[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n"
  },
  {
    "path": "Chapter01/mnist_pytorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/anaconda3/lib/python3.8/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (3.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (3.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2) (2023.10.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->torch==2.2) (2.1.1)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/anaconda3/lib/python3.8/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.8/site-packages (from torchvision==0.17) (1.23.1)\\n\",\n      \"Requirement already satisfied: requests in /opt/anaconda3/lib/python3.8/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/anaconda3/lib/python3.8/site-packages (from torchvision==0.17) (10.0.1)\\n\",\n      \"Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (3.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (3.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/anaconda3/lib/python3.8/site-packages (from torch==2.2.0->torchvision==0.17) (2023.10.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/anaconda3/lib/python3.8/site-packages (from requests->torchvision==0.17) (2.0.4)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.8/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/anaconda3/lib/python3.8/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.8/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.1)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Collecting matplotlib==3.5.2\\n\",\n      \"  Downloading matplotlib-3.5.2-cp38-cp38-macosx_10_9_x86_64.whl (7.3 MB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m7.3/7.3 MB\\u001b[0m \\u001b[31m5.3 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m00:01\\u001b[0mm\\n\",\n      \"\\u001b[?25hRequirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (0.11.0)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (4.25.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (1.4.4)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (1.23.1)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (10.0.1)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Installing collected packages: matplotlib\\n\",\n      \"  Attempting uninstall: matplotlib\\n\",\n      \"    Found existing installation: matplotlib 3.7.2\\n\",\n      \"    Uninstalling matplotlib-3.7.2:\\n\",\n      \"      Successfully uninstalled matplotlib-3.7.2\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"catboost 0.26.1 requires graphviz, which is not installed.\\n\",\n      \"catboost 0.26.1 requires plotly, which is not installed.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed matplotlib-3.5.2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 10 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset),\\n\",\n    \"        100. * success / len(test_dataloader.dataset)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\\n\",\n    \"    batch_size=32, shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,)) \\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=500, shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(0)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = optim.Adadelta(model.parameters(), lr=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.310609\\n\",\n      \"epoch: 1 [320/60000 (1%)]\\t training loss: 1.924133\\n\",\n      \"epoch: 1 [640/60000 (1%)]\\t training loss: 1.313336\\n\",\n      \"epoch: 1 [960/60000 (2%)]\\t training loss: 0.796470\\n\",\n      \"epoch: 1 [1280/60000 (2%)]\\t training loss: 0.819801\\n\",\n      \"epoch: 1 [1600/60000 (3%)]\\t training loss: 0.678443\\n\",\n      \"epoch: 1 [1920/60000 (3%)]\\t training loss: 0.477794\\n\",\n      \"epoch: 1 [2240/60000 (4%)]\\t training loss: 0.527351\\n\",\n      \"epoch: 1 [2560/60000 (4%)]\\t training loss: 0.469222\\n\",\n      \"epoch: 1 [2880/60000 (5%)]\\t training loss: 0.243136\\n\",\n      \"epoch: 1 [3200/60000 (5%)]\\t training loss: 0.526528\\n\",\n      \"epoch: 1 [3520/60000 (6%)]\\t training loss: 0.271501\\n\",\n      \"epoch: 1 [3840/60000 (6%)]\\t training loss: 0.473249\\n\",\n      \"epoch: 1 [4160/60000 (7%)]\\t training loss: 0.425321\\n\",\n      \"epoch: 1 [4480/60000 (7%)]\\t training loss: 0.321529\\n\",\n      \"epoch: 1 [4800/60000 (8%)]\\t training loss: 0.492590\\n\",\n      \"epoch: 1 [5120/60000 (9%)]\\t training loss: 0.153583\\n\",\n      \"epoch: 1 [5440/60000 (9%)]\\t training loss: 0.378470\\n\",\n      \"epoch: 1 [5760/60000 (10%)]\\t training loss: 0.081257\\n\",\n      \"epoch: 1 [6080/60000 (10%)]\\t training loss: 0.179030\\n\",\n      \"epoch: 1 [6400/60000 (11%)]\\t training loss: 0.288427\\n\",\n      \"epoch: 1 [6720/60000 (11%)]\\t training loss: 0.074618\\n\",\n      \"epoch: 1 [7040/60000 (12%)]\\t training loss: 0.361489\\n\",\n      \"epoch: 1 [7360/60000 (12%)]\\t training loss: 0.040892\\n\",\n      \"epoch: 1 [7680/60000 (13%)]\\t training loss: 0.366419\\n\",\n      \"epoch: 1 [8000/60000 (13%)]\\t training loss: 0.233954\\n\",\n      \"epoch: 1 [8320/60000 (14%)]\\t training loss: 0.352702\\n\",\n      \"epoch: 1 [8640/60000 (14%)]\\t training loss: 0.197109\\n\",\n      \"epoch: 1 [8960/60000 (15%)]\\t training loss: 0.052798\\n\",\n      \"epoch: 1 [9280/60000 (15%)]\\t training loss: 0.335245\\n\",\n      \"epoch: 1 [9600/60000 (16%)]\\t training loss: 0.237222\\n\",\n      \"epoch: 1 [9920/60000 (17%)]\\t training loss: 0.201199\\n\",\n      \"epoch: 1 [10240/60000 (17%)]\\t training loss: 0.176377\\n\",\n      \"epoch: 1 [10560/60000 (18%)]\\t training loss: 0.373226\\n\",\n      \"epoch: 1 [10880/60000 (18%)]\\t training loss: 0.076131\\n\",\n      \"epoch: 1 [11200/60000 (19%)]\\t training loss: 0.058454\\n\",\n      \"epoch: 1 [11520/60000 (19%)]\\t training loss: 0.224688\\n\",\n      \"epoch: 1 [11840/60000 (20%)]\\t training loss: 0.055108\\n\",\n      \"epoch: 1 [12160/60000 (20%)]\\t training loss: 0.394658\\n\",\n      \"epoch: 1 [12480/60000 (21%)]\\t training loss: 0.023986\\n\",\n      \"epoch: 1 [12800/60000 (21%)]\\t training loss: 0.076887\\n\",\n      \"epoch: 1 [13120/60000 (22%)]\\t training loss: 0.130194\\n\",\n      \"epoch: 1 [13440/60000 (22%)]\\t training loss: 0.169833\\n\",\n      \"epoch: 1 [13760/60000 (23%)]\\t training loss: 0.063333\\n\",\n      \"epoch: 1 [14080/60000 (23%)]\\t training loss: 0.057037\\n\",\n      \"epoch: 1 [14400/60000 (24%)]\\t training loss: 0.076323\\n\",\n      \"epoch: 1 [14720/60000 (25%)]\\t training loss: 0.150843\\n\",\n      \"epoch: 1 [15040/60000 (25%)]\\t training loss: 0.344049\\n\",\n      \"epoch: 1 [15360/60000 (26%)]\\t training loss: 0.075826\\n\",\n      \"epoch: 1 [15680/60000 (26%)]\\t training loss: 0.104628\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 0.106275\\n\",\n      \"epoch: 1 [16320/60000 (27%)]\\t training loss: 0.165156\\n\",\n      \"epoch: 1 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training loss: 0.002653\\n\",\n      \"epoch: 2 [32640/60000 (54%)]\\t training loss: 0.020022\\n\",\n      \"epoch: 2 [32960/60000 (55%)]\\t training loss: 0.001444\\n\",\n      \"epoch: 2 [33280/60000 (55%)]\\t training loss: 0.146469\\n\",\n      \"epoch: 2 [33600/60000 (56%)]\\t training loss: 0.011301\\n\",\n      \"epoch: 2 [33920/60000 (57%)]\\t training loss: 0.016578\\n\",\n      \"epoch: 2 [34240/60000 (57%)]\\t training loss: 0.318212\\n\",\n      \"epoch: 2 [34560/60000 (58%)]\\t training loss: 0.004377\\n\",\n      \"epoch: 2 [34880/60000 (58%)]\\t training loss: 0.088107\\n\",\n      \"epoch: 2 [35200/60000 (59%)]\\t training loss: 0.010620\\n\",\n      \"epoch: 2 [35520/60000 (59%)]\\t training loss: 0.040719\\n\",\n      \"epoch: 2 [35840/60000 (60%)]\\t training loss: 0.008403\\n\",\n      \"epoch: 2 [36160/60000 (60%)]\\t training loss: 0.028752\\n\",\n      \"epoch: 2 [36480/60000 (61%)]\\t training loss: 0.088192\\n\",\n      \"epoch: 2 [36800/60000 (61%)]\\t 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training loss: 0.068610\\n\",\n      \"epoch: 2 [41600/60000 (69%)]\\t training loss: 0.177852\\n\",\n      \"epoch: 2 [41920/60000 (70%)]\\t training loss: 0.005523\\n\",\n      \"epoch: 2 [42240/60000 (70%)]\\t training loss: 0.008331\\n\",\n      \"epoch: 2 [42560/60000 (71%)]\\t training loss: 0.056608\\n\",\n      \"epoch: 2 [42880/60000 (71%)]\\t training loss: 0.007651\\n\",\n      \"epoch: 2 [43200/60000 (72%)]\\t training loss: 0.003149\\n\",\n      \"epoch: 2 [43520/60000 (73%)]\\t training loss: 0.052926\\n\",\n      \"epoch: 2 [43840/60000 (73%)]\\t training loss: 0.267129\\n\",\n      \"epoch: 2 [44160/60000 (74%)]\\t training loss: 0.175253\\n\",\n      \"epoch: 2 [44480/60000 (74%)]\\t training loss: 0.009212\\n\",\n      \"epoch: 2 [44800/60000 (75%)]\\t training loss: 0.003933\\n\",\n      \"epoch: 2 [45120/60000 (75%)]\\t training loss: 0.056013\\n\",\n      \"epoch: 2 [45440/60000 (76%)]\\t training loss: 0.033882\\n\",\n      \"epoch: 2 [45760/60000 (76%)]\\t training loss: 0.049231\\n\",\n      \"epoch: 2 [46080/60000 (77%)]\\t training loss: 0.099665\\n\",\n      \"epoch: 2 [46400/60000 (77%)]\\t training loss: 0.021581\\n\",\n      \"epoch: 2 [46720/60000 (78%)]\\t training loss: 0.108349\\n\",\n      \"epoch: 2 [47040/60000 (78%)]\\t training loss: 0.065199\\n\",\n      \"epoch: 2 [47360/60000 (79%)]\\t training loss: 0.038825\\n\",\n      \"epoch: 2 [47680/60000 (79%)]\\t training loss: 0.032651\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 0.101691\\n\",\n      \"epoch: 2 [48320/60000 (81%)]\\t training loss: 0.225020\\n\",\n      \"epoch: 2 [48640/60000 (81%)]\\t training loss: 0.023284\\n\",\n      \"epoch: 2 [48960/60000 (82%)]\\t training loss: 0.009814\\n\",\n      \"epoch: 2 [49280/60000 (82%)]\\t training loss: 0.002712\\n\",\n      \"epoch: 2 [49600/60000 (83%)]\\t training loss: 0.008352\\n\",\n      \"epoch: 2 [49920/60000 (83%)]\\t training loss: 0.116699\\n\",\n      \"epoch: 2 [50240/60000 (84%)]\\t training loss: 0.034111\\n\",\n      \"epoch: 2 [50560/60000 (84%)]\\t training loss: 0.002953\\n\",\n      \"epoch: 2 [50880/60000 (85%)]\\t training loss: 0.007287\\n\",\n      \"epoch: 2 [51200/60000 (85%)]\\t training loss: 0.003475\\n\",\n      \"epoch: 2 [51520/60000 (86%)]\\t training loss: 0.221789\\n\",\n      \"epoch: 2 [51840/60000 (86%)]\\t training loss: 0.004132\\n\",\n      \"epoch: 2 [52160/60000 (87%)]\\t training loss: 0.003032\\n\",\n      \"epoch: 2 [52480/60000 (87%)]\\t training loss: 0.111290\\n\",\n      \"epoch: 2 [52800/60000 (88%)]\\t training loss: 0.038240\\n\",\n      \"epoch: 2 [53120/60000 (89%)]\\t training loss: 0.003252\\n\",\n      \"epoch: 2 [53440/60000 (89%)]\\t training loss: 0.127635\\n\",\n      \"epoch: 2 [53760/60000 (90%)]\\t training loss: 0.197244\\n\",\n      \"epoch: 2 [54080/60000 (90%)]\\t training loss: 0.002290\\n\",\n      \"epoch: 2 [54400/60000 (91%)]\\t training loss: 0.404068\\n\",\n      \"epoch: 2 [54720/60000 (91%)]\\t training loss: 0.105248\\n\",\n      \"epoch: 2 [55040/60000 (92%)]\\t training loss: 0.021728\\n\",\n      \"epoch: 2 [55360/60000 (92%)]\\t training loss: 0.073713\\n\",\n      \"epoch: 2 [55680/60000 (93%)]\\t training loss: 0.005018\\n\",\n      \"epoch: 2 [56000/60000 (93%)]\\t training loss: 0.028373\\n\",\n      \"epoch: 2 [56320/60000 (94%)]\\t training loss: 0.000962\\n\",\n      \"epoch: 2 [56640/60000 (94%)]\\t training loss: 0.033431\\n\",\n      \"epoch: 2 [56960/60000 (95%)]\\t training loss: 0.011923\\n\",\n      \"epoch: 2 [57280/60000 (95%)]\\t training loss: 0.091363\\n\",\n      \"epoch: 2 [57600/60000 (96%)]\\t training loss: 0.077149\\n\",\n      \"epoch: 2 [57920/60000 (97%)]\\t training loss: 0.144766\\n\",\n      \"epoch: 2 [58240/60000 (97%)]\\t training loss: 0.071787\\n\",\n      \"epoch: 2 [58560/60000 (98%)]\\t training loss: 0.003696\\n\",\n      \"epoch: 2 [58880/60000 (98%)]\\t training loss: 0.006127\\n\",\n      \"epoch: 2 [59200/60000 (99%)]\\t training loss: 0.020462\\n\",\n      \"epoch: 2 [59520/60000 (99%)]\\t training loss: 0.017625\\n\",\n      \"epoch: 2 [59840/60000 (100%)]\\t training loss: 0.011167\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0420, Overall Accuracy: 9859/10000 (99%)\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 2 [37440/60000 (62%)]\\t training loss: 0.004772\\n\",\n      \"epoch: 2 [37760/60000 (63%)]\\t training loss: 0.067643\\n\",\n      \"epoch: 2 [38080/60000 (63%)]\\t training loss: 0.014400\\n\",\n      \"epoch: 2 [38400/60000 (64%)]\\t training loss: 0.029562\\n\",\n      \"epoch: 2 [38720/60000 (65%)]\\t training loss: 0.091197\\n\",\n      \"epoch: 2 [39040/60000 (65%)]\\t training loss: 0.013406\\n\",\n      \"epoch: 2 [39360/60000 (66%)]\\t training loss: 0.004593\\n\",\n      \"epoch: 2 [39680/60000 (66%)]\\t training loss: 0.168198\\n\",\n      \"epoch: 2 [40000/60000 (67%)]\\t training loss: 0.011329\\n\",\n      \"epoch: 2 [40320/60000 (67%)]\\t training loss: 0.245803\\n\",\n      \"epoch: 2 [40640/60000 (68%)]\\t training loss: 0.528659\\n\",\n      \"epoch: 2 [40960/60000 (68%)]\\t training loss: 0.044822\\n\",\n      \"epoch: 2 [41280/60000 (69%)]\\t training loss: 0.083322\\n\",\n      \"epoch: 2 [41600/60000 (69%)]\\t training loss: 0.153098\\n\",\n      \"epoch: 2 [41920/60000 (70%)]\\t training loss: 0.009117\\n\",\n      \"epoch: 2 [42240/60000 (70%)]\\t training loss: 0.011295\\n\",\n      \"epoch: 2 [42560/60000 (71%)]\\t training loss: 0.046957\\n\",\n      \"epoch: 2 [42880/60000 (71%)]\\t training loss: 0.005710\\n\",\n      \"epoch: 2 [43200/60000 (72%)]\\t training loss: 0.001676\\n\",\n      \"epoch: 2 [43520/60000 (73%)]\\t training loss: 0.046940\\n\",\n      \"epoch: 2 [43840/60000 (73%)]\\t training loss: 0.260874\\n\",\n      \"epoch: 2 [44160/60000 (74%)]\\t training loss: 0.129744\\n\",\n      \"epoch: 2 [44480/60000 (74%)]\\t training loss: 0.009345\\n\",\n      \"epoch: 2 [44800/60000 (75%)]\\t training loss: 0.004784\\n\",\n      \"epoch: 2 [45120/60000 (75%)]\\t training loss: 0.060891\\n\",\n      \"epoch: 2 [45440/60000 (76%)]\\t training loss: 0.061104\\n\",\n      \"epoch: 2 [45760/60000 (76%)]\\t training loss: 0.020632\\n\",\n      \"epoch: 2 [46080/60000 (77%)]\\t training loss: 0.083816\\n\",\n      \"epoch: 2 [46400/60000 (77%)]\\t training loss: 0.042209\\n\",\n      \"epoch: 2 [46720/60000 (78%)]\\t training loss: 0.136387\\n\",\n      \"epoch: 2 [47040/60000 (78%)]\\t training loss: 0.026391\\n\",\n      \"epoch: 2 [47360/60000 (79%)]\\t training loss: 0.043673\\n\",\n      \"epoch: 2 [47680/60000 (79%)]\\t training loss: 0.024578\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 0.070994\\n\",\n      \"epoch: 2 [48320/60000 (81%)]\\t training loss: 0.221534\\n\",\n      \"epoch: 2 [48640/60000 (81%)]\\t training loss: 0.063217\\n\",\n      \"epoch: 2 [48960/60000 (82%)]\\t training loss: 0.008979\\n\",\n      \"epoch: 2 [49280/60000 (82%)]\\t training loss: 0.002270\\n\",\n      \"epoch: 2 [49600/60000 (83%)]\\t training loss: 0.011848\\n\",\n      \"epoch: 2 [49920/60000 (83%)]\\t training loss: 0.112140\\n\",\n      \"epoch: 2 [50240/60000 (84%)]\\t training loss: 0.016166\\n\",\n      \"epoch: 2 [50560/60000 (84%)]\\t training loss: 0.003836\\n\",\n      \"epoch: 2 [50880/60000 (85%)]\\t training loss: 0.003403\\n\",\n      \"epoch: 2 [51200/60000 (85%)]\\t training loss: 0.033328\\n\",\n      \"epoch: 2 [51520/60000 (86%)]\\t training loss: 0.188970\\n\",\n      \"epoch: 2 [51840/60000 (86%)]\\t training loss: 0.008271\\n\",\n      \"epoch: 2 [52160/60000 (87%)]\\t training loss: 0.004005\\n\",\n      \"epoch: 2 [52480/60000 (87%)]\\t training loss: 0.134851\\n\",\n      \"epoch: 2 [52800/60000 (88%)]\\t training loss: 0.030210\\n\",\n      \"epoch: 2 [53120/60000 (89%)]\\t training loss: 0.003869\\n\",\n      \"epoch: 2 [53440/60000 (89%)]\\t training loss: 0.136203\\n\",\n      \"epoch: 2 [53760/60000 (90%)]\\t training loss: 0.209959\\n\",\n      \"epoch: 2 [54080/60000 (90%)]\\t training loss: 0.003821\\n\",\n      \"epoch: 2 [54400/60000 (91%)]\\t training loss: 0.423098\\n\",\n      \"epoch: 2 [54720/60000 (91%)]\\t training loss: 0.145483\\n\",\n      \"epoch: 2 [55040/60000 (92%)]\\t training loss: 0.032753\\n\",\n      \"epoch: 2 [55360/60000 (92%)]\\t training loss: 0.091253\\n\",\n      \"epoch: 2 [55680/60000 (93%)]\\t training loss: 0.008338\\n\",\n      \"epoch: 2 [56000/60000 (93%)]\\t training loss: 0.038908\\n\",\n      \"epoch: 2 [56320/60000 (94%)]\\t training loss: 0.001388\\n\",\n      \"epoch: 2 [56640/60000 (94%)]\\t training loss: 0.070186\\n\",\n      \"epoch: 2 [56960/60000 (95%)]\\t training loss: 0.014708\\n\",\n      \"epoch: 2 [57280/60000 (95%)]\\t training loss: 0.019372\\n\",\n      \"epoch: 2 [57600/60000 (96%)]\\t training loss: 0.054575\\n\",\n      \"epoch: 2 [57920/60000 (97%)]\\t training loss: 0.126219\\n\",\n      \"epoch: 2 [58240/60000 (97%)]\\t training loss: 0.055399\\n\",\n      \"epoch: 2 [58560/60000 (98%)]\\t training loss: 0.007698\\n\",\n      \"epoch: 2 [58880/60000 (98%)]\\t training loss: 0.002685\\n\",\n      \"epoch: 2 [59200/60000 (99%)]\\t training loss: 0.016287\\n\",\n      \"epoch: 2 [59520/60000 (99%)]\\t training loss: 0.012645\\n\",\n      \"epoch: 2 [59840/60000 (100%)]\\t training loss: 0.007993\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0416, Overall Accuracy: 9864/10000 (99%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(1, 3):\\n\",\n    \"    train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"    test(model, device, test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"\\n\",\n    \"plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 7\\n\",\n      \"Ground truth is : 7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter01/mnist_tensorflow.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Collecting tensorflow\\n\",\n      \"  Downloading tensorflow-2.11.1-cp39-cp39-macosx_10_14_x86_64.whl (244.3 MB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m244.3/244.3 MB\\u001b[0m \\u001b[31m3.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m00:02\\u001b[0m\\n\",\n      \"\\u001b[?25hCollecting astunparse>=1.6.0\\n\",\n      \"  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\\n\",\n      \"Collecting flatbuffers>=2.0\\n\",\n      \"  Downloading flatbuffers-23.3.3-py2.py3-none-any.whl (26 kB)\\n\",\n      \"Requirement already satisfied: absl-py>=1.0.0 in 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(2.2.2)\\n\",\n      \"Collecting tensorboard-data-server<0.7.0,>=0.6.0\\n\",\n      \"  Using cached tensorboard_data_server-0.6.1-py3-none-macosx_10_9_x86_64.whl (3.5 MB)\\n\",\n      \"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (1.8.1)\\n\",\n      \"Requirement already satisfied: google-auth<3,>=1.6.3 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from tensorboard<2.12,>=2.11->tensorflow) (2.16.2)\\n\",\n      \"Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.2.8)\\n\",\n      \"Requirement already satisfied: rsa<5,>=3.1.4 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from 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  \"Requirement already satisfied: charset-normalizer<3,>=2 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (2.1.1)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (3.4)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow) (2022.12.7)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from werkzeug>=1.0.1->tensorboard<2.12,>=2.11->tensorflow) (2.1.2)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow) (3.11.0)\\n\",\n      \"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow) (0.4.8)\\n\",\n      \"Requirement already satisfied: oauthlib>=3.0.0 in /Users/ashish.jha/opt/anaconda3/envs/mastering_pytorch/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow) (3.2.2)\\n\",\n      \"Installing collected packages: libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, protobuf, opt-einsum, keras, google-pasta, gast, astunparse, tensorboard, tensorflow\\n\",\n      \"  Attempting uninstall: tensorboard-data-server\\n\",\n      \"    Found existing installation: tensorboard-data-server 0.7.0\\n\",\n      \"    Uninstalling tensorboard-data-server-0.7.0:\\n\",\n      \"      Successfully uninstalled tensorboard-data-server-0.7.0\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"  Attempting uninstall: protobuf\\n\",\n      \"    Found existing installation: protobuf 4.21.9\\n\",\n      \"    Uninstalling protobuf-4.21.9:\\n\",\n      \"      Successfully uninstalled protobuf-4.21.9\\n\",\n      \"  Attempting uninstall: tensorboard\\n\",\n      \"    Found existing installation: tensorboard 2.12.0\\n\",\n      \"    Uninstalling tensorboard-2.12.0:\\n\",\n      \"      Successfully uninstalled tensorboard-2.12.0\\n\",\n      \"Successfully installed astunparse-1.6.3 flatbuffers-23.3.3 gast-0.4.0 google-pasta-0.2.0 keras-2.11.0 libclang-15.0.6.1 opt-einsum-3.3.0 protobuf-3.19.6 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorflow-2.11.1 tensorflow-estimator-2.11.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.2.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install tensorflow==2.11.1\\n\",\n    \"!pip install matplotlib==3.5.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2023-03-24 23:11:48.683362: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\\n\",\n      \"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(tf.keras.Model):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=(28, 28, 1))\\n\",\n    \"        self.cn2 = tf.keras.layers.Conv2D(32, 3, activation='relu')\\n\",\n    \"        self.dp1 = tf.keras.layers.Dropout(0.10)\\n\",\n    \"        self.dp2 = tf.keras.layers.Dropout(0.25)\\n\",\n    \"        self.flatten = tf.keras.layers.Flatten()\\n\",\n    \"        self.fc1 = tf.keras.layers.Dense(64, activation='relu')\\n\",\n    \"        self.fc2 = tf.keras.layers.Dense(10, activation='softmax')\\n\",\n    \"        \\n\",\n    \"    def call(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = self.flatten(x)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2023-03-24 23:11:52.845124: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\\n\",\n      \"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the MNIST dataset.\\n\",\n    \"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() \\n\",\n    \"\\n\",\n    \"# Normalize pixel values between 0 and 1\\n\",\n    \"x_train = x_train.astype(\\\"float32\\\") / 255.0\\n\",\n    \"x_test = x_test.astype(\\\"float32\\\") / 255.0\\n\",\n    \"\\n\",\n    \"# Add a channels dimension (required for CNN)\\n\",\n    \"x_train = x_train[..., tf.newaxis]\\n\",\n    \"x_test = x_test[..., tf.newaxis]\\n\",\n    \"\\n\",\n    \"# Create a dataloader for training.\\n\",\n    \"train_dataloader = tf.data.Dataset.from_tensor_slices((x_train, y_train))\\n\",\n    \"train_dataloader = train_dataloader.shuffle(10000)\\n\",\n    \"train_dataloader = train_dataloader.batch(32)\\n\",\n    \"\\n\",\n    \"# Create a dataloader for testing.\\n\",\n    \"test_dataloader = tf.data.Dataset.from_tensor_slices((x_test, y_test))\\n\",\n    \"test_dataloader = test_dataloader.batch(500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(0)\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = tf.keras.optimizers.experimental.Adadelta(learning_rate=0.5)\\n\",\n    \"model.compile(optimizer=optimizer,\\n\",\n    \"loss='sparse_categorical_crossentropy',\\n\",\n    \"metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/3\\n\",\n      \"1875/1875 [==============================] - 36s 19ms/step - loss: 0.2213 - accuracy: 0.9328 - val_loss: 0.0611 - val_accuracy: 0.9798\\n\",\n      \"Epoch 2/3\\n\",\n      \"1875/1875 [==============================] - 50s 27ms/step - loss: 0.0857 - accuracy: 0.9751 - val_loss: 0.0423 - val_accuracy: 0.9857\\n\",\n      \"Epoch 3/3\\n\",\n      \"1875/1875 [==============================] - 59s 31ms/step - loss: 0.0641 - accuracy: 0.9807 - val_loss: 0.0412 - val_accuracy: 0.9862\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f80a8499ca0>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Train the model using the tf.data.Dataset API\\n\",\n    \"model.fit(train_dataloader, epochs=3, validation_data=test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"plt.imshow(sample_data[0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 7\\n\",\n      \"Ground truth is : 7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {tf.math.argmax(model(sample_data)[0])}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.16\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/DenseNetBlock.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class DenseBlock(nn.Module):\\n\",\n    \"    def __init__(self, input_num_planes, rate_inc):\\n\",\n    \"        super(DenseBlock, self).__init__()\\n\",\n    \"        self.batch_norm1 = nn.BatchNorm2d(input_num_planes)\\n\",\n    \"        self.conv_layer1 = nn.Conv2d(in_channels=input_num_planes, out_channels=4*rate_inc, kernel_size=1, bias=False)\\n\",\n    \"        self.batch_norm2 = nn.BatchNorm2d(4*rate_inc)\\n\",\n    \"        self.conv_layer2 = nn.Conv2d(in_channels=4*rate_inc, out_channels=rate_inc, kernel_size=3, padding=1, bias=False)\\n\",\n    \"    def forward(self, inp):\\n\",\n    \"        op = self.conv_layer1(F.relu(self.batch_norm1(inp)))\\n\",\n    \"        op = self.conv_layer2(F.relu(self.batch_norm2(op)))\\n\",\n    \"        op = torch.cat([op,inp], 1)\\n\",\n    \"        return op\\n\",\n    \"\\n\",\n    \"class TransBlock(nn.Module):\\n\",\n    \"    def __init__(self, input_num_planes, output_num_planes):\\n\",\n    \"        super(TransBlock, self).__init__()\\n\",\n    \"        self.batch_norm = nn.BatchNorm2d(input_num_planes)\\n\",\n    \"        self.conv_layer = nn.Conv2d(in_channels=input_num_planes, out_channels=output_num_planes, kernel_size=1, bias=False)\\n\",\n    \"    def forward(self, inp):\\n\",\n    \"        op = self.conv_layer(F.relu(self.batch_norm(inp)))\\n\",\n    \"        op = F.avg_pool2d(op, 2)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/GoogLeNet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class InceptionModule(nn.Module):\\n\",\n    \"    def __init__(self, input_planes, n_channels1x1, n_channels3x3red, n_channels3x3, n_channels5x5red, n_channels5x5, pooling_planes):\\n\",\n    \"        super(InceptionModule, self).__init__()\\n\",\n    \"        # 1x1 convolution branch\\n\",\n    \"        self.block1 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(input_planes, n_channels1x1, kernel_size=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels1x1),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"        # 1x1 convolution -> 3x3 convolution branch\\n\",\n    \"        self.block2 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(input_planes, n_channels3x3red, kernel_size=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels3x3red),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Conv2d(n_channels3x3red, n_channels3x3, kernel_size=3, padding=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels3x3),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"        # 1x1 conv -> 5x5 conv branch\\n\",\n    \"        self.block3 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(input_planes, n_channels5x5red, kernel_size=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels5x5red),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Conv2d(n_channels5x5red, n_channels5x5, kernel_size=3, padding=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels5x5),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Conv2d(n_channels5x5, n_channels5x5, kernel_size=3, padding=1),\\n\",\n    \"            nn.BatchNorm2d(n_channels5x5),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"        # 3x3 pool -> 1x1 conv branch\\n\",\n    \"        self.block4 = nn.Sequential(\\n\",\n    \"            nn.MaxPool2d(3, stride=1, padding=1),\\n\",\n    \"            nn.Conv2d(input_planes, pooling_planes, kernel_size=1),\\n\",\n    \"            nn.BatchNorm2d(pooling_planes),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"    def forward(self, ip):\\n\",\n    \"        op1 = self.block1(ip)\\n\",\n    \"        op2 = self.block2(ip)\\n\",\n    \"        op3 = self.block3(ip)\\n\",\n    \"        op4 = self.block4(ip)\\n\",\n    \"        return torch.cat([op1,op2,op3,op4], 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class GoogLeNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(GoogLeNet, self).__init__()\\n\",\n    \"        self.stem = nn.Sequential(\\n\",\n    \"            nn.Conv2d(3, 192, kernel_size=3, padding=1),\\n\",\n    \"            nn.BatchNorm2d(192),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"        )\\n\",\n    \" \\n\",\n    \"        self.im1 = InceptionModule(192,  64,  96, 128, 16, 32, 32)\\n\",\n    \"        self.im2 = InceptionModule(256, 128, 128, 192, 32, 96, 64)\\n\",\n    \" \\n\",\n    \"        self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)\\n\",\n    \" \\n\",\n    \"        self.im3 = InceptionModule(480, 192,  96, 208, 16,  48,  64)\\n\",\n    \"        self.im4 = InceptionModule(512, 160, 112, 224, 24,  64,  64)\\n\",\n    \"        self.im5 = InceptionModule(512, 128, 128, 256, 24,  64,  64)\\n\",\n    \"        self.im6 = InceptionModule(512, 112, 144, 288, 32,  64,  64)\\n\",\n    \"        self.im7 = InceptionModule(528, 256, 160, 320, 32, 128, 128)\\n\",\n    \" \\n\",\n    \"        self.im8 = InceptionModule(832, 256, 160, 320, 32, 128, 128)\\n\",\n    \"        self.im9 = InceptionModule(832, 384, 192, 384, 48, 128, 128)\\n\",\n    \" \\n\",\n    \"        self.average_pool = nn.AvgPool2d(7, stride=1)\\n\",\n    \"        self.fc = nn.Linear(4096, 1000)\\n\",\n    \" \\n\",\n    \"    def forward(self, ip):\\n\",\n    \"        op = self.stem(ip)\\n\",\n    \"        out = self.im1(op)\\n\",\n    \"        out = self.im2(op)\\n\",\n    \"        op = self.maxpool(op)\\n\",\n    \"        op = self.a4(op)\\n\",\n    \"        op = self.b4(op)\\n\",\n    \"        op = self.c4(op)\\n\",\n    \"        op = self.d4(op)\\n\",\n    \"        op = self.e4(op)\\n\",\n    \"        op = self.max_pool(op)\\n\",\n    \"        op = self.a5(op)\\n\",\n    \"        op = self.b5(op)\\n\",\n    \"        op = self.avgerage_pool(op)\\n\",\n    \"        op = op.view(op.size(0), -1)\\n\",\n    \"        op = self.fc(op)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/ResNetBlock.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class BasicBlock(nn.Module):\\n\",\n    \"    multiplier=1\\n\",\n    \"    def __init__(self, input_num_planes, num_planes, strd=1):\\n\",\n    \"        super(BasicBlock, self).__init__()\\n\",\n    \"        self.conv_layer1 = nn.Conv2d(in_channels=input_num_planes, out_channels=num_planes, kernel_size=3, stride=stride, padding=1, bias=False)\\n\",\n    \"        self.batch_norm1 = nn.BatchNorm2d(num_planes)\\n\",\n    \"        self.conv_layer2 = nn.Conv2d(in_channels=num_planes, out_channels=num_planes, kernel_size=3, stride=1, padding=1, bias=False)\\n\",\n    \"        self.batch_norm2 = nn.BatchNorm2d(num_planes)\\n\",\n    \" \\n\",\n    \"        self.res_connnection = nn.Sequential()\\n\",\n    \"        if strd > 1 or input_num_planes != self.multiplier*num_planes:\\n\",\n    \"            self.res_connnection = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=input_num_planes, out_channels=self.multiplier*num_planes, kernel_size=1, stride=strd, bias=False),\\n\",\n    \"                nn.BatchNorm2d(self.multiplier*num_planes)\\n\",\n    \"            )\\n\",\n    \"    def forward(self, inp):\\n\",\n    \"        op = F.relu(self.batch_norm1(self.conv_layer1(inp)))\\n\",\n    \"        op = self.batch_norm2(self.conv_layer2(op))\\n\",\n    \"        op += self.res_connnection(inp)\\n\",\n    \"        op = F.relu(op)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/imagenet1000_clsidx_to_labels.txt",
    "content": "{0: 'tench, Tinca tinca',\n 1: 'goldfish, Carassius auratus',\n 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',\n 3: 'tiger shark, Galeocerdo cuvieri',\n 4: 'hammerhead, hammerhead shark',\n 5: 'electric ray, crampfish, numbfish, torpedo',\n 6: 'stingray',\n 7: 'cock',\n 8: 'hen',\n 9: 'ostrich, Struthio camelus',\n 10: 'brambling, Fringilla montifringilla',\n 11: 'goldfinch, Carduelis carduelis',\n 12: 'house finch, linnet, Carpodacus mexicanus',\n 13: 'junco, snowbird',\n 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',\n 15: 'robin, American robin, Turdus migratorius',\n 16: 'bulbul',\n 17: 'jay',\n 18: 'magpie',\n 19: 'chickadee',\n 20: 'water ouzel, dipper',\n 21: 'kite',\n 22: 'bald eagle, American eagle, Haliaeetus leucocephalus',\n 23: 'vulture',\n 24: 'great grey owl, great gray owl, Strix nebulosa',\n 25: 'European fire salamander, Salamandra salamandra',\n 26: 'common newt, Triturus vulgaris',\n 27: 'eft',\n 28: 'spotted salamander, Ambystoma maculatum',\n 29: 'axolotl, mud puppy, Ambystoma mexicanum',\n 30: 'bullfrog, Rana catesbeiana',\n 31: 'tree frog, tree-frog',\n 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',\n 33: 'loggerhead, loggerhead turtle, Caretta caretta',\n 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',\n 35: 'mud turtle',\n 36: 'terrapin',\n 37: 'box turtle, box tortoise',\n 38: 'banded gecko',\n 39: 'common iguana, iguana, Iguana iguana',\n 40: 'American chameleon, anole, Anolis carolinensis',\n 41: 'whiptail, whiptail lizard',\n 42: 'agama',\n 43: 'frilled lizard, Chlamydosaurus kingi',\n 44: 'alligator lizard',\n 45: 'Gila monster, Heloderma suspectum',\n 46: 'green lizard, Lacerta viridis',\n 47: 'African chameleon, Chamaeleo chamaeleon',\n 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',\n 49: 'African crocodile, Nile crocodile, Crocodylus niloticus',\n 50: 'American alligator, Alligator mississipiensis',\n 51: 'triceratops',\n 52: 'thunder snake, worm snake, Carphophis amoenus',\n 53: 'ringneck snake, ring-necked snake, ring snake',\n 54: 'hognose snake, puff adder, sand viper',\n 55: 'green snake, grass snake',\n 56: 'king snake, kingsnake',\n 57: 'garter snake, grass snake',\n 58: 'water snake',\n 59: 'vine snake',\n 60: 'night snake, Hypsiglena torquata',\n 61: 'boa constrictor, Constrictor constrictor',\n 62: 'rock python, rock snake, Python sebae',\n 63: 'Indian cobra, Naja naja',\n 64: 'green mamba',\n 65: 'sea snake',\n 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',\n 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',\n 68: 'sidewinder, horned rattlesnake, Crotalus cerastes',\n 69: 'trilobite',\n 70: 'harvestman, daddy longlegs, Phalangium opilio',\n 71: 'scorpion',\n 72: 'black and gold garden spider, Argiope aurantia',\n 73: 'barn spider, Araneus cavaticus',\n 74: 'garden spider, Aranea diademata',\n 75: 'black widow, Latrodectus mactans',\n 76: 'tarantula',\n 77: 'wolf spider, hunting spider',\n 78: 'tick',\n 79: 'centipede',\n 80: 'black grouse',\n 81: 'ptarmigan',\n 82: 'ruffed grouse, partridge, Bonasa umbellus',\n 83: 'prairie chicken, prairie grouse, prairie fowl',\n 84: 'peacock',\n 85: 'quail',\n 86: 'partridge',\n 87: 'African grey, African gray, Psittacus erithacus',\n 88: 'macaw',\n 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',\n 90: 'lorikeet',\n 91: 'coucal',\n 92: 'bee eater',\n 93: 'hornbill',\n 94: 'hummingbird',\n 95: 'jacamar',\n 96: 'toucan',\n 97: 'drake',\n 98: 'red-breasted merganser, Mergus serrator',\n 99: 'goose',\n 100: 'black swan, Cygnus atratus',\n 101: 'tusker',\n 102: 'echidna, spiny anteater, anteater',\n 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',\n 104: 'wallaby, brush kangaroo',\n 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',\n 106: 'wombat',\n 107: 'jellyfish',\n 108: 'sea anemone, anemone',\n 109: 'brain coral',\n 110: 'flatworm, platyhelminth',\n 111: 'nematode, nematode worm, roundworm',\n 112: 'conch',\n 113: 'snail',\n 114: 'slug',\n 115: 'sea slug, nudibranch',\n 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',\n 117: 'chambered nautilus, pearly nautilus, nautilus',\n 118: 'Dungeness crab, Cancer magister',\n 119: 'rock crab, Cancer irroratus',\n 120: 'fiddler crab',\n 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',\n 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',\n 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',\n 124: 'crayfish, crawfish, crawdad, crawdaddy',\n 125: 'hermit crab',\n 126: 'isopod',\n 127: 'white stork, Ciconia ciconia',\n 128: 'black stork, Ciconia nigra',\n 129: 'spoonbill',\n 130: 'flamingo',\n 131: 'little blue heron, Egretta caerulea',\n 132: 'American egret, great white heron, Egretta albus',\n 133: 'bittern',\n 134: 'crane',\n 135: 'limpkin, Aramus pictus',\n 136: 'European gallinule, Porphyrio porphyrio',\n 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',\n 138: 'bustard',\n 139: 'ruddy turnstone, Arenaria interpres',\n 140: 'red-backed sandpiper, dunlin, Erolia alpina',\n 141: 'redshank, Tringa totanus',\n 142: 'dowitcher',\n 143: 'oystercatcher, oyster catcher',\n 144: 'pelican',\n 145: 'king penguin, Aptenodytes patagonica',\n 146: 'albatross, mollymawk',\n 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',\n 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',\n 149: 'dugong, Dugong dugon',\n 150: 'sea lion',\n 151: 'Chihuahua',\n 152: 'Japanese spaniel',\n 153: 'Maltese dog, Maltese terrier, Maltese',\n 154: 'Pekinese, Pekingese, Peke',\n 155: 'Shih-Tzu',\n 156: 'Blenheim spaniel',\n 157: 'papillon',\n 158: 'toy terrier',\n 159: 'Rhodesian ridgeback',\n 160: 'Afghan hound, Afghan',\n 161: 'basset, basset hound',\n 162: 'beagle',\n 163: 'bloodhound, sleuthhound',\n 164: 'bluetick',\n 165: 'black-and-tan coonhound',\n 166: 'Walker hound, Walker foxhound',\n 167: 'English foxhound',\n 168: 'redbone',\n 169: 'borzoi, Russian wolfhound',\n 170: 'Irish wolfhound',\n 171: 'Italian greyhound',\n 172: 'whippet',\n 173: 'Ibizan hound, Ibizan Podenco',\n 174: 'Norwegian elkhound, elkhound',\n 175: 'otterhound, otter hound',\n 176: 'Saluki, gazelle hound',\n 177: 'Scottish deerhound, deerhound',\n 178: 'Weimaraner',\n 179: 'Staffordshire bullterrier, Staffordshire bull terrier',\n 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',\n 181: 'Bedlington terrier',\n 182: 'Border terrier',\n 183: 'Kerry blue terrier',\n 184: 'Irish terrier',\n 185: 'Norfolk terrier',\n 186: 'Norwich terrier',\n 187: 'Yorkshire terrier',\n 188: 'wire-haired fox terrier',\n 189: 'Lakeland terrier',\n 190: 'Sealyham terrier, Sealyham',\n 191: 'Airedale, Airedale terrier',\n 192: 'cairn, cairn terrier',\n 193: 'Australian terrier',\n 194: 'Dandie Dinmont, Dandie Dinmont terrier',\n 195: 'Boston bull, Boston terrier',\n 196: 'miniature schnauzer',\n 197: 'giant schnauzer',\n 198: 'standard schnauzer',\n 199: 'Scotch terrier, Scottish terrier, Scottie',\n 200: 'Tibetan terrier, chrysanthemum dog',\n 201: 'silky terrier, Sydney silky',\n 202: 'soft-coated wheaten terrier',\n 203: 'West Highland white terrier',\n 204: 'Lhasa, Lhasa apso',\n 205: 'flat-coated retriever',\n 206: 'curly-coated retriever',\n 207: 'golden retriever',\n 208: 'Labrador retriever',\n 209: 'Chesapeake Bay retriever',\n 210: 'German short-haired pointer',\n 211: 'vizsla, Hungarian pointer',\n 212: 'English setter',\n 213: 'Irish setter, red setter',\n 214: 'Gordon setter',\n 215: 'Brittany spaniel',\n 216: 'clumber, clumber spaniel',\n 217: 'English springer, English springer spaniel',\n 218: 'Welsh springer spaniel',\n 219: 'cocker spaniel, English cocker spaniel, cocker',\n 220: 'Sussex spaniel',\n 221: 'Irish water spaniel',\n 222: 'kuvasz',\n 223: 'schipperke',\n 224: 'groenendael',\n 225: 'malinois',\n 226: 'briard',\n 227: 'kelpie',\n 228: 'komondor',\n 229: 'Old English sheepdog, bobtail',\n 230: 'Shetland sheepdog, Shetland sheep dog, Shetland',\n 231: 'collie',\n 232: 'Border collie',\n 233: 'Bouvier des Flandres, Bouviers des Flandres',\n 234: 'Rottweiler',\n 235: 'German shepherd, German shepherd dog, German police dog, alsatian',\n 236: 'Doberman, Doberman pinscher',\n 237: 'miniature pinscher',\n 238: 'Greater Swiss Mountain dog',\n 239: 'Bernese mountain dog',\n 240: 'Appenzeller',\n 241: 'EntleBucher',\n 242: 'boxer',\n 243: 'bull mastiff',\n 244: 'Tibetan mastiff',\n 245: 'French bulldog',\n 246: 'Great Dane',\n 247: 'Saint Bernard, St Bernard',\n 248: 'Eskimo dog, husky',\n 249: 'malamute, malemute, Alaskan malamute',\n 250: 'Siberian husky',\n 251: 'dalmatian, coach dog, carriage dog',\n 252: 'affenpinscher, monkey pinscher, monkey dog',\n 253: 'basenji',\n 254: 'pug, pug-dog',\n 255: 'Leonberg',\n 256: 'Newfoundland, Newfoundland dog',\n 257: 'Great Pyrenees',\n 258: 'Samoyed, Samoyede',\n 259: 'Pomeranian',\n 260: 'chow, chow chow',\n 261: 'keeshond',\n 262: 'Brabancon griffon',\n 263: 'Pembroke, Pembroke Welsh corgi',\n 264: 'Cardigan, Cardigan Welsh corgi',\n 265: 'toy poodle',\n 266: 'miniature poodle',\n 267: 'standard poodle',\n 268: 'Mexican hairless',\n 269: 'timber wolf, grey wolf, gray wolf, Canis lupus',\n 270: 'white wolf, Arctic wolf, Canis lupus tundrarum',\n 271: 'red wolf, maned wolf, Canis rufus, Canis niger',\n 272: 'coyote, prairie wolf, brush wolf, Canis latrans',\n 273: 'dingo, warrigal, warragal, Canis dingo',\n 274: 'dhole, Cuon alpinus',\n 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',\n 276: 'hyena, hyaena',\n 277: 'red fox, Vulpes vulpes',\n 278: 'kit fox, Vulpes macrotis',\n 279: 'Arctic fox, white fox, Alopex lagopus',\n 280: 'grey fox, gray fox, Urocyon cinereoargenteus',\n 281: 'tabby, tabby cat',\n 282: 'tiger cat',\n 283: 'Persian cat',\n 284: 'Siamese cat, Siamese',\n 285: 'Egyptian cat',\n 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',\n 287: 'lynx, catamount',\n 288: 'leopard, Panthera pardus',\n 289: 'snow leopard, ounce, Panthera uncia',\n 290: 'jaguar, panther, Panthera onca, Felis onca',\n 291: 'lion, king of beasts, Panthera leo',\n 292: 'tiger, Panthera tigris',\n 293: 'cheetah, chetah, Acinonyx jubatus',\n 294: 'brown bear, bruin, Ursus arctos',\n 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',\n 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',\n 297: 'sloth bear, Melursus ursinus, Ursus ursinus',\n 298: 'mongoose',\n 299: 'meerkat, mierkat',\n 300: 'tiger beetle',\n 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',\n 302: 'ground beetle, carabid beetle',\n 303: 'long-horned beetle, longicorn, longicorn beetle',\n 304: 'leaf beetle, chrysomelid',\n 305: 'dung beetle',\n 306: 'rhinoceros beetle',\n 307: 'weevil',\n 308: 'fly',\n 309: 'bee',\n 310: 'ant, emmet, pismire',\n 311: 'grasshopper, hopper',\n 312: 'cricket',\n 313: 'walking stick, walkingstick, stick insect',\n 314: 'cockroach, roach',\n 315: 'mantis, mantid',\n 316: 'cicada, cicala',\n 317: 'leafhopper',\n 318: 'lacewing, lacewing fly',\n 319: \"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\",\n 320: 'damselfly',\n 321: 'admiral',\n 322: 'ringlet, ringlet butterfly',\n 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',\n 324: 'cabbage butterfly',\n 325: 'sulphur butterfly, sulfur butterfly',\n 326: 'lycaenid, lycaenid butterfly',\n 327: 'starfish, sea star',\n 328: 'sea urchin',\n 329: 'sea cucumber, holothurian',\n 330: 'wood rabbit, cottontail, cottontail rabbit',\n 331: 'hare',\n 332: 'Angora, Angora rabbit',\n 333: 'hamster',\n 334: 'porcupine, hedgehog',\n 335: 'fox squirrel, eastern fox squirrel, Sciurus niger',\n 336: 'marmot',\n 337: 'beaver',\n 338: 'guinea pig, Cavia cobaya',\n 339: 'sorrel',\n 340: 'zebra',\n 341: 'hog, pig, grunter, squealer, Sus scrofa',\n 342: 'wild boar, boar, Sus scrofa',\n 343: 'warthog',\n 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',\n 345: 'ox',\n 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',\n 347: 'bison',\n 348: 'ram, tup',\n 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',\n 350: 'ibex, Capra ibex',\n 351: 'hartebeest',\n 352: 'impala, Aepyceros melampus',\n 353: 'gazelle',\n 354: 'Arabian camel, dromedary, Camelus dromedarius',\n 355: 'llama',\n 356: 'weasel',\n 357: 'mink',\n 358: 'polecat, fitch, foulmart, foumart, Mustela putorius',\n 359: 'black-footed ferret, ferret, Mustela nigripes',\n 360: 'otter',\n 361: 'skunk, polecat, wood pussy',\n 362: 'badger',\n 363: 'armadillo',\n 364: 'three-toed sloth, ai, Bradypus tridactylus',\n 365: 'orangutan, orang, orangutang, Pongo pygmaeus',\n 366: 'gorilla, Gorilla gorilla',\n 367: 'chimpanzee, chimp, Pan troglodytes',\n 368: 'gibbon, Hylobates lar',\n 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',\n 370: 'guenon, guenon monkey',\n 371: 'patas, hussar monkey, Erythrocebus patas',\n 372: 'baboon',\n 373: 'macaque',\n 374: 'langur',\n 375: 'colobus, colobus monkey',\n 376: 'proboscis monkey, Nasalis larvatus',\n 377: 'marmoset',\n 378: 'capuchin, ringtail, Cebus capucinus',\n 379: 'howler monkey, howler',\n 380: 'titi, titi monkey',\n 381: 'spider monkey, Ateles geoffroyi',\n 382: 'squirrel monkey, Saimiri sciureus',\n 383: 'Madagascar cat, ring-tailed lemur, Lemur catta',\n 384: 'indri, indris, Indri indri, Indri brevicaudatus',\n 385: 'Indian elephant, Elephas maximus',\n 386: 'African elephant, Loxodonta africana',\n 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',\n 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n 389: 'barracouta, snoek',\n 390: 'eel',\n 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',\n 392: 'rock beauty, Holocanthus tricolor',\n 393: 'anemone fish',\n 394: 'sturgeon',\n 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',\n 396: 'lionfish',\n 397: 'puffer, pufferfish, blowfish, globefish',\n 398: 'abacus',\n 399: 'abaya',\n 400: \"academic gown, academic robe, judge's robe\",\n 401: 'accordion, piano accordion, squeeze box',\n 402: 'acoustic guitar',\n 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',\n 404: 'airliner',\n 405: 'airship, dirigible',\n 406: 'altar',\n 407: 'ambulance',\n 408: 'amphibian, amphibious vehicle',\n 409: 'analog clock',\n 410: 'apiary, bee house',\n 411: 'apron',\n 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',\n 413: 'assault rifle, assault gun',\n 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',\n 415: 'bakery, bakeshop, bakehouse',\n 416: 'balance beam, beam',\n 417: 'balloon',\n 418: 'ballpoint, ballpoint pen, ballpen, Biro',\n 419: 'Band Aid',\n 420: 'banjo',\n 421: 'bannister, banister, balustrade, balusters, handrail',\n 422: 'barbell',\n 423: 'barber chair',\n 424: 'barbershop',\n 425: 'barn',\n 426: 'barometer',\n 427: 'barrel, cask',\n 428: 'barrow, garden cart, lawn cart, wheelbarrow',\n 429: 'baseball',\n 430: 'basketball',\n 431: 'bassinet',\n 432: 'bassoon',\n 433: 'bathing cap, swimming cap',\n 434: 'bath towel',\n 435: 'bathtub, bathing tub, bath, tub',\n 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',\n 437: 'beacon, lighthouse, beacon light, pharos',\n 438: 'beaker',\n 439: 'bearskin, busby, shako',\n 440: 'beer bottle',\n 441: 'beer glass',\n 442: 'bell cote, bell cot',\n 443: 'bib',\n 444: 'bicycle-built-for-two, tandem bicycle, tandem',\n 445: 'bikini, two-piece',\n 446: 'binder, ring-binder',\n 447: 'binoculars, field glasses, opera glasses',\n 448: 'birdhouse',\n 449: 'boathouse',\n 450: 'bobsled, bobsleigh, bob',\n 451: 'bolo tie, bolo, bola tie, bola',\n 452: 'bonnet, poke bonnet',\n 453: 'bookcase',\n 454: 'bookshop, bookstore, bookstall',\n 455: 'bottlecap',\n 456: 'bow',\n 457: 'bow tie, bow-tie, bowtie',\n 458: 'brass, memorial tablet, plaque',\n 459: 'brassiere, bra, bandeau',\n 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',\n 461: 'breastplate, aegis, egis',\n 462: 'broom',\n 463: 'bucket, pail',\n 464: 'buckle',\n 465: 'bulletproof vest',\n 466: 'bullet train, bullet',\n 467: 'butcher shop, meat market',\n 468: 'cab, hack, taxi, taxicab',\n 469: 'caldron, cauldron',\n 470: 'candle, taper, wax light',\n 471: 'cannon',\n 472: 'canoe',\n 473: 'can opener, tin opener',\n 474: 'cardigan',\n 475: 'car mirror',\n 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',\n 477: \"carpenter's kit, tool kit\",\n 478: 'carton',\n 479: 'car wheel',\n 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',\n 481: 'cassette',\n 482: 'cassette player',\n 483: 'castle',\n 484: 'catamaran',\n 485: 'CD player',\n 486: 'cello, violoncello',\n 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',\n 488: 'chain',\n 489: 'chainlink fence',\n 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',\n 491: 'chain saw, chainsaw',\n 492: 'chest',\n 493: 'chiffonier, commode',\n 494: 'chime, bell, gong',\n 495: 'china cabinet, china closet',\n 496: 'Christmas stocking',\n 497: 'church, church building',\n 498: 'cinema, movie theater, movie theatre, movie house, picture palace',\n 499: 'cleaver, meat cleaver, chopper',\n 500: 'cliff dwelling',\n 501: 'cloak',\n 502: 'clog, geta, patten, sabot',\n 503: 'cocktail shaker',\n 504: 'coffee mug',\n 505: 'coffeepot',\n 506: 'coil, spiral, volute, whorl, helix',\n 507: 'combination lock',\n 508: 'computer keyboard, keypad',\n 509: 'confectionery, confectionary, candy store',\n 510: 'container ship, containership, container vessel',\n 511: 'convertible',\n 512: 'corkscrew, bottle screw',\n 513: 'cornet, horn, trumpet, trump',\n 514: 'cowboy boot',\n 515: 'cowboy hat, ten-gallon hat',\n 516: 'cradle',\n 517: 'crane',\n 518: 'crash helmet',\n 519: 'crate',\n 520: 'crib, cot',\n 521: 'Crock Pot',\n 522: 'croquet ball',\n 523: 'crutch',\n 524: 'cuirass',\n 525: 'dam, dike, dyke',\n 526: 'desk',\n 527: 'desktop computer',\n 528: 'dial telephone, dial phone',\n 529: 'diaper, nappy, napkin',\n 530: 'digital clock',\n 531: 'digital watch',\n 532: 'dining table, board',\n 533: 'dishrag, dishcloth',\n 534: 'dishwasher, dish washer, dishwashing machine',\n 535: 'disk brake, disc brake',\n 536: 'dock, dockage, docking facility',\n 537: 'dogsled, dog sled, dog sleigh',\n 538: 'dome',\n 539: 'doormat, welcome mat',\n 540: 'drilling platform, offshore rig',\n 541: 'drum, membranophone, tympan',\n 542: 'drumstick',\n 543: 'dumbbell',\n 544: 'Dutch oven',\n 545: 'electric fan, blower',\n 546: 'electric guitar',\n 547: 'electric locomotive',\n 548: 'entertainment center',\n 549: 'envelope',\n 550: 'espresso maker',\n 551: 'face powder',\n 552: 'feather boa, boa',\n 553: 'file, file cabinet, filing cabinet',\n 554: 'fireboat',\n 555: 'fire engine, fire truck',\n 556: 'fire screen, fireguard',\n 557: 'flagpole, flagstaff',\n 558: 'flute, transverse flute',\n 559: 'folding chair',\n 560: 'football helmet',\n 561: 'forklift',\n 562: 'fountain',\n 563: 'fountain pen',\n 564: 'four-poster',\n 565: 'freight car',\n 566: 'French horn, horn',\n 567: 'frying pan, frypan, skillet',\n 568: 'fur coat',\n 569: 'garbage truck, dustcart',\n 570: 'gasmask, respirator, gas helmet',\n 571: 'gas pump, gasoline pump, petrol pump, island dispenser',\n 572: 'goblet',\n 573: 'go-kart',\n 574: 'golf ball',\n 575: 'golfcart, golf cart',\n 576: 'gondola',\n 577: 'gong, tam-tam',\n 578: 'gown',\n 579: 'grand piano, grand',\n 580: 'greenhouse, nursery, glasshouse',\n 581: 'grille, radiator grille',\n 582: 'grocery store, grocery, food market, market',\n 583: 'guillotine',\n 584: 'hair slide',\n 585: 'hair spray',\n 586: 'half track',\n 587: 'hammer',\n 588: 'hamper',\n 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',\n 590: 'hand-held computer, hand-held microcomputer',\n 591: 'handkerchief, hankie, hanky, hankey',\n 592: 'hard disc, hard disk, fixed disk',\n 593: 'harmonica, mouth organ, harp, mouth harp',\n 594: 'harp',\n 595: 'harvester, reaper',\n 596: 'hatchet',\n 597: 'holster',\n 598: 'home theater, home theatre',\n 599: 'honeycomb',\n 600: 'hook, claw',\n 601: 'hoopskirt, crinoline',\n 602: 'horizontal bar, high bar',\n 603: 'horse cart, horse-cart',\n 604: 'hourglass',\n 605: 'iPod',\n 606: 'iron, smoothing iron',\n 607: \"jack-o'-lantern\",\n 608: 'jean, blue jean, denim',\n 609: 'jeep, landrover',\n 610: 'jersey, T-shirt, tee shirt',\n 611: 'jigsaw puzzle',\n 612: 'jinrikisha, ricksha, rickshaw',\n 613: 'joystick',\n 614: 'kimono',\n 615: 'knee pad',\n 616: 'knot',\n 617: 'lab coat, laboratory coat',\n 618: 'ladle',\n 619: 'lampshade, lamp shade',\n 620: 'laptop, laptop computer',\n 621: 'lawn mower, mower',\n 622: 'lens cap, lens cover',\n 623: 'letter opener, paper knife, paperknife',\n 624: 'library',\n 625: 'lifeboat',\n 626: 'lighter, light, igniter, ignitor',\n 627: 'limousine, limo',\n 628: 'liner, ocean liner',\n 629: 'lipstick, lip rouge',\n 630: 'Loafer',\n 631: 'lotion',\n 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',\n 633: \"loupe, jeweler's loupe\",\n 634: 'lumbermill, sawmill',\n 635: 'magnetic compass',\n 636: 'mailbag, postbag',\n 637: 'mailbox, letter box',\n 638: 'maillot',\n 639: 'maillot, tank suit',\n 640: 'manhole cover',\n 641: 'maraca',\n 642: 'marimba, xylophone',\n 643: 'mask',\n 644: 'matchstick',\n 645: 'maypole',\n 646: 'maze, labyrinth',\n 647: 'measuring cup',\n 648: 'medicine chest, medicine cabinet',\n 649: 'megalith, megalithic structure',\n 650: 'microphone, mike',\n 651: 'microwave, microwave oven',\n 652: 'military uniform',\n 653: 'milk can',\n 654: 'minibus',\n 655: 'miniskirt, mini',\n 656: 'minivan',\n 657: 'missile',\n 658: 'mitten',\n 659: 'mixing bowl',\n 660: 'mobile home, manufactured home',\n 661: 'Model T',\n 662: 'modem',\n 663: 'monastery',\n 664: 'monitor',\n 665: 'moped',\n 666: 'mortar',\n 667: 'mortarboard',\n 668: 'mosque',\n 669: 'mosquito net',\n 670: 'motor scooter, scooter',\n 671: 'mountain bike, all-terrain bike, off-roader',\n 672: 'mountain tent',\n 673: 'mouse, computer mouse',\n 674: 'mousetrap',\n 675: 'moving van',\n 676: 'muzzle',\n 677: 'nail',\n 678: 'neck brace',\n 679: 'necklace',\n 680: 'nipple',\n 681: 'notebook, notebook computer',\n 682: 'obelisk',\n 683: 'oboe, hautboy, hautbois',\n 684: 'ocarina, sweet potato',\n 685: 'odometer, hodometer, mileometer, milometer',\n 686: 'oil filter',\n 687: 'organ, pipe organ',\n 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',\n 689: 'overskirt',\n 690: 'oxcart',\n 691: 'oxygen mask',\n 692: 'packet',\n 693: 'paddle, boat paddle',\n 694: 'paddlewheel, paddle wheel',\n 695: 'padlock',\n 696: 'paintbrush',\n 697: \"pajama, pyjama, pj's, jammies\",\n 698: 'palace',\n 699: 'panpipe, pandean pipe, syrinx',\n 700: 'paper towel',\n 701: 'parachute, chute',\n 702: 'parallel bars, bars',\n 703: 'park bench',\n 704: 'parking meter',\n 705: 'passenger car, coach, carriage',\n 706: 'patio, terrace',\n 707: 'pay-phone, pay-station',\n 708: 'pedestal, plinth, footstall',\n 709: 'pencil box, pencil case',\n 710: 'pencil sharpener',\n 711: 'perfume, essence',\n 712: 'Petri dish',\n 713: 'photocopier',\n 714: 'pick, plectrum, plectron',\n 715: 'pickelhaube',\n 716: 'picket fence, paling',\n 717: 'pickup, pickup truck',\n 718: 'pier',\n 719: 'piggy bank, penny bank',\n 720: 'pill bottle',\n 721: 'pillow',\n 722: 'ping-pong ball',\n 723: 'pinwheel',\n 724: 'pirate, pirate ship',\n 725: 'pitcher, ewer',\n 726: \"plane, carpenter's plane, woodworking plane\",\n 727: 'planetarium',\n 728: 'plastic bag',\n 729: 'plate rack',\n 730: 'plow, plough',\n 731: \"plunger, plumber's helper\",\n 732: 'Polaroid camera, Polaroid Land camera',\n 733: 'pole',\n 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',\n 735: 'poncho',\n 736: 'pool table, billiard table, snooker table',\n 737: 'pop bottle, soda bottle',\n 738: 'pot, flowerpot',\n 739: \"potter's wheel\",\n 740: 'power drill',\n 741: 'prayer rug, prayer mat',\n 742: 'printer',\n 743: 'prison, prison house',\n 744: 'projectile, missile',\n 745: 'projector',\n 746: 'puck, hockey puck',\n 747: 'punching bag, punch bag, punching ball, punchball',\n 748: 'purse',\n 749: 'quill, quill pen',\n 750: 'quilt, comforter, comfort, puff',\n 751: 'racer, race car, racing car',\n 752: 'racket, racquet',\n 753: 'radiator',\n 754: 'radio, wireless',\n 755: 'radio telescope, radio reflector',\n 756: 'rain barrel',\n 757: 'recreational vehicle, RV, R.V.',\n 758: 'reel',\n 759: 'reflex camera',\n 760: 'refrigerator, icebox',\n 761: 'remote control, remote',\n 762: 'restaurant, eating house, eating place, eatery',\n 763: 'revolver, six-gun, six-shooter',\n 764: 'rifle',\n 765: 'rocking chair, rocker',\n 766: 'rotisserie',\n 767: 'rubber eraser, rubber, pencil eraser',\n 768: 'rugby ball',\n 769: 'rule, ruler',\n 770: 'running shoe',\n 771: 'safe',\n 772: 'safety pin',\n 773: 'saltshaker, salt shaker',\n 774: 'sandal',\n 775: 'sarong',\n 776: 'sax, saxophone',\n 777: 'scabbard',\n 778: 'scale, weighing machine',\n 779: 'school bus',\n 780: 'schooner',\n 781: 'scoreboard',\n 782: 'screen, CRT screen',\n 783: 'screw',\n 784: 'screwdriver',\n 785: 'seat belt, seatbelt',\n 786: 'sewing machine',\n 787: 'shield, buckler',\n 788: 'shoe shop, shoe-shop, shoe store',\n 789: 'shoji',\n 790: 'shopping basket',\n 791: 'shopping cart',\n 792: 'shovel',\n 793: 'shower cap',\n 794: 'shower curtain',\n 795: 'ski',\n 796: 'ski mask',\n 797: 'sleeping bag',\n 798: 'slide rule, slipstick',\n 799: 'sliding door',\n 800: 'slot, one-armed bandit',\n 801: 'snorkel',\n 802: 'snowmobile',\n 803: 'snowplow, snowplough',\n 804: 'soap dispenser',\n 805: 'soccer ball',\n 806: 'sock',\n 807: 'solar dish, solar collector, solar furnace',\n 808: 'sombrero',\n 809: 'soup bowl',\n 810: 'space bar',\n 811: 'space heater',\n 812: 'space shuttle',\n 813: 'spatula',\n 814: 'speedboat',\n 815: \"spider web, spider's web\",\n 816: 'spindle',\n 817: 'sports car, sport car',\n 818: 'spotlight, spot',\n 819: 'stage',\n 820: 'steam locomotive',\n 821: 'steel arch bridge',\n 822: 'steel drum',\n 823: 'stethoscope',\n 824: 'stole',\n 825: 'stone wall',\n 826: 'stopwatch, stop watch',\n 827: 'stove',\n 828: 'strainer',\n 829: 'streetcar, tram, tramcar, trolley, trolley car',\n 830: 'stretcher',\n 831: 'studio couch, day bed',\n 832: 'stupa, tope',\n 833: 'submarine, pigboat, sub, U-boat',\n 834: 'suit, suit of clothes',\n 835: 'sundial',\n 836: 'sunglass',\n 837: 'sunglasses, dark glasses, shades',\n 838: 'sunscreen, sunblock, sun blocker',\n 839: 'suspension bridge',\n 840: 'swab, swob, mop',\n 841: 'sweatshirt',\n 842: 'swimming trunks, bathing trunks',\n 843: 'swing',\n 844: 'switch, electric switch, electrical switch',\n 845: 'syringe',\n 846: 'table lamp',\n 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',\n 848: 'tape player',\n 849: 'teapot',\n 850: 'teddy, teddy bear',\n 851: 'television, television system',\n 852: 'tennis ball',\n 853: 'thatch, thatched roof',\n 854: 'theater curtain, theatre curtain',\n 855: 'thimble',\n 856: 'thresher, thrasher, threshing machine',\n 857: 'throne',\n 858: 'tile roof',\n 859: 'toaster',\n 860: 'tobacco shop, tobacconist shop, tobacconist',\n 861: 'toilet seat',\n 862: 'torch',\n 863: 'totem pole',\n 864: 'tow truck, tow car, wrecker',\n 865: 'toyshop',\n 866: 'tractor',\n 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',\n 868: 'tray',\n 869: 'trench coat',\n 870: 'tricycle, trike, velocipede',\n 871: 'trimaran',\n 872: 'tripod',\n 873: 'triumphal arch',\n 874: 'trolleybus, trolley coach, trackless trolley',\n 875: 'trombone',\n 876: 'tub, vat',\n 877: 'turnstile',\n 878: 'typewriter keyboard',\n 879: 'umbrella',\n 880: 'unicycle, monocycle',\n 881: 'upright, upright piano',\n 882: 'vacuum, vacuum cleaner',\n 883: 'vase',\n 884: 'vault',\n 885: 'velvet',\n 886: 'vending machine',\n 887: 'vestment',\n 888: 'viaduct',\n 889: 'violin, fiddle',\n 890: 'volleyball',\n 891: 'waffle iron',\n 892: 'wall clock',\n 893: 'wallet, billfold, notecase, pocketbook',\n 894: 'wardrobe, closet, press',\n 895: 'warplane, military plane',\n 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',\n 897: 'washer, automatic washer, washing machine',\n 898: 'water bottle',\n 899: 'water jug',\n 900: 'water tower',\n 901: 'whiskey jug',\n 902: 'whistle',\n 903: 'wig',\n 904: 'window screen',\n 905: 'window shade',\n 906: 'Windsor tie',\n 907: 'wine bottle',\n 908: 'wing',\n 909: 'wok',\n 910: 'wooden spoon',\n 911: 'wool, woolen, woollen',\n 912: 'worm fence, snake fence, snake-rail fence, Virginia fence',\n 913: 'wreck',\n 914: 'yawl',\n 915: 'yurt',\n 916: 'web site, website, internet site, site',\n 917: 'comic book',\n 918: 'crossword puzzle, crossword',\n 919: 'street sign',\n 920: 'traffic light, traffic signal, stoplight',\n 921: 'book jacket, dust cover, dust jacket, dust wrapper',\n 922: 'menu',\n 923: 'plate',\n 924: 'guacamole',\n 925: 'consomme',\n 926: 'hot pot, hotpot',\n 927: 'trifle',\n 928: 'ice cream, icecream',\n 929: 'ice lolly, lolly, lollipop, popsicle',\n 930: 'French loaf',\n 931: 'bagel, beigel',\n 932: 'pretzel',\n 933: 'cheeseburger',\n 934: 'hotdog, hot dog, red hot',\n 935: 'mashed potato',\n 936: 'head cabbage',\n 937: 'broccoli',\n 938: 'cauliflower',\n 939: 'zucchini, courgette',\n 940: 'spaghetti squash',\n 941: 'acorn squash',\n 942: 'butternut squash',\n 943: 'cucumber, cuke',\n 944: 'artichoke, globe artichoke',\n 945: 'bell pepper',\n 946: 'cardoon',\n 947: 'mushroom',\n 948: 'Granny Smith',\n 949: 'strawberry',\n 950: 'orange',\n 951: 'lemon',\n 952: 'fig',\n 953: 'pineapple, ananas',\n 954: 'banana',\n 955: 'jackfruit, jak, jack',\n 956: 'custard apple',\n 957: 'pomegranate',\n 958: 'hay',\n 959: 'carbonara',\n 960: 'chocolate sauce, chocolate syrup',\n 961: 'dough',\n 962: 'meat loaf, meatloaf',\n 963: 'pizza, pizza pie',\n 964: 'potpie',\n 965: 'burrito',\n 966: 'red wine',\n 967: 'espresso',\n 968: 'cup',\n 969: 'eggnog',\n 970: 'alp',\n 971: 'bubble',\n 972: 'cliff, drop, drop-off',\n 973: 'coral reef',\n 974: 'geyser',\n 975: 'lakeside, lakeshore',\n 976: 'promontory, headland, head, foreland',\n 977: 'sandbar, sand bar',\n 978: 'seashore, coast, seacoast, sea-coast',\n 979: 'valley, vale',\n 980: 'volcano',\n 981: 'ballplayer, baseball player',\n 982: 'groom, bridegroom',\n 983: 'scuba diver',\n 984: 'rapeseed',\n 985: 'daisy',\n 986: \"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\",\n 987: 'corn',\n 988: 'acorn',\n 989: 'hip, rose hip, rosehip',\n 990: 'buckeye, horse chestnut, conker',\n 991: 'coral fungus',\n 992: 'agaric',\n 993: 'gyromitra',\n 994: 'stinkhorn, carrion fungus',\n 995: 'earthstar',\n 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',\n 997: 'bolete',\n 998: 'ear, spike, capitulum',\n 999: 'toilet tissue, toilet paper, bathroom tissue'}"
  },
  {
    "path": "Chapter02/lenet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Collecting torch==2.2\\n\",\n      \"  Downloading torch-2.2.0-cp310-cp310-manylinux1_x86_64.whl.metadata (25 kB)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Collecting typing-extensions>=4.8.0 (from torch==2.2)\\n\",\n      \"  Downloading typing_extensions-4.9.0-py3-none-any.whl.metadata (3.0 kB)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Collecting nvidia-nccl-cu12==2.19.3 (from torch==2.2)\\n\",\n      \"  Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Collecting triton==2.2.0 (from torch==2.2)\\n\",\n      \"  Downloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.4 kB)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Downloading torch-2.2.0-cp310-cp310-manylinux1_x86_64.whl (755.5 MB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m755.5/755.5 MB\\u001b[0m \\u001b[31m3.1 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m:00:01\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hDownloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m166.0/166.0 MB\\u001b[0m \\u001b[31m14.1 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hDownloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m167.9/167.9 MB\\u001b[0m \\u001b[31m13.8 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hDownloading typing_extensions-4.9.0-py3-none-any.whl (32 kB)\\n\",\n      \"Installing collected packages: typing-extensions, triton, nvidia-nccl-cu12, torch\\n\",\n      \"  Attempting uninstall: typing-extensions\\n\",\n      \"    Found existing installation: typing_extensions 4.3.0\\n\",\n      \"    Uninstalling typing_extensions-4.3.0:\\n\",\n      \"      Successfully uninstalled typing_extensions-4.3.0\\n\",\n      \"  Attempting uninstall: triton\\n\",\n      \"    Found existing installation: triton 2.1.0\\n\",\n      \"    Uninstalling triton-2.1.0:\\n\",\n      \"      Successfully uninstalled triton-2.1.0\\n\",\n      \"  Attempting uninstall: nvidia-nccl-cu12\\n\",\n      \"    Found existing installation: nvidia-nccl-cu12 2.18.1\\n\",\n      \"    Uninstalling nvidia-nccl-cu12-2.18.1:\\n\",\n      \"      Successfully uninstalled nvidia-nccl-cu12-2.18.1\\n\",\n      \"  Attempting uninstall: torch\\n\",\n      \"    Found existing installation: torch 2.1.2\\n\",\n      \"    Uninstalling torch-2.1.2:\\n\",\n      \"      Successfully uninstalled torch-2.1.2\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"torchaudio 2.1.2 requires torch==2.1.2, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"torchvision 0.16.2 requires torch==2.1.2, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"ydata-profiling 4.6.0 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires attrs==21.4.0, but you have attrs 23.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires filelock==3.7.1, but you have filelock 3.13.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires fsspec==2022.5.0, but you have fsspec 2023.12.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires huggingface-hub==0.8.1, but you have huggingface-hub 0.20.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires numpy==1.23.1, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires Pillow==9.2.0, but you have pillow 10.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tokenizers==0.12.1, but you have tokenizers 0.15.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torch==1.12.0, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torchvision==0.13.0, but you have torchvision 0.16.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tqdm==4.64.0, but you have tqdm 4.66.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires transformers==4.20.1, but you have transformers 4.36.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires typing_extensions==4.3.0, but you have typing-extensions 4.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed nvidia-nccl-cu12-2.19.3 torch-2.2.0 triton-2.2.0 typing-extensions-4.9.0\\n\",\n      \"Collecting torchvision==0.17\\n\",\n      \"  Downloading torchvision-0.17.0-cp310-cp310-manylinux1_x86_64.whl.metadata (6.6 kB)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Downloading torchvision-0.17.0-cp310-cp310-manylinux1_x86_64.whl (6.9 MB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m6.9/6.9 MB\\u001b[0m \\u001b[31m95.0 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m:00:01\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hInstalling collected packages: torchvision\\n\",\n      \"  Attempting uninstall: torchvision\\n\",\n      \"    Found existing installation: torchvision 0.16.2\\n\",\n      \"    Uninstalling torchvision-0.16.2:\\n\",\n      \"      Successfully uninstalled torchvision-0.16.2\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"minimagen 0.0.9 requires attrs==21.4.0, but you have attrs 23.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires filelock==3.7.1, but you have filelock 3.13.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires fsspec==2022.5.0, but you have fsspec 2023.12.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires huggingface-hub==0.8.1, but you have huggingface-hub 0.20.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires numpy==1.23.1, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires Pillow==9.2.0, but you have pillow 10.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tokenizers==0.12.1, but you have tokenizers 0.15.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torch==1.12.0, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torchvision==0.13.0, but you have torchvision 0.17.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tqdm==4.64.0, but you have tqdm 4.66.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires transformers==4.20.1, but you have transformers 4.36.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires typing_extensions==4.3.0, but you have typing-extensions 4.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed torchvision-0.17.0\\n\",\n      \"Collecting nltk==3.7\\n\",\n      \"  Downloading nltk-3.7-py3-none-any.whl (1.5 MB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m1.5/1.5 MB\\u001b[0m \\u001b[31m28.9 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hRequirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\",\n      \"Installing collected packages: nltk\\n\",\n      \"Successfully installed nltk-3.7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \" \\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torchvision.transforms as transforms\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LeNet(\\n\",\n      \"  (cn1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\\n\",\n      \"  (cn2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\\n\",\n      \"  (fc1): Linear(in_features=400, out_features=120, bias=True)\\n\",\n      \"  (fc2): Linear(in_features=120, out_features=84, bias=True)\\n\",\n      \"  (fc3): Linear(in_features=84, out_features=10, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class LeNet(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(LeNet, self).__init__()\\n\",\n    \"        # 3 input image channel, 6 output feature maps and 5x5 conv kernel\\n\",\n    \"        self.cn1 = nn.Conv2d(3, 6, 5)\\n\",\n    \"        # 6 input image channel, 16 output feature maps and 5x5 conv kernel\\n\",\n    \"        self.cn2 = nn.Conv2d(6, 16, 5)\\n\",\n    \"        # fully connected layers of size 120, 84 and 10\\n\",\n    \"        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5*5 is the spatial dimension at this layer\\n\",\n    \"        self.fc2 = nn.Linear(120, 84)\\n\",\n    \"        self.fc3 = nn.Linear(84, 10)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        # Convolution with 5x5 kernel\\n\",\n    \"        x = F.relu(self.cn1(x))\\n\",\n    \"        # Max pooling over a (2, 2) window\\n\",\n    \"        x = F.max_pool2d(x, (2, 2))\\n\",\n    \"        # Convolution with 5x5 kernel\\n\",\n    \"        x = F.relu(self.cn2(x))\\n\",\n    \"        # Max pooling over a (2, 2) window\\n\",\n    \"        x = F.max_pool2d(x, (2, 2))\\n\",\n    \"        # Flatten spatial and depth dimensions into a single vector\\n\",\n    \"        x = x.view(-1, self.flattened_features(x))\\n\",\n    \"        # Fully connected operations\\n\",\n    \"        x = F.relu(self.fc1(x))\\n\",\n    \"        x = F.relu(self.fc2(x))\\n\",\n    \"        x = self.fc3(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    def flattened_features(self, x):\\n\",\n    \"        # all except the first (batch) dimension\\n\",\n    \"        size = x.size()[1:]  \\n\",\n    \"        num_feats = 1\\n\",\n    \"        for s in size:\\n\",\n    \"            num_feats *= s\\n\",\n    \"        return num_feats\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lenet = LeNet()\\n\",\n    \"print(lenet)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(net, trainloader, optim, epoch):\\n\",\n    \"    # initialize loss\\n\",\n    \"    loss_total = 0.0\\n\",\n    \"    \\n\",\n    \"    for i, data in enumerate(trainloader, 0):\\n\",\n    \"        # get the inputs; data is a list of [inputs, labels]\\n\",\n    \"        # ip refers to the input images, and ground_truth refers to the output classes the images belong to\\n\",\n    \"        ip, ground_truth = data\\n\",\n    \"\\n\",\n    \"        # zero the parameter gradients\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"\\n\",\n    \"        # forward pass + backward pass + optimization step\\n\",\n    \"        op = net(ip)\\n\",\n    \"        loss = nn.CrossEntropyLoss()(op, ground_truth)\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"\\n\",\n    \"        # update loss\\n\",\n    \"        loss_total += loss.item()\\n\",\n    \"        \\n\",\n    \"        # print loss statistics\\n\",\n    \"        if (i+1) % 1000 == 0:    # print at the interval of 1000 mini-batches\\n\",\n    \"            print('[Epoch number : %d, Mini-batches: %5d] loss: %.3f' %\\n\",\n    \"                  (epoch + 1, i + 1, loss_total / 200))\\n\",\n    \"            loss_total = 0.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(net, testloader):\\n\",\n    \"    success = 0\\n\",\n    \"    counter = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for data in testloader:\\n\",\n    \"            im, ground_truth = data\\n\",\n    \"            op = net(im)\\n\",\n    \"            _, pred = torch.max(op.data, 1)\\n\",\n    \"            counter += ground_truth.size(0)\\n\",\n    \"            success += (pred == ground_truth).sum().item()\\n\",\n    \"\\n\",\n    \"    print('LeNet accuracy on 10000 images from test dataset: %d %%' % (\\n\",\n    \"        100 * success / counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The mean and std are kept as 0.5 for normalizing pixel values as the pixel values are originally in the range 0 to 1\\n\",\n    \"train_transform = transforms.Compose([transforms.RandomHorizontalFlip(),\\n\",\n    \"                                      transforms.RandomCrop(32, 4),\\n\",\n    \"                                      transforms.ToTensor(),\\n\",\n    \"                                      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform)\\n\",\n    \"trainloader = torch.utils.data.DataLoader(trainset, batch_size=8, shuffle=True)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform)\\n\",\n    \"testloader = torch.utils.data.DataLoader(testset, batch_size=10000, shuffle=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# ordering is important\\n\",\n    \"classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"    horse  ||  deer  ||  plane  ||  dog\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# define a function that displays an image\\n\",\n    \"def imageshow(image):\\n\",\n    \"    # un-normalize the image\\n\",\n    \"    image = image/2 + 0.5     \\n\",\n    \"    npimage = image.numpy()\\n\",\n    \"    plt.imshow(np.transpose(npimage, (1, 2, 0)))\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# sample images from training set\\n\",\n    \"dataiter = iter(trainloader)\\n\",\n    \"images, labels = next(dataiter)\\n\",\n    \"\\n\",\n    \"# display images in a grid\\n\",\n    \"num_images = 4\\n\",\n    \"imageshow(torchvision.utils.make_grid(images[:num_images]))\\n\",\n    \"# print labels\\n\",\n    \"print('    '+'  ||  '.join(classes[labels[j]] for j in range(num_images)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[Epoch number : 1, Mini-batches:  1000] loss: 9.887\\n\",\n      \"[Epoch number : 1, Mini-batches:  2000] loss: 8.766\\n\",\n      \"[Epoch number : 1, Mini-batches:  3000] loss: 8.350\\n\",\n      \"[Epoch number : 1, Mini-batches:  4000] loss: 8.136\\n\",\n      \"[Epoch number : 1, Mini-batches:  5000] loss: 7.806\\n\",\n      \"[Epoch number : 1, Mini-batches:  6000] loss: 7.584\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 46 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 2, Mini-batches:  1000] loss: 7.502\\n\",\n      \"[Epoch number : 2, Mini-batches:  2000] loss: 7.380\\n\",\n      \"[Epoch number : 2, Mini-batches:  3000] loss: 7.173\\n\",\n      \"[Epoch number : 2, Mini-batches:  4000] loss: 7.106\\n\",\n      \"[Epoch number : 2, Mini-batches:  5000] loss: 7.109\\n\",\n      \"[Epoch number : 2, Mini-batches:  6000] loss: 7.039\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 51 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 3, Mini-batches:  1000] loss: 6.959\\n\",\n      \"[Epoch number : 3, Mini-batches:  2000] loss: 6.903\\n\",\n      \"[Epoch number : 3, Mini-batches:  3000] loss: 6.900\\n\",\n      \"[Epoch number : 3, Mini-batches:  4000] loss: 7.021\\n\",\n      \"[Epoch number : 3, Mini-batches:  5000] loss: 6.664\\n\",\n      \"[Epoch number : 3, Mini-batches:  6000] loss: 6.599\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 54 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 4, Mini-batches:  1000] loss: 6.680\\n\",\n      \"[Epoch number : 4, Mini-batches:  2000] loss: 6.588\\n\",\n      \"[Epoch number : 4, Mini-batches:  3000] loss: 6.650\\n\",\n      \"[Epoch number : 4, Mini-batches:  4000] loss: 6.549\\n\",\n      \"[Epoch number : 4, Mini-batches:  5000] loss: 6.576\\n\",\n      \"[Epoch number : 4, Mini-batches:  6000] loss: 6.487\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 57 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 5, Mini-batches:  1000] loss: 6.418\\n\",\n      \"[Epoch number : 5, Mini-batches:  2000] loss: 6.424\\n\",\n      \"[Epoch number : 5, Mini-batches:  3000] loss: 6.482\\n\",\n      \"[Epoch number : 5, Mini-batches:  4000] loss: 6.401\\n\",\n      \"[Epoch number : 5, Mini-batches:  5000] loss: 6.400\\n\",\n      \"[Epoch number : 5, Mini-batches:  6000] loss: 6.274\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 58 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 6, Mini-batches:  1000] loss: 6.238\\n\",\n      \"[Epoch number : 6, Mini-batches:  2000] loss: 6.196\\n\",\n      \"[Epoch number : 6, Mini-batches:  3000] loss: 6.319\\n\",\n      \"[Epoch number : 6, Mini-batches:  4000] loss: 6.271\\n\",\n      \"[Epoch number : 6, Mini-batches:  5000] loss: 6.148\\n\",\n      \"[Epoch number : 6, Mini-batches:  6000] loss: 6.217\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 58 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 7, Mini-batches:  1000] loss: 6.156\\n\",\n      \"[Epoch number : 7, Mini-batches:  2000] loss: 6.053\\n\",\n      \"[Epoch number : 7, Mini-batches:  3000] loss: 6.147\\n\",\n      \"[Epoch number : 7, Mini-batches:  4000] loss: 6.060\\n\",\n      \"[Epoch number : 7, Mini-batches:  5000] loss: 6.067\\n\",\n      \"[Epoch number : 7, Mini-batches:  6000] loss: 6.200\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 59 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 8, Mini-batches:  1000] loss: 5.992\\n\",\n      \"[Epoch number : 8, Mini-batches:  2000] loss: 6.001\\n\",\n      \"[Epoch number : 8, Mini-batches:  3000] loss: 6.057\\n\",\n      \"[Epoch number : 8, Mini-batches:  4000] loss: 5.926\\n\",\n      \"[Epoch number : 8, Mini-batches:  5000] loss: 6.069\\n\",\n      \"[Epoch number : 8, Mini-batches:  6000] loss: 6.044\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 61 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 9, Mini-batches:  1000] loss: 5.948\\n\",\n      \"[Epoch number : 9, Mini-batches:  2000] loss: 5.840\\n\",\n      \"[Epoch number : 9, Mini-batches:  3000] loss: 5.897\\n\",\n      \"[Epoch number : 9, Mini-batches:  4000] loss: 5.856\\n\",\n      \"[Epoch number : 9, Mini-batches:  5000] loss: 5.847\\n\",\n      \"[Epoch number : 9, Mini-batches:  6000] loss: 6.045\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 60 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 10, Mini-batches:  1000] loss: 5.873\\n\",\n      \"[Epoch number : 10, Mini-batches:  2000] loss: 5.963\\n\",\n      \"[Epoch number : 10, Mini-batches:  3000] loss: 5.907\\n\",\n      \"[Epoch number : 10, Mini-batches:  4000] loss: 5.846\\n\",\n      \"[Epoch number : 10, Mini-batches:  5000] loss: 5.864\\n\",\n      \"[Epoch number : 10, Mini-batches:  6000] loss: 5.741\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 61 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 11, Mini-batches:  1000] loss: 5.710\\n\",\n      \"[Epoch number : 11, Mini-batches:  2000] loss: 5.795\\n\",\n      \"[Epoch number : 11, Mini-batches:  3000] loss: 5.881\\n\",\n      \"[Epoch number : 11, Mini-batches:  4000] loss: 5.849\\n\",\n      \"[Epoch number : 11, Mini-batches:  5000] loss: 5.751\\n\",\n      \"[Epoch number : 11, Mini-batches:  6000] loss: 5.729\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 63 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 12, Mini-batches:  1000] loss: 5.732\\n\",\n      \"[Epoch number : 12, Mini-batches:  2000] loss: 5.732\\n\",\n      \"[Epoch number : 12, Mini-batches:  3000] loss: 5.701\\n\",\n      \"[Epoch number : 12, Mini-batches:  4000] loss: 5.761\\n\",\n      \"[Epoch number : 12, Mini-batches:  5000] loss: 5.734\\n\",\n      \"[Epoch number : 12, Mini-batches:  6000] loss: 5.684\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 62 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 13, Mini-batches:  1000] loss: 5.674\\n\",\n      \"[Epoch number : 13, Mini-batches:  2000] loss: 5.703\\n\",\n      \"[Epoch number : 13, Mini-batches:  3000] loss: 5.653\\n\",\n      \"[Epoch number : 13, Mini-batches:  4000] loss: 5.652\\n\",\n      \"[Epoch number : 13, Mini-batches:  5000] loss: 5.700\\n\",\n      \"[Epoch number : 13, Mini-batches:  6000] loss: 5.794\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 62 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 14, Mini-batches:  1000] loss: 5.745\\n\",\n      \"[Epoch number : 14, Mini-batches:  2000] loss: 5.605\\n\",\n      \"[Epoch number : 14, Mini-batches:  3000] loss: 5.685\\n\",\n      \"[Epoch number : 14, Mini-batches:  4000] loss: 5.550\\n\",\n      \"[Epoch number : 14, Mini-batches:  5000] loss: 5.623\\n\",\n      \"[Epoch number : 14, Mini-batches:  6000] loss: 5.631\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 61 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 15, Mini-batches:  1000] loss: 5.637\\n\",\n      \"[Epoch number : 15, Mini-batches:  2000] loss: 5.594\\n\",\n      \"[Epoch number : 15, Mini-batches:  3000] loss: 5.581\\n\",\n      \"[Epoch number : 15, Mini-batches:  4000] loss: 5.522\\n\",\n      \"[Epoch number : 15, Mini-batches:  5000] loss: 5.532\\n\",\n      \"[Epoch number : 15, Mini-batches:  6000] loss: 5.615\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 62 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 16, Mini-batches:  1000] loss: 5.627\\n\",\n      \"[Epoch number : 16, Mini-batches:  2000] loss: 5.543\\n\",\n      \"[Epoch number : 16, Mini-batches:  3000] loss: 5.557\\n\",\n      \"[Epoch number : 16, Mini-batches:  4000] loss: 5.572\\n\",\n      \"[Epoch number : 16, Mini-batches:  5000] loss: 5.535\\n\",\n      \"[Epoch number : 16, Mini-batches:  6000] loss: 5.603\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 62 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 17, Mini-batches:  1000] loss: 5.411\\n\",\n      \"[Epoch number : 17, Mini-batches:  2000] loss: 5.526\\n\",\n      \"[Epoch number : 17, Mini-batches:  3000] loss: 5.572\\n\",\n      \"[Epoch number : 17, Mini-batches:  4000] loss: 5.533\\n\",\n      \"[Epoch number : 17, Mini-batches:  5000] loss: 5.590\\n\",\n      \"[Epoch number : 17, Mini-batches:  6000] loss: 5.395\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 18, Mini-batches:  1000] loss: 5.455\\n\",\n      \"[Epoch number : 18, Mini-batches:  2000] loss: 5.596\\n\",\n      \"[Epoch number : 18, Mini-batches:  3000] loss: 5.455\\n\",\n      \"[Epoch number : 18, Mini-batches:  4000] loss: 5.430\\n\",\n      \"[Epoch number : 18, Mini-batches:  5000] loss: 5.441\\n\",\n      \"[Epoch number : 18, Mini-batches:  6000] loss: 5.424\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 19, Mini-batches:  1000] loss: 5.441\\n\",\n      \"[Epoch number : 19, Mini-batches:  2000] loss: 5.513\\n\",\n      \"[Epoch number : 19, Mini-batches:  3000] loss: 5.546\\n\",\n      \"[Epoch number : 19, Mini-batches:  4000] loss: 5.492\\n\",\n      \"[Epoch number : 19, Mini-batches:  5000] loss: 5.348\\n\",\n      \"[Epoch number : 19, Mini-batches:  6000] loss: 5.475\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 20, Mini-batches:  1000] loss: 5.308\\n\",\n      \"[Epoch number : 20, Mini-batches:  2000] loss: 5.449\\n\",\n      \"[Epoch number : 20, Mini-batches:  3000] loss: 5.489\\n\",\n      \"[Epoch number : 20, Mini-batches:  4000] loss: 5.411\\n\",\n      \"[Epoch number : 20, Mini-batches:  5000] loss: 5.492\\n\",\n      \"[Epoch number : 20, Mini-batches:  6000] loss: 5.449\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 21, Mini-batches:  1000] loss: 5.417\\n\",\n      \"[Epoch number : 21, Mini-batches:  2000] loss: 5.461\\n\",\n      \"[Epoch number : 21, Mini-batches:  3000] loss: 5.266\\n\",\n      \"[Epoch number : 21, Mini-batches:  4000] loss: 5.411\\n\",\n      \"[Epoch number : 21, Mini-batches:  5000] loss: 5.460\\n\",\n      \"[Epoch number : 21, Mini-batches:  6000] loss: 5.451\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 22, Mini-batches:  1000] loss: 5.332\\n\",\n      \"[Epoch number : 22, Mini-batches:  2000] loss: 5.474\\n\",\n      \"[Epoch number : 22, Mini-batches:  3000] loss: 5.373\\n\",\n      \"[Epoch number : 22, Mini-batches:  4000] loss: 5.412\\n\",\n      \"[Epoch number : 22, Mini-batches:  5000] loss: 5.334\\n\",\n      \"[Epoch number : 22, Mini-batches:  6000] loss: 5.371\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 63 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 23, Mini-batches:  1000] loss: 5.378\\n\",\n      \"[Epoch number : 23, Mini-batches:  2000] loss: 5.376\\n\",\n      \"[Epoch number : 23, Mini-batches:  3000] loss: 5.365\\n\",\n      \"[Epoch number : 23, Mini-batches:  4000] loss: 5.452\\n\",\n      \"[Epoch number : 23, Mini-batches:  5000] loss: 5.362\\n\",\n      \"[Epoch number : 23, Mini-batches:  6000] loss: 5.328\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 24, Mini-batches:  1000] loss: 5.217\\n\",\n      \"[Epoch number : 24, Mini-batches:  2000] loss: 5.435\\n\",\n      \"[Epoch number : 24, Mini-batches:  3000] loss: 5.383\\n\",\n      \"[Epoch number : 24, Mini-batches:  4000] loss: 5.400\\n\",\n      \"[Epoch number : 24, Mini-batches:  5000] loss: 5.364\\n\",\n      \"[Epoch number : 24, Mini-batches:  6000] loss: 5.263\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 25, Mini-batches:  1000] loss: 5.255\\n\",\n      \"[Epoch number : 25, Mini-batches:  2000] loss: 5.392\\n\",\n      \"[Epoch number : 25, Mini-batches:  3000] loss: 5.320\\n\",\n      \"[Epoch number : 25, Mini-batches:  4000] loss: 5.278\\n\",\n      \"[Epoch number : 25, Mini-batches:  5000] loss: 5.261\\n\",\n      \"[Epoch number : 25, Mini-batches:  6000] loss: 5.187\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 26, Mini-batches:  1000] loss: 5.267\\n\",\n      \"[Epoch number : 26, Mini-batches:  2000] loss: 5.323\\n\",\n      \"[Epoch number : 26, Mini-batches:  3000] loss: 5.307\\n\",\n      \"[Epoch number : 26, Mini-batches:  4000] loss: 5.257\\n\",\n      \"[Epoch number : 26, Mini-batches:  5000] loss: 5.373\\n\",\n      \"[Epoch number : 26, Mini-batches:  6000] loss: 5.279\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 27, Mini-batches:  1000] loss: 5.342\\n\",\n      \"[Epoch number : 27, Mini-batches:  2000] loss: 5.241\\n\",\n      \"[Epoch number : 27, Mini-batches:  3000] loss: 5.224\\n\",\n      \"[Epoch number : 27, Mini-batches:  4000] loss: 5.312\\n\",\n      \"[Epoch number : 27, Mini-batches:  5000] loss: 5.297\\n\",\n      \"[Epoch number : 27, Mini-batches:  6000] loss: 5.257\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 28, Mini-batches:  1000] loss: 5.200\\n\",\n      \"[Epoch number : 28, Mini-batches:  2000] loss: 5.226\\n\",\n      \"[Epoch number : 28, Mini-batches:  3000] loss: 5.478\\n\",\n      \"[Epoch number : 28, Mini-batches:  4000] loss: 5.274\\n\",\n      \"[Epoch number : 28, Mini-batches:  5000] loss: 5.341\\n\",\n      \"[Epoch number : 28, Mini-batches:  6000] loss: 5.284\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 63 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 29, Mini-batches:  1000] loss: 5.187\\n\",\n      \"[Epoch number : 29, Mini-batches:  2000] loss: 5.323\\n\",\n      \"[Epoch number : 29, Mini-batches:  3000] loss: 5.350\\n\",\n      \"[Epoch number : 29, Mini-batches:  4000] loss: 5.278\\n\",\n      \"[Epoch number : 29, Mini-batches:  5000] loss: 5.212\\n\",\n      \"[Epoch number : 29, Mini-batches:  6000] loss: 5.291\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 30, Mini-batches:  1000] loss: 5.278\\n\",\n      \"[Epoch number : 30, Mini-batches:  2000] loss: 5.200\\n\",\n      \"[Epoch number : 30, Mini-batches:  3000] loss: 5.122\\n\",\n      \"[Epoch number : 30, Mini-batches:  4000] loss: 5.264\\n\",\n      \"[Epoch number : 30, Mini-batches:  5000] loss: 5.274\\n\",\n      \"[Epoch number : 30, Mini-batches:  6000] loss: 5.334\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 31, Mini-batches:  1000] loss: 5.158\\n\",\n      \"[Epoch number : 31, Mini-batches:  2000] loss: 5.076\\n\",\n      \"[Epoch number : 31, Mini-batches:  3000] loss: 5.238\\n\",\n      \"[Epoch number : 31, Mini-batches:  4000] loss: 5.370\\n\",\n      \"[Epoch number : 31, Mini-batches:  5000] loss: 5.139\\n\",\n      \"[Epoch number : 31, Mini-batches:  6000] loss: 5.246\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 32, Mini-batches:  1000] loss: 5.234\\n\",\n      \"[Epoch number : 32, Mini-batches:  2000] loss: 5.215\\n\",\n      \"[Epoch number : 32, Mini-batches:  3000] loss: 5.256\\n\",\n      \"[Epoch number : 32, Mini-batches:  4000] loss: 5.161\\n\",\n      \"[Epoch number : 32, Mini-batches:  5000] loss: 5.028\\n\",\n      \"[Epoch number : 32, Mini-batches:  6000] loss: 5.264\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 63 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 33, Mini-batches:  1000] loss: 5.198\\n\",\n      \"[Epoch number : 33, Mini-batches:  2000] loss: 5.196\\n\",\n      \"[Epoch number : 33, Mini-batches:  3000] loss: 5.177\\n\",\n      \"[Epoch number : 33, Mini-batches:  4000] loss: 5.111\\n\",\n      \"[Epoch number : 33, Mini-batches:  5000] loss: 5.333\\n\",\n      \"[Epoch number : 33, Mini-batches:  6000] loss: 5.214\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 34, Mini-batches:  1000] loss: 5.189\\n\",\n      \"[Epoch number : 34, Mini-batches:  2000] loss: 5.221\\n\",\n      \"[Epoch number : 34, Mini-batches:  3000] loss: 5.151\\n\",\n      \"[Epoch number : 34, Mini-batches:  4000] loss: 5.220\\n\",\n      \"[Epoch number : 34, Mini-batches:  5000] loss: 5.301\\n\",\n      \"[Epoch number : 34, Mini-batches:  6000] loss: 5.172\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 35, Mini-batches:  1000] loss: 5.087\\n\",\n      \"[Epoch number : 35, Mini-batches:  2000] loss: 5.227\\n\",\n      \"[Epoch number : 35, Mini-batches:  3000] loss: 5.275\\n\",\n      \"[Epoch number : 35, Mini-batches:  4000] loss: 5.156\\n\",\n      \"[Epoch number : 35, Mini-batches:  5000] loss: 5.131\\n\",\n      \"[Epoch number : 35, Mini-batches:  6000] loss: 5.228\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 36, Mini-batches:  1000] loss: 5.083\\n\",\n      \"[Epoch number : 36, Mini-batches:  2000] loss: 5.078\\n\",\n      \"[Epoch number : 36, Mini-batches:  3000] loss: 5.193\\n\",\n      \"[Epoch number : 36, Mini-batches:  4000] loss: 5.221\\n\",\n      \"[Epoch number : 36, Mini-batches:  5000] loss: 5.222\\n\",\n      \"[Epoch number : 36, Mini-batches:  6000] loss: 5.238\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 37, Mini-batches:  1000] loss: 5.130\\n\",\n      \"[Epoch number : 37, Mini-batches:  2000] loss: 5.166\\n\",\n      \"[Epoch number : 37, Mini-batches:  3000] loss: 5.093\\n\",\n      \"[Epoch number : 37, Mini-batches:  4000] loss: 5.246\\n\",\n      \"[Epoch number : 37, Mini-batches:  5000] loss: 5.137\\n\",\n      \"[Epoch number : 37, Mini-batches:  6000] loss: 5.147\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 38, Mini-batches:  1000] loss: 5.033\\n\",\n      \"[Epoch number : 38, Mini-batches:  2000] loss: 5.109\\n\",\n      \"[Epoch number : 38, Mini-batches:  3000] loss: 5.077\\n\",\n      \"[Epoch number : 38, Mini-batches:  4000] loss: 5.176\\n\",\n      \"[Epoch number : 38, Mini-batches:  5000] loss: 5.188\\n\",\n      \"[Epoch number : 38, Mini-batches:  6000] loss: 5.229\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 39, Mini-batches:  1000] loss: 5.122\\n\",\n      \"[Epoch number : 39, Mini-batches:  2000] loss: 5.079\\n\",\n      \"[Epoch number : 39, Mini-batches:  3000] loss: 5.116\\n\",\n      \"[Epoch number : 39, Mini-batches:  4000] loss: 4.991\\n\",\n      \"[Epoch number : 39, Mini-batches:  5000] loss: 5.109\\n\",\n      \"[Epoch number : 39, Mini-batches:  6000] loss: 5.157\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 65 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 40, Mini-batches:  1000] loss: 5.091\\n\",\n      \"[Epoch number : 40, Mini-batches:  2000] loss: 5.166\\n\",\n      \"[Epoch number : 40, Mini-batches:  3000] loss: 5.136\\n\",\n      \"[Epoch number : 40, Mini-batches:  4000] loss: 5.131\\n\",\n      \"[Epoch number : 40, Mini-batches:  5000] loss: 5.179\\n\",\n      \"[Epoch number : 40, Mini-batches:  6000] loss: 5.104\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 41, Mini-batches:  1000] loss: 5.090\\n\",\n      \"[Epoch number : 41, Mini-batches:  2000] loss: 5.137\\n\",\n      \"[Epoch number : 41, Mini-batches:  3000] loss: 5.074\\n\",\n      \"[Epoch number : 41, Mini-batches:  4000] loss: 5.262\\n\",\n      \"[Epoch number : 41, Mini-batches:  5000] loss: 5.030\\n\",\n      \"[Epoch number : 41, Mini-batches:  6000] loss: 5.105\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 42, Mini-batches:  1000] loss: 5.090\\n\",\n      \"[Epoch number : 42, Mini-batches:  2000] loss: 5.021\\n\",\n      \"[Epoch number : 42, Mini-batches:  3000] loss: 5.134\\n\",\n      \"[Epoch number : 42, Mini-batches:  4000] loss: 5.113\\n\",\n      \"[Epoch number : 42, Mini-batches:  5000] loss: 5.131\\n\",\n      \"[Epoch number : 42, Mini-batches:  6000] loss: 5.099\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 43, Mini-batches:  1000] loss: 4.968\\n\",\n      \"[Epoch number : 43, Mini-batches:  2000] loss: 5.034\\n\",\n      \"[Epoch number : 43, Mini-batches:  3000] loss: 5.095\\n\",\n      \"[Epoch number : 43, Mini-batches:  4000] loss: 5.175\\n\",\n      \"[Epoch number : 43, Mini-batches:  5000] loss: 5.099\\n\",\n      \"[Epoch number : 43, Mini-batches:  6000] loss: 5.199\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 44, Mini-batches:  1000] loss: 5.122\\n\",\n      \"[Epoch number : 44, Mini-batches:  2000] loss: 5.094\\n\",\n      \"[Epoch number : 44, Mini-batches:  3000] loss: 5.053\\n\",\n      \"[Epoch number : 44, Mini-batches:  4000] loss: 5.118\\n\",\n      \"[Epoch number : 44, Mini-batches:  5000] loss: 5.125\\n\",\n      \"[Epoch number : 44, Mini-batches:  6000] loss: 5.087\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 68 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 45, Mini-batches:  1000] loss: 5.004\\n\",\n      \"[Epoch number : 45, Mini-batches:  2000] loss: 5.104\\n\",\n      \"[Epoch number : 45, Mini-batches:  3000] loss: 5.182\\n\",\n      \"[Epoch number : 45, Mini-batches:  4000] loss: 5.023\\n\",\n      \"[Epoch number : 45, Mini-batches:  5000] loss: 5.122\\n\",\n      \"[Epoch number : 45, Mini-batches:  6000] loss: 5.056\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 46, Mini-batches:  1000] loss: 5.044\\n\",\n      \"[Epoch number : 46, Mini-batches:  2000] loss: 5.173\\n\",\n      \"[Epoch number : 46, Mini-batches:  3000] loss: 5.134\\n\",\n      \"[Epoch number : 46, Mini-batches:  4000] loss: 4.978\\n\",\n      \"[Epoch number : 46, Mini-batches:  5000] loss: 5.118\\n\",\n      \"[Epoch number : 46, Mini-batches:  6000] loss: 5.053\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 47, Mini-batches:  1000] loss: 5.053\\n\",\n      \"[Epoch number : 47, Mini-batches:  2000] loss: 4.968\\n\",\n      \"[Epoch number : 47, Mini-batches:  3000] loss: 5.028\\n\",\n      \"[Epoch number : 47, Mini-batches:  4000] loss: 5.106\\n\",\n      \"[Epoch number : 47, Mini-batches:  5000] loss: 5.084\\n\",\n      \"[Epoch number : 47, Mini-batches:  6000] loss: 5.099\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 66 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 48, Mini-batches:  1000] loss: 4.947\\n\",\n      \"[Epoch number : 48, Mini-batches:  2000] loss: 5.073\\n\",\n      \"[Epoch number : 48, Mini-batches:  3000] loss: 5.047\\n\",\n      \"[Epoch number : 48, Mini-batches:  4000] loss: 5.097\\n\",\n      \"[Epoch number : 48, Mini-batches:  5000] loss: 5.164\\n\",\n      \"[Epoch number : 48, Mini-batches:  6000] loss: 5.010\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 64 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 49, Mini-batches:  1000] loss: 5.069\\n\",\n      \"[Epoch number : 49, Mini-batches:  2000] loss: 5.035\\n\",\n      \"[Epoch number : 49, Mini-batches:  3000] loss: 5.044\\n\",\n      \"[Epoch number : 49, Mini-batches:  4000] loss: 5.109\\n\",\n      \"[Epoch number : 49, Mini-batches:  5000] loss: 5.019\\n\",\n      \"[Epoch number : 49, Mini-batches:  6000] loss: 5.063\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 67 %\\n\",\n      \"\\n\",\n      \"[Epoch number : 50, Mini-batches:  1000] loss: 5.016\\n\",\n      \"[Epoch number : 50, Mini-batches:  2000] loss: 5.016\\n\",\n      \"[Epoch number : 50, Mini-batches:  3000] loss: 5.065\\n\",\n      \"[Epoch number : 50, Mini-batches:  4000] loss: 5.076\\n\",\n      \"[Epoch number : 50, Mini-batches:  5000] loss: 5.094\\n\",\n      \"[Epoch number : 50, Mini-batches:  6000] loss: 5.019\\n\",\n      \"\\n\",\n      \"LeNet accuracy on 10000 images from test dataset: 68 %\\n\",\n      \"\\n\",\n      \"Finished Training\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# define optimizer\\n\",\n    \"optim = torch.optim.Adam(lenet.parameters(), lr=0.001)\\n\",\n    \"\\n\",\n    \"# training loop over the dataset multiple times\\n\",\n    \"for epoch in range(50):  \\n\",\n    \"    train(lenet, trainloader, optim, epoch)\\n\",\n    \"    print()\\n\",\n    \"    test(lenet, testloader)\\n\",\n    \"    print()\\n\",\n    \"\\n\",\n    \"print('Finished Training')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_path = './cifar_model.pth'\\n\",\n    \"torch.save(lenet.state_dict(), model_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Label:         cat  ship  ship plane\\n\",\n      \"Prediction:    cat  ship  ship  ship\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# load test dataset images\\n\",\n    \"d_iter = iter(testloader)\\n\",\n    \"im, ground_truth = next(d_iter)\\n\",\n    \"\\n\",\n    \"# print images and ground truth\\n\",\n    \"imageshow(torchvision.utils.make_grid(im[:4]))\\n\",\n    \"print('Label:      ', ' '.join('%5s' % classes[ground_truth[j]] for j in range(4)))\\n\",\n    \"\\n\",\n    \"# load model\\n\",\n    \"lenet_cached = LeNet()\\n\",\n    \"lenet_cached.load_state_dict(torch.load(model_path))\\n\",\n    \"\\n\",\n    \"# model inference\\n\",\n    \"op = lenet_cached(im)\\n\",\n    \"\\n\",\n    \"# print predictions\\n\",\n    \"_, pred = torch.max(op, 1)\\n\",\n    \"\\n\",\n    \"print('Prediction: ', ' '.join('%5s' % classes[pred[j]] for j in range(4)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model accuracy on 10000 images from test dataset: 68 %\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"success = 0\\n\",\n    \"counter = 0\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for data in testloader:\\n\",\n    \"        im, ground_truth = data\\n\",\n    \"        op = lenet_cached(im)\\n\",\n    \"        _, pred = torch.max(op.data, 1)\\n\",\n    \"        counter += ground_truth.size(0)\\n\",\n    \"        success += (pred == ground_truth).sum().item()\\n\",\n    \"\\n\",\n    \"print('Model accuracy on 10000 images from test dataset: %d %%' % (\\n\",\n    \"    100 * success / counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model accuracy for class plane : 69 %\\n\",\n      \"Model accuracy for class   car : 81 %\\n\",\n      \"Model accuracy for class  bird : 56 %\\n\",\n      \"Model accuracy for class   cat : 52 %\\n\",\n      \"Model accuracy for class  deer : 57 %\\n\",\n      \"Model accuracy for class   dog : 49 %\\n\",\n      \"Model accuracy for class  frog : 71 %\\n\",\n      \"Model accuracy for class horse : 78 %\\n\",\n      \"Model accuracy for class  ship : 84 %\\n\",\n      \"Model accuracy for class truck : 80 %\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class_sucess = list(0. for i in range(10))\\n\",\n    \"class_counter = list(0. for i in range(10))\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for data in testloader:\\n\",\n    \"        im, ground_truth = data\\n\",\n    \"        op = lenet_cached(im)\\n\",\n    \"        _, pred = torch.max(op, 1)\\n\",\n    \"        c = (pred == ground_truth).squeeze()\\n\",\n    \"        for i in range(10000):\\n\",\n    \"            ground_truth_curr = ground_truth[i]\\n\",\n    \"            class_sucess[ground_truth_curr] += c[i].item()\\n\",\n    \"            class_counter[ground_truth_curr] += 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i in range(10):\\n\",\n    \"    print('Model accuracy for class %5s : %2d %%' % (\\n\",\n    \"        classes[i], 100 * class_sucess[i] / class_counter[i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/transfer_learning_alexnet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import copy\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.optim import lr_scheduler\\n\",\n    \"from torchvision import datasets, models, transforms\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Dataset information\\n\",\n    \"- There are 240 training images and 150 validation images divided equally between the two classes (bees and ants). \\n\",\n    \"- We download the dataset from this link - https://www.kaggle.com/ajayrana/hymenoptera-data and store it in the current working directory. \\n\",\n    \"- In order to download the dataset you will need to login to kaggle. If you do not already have a kaggle account, you will need to register.\\n\",\n    \"- More information about the dataset can be found here: https://hymenoptera.elsiklab.missouri.edu/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ddir = 'hymenoptera_data'\\n\",\n    \"\\n\",\n    \"# Data normalization and augmentation transformations for train dataset\\n\",\n    \"# Only normalization transformation for validation dataset\\n\",\n    \"# The mean and std for normalization are calculated as the mean of all pixel values for all images in the training set per each image channel - R, G and B\\n\",\n    \"\\n\",\n    \"data_transformers = {\\n\",\n    \"    'train': transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(),\\n\",\n    \"                                    transforms.ToTensor(), \\n\",\n    \"                                    transforms.Normalize([0.490, 0.449, 0.411], [0.231, 0.221, 0.230])]),\\n\",\n    \"    'val': transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), \\n\",\n    \"                                      transforms.ToTensor(), \\n\",\n    \"                                      transforms.Normalize([0.490, 0.449, 0.411], [0.231, 0.221, 0.230])])}\\n\",\n    \"\\n\",\n    \"img_data = {k: datasets.ImageFolder(os.path.join(ddir, k), data_transformers[k]) for k in ['train', 'val']}\\n\",\n    \"dloaders = {k: torch.utils.data.DataLoader(img_data[k], batch_size=8, shuffle=True) \\n\",\n    \"            for k in ['train', 'val']}\\n\",\n    \"dset_sizes = {x: len(img_data[x]) for x in ['train', 'val']}\\n\",\n    \"classes = img_data['train'].classes\\n\",\n    \"dvc = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def imageshow(img, text=None):\\n\",\n    \"    img = img.numpy().transpose((1, 2, 0))\\n\",\n    \"    avg = np.array([0.490, 0.449, 0.411])\\n\",\n    \"    stddev = np.array([0.231, 0.221, 0.230])\\n\",\n    \"    img = stddev * img + avg\\n\",\n    \"    img = np.clip(img, 0, 1)\\n\",\n    \"    plt.imshow(img)\\n\",\n    \"    if text is not None:\\n\",\n    \"        plt.title(text)\\n\",\n    \"\\n\",\n    \"# Generate one train dataset batch\\n\",\n    \"imgs, cls = next(iter(dloaders['train']))\\n\",\n    \"\\n\",\n    \"# Generate a grid from batch\\n\",\n    \"grid = torchvision.utils.make_grid(imgs)\\n\",\n    \"\\n\",\n    \"imageshow(grid, text=[classes[c] for c in cls])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def finetune_model(pretrained_model, loss_func, optim, epochs=10):\\n\",\n    \"    start = time.time()\\n\",\n    \"\\n\",\n    \"    model_weights = copy.deepcopy(pretrained_model.state_dict())\\n\",\n    \"    accuracy = 0.0\\n\",\n    \"\\n\",\n    \"    for e in range(epochs):\\n\",\n    \"        print(f'Epoch number {e}/{epochs - 1}')\\n\",\n    \"        print('=' * 20)\\n\",\n    \"\\n\",\n    \"        # for each epoch we run through the training and validation set\\n\",\n    \"        for dset in ['train', 'val']:\\n\",\n    \"            if dset == 'train':\\n\",\n    \"                pretrained_model.train()  # set model to train mode (i.e. trainbale weights)\\n\",\n    \"            else:\\n\",\n    \"                pretrained_model.eval()   # set model to validation mode\\n\",\n    \"\\n\",\n    \"            loss = 0.0\\n\",\n    \"            successes = 0\\n\",\n    \"\\n\",\n    \"            # iterate over the (training/validation) data.\\n\",\n    \"            for imgs, tgts in dloaders[dset]:\\n\",\n    \"                imgs = imgs.to(dvc)\\n\",\n    \"                tgts = tgts.to(dvc)\\n\",\n    \"                optim.zero_grad()\\n\",\n    \"                \\n\",\n    \"                with torch.set_grad_enabled(dset == 'train'):\\n\",\n    \"                    ops = pretrained_model(imgs)\\n\",\n    \"                    _, preds = torch.max(ops, 1)\\n\",\n    \"                    loss_curr = loss_func(ops, tgts)\\n\",\n    \"                    # backward pass only if in training mode\\n\",\n    \"                    if dset == 'train':\\n\",\n    \"                        loss_curr.backward()\\n\",\n    \"                        optim.step()\\n\",\n    \"\\n\",\n    \"                loss += loss_curr.item() * imgs.size(0)\\n\",\n    \"                successes += torch.sum(preds == tgts.data)\\n\",\n    \"\\n\",\n    \"            loss_epoch = loss / dset_sizes[dset]\\n\",\n    \"            accuracy_epoch = successes.double() / dset_sizes[dset]\\n\",\n    \"\\n\",\n    \"            print(f'{dset} loss in this epoch: {loss_epoch}, accuracy in this epoch: {accuracy_epoch}')\\n\",\n    \"            if dset == 'val' and accuracy_epoch > accuracy:\\n\",\n    \"                accuracy = accuracy_epoch\\n\",\n    \"                model_weights = copy.deepcopy(pretrained_model.state_dict())\\n\",\n    \"        print()\\n\",\n    \"\\n\",\n    \"    time_delta = time.time() - start\\n\",\n    \"    print(f'Training finished in {time_delta // 60}mins {time_delta % 60}secs')\\n\",\n    \"    print(f'Best validation set accuracy: {accuracy}')\\n\",\n    \"\\n\",\n    \"    # load the best model version (weights)\\n\",\n    \"    pretrained_model.load_state_dict(model_weights)\\n\",\n    \"    return pretrained_model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def visualize_predictions(pretrained_model, max_num_imgs=4):\\n\",\n    \"    torch.manual_seed(1)\\n\",\n    \"    was_model_training = pretrained_model.training\\n\",\n    \"    pretrained_model.eval()\\n\",\n    \"    imgs_counter = 0\\n\",\n    \"    fig = plt.figure()\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for i, (imgs, tgts) in enumerate(dloaders['val']):\\n\",\n    \"            imgs = imgs.to(dvc)\\n\",\n    \"            tgts = tgts.to(dvc)\\n\",\n    \"            ops = pretrained_model(imgs)\\n\",\n    \"            _, preds = torch.max(ops, 1)\\n\",\n    \"            \\n\",\n    \"            for j in range(imgs.size()[0]):\\n\",\n    \"                imgs_counter += 1\\n\",\n    \"                ax = plt.subplot(max_num_imgs//2, 2, imgs_counter)\\n\",\n    \"                ax.axis('off')\\n\",\n    \"                ax.set_title(f'pred: {classes[preds[j]]} || target: {classes[tgts[j]]}')\\n\",\n    \"                imageshow(imgs.cpu().data[j])\\n\",\n    \"\\n\",\n    \"                if imgs_counter == max_num_imgs:\\n\",\n    \"                    pretrained_model.train(mode=was_model_training)\\n\",\n    \"                    return\\n\",\n    \"        pretrained_model.train(mode=was_model_training)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\\n\",\n      \"  warnings.warn(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\\n\",\n      \"  warnings.warn(msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential(\\n\",\n      \"  (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\\n\",\n      \"  (1): ReLU(inplace=True)\\n\",\n      \"  (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"  (4): ReLU(inplace=True)\\n\",\n      \"  (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (7): ReLU(inplace=True)\\n\",\n      \"  (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (9): ReLU(inplace=True)\\n\",\n      \"  (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (11): ReLU(inplace=True)\\n\",\n      \"  (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_finetune = models.alexnet(pretrained=True)\\n\",\n    \"print(model_finetune.features)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential(\\n\",\n      \"  (0): Dropout(p=0.5, inplace=False)\\n\",\n      \"  (1): Linear(in_features=9216, out_features=4096, bias=True)\\n\",\n      \"  (2): ReLU(inplace=True)\\n\",\n      \"  (3): Dropout(p=0.5, inplace=False)\\n\",\n      \"  (4): Linear(in_features=4096, out_features=4096, bias=True)\\n\",\n      \"  (5): ReLU(inplace=True)\\n\",\n      \"  (6): Linear(in_features=4096, out_features=1000, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(model_finetune.classifier)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch number 0/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.7751872637232796, accuracy in this epoch: 0.5204918032786885\\n\",\n      \"val loss in this epoch: 0.6339419616593255, accuracy in this epoch: 0.6339869281045751\\n\",\n      \"\\n\",\n      \"Epoch number 1/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.5628533636937376, accuracy in this epoch: 0.6885245901639344\\n\",\n      \"val loss in this epoch: 0.5051289816307866, accuracy in this epoch: 0.7777777777777778\\n\",\n      \"\\n\",\n      \"Epoch number 2/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.4783363899246591, accuracy in this epoch: 0.7622950819672131\\n\",\n      \"val loss in this epoch: 0.4450346809976241, accuracy in this epoch: 0.8104575163398693\\n\",\n      \"\\n\",\n      \"Epoch number 3/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.4173249428878065, accuracy in this epoch: 0.8237704918032787\\n\",\n      \"val loss in this epoch: 0.3971024395204058, accuracy in this epoch: 0.8431372549019608\\n\",\n      \"\\n\",\n      \"Epoch number 4/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.40321881458407544, accuracy in this epoch: 0.819672131147541\\n\",\n      \"val loss in this epoch: 0.36537496151487814, accuracy in this epoch: 0.8627450980392157\\n\",\n      \"\\n\",\n      \"Epoch number 5/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.36007155600141305, accuracy in this epoch: 0.8647540983606558\\n\",\n      \"val loss in this epoch: 0.3422834701787413, accuracy in this epoch: 0.8758169934640523\\n\",\n      \"\\n\",\n      \"Epoch number 6/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.3468564975945676, accuracy in this epoch: 0.8852459016393442\\n\",\n      \"val loss in this epoch: 0.32399294603603823, accuracy in this epoch: 0.8823529411764706\\n\",\n      \"\\n\",\n      \"Epoch number 7/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.3006822402115728, accuracy in this epoch: 0.8852459016393442\\n\",\n      \"val loss in this epoch: 0.3107166847372367, accuracy in this epoch: 0.8823529411764706\\n\",\n      \"\\n\",\n      \"Epoch number 8/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.33582790696718656, accuracy in this epoch: 0.860655737704918\\n\",\n      \"val loss in this epoch: 0.30031758532220243, accuracy in this epoch: 0.8954248366013072\\n\",\n      \"\\n\",\n      \"Epoch number 9/9\\n\",\n      \"====================\\n\",\n      \"train loss in this epoch: 0.31155503187023226, accuracy in this epoch: 0.860655737704918\\n\",\n      \"val loss in this epoch: 0.2928269924291598, accuracy in this epoch: 0.9019607843137255\\n\",\n      \"\\n\",\n      \"Training finished in 1.0mins 57.03927659988403secs\\n\",\n      \"Best validation set accuracy: 0.9019607843137255\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# change the last layer from 1000 classes to 2 classes\\n\",\n    \"model_finetune.classifier[6] = nn.Linear(4096, len(classes))\\n\",\n    \"\\n\",\n    \"loss_func = nn.CrossEntropyLoss()\\n\",\n    \"optim_finetune = optim.SGD(model_finetune.parameters(), lr=0.0001)\\n\",\n    \"\\n\",\n    \"# train (fine-tune) and validate the model\\n\",\n    \"model_finetune = finetune_model(model_finetune, loss_func, optim_finetune, epochs=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"visualize_predictions(model_finetune)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter02/vgg13_pretrained_run_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import copy\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.optim import lr_scheduler\\n\",\n    \"from torchvision import datasets, models, transforms\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ddir = 'hymenoptera_data'\\n\",\n    \"\\n\",\n    \"# Data normalization and augmentation transformations for train dataset\\n\",\n    \"# Only normalization transformation for validation dataset\\n\",\n    \"\\n\",\n    \"data_transformers = {\\n\",\n    \"    'train': transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(),\\n\",\n    \"                                    transforms.ToTensor(), \\n\",\n    \"                                    transforms.Normalize([0.490, 0.449, 0.411], [0.231, 0.221, 0.230])]),\\n\",\n    \"    'val': transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), \\n\",\n    \"                                      transforms.ToTensor(), \\n\",\n    \"                                      transforms.Normalize([0.490, 0.449, 0.411], [0.231, 0.221, 0.230])])}\\n\",\n    \"\\n\",\n    \"img_data = {k: datasets.ImageFolder(os.path.join(ddir, k), data_transformers[k]) for k in ['train', 'val']}\\n\",\n    \"dloaders = {k: torch.utils.data.DataLoader(img_data[k], batch_size=8, shuffle=True, num_workers=2) \\n\",\n    \"            for k in ['train', 'val']}\\n\",\n    \"dset_sizes = {x: len(img_data[x]) for x in ['train', 'val']}\\n\",\n    \"dvc = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{0: 'tench, Tinca tinca', 1: 'goldfish, Carassius auratus', 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 3: 'tiger shark, Galeocerdo cuvieri', 4: 'hammerhead, hammerhead shark'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import ast\\n\",\n    \"with open('./imagenet1000_clsidx_to_labels.txt') as f:\\n\",\n    \"    classes_data = f.read()\\n\",\n    \"classes_dict = ast.literal_eval(classes_data)\\n\",\n    \"print({k: classes_dict[k] for k in list(classes_dict)[:5]})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def imageshow(img, text=None):\\n\",\n    \"    img = img.numpy().transpose((1, 2, 0))\\n\",\n    \"    avg = np.array([0.490, 0.449, 0.411])\\n\",\n    \"    stddev = np.array([0.231, 0.221, 0.230])\\n\",\n    \"    img = stddev * img + avg\\n\",\n    \"    img = np.clip(img, 0, 1)\\n\",\n    \"    plt.imshow(img)\\n\",\n    \"    if text is not None:\\n\",\n    \"        plt.title(text)\\n\",\n    \"\\n\",\n    \"def visualize_predictions(pretrained_model, max_num_imgs=4):\\n\",\n    \"    torch.manual_seed(1)\\n\",\n    \"    was_model_training = pretrained_model.training\\n\",\n    \"    pretrained_model.eval()\\n\",\n    \"    imgs_counter = 0\\n\",\n    \"    fig = plt.figure()\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for i, (imgs, tgts) in enumerate(dloaders['val']):\\n\",\n    \"            imgs = imgs.to(dvc)\\n\",\n    \"            ops = pretrained_model(imgs)\\n\",\n    \"            _, preds = torch.max(ops, 1)\\n\",\n    \"            \\n\",\n    \"            for j in range(imgs.size()[0]):\\n\",\n    \"                imgs_counter += 1\\n\",\n    \"                ax = plt.subplot(max_num_imgs//2, 2, imgs_counter)\\n\",\n    \"                ax.axis('off')\\n\",\n    \"                ax.set_title(f'pred: {classes_dict[int(preds[j])]}')\\n\",\n    \"                imageshow(imgs.cpu().data[j])\\n\",\n    \"\\n\",\n    \"                if imgs_counter == max_num_imgs:\\n\",\n    \"                    pretrained_model.train(mode=was_model_training)\\n\",\n    \"                    return\\n\",\n    \"        pretrained_model.train(mode=was_model_training)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\\n\",\n      \"  warnings.warn(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG13_Weights.IMAGENET1K_V1`. You can also use `weights=VGG13_Weights.DEFAULT` to get the most up-to-date weights.\\n\",\n      \"  warnings.warn(msg)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = models.vgg13(pretrained=True) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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lLf+kvfdx9X/nKV/K2t72NL/zCL+TFL34xt956KxcvXuSP/uiPeOtb38rFixcBePGLX8xrX/tanv/85/OHf/iHPOxhD+ONb3xjv3CM5b//9//OV3zFV/Dyl7/8QQUk3XTTTfzIj/wId9xxB7fccgu/+Iu/yP/6X/+LN7zhDb0f8Ju/+Zv5pV/6Jb7927+dt73tbXzJl3wJKSXe/e5380u/9Eu85S1v4Qu+4At47GMfyw/+4A/yfd/3fdxxxx0861nP4uzZs7z//e/nV3/1V/nWb/1WXvayl31iA7yVzwj5dJ0nT3jCE3jsYx/Ly172Mu68807OnTvHm9/85k/KJz+fz7n11lv5xV/8RW655RauvvpqnvSkJ/GkJz2JO+64g8c85jG84AUv4N/+23/7gMf4dJprD3b9AHjqU5/KW9/6Vl796ldz00038ZjHPKYPfBMRnva0pz2oksS33HILL3rRi/gf/+N/cMMNN/AzP/Mz3H333fzsz/7sA+7zW7/1W7z0pS/lG7/xG7nlllvouo43vvGNvdL5UMhP/MRP8KVf+qXcdtttvPjFL+ZzPudzuPvuu/n93/99PvzhD/OOd7zjwR3oQcfzb+VEKakqf/zHf6zPec5z9OzZs3rVVVfpS1/6Ul0ulxvbAvqSl7zkxOPcfffd+pKXvERvvvlmretab7zxRn3GM56hb3jDGza2+8AHPqDPfOYzdWdnR6+99lr9ru/6Lv2N3/iNTzot74lPfKL+z//5P/WLvuiLdDab6aMe9Sh97Wtfe2zbpmn0R37kR/SJT3yiTqdTveqqq/SpT32q3n777XrlypWNbd/85jfrl37pl+ru7q7u7u7qE57wBH3JS16if/Inf/Jxr2krn1nymTBP/viP/1i/8iu/Us+cOaPXXnutvvjFL9Z3vOMdx9LXXvCCF+ju7u4DjsFYfu/3fk+f+tSn6mQy2biOd77znQro937v937c61J9cHOtzPOj8qhHPUq/5mu+5tjnR59Duf577713Y7sHut+Tzvdg1493v/vd+uVf/uU6n88V6FP09vf3FdDnPe95H3dMyn295S1v0Sc/+ck6nU71CU94wrF0u6Npee973/v0W77lW/Sxj32szmYzvfrqq/UrvuIr9K1vfevHHB/VIbXuR3/0R088x/jcD5SWd3TfIn/2Z3+mz3/+8/XGG2/Uuq714Q9/uH7t136t/vIv//LHHYsi4he+lT+nvOIVr+D222/n3nvvPRalupWtbMVkO08+MXnd617H93zP9/Bnf/Zn3HDDDQ/15Zwa+fVf/3W+9mu/lne84x3cdtttH3PbRz/60TzpSU/iP//n//z/6OpOv2x9+FvZyla2csrkbW97G9/5nd+5Bfsj8ra3vY3nPe95Hxfst3KybH34W9nKVrZyyuRNb3rTQ30Jp1J+9Ed/9KG+hE9r2Vr4W9nKVrayla18FsjWh7+VrWxlK1vZymeBbC38rWxlK1vZylY+C2QL+FvZyla2spWtfBbIqQra++Bde7SMalKrEFVoJaMilDYQQaEhA0IUUPC/hJAhoyAQ/bOEomoFGzL2fS1CVrV/okwkUmUBzaQoJN9ObEdiVqIKXU40KEFBUiJFIWuikkACYsq0omgWRAKCgh9HARFAQQWy2vVBhiAEhCiRFARRoQqRICDB7qUTAQQV28vUNUWSQAhkoFIlBjurILRkNAjBTx6ylqElKHR+PXY+QVCSQAJCVlR8P4SgAmIjnQRUBEWpkpKCUKvfraiNe9kcJYiQRRF3IInfOVlpBQSoCICiqiRRag002pFUkez7ilUQS6oQAlW2DztNCELWTAjBxluVLCD+7DtRJtneB4nB3h+1caCOZDITIipCCAEVIaP9+N181exT+LZv5VMpr/1X/1//zUqoHi3NGkIAIuNPVW1dKC+l+tIjAqplS9moYCc+B4fzaH+s8XHxNQfUj3f0issHw7HKfnZO6TdRxse2TfpGp6JAglHjU5Hg12klZYexkNF1lePljWtVjvyt2BqZBc2gZB83tb/LNeqw79HzDj8hBO3vt1zT8KiG7QSbgyGE/li2v/izPPqd/Qsh+iIr/ff9McXegzC6LvtcECrbVnS4no2xszsL/WeKkEEi9l75McP4QQc7BvDMZ30zp0FOFeAnzYQAqNJhYKIoOSsSIAnEDFkNIGX0sgHgi7q/1iRRogSyKgQhZp8fwV9LBSFQYwCSJEOEpEp0MEgY2CmZJEAI1Kp0rhgIgESyv/BZIKigAlEVFbsH0UzAgLnTjKgQJdgdlhdUIZNRFQKB5BNZCGQFjfbClYUpqmCvvk81UR81CFnI0a4lOsihavcSsL8xsFNMoUBtjFFsO4Eo9rM8n4i98FEV1x1s27IQiSlREXs+2cHe149e2cGBuEwI/JkJYoqMCDkrMUQkZ1PqVImqdCghRq+RbXsGVwQmBDpVkp+3Ajofk4iQgk3VoJAFWn9HgtoxWslUEv2dEiSaEvmpqey9lb8wkaI8jxZkCX0d9fJZv3n505VBVUWCMCwlm78P+40B/+OFPw0Afvzz8c8BlIqiIaKjbwdQ7JWUsoDhC9qxayzHldEV+GRnDM5lOzWwo1zD+N50Y7NyTepzWI+Mw1GwH37XBxi24+M7ALufbQTOYMC/Cfam6Cjiz7SMF4QwAH65dnqg9/OX793AOaqsDKNZrjP079tw+eX+Np/3p6ovwKdCThXgNyh1Un/QguQMMVCl4cVSH18Dk+HVrdSgLgWlIiBqyoEU61ahwwALVVMMgi3mZOhEicEsySgBVMliVroxBgbOUaDqIIlQqTEBwxJgL6gIZFFSynSAqBLA2IicAIgYI3Bkyvn7Y3cVkpCDATtBqBCnBQRJ9nKpQIqBTLazi1nWSZSQzNovlrWKAXrQsa1j4KwonSqRQHalq2jCUQcLJAdffFwJKxNGRM0aHltHglnQ/rySK2mCj50PXKXZ792YjeDAr479SKCNSsyCpGxaNgbY6ou1ikAGUWMammDjmKVYH2bdt9HeleznDhKY5NDzSsXgK4uBlsV2G9p6qkWcVRosRgMNXBktoF4epEhkAOQRXj7Asz45tPmo5T/+XI5Y9icBvxz5bmzt0rMHm9sN340IATSbgjPawPfQjSMUhmzzEnR028NvqkWnGBQmHWFaWVOGy9q8zgKyRSlHhv0HJUdgtG6Ks2vSswQGrEEGYLX7HCteY890GfvhWozx8HfkmHUf+t/76zlJWRl/p0WjGBS1cv/26oV+k02G5aGXUwX4tQayZLqciWqAKNmARBzcQnbKWYScoTaGmdat25ghoiQKBZ570DF+HGY50PpLnlFygCoDSdBYQAkk595SpFgBDkSVI47RzaBRqBI0ooTsbASBmGzK5RgQzagDeKeZ7C9MBU4fGxiNLdKgkHKiIpAxy19FyRWAIiqIKpUvFikPlmzrgDzJ0v9uQOssgC+ECSXEYOdSV0Y0UWHHQN2lEJREJqhQiTg7YOApIlQKrcJEIUf7W8UUjugKUu4XE1OCVECDL9VqE7vSSMqZRpzFEHOlVAg5uNsjZ6KaewNRquzPM5iCFV05AYgSzaVANoWoV0KEWoQm9KsQdQ6o+YIGCyCEnk3ayukUwyNDwLFFXxZ72+Zky6uwALbI0ANTKMqkbgLxWFEYH6sAqYxAdfz5pnKx6So46fo2zzcoLga82ZQWCUDyyQxG7Y9Q2MG2GEqbgOuozbBL3+ZX/Vi+5lHAv5jPrmj0sKrqYFcAebjfTSkAPdxr2d4A2QC4UOe98efzvCgB9P+kB2PDYDlybOnnce9GtFXcEbl/IKP9h7k/UPgbD2p4KiPG5+jvMnYPnBI5VYCfRenUKOIkBgipVynt3WzFwHki7mNHIQvBnfkq0FJ8UEIkgC/0QY3SWjnNXhebQAxEUfNxixTqXJwOMq1O3D2gVSBrRkOArESUkJVVsTLElIDgCoYBtVnPrWYaNRYiuqWZgUlWB+Lgrvns1mkg+eJQZQNPVVOOYGCPirujk0wOwXzyOAMSylgUGs7vxed79MWkuBeCZqIIXbRn4SNIkIBkX8QwqrxM3YwrWIq7DBzMwVwKxd0Q3PJyRS1rNgvCp6LFHAR7FiKEZPdbhQDJaVegChFRZUWmQojBffNYvEStQuvKuGIujMrjHCS4ewDoABWLzzBLAmN3MHeMPce8BfxTLptU+0DzFgXAt2IAA5s/pZ+8AXOxynXDCuXY70ctb0747ui1HfuUsdtgoK9POs5J7oVNJcIw2RURPXp9Y7/9cC26+T9XBqS39ItiYt/5OI3veaQc9ePfg9wYZH2h6RUEsyIGpWzYv/jYy3CIW3rH4wGGOAWCn1OqB1Saemu7Vx4K1p9A34+VhAdQEo9/dlwxKG6H0ySnCvA7zFJN/vLG7CDtDzY46BdruxXzu5YJUQLyphpoPfBLFA+o095CFwExs5REpsoZ1xsor2RWA4KAINkUBJtUGZVogJrdilax+AOCBcWJATwYYI4nTtENVQyAsysfa8o0MIq9FWMzqqBU2YJdxGdBVHHlOzsgBxoMDCv3Y1msnFDMaAvQU2qCKTjQ0+gd9iJkydS4e0SCWejuD6/E6PaVX4+FqRQqXAk5E4IMLheKIqAQzJcaQzRQzr4ciFnU2RWAFHovvgcmmpVuAI8FJmYlayb6rRlrYMzEJAc6Nz2yiMc0YAyMP/9JDDSS7R7E2JQqQwo2BlEtDgTVfnzJW0b/tIv4mmFWVZkr0htx9rrFXrEtwLUJtAWA7O1V9UVBtAfG3so9Mq/HVrhdT7HIi7W/6Yfv1wOBYvEOFP7wfXFDjHHDri/226oWaC1zuvxvUG4GGC8Efvk9D+dR6b/rfxQFKJj2Hoj0PnuPog1hYO20XKv2N9dfSxiN18AC2BwTBBn55cu5bZ1wq96v9bj1LKMnCgV8g5RjDONZFBJTGHx9GCkdJ4F3UTrGxy7PpGeHMCbJXByj6994Rx56OVWAH30yJswCblGCFHrbFuXgiFJo/kJRgwXZRYw2jmLHM8VysC77x1+O4S8aPqe1BJyJe9J7xdUXADEFIIitBYFACgl1EAtBaDXbC5U9mhX3GaMENUu0WMDlYmwhMgUguHJTAzlY8Bo5EMiEWFGiDgOBLhidPcE03oxZs9MQSdknsRilV/UZDDY5ahkU/ArISZAodGIxAFMNFoOAAaoITDR4IKKzMApdgIlGkpirImBjUyGuPAgqFv0uOY+0cw8SFCFX6sGKZmkESiCVKWu9dRBsXCWbMlYRPGPDxiqoPbPsPysFjcUKMYamws7ZaCZKoPPtAFJ5K3SwekqmwlZOsxQwKGDiANfTxbbVEEC6aWVu+vkd6LKgkd6PnbMeWcCHxb8oCBvKwghoBv9xD8v9McYAMxz/JBVT2QD44senrF/lsxH4+aFKfNGgPAwuyvHGvYU/prQsMMcOlaUHdg06umcYuyjUDZAxy1JiKuw+B6UDdb99KGyA7VfYVfG1jd4XLxQfv41FyccaPxv7dWAP6C17ynFl8/nJxvbj6ywYMdpn497NiPKboTAEYeMhnA45VYBfhqUWQbMtzEFCH/hWiZA0+YLslLXPqSHIw7axaPSAJLXUMwdwc31rH1keCD29GxwoEAPeMgHArH17oYNT4CVdy88bAl22MMLKbySLLRi4IlNhKYb9S+SfF7o8YdeRsQC/DrUI9TKB1QApSqHzxf3a9spHFY+qt9gDAiTxYD6CHTSPJ7LFO4BYQJ0MVpGlASprMhMx/74qaDSlQnxByzEYm6HleJFKlDYoHTAlQIYKz5pwf30Wew45YJ+JxSNEB1gFi+MQf+5ZCQ7YMZjy07kyJWoWTK3mzkG1j6EIqH2GWfN1uQ8Gd0SJPsqYe8FSCOnTDFEbuq2cZgmjyO2wucj3FmQBiZI2C8fBuewXCuVmIJohxjgCtcGa38wEKDIA0iaYM9puOMbw+VgxGFKUx8Bath0zBeWc5TyjLQteH4cdv/9NXbZYAf5rf29lrS2/b8YglN+PpjAeZSYGxsOULzQMln0P2erPsoD7SJFzUPb/+XY9rbLx3EPZf/TZmNYfAD6w+fxOciGMlMdQzl+O6SuJuItS6BWXQRE5HXKqAL+kSsUsNGQL0FLzr6YjD2dg2rT3MSexOIBKLZiuT+GyXD8LfHMwyABSXAbqFrMHgTmwu5vZgsAQjBK0XG/xqPk2WNR45RH1STIpQ63+YhAIyVLnVMy33PmLUVLzMpmpaA9WiPmwywuDuLWvZr0H7ZBgWqUxGhkIRkWLa5hukQeBSgNZA13I/XiprwoJy68v1nQh+epsYDfV0EfDR7GshFqFwsjZGiBoUCaYQiIqPdMSShqiP98gBuqtsyt9QCV2zJB8vQmQ+7iGEj9hwZKdK3OeGGzkZraaAnY/FgkhOkTygzE+ZQEUjElBsikBQOXvVfHhm+blx8nD4ruV0yfSU9wcW+DpSeORRdtTwuOgvAH0e9AtvHzJr954DYpVP2YRzBU0HCuMqLyS33myb3cTHGFIjxu2H1wKurHP+HiqhaIfHbPguB4/r/qBSuyCiLEF5fiCbDCFvZE1Cs4bLmt8EyPNoQfZsYbDCHzFjznQ9cP25UaiW9lHx6ysewNrcywkQgqzq/3zL1cSRgqiWetH2aGiKGyeY+wCoD/W6DmM7v60yOkC/GCTr8vma24l2zT13O6q00Fry9lT6+ipFshm1YkBQs6ZygFcgxCTBZNVIsWu7i3s4u4OIViUfc6kaFZzhUW9Jwft6BZfyLk8f4vuT4CYxqrZ1IWs7pZQNRYx29IzVQPJYn03GCMA5rbIPon8NacjEyRSpaLVFs04MPEUv4BHlTJYMkLs3QkVgRSVWfaYBzVmM4hZ0sXax4E5C5YO6DEARSMuro2gYla2eKAlmUot/a5CWKE0KVPhiopNmX7kK7Go+4RSpxG9jz/vEJhk6HIyS7/EAoDFTRBIkq1AT8jknIkhoFGo1dwXxbKJEozByfb8zP3gloCPc+uWR+sWf+fXq9oRRoCylVMoDgxHrfsxyNjTdN9tsN+hFIMZAZG/Eb07p+ytHhanm1b9pqVr246ZhREq99c1gOD4JsbXML6WsSVffh/N8dEpAHIu242s87LfMfQZfRDK3+KMlhQ6jD7uIeI1R4rtWtbSgeUrx1RXcI7GIYSB63ZLvIB9+SejexruobAtG9uC1WTxDcZWfR9hf/R9KAZ5eX5FCRtZ/v3j6o0u+ye+7Tj2Y1PZcvbV99H+QKdDThXgd2DvjRgNLzr4nANiPn20968aBWwPrPhjJStWtc7y6A3gjS7vQqG2XPP2B1KJIBk6TVSxohMliYFyFg/+A48LsBcjq9IGQbIBl4oSolXkM3CM/QJRJlIpciNki0dArPZAFvPFl1oCxaAoLwzuHkARsXScUmgGzJoN/nIOgYnDW6tCHxkfRZyWNzC0wMGyhDixgAUNimdKWFyF5/CXa3IGRZyi74IF43UheMqbMvF7TsHyFMwVUCwNaIKNXcx4cJ4xHGWJU3XgJ9CobiwmKUCXLB6gc2O/LCRlkg3HwVmQEpfh1g+g2ao44gpiGN4OECuwVPUH3cpplVDYtBD6RXlYvEswXxiZXcViLuDvohEZ5VGXtLz+a8br98AIjNPwGCuHGxZfCS0rygAPCMCbKXtldtJj9hAcuHmqwgCMLdnRUU84VznfEUWk8G5u1ZfKlaYMbGb3DzMWNI+VkwK05foGeruMo+gIhDmuvGz634fvSzjAcX/7saEENiPmh7x8ObZ/YYDGz+YouB8Nwhw+H1+79BVRN+ojPMRyqgDfY9NMO0sZPJq8zMcUhAmxz1MvhWsEo/3VzTnptVIPGMsGjAVISyEakVKAxqyAGquY1y/4mB9YsufPY1agBNuiUgPCVjxAUIRJNFDpFANDMwdQtE/TM0XGXRDZYwEoxWDE0KtoqcWq8H0DEDRDDsQghBIsIuaHx0E8+XlEjIovylOGvhiNBBvfVGIaZPymu+UutkNCqdQteX8eSslX9oBL17ITNoAxG4Mg0DMWpfKh+oTLPth9GrHYuaIDuAU42pcN6nEbluIonsIjySov9sFFavEPJRdfko+bj7nKUHo3ZHvGq41lCwv+80oJFYFRBM5WTqUIVkxn/BT9mx4YABID1VyszrKPvSUG+Inin80jhn4UDzgEzNnZR7pEHrb3vPiMx+6M9rG602OAPOGufE6pW7pl/7HVvFnKdwz85ejS/1GU9R7niqLS31xRADKyiWiIq8O2zJYZ7ffu7geJI0WK0ZpsVz26pyEuoM8NOHEYjpa5tGcyHrWhVG4YVBAZ7jsECFKMpSF8sbgqGFnjtgQGijV/NAbAfnqhjrFCo8FXTQ8exdZKRkrOaZBTBfilEIxiFm3EAy+82I75YD2n3vcplmDw8rYiXgdeDMgqD7wKUgBN+7Vbslu0mgkhesCe9uCVjXvuX4bomqh4jneNU8VYoKGoVfALwWvW2xtAVggaCSiduynKSzARr3nnlL+qUhH9ehlAEntBp0Q617pTCWyB/kXusEJBoQd07cvoIgZk6guZVR30iSgeJKh2X6Iy5MqXsXYtlhJAJNIX6DQq37IQLKPB9mmLC0SFNqj1ShCrk18s7+CpcOJ+CQE0OsvgjEqV1Ar/+DVYPIBH7Isw0UBXohnEAzpL4Kw/M+zxUir5JTEFU1Gv+S+UR65kOhWvd6B0psZs5ZTKQAMPVqATN9Bb+6Ol3oEUZHhR1PFPPB1ztKAXyzm4YtmXuB1ZyL2iPFIICsMQyottfzJQ/ZvKydFiPMWaDEXTKMdztvKkwLkxQpoSTc+KomwAmY62ZWSJDmO4GXgIbsWPLOOj590cmwKAebTNGETHqXab0Hg06G+83dFAyMLu9XT+qM7+kNM/bL/BtIyPKYLEgbrvz8NJNH45Z3kuhRkpik04dk8PtZwqwC/eoJiFjKV/BR0s36DQqXrqmgGsBZJIT9mW/8g2KUzT0h6wO6w6Xwpm7dVqVeNKWdhCOwUBiYUlMIDRUYGfKluE+UzNF5xcWTSLQJHounUyoAxZEZIzCoFEpi6au1PyUqzkIBYVXOguf7Es39zu2RYWdbA2+6JSWxgiQ+Zph/vc3PKPhZISCx6MbhznHlyVJmSiFZ3vg/qNJSiV8iyorQBr8VqhQotQSQmeDFZ1z4h0C/bDcuTLYhc19AuE3xXR/DC9otDhLEhWcoQaK3xUNGiCRfFLsgXEXAeCaqJSeqWkpFGqK15lUdIA0xxYYxa/eMBC9JLLY+ttK6dTjgZ7jWut49beePHtg/n6jxzYeuXYFP5SgtfeTZOhyY70isAGs9CDRNlhTOWXHzLaR30zPQYQvRUsA5s5XM2GGT/8fnRsdNi+HF419BatFb4aoeLo3OIW9YZeUFIVN2QT9AclKQxgP/bnUWIrxgeS0X4wBvWjVnYPsCOl4DjYl+MfB97yxIoFLqP9KZH2zmowWoOPv0M6Ok85UlHSyv2cntXjVAE+3q0NMctePVKcYPp2QpngdDlWxlbRvtlJwny6Ew10kp0hcODyYiqVP7oSpFcj/YsYyucSqPACLEApo2NlZa2aXYhCkEjyojs1pi1nEWIEIdKSkaBMVFiFjk7FAuyCBeZpFrIXsqmIaFBSH72vfg+24GRXSlJRgsCb+1h7H9VgAYoCktXK0mbLLijxATb1LAYiudXRYMpMLK4FlUJJgJaUu77+X8+gFCWorFkZtzhKgKIrYeLWTBYb+448lB1Wy1awzAghSe5jNixTwEaf4kYRe2HFmyxJVkt7xAIoU7BUvg4rp4xnQBRFUEnuThCyK0Al/Chp8fPavSVMySkxItXpmbNbOUFEdMOqsw+hlGMN7u4aR16Xxdk2D0ZLlyjPkYQQvDpDcAZgnHvO6Odmml0PqIh7EQYw6Clwv46jaW0FsE7GiuHDHn9k9E2PmHrSLiOrvsRDFXN+PDajuAfELfESzJhd2TEnYVEMdKRYFFffcHGB0mmzsB7jC99gXPpxGRQUYKN7nk1vB/gNxQA2o/w3KXkRW7h6ZqAvHkTPBAwKmW1U4nn6+xx9NzbIhloL46yNTSXhoZZTBfgZp1lzZu6Bdm1wUIZeY4peQKYKQlADdCvIE5EspKBM1DSzQnFXDrSC9FXpigXsrRp6Ortf8JX+5coCIVSIZmoClU+ETrOVfRXTkaMatV35+bMoXc7McuVANKoh7znyQbxYj+vCQ+VZA8hKgisBgHqp3GxMRx0ipcuVgGUiiKUFZsFzz0s6YvbJYeMY1DMWvDGQaPZaAEWhGIoVFT96JaVDn/SFblKyKnuV9q86ou7Ap8RMOEVPoM6jyR4EaTNVsHbAIpAqpc5qmRLupy+le8WVGE2WtlmpMRhtLdTJ4xfU0wPx5+gLmoDdTy7KnVH5krJnSGi/Xc7m/y9KS3eKJu1WPraMadjB+sqUYLpx29QegNQXcrFtlEyMFrCnummJZ4YIeRhod/V3sad0/RoAqwHgwOdhPZwcbzCA8RAs6ECoxjVsgmPhH8YyAkoZrs2/GX2+eZRyVFVjR+kBU0fWuV+32hjZfr2NPDp3SfEb36P0m5UndEy7Go3BAwHmBoAHT7k8ss/R/Qbwt/NKMfT6d6RsNxQ22jgXHiswYkN8b2vJ2+tNm+c8bXKqAL9Wo9iyWI90rSzqHaexDJy9BKxaMZigQhc88MxVPHFQLMFqAn01vhysec4a7S1eFXp/dip57DjVL9ki/8WtQbGXOOEZBNEDC/2lLn6elWQmWbyXhU0Ezco6+qukhUo3+j6JmO9cPLId3UgRlBBHsUZDn/ecrdTvLCnRCt2DV+erJbr/3qjwQqcNk9B86rVE71kg3hSo1JoXShqcBe2ZBSTiAYeajfKPhT0YJSlryXe3cQseIB18ESvGO0BbKZXa3rVaRrUFXQevmWBjF90KCVkJwZIIS4pQraFflIycCKhmjxXwADw1l44IfWpfVzR8yu5mneTCLfqYxxMWpq2cHjnRt+sKZqn4Zj7Vo0VWvFtESPb8ddO3LW6NpzS8C4ODpwC+w1tR5nVIWxuDcQ/+vmuBXHUNQAqI9ma9pRAerQEx+NSHam89M9BfVdm2gOD4ikZA68qOLX+ZsZtD+nferH3Fsm0KKBpDFrz6XjnPYO32wMpR4B9Ov/HR6D5cR+8ZmTB6br317sHORz/vFZUwPMPx+1GYgF7ZKwZgKErKYCBsKoehZzwKuEu51w1FY/hZRuG0yKkC/BzoI8VNj5Let90F9faySpQ+Y5ys0qePBvHodOxFCSrEWLrZ0z/MEAJTscU9u4aX/VyVeGlZIJoTzwO7cNA337IdyyeU4qVrlRwSgnihnuKHh1yJF/vPXppXDMQ9CnWwjkeZA8aRW4MXp9+iR6IGLcqPUufs1fZc23WFoRQI8k66NmZKHwMB2itF9O+sX7crTgSzzsGD+sSihTRnplja41i7NXeBW9LY+EQBUfU0xzxMBPfPR8S7AfrymC3YL7gSlg3nUU+91OgWfHaFLhhwl74LpvVnii5usYBCIjljWxZ07WMaLPDTr6EswB4rICVCaiunWo62fbU1PZhVJnGUmjX4fW3TglblOwF1Fc+DyqpqZJm7K9FeYumN3+IN6APWNKBsgvVwseV/gngAq9L1RIIn6jiQjunyYoUeuW8dAcwYOLUoIMUS93seDUCp72M4N85cgBIBs6k4uN+6dwccuTcpSshwrt5Y6cGWUczC8esq5c77/aX452VUsnfY1WyBYrmXexnAvv+s7DP6WySYQnGC8jAoh8OJNhikUMZjdDGMQH942KdCThngW3/6Kpj/OwIEA2B08Ln2/uCSD2q0ABn6IjnF55rJXt3Nh97b7dqhxfPKtbcUzSZwBPfYAREsGtzPpVife9uaHuyyBwJm7Px9z/VSZ59ILYE2JIviV6UL5sen8+JADJZxSMP9GYtAD0hTT29Djd3oJFmEf8BKzYqBcafZXCB+zAg0Ql9PPgMtySYSZj1ktC9rW8C+KufunQ2MUv9sLHq/t4+nhOAlcW2bhmS/BCUkq6JIuQ7xNshYL/sq24SPYVDiJgwBfiYKobgjvLCQH6fCgj2rLHZ/GXeNZM/mKEWAPFAUy8Pvuxdq8KZBtvBvo/Q/faRf7MuCfcTC861G4C9QgrN09L2ldzjw2iJUjmEAPyzk44j5vuSthmNLfWEAc85sNKwpKUOujJbo9XK84VqNuXqgQLCiJIzl4wWNyWin3kruAa6kIW+OHQw6y1E6XQsASlkpBvDt05P7ixuOd+ya/OM+Ot5BfXyvvbV+BKCP/uuPycfeTjzVuaz1Y2t9cAnoxt/GuAxKztHznSY5VYAfJFCFQqPYZ0k951rMX9wGa3krannhqfhhS4CWv4UJ19xcE7fAK++ihvVBbz3wKyoQgwEnnpMefP4ln/ChgI0OVvs4wAaL/FcM4FNhsAQ0Wmc99Xkdnd7qxGoNhKS0IffTSMCv397k6IAWxBt4hNBT8FktnU+yKUBZM1UCKvPtFzaiaLWd2kPvPGagpEJa8IsvXBl8UD0wxzVZzQQirUeuBy9MRKEwg2UfmI8fkjcPMhrfFzqslXBxuwB9kJ+oWQJBTHEo3RArN26SWlGjFE0tK4tU60GTSUrmwmiRFB9vdxmUSS2qPaNQnmWh3/qSzFK2s3dpK58G0oNAsdg8eEwy0rdPHS/IThUzgIpZtOW7SKlmN/h2faaq0+1uWpv+rX3LXR1HvwMQNtLHNJcYAc990UipUV8UCsFSznoltcdut4LoTw+M0gU3MF76tWiQI2TzBtCPgM6DGa3qto6222Qu+l3LPcvg5iznGaz38SWcDIq9Ve4HH4L4RgqE0/xH0+VOAtyPBfYhlIh6v24J/Q1t+P3HCgb0IL95vs3PTpucKsCXfkRdU6ZUgjMkkgCxs3xs8Z7vfbOC4r8V84dbSduiVbpfV0qEuQODswK9z1uFVTRFQrLSxSGnPnnuuHhqWIdRvVX/mSkCuVDB7ncLOtBDWUBioE6ZVrwMsObeonej1qx/vNQsCmIgmVV6Fd62zd7UxhWdGPrKearOkGCV9aIvEFmGeAdRet9+xtiPgLkfJBvQTd353qEEtUV0gvQFfFAIeXCrwJDuor76KhaTUTnDUBHJoaT2WWvfmkAULLNBByLUUuksh9/aFNuxewYlZWu25IqBvRM2dtEnXszQxGz5DJ25FYoPP3oENpTnZ+9Qh2dElMXvdM7frbhs+lGdxi/Ub09Tw7CIF9+s+fDLAr1ZMc+ONabQi+LXN4BxQ0BEfF0RxJvshH5SBDyYhwGkAxpy7yYoAK8qHpdTIujpW0UPgF6OM6b5j9L4x8eoYL74+tArOsUqLTFAPeh7hH6vMJQUtHJfeTQ3xmDuobuev6iKWeaFMfWrsQQqYdibYfsRaA6ld8dZBK5GlDWSogD4Uwtj8NXRcy8gX2IC8K58gxLRK4FSLPfh2kLYVDqGn2Wjo0zSpuLxUMupAvySP95m717nA9+RqVRoNEG0AjpdzgT304tatLx1mlPULWj1yNji0y6Ld3llFfriNtm/n+XQB+ORLU2sEyyfXIbcfhGzstVLrxZrV327ojsHP0EWA9Tk1Z4qPCMh4cEnkS4lU27ctZHVJ5S7BCzi3I6v2Oc5WGaApe1Bysk1VSvWE4JZEtFBnRio1dLyBAN2HVsQKHVSKgdiUUtZKymHPYUveHU8QQJOeFsAJCG49ZIhmushZKUk97UezyC+MtUqlpLoz8RiE6wlr7El6vX4MeVMMx2WftlIMpoQK72sZezErK2ywkQiMTur4s8jRfqg0CBec0AG9072cerUsy62cnpFcLfUsLgWMDUgiBuAsQn6A+2/WURmU9Mbd6czw35TSSgMIBKO0OhjwC8gOGIJy0JUzizSFwrTciPjW+2v0/71OP9ANH85fM9iDbd11LdfPrPzaK9ojMF+ALpxHtsJVvXocsY1ToolfGz7jXtkeDZHrOijgFuONQC3jq5Rjh3H0vsGQ2wIzJPetVmMSOGk8x7/bDzaW8B/kNJK7iur+dpLR/IX3+CiNIbwjFC32rXXUq3oiqFSlODpa5iP33NJ+yAVfDKopfG1JUpTBUlelrUcL3jrF3EfNx5NL0BWki8upS9XxnvFO5iUIjodxXIfrOSyrVWyy7TiFrMWZcSBUKU/n011Azcl97nlKsFiAUrvbjF/fNF0DB5tnM3KtpegcjoyImgQVqpWOlh9reqn/rgIjfm8Q2FgZNgOhvQ9kN7dMRTZGaUBCQR3lRAsRqKQoYK5bDTa861UKM6PTpNNWp+Wlq1oVxnVFqnxYlgW9cqbB4hidQ/E4yn9/q3Tot+rmouie6Dgq62cCukX1QIsPcCE4d8G0I//9jdIZMOHvwlBw7sLEOPg4hsDv30/ABqMsVz8j9DPgWAWAlmt1kbfDCeIMXr9VcjGuXqXgZi1nsdtrx9wkDbs0OEORUugfn/+/v5FEckb3eeOsiXjE2yAoo6/0+HZMFYWxsfUI9s64Kp4d9Dx+YY242Wfgd4fxzyUn/RBmzEO1v3JFP+m1b757mwC+VEwP43V9cZyqgA/K8w8NzV5T/cCuNkXe8ULykTz3fZvX09/0U/enBwWxWjuiJlxFQLRQKFTo9UyDmx+SMtHt/Q7wdLCJAxpfJq19zdrECsZi/mLo5brhoFOcg3c76OUx61yZE3yHD3zlyvatwP2120IWOz1bzu2Yi1hxa1xy3m3e7J8fFNUmkJvlYku1k42iFUvrMQUjqRD8KKIel17s1qsuI4X7VFjKYrd4tBMQMm5AOyQ/RyQIW4g442NFIrrYdRvVz3lEszPbwF8riL4xaesfkab5G32EkSFEUJ6JUj8uZRIfPW4A2Mn7J0owZA2nvZ3KiwF0sdrbOV0ysai7fU2RAIhxA0rr6RWFfp38KmDAU3o9z0p+G0zaPTkhX1TKS6gasdXhsCuPsAriAXaml1h7sCSHujv6fFc+3JQd1swCuTbUFrsvAXYh9nKwPFvcJ7lusaOdqXQ3Bt3eSLgjSzgsW8dZRMsh/OO/eT9NZTP+3u0fcshfSndVE76Y28qB2Nf/UDjHwfugR0o11MUo00O5GNZ+IPr53SC/qkC/JmnsSTcmg1m+VVG7pNC9hz17A1QoLcoo1vmQSAZ+PZ10bVEuLtmDqBmtWnwBK5sVK5tb1Zu9k5qJYVN1XvCp6FITnKNOKhV50sorQNxjOL+beljvK2Qnee0g1HsGU9XC1aWVrMrCgEyfdEbxZiC0vNdgnmfcwbJAkFJGUp5WWuIA6tQ+sNbhkMM7l/Pqde2S+W+1tPbVKIdIwttGKzb2iPd7V5KQZxS6tieR8qZlDqzmAjUnn0QNEAoPRGcoTAVob/W7PEFWUzJC9m6HEZndpJ4Yx2f5FmzB+15bf5gEfao9o2QSvngFGwBTQHovKeBQk7OFHnmRIsFPmZR8+nnjIaTFtytnBbZpGvNog8h9nStfW5NT4wyDx6R7SDvCkGQEnIKYxDp6XqOAv8Aan2+vDoHOYoH0LHCmh283LhRFYIMDbGLa+3jSaH7xyCjWYd9CwOwuUMPq4ZwmZIjX6LN6dX4o6A1AsKeKTkKnGW7TRB3Vct1kaE4TzHIxqyBrU2yGYOB+s+B/S3F044Wzhn74Pvn7e+FbXOE3dn4Fzbvww0unJnQI5b+yQzHWImE4+P40MmpAvzeexS8ZrwKnoA9VKITs9KSa8dWW97TqtDen01JMwOjhF17D1n7jmwiDE0l3EK2h6q9b7tYqgSjd7viexN633ZQC7BJYpSgiIFMofYFLxok6hS8RaBbdLoxB+r1BLJPiqCBFCx2Ifm5UvFNqdPP2QBPPXUN7NVKClUy27qTwjwYEyHZxixkq1RopXptErQ5E8Ow4EiZ4NkCFyyn35QRdSsbsSDDHOxnyomu7Wx7EUQVqS06WoO6lTF0tepzoP059lW9conut0WslZLyZ9G/bTbiP4mVJm6zBT9aDwAvQwy93zIlSFHQlLwksbgbxhbaNhVrXvtqi2gGtYp+8vHX3608hDJYcIN1Xz4/yY8rAU8XDUDs9wthvCQWgB+sbft7RN/rALwWwEdPj29S2m5yKPR9sj2+R8HrgeBl3K2gzWDtDoWA7NyFQdjkEfp23H2sQe6VARVnHUesQb/Ybo6krzFHPy/Ky+a4AiMg5cg4j47p34ce8PtBsm+PMA0a8sZzG65NGLIFoBQC6o9VDtUrJ5uNdMq1D6BcLPuj70q515ImOWIdBvOf/sZ7tBmvbyMG+pTIqQL8zhf/7KldWQxgDAyl7/qWfdCzR+UoZhXXYP5tB/2gxrn3ee1+jOI/Lk2fgpqfvxRuAaf0e5ZBzNIWe/zWYhdArIa+0ANCqRFQ+kiXCm3qdD1SauB75D+hjyqfBCskhGcf5GjfiVfOqRHWGP1ut2rXG7wKXQreFtavvxVhomINggA8LiGrgX8iO5Wdh/Q9nAnpI2xloD2LlVM+E8ySFkFSNsu+aVm3DdNYoXVl6YSI9z2wg2vwNa9YQJhbIQWrtBcyrD1uYprcKh9NquyKQ0ilvkDR/i3QslNoc7L2uTpKtUz0MR05GKuj6sWAglIne6dayUNWhxqr8mAsrq08dDKmYiVoX0LavpPRNoEg0ddowYL5/G+3bKVo1QVEdEzDF/ecK4u2+AwWYP/P1wBGLgAdFN1C7xes2zglASR5ETKhAIgFk3o/iDHjkNV005EP4sT3tWC8p82NoXQYR18pezAb0sxOorLH0e5HATUcOfhQq35UzW+0z7iU8Ek4edyHzpHzFv+9V0/0bcwnP2YnnJUNYfO98TV9eAdG5+iPz8Y1FGQ5SU4jrX+qAF+QvjtbeRn7CmyuoQZPfSuBbCJ4gJaDaEHjkRZrgV7QBvPtAn0ATPF1TymWuyIa/LxWn73C6P0QvJiLQof57ZNfpzVusWnWamZWGvpky5OvPSs1M1R+KzXrCcosCQRLCcRz9kv+eZbBi2fWp7i/30TFaPWoNvlTDPaSdxmNgU4wjiFGonheuRsQEa/F7wxGVu9tH50Wd7eKwaq5UoLX3i9FNATociapBfGljBXP6TJa1x5tbxa7YqWCCZYuZylHAmSqLHSaqNTSHTNWLyB5ZL0qtMA0GcPQ6Uj58oyKvjyOeMomxaCymIsUrN+AxVAMi2IsVL9KX/63cg1d9PRN3K1sysm0bGAMKCEUsI9m4RcgoBhpBezKoj+yOHXIgR8s/JINBKVojnuFenq8AL7moqCDWX55pEg44JUI/pHaMCKrKX76Y+4FYLPW/dHB6f83+tuOrlkJcVB0BvDLveL0QPS3AeY4FS70lnthTAbvd/bngFenK4A4BtEyHpvMxfjaj1Po4+dfANyyIo4qIsE9FVKYABldc7l/pI89EL/g8bgcvStKqd1+Dx39Pv77dMipAnwzpD3ITkE87axY8QY7YgpqZqhKp2aJFU9zkAJWRv8Xn3qFOD07pKcEDBAaUWYlLkBtojYyUOoqVuTH4gsMZUr5XFGjm1HtG86UjAMLIjOXBF77uZS2rRAawWh2yUQNZp2bq9siw6NHime33v36SkU4FQNTcaA3q9YALKggyRISNRrABg9osrXHlRx1K18s1iBh1ryI6a4qON3tY+imiKh4JkK2jmLJUw7rCvVIWKrgrpHsyhQItm2VPTrelTnNVolP1boGkiy3Ofm44orbCqXKwQvk5L69cbHkxRWBFErhJnFrPUMHSSyeoRRXqjXSBagydDmN1pihvsCW0j/dMlh/RtGPgWSwQktOrfbbblilZcHvj1XS4or1V6QYC6ad56JBq7ntshb89RLPYsxdD2RajuF/alGfC3XvJXkVn3zi7iV6xUNztG21dMsIIEURGCzl4y/uycrrJtgPn1GAdLTdOC7imCLgQzGcq4DjiDoPo+OPrGkj9U4CyaMR/TK4HKSA9wD4RePYvB/tu+ttWu/luW/a6tJf7xFW4chI9ipZ/4Wff/zinKK141QBfoVby8rIqtK+NSoKTfa2t6qkUjTGtVuztgMh2vyImO+16lVO90Ez0MqSXUHAJktXmAHxgjsOsqFM8DBUgDNdxB5w5UqDJ++hxVIu2qXfTflMRZAMlWbrCKim+VrOOohEuz5XTipRq7Lntn4XICQ/ntklbgHnvp7BOhjAVqpmPYvVko+hwor22LiVpYY+kU57i1qkLD+OgiW2gSFeIgnUQcBqAhKdy4tVoI7GBlhVXelrF9RBaDUZXR5sHDtwYIfGiwrVaoFVyZ9PEPPjl3TNcdvagNH0Iu66cbqoE2/6Eez8FRYwZc/XlC11VC9R++b/l6EWwimatFs5LgPgRKvPsRGsFzheaMWrq2G0fonOtziAIULd1gL7e9xzocTfmMvLlNGy+AdnJAtYFyayP2yG48VzioUeUE2j33Xkty+aqANkLuBeGDiGvx3kHnjA7H9HYxvKWBblJ4h1qwylI2gIfYrceFzxewwji9pisMaAr6NxGIC6MCclin6cfliuZ/BWlM9LKHVJGfRKoeX6Ga7pKLV+NDZgDPbDXB8rTkXJKL/rxr6bLMz4mdKvo6dFThXgN5qZqAOEF7kxGswnoweHlbnUW3ROI5U2upKLdu40uFhRFigBauqpXpaLntwqTGjfPrXKELymf4sycdDvVK3iHyVFz5SAnLJr+Phxh9r/ggXlhKy0kb6Snqg4dQ7lBUtRiWq93VWH4LnStFYUpupBfaKm2RDoMKVhJubaEMVzG8wKabJCTkyqCdpmQugI1YScOnRxhVDNyTEi0XOWY/D4hWgKDxblnkIHWpkS69Xviuuh8g52iQhiYLlOqWcCBDXfugjSeSlggZSSpeap1SnofLFKqiSP8BePbM7qn/viXWVl7dRpCdwMhfIUUxhUPIUTQTSwwlIBax2CHSvPELEnU9wPQnbrJp3sptvKKZGSajcG+5P/DTn49BZcxeD/3QQ/KOBczlPMcpwpG+Ja1OON1Is3Wac7+my43hHma8SAIOM7sclkRkMB+2JYDJvraOvxfgPmaK9ofOxxG+5pTIGf+C8cV6Q2LP3+eOWKijJhc78viCOF7RgrCsPzwO9dHH1zHtffH5SeoRDQoKSFnmI3gC5Wfdn3qNVe/n+0ec6YedgU3fyuXC9F5RqnZH6cwX8I5FQBvspQSjXr4LsG0GypW3GU0lL6qls51ADeHc6NNcAa71QefZ/F6tIXy1QVb69r9mtW8Yp0mU4MbCsEUfPXl8C2kHEfoKX0RZ9ZXW8JZyQZICuWU28V8cwiVQnkYC10bXLKYDVn+z5iueMdCc2pt27JyYHY7jGJ1QqwbEQHNWwMyEqQhCSv7Ncuybqy4MNmyWx+hm5xL8v7PkSqz7OeRHamu8SqYnbmagITUghQBbIEgkZSSEQNdN2o/K1b1cWiTmRrVOOgXcC67BEU1pos9RJ/5s5mFC+muI+/VaV1Wj6IZT8YE5DJKrTQV0lUvEqejNsc0wctSzY3T1RTHNae5mNrkBCiBW+pQJfVXqwIMQsxnSI1fSvHJEi9kY439jP3f7v1P6Z6zW1V5kypdT/4hnGr/6jffKD06d/9EtBXdlWM4gevwumBRwX0Qgg2Z7VY1cM5IXjQnvvtEYsZcOOgB08dAKa/Jrfej5uWIwu+/7kJgifR9UNlutH3YmWpzWIf2IChjLFs/F7m2Ah7R0BdvjftyMbV62aEYAzK6Po3fx8pcKN72rTOBz/7hk0/flBlP9kclxKseWzcjrEGvsg8EDtwSuRUAb5gQVo+6t4tzixqUfqgLMW8dCX9RZ1i7zBS2bNdiBq8y5sd05gf7X3UFo0uzDOsxCzjaYh9QZ6SKy+YBpq83r6KWaeVa+mFcjLWx5UQ8ZjfYEpGTxKKn1uVCaH38aMeae+UPuJAFjxFJ2UvLRssiC0bTR0sWR5UqQr1JUpKGXIkxEDuloT2EFaHNOsDpFvS7V9iMt1heXA3+3e+nzg/QzeZwWSXMK3hzHUstSJOJsh8TrV7DSGeJUwjWs0IIZKDNdYQd6ZkEa9ngLkctDyXUkKJgXMoaYkKEymBma7Ro6ScLcMgl5LCmYwxAMEXNcEs/SEJprhaBmbEqiGa4liJuXTs+RuLU1bgFEpKqFBrhJBdcTEFLp6eObuVEySEMZiPgSt6/j3+fXArkOGNPMIAwOYiPV7oCwgVBaDPvQ/0bjVQt+azVwktgOapeb497k4scUu27PW88ciYFWcQBsp7U+Qo/oy22fxu05qX0XjJBqgfA/5wxD0Cvo/dV1GUys/BAh9YjLFRPLAtZY0tG9g+J9/n5j37nY5Ann6MxvdrQXxHQLuMiy/cWg4pZe0wpeMo23P0EoaqBqb0HMuOOGXrxqkC/ODpIIoFYwnBg+Hss1wAXnUoZiPCVAESE0xjju7MUc/PNAVueBQdDsaqNO64qfzb1KWStdITvOVHaaXbiOX9W+S3eg12BxfMEq+z1YDPeWPOAl4nwF+N0rYWiqVr4FMa/0S1QMOUjRK0jnGhT69DrO59zpnG2QtQpGvsDppAuPJhllfegx5cQvcv06QFzf5lVmHCcrXP/v33MJlOaahoJViwYDVFBSa7O9TzC8yuvpn67I2suwSzs8x2r2F61Q1UszkSKyTWRrWrIlVNzsnK9rpWlgvB52WBp57ymPyeSjOPMkh9BoM/AFtMldZBvRpr0zY3B8PGay2gxaFiDAJa6FQP4FHx6mb23rVeAKgSy9PPwVikLIJ3ItrKKZVNa3QAqxjjACxaviuUvvvxnQEsEekcA4zBwj+6+McQ+j4UGTz2yLpWWuVLBmAJmewdpwT/zmtjBI8v6c+JrVkSrDRVskmEAelxa7YYHANQDpT45rbDPY1/L8B4dPzK7zFugn0smUBiMRElF38AfFft+6BBs94tlqYM8aCMjGn3cs1Bhtx34JgCcLyDpZ+713WGeKyy/RDY50xNMGXgJKt9WD0eSNEaGANfXo7dw6C1nQ45ZYBfgFb6Mepz3AUHP8tdLwVzRIEMXbAFXvyBZ3HwVc8rT8HbqtqkNBrPFvhO1bu4lQuw8qvJNb3OFYiAvdjWhtfOH0u1LDxfP3qgnwemFEzqu2z5vVaEvgdANWKDgtNeMuqYJ3mI7q+RvtRw8lcyZCtKRIacO0JO6HKN6v3kJTQfeQ/5rv9Jd3iZ7mCPJjc0y0MiFV2zZrnYJ2dltfYgutyABCbzmlhPmEzn1GeuQifnWawXNLmivnAD19z4OKpz1zLdvcD0zNWsOqWazLj6+hvJEkghoPUUCbGvly9YsZzeJ+7jWKoiBtfwgwO+lTg2Ja3z5x1ESO5+CVKUKe0BvCtR/+NJqjgtKrZ8Su/1tFROhCq7lzV4gZ+sVqaYkfK3lVMpxZIfrNIwApVCN8f+OxgB39iSPgEMy++bDWs8b9xLPBd2aWCyAiq5X6dsOfBIevwNl+SFuob8/p4ez1j8iOSePcihgL6pAKUkLyqDetwDrBz5Obbu2RybHthL4BwbVn8IgRhOUgbw1L1C+Q+57370EYiGvuTxOLK+31JgUE4w9yxq7GavrIzn4CaIun7fu+/KatJfR2mK40MkxXVTjqolMBsolUihZ40234ejisHGlRy57+PX+lDKqQL8tSail3k1jdgi76NbflYkxkBwvABrjF55r1Rus/EuaV9BgKBI5znxHqDT908Pwf3jSotFzheLVEuGAF7dToUJlvYXxPzKLV6+1S3swjyIWB6/xQd6Pf/RAoFIH4VvczmTxWjyLgDZJr5RymaJNCWYz/POQWktER1yh3Qr8nofOYTl+qOEK3ss7no/q7vfR1i3LA+ukJqGnDrILU2TWK9bVuuW5bqhaxOiLSKRWFdIUM7t7kD4KDlHGjKrdYfMaj5Un4dql91rHk6cV1QyYeeah3PPNQ+nJXDuzFWcv+Zmqp05uZ6wO5sTpnNzBdSRELzBUZl4Yt54zZapQSgBnADiVRKD5ftjk1GDVRx0rw1FV5Rc4kDK7uK1D6wgEXkUJWymGYXa69TyF2us3kKlmfXpmbNbOUHGZXSLpToGsxBKbnboKflNSvt4NbaPSecWor43KE0h39zEKO+SyRM8JTj7ulSUEiMi3QApRqooobiZsifriVoDMAHJFhiraixj36tazL9fXAuwaRkP8QsDlX/SPfeNZo758otlL27ZjxUs8flXXG7hyHmLJnAU8As+jhgOZ0rGVQaPW/RjH3vxuSulQdGJyo4ORl95huIKgichA0OxtgLchbQvTMpw6PL08fVj/OXpWzROFeCXiOoKC86TbLXdrS2tUfghqdfADm69G007SRYx3zn2JTJ1iG5Jl5KWnrqhyQrIhGD566U0LABeSCMEQrLUtzpYQ5aA0XdWB8DANvkLUXL9tZxfM13wDAHxZjPetS776xOz1b5vY6B261SxjILaq+d1JNOMA0jrRYQyrJMSNCM5uV/blIB2tUQPLtLuX0T3l+zf8270vjvoLl4Eidx3sKBqWyIZcsPesqVZJZpmyd4qsVzDNAiH6wXzacWZM1M0rElp3wLZVFk2mek+XFzeSa6mzO56P00HkzAhT2rWRCQru2fPMb3mKq695uFMzl7gugvXcu11NxPPnKU+c57JmWuZzneM6QgRqXbo6kggekZER+0TN6q5dKKqW0+WuldhNQ8qLLCyUqHy78sk7CexL4I7GrxaI+ZKwBQyYw8CUaDxzyZibYJnWwP/VMsD+Z83gfxjA/tRy3eDOh8Fw/X0ORZsKx6LEsIQl1IYAQtoLYyAknNAglrr+RDcpSSUipZaDBevBGnNdAoQaq9AaNC+MVQmI5G+EqbzfljAXx4Zm+OqckfGanS/Rz8bW/SFvu+VggAhDtT8uBmR9Fa2jsYShjoImyJugIUw+NwLA7jxvPq7secxALx/JuVv7bfqz9FH/NA/z+H4viac8H7JGLylrNWjw+Dqw2iz07hknCrAV/ViMVhONljwV61CTFYKNsjgw0fN19vlRBdKfXxxRddT6DRZ8R6BkI1Otp7q2heXyT5JnRinKJsd2lNWYlQDFU4decBsiZ4vBV8ku0sCC/ArroAsDAV/nNo2l5wCCW1xflqRnMgILYmgygShVQO4mDOdd9OKKrQ5gPXCI6vSdB1pueTgvjvZWXes9hek/fvRZkGTA8vFgnkQmtxCgi5Z6txegoO1slglYoistWYn1lBHFl0mtxltlURHkyJNTrRJSF3LurtCpztk3Sepfd61wtnp3dT3nePK7IOc352yvDDn8Jpr2Tl3nvrcNczPP5wLV99sMQMhsHP2BvLZC4Tda9HpnCpGUrSCOqX4R+c6f0mjGdrYGk2Xi2+g+AF7xsVTAH2hLqmCKpFERjQjRFs8QsR8joEuKjExdtBt5RRK8dUfBfmN4DMeAOxxSpqB4i/WXXEploVc3S+vvQLJoFy6FLAf+9BVIWtCiFb7Iwz0Y87en4KARM/GUUAyIQdSVEQyKYkZKx4rE7Ofx85qLk1PBRgi3S1dsFwXjJUjdx+I9Cl3MRpTUnLvx+B+0tiGaJY+DEGRhp1CqXYHw/gV6/gk4qQAfl8gCWcLRuMafCy1B353rehwb/1nG6Ijun/0aXl2/WnKUYfjl/dB+4t25eDoGcY36crYsSC+h1hOFeBLyqWRFClYL6lSWU7DUIpWAM3eu1xsUBUb55CteE52pkCBCeK525aOFdXTwPzlm6hN+FaT+cXFFAZLkxPaaIGAZmWWwDnP8/fiNtmVEC20EvgELNSRpZgp5mpQ1FP9vL5AtmJCmUyiI2X3P6nSKVZoSxKhM8u+S4ll7shJkdxCk1m3KypN5OWKdn/NPZfeT3twSFgeELrEulkhqaHplDpULLPSpkTKmaZR2iSsPTdyenYOMym9bliXa8nQIJBs8q9RUg5otyQh1NWMhSTWyxVVl5mfU9aLfe4/uJ9pukDVLlld+hDTac1q5yzrCzeCZCR1rM/fQHX1jcyuvYXZ+YdTXbgRme6Q6wkp0Je8NKUJ7/pns9h62RtNE5W+4U0KPvqaqbDUTaTEY1g2RkapJVDqmJh9YCt9peI9DU7XxN3KpojEHsjKP1MCRsDPAPYwxH+UAjzl2W9kWG2eZYAD7b35trF/cDS4y/4ertGsa0XVgsdMSSlUtNHRWTLi61v2IKUhcM0tfh1qRpjyYUW1+mh0M3WdcZCN65Hewh6AnNG4DdsdZT6Ofl7iI3zsRCh17PvYCI7u31/ckec3HL9Y6/j99cq7FINMUAn9/ZuJKP2+5RmMp6z2TMr42dAfW7xSojEmg7U/MArF7ehgPmIHhqcg/t4Mip59eYJ28xDJqQL8tSbzu7vvNmC947NYN7TKB7LVPDTHUKPgrTiL0b2ol0cd5emLW+LFEgYQLd2tlDrnnjmQACRlEqwYS+ws6jupWstbfweDDimA4r55nFmosHS7mNW76blSkK1sbMkZXydLaau7bPn/uaPLLeItfrucQC3qVx35Q8qsu5aUOjS1tDnRtUvC4m7q3LJYrGiWV7hy3/20h5eomgNithQiAZou0QDrLrPqModtoGHCmkxHx+VFx4VZy/XnpjQ5efZERVO1NI3QEdAIsyowIbDO0dPwAutOCbkiTiOrlKjDkquvuZp7LrXct7+H6Io0qdAJyPIiy0t3M6sEtKP56J9RnTmPnHkXs2sexVUPuwU9ez2Tax5JuOoGZDYjSLA4huxBmZjyFt23jwhNUCqsCFDQUt3L4g9qLS19LdUviFBTWXnfUCwIz+VHvB1xyX7YymmVk635ccpdSccbA9tgx4HRvcGt05JIK0cUvWLxFwNDKFa29MbKphiom4UvTssXmt8s9wLItoZkC9KVbEXGvFy1/fRc/qCkLKQCbJQCPZmgAdWMepfM4sYaR+6PLf2CxkdTGjes+Y0c/CFAL4SRK6AoLzIwCbIx3iOAfUDALwMLA5gO4BlcoaJ/Rv5TBc3DMy3nHOyukUuhaHUUxadcQeizNewhF/bHYyS8f8hwjHKuYuCJszTS6yjl+o5zAQ+dnCrAzym798koGg1WCx3xlDcH90qshpxks7gnyV6oJNBpIgSz7pMY9ZvVW6Pm3GdXlZr4UYRGsycBCjmbpqxZkJz7h5w1ETELV5MVyEmahwUgG53eqXHIy66BLrHOCVJGNVn9+5RpUwNdZz73roXUsO4acm7NEm/tbwU0J1JKSDZlIGli2inrdslh10FuSAlmcc15Gi6097GXA4dXDlkd7LHY32eSlHnsoLO6312baJPFLnQpk0RYk1lJxeUQyefn1LNElkiTOyqJrDOscuKwVQiB3UrocmQdhFWYEHJmNq3RnJkSOGwFXa1ZdJmrZM3DrzvH3l7Lol2jkhAmkBskJGdvMq0skcUh9eWLtBc/Qnv/+6jOXcPsusdy9ub/D9MbPhfOnmOi0S2KTFaLIM5iC2pA+va2R6da5UVKothimUnEbEGcnedMlyyA2t+RHE0hSJLZyumVkjZmUoyBYnEabT8G+42CLzqAU1nIh+Xc/j+4ghyCRHu/vtngI/DfoH79NzMdESlFvwZrc0j5M+YwY0pAzuaOTBkkClGi9wXTHm9yLoDvoKMZNJKtXrUbl4pq8us4asEXUA/HaPv+9xAI0YNs41DXYDMAcPDtD/n1A/iO6wvICYBvo6UFy499A3hJXhk+cmpfCWhh/0p1I2J/nKOsy/BkR8yOFEonjEMN/Z0oVnux8guYM2h+x67XlIXh4Z8OOVWAn3Kiip5y55GmKQazsjx4r/YFGSzQrhFlks2Kn7j11jhFVrtWlhVSMnpWnNqvklnYbXlBWrO8l7klJitcE0Qhm2WdNKHakVKi6TroEim1dCnRdhlNHQfdiuVywbpZs7e4Qm4Ta03UKTHJmU4TKTV0bUPbtSxXa7qmgWbBSltS06Bk2tYp7pCZa2YaMnVVsdZAHStqiWhesUpKnTLXn53TacvOTsXFtMdUD1lfUdLBIetVS5szUndIbrBFIZCSsuwyoevIGlg3StdkzpzdJc4n3DRfUYuybiOLDDkp6yQsE1yoFJGKhQRCrJiETJchV8K5M2epQ6a5CAfrwKWlUl9ec9MF2N2B5Vq5smqZB1iu12gIVKEi5mSWiy6Ik8DZxRUWBxfZ2dlhfe/7yVc+ypn9v8zuo55CuHAjRAvuSxKosGfad87zCebLksWDBAP85BS+sUfR03VsoY8hgvtHQwjUalRd8MyJrZxe2SinyxCNP7b8B+t2SM2DYjAOIH1SQFmJnpdRHv0GhPi7MvjvvSjVCNxC0J4SVnfhxxj74j2lkI8dIzH4w9UzVOyz7O14o4CEcfldTwP0dMEhj7yPJOgB96RKemPLvqThxRh7/36pWjjOuTeg34yXgEEJ6HWowmKEkwG/KF62/UkKgTFw5TjFH9/T+66wDbhuz3mIoSjmPr7d4NbolbsgeOvD8glDat/4MzuQwrF3ZbMGwvGsgodaThXgo5m265BoKW6TUBE6G8AuaF+bvlOlEyVko5SsdnymLYUpWqNgV9pQtTYJUko0TSKljtAlFqlB25aua2lzg3Qd63ZFm1pS29C1LSl3kDqarmPVrkFbyB1XmhWhS7RdS7te0+SWyaLlSrdi//CQg8NDVssVcbWGWixjbt3Shsy8qi1KPyUCySzSSqmmM3aqOZPdOYQJs53zTGdT1otDKu0IElh3DXOUdr2mzS2zpOTcMruy4Mw8UB021Lokdy1hvSSnNXWbOFwlZpPItDbXQhegClBnq21PVuqktB1cuu8K850p+vAJswqaSohti+TELHXUOxPmE9BcEUKmSYmdOOEgJmqBWe6Y1BPOzafM5jNUhHVW7l12nA/CtKpZRWWRVuyEjlYjqcmkvGYaQFNgcZio5w0sluSDit2D+7lyeJHDSx/kwqWPcuGWL2Vyw2NI05mlM4qSJBLFWxeLFQrpUkcVKouRECVKMD+/vzNQfkIttcV3iPlQwVwGCB5kueX0T7OMy78KsQ/iDCX1TYo950AHGLjTuwfFeechnayHyWFh7+nmUj+vAD1IHoLjBuu9bC+IlIqfXuI5e++H3jr1fXK2CiFCHxCnYhH/qtamG7VKfkLx61s6a09/e2CybTfck7g7YQz6G7n2MY6UgEFhqkowXxgo/bHlPo7sp1j+nkJvJEPox28czDiwMUM2wUkKl22pDsqCOXEL7NJXYi2MzgDRw08ohbcKozO8Az1VA4hYV7ajrMCYLxzgf8w4lHiA0UcI6OlZO04V4GcyZEG0Q6Qmh9zXyY9gFL4/iKCWotfmhHQd0rWklFl3mWa1IHcdy2ZNu1rSpobVeslq/5DDdkG3XpNyQ+oaUurITYOmjkW7RFND2zXk1ix9UkdDMiu5S0hKtOsGJbBoG9q2I4qgyZr/NG3Hum0RUWaambXCdFbDNKExcm42R1BiTmQSEoRJJQSJxHlNrBKal6gu6ZbAaomkRO4yuetoJoGgmSmZNrdIXrFslMVSubCbqbIxEd1iQUoVNZmd0JE1e4CfpeqklNG2s/mTrXnNmUmgYsJkKuS2ISNUNGgVSU2gkchUOmIOBGlZ5sC0UmIFF3Z2oBNiDTHCNefnrEikdcu0jgiBGKBtlPOzGlkrrRdM0pSs4U5nRQvaTlms19AF9paBLi2omzWztkH3F7SrPa574l+nesQtyHQXggF5JV7et1+EzMavZLTIu5/fmvlYkGXOxelmLqUsZYkUWrW+AGwB/1RLyQ23wDh71iEYPvRBZVoUAgfA8vvYH0tfVLUHnrFt5/ab/dGXjjVgleJXPvaqjBQK/DpVzC2QPZA4eMiRmsUqGGDmDBILVS0o0XpWZMtsUY/vEX/31W7UL8vZDB1KbiNKkAL6m9H3JTr/qC8/hk2wH/vyQTdT9Xqav4zmaPzL+Hisk8/SnmErZYcfCPDHBnbP5KBeOr2oAKN/Y6B1UFeGkgVHz7UZ3BgZ6hmUDcLA4h+5HtupOHeGL+1RnB5L/1QBPknRkK2IjSSbCFhzm9CBJotOXzcNpIQ0LYvFgv3VIc1iyXK5YG+15HC5R7s65HBxwOHBFehaLh1cYbU8ZB5Bm4aqjqSmISUvAZux+vPlpezMqs7eDrYJFWndQQCtggUQhglZArshEgOsckPSjl2ECUIlgTmZrOaPT82KLjfUteWJdxFCEkKqrAlO20DooIo0EUQjde6QUFNNItKuOXt2l/1Ll5B2Qbc8JLewCC11VXGYBEJCM4QusF4dIGqUe07ZYgESSMzQWDBgl5XQQd1lzkfhrDa0QQjtlETLGZQFSqMdMVtN+ZQT5Mx0MiXEwEwyLYk2BCYhUucWqa35TZpXzKIyqyMzhcsa2Z1DnM6553KLJC+SXE3oWiXSogKHOTBnTZQGbQJdZzETV65cYbHaJ8TItXHC5KbHwWxCCNY4STPUEpHsRYzcFSRinfXACygxzFfrUUC/CEUP5sxY2WLVfLyoylZOlRjg2FM16zL0YCMlIKtPGyu0/lGwL5bfAMxgbOOGrTeqZrcZCV7ACweL7JHzpVpcURBgUACsO4gFDGPrixj4iNPLhf4XwRQD6AOMyxELO6FESseRUrBMQ6HBtWc0BkqfE90eY7CPsaTrDRQ+fj0igeiNJkxhMFjt6XIZrOkxszKApYx+ngSMY4gNR5Qpt6gJo203tILRr8MzPsbO99uE0c9yrJN8/w8kMjrnsO8DKjAPgZwqwM9tAjqkU1apo+ssP7pNnYF903KwXLJYHnJ4uE+7POD+/Stc2ruP5f4e7WrJer0kLQ6gWbE6XLDYu8yFMOEgtTCfUu3s0HUdEi0tLYnQqlLVE3KAqZhft6sjldRoWhmNRrQ896RMspK1pVaIXcsir+i6hCQl50TA0gsPIlxJQrO/xzXXnWO+MyGkjhRrhMBUJlTVhBgqy60nM60mLOeBmITZZIdqPiMfHLBcHNA2DR/9s4+yXO1RRYvST20mhBW78xkTqQClyWqg1WU6ScTK6MZVaplpRZdali3k1hea1jtT0SKhI7fKuusIO75gaodmdwekjsCEdc5UuaHrKiq1+65kSgyZKpi7I0wqalVCtkVBEeYxUHWZqjZgPVi0SJ1oVkqlgSpbq52czFpo28w6tUxFOVwrKzngalXue8//D1LmTPt05o+8DWa7VGK6fkWmE3tmISmVZo9cVroAguVsq3iapxdSCpjl34hlV8QQ3NrzFsBbObVSLE51RtAi8gND0J444zNQ/LbfYGmW6mw9xev+9gGfdMO/rD16mGVnhXcGcAXBOr0ZUJXAr7Fv3cDVDpb9eINP33q9qxTzPzAu9KxehMsi84uP28KeB1+yHbN00VMp4YuD753RtRz160sfqFc+L+A+/H00hW/DRdIrT5soW8Z4/Png/tgEWUthpGg2/tlxl8lm9L9sjHH5zJ704KrZNNfH/47u6+9Kf4qTFgQZfSzDR6do7ThVgH/5rntomxWrxSFXVgv2FwtSt6BbrjlcrdlfHtItFyz399lbXKZq1+SmpTk8IDQdRJjNAgeX91nsr0g5sYqJvbSm66DdW3JXvU8tgaQW2RlCgCjkKjKNkVkFVawIVUA76LT1eIFIzg0qEbqGjNKI1YfWnC3KO2cCNS2Zxn1ubaPoOtGuWrSqyGR2VhCkRmpr0LKSDLUwO3OW2ZnzyGRKqGrOnbse6TL3tx8kXzlA0pqlrmiixTFMIqQ2sVwlVnlJ19bMp1AFQRIWSEdD0kwlgCqtNFaeNniRHiz7QdQyCCoJaFJSlVk2AjWkbEtUFzKaLBiuDpYG13UNUDGjhqpjlYwOa9sOUsO5qfUab5cNa6kIXcf+umU+mzIJwqQWutTRtka5JunICSS3tLFi2SQmQTlTgVYNoappmhWHd32Q9XrNhZy5KU6ZPOIW8mTHlLWcUTIpeAnidkmriXmMpDghBCsYNHG/ZM545TKlw+jW5HEkrWZrzbsN2jvVImJJuyIWzT7kgo99+OMa+1DA3YnnUS/1/qBQ/NL9Sm50edbiHS5WnKXV2jTT3tILoRR8UdBIcR3Rp+UNhXPK8US0lMz3lOIR1W01xD1SXPv69Opssuom6zD2i4uUqncj9uIEy34A8iEwb/g3bBtj2PgsBHEwHfv4N+fNUNJ4uK7jMjwHLalyKKUrqCdhD9kKI5agT5NmBPlFSeg/75+kB/udJIM1P1zBpjJ30j7ldk56BqdBThXg/9Ef/T4H+5fYO7jMcm+fK4eHLJb7LNZLYptQyVyTJzRdS1hbEF2TO9ZNYq9d0UVlEivysmPZduRJTceUtE4s1ytmCloLIYHkjipCo4F1zpyfVEAgkahmFTqxPO4UggcRClUdzZeuAch0UlnZ3U6pyEzIIAkRIVeV+b/nEanO0VSR3Ci7YtHkbWiYdpF1lWlyoNYp0gXOzq7lkY/+PK4+dy3TqubK4oDd2VnuntQc3qXIxY8SU7LAxIkVidhvKioNZBX2Fh07lbBbKfMqoLlj1WTmlfmjm5RIHazbwKJTCIFahJCS1RjwhdE47UCjlR0jdTQpE6WilQ5EyF0sxQGBjgjkrkNjzXxWs1w3xBSRiGU5JIsvWK8aG68AO5pYE4BEmzuiW/coaFXRCmjuCJ2QcmKa4PAg0LX3EZf7LDpBk/KwbsmZm5+M7pyh04S2iXoaWDV75NWKrJl2NkWqCaldo+2KNNlFJBLFShyTvXxvcO+gBzxlhkC+rZxOCc5uBQe2UADfc81F6H34JgXs/a8eEAcZWADbvuzXg9DYh49ljeiIBdARgBdwN6vcy++izh5YvQ/wYLtsn6iod5e0ip9koRShCzqqJ+L/M+W1RPkP5zzpfk6y7DfT8UZWvpy03aYC0AdHMgROnoSlx6L4j1nKR2h5LUBdgv7M/88IWDeej/8IPUzbDZcQyzH2is/tE66y3zJsbD3u4HmynFTh7zTJqQL8//k7v0nbrMjNGrrMlbaj7daENnG2mjANcEmvkJqOlFo0tyQRYpyiGc7JlGaV2esyOStd6rz+eyBWQpc7YgpUWI17DZHrzk1Iy8T6cEFHZ+leBwuYTEGUZtGx1o7zZ2c09ZQIpBghJpK3hNUYaQlEhYlYK1vWCpNAkEiOllYzVaPt15KI1KRpJItyZj7lwrXXkEMgLS/ywT/5H9whFZItNSyGgK4zWafU1Vm60HGYoVlDlMhh6lgftpw/G5lrRwwT1utMXLfsRGEikYsrczUEFXID6xa0EyDRVRFFmGikiUqI5jeUdSKrWUmrJHRdYCZW6vOgTcQsxKl1MExEQs5MpELbgFYgIbLWjpg6q1ZLZ01BYoSc6WhIGVKbkWSLU/J2tJohtB0hZbIIq9aCAxMN9x9kdnOmXndU3Z+aJZ4bHjk9S/WwR9O0DRNV2rBLWu6RFvtMpjuo1qR1Q9vssVNP6dKa0K0gTghx2rctRr2PuVOIWpyrWzm1EsUi9ES8jkehr8WazJj1v5kf7mjrUFDo/FEa1ojqFQ+EU7FqjtLzwfTBxAbEjOhmBbE5r16LO5N6S9vKaA/VIsc+3wJyQQzwM1ZMp1jdpnSIl+fN1hK6BA6qngimdjsDgPefjX/KOACyZC4cVwr84zJqXqpaejcAPaWv/XHHqXAmI3/JRv2CAuSjErv9bmX74Rhjq3pUKX/0bP36R2f1PQd2pbhTwIstDSxNeTbaH3Ow4Addo7hzTlJgTg/onyrA14/cya5YoMiV3FGlxEQzXbsmq3LZiHO0U7QS5jtnmGQsinwtHCRYTiFPJpbyEpSqWSOaWZKt6h2ZOAnsxMiFnV3Ondnh/Xv3kro1VV2xW0XaNrM6WFBNJizaFbMQkHVLnZUmJyqEMKuYeBncPImsU+BKhC5k5lJDo0BiFqGKQl1P0BDIGtFKSHSggaAJ0TU72nLtVRc4c9UFdnbPk1RYHB7S7C249+67YXWFptlDNDEJU5a0LDqloqOqFI2Bc3VkN5hvOkpgvUw0UTgXIaUOkrXcXDaJ1EKVrD70NEL0BUsTXg44kIJY8JsCnVUOzJNAyNAlpUGYtsZ2JO2IRKo6IRoIZKZASIm1tkStobUJFGNF17Wsc6LLgVWXLEAu44GbthA2qaHDetJrziQJtAnWQdHQUpOY5QV7H/0AYXaO8ze9i+vPXc3+cp+rYyDEwOryXcjygPqqG8lpznLvXqZ5hZ45B8srSD2FnfMWqR9qq3YWohVmwhb+nKE+0RLYymkRcfcMBSgoAD+ilpVRDfbA0OxEKFzwYHkOgWa2yo8VgOF3McN7CBvzYD0YUftqRHfppyE9MBjo9+BRyuy6D9+8BzoU/RkBYgF9PKNEejp9UDZUH1hJ3bDwR59tUvQ2dhHpe1f014lnQPSpkMU9UEoGZ3dheM5+0YY8vmCoTufjWNIPN3zkyoD5tv3JXnr7fuyqOXK3o9/CBmKP9AbzGox36y9mBP6FoRh9XcoYl8McjUE4TcbCqQL8rMqBZLIqy5Ssp7xYH/QYhU4DTdsiqlRZyE3H3mrllasiKUEn1ut8nUxfS3TUCJOUSSQm8xnnZzNknbn74gEfuXyZRdsymU65Iom2XbO/7shthywTk1mgVWW5amgWKxYt7AalrqzDXZ4IsQ7kHKgroQkNbTXn6nM7tLljtVhTTzKV7CCxYr4zpa7NGplWE5rUklctd/zJ+/jgn8FsGjh/1QU6DTzy4Tdx7rrredT8GpbrOZfvm/DRj1xkoQGZK11d04gw6TIHyxUhr5jKoRXCQZgSyG1L12TaBDTQSraI905p1CZp12amFUhSNFqMr0QlZavMt1JBu0w9CdRRadpMm61VZytK7qIF/UmLamQWMxPNtE7PN12ELjPRDlSQqiOESNPBorXI/5wgJ/GcYisRWoUpWTuMpKnRNlkJX5TcwpIMcYWsKqpLd3Nw3we5YbHP3v0f5uoz52kEDu59v3W6272AHl7h0p3v48brbuLwyl1MVAnzc2hdk5gQZI1IRCY7vWVTUor05LqpWzklEkJ0Hzzec2EA7gJgpTCOiWxYf0fJXgNq8f2P+GxHIKHF+h8fVQYr309g14iSiUjIXhEvuNVuSnMPaoWRcKApTbfKtQ6A2V+ElQ0PQ039Pir9ASLGxu2Bx7IZgOe0vgSL+O8/7z0l/e1ZCmSx5kvBIG9GVfz6wGZgHSec//i12niObrf/YwTkcuT5bXw+tvKlDNlwwP68J8/x44F60v8Yv01GEpxu19+pAvyLzYogsIzQLDpLU4vWDGUZW6IAXtq1ARarhoRAmzjMDTWBNgmqa1BFU7BGNF1rxWQEDvYPuXJ5iaB0OVNNhaiZpm3Jy8x9TcNhUqZBCLTEVkgJDlHWWYmhZomwO1OL+s3KtMnUsUWrOavccnhlxd7hHo+4bpdZBWd2aqoYqWJkMp9wfh65UEcI0IUJVBOq6cMJkxm6bljsL2iuXOadH/5frLvM/OwOZ87N7QWLHVfffD133nkvq719mv0VTUg0IbJqA50Ye1HHhnlQqhg5kMA6ZaJk1q1F+HbAwRpCnZkidCHQRItGr1OilkjSzKqFVi24bjKLHiecyO6xtDL42Rv8tKxjTSSQIyCBZbMiUUOTWGdlGgM5dYQpTELksFuzbK0gRrZGv6ROqCYwEaVuAyodq5zISQli3RPXTaKeRpIK6ywcLpYc7l1mfXAP+eL7advryc1lVne/l8nZG1gtD/jge97BtWd3WB0EEkKba2Ztw0wSEnZZ58xkOkerCaGaWK0C3Dd8imi5rRyXSszCtzz6wVKF8YIPA9VslvcARCcoCAU0Q3ADX3uLerBQy+8DxysaNkB5KPfqlrDQX6e6ywHMQFFLM4Ds5yk+6yPv3/ic4kdWUQsmDGH0/Yht6HdmACux7WOwTJqjUfelsY7FtlgQoriSclQ5MD2pxA2IbRcKU4Lz9AOoq5bxG+5pcEl4MaJsZYY3C9gIhV8fYgI8hbFnS2Tj56AA5n7MyvMoHv4Hcr+X9+aoktD/VhqpcZIcLeDz0MqpAvzD1YoggdRl2tSaryyJWZxisZlrbVk2wupQiR0E6YhizVAUJSVLKZtktfrnGqwm9QKaTqFLSBWZ1JkggR2ZcqaecXm9YN0pldTE3HLYZuqsXAmJs7mii4mOSEgda2CdhOms46r5jKumM1KMXF4m7r60T9c1nKknNJq5sDPnhi5SzRq6uOBM2xAvzAi7cwiJ2c6EOi850yQaaia7Mx756Jtp5fHcf/8h7/+TP2Wxdz9X7rjExcMVXQ7Uk7u59pqruOGGG1le3bE8POCjd93LwcECrTuaWqiCkutAm9akHFhLJseMdkK3yrQ5sMLAulGhzcqkykyDNcJpcyarFTKK04rpTsU0BpbLTKuW1tbkltABJDRUpNaUgAWZbmFVAMlCXScikdytaEnMsLoKOQutWCe6GiVphVUise+7CjRGC7ZK1tdANbBoM9M6Qha0UTQm8uEhq4t3sffR97K46300kji4Iiwv3W1Bjx/4Pyzu/gjx3K3sf/QO4uwcGiL15EYOLl+BcAVQ5PyN1POzppSEigarQniKWLmtnCAWMe4gIYVGPQ72G0v9eLveL13Aa+TzlWH/vsV1OVzxBI9IAtHBCu8juh3wgsoQK4IFtyEjMAnYOfzYCMYI5ONAnHNGvDlI6bbX6zK9zXnE2lUYF7jpqfswRNyPQd9vyPzyXk5gXLhnUKpKw6IC+oBkxjn5NsyhB8nhmXh6cCigb9+bT9zHSseAWh5IeZaF+fDnMQb78hzHjv6yu9c+MAXuhJfqGPNzXLS4ZfLmp8MLMVIGT4GcKsCfTwNJIx2JOlTWcS4lctOx6FbsxUBYrQmTGbMqWJoYCYkWTHbYJnaiUOVAK5mdKhDbyEpg3SW6BPN5ZDadsVsJs/mU+bRmXgV2lpHpZM7+csWVZcMqKeuu5WyyrmpLOuYSmEpAJpGQhauumrM7C7CfuXzxCstl4toqIXUEScxFSCQ+ev8ebQ6kKMz1kI+enbAzj+zOA+dnE3bOTjm/O2FS11zQNQug6QJXnd1BHnc191yecLhYMd1r+PAH7+fOD93HvR+5wjQGLlxzhnon8LDrznFmd0ZqDmjaNYuDBQeTCC20XSbmgIbMBPMTdlmRLtMAycvOoua71rUgS+vWFYJwfmYVuDKBZqU0olSVMRyWhpdos0JSprW1GN4/hCZnYgfVDM5NA60GuiWE+ZSWDF1HSspKMkkteyKEihCs4O26TWhWqiBAJIRsrXi9jHJeZ5YxMIuZuFhzuNjj7rv+jIN7PsTlSWD/YEW3d5n1hQPu/cidXH32Wu6+8/3kpqGKe5y5+ioOZzNk3TLb3WW5bqEJTOMuurNDqC3oK8C2Pe4plyEgD0QiSGBsPQKDRd+v4QXw6enqY7/7ZoWJHsC+8MLSs/8lv37D+i6g1u8vlIjzUmu/FL8de+GHe0ne3GcA2HHOu46vQccwrz3YlViCAV8HLjqMwFGkWMrHz1MGQo4A4DBOChvjKhT3hmwoYCMFycfAjqOjn4UeN/Dvsx105Dc/gsOldsFwXWUbPfIsZYS/unE35d4+1kzvH3u/9RGGp7//k35/6OVUAX6HkHJmEgLrac1q/5B2sSZrpiEQNJCnM/MnZSuk0iTIkhDNpA72pUPb1qI1c6TLjfdOFmuYKsKia0kqaBXRlNnrWuZn5lS10i7XXHPVjNlsTjUJ7Oaa/fWSvXaFZstn3wnQ7Dcc7i+4+2LisOlIanT0+Z2KnQtzrlxeElDSqiPnhihiVvFEmHSRxRoWi45LumJWKbIzZVpHzs5rzswvcWZ3wnVXn6GqJsybhqvPTVlfuJprH3YTH370Rd73zj/lcHlIuu8yXZeZBEUmEa2s2U41jx5QFGhoQStCJ+x3iQposkKOoImuA9HMbg2pE5pGWKdMHSJROnZ34HCdydqyTsositesV1QCOVrufqloFrOw8M6Ha5TDRSaGZIp6grbJ7NRC7gK5SyjKkkDUTOwyMQqSlDWmBJCUFKAVJWTrZy/JK6BFYd111Dmxyg2X7/kgujpg7+Jd7O1dIWSl++id7N37UaoUuPKRAyZVTT3Zpev24b67CTEwn5wnTSP33f1hbtnZJYYbCEAnkVzXVH2fxa2cRgmhWMuYQ3kUdGcgqD3gDyIMUWFu2R61iOlJcQMgCo3bH8Etcj2CFAZ2OQ809NBUp1DiRrnLWPFAgGRBeah/njHt++hde6Gv0tmnfKoFIumtWdUCnPQ0e7Hsowc8GqU/aosrA2AHr1w4Zko20xYdlP18MhrLAdTHw1MYjl7FGV3/0DRI3fmxmfuuJ1jk5fyDdmbHkdF4h74AEUfiHDaUw/4hh+HYw27D1/31lp1OuqbTJacK8LXJRo1kJaaWLgY4MyWQ2RWhISKt0nUdTVY0JSRD0yo5NSxStsCYlKkzrMSqtjW50DugBy2VRCQoe/srdupAlxNh74B5CLQIiSWie5zbrdnRilgFLi4OWTfWnlaSrSk5Wyx3FW3Bmc9MN80ps1wquV0idc1s7i9OCITcsUxLqGrOn5mzs7NL9AmeV8rFvSX33LvgTB34UHUPZ87sMp3U7J6bUlVrrrv5OurpdbT33c/utY9lceUyl+67THPlMgcHK1arllllBYjiBOpKmElgvUo0rXCw7FCN5BRJQWlzICKcixXSWCvfNkHbKQ0wrSOHnTJZWu5vKxDU+nEjVjd/UlW0ySZmxBoZ7U6UVWfVx2IbWa+VWNlqkFSpG6FRi/avFLrc0Wmg9tz3NlvR0drpTVErdhLVWpVmzaQYaNpMlRKzqTANE9aLPUK7YHEwZ335Eg0V6fIBq8U+i4MlScXKK4cJly5Omc9q1mSuOvcwqhoO1muuPX8dV03mZG3I1ZRJOAf1ltM/zWIV9TxfbsNvGyg10OmBD1+fXTEo+4wQaQjo81Q7xuRsWch18N1mHQx5KTTxGOyP+tIHSjsQyYhH1adeEQCLUkfHFnOh4c3zbPkw42u2LAC7HLeWtbAPdk/HO+Vt1sk/3l2wH2QGqjoMClS/ifYBg0JhC8o3g9tCRuO3AZSFKhkewjFGvAdpAM2b7AWA9OqZsxvB0wRHz21DWzh6f36FJfVSj2w5YuvH8QLHAwlPH9jDKQP8EAJhGpnESJcyU62JKAe0SGeA1XQNbbvmsO3YqSaoCmEtdMEa0zWdMpeKtlayJtaN9n637FWtVpp8Jgc0JVaaSZqZhIhoZBogxJYrOfFRt0Bzk5BamHSWSqaSCaocBqg7taj0GGjWgWm7Yt0qSWouTCLrdQMJ1hVMQ0SmGW0hHBwiXYu2DWfOz7nuqjPoZEabhEigPej4yP0LVquOrMpsPuGaj94LVU2cJaY1tNWUc/MJZx/xOO6/7x7uv+cizd6aw31hmRp2ZpmqisxnwnwCrUYuNbBuEqlRUghMQ+BSCqxCYJobKpRWI01j0fhnDgSdWFZdjkKDElWp1diYLBZ1b/Mzo9pRTWBWAbkiT5Suw9rNiikTh6LkDsiBUEfoWtpky2fKQucTJwrWHRH1fvaZtjODR3Igp0yuBaY1VRRW6wZdL1hUC/b2r7DMAW1bmmZJ112C+gy1LpmEmul0zmKqUE/p9i8xqSYsO/hA9Uecv/axLNOKWbUDcU6Ip4ua28qmFMA3TDJns0XlB6tJL1jTmR6yHdAI5ODFamDIvuNkm228nB+lcksw/0mF2Dbz46W3Kk3pUK8doGTr74xKR86lGG/wwjtWq34IalMvsluuaDh31MGyVpENkBvAPBDj0Aa3NME5DvjaMyUl5kBGpYh7HM3i5X2hpCIeQWvbvkfSI4PUxz0Ml6tKnyFzPFp+tN/GV+XMGUiWDdi3z409OI9r/NujLIqfK0/i7I2ODi3D7+NhHYcIDM/Cxm4btPcA0tYVMQqpqtGqIzUdh21mddCwWjcs19Zbfhate15cd3SirEJiPquZTCfkVqiBc2d2OHN2zh0fvoed3Tl7647Fasm0ilTBejxPQoSuY5k6KlFCBVUMtGvYbxL7By1KJojS5Gx11hHIHSFGYgVzgavmU5JUnN2pmZ2ZgQTONC3z3RomMy5dvIQsF1w9i+QuUceaHQ1MZ1Nkaj3Y9y4v0P01MUI9qZnsVtSTGTc84ixNlzm4vOSe+y5z7533krVmfrZifvUVgkSuu/o8q+UKXTU86mHXc3h9x8VL9zFfKuvDTLvIHBwkZlOFKFyYRhqUi4vMOimSIC/WBIlMzlboRFgdtnQrqJrApS4zqQNdm5BVJk8ys5lVM4vaMkOYRNAuMxHlXC0ErZmL0ImCBNJEaTur0BdbLKdfE51CSJEVlhVgKZYW5JdU6TJWDwBL3VMZOnV160SrUE8rQhCWyz265Rq6FpmtudK05EZZLVa06zV7CXbCCq0r2nbBuXqfOIvszM6i9YqDIDSayEyZvOt3mV17HddddxM7JKruDFy9+xDPkK08kJTObZuGegGjbKxs8RMTUM/DR4wVDCOK3u1i+0AHahrcpi9NccZ2a59zb0c5ljqHIBJ7S38AAqMQRC0dmVAYA1MGrI2tgX0WY0CFQNaRXV+C5NwFoKqu3DiN3+OYA3hQT6sTB3n8GGHD4i9KwYYvXzJWxlhG31v0Pt6/wAA7j66paEKbkfSb4zBiX/ohdfB3tWdQr8RrGfizLWPs7YfLvmCgnrM/x3GMBoye4Ujx0EH5wpmR4c1geDM0U8o292/MMV/+6ZNTBfiHBwsCgaArUoysUqJrlboJkCOTkNgNgXOTmjrUpElkDSzbhhgqctMxCUqsa9qu4677r5AmNRoyZ2aB82d2qOoJZ+qaK4slKSlRIuvcEhVyC5f3G5ZNQomkkJAqMJ1W7KrStBByZhoDTVVx/VVTrprNaRGYVsSonNnZBYlUUVjvrdjbO6BZdkwIoAmJQqXJKvV1Sj3ZJcYpTGeEiUDbsF60XLp8QJCITCum0wlTIjdcPeHiBA73Wg6Way7euaKSyOq+PUIlnNutAUHWHWcmFbmCOBFiWrF3GGhXmabJqCSSCPNJYBbEav+3QttlmqVaWdK6ppu1rBtFl9YvbKZWVjhqIKwsBZIoXFClqyFlmAVYtUolHVWMrNpMECEGT3BTBVGSWm2FUEVyq5YnqIJ2CY1C1MAqdbYaJagSrFVpAiAdmgVtM1LXVCEQUFb7V+hyJuSEXrnIerVgsUp0q44rhys+soTrgpBjTacdB7OKnfWEttmD3TmxnrLsEly+iw/+ye9y7d7DqJrHI6mjOnMOHvnIh3R+bOWBRZ3C3gBrwPvMjf62jnrGlHu0+ijtbdNeLr+VIDvd+MfoXKpDAJhZf+VqjqZrjTzBUva2a+wr8Klb5d71bvCjF2Ygu8tg7EcXNhr7uOt/CBEsp1cIFSLZQD9En5/H0+w2LX0vqDMKJehz8oOTKgz0iKp4paGSkqgM4Dwe2wHYx89po1rhUYO+H0kDeRv7ACqkPBTRHbsjXOXq/+ifdo/jY8XuyAnHilz/qnwsxm/89pwuBeBUAb4uliQETaCxYlpPqapIXQXaMKFbwMHKu9clRbtMyBXLnJG2YxYrVrkjNStEIy2ZUE3YW7fM68i83iECy+Wa1Tpz+XBF11gVtyoKQaCNgekUNATmESazHQiwEyxjYJ0ysWuoEWZnpyxb5f6LB1SVIK1yudpjultTTyfMQ0WoAzvTmtlshxpYN0s0e2353NF1e5yZTajiHJkGqvkuqcrMuzmL1RpZtewtD8wnFzOaamaTip2p0IaK9Uq50jVMVoG2y9x/8ZDdOjI9ExDpTGmIgXPX1LRtx+ywY+/QAhwTwiwqVWWKTJuFvUZZpkyuA9NJRSMdh2vQA5hHmEVB18J+ytZUSIT1Ujl3JjOthHUKHDRwRjJdiiw7pSYTrVWB96GHicdV5Ch0QamDMPG+Bl02Db5TIaqgKdNiQYI1whzhIAtChQLTGFmHzKpboW1HUGG5bFgsOvYOO5q25Z69zP2LyJUYiMFaL59bJ3YmS5brCCEyRxGp6JoFzZW72JMVO3XFRGomy/PAlz6Es2MrH0tyGgLMsgzUuUkx36OD+whMe8/3UTlqtRWr3Rq4lECyXkHQo4v80Dt9M8G7UBAOLyVgDOkr7IlzxZaOn92aLwBvDbtCBkJEFa+fr5YzXyAmD4zGcHMWL+AmvXXC8xz8ozT+GCzHuezjw/UA77fUxx6oxSPohlVtJYYfENT74x/5Xkffy/hHqWiY7HwlSLPUxzzCroCN5bF+CU7dl/McLQR0ZMsyIBvXOKbx7bPy5E8X2MMpA/zD1DJVhbpmNyRSt6BRWK5h2XYIynKZuGfZENSjWyWiopyTyKG0UAUmE4gkmhaavKbTzEob7g9rpLXo/iRCi5VxrSWwVlv0CUqrSp0MsLquo8qJlQSyQldlQopEFf70A3v2glXCLEEOiXmXWe0r0iqTOlN704tm0bBTqfmOa6FVQDpCm40eSgtUZ+h8hkqink85P5nDGaWrI4f37SGrjsuLJblpSFXFdBqYA93ZCXVn3av2Fy2H+4eEfaGqlHqWvPKd1Rufna+oZ4nFClgoiwPL5W1FqCtBUrZWNislzFqmUZBppI5CrbDKCWmFQ0+jE1UOOyvOc811UyQo+9pZ7nyTWHbKLMIcpZOhy1gdI0Gt3W5GUEnW9zuIFdORTKUVnUKbrRdBFyClyDpmlsAcrFb6bE6rgaZJpGRlfptVZn+Z6VaZ5Uq5tI6sMzRYj4JFt+L+pXJ13TG/bobGCZAgCCllQrNAFhV7936QrJlr03UP4czYyseXAspm7VsgeqHSBwerjClgtK+hPuTLB8wn7G1Vxuu2Do1wHCE8XbOAyhBp31v3xTIsufnlexnQSzUXBt9OkzHLVSxWqPR1GIL8hBSkb4vb30twmlqtLoF6rJIqaDIgDFITJFusYohUErzhkKG2bID3kJInOvS678GvBMipN/wJJc1w7Lu2GztaCKh/akf828M+I5p/A1B1AFx3Uxi+5n7bMWaPm9mYHqX9H5vQPigGH0vGoL+h5B1lIUbvzCly4Z8uwD9YdixjZKKdv+iR1bpl/zDRaEcdAq0Ikoy+UX+ASeFyTNQSYJ1Zd8rOJCJSoXlNEFhEq9yWZ1iRiSTM/MWyFBIlx5ZKKrog5ByIEsgxQZwgSaDLzJIgM6HpIMaaOifWObNoQUPNUjtqEhMJXNnriLUyRVDJXAqBnVo5MwOphFktTGZz2lhRnZ2yTpnl5UOqeUWdIvV0wrkL51ldPqSbdXRnhHTXkqyRdSOs1muqEIwBITOfzZjtVqxkzeF+R95LzHegmyk704RMKqKA1kCGmXifrga6NrNKVjkvqhCiEJMXJzoj7Fx3hpADi70lqcmkdaZbJhqBJneERSJcUc6cTyiZqhGriw+sMEqsIxM1UnpUSQjkpGhOZIQkUBEtYj8pbbbMgJwhh0BGWHW2yMYQaFQJGUJVMw01y8YYGA1K16w5bE3hOOgCMVbo4SHEyOW24ewU4u55lusD4u4FunVrTYPItNqx6KaEdQfLK8SDGjl7qqbKVo7IUaBIaWjDOrb2jG4v+4BifvGy/7jDnQj9d/Z1QZ8CLmM/fUAkDzT9xipfGqsMFuwG3SzBA4uFEpFvkQVegEdK2liPxj4vxMsJl9Y9BdRG/mbXJPqPYqHxo/vyre+GSMCM/yMpeW47W/Mgz3iQcv2bFnrO0o+PeMT92M5WH9eeUj+GhELxm4+j+gsTMxTl0X6cLC/ILX0pn5VUTClHZTid9p8V1WWcwbCxQ2/60289OtDIvTN60hpc6SqFwoYMjdMgp2sVq2qWKsQ2c7FS6FqytmZVd2b5EXr1HQSm2AveSGadO0SEmAK0SmYNKZGDuGIZqUTJQVl32XzVwNRpnRyhISGVQNORJThtHVh3DRWwIFBrZB6FXGfaTqirQK6FSYYG66C3IKLaETulTZbr3sbEPi2XZUKohTNRmQYlRagmK87OhTMV1E1C8oqQLnP/B+9m3XWW7y4wy0ITK3TaWhvZuoIuk1Q5aBLSgqxMuTmIllN/sOyYVmuqScvOvCJVAq0VBjp7viZLRpaZw0uRplVoM7NaSVhZ4Zlkrg6Balax1Cm57Th3qBxOlKU2HE6E5Vq5d2/NslHSDjRVYBISVbAOgnsCkxCIksk10OV+6nWihGTpRWvtSOrBSTkQVFmRrWRyFKIodEqSRF0J07riTB2ROrBuMqnpaFNm/1BZrloO1xVM5jRtx8EqUNUdyv+fvT9rkiTL7jyx37mLLmbm7rFkVmYVqlCFrdENdBPTI6TMSAtJIR/I1xHh8u34Efg0QiGF8zBCUkaEw+7mkDPdPT1AA2gUqoCsXCLCNzNT1XvvOXw4quYemYUa8GUyHuJKeka4h21upqrnnv/5L0IYr0ASh6uRHI2pnli0IyWjrhuLvhZyS0gpLPX0fZwRH9ffc5lt3eMTjG8mKwleVg6WXZjVF2tcWLvs9T7y1PkbTx3zs2d6D4p+Hzr2P/RSHNaG4jIrDs8KDRc3PS/27xciQ1AJuFQvsg7k/V83md4l3/7Z/Fm27poV/QxoE86nr6mP/wLNn3L49J8xxoZIfNbZ/5ovWLv/zU4XNrb7+2/KCpm3jYuwvRWyJs89lVnzGcQqe9tep3zrMZ/NCbi0+E+1d3sHNxb8s8+G5+/9ps741r7iopjwNm99rvd/l/fHCxv6Y7CF7zwz+nm/0183Opsd8N8TOfgfan1YBX8Bi4WjGeNZKEDBPOs9rgc3Sif+MU+mTGZ0FlCaG/IYQIMGGeXBlNQgqHFkIZt4spY1Jo3k9WOpArq47Kwjs9RGtoIGmNoCCA1xCVoLHmBzjnjOm5FkDf4JEUgUK+wlk9Wd7XJvdCTUoMWGSuB2AYsLoUJ5mNi1RgmBJIEXu4CMgUEEsYbR2EtkNqMXIQEhKTnMYJHj1EAgJHGTmhiJB0FmHy+cqtGqcp4qkqDPQu4qYx7odomrDLcSuDt5kt5DUdrqNyBF+PL2zJiFIUQWhC47W38XBq6GntNx4d1xYl4CU59pQI9xSB7S86jGPjtiEs2QEMgiaxRupqliCaL6555CpOHe4LkKPcJUA+dmbs2blN3Qc/VyT+gic+gpREwq98cTdyfj4RS5X6DVidSPDIMn7x3PhfvbB169Vm66yHl5cLYyQohKbK7xnxWGYtRa+Pph+r7Oio/r77GemPPPOmdbu1J8nhxWbbsZq+59Q6Xtcl3e4nQvvvZsj7n1qE8Q+rf96Y1nNrIELr63YgTZpH/yxAFjwxSc2NoMmrmkX9exgj/UWriaPus2125eBXA086ng+b8FKhaMQqIuwt2//SvOb/8f2P/iE/of/UO6xMUQ7LlhzbaejHe2n+sFmXjq0LdO/lmhvxTyxmrHtV65HbkIhFVFsPbh9uS+tzEq3sNHVpTmPcKcGEjzx7DnqMbz7n1DE+TZIz7dLsjKyH9v02be+a3fG08bM9/M6eXD20YIGxohl+dxxMURGeX9d/X7XR9UwZ/KjFTXXhdzaDaYMJDcZjU4sxQqou6clwyqVTBoYUuNShQRqgidJjBlMld2hyQMCY4FsEpDqLIRP8A00IqfcEW3nPaw7rgVM6UJNFGilpVRLixN/HAODQ1Kt7jaYInepaYQiLQLTD0KK4QeGKK7xs0xuL4c4e7knfqtFaI4JH3XGUGjx/Im2OfAqW6MYyPZRKgRy4GxE0Ls0EEJfeA8FczW2Z5UaEKbMpozEiO5g3xVyWbE6O/58dwwUU5L4GRKL4ExnMkxkypoNFpSEh3DFVx1wnJsTLOz9kOOTCxEImVRHq0SRrgmMquyrBfBWgImjb76+48Jsa0zyWYYkQkDa+wkUEVolqgh8MnVwH7sqQQ0KpMKbyfh8VE5WYTYs4uKRRivR2KtfNoPhCGgtfJ4WkgpcZUdiUkIXegQacxVmSbF7ifuH778Pk+Nj+u/Z106tWdFWG2Vp9lzaFVxb6/NWS1cJHngefVg3yp+z4r85fv3O7un2fQz7TpcfPhNXPr3ZF27lZ6nYmM8adDVGtAQa5dCEuSJKLhpx+USCXwZFjwjykWiOUzfv/yccv0f86t/8e/Iv/tXfPpbf0hIWxfP5bW/F3f7nLj3XHP/7L1xrwBlk+M93cXWvYAz5201yRHxzVaQeCmWlwb60tVvOwe9fJyXZ3y2W7IVOLFV7vf0PvLenUzWwr05/m0P82yTdtksbhsHefqktz3hc7Bh+/yfazMur2DbIGx3+4Aq/gdV8BWf5WJwtXOSHAZaqneCBLJEVCvFjNnceKLH3dcMW536KpYiSSJ57dxU/bAcxCVcvQSa6GrwEogEtgQEa7qCPUYWYREjBmPIkWpwSBHpAlXh1Zg5L5XHZUGL21Pu9z2hKtNc2Q8r5D+bR9IGI6pL9LIqQdxkiCbkEOlEyMk40+gk0mxFERROs/nmwrxz/zoqgzgKoNHHG0kUicYuQwyBoY+Mg9HNMD0sZANNEUzRptTTzCwR6yLJoJPA0iXIzvrVuVFbYFLhsRR6VcahMCahlIZlY0iBl2NPv9tRxsbt3ZnjNNPVRthF8tpZ1dbQFpnWCOTNK6y2QhcDTeGk6kgNHtPbIizmyYYVI4r76EsQiJlmgnSZclw4LZU3b0/cHwsPGhhThxUjauTcYOg6DvtAHg/cnytaFuoy0WpDdpFFIDUlihf+YnAsM/MU+Of//lffz0nxcf0911N3ezGcMUNNnf2+beitrR30ei8Lz+66OcTZs8fzbnLrcp9rrd+D9MNTMQkS179VkOiIYnPynWfIu73ue0ivbSDyRhi09QqkK0qxFq4AoptVsN9VxAl1T8i0/z/gCLwE5SrD9R/8jOU/v+buyyNII4RMMLu4C7u2fi1ha+XeZvFbof2uhTDf6e43cqE247wcuX94y3S6JVI5XL9gd/0Dunjw3/Hbj7XtGi7P813jGt9zvL+5k5Xz8PxGl5J74RJsn92mIHj2qzztKJ4mCWzjGfwxfm3hfpIBPidlPm0Avj3++H7XB1XwcxBmE3JMpFgxSyjCcSkYEE2ZvTbS1q474LntATBVAh764ilwjRCMmgIpRUTdGCYOkR0BORbmumbHqzCJkEWoOLLQ3H0fwZ2rplpJFphMyUAcOiKRPhhLyiDQpQiLcjYjd5GQhBQjNVRqSoxSvdA2h6yjKdbj6gSt7rBXFVSoVghdIInQUPYGJSiZQMQ3H7PCWc2JP5Nw3WCOSkpub9t1HbuxQ8bMLiaoQo2GzYVynLkvRl8rMQglNjQnWjfSC4ROebgrTFpYpkqslSOC5MhuhCsTGgEVc2vhnEn7kU9eHvjiy3e8eXOkP8OrbOTsUcK6rEE5ZpQAyZpvqlCCrhr/IFhQNyRpQkIum7+y9hO9QM6Jowk2VdSEX3115pu7Si2RkCJT11NVaW1BWyRGo+8SGgP3xzN9O/npr5HSRe+mWoKoNFWyBWpbePt25t/9xdff34nxcf33Lre59j27S9zWC7Y17+5XQpmaobpBrutF2cIl7WwLqfEHW38WnqBsrwSbfeszxr7AZrUXSFxKaGgEjauZjaIS1+S7raO1Z0VUcX28eqHZeAlbEyxhrV/qBU9WOFzaKst7PnO+vDMgxs7g1atIv3vBUiElZ+fH9fabgY5circXxvf85Pl2t7qNWZ8GHf6rKEtb+PKLX/JX//L/xuMXf4G0e15fB17+4T/h8z/+X5Ourv11B3MIXdZHEbtA8c/X+0XfP0uxbf5+0VQ8r9sX86HLD95/xPe7b+GJC/FrNhhPqMIGYzzbWq2IiLKhBs+fIVz8Hj6E9UEV/CXuGFCGFEkpoeZ60f0+giSWWqjVmbCdGmgjRPfSN1w2lsTd8mTdTa/yb3IXSCEyNxhzIBOwnXEq8Nh83hLF1lNV6GPibOtGAmXEHSdaFKS6Q1ydjC9OxW9jCaTS1LPhCYFqQq1KaYF5Dk5ytbUjTw7TRzGGFLmOClHIASQYWqCpURajaqMCxQKpQrRCSWkthI1MQMWT7h7F0AazRqbJGMOZt11glztC9JldGyOSAq0LLPOCTM1zDERpGfphJg49TTKWhTA5XyDmQFbIUQmpR1IgtUbMgRSETgLX+x3c7JlC5MtvzqTTTNnBkCIxKVUBDfRh7b7WC2mokYIhwXfMIWzjGtfPOmM5EBSSGGPf0wgss1J14Tgbv/jmyFyU/XAg5+xmQkVp0bkT53MlzMq1RK52HblAYyJFcVTB2yFCWs/pALUab+9OnGr7Xs6Jj+vvt1Q3tj0rfO8/b6Y0T1xG1dZ89Q06Xy/6urV6z2RoK3HNzJDwJOVz610vTAYXkt/WBZttY0efjbuTnXfOMRgSmquCRFwtZLCZ0xiKWCNc2N/Ni3DYGPyBZkawgOhWeJ42I0/Jdlt18k3Ctj3pcgQZiP2OlKKjDeuBHkJ8Nqtf2xxbkYrnj7n2vReI358ZTPzctcRUTvz7f/dn/Nv/7P8K5R03v/0PuPrkh9xc7xg/eU2MP9h2Zmuwma2vdeuHtxHBcwTgeTu+ltfnfALssonb1vt/2/rtcCnaugE48KzI27d+3yeY/+km7xf958vk6Zkuz2zfvd33tT6ogp+borJgoSPESGgLnSWolaNWQjG6oNQm9Em4HuCsYF0moTRzJntSYXZmDhbEE/VmY4mQDyM5G4GOQ+rpaqEtHv4SrJJDdiJfNTpzX/fOjFlA1lS6qIkqSjOHCMtKTskpIc315lfqXWggoK1BqDSDXeqoavQJOmksIXIQYZ8iKdjKao9YDxnjlA1VT7wrqmiF8+ybmyPGsBJglIpGCJJYxDjhtgIPKnCGVgoz0JVIHIzQwSQDNSVkKSytEs3o40DVhD02rIPYEld9pNsFUmzUZeFcGssSiCmzT5E4GvurgWG3Zzjs0NwzpMQLEb5pbkiUu8aQHa2IFjCtfmJEMI2UZsyAZWGvjS0hrOJ+CTRBsxv0XKXIzacHWtfxzd0JiYlvvjnxcDuT08AJw0pjv8vsdhGtzgOoYpSp8e6biWEfqSL0AhYi7aQsnZBQIomcPAQpqXIYMi8++2ir+yGvtso1n8+FVaGZOVG24SMldYj/SW5nzwhkXrQ32dkmDtugblap3nYpN9u87JWLTMx8sxpC8Jk7ikj17+OzDUDY3O1Yu1pwXYyyycTC2vE7ju+4+zYskLCiC5uqADbF3tNaE+tkRQuCLByL8ermmpiFaA3HzzYm/vMCnmBFOLde9cnDIOAOd5chiL8nGtDwyBe/+CX/5f/x/87LT2/4p/+b/4Tr179NSgMEbzj66PHhZk9qiG2j8lSwn3fF3+7Uv1vU7Tu3e77s6fPZnmvjQlzwCUd3fpP5zrd//jyj4Hln/4xduO4f3jf7+T7XB1XwR1moGKUVOjMIEdVG7mFcoKpwIqPZDVbOs+8qY/NsdAuBszYwD1qpwfwgI5KrMQUlL43HJHz+o45+1yPnwP6u8HaeCHXBcqaqYgSSGbM2gvkJGhS6LMx4kU8EdkE44t11ED9F+iTE3CAkrg89ZWm8u1eWaixLoceJLPMQiLUxD9BLJqRG6H0el8QtZV8kQSlUBqQJpRXqaMxV6IpwrIqpmwUVNd8AhcA1BZXEEiOWfPTRCQxxRqMRlkBLHtXbmnK1z6QADD0nM+rtiVSV/Vh58WKg7wKlVN4Vw9Q9uE09WCi1CM2o1ZiPhdO7R+a7N+x2gVKNhzN0uH9Byua/TxB6DV6Mk9IwskEvjsnYyrmQ1a3rHA1dhBSM4WaEbmCKkeN55vbhgXfvZuZihNzQeWZ3NZDnBmoEErIfeTgq01ToOmG+b1xLww6ZNlceAuQ5kSSQg9FJollAcqAsBqfH7/fk+Lh+46raUHVVBwAm6/dKU5eX+nin0Vrz0KWVyPcc+vUZ/HMduqxd4DNp2rPuz54TywxYg268+G6adoesQ5Q1kc6JyTE4ifgyJ8cInvvsjy44M/9Z8ZN1g6Ers397PcKGJDz7TZ7N3QWwcubcel5/9rk/t8ULhP3kx7/+Dtsce934+OYorIz4dUsSVgIi4iiFzCzLA//Ff/avkV3kn/1v/1e8+vy38CgwR8jceEtXSd762Jf3zp62D/J8dMKlq34iycl3yvuG7ny3XK9uAhbWov78M9ze6w0V0t9QoLfQIn+Ptte0qSou7zuwzfw/JMIefGAF/2yChsBOMkUCWWDCpWiLj1Zp80KInvgQG5wWl0u5BaUQ1CC6hE7WA1RNmcXlG7Up3fUVP/jsCmnGKfSUZebN48SpGaFUp39IXedCihDdgU6ENvtMLYkhVGoQhpxoOIt26CK7rqNLMNfGdF6YFmGalcelAsa8HmzxLIwBokUeJqWNSiZDF7C4Mv4lEszIGBpdtqYEshVygdAEXRrHZoTmF5EmAkEYaWRzM51gARVhAAiCNqhL41wroVS6GEhDT2VBaiHVM8PQc3M1MHYwt8ZUjDAbqUELShcG98hfCufbiXZfeYMRakX1SM7GTRc4FuE0+2wyR9glkOYX51mhT0LIATWjYbTmG5guRB9nKNQGzYQk8O40sU8nppR5dy68eyy8q5WhubvY2HVYUd6dT3RX10jqmH51x7uHN7TqwUc3hwNpyNRTZT8KqYscj5UuB4JFglZS7JEM0/lMb/X7Oi0+rr/HalZRfbrI2mbYZIo2827/WcGvtXkYk3mIk63JdOFZl7vNxH08uBX89x3kYHs+ZxSpGYiusL/nzYu4KYx39hCDEOOaJVG35xGw5vK91SXQf+7KoC3e2zcedpHDB9a7rvPizXr30nl7602Myu03t9B1/Pj3e2JoROsuaIKjBvjv9C0rYMONgTBb+Qfb795AKkbBOFHme/7sT7/kb76853//v/sPefWD14gpYvHSWQs+QrH12qrfqogGfp/LxuoJ1t8e4wmg/9Ydgc2Q7fLa127eX+t3LHYu2AXPNgFuVfxM7bDd1l/csw3Bdnywqg+2l7zt2HTduHyE9H/tmqwSaiR28PrlFV0S3i2FnRpahfNJOZUzXT8QQsZqIWhxvM4Cdd2BiRoNd4mL6h9qfbbT7QPkHEh9IGZjmQvdEFlKJGwe1BIIpjQJ5ODkFglQFfIuMkogR4hjRmNHGDNXsaMfM/1uRygTpTTOx0dOb+452+Ie36rMODnQVCkWCEvj1CBFYUkNC5V97JEc6YhUKqLmJ06BUZUShJYiB1GaRVKEKsLUjCYeMnOVMn0UoimnCYpWrAoWAkRlp4ZRaGKIJqZzIQvO1u8y11cdu11GqrHMFa3GvsuUGAlACpCSUKrHB5/MNezZCkMyht5HIm/UqOpqh7MEH5ms50EfhGRg6o6JDV0JesKijarG3NxGVAyswhB7auq4nQrnaWE6NyiueLBozKLY0shLZdkbD/dnpndv0DZTopCLEczY/ehzHt7c8ub0yCe9yzynCXKu5NSz7wKPptRWeX3z4ns9Nz6u37xMXaGj9jTPb+vfVUGbUdVora5FX2nNxzaGK1a8qG3zWttSdi+Fyv2+2gqlB2Rl/GyEQZ87P5mCgZ9rhqx+9awdvjifJnjRjyH4tckaUbyh8OcODqNfkIatE1fv0MPmeufdedA1037zsJe42ebTzHj79Uy360H/ip4ei69pFglr3LeRVonvs6K4Wpj7exCcvbxurFRmTM5oW/jib7/g3/xXf87P/+qBP/mPXvGT3/8JYplgglv8hosMTm3Trfvjf7d4P8kft/tsSMLT/uAJkbgA6PbdYn6xTgbWGe+3BgRPD/KU8Bcv4x6PXH4q7JfHXZGfywhJ5PI6npD+daMUPhb8X7uaBhoNzXDz6Ug2oWt7Ko25VsrXC5wSKWaCJB5lRlLyHbwYUeTZ7s/WAssK66+ab4z7xxPLaeb65sBclZQCXe6BBYvKmDyFL604/WZzOQwd+y7SUmTsEhqNoevoupHhaqSLA7p+8MWEc5m4PQoUYy+B43pi5hi57gLnUFlmoHoSYGyRkDv6Q2Y/DMSNvS8dqF+wpASKeYHrVghqUqW3AGosS2WujaNFdl2G1dWuDwo1sCwwn43WAuMAV9JTUWjOOSgNkirxKnnBj4nzXLiSyNyDJUGWgLWCSqUWg6o8GERr0AqIcghwNXaMg9GnwtezS/6CGVNRkkBO4uqAqlS881HxtL6ixmyCVjjie7oR36h0u4FHVercqGeltEa2REiBGqDIQrCBucEwLyyl+nEVjLhma5ei9JLRTz7j3c8n+rkRMIZzYd936LjKAs34yScHrn968z/w2fBx/f+ztClNnzp5bYKaUZureFR95NRqo7WKqhf81hpNm0PnzUd5m2ufBLlcq9Xs4s/vXX5EpHoRWufZqi7/RZ46QBVBg0tko9gqy8OtqyOeURE2W171Ah03hF8uTHpbu9WwxuU6QhAu0bab3M+T72TlDDTMnJBHbFx9esDCwP/p//Df8B/9L7/iD/7J7zBc/QEqL9fiFd11E/89twTA6FuW9drqfihYZV7u+frrb/jzP/2C//b/+yu0FH7/j17xP/2f/QmpOzjKsj3WaljjV8fm44L15++z6f1922busG6+5OnKDrYSGZ8kcRvf8H2BxYokvNdhP7fgeWbX++y5vz3H/+5c/6nOOElvtUG29+f84bnE8ANZH1TBDzizNUfY94mpCV2MXA8Dd8eFH7yIPD4Eqs4YjS4IS45UW1hbeNeVCiSLVFNK2IweAnmF58/aWKpyc91RqrEEZVqU0pRlmWhFMSIxOOGmCuRdj0QhIuxSJNWKxkTXdYRaqQ8PRDlyLsbdaaEuM30wplKR2rAYsOamHika/aj0KRN3rgQ4h4G8E/pD5MXNgXHMdHG9MAgQIkUbdW7YUknVyDHQJ+EGdWc6a8xL4XxqPM6NuhSmpfIwKcvJ3Qe7oUO6hi2gSbg6JFoQWoVQjGaegjd0wst9dOZ+gUVgsMDRlBQaKWVE4HFeoFW64M5eozV22bgajDEXggjXLROzYdU4z5Vlg8vUGdCBQAn++RWDZEo0RyaIwk5hEldGvNqNhP1AUqFSuF8aFhxinARcKJkpCtUaVio5+glX1vOvmlDPlePtievf+y1Oj58x1W9oU6Gacn0Q7/6sOWFxyIT44ezSP67vrtZ0ndfbWsi9269NqbVRW/OEyNreg/ZbbQ771xUSYHPZ41JktpnvZtJy6bzZtNzqWRDNPfxZ5XMqK2Ilnv0Q8GIsYqTg/hwxiqNq0aE1L9zmnvcrbn8Rv61qkiwQk98uREgh+Obk4o0vxGgEFYIoIRpQ+fxHn/AP/yc/5V//i8T/+T+d+d1/81f8x//zyo//8LcxeYVsefV4t+srIBLXDUgAVZpN3N695b/7t1/xV3/5htpm/ug//DE/+emeT3/wmr5/SdNuNRL29471uvxe2b6oJXjq+J+tTSXB5fbPvAt+zXBcnv1c5Lsz/m+v57Y5F87Dr11P3bw/9vo8m5Z/+6WejRKe+v0Pa31QBT+mhprweFz48u7Ey6sDu13PsRRE3dN+HAceHidEAx24lK0fOLYJQSjiev1FmkNT5pIxCcKsSsZn0I+3C2bGGIUxB17cZKbW8+4hciwnJAul2Gq1GcAiUpUalf0Q6PKOkALzXCmtsVOhUrl7WJiXxuEwEGnsl8YyuGFQqAunpjzMruM/XAUkCV3OSBV2aWDfdfTBpX+FQI8wrxen41K4P1eaRkI09tl3jzn6beZSKbOhREJuhOJ2naUo1kWuDwM3NyOLKOWsNFMGcce/mIyzFupU6LvAEBIxKiGCdoB56ty0LKQciEHpIxAUiZEhF8Ys3Fhk7JSxC85rWIwxuhnQw/FpxzsDaXFIs0U3FJJVQ93EiAhpJdIUce0+Ggh9pJJ4vJ95++ZMmypDhlkgWaCGCOJRugWIOiEx+qbGWF0PI93VFZ/+6If8+Hd/Rrp5wZv/6kjfRRYap9PCaTD6XWQkIFHQ8FGW9yGvpu0ZVN9QFZo+K/i1oBpoVanrDN+/PLtBVf34W+fxF/Y1W3iM8wH8Gh5Z25MLFNzw+OzWXAZogIpRaS4yk0AUbzwCENfzNqVIF3HW/Arvh+iQvWxQ/4bLiydeFhFSEmLykZqGSAiRuIbf6Ppag3rhF4xoib674k/+2c/4vX/8KV9++cgvf7Hw//p/Ft58+Uv+6D94pHt5Q9UrxAzV4p2pGFF6xDIQqBb44ldv+H//l/+eZo0//pMX/PC3XnAYb+jDQKPHmqNp8HTOfIvS9lQKfXfB886blcR4iZl9mi88q/dyuftW2mXdYjzV419TcC8z/ecdPHhb+L5F8Pb5Gy6F3MY0/pq2XIT1s1m/k40oyVO3/yER9z6ogp9TQtbddx8SP/r0imwReTRCU3LoeHs3MoVCxU8cn5Fkul1waWatqBayiXfVKwEjxEAogsVIBt6+nfjmbuJHnx14WXusBu7Oypu7e2KXsKmscL4x5kSyisWESUJb4mGaoBaSCOkqk6NRZuNqn7m56t1cpgVOcebly2vKcWE+Lz6DFlACx6mxVLjuobvZM17v6MbAYqDVQOFUJqZSKA2WqTJVOM9nrvaJbpeZq2GTJ/ZNqmgxRI1lnmE2iiiH0PPiByPjzQ6J0J+Ed5yZizKfCyHAOIo7zkWFkJnmyldvTnQCEgOPS+PuQTnXytBlhsFDiVSUqIluN3I9BPqwkCguhcL9/aO5+AernoOgxlmVZZ2VjU0uITmCUMWti4tEigqLCrM1jxrOidt54ZuHI3P12aCqErMrEUIWVJXUhBabm6CIoSkRWmMXAv3+wIsff87LH33KDz/7lN0nn/Av//KX6PGvSRnmpdGsIzTB1C1AZZvNflwf5Grm0Hy9QPbe5ddqa4GvK6TvnIzajNaUUiut6iV33tpW8L1JwISA6+GfuAGe0ra2lJitI4PWHFVcH68JlObBVhuY7dcsJQehT5GcA10WUhZSFFIK/uUVH1sNezapYJTgCZlRSJ2jBCknUsokTV7gRVBzs5+mwZ09YwWFGHa8ejHy8vWef/THjdt3E3/6p3f8p/+Xv+Yf/57w+Y/3nGtjmqDVSLXEfj/w4qanHzJ/8XPlX/03X/M7v73nD/74hwzdFViH+eQSVFGcb1NZve63ov2dwrcWyTW3QLeCeSnbW9MtF8TFi+f7EP0TZP7c3W792or7r91pbLN/2z6ZZ/LA7VaOyKjhiqFtxiO4h4A9bfw2PoBh39lUfCjrgyr4RaIzWa1yv7jHubaGaKVgtGRcv9hx/3DkVBeadLSQ6YOfJB2BpQTOxdAKhECfIqbOBt3tdjR1177H1vjbbx75/PMDfS/sloAMyb3lDcY+EFKknhdCiFhylvs4JOZmzM3YhcRnn4zYEAilMiY418bxWBj3A49lou96l+M1ZVKjmNB3So3CaXb9sFnlx6+U47zQ6kpoScE7jlYAqObWs3MwujGwOyQWDSzHhaKNh9rQArEJyRQNge5aSV3HmHtevhhpKTKdKu8eF6bF3fxaCFgnHEsha+LwcuBcjdND5d3jRDIjDkJZ4Fh8jtlHZRwSxMyxVXRqLM1YtFtzizy2Vk0waaQsiAqHLBxR5sXzBJo0qgmPuJ2uqZsmVXx+ribrXNZh2g73PpgXYxE8hRAlSoSmaBe9m4luXaoSaW3NMYidz1xzoH954PDqmn63J3aZfRL2rz/h9u0vmDtlssjcjAkhqhBbw/QjS/9DXm0t3K1V79pXUl6tXAp+a0qrldo8UbFWdf1+XTMytmNNDdONrLU5vzkK4MekXMxvWf+tqSMHxZpzUppSTWnNyafNHJ6OpgRT+mAMfaKLiZyFrouk5NB+zomYEpKC2/Gil7l8CkIXAzVHUou0LpLVSYeWG2aZGOOzd8YLbjWcwxJcDocmlJmr/sx/8AeNv+hm/vP/4muubhbeSc9XD5mlJnLc8dlN4h/+OPLpdeYv/2LiH/+Tz/j9P/wRai8QSyh6ycDwzZJeyGyCrIZ8z8H8bTlX4lJ8t1m5PRVx2+Rvl9v/uiXf+hO2Z3/vNuto5jcy558RBv1ujvEIPOvetw3FKpV4RnLcRhAS5OLe+CGtD6rgx9gT6wQi3B8XSlvouo5+NzKGyrxUxqFjuNrRHh/dGCV3mCp9SFjKDLknlIyWxrRG45o5O7dfP/Q+BxYRfvXmxMN95dVVz7nNzKcjsQlDP1I7pT1MxJyJfcCmQtf1zKWQeyG1QL4ZORuEArFmTsuJ2/sz+dDTThNo4zQX5OwOfLYEFKOKO36V6gf3uSoPS2G6C2htDIeRITbaVMEqKSYsB4iZLsB+nzi2wvzoerWHpXF+KJyaMvSJV4dAN3SEJOzTwG5IdKnjcSrcvz3ycDQsRkIX6cfEVJXzmzOHGAmSqK0SrHGc/ZAezLPph5U5XGJgEqM7Lyy1ONmvGKVTgrpfAq158e2MffZzNqZIOgZutREEijYWM3oCMUTOwZhroBGIwQjqcqm5GudZiEMiDB2hJJYZKtDE389AYLRIqMIpGzFGYvVZam6RJEoB6Dryfs/VeGDIHYsappHueuRMR98mLNtKvhI0BkKOz+aRH9eHuJyEV1c4fy3Oqza/1a3D3/6sjgasiZC1rCS+FRVQfR+GNZz4t82cdVMD2Na5rs9hyqKNpTVHz0qlFaVqc7c/MwKNiDIEGLpInzvv8vtIl6N3+Tm6FXgUCA5Uh+DSwBSFLkX6rqPrE6oRs7QaWeV1puzH/6YtN/OxRDChqSJtobXGUk6czo+8e/MN//7nX/Crt2/44i8XTvtr/vb8KZ++3lFy5K818+btwlV7wx/+Qc+PPnOEI4rQtMNsuZgegcPt7sm/drzK0+ZofUefvbkund6+fYaGPE38n6Dx9w16VhRghdU9nc7/3Ql5+qSvfzaAf9L8bx4CK3S/3v7JsveZ1e4mx3yG9wtPxL8nuH/lDyhP+4+PkP6vX5IgrPOid/cTX7w584OXkS4JvRgEwbrGJy9GpnNhrg2rhgWDoSNZx0kqzXzGps2hs1J9t3+aT9SUmFbHp/ig/MUvbon/8BNacL/p10PgMRiPd/dIVV6+uMYWBXPZWqDSWkKj0krly3czsxgvh8RX747cXI/MtxWhMqswVuMYK9OxuGywM6oJVkGlrQe8cPvNQsiV2Pc8zCeX3mghd4F9Z/Q18WiVqyHQi/DuVHkoxrIYd7dnDxFKkZADbxdlZ4GhMw4D9DkxnwuPjzPzUqji6EUWKLPw8O7M/aTodaKdC8vspkJJFRVlXgJVlJw7ainYXKAqx6kS+zXP4ASTmbvhAdkCthKPWoRenAzVFWEoXqyHLlEQZhVaDGgz3hGYqzGXysucMBNqbSzW6PcdS+x5/OYtIgtdDJQQqAKduf9Ai8bYJZDEqRSiejwv6qjBJlmqWrlbTuSHIxKSE6ziwONSeTUsTOdCmxYsdhSB3D4W/A952QbJm646+3rR29faqKVRm1JKpZRCq41Sm0P+xTcApemzgv8MJja3uYbVzEed6NfM3G1NoC1KaZW5VuaqzMU5NaaNVhxJQN05T1CmAF2X6LtM1ydC9PO0i3Et+O7MJzEQxCF+Z+Y7Cjb2M7vWYZZRzZQU6FvDrJFTj1kHeH2KSdbXD6oL8zLx+HDH7ds7fvnFLX/7q6/46s0tbX7D45sTfzTCj378Q8ZXO86T8oe/M/IXf3bPF798w+H6JX/6VzO/87M7xkNPZC20BnZxHNxm7E9F/jLDf1YY/ed6CajZ4PoLdP+tU+5SXL/92bPJ71ZJlj2hL8/oGE6C/JYs7+kxYMtC8P9vfgRbmp6tnvjhqdBfXrQP7W173cZl82H83WDC97E+qIJfm7r0JCbqqfD2zZGf/uAGLZUXVz2nWSnNyH2g6xLnyXfVvQY4FywqnCdKbVhV0MZjMbe2berM19qAgHXGFIwvvrrnBz95xdgNfPJDo3155stfvsOa8cln197Vz5W7uxMEoQs9NMP6wHSamY4Vhszbb87UILx7OznjVxQLiaUP2FKozdm6XUhQFUtCW9Q7ZlXmudJpIFaHw7ocGTFSSjxW+KbM9F0mzHA8H3l3bljwKF/JgSEFWqscH088LpEpz/zo9RVVhNupMJ0rS52pUhn6jsmUU4XSKi0Ebl7u2OVAmRdsbpxLY0rOAFZ1eVJDGXo3RDovPgcdzJnApShLjGRTJEckCUOEFNzhLwajr0KjOimJQGmKGDRJWBFOVVgm5bQoWoW5VloTJnPIPw6Zeao8TMZUYJmrQ34qWBKm6DNOn3U6l6G0hqmyaHUudW2EpbDMJ073R+5Dgi5hpVCAsgjT3CFXgWqReXLCaA35+z49Pq7fsOxywdf3yHutlWcyPO/uS3F4vywuKy2lehpj802BowTu6bHJrdaUXe9W1QmvbS0r1T18qbUxlcU7/GUr+BVrRqu4M6U1sMYiQu4T05LJSyImHz92MRJzcMldDISYyCGss3oPAUspeC7AWtRUffMguCT2+WZFgq3F3jcq03Lm/v6Bd28f+eqLW/7tX37NN19/w+3bN8jpkdh23Cb4wbXy7jbShSv+3X/9hl/+/Fc8Lnf0X+7o/vzMEm7549/fIbkhdkCpl8J/gbq/XVqfFXt79sMn3fr2Wa5Wxt8q+l5MN5+DrateP5TL39c7bUX328/z3nzePIRoe/yVJ/HtTYkX/e01PntR3x41rP+kttkyuzT0Y4f/d6yMG1M4mzVxLsJybsxN6bvMUjwVKURlHDP3j5VOGyoLrcK5VOpSfeetq7QmCFFh252HYNAKnfbQw3ku/O1fv+Onv/OSfRdIVF5d9+yuRoax5939gs6FsY+EPBInJXZGWwrTslBohMnIQXmhCQ3mM/0gXB0y/a4nNuN4nnj7uEB1d765uOY32EaG9ZCdasaui4hBkY7l7HawmoVDE0hQtfJ4KvQ5eWxvypTN21cELNDlBN3AwwxymrBkzi02I48RrRGdGmnIxJw5ZNAmPD4W2mOh72HXJ7IZczSyJpIISzaaQrGGRMGD/VyJOmOkEokYQ6oEoIvQiTsoVmm+6YpCq8JUjXkxHpvLJIvCYkoXAv0+Yuqf/bQIh+hjg2MrnFvj/FAJFliCgEKR4ErhENGqBEk0851/MScGCca5FU6nE8PjEfoHGsYuDxznM1kCtMxJjZMKSxo4t4X+XIn5gzpVPq7vLC/2tvnPi0O0m9ZbNxZ/LdRSWco6a1+UpZSVvFfXsJ26kvfgYpFKWK8rTx2rwTr3f878L7SyoKUgzX37nTy4jhvaglijiVCXxJIiqUvkLlNzZo7Jw79WrX7OPV2ILuHrEiknck6uWkEIlLX8KEIDiWwJeYYTj1FF28JpOnP3cOJ4+8jD7Ykvf3XPz//6S+r5LaneE1ri5ubAvLvhcXrB7/+jH3G4uuFf/POFn//yjs9u3oL8kH/3zZH9Xvj9H+/pco8hq4JhTfOjrXC4PXunns/Un0H2G2L+HCq/OPltVfbJY/87c/n1cRxVf46hy3dm9aprKM+zV2HyvIDL5X5bjoIbBW15Culb7brj9hdqgDzdX1ZUUZ4hDB/C+qCuYiINSFhTmlR+9eaOL3/6it+67qApXS/kKuw1U8cdx77wcDxxqgrV0OY7d38s/HhRowkOp+GM64ZwbkpuEUmZr7584MXrnpdjz+sXPS9e7ei6ntujsSxnghlj7AhN4Toh88zVvkf2I7kz0pA4HCK99cwZupwIEtiHxmEcCS92HN888uVXD0w0psX463//FXo80RZ1qBlZHQOF8wKdCUqjAGmFDqdTJQ3K0oymgVKMGCsPWpzcow4B5uTxu6fHSuoDYo2xGdaMcdxhuaOrhTAKJ3ETjxqcU3BePO1vNyT2Kbl0L69OeSaEpg6HSiADS1OG6F12nYVJjENeZUdpMzxKyNqRtOCs/VZgLoHbYjyelLOJy4EizuQPnqTX94FZldPKxj+fjXJeiMEZ1bn4SRxrJHWZocs08ePBAKtKaKAhoxSWCnePZ9LwiJI5L2f6PDDPJ85aiSHxkALf3C68emn0fU8pDVs+oLP24/o1q+IuW8pK+cR4noynFzZ9a17kl9IopVFLpdZyme2rrna8Bk7WA/Djz8yDcWAN5mnu119VVrveijaH8t1JU6k0mha0VqgFUIoaGiOtRkqJ1JKpOXk0ePD5fYoR7TztseZIVkcudJ2JBy1EIjFmJGSHqyWDFExmVNcAnFqZTwu3dxNff/PA6eGedjry9ptb3t3ekuuJQRbSYeSP/tEP+fGPfp9z/Izf/52f8eWXR/5Hv3PNL/7Njs+y8k9/tPCvjwWbFYkzqhWoiKV1g7WttcivnIKLY+FWW591ySGE94rilsR3oc891+Jj3+rAHUnw27xPVrzc48ItAKcUP83atwL+XiTyas512W9cfofVdAl4L0hovbEbFXmFf5J3yseC/3etSiaqQ2XahHYqfPXNI7/96jMkGckW+uTdYdcb4yi8vVPK7DvZiJD6gbLMoNUz4mNcXbIiISbQBipoqbQYyVF4WBq/+MUd3U9eE7ELpJYzpC7RgOsxk6JBn7j+dFxz7t0chwgpROpkBGt0zRivEqSIBMgEsgqxTlxdX/HiZWI3RP7qF2/48m/fIdWAhOHwsWlglrYeloFFjFAh7wfOtVCqF7tmkZNCzIEuZ5YmJCIhCTlFd8RqsGjgtJy4GTokgxV3FUtDR5yMUivzEDjPhaU1SIE0ZrooaBXiEKkNpCldgLM2uupRtUNUxmwsDWIzht41xSEGclT6VUOsdZ2PqSMJd0W4bc03EhGsuA95p0ZIwrQos3nIiCHkoEhyaFVyxrTQqifuKR4h7L7kiaHrOKnP+M+miApIT0AJ0ijTwv39I7MJ/TkhuaNZY6qFAxmkc6VA89RGmEE+6vA/6LUWey6pb+u8fMtXF2At+r4RaDR18prL9Cq1FTdcWkl/rbXLhsH71W0D4XPczcbXNwgRa4qtIyRTw5oi2pBasVJZcX0vCOt1zsxtnpuAqEJsTqgNAYvR8+771Z2veDGMUtBQKQHmRYjnBbVIzpFmmdqENLvGHxPaUpiPhdt3Z96+eWB+mGiPd8wPd/RhItpCUuXHe+O3Do/I4y/pMozn13wWAunqDf/sdxPf/E3kzZ/9DbtDh36S1iLYYRZ5rrm/EOe2hndlsW8bpctwG2FLARR4Ithdht/PIfi1w9+K9HtOeH6f9+f16+vYPr/15ttm6cnUZ0UT5OnnASfpXub2ts7/V7aBmFxiveU5QrB+nu+/HvtY8P+uZRqR6DOpqEJR4cuvH7j7vU+4yglQqhrnWjmHxosXBx7nSvn6jhTcQ17N6CQgqaOGSFTfkbXICumHS8CMBKMuDnfdfrPwxRj55KajmytWhH3f8ZPXA0NMpD5SaiNkZcyZoo1papyPlak1jpOu+euNVI3Dqx27KyNV2B+OzNOJv/zVHcOXZ3ZXkeEwcnM1UG4OLGVhnoFqJIk0MZIEljXKUwxSikytME2FQCCJ0YIx9pFd11NV6HshdwopEXNyPa42HuZGPxn9VQfmcjZyh5IobaIsCktjmrw4hur5VpYjGoShS7SlMUpDLUAQ9qkxJvNZuQhFXSffpYQEn/NHCbRgJA/vJVQ/be9L424WJjViSkzY6ojn1rgpQUcimDJpQGjsu0jeD8QFdsPA21pZABMhidJkoTZnQidJ7IbAsZyRk2FWSHkPuadQSGZwLpzb40rgjIQhkYAW3ZN/ksijBJb7xq7PpI8F/8Ne4kVZLt3kuhW0BrIW/2BINMIaTBVEQRpGRfXJY/+J4e+bCN2C0zcS2OqvbmzIwTpC1IZpQ1QJZkRrhNaQVh1d2ObOsibNmeGzAxBzJ76wQeG2/XvAWvVvA5guaCsu91u8Ba0KQw1O9puEFINb6wZxU6G5YueF88MZHibssfJw+8h0euR3XjbKCbop8ANOtL/5ayZ7IOQzf3E8EUj87PWX/OSFcnr8Lf7s57dcv74l/O4LhG6tywFHWJ7b16xQ/aUjXz+m99zs3mvrnwrz5S8btK+rxG0br7DW6JVUZ08hRpfHkucYgRML7SK5e78Qy+a/vr6itiIVXuS3TcomH9SVY7DyBFY1gmvvuWj2bc0LUP1Y8P/ONXRuTWlAEbBqfPnFW/7Vf9vxBz/9lE4CXfRQmKCCdJEfyyvOp4lpcQMZN8zoASWFNZ/azIll5lBbkUCfe2e+pkCSHgtCOQmlC8Q+EWIgWePq0LHPCdWFc1loR+VeC49zYT43TlPhWCMWAiVlcsrQe0Ld/BiwZlxbo5wKD/eVvzmdiF8pYwp0fWJ/teOTwwvuvnnL3TE6WjAbFeXFkCgNHhejZsOK+4WqeSxel5xXYEHcmQvjsQnXq/XnIoJUZXmcIW7adsWakXrhuCgPs7mlcIXJQFNkMONUDOuFYUzk4L9f6HtOcvaUsSYUESRkmrg8LnYBWYl+NfjJGK1R8MCMunqdJwmkaDwskaUqbTbGEKjiJhaxH9gNO06PR0qdEYx+P7AQqFbQaIwxuk1ua4TkLmXZAqFBoxByxxA7Kh2tVHRQYteRJGLNpXxSK00CU4C0GClnUsiIuptfPMPu89ecbx9IH9BJ+3F9d3nhdUIc0rwzE8NT0hoS2lrsq38FvSTgbRfyrav3Yt+e0AC9tIuorgU5OOFrk3OZ6Vq8nSsSaBAamhoBpRMfUZlFNv/4jdHtCXrmx3By29yNPBajW3GHUJHgM+NanZtQSmSeIaXKY1SPtw6ARUyhFh//BVX6shBrobeFnRUe9YyUR0yVLjc+iUqejeXre0514ty9Zukzf/A7n/InPz7z9s/fcfgHv8eLYaYvZ26uR7o8uBGNVi58vQ0iBzdFW0vsJbpXnm8C5NnX0+bpQu6zFRBh6+hXHsDzc9HWND/gyYBnZTVcUAS5bBgcmdk68Q0J8kTDywzeDJXmgV0S1s/t+TzCN4ByGQdsGwi7/LOuoL+t17wPZX1QBb82aNU/lF6FYo1a4Iu/PfLJ9YEfXI8kSeRYERHuHidUYbzZMb97JMzND8AoFIRRhKX6UVNVobpHu3VCVGUXO4YYGA8d17uBq31PNyayCKKNbLC0hfOycGqNcl44TcrD48JxgaM2dmkkjQNVjbQs1ARD6CgmWDN2ydPfHoug60Gv1V9fikYfjNwW1JQhRQiFk4FqZSJi0RP9dIFWt6hMd+DqQ+YmB65/6wW3x5mHd2fK1LiViTRCWQyZ1YkpAd5MCrOipbG/SdzOxu1DJTFzM+4YRCni7OBzbYwlEHpBU/BxSRD6lClByUEJtMsufi8estFJ8I4qrLtelXXXLGg0bIG4j6AQ5spYKicRhjFSNFCLIHFAPv2E6zhQH95QtMIuY9VIJmTpqLFBZ0Q1YoAUE6zjm2iBFgKSApoCaWnkGmg5e3fl57n/rtIIIZJCRx8TMQgN6PvEu/uJn/2DyEO84eHh+P2eHB/Xb1xRFxSXw/qUzTfGHnBTCdRL5x/EPegleKFH6qXTdy2/d/uwQspbd8lWiwLSvDjYashj5hn1Epw8F6U52tAMd3p8goQxp5BeqqS4xXRKRsqQcrjozWMyQipIcPTS2mrxuwDBPADOtrlxdalhSZTliZjYJ+VFUl71ym5s9LKwjwuPFGwyLAVOITBWxTrh+sXAz372OYff+Rn9y0/557+aEW385Lc/5cXhwPTmK37vD1/R9XuwfBmnBDZ0wkP1bEMzNsj+AstfWvmnTNlL478V/6e5f0R8rL6m4V0scy+F/7noHTZ53YYA2DYa2D7R94rz1rU/fbasJD3dCv3lcSMbEXBT8W/jhm2csBX/rcG5zPI/kPVBFXzT4hnQFmnRPdWpgdv7ib/65Rvyzz6hS+L51ZgfKwleXO9op8a0TKhV0mqBOLVCQqjiTm1REpKMXZfpdj03+46rXU+/73k1dPSDw2KtVNrcOC+VeVk4zsqpKFKMtws8niq1rYUjC2kIxKa8nSthcv6Bis/8Y/K5XqnN1SMhQDAsC7JPdF2go5IXQYMSLGC1UlaXLvez3xyshBgiEoS+T3z+2Y7Xr/e8nZX57kQ8nuhjxlIkFBhNsRRp4iFAZVqYFqGzQGmBVBeyBPq+w7pEWZaLU16yQKlKnJ0TEHKkIFhpviEKYSUsVZIpObt5TqtG2maeIlSgqlFMmZcGoafbHegez+xCYcrCLkaX3M1CK8piwouxo//sFZPOFCa6nAnq44QYjdxlcl1YamPsEpZhMUODQE4YRo1K7AJ1CbBUJGaiCCUIQnR1QRJySIhEkgRUPHVQYs9Xx5l3b8+k3chcy/d7cnxcv3GFOhFVsVZ9E2jmsdK2fRW3RmUjXTm0T1RMCmoVNSfXOXnPU+N0LfhPl+zNrOWJ8GWqK7zvV30J7p0fVsfLEDYkYC2J4sX5KaDGTXJigpD04onv3WlBTXzjvOn/W3WOk63ugtVYlsayLCxzYVnEJYfNYfZ9nwgHYXfITChDqGRTDsCyzqipMFXhAXi9D9y8nMntgenOeJN7hhc7fvu3rnl89Sntd3p+8KNrhP36FugK4z9B5k9cCjcMeir4T136pcm//Msz+H/dD2yTD0+eM0dbLkX4WeF+PoffHmC7Dple3JT0Mld/Ksabo+K2UXges+sGOut3q+TXDEzD5TPfir1Zu0D9nuPgr0w/oBb/gyr4agkjoKbrLj1iEVDjzZsz19cPfP7JFUECXTL6DmpVeoG+C5ySIMWIKNNcWAhYUKJCl4QuJmISbl7s+PSTa3Z94GWK6JiJQUi1+byrLixT4ZvHheVUuCvwrhp9c2tcNei6SB8DrYesHtE6REhUus4h8DZXiMI8N06PM2etiCjBICFca2SXO9IQub6Gb1plua2oCUJwBuzqXLdZVObQMewCr19E8g6Op4UvfnHH4/0Z0YqEQhh7DmOiE0E6oRszXX9Aq5B2QkwBincLV1dCN2TqBEkDtVV3EgsO2R8tIA1SbJwLPJwKLQRGw+f0ASqBUXxWemrC0SKdCs2aO4WpNyOBTH11zXmJWH2kGxOlQFah6zsWi7SoaMoMeSDse+LDCTs1dil5CloK5AR9l4ld4rY8oBZZohBrI0pFpTGmHukzD2Pl7lypbSZpIsWeJkpKWzZ5IsZIEvF0hjUxTIIw18RXX0/86CcdNnTf89nxcf2mJToT1JBWsFqhKaEpUY2mDTFFmgOtIg2kepIc5qZMtV3MeEpZWfp4Adkg4EstWgvAc/fF5zNcwQu+iWv5Y1wL0wYRbLaxto0TvMiEsMqGRby4may24G4C42l8jVILdaksxRuSeW5Mp+rywlJWB8GVrBYN3Ude50DpBo4pUoPnDXTRkzoDSkrCOIDFyuP5ni9/8WfE3RnZ/Yj59TXTsOeX/+qe//E/PfDTP/iEkHYYHUJDULaEW5HnRVsvjfx7ML7JUxf+rFt/TpSXtStHLgD9e+Q7v936/q3Q/HZH9wNQZI2t1dYuRb/Z6pq4IiNbLZZnn4us8jznQNmz/YVfG3wuH1wIutox+4hmlemZoKv1i67Kig9lfVAFH6AXhWiIegLckISgxjzP/PKXt+z2Ha+6ntmUZWlorQSEV692qBi3Dw1dKiHATn2enHL24r7v6Q4DV9c7dsPqv2+BagLnynGeWVqjlIXpvPDmwahLZdaMVWVpisb14E1GTokxCqlVCMZVF+gw8piZZuXNuwe0ZBrGsVXf6aofoJ0a/S5zfXMgpsBpmOGucC4zEg2Kk4V8g+nJcR3QqESLyBJ4fDPxMD/yeJxppnQW0ADMCzVAeDny2WEgZu/o6SIvknEOAZMF9tkDglKkzTPjkNAgPJ4L45gYxwTNONdGZ2BLZK6NYIWaE50oIYa1g09I1LWzwi1rI8TVXGduhuUdmgdOX5+QGOm7QDsaB8nsb3bUUKEIu2QkGkM/srx4TZ0LmUaTxiFnYu5oArt2w/nxjKiRRBh2PWOXyM0YRlcDDF3iHANLU6hnComcVnDVEhXneLS4BWMIWCBJIg2ejWAm3HT993pefFy/eYXiDHuWgpUZa6u//GrzrNXn8qJKxAudakNbuQTtuEzPPfZrM6rZSuDypmGbIm8wdfC+cv1+TWbfjiEEEZeJbaME2bpIAYgrkexp/m+ES6eo+jT/9a5WWVZ74HkpzOeZaW5M88Q0zZRl9a9XpbUNohYnwVvzzY7O1NojISC6jui21L7oGRQxuXT/bI+8vLkjv9jTHXa8/tlLXt/8mJ/85DWQWSqwhmPxXiF+Pzv+qYvn2YzdWMkGz77fJuwrarK+VxtPwt9Hu9z3Mu/fbrvV/Ut11pWHYZfQo01RUdZwJd28FnAk//J61lGLhxCt1+CVD7AVeLWAmVHrRvLc7JddXV2b36605sZvH8j6oAp+xrvLzgKkxnXfcTyevPOWwPFh4hc/f0f48UsolTA1ui6hQdHHmSEKvQWmBjFHdsPI4ZAYh55dl8mdcPWq55B6t5BthSiBuhjHh4np8cx9VaZa4VR4rAmTwFkVtCDiE8K9KTI3oBAskHaZoQvkfkByQkUot/dwP/FQGqmLdKaMGZBEK5VxFIadd7Y5CDb0tNsTDaMLgSbbDEtI5gVqF52f8HLM7PYd81k5HRtWcRhKHGZqSYgpMeaOEDvMPECmy4GckkODkgjSqCFQUYbkiVtHW+jnQu6EXQ7MZWGaff5tU+VkRlYlN7gDBgskrTyokgUO0Y2BJkuIuqVnnZW6gLzMzMVZwzl3NKmMQ2R/tae76tkdjyza0LmwPJ55/Sow/vhT7g6J5fiGw3TiandAiZAg5p77x9dMj3dQjMM4MOw7lmn2i0GEloFdIB5Bq9Kb0hFZgm/aVBRC9HRFlCTK2RrXGghhQOeJIJA+xCSMj+tp1YpVg1L8782/19UqV9UlcVunZ2x6+0or7qtfqzvt1aaUph7YpStUrYqY+2GYOJs+wVrbw9OE39z3viEupQv+pwTnDhDsMp+G4J2hLmsRWTtHdWb49tqtrTHR1ZiLcJqE46kxnQtlWWil+ChBAiJGioAGhEgUJdNW0y0l1ZkYIsEUidClwJCNLgVCEsY+ICkzXPcMozC86nnxk5FPfnTD1WGgtW27VNgwjg24MLjM67dNwKX221OBv2AlshV5e2LnXwYu769tE7F1++0ZIdAVEz7yYPupuT/CVoxtDeByB8Zl7b63bn2jFDhKYDxxK5x/xKb4RJtSTVa+mYcmtdXbwUmfRlVbjyFHjD52+H/Haq2R1Tiq0tWI5olWKhNCqI1G46uvKn0v/PgHLwlR+ObdkdM008cBVZAucC0d+0PH1fWe613iMPauVUdIDazNtKbU6czdVHio8PhYOJ0mji1SU0RKT1FFzOfqEjM6ZnaiMAVqNGIn9IfIq+uRJUW6ICRJ3J8LizSGvQcxJDWIgV0YkaERU89+zFz3A5IjcwtQI20CLa5Q8DmSF3tkzXHvBl5cJ168uCJe9dw/HimPnj3d1GghOkwZEvQZazN37xasRWInpD6Sxh4txrJUEoaNkVYauzG7rziVukscJNN1EZbkZMFaaNFICHFVPiCuQighUgpILMwaGBc/aUp2PsB5MWYVdqlnmY3URSwYNgnXfSYdelJYuyGDWozzg1uUvnr5gtwN/M2/uSMGlxnSd8SQ2OeO3/6tz/nrXwntbiasfgjjuMOArgX2IaNj42GeCdKx6MIcek/9s0ZnkdyUlgyzjiaNwSKKsvSRqTgHuMqHc9J+XN9dVt3S2toCdSvkrj5Zml+AK8ZSG6UaZamUIiw1sxSl1EDVTFFj0UZdU/NU26rJ1idWFrJKwQO2FfT1dWwSvvg85G1jmMenrPWLP7tnS15UAT6jX2fEGi7wtJdGJ+xWCSwaWdq8EnndqiZFI8fVfkb9sVMwdmutnavSB6FrhRSMFIyYIA+uDtgNgUMvkDv6w8jhqkf6RAoDogO1DWuB1pXw+IzQKAJrNsglCmdzrvPfmIvH/XsEvbVRsadRgLAy7O3pPdxGKpf5+7P5/fa+bzkHDruzFnwuVsnajNIKrS1otUux3tJU2WR7qyTS1u5eVZ4hLl7QS/UNRG26RjJ7sS9NWVTXnIY1qKl+ONeOD6rg910iqbIsFZNIrUaUsO6gXSaBCt/86oEhuAb0eF643neMfQYyN2Ny+d4hcxjdhnKICRGjauF8qiy1cDwX7s+VeTLuysI8K03FLWqrz9KW0AhFyKb0WTAt9DEwjpE4RnZ94rDvGYfEmCKNwFyVUAuHXti/HskRPxhmox8yh32mG3pInYdlpMDt0ng8HamlEjGPpBVhRtiiJkMUJlFyFG72kYVAOCuxemRnSAEkEWNj3yfGEN2dD/ceaA1osGvKvQQYOoYe5gm0BPIYOLZKT2AXArmPdAjHGLHqsGCKQh+EIUTGPrBUKKbOzM8+azxXY7/AEp0Q14qwFIGuIxLQ1tAQSATGPhEk0QWwtIaGHDpkNrROlPMJ1Rv6PiP9yOl4x7hrdClQYqalwD5ldsPI/bkw5kwiIq5RpMhMlyLj2LEUY17E/XNaIxGZo9IhVMmIKXGN0s3BcbmuN27vF47Hgr4XOfpxfXCrHp1w2iragFrRqh5qU/0ivZix1EqpMJfKUiO1Rlpzcy2ViEbxC3xbUHO4/9L5rV2lhOhkPlGSbF2ud/2b/h3WrnRtd9/Xnz8vXKzQ/lqYzMcJ1gJO5vOuPYZAFyDHdUwx9kRTJ91pQ6yRQ6VPSsLRARHnN43RkYbajLkoOfu4LcTsct4QSRG6mD1aOvakvIOwI4ZM1wkSFaXg/prh0sFvhrmbBE/WOfrzefwWQnMZyJtcUJGN2LepEr7NareNznfRzT8j3G3v49rF62Wc4WiJF3tdHRDXUCUttDbTCjStl/vV5oojXUeu2yjAUw4Dtj5Wa0ZRe4pAruZdvCpVoVRPS6y1rg6O7rz4oawPquAPu8BUAplAy4JoJmRjnhp99nCYQKI14d2bB158tucnn12zixkbnJRHg2hGCdCFhgSl1pm5NspSmafC6bwwHRfenWFOgdRndGmca0WAPniARdNGF2QNsoBBjd0gDIfEbhgYIvT9mmIlgWlRwlywVun7DsHIuRC1gwFefrLncHPw7GjcivJcKo8Pj0xLdT9uAURcaWBuA+ns1USnxpA68tXI9LDQzuvuPkDoMxYS2QLdkBh2idgPzFWR5rrkQ9/R7zvGYyNmoU+BuZzYd4kkYKUgwclDKWd/3lYwMwoQVYnimdzSiUOETeiTsERhmgKpKbO6aoESmGtgUSOHBMFtc+8sEC2QU7zojdPiow8flRhlnpnvTpw/KRyuM/uXO9rRO/th7Nl1A2PuOS4Lr/Y9pXUe+LM0uti4OuzolsqMF/d+zLR5RvqRsqYAmgnWApNUupoIydwvnbVjIFNMuH+YGV99nOF/0GuZ3UGzecHUVi+ze6tOwqtAq41lEeZiLB5tgYpAEFKGnWSSGJMZs7mNtMO+TwVHVtlpDGE18nKzGyysEP46O9+c/tbR3DpqvqytK3Wo2X35t8hecO19iIEuGkOKdESQRh+EPmaOGeYhUOqC1kq0mS4WYvMuOkqgj0afPGHPYmRZZ9mdGH2Gvo90XST1mSUElIzKDrUeWsdeE0GrywwpmC2IZEcQNj6CrXj3JTTG15MKYS3qq3DP/3u6nb8rein220bIGfVPj7G57vnn4fP1rcBvXbxD64Zuf5peopH14qq4eGhSre6seAla2iB/D0faxgBqruMvtVHbU2dfmrE0o6gXf1V9ykyonsxYW8E+zvB//RqGHUaDYBR1Bmdh4bCLNBq5JCRlyAHp8fS6YWDoO7CFQSD0gYpRlkI5u3XmVJVymrmfFh6XQjsHpBhHMaDnHGTt7gyLSkjCLke0dvRSiJ2w7zI3XeRm38EQySlB9HzqqtCKMR9PFG2kFJjMCNPsJ0Pqefl6z+c/umY37DieC22Bu6Xwzd2Z47t7pFaW1jxwJgmlKbVBIBAEcgz0Ehiv9+Rhx/mrE6dpoqqSIoQUSLlHRajBIHfM55n7eaEfMj/Y7clDRC1QzCih0WbICLGPHKsX2pSEToQZ81jP0pyNj7KY8wv6ztnDpSm7LlGCEM++E44BJnXlwrIYU9MV9hROBebim4dzSCRzK906FYZd4uVNx91fz9yfK4HG8PaO/c1bhpzZ7Xe0XU+KxtU40u935HQgPjwQXl+z6wK//OaW2/OMtUB3v3B1yNQ5IEUYdvB4WojakJwJpTp/SoSdeVevIgQTFlyJkVrl3aL8zdtHfm//scP/kNc8FUrzRMctrGZpT6z7qjhcXyvzHJiWxLw4+tRMiMHIFDBlViGkCMU5MdW2ABXvamNw58suRLoEXXRJna3FPaw39M2srcV/7UrbWtTY3PyMWtdCU72QaNs6YueO9FE4ZGEIEINy6AK7Hh7HzGkR5hlaiVAFMfzYViOL0CVjHCJDPzL0I+O4g2lhN0y8uF7Y73ZIHlgksEim5Z5+2CO7lzBeYTmhzbC50jgDjRgzIXU+yzfPJwnP8Xd5NuN4ztrfDGv8G5zRvjEA5JlcTlbWu21qOpyEx6Xgqz119D6nX4v382K//vtF2VAqc11Y6uwyxtJWyocnHTY1WlXfJK4GTG2dx7e2IkXr36u662tZiXlttVNuS8Gq2ytrM1QLfCz4v35JFK73gdNUEJJHsk4dmUApM7VXhiHSj5FPx57dLjGsDNPWoGhFVFyjOi8s05n7aeY8Vc4LnGaYi9Ba5TAcCK1RjpV2rqTkEbBCpKOtabqNPgvXY8f1YaQfhd2YMRKtuWZWFUSNuUxMzYBIq41oSnX1Gy/3gc8/f8Gnr18wtYoslckWTvcnzncPLMEPuGDi5Js1TCIEIalD6UTY9cLLm5E6V94+LDzOzkQPXWLf74mHkWQRWSaW00QfhRdXA9e7ni4nctdBrVz1kZATAWPZG0HApsaQelqSC6M5mDEMkaKRqZwJuDGIIhQTUp/pcuD84CdbjEYXE2OGkBrH2iglEPvAbr/zZEAgxkRoRkouZRljR+47SjOGcOQsihJQaXTaaOcztcGSMuM8IcCu27Fo47AfiV2i73tOAaa/vWVYMwC6kNhlPMGwZF5e7bh9dyY3oYWINIWgqLq6QURpKEkbEaMW4Tz5nPK4fM8nx8f1G5d3ax5EVZs4WrYR8WrzYt8cATrPgfMcOS/Col6cUhCCVc+fCC73Wq1VLpKyzZY7xUAOkRQDaS32Mcmlzjm9ZYOpw2XeratMS61dulIv9GuHqV70LzNqMaImD/5qECRh4r4YuxWuvwqRc8osNVCW4OOAEKAZORq7rOx3Hfv9nk9eXPPyB6/44puFniPDbuLlVWYJHbVCSj3j0HN9NbB/0TPsd8RuABXaXLDySBEPqer6HSEOsOr4g8UL1/45fO97nyflwhoj9+xn8ATlC2rPmfCezLntJXwuvxLwdOvMlaYNU3P4fCv0a7Gvq9yy1sJcFubSKMWYF/Wvqg7Pl3XeXytaK7YeT1Wbd+rVWMqaubA+/lb0Wb83My/2TaEZou7++NF45+9Y+zzysMyEBNECqNKHRgwBSQMvBrjaDeyuMi/GkZyU3szT0BrMawpWKY1lnjmeCu/uF6YiLBppVqEFxIRzgg6BEBmzs/oxfGcvMEToAry82nH9yY5eAkGEIfXE4Da1iwXOpWBqLOfKpM27bW0IAZqAwuGwY3c9YAHm48Lp8czpVJhujwR1o5pKRIE+eu71bI19EGoSchZSCFx/doVdjdx+c6bcLVhtxOiFLeTA4cVAkoTeG6EZ/T7Rdx198tceIxybk2T6bOvzjehyJiaFlJHZyUi7kIitEbvAdGoEyWhrhBgRGocEfchUbShKv+voze2RDwloxqmBdYFdP5API1PxzyloY1ZjXGeIw5AY+kgicXs18LhMlCIchsCLn11xqpnlVw+0lFwW2GZCdqdBJGDTxJACoes4dDu+vr2lToVaHWo9VWWfe8abyMNilIeF1ENL2bsBKahEhiYQzHkkIpxKdSh4Vm7vPxrvfMjLbKLh8tNSjakoc1FKg6UqU2uci3IqkemcmebAsRmLAcHoDLrqHWup7lDnhL3mOQrB7ZuDRB9riazFXghZSBGc5uFjITaP9QvpDEBRa++F89RS1y61rRrxhrm3nPNdMKZ1I9HMr11JPOo6R6WLwq5Ba8LUJY5loMRM0EYOMHbG9aHj9esbfv93X/GHP33Jf/e3EE5H9ulMCoXTOVJiJPcD+6vE9c3A4SbTjRFiQC1weiiYKUuAbhzQArFrpJQJEmghIyGuQkXhyYX+WbH3t+dbMjt/jzZGfTM3KDMV2kaWM28ynGAntCZroV+78dUK2clzbQ1EUoo2l81Vo9bCUtSPi8UoBUoR5ur23Ftqom3z/pV0t9SFZSmUopzLspLw3MfA0xJ1jTj33ypuv6N5wVer789xvuf1QRX8c/Mcc0vOcq0L5F0gLHXNjA6kFN0PvTREYRalM+OxztjceFgqj48T96eZ6dE4Vb2wPtWUJI1CYP+4oDnQ7Y2h78i1MSHkEOh65VXfk3eZw/6KPCSPhRUgG3HoGCss84KGwF2ZKWZ06i6AhcCYIp8cAv2w4/pqJJTKw7tHyqkQqtJpJQ7VA2eOmRwq4xDo+oyJkouivTDGSI8wjgMvPnlBUGG6e2ReFp/zEwkpEXNHzB3JgmvMs2vQLQlzUHZdT8Pn9P3NjgxYEqZpQTXSdztqEMp5ok/m+vrq7OAU3LJ4skDfB0QiS50JSTGEsQtcjR1G5br3i9XDbaGTRH/IHK6vgETEWEKkNSVLpEuBm5yIw+DmNxniOKKcERbujjNf/tlXcDhwMm8mtIFpJaTIsD8w1Ykh9rD05DDy6volr5eJX/z8FyynM9KNTOdHroLRxZ7X1wvnvuPx9oFBoOUBUyNqYArGYA3NGVV4fDxCqDxoh5SPBf9DXktVL/bNOJfGcfE/l1o5l8qpGI/nkakk5irMRGaDpQqlCW2ZyLaQWkHLwlwWz3EwIwYv7G7Li1sxp0jOga5Tt8MNgRgFaIRLTKys0jpHsi7+/GvRr9sM2Zys52xxxWydmauhVpgJTHPjPnpj0gnsstBHYxehj0BUxqzss7JkQTWSUmLXdbw6jLx+ceAHrw780ec9L28O/NUXe+7vb/lymjgRIQeGMdMfRvL1jjRGLHmQjGqhzpW6dBQiy3lhHqAblDw0Uk7+PtCB5Pf1eCvncbOc3ZbHDHOZ26sa9bmMrkVH3i7phuKbb3WfBG2s8/cVem+VWsr6901eadTm5myl4Zu/BouCB4ZuXICwqjBW8uFqzNeajzTrXFiWwjzPflzMBS3FNy5qhOibwBie7NOd2NgQq4RLlO73vz6ogn89JIr4jFkoDH2kLIbGRM6CSqBVY7qfqVnpo7tZJVOO08w0Kw/nwuOxMC2bx7UxK/Tms7VGIISI5sDQwbDfkbuELo0OZZ8iuzFz2HfcHDrSMLBMikmll0jfZ4KIR8nOhfNUaYsfuLUqzQJ9UF4eEq9vEuM4MOxHd94rPldUc3bq0HVQDE2ChDOfvjigIXJ8ONOi0QVn2KcUefVq5PBix3y38M3DxNkgpISFROuycwoaMBeaVboUaEujVRh3PUEFWuWm70l9JhjczYU6LwypY07NiSwCV7seMfeiDzkwxkhdKq0t7PcJbUKJyq6LTEXZjzBmQ2sgj5G7s/BgkU/2md04sNvvKFVQGrMkDCUHJ+21IKQAKSRyzLzcG1+mxFQb7+YKf/EF3dWB9OKaPiuKEE3Zp0zMmUCB1CGdP35rgeFwYCc9v/jiF1Ar44s997d3MIy8qB3XWdDqXgyBhvQjhEiHMgbniTw8nLDjTE6RYpG//frx+z49Pq7fsO7OjRlhVu/gpuKmSefafJw3Be4nYapO6CVASJlogaow6cLbyajnhTAfSTYRQ6NbbW5FIjG4532MgbQib7mL5OyW12GF7oNt9quu4d7MdLzQP3Wheonf3QJ4tiLoeF9QRapRi7FopFpFY6ALkeshcdUpqcM3HjEQorELRkl+Hc0pcug7XrzouR476pL4+VfK4dMd+Xrk4RgpvKMbhN0Oxv2OOO5ooWexRKuJVgOheZfcWqGqYNpRa2KaM3keGfcDIfRISISw+hVodHtNnor6t1tdzyt4ih12DZzzHJquRd304nSojbWQN1rdZvTOhDd1VGZj7NeVCKlV11EKoHGlDSrxYvCvxBCoUVBzlLVada+GdcQn2rDiEcc6FeoyU0txyXYQRBISkzdfLkAghUZYDX+wD6fF/6AKvpZCN/a86jpS2iNBWUplqb5bChhzmdEaWJp5HnqbOZ6UMlVOxZn4rZqnFVkArQyr6cOsgSFHxrFHB+i7yGGf6EVoKTPkyGFUhq6n9YF9F1GEmsC0d116iEznhYe6MM2F01Rdg6qVWhckJnaDE9BevRxQizzcn5153/BAIDNOAoVACx2NRtdn9jnx7jwTNHjXLo0+Ja53A599/pKhG7h/eEM5F3d/isKQItK5ZXBoFW2FITT6FDlPhdQJFoxFC2MSrq52LBJXnejCeOhct99ASmEUoUuJjoiEQEiBuTiDti9K7BODCgkjSUYopCGQqVgN5C4xPS6M40B/SPSHkXEckePMI94JiSk5d6SUIUa63DEMgS4nrvYDN9cjixYkg7ZGmWZ0WvwkVQjTglihSSOHHX0yziIkEloXMGH47BNurne8ffeG6fxIFOVclWUc6ary8lA4pcTdQ8Fspht35JApEjg+HinTwpgiOQaW0pge5u/57Pi4ftOaqrFQmUpjrsJUxIt9a5yWyOkceZyUkwRy13NImS5mrGucpzNSlekkPD4qdpwYwiP7nWDPchZyTqTkSZre3Ue6PpJyIIqtcbvyRCbDGfGwpvexFfbVAEjWLxcFPrHXRQhu3QGl0WqlFWEqCwtCJFKGgeUwoGTnF0Sljx5eNZDRFOiHzNUwcnXVsx+Fthh//rXxR68nDoc9c3ftzqCDcBgDaexIecAYmcqW+e55GWarPL01tM6YVqwppQimQk6REIsTdAmIReflr3I7X08bgPeC55DVDc+/MH/MZh79uyEAujLkW9XLe1xre0bOk4vH/cbwN1Yio7kPvkQhiIdqteTkY2tOuq5VqBWWqFuGDmXRi9rCWkVr8c9j5fkIgRzxlMM12juIkcMaIGSCfjiqvA+r4JvAGCK7LiNJyF3ihbhb1DzDXBptSpw6J28kU+6Pgce5UBblXCvWDMUQXUkwJHIIpKzsiQzXHbs+M6zFZcwByZGrznfsY5fA/GBzvThcx+DhMX3k4WHi7lhgqdSpgUU0VqIIw9DTJbg+ZK4/2ZFi4m9+8YbTaSZmPxqWJiQCU8V3s8n9419dXbG0Qp4LYZ+4oqEhs8+RH3564PWrPeelcT/PPJaCNecg2BDpU0cV6JgQFrrY6LLPqUL0IjnGROoSMQVyUeapMUhHP0akGanP3BqMOXCzG6hAiJ5UlVCW1khB6GKk10Y/ZmqIvOojkoTlPJGGRItwvYvsc6Qf4Gp/RYiJwkJORk7CMmVu9pndmAg5sus6duNAH3tOrwbGr+7pH2bEFo5EcjRkeqCe4WY0ctxjZSYgxG5EwsI+ZapGJBh9P3JujXG3Zz/uKOdHXt18wt35ka9vH3l8c0uPMesJNajHCZbGQ1QSwq5PXI+J41wp58CwzFzJw/d8dnxcv2kV8ahlN9nxGFtTpS5KWQJLcQiYFOlT4pB3jJ2ATuwNwtw4y5EHHpnqiRLO1ObKjLBufFN2CVtMka5L5BzIXSQlIYohEjFzZvdGXhNTZCXdPV9yYfELgQjBTbpsIwnSMJr7/ptBW1UzLbBYpYVAqAODdRwNshodMGSlz5E0dPS7gf0wcNglxi5RcBLq3buFm8+u2B0yVl8y7rzop24ghh7IqIULydBCRXIlWKHr3J7YGiiNuswsFOpgWDLcKiXi/p3hUtmfYoh9PTW9G1P//e/9nQgXVr4jIk9mSBv0r2qXDv7C8F9Z/pgXdwLkNeY2qdLjt20W/XepgdbEZ/ZFmBcP6MKUuiTmGBEKqtVZ91qJ5pbEQxfostJnIwfzmOPgjU3QNSb3A/Lw+KAK/v565LAfuEoREyVaRaQitRFbJbTG4zRRizFEqOZa+UWEWouT9yRg5iY9MUZSjHQ5cDhEuq7H+shBjaubgatdx37o6GIkjZGmDu+k0ljEd2+SjNMkLFp5/ObEV18dKc0lMn2CkI1d6jhpI5pxlY2bq8QwF+7nI6HPZITpPHM+V1LMnM0oKwO1akWCcGwLsRnXfccjjaQdIFzvEp+8fk3OI1+8ueP24UwgkAK0FAhEN5spzv4NpcE+riSiSCkzY59gSMQUnaiy6kZTdmmRrDyBmpS+U8bDiC2FWQTRQh4TO8s8VKEPmZSh6xKDCbtdZBEhWHSIHmWfoM+RPkfybs9UjT5nZ7RYQm4S+7HjaozEbschd5gkUna4/fXVwO2be05VXUmhDa0dSER7aJ1iUei7HTHtsGbEkIg0NCYkdwSd3P60y/T5mptXn/ITq0zzwldv3/EXX37Fw7/+c/c6MCdT7UU4HDLd7iW3797SlgIS+OF15E9+evi+T4+P6zesqQpLw4/tqpTVF7/MRpkDy6JMxSitcg5KJxUS7JOwG+F4rmSZGdKMxskT9gQkNEJ0uWpM4p19H+l7N/UKUYhBYA3i0bV9dSLampx3mesKTmVbnfPWgg+44c0q1A+sfJU11jqYd4vBwJrRcEXAfoz88Cay7wOxGTEoKTaGLjGMHcOu42oX2Pc9JfQcl8bUjHdfnfitTzp++oM9f3M/kntDhkDMPVEGhOhmXqunvAUQKlEWEhUx15lbidR53ajMZ6wHUoCYMXG5nq4eBJuBzpP2fvvbk85exN8bESGI0miXPILV0m914F95SqJrhoHfN4hi4t19lADRUd4Yn8v/9GIOZDjaaOrqgFoTZZlJE2CNskRiCqvPvr7nBCgBcoa+gyFDn40uQRSc0BkEiBdy4YeyPqiC/8mhJ4WGipIEYise3tIaj+eFx4czX351olUIMWApMbeF05QIJlR8xpWkQ3thGAJ5Lk4cO4xEIvtdIPWRqzGz33UcciYLhJyYWiM1ZRKD1Vv+3dtb/vZXD9wfK9O5sDR3vap9RMfIdRexEMkSCHWmHwI5G6UuVIksutDmhVJhqZHzNDE3IAaaCWbOLgWhiBFyYC+BopAkcLja0d+MlKLc3Z15mIub80TIfaIfhOazAnSp7E3pRqiSOdcKZqhPRBxeTEKZlBiFvssgRk4RbY2rIdOtB65KB6qEEIkCXQwMvTIMMIbsB3QQxn0iVJcP5eyzt5QjIQTG2FFjIKrS9ZmrmN2oIghXL/bcDBnS4AhAhZQzQxD6q5ExdizLhElgLoW+QegSx5J5VOMHoZHEu7gYEiYJiU4NFLVL1nnXrW5lIZIlMQw7rg8HfvT5D0jS8V//f/4VX3/zyJDc9dCAx3lBl9nPXhGIPaQP56T9uL67Zg0sWlmsUVko6Hp+GU3d+9wIhFaYzoVKZhEldUqaPR2TIkRVcgTMj2EPlHHovh8i3SCMQyLnSM5xLVJrkxHUDWlMngJzLpp8kGBO/Ate+DdDHjMvXmaKmFtXi0SaNvSZRp2VGxBS5HpM/OyF8E8/D2hMfP3OrwGhGVkCQ0zsUmCMgUUi37TEXasYfh3rjsYffR4ouufcOkSEKIkQMp5T6hcZWSNhRRowIFYxCqSCBCWjhCZIK4QasTph0jnaIRlRNwbblmxufN+hrq+BROvcP7LyH9boYUWIolhYdz5erpHgbH4RQUPw3JvASvhzH5MtbEdWV8TI+lkEZwGrBWoTYmzO82rGklyBgHjegZrSzL30zYycI12GPnseQZ+ELru1dwrBr0nmJFL0ow7/165eFKUhGihSOJWF+VT56u7I27uJMjVKha4bGPvgXvBVGCMskrkKiVNxHXvuA/sBqvS8vN5zMyZCFxn7zFXf0feR1CWCCAtGFwq5ehdwLsqQKl98eeTnf3nH/enIfS0Ec0i79E5cW05K1UA/GIcuczV2jJ0QWmRGOM+LSwQ18lgaM4VqQhMnpGCOCkQTOhOaZJpBL5CCMqaOm9fX9H3i7eOJ23cnptkILSDRQy5ClwkWPMebyn7N4hay8x5iJodEtFUBYUoOjZyjn+AIKUbOtdLnhOTI0K2+3SWREIY+MA2NsUCwSh6849g5dsWihXzo6GMjqK0EJlcNWK0+BrhyE6OEwYuDb4sNinSY9EhWSnATjJQyoVPSojTpiMG1s53BPBvHB0M1M5dGyIUOISKYRDBn8vbiJz8hAo0+u43v0hopd9x0kX/2T/8h0/nM3b/8tyRxadLjUpH2wF6MGUg50kKmCx+d9j7kNatRmnucL4bHWK/ubN6lJ0aFKo1SbjlNCw+Pibv+RF8r03liqTOC0uWAWSbGTIw9KXX0Q2K/TwxjpOvCOrP26mK2ptwheOrdZq6zyc+24r960K8eG6pbl8+KCgSPql1H+x7CA4t4p6zq8/3UBw6Hnp992vMHnyb++txzfLvQ5uAa/04ps3LuAu+0Y0nCnadNMcjMfZv5m78588fXj/zuzYE/vRsw66AmNKS1yMd1ROEyZZO2dtkdEirIAmlGdCaZZ53UaUKDEKQjxEQMiU2psHrnAY4IXOJxBMKKdBisNsJ+68SaYbB9ktbW9EJWvxUXAMpqyrNK4t191ATVgAsYXWocQlivjbZ+Hg1Wln74/7H3Z72WZcmWHvaZzWattfc+nfceTXaRt6kiiyyIICiClCgBAsEn/UT9B+lBkJ4kqEGxhKJIqqp463bZRUaE93663aw1G9ODreOZYl1RVy+8/hATcGSEu+eJ0+y1zabZGN+o6mP7ZoTQEW20LrQOpbtf/7Q0lurRQUPSdX3iOqoxBoYYiCkiMfs6ooGEjn4+9f7zKvi9FgZ1T+Tt3YGPNwdubxuv7meWpZJD4unVjnwxMW4y9Xgih47NRm2NgwV660xT4GozMMSObBNPzybOdhnTyiY7drZVv2EvXUjROdPHBh9vTrQYuJ8rP3x3w75VF7GpZ2YjRrJOIhI7nJZKNyNg7IaHjj9yO88UiyzVuF0a1XxMNgTx6E4RqrmQMKuDQ0rvWFCnZKmyuzpn9/icXit3dws39zOlduiwTeLwDxJQiTSGCuMEw2ZEB2UaBogJQym9MUWlrGsLE5+IbPLIbJUAhGBI81VGN2FYJ5A5BloFS04VHFhDOoZAbUYiMQ4JWPyNqwuSBlr3LASLHu+LGsEqpgEhMOMsg74YeZ10nkojiJJ7JKLELLBkB6eIrfxrH8HGUsiSsABdGiaeXR0QVANN/AWu4iLI2FcEah5ZWuFMGv/F/+w/JZ8947/5Z/+C5f6eQQ20rXAsYRx9wtHL9h/qsfjx/D3OH8hnQqmBUoVWhdY7Kp0pNZIIpVWkLsxL4bBEDrMz6EOpSGiMo9DiACh5yIxTYtoMTJvIdheYxuzNsoY/jO4fEvnMhWX6cCv+NL7/VPr9Jq3yqUEQZJWQ26qZ4aFEQQh0bSt6XvxWai5ODXEgj1t6HPhgE69OyunmyDAUdtbZ9MbQG2HwKVdMgUkD2wGqCn9zXeBXjUe/PKJhorbEIoL0h1u9fhIRqihIwNP9GtIU1QwYgUKwmd4rh6N/RUES5EQmEDV54/Cp4LMW+xU5zMOg/4/n5Y7tVukE7Qgd1TXZrgqo0myF90hHo39o6Y45/wTxeSj4Iog8KOqVKCCyNmbd18JmUNVFfrW7P/9Ujfu5cn+q7I8nTsdCb52UOiksfpOXQIjJ16PR/5kQ3F0gQuirbP8zOZ9XwS9H3h8qHz7ueXtz5Pq+0mtiCUZKE2fbibTLmMKpwfb8nDAl4mHm8OqGsnS3opxldrvEGDNnu8DF+cQQlK6RszHRu1DmzhzcR9/Nb5bv3txzfbfAJrEcFubqlpeqQlggmrCXCuYq9Tkmx25WY1iMnCJk4XrppCbc7mf2M5T1JR40Uay7GFAhNmHpRpEGKMFgqUbTxuXFjpcvz8kxsz8Wru8X2tI/MayLesCPiYMfSjOywDgG4iYi48glyt3erXlBheNcUYVswql0YvQOuiyFSQLNHMdpFlhKh9DYaKSsmCuLrNEZiiQBi1gpxCmBVYIkuiqyjrS6NE8CxP3LnciNdVKFIbhQ6e7UkGOh5AjaoArajeF8ZDgl2proFSXiH6ojqhznxuZupm+BnBELECPSO6o+pjNZAzxDwCRQtJEkojER6ahu2J4/4j//T0Z+8vgF/6d/9s949e3vOEuNU1voFqhAipm7+Ucf/ud8/hh32ppiTejNb99BO5J8zaPVb4fYwtBnqgLayQPElNfEs4FqTtLbbBJnu5Hzs8Bu4yCrkFzKavgt3XOpnLCm60j54eYurBCedTukurYA/eH274X8gckbcJW7YYipi/mCItLQKP57FjAyNWR0GBk3E5YTb0vBamVrcN47F31m2gbOQmYcfYIXpoGYEjMz318rF3sISZhnBVUfgatzUDC/cfvn4gW5d/EQLOuYrKCi3j2mtzV0gSzqzysu6P2E1hXx53et77qW+oeJyB8fWw38gosme/f3uxAc7cO64gj2hx0+qnwK1jFvWmwNX1PUc0JECWorF0DouDq/dx/XlyVwOhnHY+V+f+TucOJwnDkdF2o5kRSG2EkKqhHVSAzB81aiEKNCACHQCK51+IySNj+rgv/P/uVr5lOja6fUgMWBHIXL3cBmMyAGcUjMh8JyKPRT4dnTc55cXHI2bflwt0dTYHt5QRgzlwkuN9DTBmveby8NjvvCjRhU37fcHQrv7w/cHKsn8u0rpfvOODRDYiDHhdsQyEfxnXswdgYtRe9bg7Lvyt21kTGWBjcno1hApJM1EA0WAk1WNrNBRWlmDNIwNVQDl5eXvHhyxvl2w7EJ1x/veP/hmrveCKsgxVrEunEA0nwi0glRmKYN23GHhMw8BeY2sz8caVm43+8Z4kRWZaZyliKHVpDePThjMXTqLMuRZp0ckj9ExcE7IwltM+TA0iPWG9M0EXJgWZpH3NqKN6Yyd1haYoiNWY3QE2Uxjii9FKJm7vcH9tWYmpJNmZfKvFTGMfJhDNjcGK3RFDIOHUkaWZbK27sbLjljqZWwwoeiBCQFWlBSV2oKLmysSqVg0465N6IoIT8mpg1n58J//r/4Kf/Zf/BL/l//+i/4P/xf/ktO71/xdn+iIxwNDnb4h304fjz/g6euVikzn592w73Y3YVnGgQxQWNjVGOaPDUuZd/6mHVoUHuktEgjEUNgOyY224Hzs8iYjRCzFyA8t7538YZ9vcV6wfE8jW4NaT6eDyqYegjLg0UvqPyB0b82CQEX4nZboS4qaDCI/nHMoIlwqonblolT4p9ebbmpjZulcvOmOCWuKNYDwSBJZ0idOCgMA2SQcWLXXUfQ6jo07z55EFuDbj4l2T2U5lV0b/65dhrV3ApXZ2M+zIRY6KGQtbuvvXugkLBODHyB742P9JXG9wd0MTxYGVdanSgP+30ebvrrz9MerIzdi/7DiqSvBf9hAuN6PXdDGA8gJNcbWXOs+bJUTqfC8fDwa+Z4XFax9ZF5WRCZydFV+TEJKTeGoTNmY8jik5TsX5eRqC3SrTnF9TM5n1XB/3gPYw7EYXDiXGhcbAZiyuznhbAZ2J/8xbLJnbOU2Gy3LHlLTBu2mzvyMKJ5IgUlpYUeAsdqHI6NMSnHU8Ma3M0ebdm6sT9UTpZIBFJwHYFG88QjVaJ0rEfOiFwr7E8L0hpD9kJe1cUj764bOUWidvrsYr6gbpcBYdaGFVamvEdMVumoCSl4N5rHzMU0MO02LEEoH2/5+HHPYb+gpWLhQXPQwQpUIQJBjG1OnJ2NxGHAWqBIZciR5eSdZumGzSduxMNCBvHmo2JEFYbs8bV3p05O0CJUK2gLWIgcl8qQlSCJpTRSjiDCXI3alVqr36idcMRxMYIUeIgNtsBcA/vWeHgE7g/LeuMJzNY5LtVDSKaRi5w4VN+ftXWPd3E2snu0ZaEy399SDMZhIA+Rs7HTY2AhMwKLdkKBgGLSfVXRFK2NFiMhRJZeGXXjlC6p/ONfPCeF/4D/7f/tv4TfvabWAl348CNL/7M+rfdPSYfyR4VlrU6w3gRD6GjupKRsNzCOnRTBVBEC9IRZphFRUXLKpDSwGSNpjYDugJoLueguBOPBDma+izeU0CHGtRAF/xzof+RKX4vret11EZp5Y0D3FZhEQbsg1Z8pWd+fTotxfQosceCbJ4H/MOx4cxz4q6USyh05FHKU1T4YiNG5/6aBLm4ze3VqzN9XlssDXTdYT26f6+qfA/xR0V+nEd3DvPz3oyvm179LbdArcjLqmGh6pFteNT1xDRXyhsyD9vqnKYiof08fXAsP+QU+ArB1neF/wspVeUjPk3VsYmuUeDen6PX1nzuGmgsCVQw1dzR5UJEjdZdlYV5OlHpiLkdO84kyL5R5pswzrS5kaQyhkhXGMDAmZcyBMQfyEEkDSPRJpisQIlEiMf33BYr/cOezKvjjNpJiwCwiU2SSjkyJ+dQxdVHfxXbH7tElm2lik0eWOLAviaMEwjQiQyKpM/g1TNwujePSOVbW3YwyW4UyoaHSh0BcImPvIAmlAh5F27W6+MuaAx0CnJfAqXV6rczNiESmOJBip4qwEFi6ssn+BlN9A0WgE1E0QcOI3fdUme4PtDa6wJgiw3kmDInjsbC/PXG4n724a3Oe9hr0krrSeyU2JUyRzS4xTtF3f23t0tdYTJs7AaV0YxZjE5T9YWGMytE6uxGswn5lSaORvhhB/CVyXIwEzDViS2WIgjXhALQauTsdiUGYkqLNMbxzbTzKjVrdtne7FOYKc4OtKPu6UM0tRmVd8VkQxqgcTgvjRoii3B+FejwQiGzihoaw3xeW5UQucH52RrdMxAgpos2dD5oSOYrHLeO3gnooBBpqkaoNlYIEYV6O9LIntsK/+/VPeP1PZ/6P1/9n2vWB3isSftzhf86nmjpqdVW/h2Ck3JwS131XqxJIWUjBdTvTJjBtXIgrwVXb2hURR8T6HtvHtkOKhGifULiGC27FzMfF4mp0+QSaabRkHjuN38o/BcY09RVEc6uYPFj0VjEZgEZfBWhrhCiE5PHU0aD1wjIfeX8Hbw4D/+7Q+bMvE9/tN9zfHTlcw5CPxByIKaEpQ3Bbq3XF2kDvgbf7PT+8W/jim06/PEEfgRWWU9un1XPQlRcg/qzqunMPIkgM9BawILTY0VYYFqOVhSXOGAtBsucQaEd0LfK+QEc1fPraTZw0qOvN/MHO2PFC7al8q1BSfLTfu7nfff2+OZ7YR/TtIWSH9ZIl3mQEHG9ce12nIYtjc08L87ywlIWlLg52q345yWJsYmQTF3c/xMiUA+MgDGMnj36T7yHhTGK/UKQUyMOPI/2/81xdDRQJWPXEufvFyNKIEnj+6BGXW+PJ1SN0M9J0x9KFVoRJAmEKnmIkYFIRTSy9M1tk1o6FRrVIjJ1AgmgsElDr7PLIAYi9UUQZuhF6Z7EBpHmKlgEoQ+lUAqf9nmqNBSNGCHkkISwSSUEo5tnvQbwPztbdrtJcfIYpmU7F2c7JhKLCtMtshh2lGMwzN8uJWgqldcwiUOlVCTlQxYjNpxEpJ8bNFoZzrAtzrYxk3veCxbTuu9z2gxlRfFfn5C5z1wHKqRkhQCZiBsfSMY0cq9v2jneVNEC1xNgDR4TjsjDPPiGIqy/2UCGHyMkEq9DxEJveXFB1VEeaisIoUNaf2xAiXSDHTthtuN0XTvdHTkdnfm+ney5PjUNRrg8Htivc4pIdWQKUxRn/ozritEBJ3W9wGgh6dKp/q1APBOksdk2TmVB9tdKHgT/78iV/+eIZN8fvObQ9MT76B302fjz/w8dsDW15EGhlI+Y/KMPN8F19Et/pN0fi5hhJY3JSpbL6PRI+N1MwV6zHlF0vIwu0Sre+3lA7ISjJzL2vtNWC5xM+ZPEd8urnR8SFsRWQVWkuYd3zr3/eK8GiF9pumHYGCVT1VWKtivSFD3cH/ub9E/7944Zvniv/0z/tXN9d8Ne/Fax8YBj4JDA0C5TuanQMD+DJnVsrHK9vGTcbehKarvtzWUVwD9Q/nCDnV3P/2r0Cu6pePJgCPTWiGK0Uaip0KqaeYue78wcxg+/ng7Q1WXBtCFZR46cCr2Hd8T+M513wt36KPrxB/mCBXPUFDub5w1pHVteG0Inia5fenJpXlsKyzCxLYVkWlnmmLAu9VQKeOqiDMoTKMDiHYTMp4xTYjMI4RuIwgma6RMQyYtFt04MyjD8W/L/zhDT4bb52UhakNR4PI09eXPHo6sp3JNOW2hWVTFdccdmMyMCysvObRldsmxGBbTDmXpnNx+tBjdiNiFF6hWAM1kjmDYA2f+FddOEkDemNiNIMWoLncWAfAteHe1qtJCDEDDEymCBRyKoE84flISfb90ydncDSA9YrUV1tH1rnIiqbIWEEWndqINVYAiiJEBvUgKlSrNO6w4UkKFPKhGmEmFiqMWMMGskRlugUsIYr2JNAE7/NPEzgT9GLMqqoJtpDBHBzVa9Y4LpAscC2Ka0L93OnIFQRhhgREfbV33iXbq4V6IoinEwAJ4TZ6ncPRJ/QmaNJid7ta++kccBsw4d44HbpHE6usv5wN/PF3Z7txTPu58ZynLlV2EblmBMmymCNMVQPHREhmaDRBUNDgCJGLd1VthSWfkLpyLIwC+iw0OfCk8sL7oY3UCvtYWz54/ksj4ZV8o2iQd3PLebJm2HdI2snqPkutyoQUHlgoD+Mql2Qh0VgTX972GGvI/uHG+SD2tzEVr88LrAzQ7sSJBPVMC2uJGdFvqoXKTNB172+41uDFy4FteZCfRKhNpIYA4EH9rvQaacjr98XXt8Kf/LM+PlV5JuXA29uN9zfLWiaPX9kubM3EwABAABJREFU9ZmzGNEaQiNE1y9sdpFWFqI4vUCJPpNcJxYqvgR/iApG7VOhBRyhmyNikdBWj4FA69CqO146ZYVbPTQ3gqzaBRNWrYKP9FX8+xHWaUlfpx8Pq5LePR7XQ7T0D/sRaZ+mIw/xubU63KytND4zpxi2FW38kIrXaqGU+ulXLdW/AHOYkmcluMNqGCLTEBlyZEyRnBI5J4iJHjJKJlpC1TUg00YYp/9xnoG/z/msCv7NfWPCRXtnYeLxT57y6ItnDNszcg60kD3UxQACWzNktWQFArE5s9p6g+5jNItA62436T5+SzFAnQm9ERgRKkWMXCH1ur4YjRONZNH9p81HXWKGjpkxRzTA7WHmfq6MozKNwzqGEmJS6M6j7qJ0hBhsHRkqgyhilSZuqlNrDGoMMRCyd+DLov4gECAm1NVHiEAKQoyBy6CMU2Z7vmHcDAwpESRwapUWPBY3oWBCWstsEDhZxRBG8VAODZBwVbqY0SVS8Bt4QDCFapCC76iswxK8W5A1prarj8pEA8kCXV1gOCdlbkY2TxM0gdCVLI1ilaaRwZSo5lMLOkkrZQhMpaDvrmlzpatwLoFUFl4+HpFhww8fPnI8Fe5ipY6FNLrQMJlBa9QHNfDa2PTm0NIZH48Obaa1Sqx7Wj1wkshwM2O9sAuGRuM8BpbPyEv74/m3j7vkfN0VwvrvaqSspOzBMirRb/9i9KVTZkEsojYiXYFKl7UQdfBu1D9O736bf2C6m4krwFfDmYljZT8J9ILv7h1RGzBxAhviFwqV7kz3zsqRl0+7cVUl4F+IBA+hMVlFbqHTkmA9oQT2t0d+9/bE+6fKV48iX18qf30RaMu0xvUqrQVq1ZVh78W8SyLIwLQVpi5s1Ojqt3MPf5FPFMGHUCAvqOvlRdfvN0qQiJCxPiBiHvlblV7WJiz612XNfy6q5pkYsrqMYKVw4kK7ByaBytpcmDsTTD4FotmnQJwHnb93IA/2x4cQnf6AWm4ewoM57tgLfqOXQq2FeamU6umKrTr3XOiEYGSUniODCnmEPEamcWDIiSEnUs70lLGQ0J5QS74GyplxowzD51NmP5/PBLhMxuNHVzx+8pjx/JLN5SUpbSF60px2D64Ah8gEMYp1JoHWCkKgV2hqvh8XpbqhmtQ7g3V682ADq5NDbwJY6xyt0YqRGVermxFWMINU706jOYXPTGDKpE0ifjhwP9+zrydi2nI2jkTNa2wivjcTc0+mRGdYBd8x0oXqJluSCmJ1LeQdyt6VwyZsUuS4hVASx7JQSidmZZOVkDPTduTJ1TlXZ+dInIBAGryw5pAYh0DtRlx35dXgUTU6nSLGGYIMwZGeDRa6p/Th0pMggOd3kaTTgvv0N+oNhBJpwXniE26fm9VhQqgxdCUFo6TqRRRouIJ1FEW670BNO9EayYwxNBYbeNbg9vKa29sCNnNqHjq0zfDscsehGq9vrvmw3/MsZ8aQIAaKKTSBAHUVBqUVBhIeOv1efT/Y7mn1xLELLAv385FeZlo1zi4vONTEq+P+H+ah+PH8vU4I3pSGIIToN8YQhTR40I0Gn+whPtYlmsfZWiRnf0bbas2Chq3qftCVFueMdC80q5r9j3b2D4S4B1yu3+ADEFmdeVSxFQCzTrVFae1h1wxiHrEaFYIEBCP2jiZBayCUTqu25sF7c9Prnt+8OedfXxWidpLBVa7cZqX3DKb0FmnFLXQ9RLeviqLJGK8CTzDGVFmCY2w0OEZYZW2eVNZ1wwMlEFTC6m0Pa8FPWE8uPkydUCPWhVpWi1xwF8ODWPdBTS/i67aHG3hfL0zS3UKn+gcc70PQzsMvWbkb/qnpatXDg328C+MhltiV+d640fuaedDWHX5nKY1SK7U1tyGKOwJSFqQqkBhyJg+QshBSIq6R5BIThIiGRIwDEEkp+fg/J4Yh/4/yDPx9zmdV8P8n/96/x7DbMG3PiNOGFkfoztXXAAPK3DpZwVb29IjfGrONqBmWPVNazAdyXQzpwtIKuXXIAQ0zvawdorh3duwdHtSUvVN6Z7CCSGCOFe0NJZMwhh4oWtFhAzrS3zdK60gp7M4vkTxR6Y5ZDOp2NYwuwTOYNThoxh4eKKfOtb4QVeg2005HTnNnXhYU4+IsgQUO90pdKiU7YnMYA9PZjs32CjZnqCZShYshOIK3NsQe8riMvN56pUE39wyNEtCka3esDovonRzUxcIiLCvBL4ojf3MHgmEWaUHZmO/hkQzBmMSFclNttJiw3knW3bGgwgmI1SAquSs9uF81wEriKowM1LZwdrZBklIOnfnYOO2PyHJkc/mIx+2SucP93TWH04HNMDDFSE11FUpGUg/kHtcxm1LV1zmxd7RECBPHYfJRXj/53g9DtwOP0wXc7jn98MP/yE/Dj+f/nxOC78FjNFIyv9EH9fjYhyZA8Cpt/r8pTGhIqMI8V2pdueq901v1AieJEJrP4fuqLFdbffTrrhv5VBhbe5guNG86ghCij9Rr07VYd2KAFlxoWKrSeoSu6//HxX7qH5zYhdYDra1C+Paw+1dyEu6WA7/+IGx0xGInBSPnwDzLJweBh43xB4GgBIKOxJAYemEiEtXXciHoOi35A+vfz0OhXW/euPNFxBtpSwlD0CTEArL8IeM+CKzTfEcPrx+vetoOfuXva5EXfx9Y6YTeY3TXV8gfhQ6t07tuf3R5Yg0serA7mqwiQ4/axdbwnXWk33v392R7eFn416/Bg5GQRGuehDgkyIORBiWMA5IypEwPCdPozY86bCjmQM6ZnD1j5HM5n1XB//JP/zFNEl1WgpG4ur01z2l/sJ81gxp8v9zNaKbkGImrf7OJETVSykK0RqnNd+oeP48iVAXtD8lUQG+YVJbmL+JAp2twql70vRzqoTm5R+4xpHcu08h9Ne4+3LI/VZZu7EIiJd+vhxxcMGJC1oRKRUg0MZrARgUkIt2oFlCr7Pd3HA8H7q/vud+fCBq4PN+gSTkbIx9vj3SMMUUuNhuupi3DZksed95pdt/Z1yb01olmLHXxKUNrLL2RckLNOFphFMUGH/t3nOOvQRhWUoiYMNJoQNRME4Hua4AsAdO46iWEFvyhzOYFv6cF1UTpnRH32lqHjfoj/ODpHYKgMdANSpmJ0qEuTClyvoucJeUDQm8FSmUKlTwoZ9vMSwu8RTmd9hwORyek2ehWJAI1RDS6FbEGIYRIFRh6ooWOxIFUK9KNXhpSTggFi5kmic3NHWXzGS3ifjz/1vHYWiOlToheUGWNLfUUMyet2breqqutbmkVW1iz1mXNWbeV4e4CP5WG9PWGz/qeIX9U+HzXh7B65lfBWaseyNJapLVMrYVSfczco6xxrD5p6G1FxIWGqq2EyOBjeV33181Jl70/aAj8tZyzcLN0fne7cDEoSxNIAe1tnZ4pQQNBIyoBFfF/DzgfpLkbJ0T9FP4DwJpDbx006HqLXkfsgk8vNTuaNqx/H5+G0quv+8zAKmZh9cHrOpr3K4in3a2RwSKotFXRDxKEFILjuNfiHnRtRkQIqnT1Yt8bqFaCOiDM1ynNv9ZVJOgfxj+WdfvUFCheE1QgaaAlZbQIJFJodBPQRg5CzkYaA+tVHwsDXaJfdMioRGJWco7EmF1jJT8W/L/zyPlzsr9kvKNu61haXNEO0K2TxffDZo2AUnunOCoDM8e3encHLRnnlqmtIlo9GrYqA0pYhRlGozclxYFtLT7qXQUgaR3dBVUWOgXFKpytD8WM8LUk3pK5Od7x5voj4zjw+OI5lpSuwYV7667bqAwSVquQh9jkkOhtQWtnf9iz3O65u77h/YdrrvcLZ9vMJsB2s6HkQo/KVJXtbiKd7zgfN5xvJmzarIW+U6sQkiGhE7qRg4tpghkboOFvgLSMAEP0/OoZI+HiGVTIGqAL0TpEpaiSulA1o6j/vGJcu/3gI7m2WmEQRKKPzXtnGwdaVNpcQYSUIk3coQCOwy2tQYg+2o9Gbp6F8Gg3cH86Ukvn/X7m7nbm8VcwsWUaz9lOO/727fd8ONwR5iPj5SUhjcQQ0ZQcxqNCUiUpjMEZAhFBZCR2I5hTBaPALg8M00TMA6el8k+Ox3+IR+LH8/c8OUdCgJgaITYnsgVIoZPECAJK90KAOR2vV2ozahdqC/Qq/qu5ZkvwyNcQbAX6rKIy1iRN8VG/hvX1K2viHT4CbtoJwbGtHvGaqNUni63ieO+mlCrUVT3vGpjq7p4ghOAefhEvlNZ1DZVZpwsipBiRKNTQ2SS4MuVQG3dNobs9zHUNfwgESimSs5IDSBROKYIo+keWcb+Nr7ffZivXnk83f1EFS1hXijX/mvEVhaysj24VkdMnjn0zR4fTOq0vHoPbunPrzTUOooaEgATn1OcAIUFW8SboU9OivvrX4B5+VjfEQ8NSfFqo4hOWTqOLcw1UjErHuq3fG9dEtRbIllatgVKrolIxKT45SZGcMzEnNCQkZFQjItkniFGJyQOXVAOQV3fV53E+n88EyJsNUWBeCks3hpSp3ZCwej4NTI3WVr7ymne8xV+YpcHSq2NWDVJQRI1WOwudQSIxevH1YAaj9UagMVTv6lMUongnPHcHOSTEVfPAIEJKndpcbHeuvs/XqJRXjaXMXO/3PHtshGGLqGA0TF1M18RHa+vbiQNvJHC/ODmwHQ7cffjIq9cfuX57z31fCFHZzp226Uwhkq4uOMtbdhdn5PMz5DATwoANO1g6QSqWV2OR+X9n7o3emnfc0RkAQYTclYzQtJFlHX/1jkhEpdNjXBXInfHBMteMIQeyZLDm4B66i/nWSND2Scbrb4KqQpZEBUJ2sEdOybHFffXaoqQUGMi0XthUWGRhvLjg4nxie3fiWI/clCMfbvc8bwsXm4GqI2ebM2Y1fvvDQl9vaTFGdBzZakZVKCn42FJBYiTESFR3JiCuTyYEchrZjCN5GBnG4eGd7x/oqfjx/H1OzgkN3THMyQE7QYPbvljWsbYnsDUegnVWcVfzoJPeVixvdzueJ6VFSktI7QgNCV4AVnLMKmwLK07aHSoPRTIGt4aJ8Cm/vdV1X93lE9K1NmdV0LL/mczIetONUdd1QVi/BvERdH/QCxiqShoykgXLlYuoHJo3Mc2MEKLH+wYl5oGUEjEGUkpoVso6yWLx8K4H1bxPEfzZ9GZHV5iNGxdlbdJFfTLQEbostH6iFTy1krK6I1xsLeJKf2iunm9uM3S+QaUT6Vo9nlgbRRvjkJgkr+8brnMIGl3nILK2WB0hIurhON6g6boGWCfD0mitE6Q78Iy2/nNbv4/+9YaotJaIyd1IkeKZANrpmgkxE4OiIYFE0ICGTAyBGAUh0nuiyaoe/YzK7OfzmQBTzlQxRk1kjKyB3oxijWqVgDgrXVaO8SqCKVTHTmIEEUaDSkWSEnrFBpy9ZkK1yrnk9WN2h6qU4Gx1YW0QCmLGgKyBGM0Vv2vyXO6dRStJA7X5Hv6L58+Imnjz5i3LfuHDzQeebndoyIQ8oQQySovCsK4ouoC1QrHmu6Ry4rS/Y76+5fTxhvene0+fOp7Y3h6JQTnb7Xj0/Cnjk+dsxx06jSyvXmOirqon+IgxOMq22kyQQIxGK8KYnPvcu8c9mlVX1K8vha36npDQiQxuRxNQS0jwGOCcjZgUq0JrHnYTYiJ0JYqn1U3Rk7f6mhqmwX3NkwafwojQVdzGKEJUJ4g39dHa3Bv1ICQWLnbnXFyesXl/z1w6oXXssBCOC+dniSVumQl8PWQIwuvXr3h1/ZFtGri4eEwVb0eGYSDnTALGNBBSpotgvTJoxJIypoFx2hBjIg7xU555+CQd+vF8jiclL/galBg7Maz2Ntra2PvouK6i1dZ85F4qtAfufnc1nQa3/YYQ3J63vi9Y8CKcwkPcqnnTGJJPzx4qBmsWnAE9IBJdNEPH+oPaXGjFFeRuH+tgkV4DtS9AJYRVeBhXUaJ5eps3DC6BU23ubw8RCcKiga0YVxvjZMqpecMRQyCGkZQHv4GGh4KfadpZLHt2ROsOw5KHRLk/iOSCepaGe+HXYupvDhAyvULtjdKN3quvAlTXSV9HpdAl0xhoQJFCodMxOpXa16ahP2QJeMx4r2WdZkzEEBnEc+p9vL8yFvxdBhMXYtqq00BWeI8uNK1ULSy1+CxTC70XTBsSGsEaWRpaBGvRg7bEGFUYY+HJ2Mhb5d0cuJ8nZCUq+jjARXsEcUtzT3RJWP98xvnwmRX8YZp879aE2juRTm3db8YWXC1fOzFH7+LUx0PDuhOKUWjNyNE7M+nCbJAkMbdKaBDMLTJbkr/YzG/dqVVPODIXbGU1LJiLOJz9uhajgIkwio/AOsnFIiq8+PIlKUZ+9+oHvv9ww+byhkcvvqa1VbmbI5L8FpBMaLUQemNuiwsMTwfu7m+4u7/j/rin1UZnpObA7akS7jv6+IzLJy/YPH/GEDdUi7DxnfM4RCz5+mIbHgQ+mdAb1gLDkIiqVO3UXgjiEcNRlRAzrTSKQlKhaaWZrPs/33IRI7G6ur3VRjIlJk/DCsFf+IP4flRSolsn9pWKFRINFwGerBFMGFOkBiWz3mDEGwFv7BpRIot2qFc8ff6M37++5ebkGoTb0jBV8nbAdCLGgcfbM4Zxw2LKzavvuT2deEEgDhkQNuNAHAY2OmDRXz8+fI2Mm4lpyGzThhoD9O4dvuJK33/IB+PH8//zjOOAhgeS2/IHnKm5BqX3RqVTrVMr1DVNr9Y/FHuRSA5CWH3XKToyG1VShJyC79t7+qOVVVhjdOUTNQ5cze9msUgI7oBhDW8Bj9duzSilU6qP/MEnDK3a2lA0VDshtlUd79NNb1DW1YI0YlwLa4Aa/OZ6ro37qIRFMYsoAZHkKy6VtQGIjEl9bVeEQ3CdgHvX1TG06uwA1v35gwBQVOnNdVYSQEJEW4KS/fva9wiFGMSFkQz0PtCIzDJRm1FKcL0ExfNFurt3TDwNT4Gsndoq+/0CEgmaGEIg4+sJghBWeFGQhyjkhkSHcFEaEhaohVpP9MNMXE600x5ZjmitSFu5A+v6ljF4xkkXxDKbaFzEyjcXgYun8G/uN/zm1UQvrvdqITrnxIJnN2hE7SGWlz+sGD6D81kV/IdOWWm0IF4o1UExmNKikoJytAUCDJrp80IV8fFObei6M24NZ+KL771yD5A60t0/W1qDbmtUbMOCeVCOCVPMfxD5rAKRtixkEe9HxTweUnwvpSFQi7EZIvn5E+6WmXcfr/n+h7ecnz0mnl2g5i6xaK4WDikwhIQlwfYLN/s9p7s7ltt7rg8HDgWKRA/qCIHWlV0a2Jzv2J1fMk7nQMYWI+7OkL4Q04jYQ0oTqLR1TBWRVlGJtN6QKgxpAPOkqJbdOpRzogd1j/Ci7v/FpymNhlGZ8oo+Fh+/S1AH+CRdLYYOEjLwXb7CEBMhRGfoG+xCpIqRQyTHhMjqIqiVFCNDHMAKrYO2LXm6YNxu2GySc88XpdSO9M4mBnQa6WSiCLs48M3PvuGvSuFUO2/vPvBy95I0jKRpJKeRKIEiHnCyiclHv1Mix0RdoSAmhtXGNOS16P840v+czzi6X93DVtInUluz5nY0cQBM653eA3Tf+YZVfNdEXLGelJiNlI203iJjCEiEKboGZGmdYj66RTKqgRgDITibA3xk35rAmronQYkx+ErRHpqMteloQqu+Wapm1OKCWw+HmQlanLhHp7VGqz4ZcDStWwJFGyKNIlBX8Nej6u6bxaJ7U7uCrrt6TaQQiTE6+39NlnN1gx8Vtwg21kAacTS3WkR6RCSh0bNGREFzp9WIaaFiiBQCQq/BmyTNFHO0eW2dUn3i4qJlBWlYK0BD1DUL1Xz1spQCx4KG9mnnjroDStSJfVUboeEiR5mprdHaTGjveRnu6Lnyq1tY9gfmj99xt79BayGEwBgzmjzmdnO2pectJxtoFkniZNJD33N6v/D+NFMsoTL4a21N+ui4NlDMEzsfdCPYjwX/7zyt+Y4siDIFpVE9Ux1XyBtCqZWUAlWhHGYC687Y22Q0KG1ZEMN91wLLOnd34E1nbs2DKIQ1HnEhZDACo3Xa2pXS2ho601fRibeAURMS3PfZkvqEIQTQxOYs84uvv2Z/ONFPlVevvudFzqhmBoOgkSENlAeLSjdO88xpPnA83FP3Rw7LTDFBiezGQMyBXUpcPrlg++yK6XKLxES0SNeFYbdFOKeJ7690VcBaaaQwOFhHE1IbTRuDGTEM1N4IOdEiSGk8UCuLGYMKEGl0OkJSz8bN6rebLkoOAQtKLy4ICqzjNXG2QU7Zb/simBhjjNTWiSkypUxrfm+ewoAG4cTJR/8xQjemPNKDUihM04YUlbjuROmdXgtRjCEn+gq7yMH4Ijzl+qsjb37/G97cfmR7tuPJODHETB5HxCBLIscAOTKEQEiJ0jpYJcr6dVlnCP7G3uqP6Tmf84mxe3ys4EIy3HZq9SF9zbA2rntlIQYX5rZ1Dx3NC3tOgTxAzo0pRlKIEJWeAlPw96XSjdKhrEpu1UTO+dNNHwTrwWlwbbWZ5fip4COGteA8r+YFX8WDtprg4r6u1N5ofUH6CWr1lUQtnkzZOtb/sIZEqrsJtDhAJ1Z2UVmKEnqmS6DWQBfFNGHi+2fRhFjynIEIvS307hjgHIWc3ENfV9tbM+hBIQ7EEBhjICRXwddasKwsaUPr4m6E5pG+tQVO5sjxsgqzfQjv2iz+yEFhD3sLUf8YuBuLUyVqIWlE8WyUZNnFe+u0TuQIFKodOBxuufn2O4bT90xXN1g2Un3CfAv9+oaxvuMiLUxDJsfIsQxM2rgaJubpilmuWPoGgFAqS5k5zQtWhatozLFzqqP/7LrXJ1kBSlGdORAwwmc0H/ysCn6OnS6drkItTn0a1G14JoGoRs8Ja41kwsmMkIMnHuE35m4VzZG8qloP8/wJdYtBxbCVPteSElsl5IyI0kJfozIb1jsteqRujg+FD2JQcgpI75+scYh6UIW40vPi0RV/9stf8u2vfsXrt+8Zdzueffm1vyFEv91W637TNH9j6vORw+HA3fFIPRp05WyK7LY7NEeeXVzw9IunvHj2nCFv/XaREnkKpLblfpmR2mC1ug3BIE80g4FIKY0YE1l9vG0IsSzQffcYc2QBr6bLTAzCEc+hjyFitqbeNUhRSTH6LUMTBGMuxWU8akwpe/efAuDwo1EhRBc/5ZywEGm9Qo5EUXSYSGlErQOVKY+MG8XKift2Il2cozljwwBloYXIYoEWAjFmTt3f5MccICX+/Kc/Yyud67dvefX+HWfjxMunz+gpQnChYo4BhkwAaq9oEDZx5GQdq41RI6X1Nbbz89rF/Xj+P09ax6oonwRYZp0qlVIr1oWgzUfA4pMpM58E+m3cfe2+klUXzCX3UEvwVVmKgZyEKSi9CnMXGiMhjKQhE0MiRGfv96osxSjFb3cprpnp4s4ciV6gSnEr7JAS4yioRkyC3+atssyNVip1bpRaOC0z87xQSgNr7jygYeKW326KyYJIZ0jCRhVqokqiYfSmIBkJGQmJIBnVAcmK4MTSHDrowpgDw+AAoNYfaH0RENIwsNmsoUIhIQEWrdTlyHabiOmMeSnuoKqNYyvcLSeOy7Ja4sQLencvfFIhAVIrcytUM8Kw2toUaqmU6rXBApTWSCeP47UQyQqDHAi2UPuJt6+veffdb0nH3xI3d/zlacPlxxO74Qdko0zpwG6TGTeP2W0CKUe+exuR/TXp8Jq4f83u0VP62WN623Fzd8/p5pq7VqDf8eLJkeHFI25JvLnecZzXzAF1foH6lndVP/x4w/87j4ZAr5UYlWEY1rFQJ6+qVgnGYkYVpbdOignr5mPh3lwoEyLRcQyYwTCMSPNu0sS7rgKU0EmmqCT6mOiLEbWwrDd/C0rHSBEajahrYcI7UBFBV4ZnMxfFVDOsdDQql5dn3D99zuF33/Pu4zVnFxecP39JkkjIgUFAq3E8HZmXE8fbO+5ubzjdzxSDNAU2ZyObPHB1tuHLr5/y1RdfcHH+2O13Na1hEP5ADNlFdWaKZedSH1pZLXlCiG5J0r64uh5oMVFPxQ3L6gCLCMTs3/cpJGrzFUaQvKqS3d8bWqeniHQXMrUqnhEdExDRNaTHMCbJxGCQAjllWmskgTxm9zqHQNPONkZ6N0pz3nVra4peHMkauTjbcbH9QJsDuxjZ5MRZjiwxgfl/wwioCXmz4Wz8R/zN7pxvf/O3/Ob3v+dsu+HLn/6crpGU/MHsJhzmo+cLRGgkQu+gxmyVfqiANzQ/ns/39J6he5Pp+3a/8ZYonBajSyeYr7jA3TsiQu+BVo2yuDVuLp5137qumemJNCSijXRNLNKZRBjW2NoqAxa2xDgxDdFhLWYsxa19nhcvn8Aw/cFTLkYKDwhZJQ8bptEz1TUEYugYheVUmRdjPjUOxwOIh0DltDqVFEo7YSwojWqK1UAlMOJpeUE9fyMYNElIGAghr/a2SIw+mXDVPaTot+sxKzkLSKP34NoWJpoEUh7Y7QaSJI6mLL0Qw8D2LDGXmXp/IuCE1FrNxYBz4XR3w3xaKN3XAM7d6CRgEAgUDl1YBEKMjHlgN41oLxSUI4a1xqzewAke9xtoKAVtlePxmo/vXiH1LY/OlOvhOYfvja/kxLj9wCYGXlwKly9e8Lo9Zq7Go7POVxcDdvuY25tzbL7h5mTs339E9cDy7g33P7zmcDTmIrx9nHm5f8Gf/ZPCF5df85evE7d3A2qKBucxqOonO+Lncj6rgp/DSIqGBKUJ5NVHE6O4j75CD40co9PwmkBdDV1jopuh4kETZsbchFZ99PtAeRLUC3f0lLjWHMzjgTKJGIRi1Tu1WtHe6dH9qRojFcFKIUZ/sLUHtwuGTsSQcXChjinPvnjJaV/4cPuB73/7ezZD4tHTL1BJLFZpbaadDtj9gXZzT90v3JdOC4Fxt6Hmkd1uy8sXj/n6Z19w8fIZcY2CNXExkJ0WYlZ6d5V7j2F9yDoDSo/idME6Q+/s9AyThJVl3XMGssB9d5xwpCM20sSbnAQrtEI4HfZcv/mOpy8ek3e7T6rnXoyUnKPdqqFWaBKw1jjb7jCULn1N6FOGh55XvNinmKjJk/ZKhSiJURPjOFC0crsUxnHLdrthtx2o4q+PUjyJLGjAYqJ3I8eI4ICSx0+fkqeJ02nmww+/529/+xuePH2OjDuadHoMaJs5HGdO8xHF+ME+sN8fOJ4OHE73TC1wsk5ZCv/r/+X//B/2Afnx/H89yzJiYSDlic0Q2eSKG7HvmcsB6UpfcbkSstvUYoduFHzfvjQo1Z+jY+sImSAZSZnQggfFoMzm43wh0SzTaqLKwDBmghiKO4sevOJRhdo6h2LMpdIsYiJMOZDVb/fFJpau9NJJlhA1NGTSJJg2mhW0REIsBDsSYyOLr98OizBpJCbIFUwivUQWIhYSQ8wMIRLVWFrwGOscyXEgZ8fAIt5k6NJoHQIDMeKNunQa/rVrGLE0kuPAkCO1w10TIDCGzmbIHKNivZGCcXMPy13luD9yurnm+PED1/dH5t5ZQwKZS0cNkjSSVqo6daWoF83Hm5HNkCBOtNLoxwNVCr3tfXWAYN2oy0wtJ4QTcTiyOd9ysGe8e11YPn6Lpj3Pwz3XLTHPnZsWuJsPbOeZuDkyngU255fcXl1weLPlL//qmt2H3/P8m4lp9zUlHdgO99AD9/sT7//610z5np//O4XtF1/xr98qd7eQ4kQMgoSVgaCfz3Tw8yr455457hnWzb2Z6whszZll7APFIGinS6RGh8JEHJUYwENnu4smuoi/oHtnvxS0ecLeQqUA3bxj9qBbJVQjS0BXCE1Zf1gd512rVTSpj84UF/xZAIlrWqSSVQjjSMyJb/7s5wy/Cby//shv/+a3hGHD5e4RRRrLfs/p/o7b21tu7m+5XzvfzTh4OE+O/OTRBV99/ZKrL14wbC6QOJIZ6FGYq2CpITmhs9v7tLufmCzIyRXwst5WhjQQ0sTd/g6tjTBkzqdEk8ZZ9VVH7X7j76WRgk9WFmvMpVB6Y3+959HTR3RTtzWarAAKp/qJ4boAU3pptPkEIfgqAyGYs/rzg7UnJ07WsMUogFpjnEbiOBFFWEpn2m2o2x2buAqgxFYgSaa1gIbEFCI9KkszxgFiadzPBxozXz9/wf76I9f7I//Vf/evefrsOR8OR+bTTKiV0zJTTwthSBzmhW0MbPPAk2lke3XBtD2j/Cja+6zPYjtiumKzu+DJk8QuV/Z3H9jfBU574e4olJqgCyFFppTYjBGNhToL+WTEfaPQkLCS9BiYTQlWyKMxTI5jqNWYZ5hLpJZID8r5ZUdjIMqJuQlz8/XAGIUhKmUGMEKfKA0qR0IPdIsswwZJE5q3HGpjPjQ4NMagnJ1PpMEY2sIcrulhJpHZjEqShWNpnOLIQGEcRlo5sbSF3itxHBlSgJYIMTCOThZUYEi4RS+7PQ/L/v7WO1Y9NMrdKR4wNPcODbbDwGY7ktLAOIycCGxaJdeF8+Q7a4kNkRN7Ft6+u+d4OnJ/uOfDzVs+vn3L7d2B2oGgtN44zmdIz+R0zRDdRpyiOANEhff3cJgGYtwCAbMZs1tavV1jkSO9K3XudArbs4Hzi0CtxrIvfLhdKMeZow1Uu0B15j4eubv/lsSRmiqHGfR94ny8p08Lb153fvjbH/h3tjc8WwR7dkn46j/j8Z8+Y3PxC979+v/BD7/7lo+vv+fi+TtevNywfdn5l8PI/jCQ5MwRwyG6g+kzOZ9VwR/yhPXuakszUodijmikelRh7YYWF3uMSekNeu3MdYEYPiWxLdLQ2tjmRG+Vgu/7CSvEvvke38IK32nut63WPrGXd2l0kRmGpDUecjFKUDQGt5P11e8rnaTR1ebqsZxqynixoX/9nFIb8/HAh9fv2G63xBTY98J8vOf2+pr74579UnzPPSZCSjw/G7n68imPv3rJ2dVzJOyoljz4osGkUIeMNQghwah08Rs0QbEz31P2ZmhyF0Sbjc2wQbJhumJCNZKiYdUgRd9fSiVnQ5uvDfKQCedbnl098uIuwtg6C0ZYb+69+Ka7mzCFgX3z+NxgDSsel1lWVrUkdwSU0lYfvmNFE8I4DpzoKL4f1O2OkCLn2zNyHLlnpq6hIzWAtIXb00ylcLcv9GVmmWeO+5lSjiRNnA0b9gLXH6+pp4Woge20ZRgyT6/O2eSRzfk5zYwxZcac2OaBvNkgQ3S85o/nsz2XF095erXj8dORi22izJ1TMEwCBWMxV4GHlEhT5OrJyMV5wGzhcKiEVEmTMcYZ6Y231503dwk0sKNxfpGIVj6J0awK82lmf1BsgPOpc3vd2QscW+e4REYC2yn6KL1GsMbZJiEmWOiEEFnayHC25cXTp+yGxP3xwN1+5rDAo7Mtz59tgM7HDwsf748MKdDDgqUITFzmzlYatAVribt5oOrM9iyQJ2fcl5MSpDEOjVoavWeMzGxCroGZwMlgrriMTvzZWroAyjhsmCmYCU+GxLNHE5vNxNIStzcn5tORXfSI7uNpBlNC8PWc1RNS76EemE8L94cj+8OBZrr+bBKzNKSfmE8Lx36PNiPmSB7dabRvjWGMpLXg1/qK0/EDtRxZUzFwrG1HQuTjHaR3DvIKlinrhedoCbt21sYg8CFv2KVzxuyOn0nh/RwYbj9wWI48fVK4fDGxfRbQi0a5+YEXz1/y6J/8U/j5f8yv/3f/jPjD/4bXbw5sr+DxZeCbC+FXOtPb6Df96JPYz+V8VgU/xQFrlRhcLNPMSOYCNyuFqIGlO1Sj1WWFohjH2MmLYUmJpuhckLAG50Qj9kQC4lIordKrL+qldZBOtkBd1fmSjLDmXftuHsKKilxwTKM172J1zcdO+Ci7hOYAmTXWkeiTgvPHj3h6qrz57jtuDgcevX/P2aNzTvd7Dh8/Mt9cczjNzAY9BoYUmPLA1aMrHn/xBY+evSTtLolxy1L5g20sCCll9of5kxBmTEpLDrwZpg2H+4MLX4JPOULs5HGilLJiLAN0RxYTF7Q1+tzJeSANEbPOpjdaaYh0wjRQe3faYeykxYhrA0DwfbcBtRYUZe4drQ3RxhAS0VzzUDBaK1g/sZ0mRILvOmMCK4wxEFOAhquSxdg+ekSvI8f797yqN/zw7Sva499xSHtev7kmqY/2Jo3EnDjfbbgaB7746iWzRP7221e8/s1viGPlq+cv+OrnP6NJJ8bEKOP6w1ZIaXV6dOK4pfTiTo8fz2d7hvMznrx8yjc/O2ebj3x8v2cukbPzDLLjfAgsrRLSht3FxPNL3/NLCzweDswb5eZQmKvQi6Cm1NooTXl/Cnw8GleTC4fPUyNZpdbG4XCCMnMTNtzPe8Yp0Kry8U4Z4wbViS6Z+2r0kHh6JpwPQpCR3iaI5wyXl5xfXpCD8X4WTt0Yo3KwzKFFxgQHUw5tgGbcmXCpAz1kJHTqcsBsIMaJpgduyx0HEkNwIlxplbNJGIYGNiNkTDIzCdGB1gNzdXHZEEC0UzxXmhQDXz2ZoI+8er+QMqQEPRk3h5nT/ZGhGzkrbZlpvXOaC/PJOM6eC5BDo5c9x/09xwWOLdNM0eg8lbFes/QTZSmcygGaosdAOhZgcWfW0Nwx0SvHw3uOR7cJu9F9IMSti6n1nmU5OdwrjAx5IMcBjQOaOnYvhJSJOpDmxBASOSqbPHMW3Yo4ijDmI4++EOxqZP7ZT7mX/5j09p+jtx9Jy19x9/YfU/7633A5fM/JHvF9SRR7xllSrjYLh1LQNBDV2QGfy/msCv5mGFknYcw0cjeO1kmlOVnPIKnSY+W+Jax1ojR2tdMGBzZYF2pODN05yaV3tHiGMlGIgmNje3OEY+9UDLU16KF77nSLvncT1XXFUOnmkMmIC+5MDIIjFN1aEvym2+0P+Ed1peuzn3xFnwtv373n+/57zucL5g+3XL97z/39Lbe1UrsSUyCHwIvtGT/94gsev3hO2p4jcWRWh20kAQuBKK6CH3MgkWkSHHaTwaoHUuiYyJq8YbGGtkqRjmb//q2ZFxTrDLuR1IXeGiPK0YxogYrRYyOKEixAjFgv1O5edjOwYohEkgqVRrVGKZXaOwQlKkCjRh8b1qUgHvnFdV8IMUF1VvmvTiesnIjDhuP9kUkbcb7n2JXYhYMK893M3V3hFxRSjGy++ordsOHi0QWX24G427m9KEaiCBXh7PKcvxgib373e37//Q/EKfPsxUvysKG7vJuCIaeFqIaFQG0LVjux/VjwP+dzc+x8+6FzDMaj85GJiWkTefGocD4u3N40rvuOYcg8eTSwyYG+HNmfCm/uO+W0sNw39r0gkmiSeHYBweA4OxTm/kaJOZG35xzbQq8nglXuTkdeq/FMvBBK3ZBsQ5DM9bHzaBt59mzi0dnAxRRoAje3RpIt09kV6WKLTAPvb498f92gJR5vBu5PgtwOvHg8EeI1KQU+7gv7cuLJsOVUZ/Ya+P6+MSmcRXi1b7zdw9nQGcuCRhehnm8GrCaWEshT5ixHxhap1pAOGxndCUVnzMapzWQCeZgYz0ZCyGzbgTx20pCw2gm1si9H+nEm2MCHVih1JtE5FPhwbCzFo8iSdDREShhocUCjkgclLbfU2yPL6Z5aTpS+oCho53D04DSzAnKk9QV6oZwUiwqxY3WLykiKAyUndFBaPVIOFU2FkhrjAOMEY1RUJ0ptFAKzzBytM/TEfW9cA0kj203iHOUiZe5kw5vrM+5uv+Wsved3t1ve/1//Bd/9zf+ep/H/zssXwl4Wet3zfp64C2dEWRhDJcRASIEQfozH/bvPlEnm2fFbFax2eqkkOj3i2MTaaVnZLEKtzYl4uXn63ar4DFURc7JWL83hDNaJJrQSOPW+CgIrrRW0ddBAaYVmriFID39ejcXqCvUR6gP+tbjnX5wf5+IcU0+iE/eJqnnXHES52G3Rb37qe+XDDR+/veF4d8/pw0fm40xqiZA72YQX44YvvnjGs6+/4vLRY/LZpXexzdARel3VxslV9GmzRcsaC9yUDqQUVma+0cxgbsQuWIjuVFg8GyAGYQjudOgaoZY1VcvYiaIiHEsnVgg5uK+fRumNIBGzSBDDevOphrr3uNWKKWiKLPPC8bT4pKYZbVk4zUekdUpp7OcjY3GQiIwB0Uwg8PjJI7abkbOLC9LZjtI6m1EYysKhdJZmXD1/zKOvf0FJl6QhYE2watyXSorKtD5sasaURv78z/6UVhr3797y+199yziMpHFkyJNDnhAkO+ioFc/HTmPC6uezh/vx/Ntn006cPnzg9XJivtlxNkCWPR9vCr9+t/CrNydME//oq8TFfeG/u77n4+2RdnfH9/d7xrGQaex7IgRlzImnF4nLrRHbkaUX9sct283Afg83x8axiO/Hk3E5GqELh9OGpBNn28g4CT0k/uRnX/PF44kYZ97+sLCfQWPg+fNnbDc7ikbuTxVr8ItHW1JYiNMWFUXawm4QTtstYRjZTAOie2a7oS3BG5CygBpRMhf5jHAWOB/dXvzxWBnj+kyg3C6dnQQ2ecMhGDf3xuUwIjGQcocKd6Xz3VyJzXicR/b7gdICvU3sj7f85tUd3ZStGts4I+OR0+nI928Xro+FRxeJnJTj3Hl7VzmeKncWsDiSQ2HJhRQbOYFqwppi84F5OTGXBVuzPLAVI8yB0jw4i4e4XQtYMVopiAolVkodkX5GCHuWU0d7WWl4Lp47z0ZOB1oRpGzJwUjBs1TavHAAxmHj6XvzyNKVEgPH19cku6cfG/d/9d8y9ANtHnn5cmD3n/wnXP/6Bf34K8LwPWX6BaZbrBeSGDl5ouHncj6fzwQ8srU7u7xjVPUktZiEHrpTlICF5PuaWD0r2fqaoe5pSwQldNDWSNapoWG4LgAtDKW5yj948l2QRg3dc6eXxcNiekPx1LalK0061iGK0IpHxUrvxO7kPE0Rqm+TQhDSCpNQFZJ44tXV5TlffvGM139zzd31R+Z37zne33LbCqgwoVzkiecvnvLVz77m6Ysv2JxdEYctEGnRU/3IPtZvKgRbCBKJWTwhrLtCvbHu56tBKajCNKqH3BpodHCQoh77aZXehTwlFyouDVP3vcawofUGNE7db7tbjZToWdG9Froq87IgvbE/zBzu7pFSKdIo+wPzYfHGRhRrjUbz0JGoDApnu4lpzITtwNlmy/luS768YJN3bK7O6LUyyJHfPj3jb37jP5/3373l7ocbLp8XwhSop+7gpqCwLGjI1FbcQWCG5JFdavzpP/5T/s1/W7n54RXff/sdu8dXhLQlicNSghghKLV0Aon5tL7Ofjyf7fm4rwy7Az9PyiQz1IUiJz7cfuQv//o1f/vmAGlLm895dx64u+7UvnARZzgtWB748ouBs3EAGehEDsV4ez/z/Cqzi4pJZrfJWA8cTsZSZxaJvLxUznPkvg1cTRfEAawngkz85Plzvnh8QcoRxNhsIr3NvDlV7pfGdmMs8xGtQF2Yy4Fxs2WclLIU3tze8OY0c1oiv/zqnPcfZl59f8fd6UAvwuOwZUS4ax1S4MX5jvPpOT98vKG2woupEiksx4JJ4fEmMWwnfn19ZF9hUGEbPE1wIpF2Ezfv3jO1zvkw8eX5OV+8fEyOmXkp/ObbQmmF168/sGXGRh/nv9sfeH1YeP3hxA/3ga8fTcTeuDvO/P7tzOnUaGVhGxaGwS9WtEQPRp5ge+YEz3534FgKWHPMLYHWFpal0greBEinmVFLxHojxCOtDbQqaD9jGCPzybVYlgq5H5EwkEV5vK28Owh92bO17us/FeYKhcwY3TrQNDCHidtlS+mBzRA4pC8ZFmMsv4b5Ne9fGfu/+FtOu5+wu/z3OdaK3bwlTxdovqBhBOmkz+iu8FkVfM0Z7Z1gnn3szGcPMIm9U6xjpqQKwSq51bUD9GxrwQjR0OZwntAaNVaCRc+37q7sD6HQWyX0SA2BnjuxVo6tMCJOwKqFpgsUJZYH8FPzHOfeQAXpsASIXZDio/EsiRohERzew4qd7EYwY7vbsh0n9vuFdrfncDpCdajPLm/cgvfNz3nyzU/YvXjBuDmny+BJUyHSi7E0W9O6jBhGH5lHh3VodXZAAQcSLYWUEyEFUjSWVrACm6QOGFJzxHAYWczdCKF5rHBzIzKlV2qpNGsc9if66eA2GDOsNJb9geNpxmYfD8oQOC0LY1filBk3Fzy+TM7J343kNfSiLJUuwqBGV88ktd4xE06nmXZzjU4LjRmNyjAEnn/5JfFf/IpS33F/d8fx/pZSTgylUNYVSqmdaRihNXJQR5Z2HHxC4Mnjx3zxi59z++GW2/2JN7/6lp//o4FhuOAk0GonqCAkogpUGPL4D/14/Hj+B87fvnrH1dWW+7lysQs8y8aQT5TyjsebPXM6eRTz/UAcLnh2GdhuBlLK/Ozrp8Tpkp/95AVjVk5L4d37j7z/m2veXENi5BdfTZRlJKeJ3RRo9Y5Hu8BmO3G5mxg2kY93J/oCLE7j/OnPfsKf/OJnpNi4PVQsBS6uRkI68du/PvA3f/MG/QXUWhmmzP3hmnfvDvzXf/2Wy93M6Xji2/cnzjcjTy933O8ip/t71BL7+8yhndiXEwSl3C2Mz58yXp4jceDrYce8nDgcD3x883tOtfL+uDAW4QkRK52rMPHNl1eMOfFmf+LUM6/e3NJ6Ync5OmrqbMswbh0TGzOPHz9Cy57YjX/zt7/hh+9uUY385lVh2gh/8uWWkAbacuK+NFgKen9PubmmlXti6FxuBpYqLAscS6eWB6eTkjK0vlAX/z3RhljBqnmGQDff+QK9Baw3Whc0LJQQiYCVxvFUGXABcy2dqQtXo7F5vCOwIcue3VQ5C43U75k1szQYwgGnAgREJmoMHIOCdceRn/8Zbf8Mkd/z5vaa3a/e84v/6L+inv+v0PkJh/0rjtdv2U1H7OoRtUY0fD4Mj8+q4G/wqMVF3b6l5vGuBA9pDFSsCafQiS3hYHWPtTVWslZrDC1SWmOuCympx9H2TqvVfeo9cGyFUDta1Z0BogxBaapoM+aojDOYdGYBrRBM0KLMCmKVClCcoRyCx8QKwqBClUbHRzrROtKgWmVsjW2AwRof68xcixPiYuTR2ZYXX7zk8U++5vL5S/L2HHT0EBszv5kHQVtHERfOifPrW3DlvoMo3VHQEfoQPAIWXKioiSoLObpqfumV3hpWjKVUyrHQaqOVwrFUain045FeG4JRDkcqkENgnBKIshsGLnYXxKTEGGlDdO/vOBHGgd47YpVsSk8RNaOWyn5e0LkClWM90Rf/7x2XhXY6oPOB+XRAbq+ZKmzPI+cvHvP4fMfp9j1lbpzKnq4O+IkhUEpDzQg50x/QzOaBPFEV61CXzsuvnnF/83Nef/s9v/3uDdM08uSnQgwjMWXE/NZTVypha/Uf4pH48fw9Ty8nvvsWtpf3bL+eaBOE9JGzsw+cne95yRVnu5/w9c/+jKeXW+a5MZ8Wet/TZOTi0VNe/uQbdkPneLwhhu/p7ZKfhcJghbNd4mIbUNuSv3hKlBPLqfD8i2+4vIgc7t5xWha+/+GGZnvGtOWbb37KuNlwv9+zHBfe//6a+sVjlralLh/4ePdb/g0fOd88Io/Gcthz//odH25ec/9RsBrZTSPtcOL1/IHvvj/y5OIpf/KLn/D0Gbz++BG1yHYUvtN3HLtxMV2QU+ZiypR64vvvf+DuuPDx7obS4MWjZyQTnl2dM+SBuD3n7mR8/8M1Fg9IU3aTUwtTStRq/O33d4zbRNbI/V3lyUUkjsKhG6cb4ckjJbHncZr42bMrHj1+xu9+9zt+//EtT3bG/XllPjUWUzQmjtEYs6BxYb6doQp92WHNgVgxCC05RKhb4TR3KC6irs3WPPuHC5jjyRsJZIdyy/5+z9JAS6eqMe7gYjAePa68n0f2x3N2OnKjFfKeL3eRTd9wuP7ILtzT8wVLSFgbSJzoDPQl0BTqAH1zhuR/Qg+FuHnNXi9Y7gNRDPQxzSrv37zjchNQ2WL6+bx3fFYFvwcl4GE5Af30Rm4mmAiRgKrRqC7O1DWBEsF6o1slNY94tOJFLiJ06Wg3avJbaqodrZGiFZGI9Oq0q+5inUUraGTpbl3rqK8JrKJq1CZINUptHkAjqz1PHAEpOEFqk0diAuiu6O+d2htijawdy5WwGGNXLjcTz59f8eyrFzx6+oxhd0WQgcWCw0IEeliLfFitbeZJ9TEKFhSaUUL3uE3zzAazxnE+OoVQPLDiVBfsumD3J47zTKuNcpzR6uLGAFT8AVNRUorkaWSznQgpIDExDZlpGyAObiXkAVnrhEITTx0MzaM/NQhqQgsBWnM8cqlwmOmtMrYNS6rsj3tEDsgslHqiXN/TaqHoQJh2jJfnvPzqKe/evuHNYc+/+Ve/5vLnv+fFLy+JJuRF0CxOH+wKIRA10LpDPDqGdOEsZv7sz7/hVI33f/u3/PbX36J54vHXX7pdsRXyGIlpcErYZ8TD/vH82+fj7T1PHlX+5MvE1Sjk0DjOB3o9409+9uc8efFLnj7+cy6ffkWOgcN+z69/82tuPr4GAtJhfzejxZiPQp5+wtOvK/Pd79BWOO4Lm2lgHHdszi4QOptxw+58opU7enV76uOrM26PnXpc+O7b33J/13n1+g3bTUHCCNPA7W3n+zf3WL9hSJ57UW4KF1vl7OqGr9PCr381k6YzxiERwsx+/4avv/qKx4++IOljHr/Y8Ojpc354e893r35PlYEuA7ULOo18PM3cfv+K24/X9FNDS+ebL57w9U+/4Vff/kD9cI/onpYz+9s97999y7iJfPmTn/PyxVPqnGjV47ovQ+TVr+/5cDdj7S3vcuDVXSdPkZ9+eUnvjV8+mbjYJD68+cBphpvbI9YKU4KLzcBpO3FUqK0zt05ZhBQy43CgLoWgJ9IkJN0BPv1rUinzATsFDrEhzS+A1ozWjJgLMFLbiOmGNGXa8oG6zKz8XTQYo3S2sdKl8+ad0PfGXrx6bzQybIQSdtR0xaJHJI9shoEUBoYstHZi+XCifSzs0znNNpyHE9MucjZ2lu+/Y8kNe/QTqj7iMI/cvDuRr64J8bk7oD6T81kV/GFVkxdpqChZZN3jrLcs8/z43BTTTtROWyt/b0ptSpdGlE7UyJKSB+PgwS5qycfsWonaOMlC1kaxgLWIlE4LSqhKLJ2EcuwLY1Bii9RW6FbIZh440wvNKtY6S+1Y6gSFpTsaNuH+dBMgNlpbqPOBQy3cm0EYGJIw9sDF5SUvvv6aJz/5CeePn8G4w0ioh9bj7CFhESFkF9NZ7dTaPN+7LNTSWE4nlnnh0BaYG+W4p9XCUiq6ODxjsUY0CBIYQ4CojJsdaUgMm8QwuUVtChkbIhojqoGdJkoOaBV678ToKxcVZVnDLqRDMsNWnnTrzSOGY3Qq39oYmRnDUrGpMJeFshRkKUiO1DlTTyf6YWWc01moHO8PpDHx8pc/4e2HG17/7W/47b/6lieX/5Kcznjxj/4xshkop8poyrCd0BCYzQFOmDhuNAs9wLkm/tGf/YJ/td9z//Ydb779jnHMPH3+gpadd2C9oCrkz+tR+fH8985/9MsnPPr6ghcJJOzYXlTo33D26M/58us/YbcZCZLp1ri920M98Pgskds5pC1pl5mX9xzvK3MbGF/+jHH5AW4W4uaKPN4jpmB31D3IGKEZH96+oUiiHRbmw7csy4FeI7fzDT+8+1cc9kJiR1mMKhOlV+Kw49kF3N37JLLf7jnt72n3nuewffwn/Cwc2V1dokvnePORJ5ejJ31SCFujWOfD/cIPb99wc3/D7dsPzPsD7XAi/Ozn7M4nnjx7wqMnO774+inLXEkx8eH9Ldu45e37Ww7HO6ZphFIYZeHNq3vevvnXXP/8a0Ia2OTOxW7LD2/e8PtXb/nV716xzHseXU4wTagJ/+l/+E/J45a3v/sLEgt/86tXvP7uFYflSLOZcRgYNo948iQxz3uWeaYeZw6l0npjTAHZOLL7jECcAr0m6iyc5sZB7pAr0Hzk5r6wlEZZOt0gq5I2RtXZMcrLe+6PlY4QQiWLMEXhPFfmZebNxwCMjDlhVkhy4nEwzuXEPA70+AsIAxb8IjNs4HyXuNhNyMn48Kt/zt3Nr9FZ+OKswsWWhURohdh+S7y5Z376Fb8/Pub24zVntwGZOlP60Yf/dx4JzsmLCCZrmf+klvKxdjUlOO+VjN8iTZQSIONkPQ2ertSskpfB9/uqdHObXgyREhZigNY7Q8sMtXFMhaEGyjJjNHpShlkwi5jNIM52l45HM3YldKHTnFC3dEwXCBEBal/o3VhoBLcQcHN7y/X+yP28cDp5EQpj4Oz8jOHRJfnqApk2mEBtlVYrVdZMm+IveC1GWwplXmitUk8V64VeO8e5ENVcJd+MUhdPtcqZOCl5msjTQAhCGAd2wwBrQY9RSUnpmnzpveZ3W1BElal2j9NMrnPQGFxA0zpT9mS8VgpdIkP0l1ZUj5YUVZqoi3XWuNlxMEqv2HwkHhdiLQxjZJknymmPRWU5Bfod9HLkeGyEkDn7+gu+utuTlhPhuKfffeDwwxsOz75gunqEz3UUTS4CHUNgDs3TAkOA4HngpRZ2ux1f/8k3/CBwe9zz5u0rpsszNvmKaoFBjJwiPX4+Xtofz799jqeR27srnn7ZGIaBmB5xttlxdn7FbrslC2CVcrrh+t233J0ajy6ec/nc6Evm1O+ppwOWzxiGyf9+2HIcJkLo5A2Uw4EYMl07OZ0h2ytaH6Ef6FOlLzvQzHwqhP1rJj3nyVc/R8dHHMqBuzffcXf9luGiY8tM7gtT6oxTRC4yIQ7c3XT2b28hbzld79ntJs6+/hn1eE/9+JZ6uOX7v97z6uNCLZWYOufJsI0whSO7WEj1xO0P73n04jmbq+fMt0f6D6958uIJv/j6a+Zy4l/9ReFwGzh79hhqI28iH/6bv6TZR37/+4XQAo8utixPrrh49oTHLy+pceZf/dfv+P6HA5vLjC2Zf/4v/jskbfniokE5sT8eWU57WnEh8k3ZoyJstmekkGnpRA1H0jzTKBz1hm0+51wDIQqzZXdTNePm+p55MaYwMG4+ovmO475xPM7U1lE6aoWxGqXB0iAkI4s7o1KMpG1kmCJsLmCIXORLAoHeZsYcyWGmznfk80e8eCRUHVlmpdRI6wsqmXHacvbiEVdffsW8v2eQe15cGDZseH275/r9PeXjHtsfOFtOxLon1nvm45Z56aj9WPD/zhPVR+etFZ+JKygePtDNQTqqSsCzrgXHrHYzoga64Vx16wQRaJ7VXPFdd7XuQTpBiarMMWLVMKvQGqFGiiwEhCYNawuhG51ACUKohWSBxYCuqCmxCVUXQvM9rzVfG5gJJ4OBDm2m0WiHA6c377h79Y67j7csS+Nst+HsbMuw3aFhoBa4udsj9wt9aSyn2cVzBnXx32u1E5pbVKoYwTq+dYgMUyKlQEiRECMxCkOKSE4MmiAPjCnRxYgEppiwoDQzFF8b1G7EVunBs+1LVHdAaELVuw8BxJSu6vu05B55FzQIEoLf7FP08qs+ibFmLNYw6z65CBkNStfsnv1WmeeZZR+xaMhNY54XzArFhKMGUu+8vLhg8/XX9NN7to8ythy5/+EtOkxsLp+QNCEYech09XzxVhpdcd1HbUgTkinPH19B/Qk//Oq33Hy85cPbV8RhYDtsiDoASis/+vA/5/PnvzyjyJHju1fcxeds//TP6HH2phShc2SZjf0y0wWS3jCkK8azF8z7wvz2e/pyy3z4gSm+oC0zqkeSLVjZEscLeoye/5BH6lzJdkvmltP+NXQY4zPYXTAKtPOAlUiKlzSJpFjJF2fs7+8ZY+PiqydUmTkbRqYnP4OY6fc3bOIHPr6/YQ6J5e4VVc84v7hk8/XPmL/4gtPtDTd/9ZfcvX1LHieufv5zUoV6d4OdGnVuCHuS3HO43SAXV/Qpk6/OqMHjcIdd5uziMfsP17z78J6sI/t9I2wC2s857guhH6mbSojnfPjYqa1ztYn8/IuJj+9myvFEHhJvvv8VOZ9xLlccDnvSmDjNcHe3YCoMyTjfjhAySSGEiXk5o93vKcuRuNmz1S15POf1x2v2d4lpVKxWJEHKFZoS1NeGORYk7ClLgb4g0hiirMJpo1twmKoEYphI04aQRlp8Qh4icXuJNmeEjENkSsXRyVY4n9y6eDwY875ytI7azDQc2Gw2XD59yvjlT0hMdE2IdK6mPbfhmqh3TLvXnPW3jKkwx5lQrqEfqe3zsfh8VgW/h46Ch6wE3873ZrQ1WziK+Zg1uOWsm3vzg3kOtomsqn3PTe/Rm4gHV4T0hohhoVNECU0d6WqBZp1YGyEEQkyUuJCK0kPkEAtDFawqEpSYhKVWVBcX/TXBbMGqUWujdY9yVKssXQinipUTx7sb3n7/ips3rzju79lsdlxdXrG7umJ7/oTSMu/e3DPcNlIMvoRfurPoxUEDGoTNkEgpIoMnXZlCCgGLATNBRMkxM8ZIV1sdBsIQM6jvtAtGlhWjqU7aCw1kJQcSIMeARs8MiBYQWIuzIrXT2wrjiYKmQDCfBph6xrWHhwR6EFRktTIasXcaCsF1GwPBE+ys03pBcyAEX+20udGHAkvlREduDoSlMu7O0T/5JfP3O4anT5FHT+mnjt4tpCuwIHRRquINUzcauFAzRlDzpiVGLkJAXz6n9cqr3/yW179+zSgbhp9+ScDpZvL5rOF+PH/H+cXXP+X29hW//9ZY7D3vf/h/citwuBCibhmniZNsVovuAt04yY40PCVzzXZ/Qa9brBw8DIsPLPNrlqqksGVZ3q9R0BuSTTTWkCgdCbsr0v49p+MPxPvfM2xe0s7/PRDhKJ1y39jfX3N/+5HTqVPSQHz+c4bHz9hsOsNmIJZ75uMNpyycvXzMVjP96Ug9FeR0R7THbK6+5N42nB59h01HdsMjzq4uKU2dnd8bX37zM7bnCZaB9+9nDn/xV2x//jVPnjziMJ/49fffcWLi+5vCd+8/8vYv/pLtxSVdlBAS12/e8fjpJY8vnjFdXXF76nx4/Ze8fPmE51/9nK+++SW3H++4ffsRi8Zhf01rxul44FiPBIGPN/eUpmQxpiGjIVDojE8uOLt8xts377j94SO3t3ds0kD+6c/ZvnzCjt9wuPuemzqw9IGbuTJskl+s8jl5yQzDCQ1CXfbU3jE2DJsdSqCcFmr3IB4JimpmzAMyTLQw0CWSwha0o1qYtLHLSsqVj6dEPwonU2728PHuSF0Ky1JIeiRJJ5HRC6EI3CydZakcDpX3t412e6C3A0s8glamcAbLHb3d0PTzKbOfz2cCZHWkpanR8HE34uN5k9WJhtEEIkoAFlw9L6x/XQwQNKhT42JDbb3BinrsZGvIinakN6y7J91CJQdBYkCS0GtgrpV8CrAI81ocq3YnyUX8RVeMuVcOy9H30LWxpzPiDoPaZ+LpyPH2I4f9R2qf2Ywjz5684OVXX3D28jlhc0FpkXJ3orbG7uKczdmGUSMyJDQERJQYlJw8YYvgzGoTv6l2gXkpqCpJo1sHRQh9/d5pQFNEccFfF/l0440u7ne/uggtBYJGsihBhbI6DSJ+2+/mK5gcHb+LqN/qoxMRaa7DcFWleSPAGkqiShKBAIt1AgEbvC2TFlnEE++21bDiMaQLAcqJPM9YyORHW7ZpwHbP2Wwndl88J8XMcD55GqI6GrT2BqU5FVCVpIEUI31tBBpuBxnThkcvnlH2R97/8AOv377FdgPPXmZSzJ/yFX48n+fRi58wxInHNfDx/WsOH2/ROKHDwvdvPxDzGeEyElKgxUdI2DDPgfF4QygfqMWoLRFkRwiQJJDGSBzBbEKGDcgVzYSlQmh7KjMxJWK4gHFi1G+pp48s+3t63tH6Pa3DcuqcTkdqW5BWif0jA3dM+QJNCTFzJfjmBUkfYUQKmVYb9eY1/2/2/jz4trSq78df6xn23uecz3DHnpvuppvBhkYszNc4oiGJiVE0gRCSMkJJMINohjImqdIIFlEsIyERUTSJMWgqUdEklTJiKNGfFU0lUUJAg4DSTc93/Exn2Hs/z7N+f6znfG5fupFuVLjIWT3cz+fcvc/Ze5+9nzW93+81Hj7M3qMfZrYojMzYvu4edhjJqSeh4Bzbp84y7YSts9czmQUkFVbuAqv7PwT7DXlyO6vVwOVz57jvwYvsHV5mkUa06ZgfzYkok7M73HzraZwU5ssVczkkDYXdW85w9va7cFvX4ZaZyYkTxBM3cf7Rixw8fERaHrK/fwEthUnTMq4G5oslcddBe5rLqTDsz4l5m/OrhuXFhrTqkbxk//CI3K4YXCaNHU6U8fJF5oMn5pbZbIenXb/FZNqzOLrMhcsHSMwcXBJ0CBQaRE/QdFPamNEyAGKT+PAEnwg+sNNmzuxO2D01YdWPjMMumgcosBoK9+17JouBBQfs9dAPPbkfmfeFsUwYciBxjq1hhZMVl5cNy37gaDHQL+bE/iJpuADjo+TRUaJjuUh0qyNc2P1UPx7Hdk05fKRmtaLGt6TOpichav3kLCYtK2rCN96ZJGoWKBS8N5odFFrvGEuxgTriGYopNwQnpBZ8Ac1CKJ4+G4AkURg1gxpgLBcYVEk5kzWT+tF0khcDpR/QYSAPA2M/oIueYehNuY9CdjB6cGVgsTikXy7JY48PgTNbJ7j5jju46em3sX3z9YzNjKPLcw4u7tm1CJ7pzpSTkw4fItl5NGHjaj1I4xHvCerNp4qguRB9gwhVPz8YUdAZat45CxoyQhCHFqVNUARGby7NpIMdQSCIpzjB2u7GZ3di2yUnuBjxwZGLgSSNIlgrLUIt2wtQqzDeGSUPpfFWVXHH4y2NWlkcEAUXHNuhIU4m7E63yTunWZUBTcn0/Sctrolwi5p4z3SLGDuatjHdASJSsHtBhK4JprgoVoEYKMigJi3qhI7AiekWcutNDOPA/qXLxAfP0c6mnNg9wcy1n9RHYWNPzVpdkAOcPHmK0J5iVSJFthjcjMP5EXJ4QHP+AfxkRPUcafC4+QHpaEbnM/1KyGSb4kbG+xWqmZIiqo4QwPvCRAbUDfRjD7kHd0DwW4SmwcsOOTXocES/+k32L1xkuXeJZVJG11C0I3TbJJcpl+4npEPK9mn6nVsYtztC19CwNO56WlHGS5TVb3M4f4A8BvJin9DdjJ94Zu6AJIXVap9xXFA4gW7dCWWgJAyTc2oXVtczHhxy4A9xXeDE086yiIcs3neJRZ9s9ogXxjwyLhKTsyfIR0fMVyOeObJasYqB8w8ccubuM6QWLt13jkcfeoQHPvxhxvmjzKYON65wwbHKS2JMOL8gTq9n1TtWq4zmBeXRh1D3EK5r2Z4KSMJrA+ki5SAzC5Gtp91Md/6Aw6Ml3gfaacOpW25ibAqzNCee2kfdQ0Qe4Gi+z2Jljl3UM+lmliCiiCiN84TYs90Kp2aek7Nttk/OKEeJg8uO5XKLk4sJk2nDvUuhHCw40cFYMjKOkEcuDZlhEOarIxa9Y3d3hcqcoxRZ9iPLZY8MS3b8HsFfxqc9Su5Y+QmshLQ8IsyGT/XjcWzXlMP3WqpCnOC8EDKU6nydAjUba3CIGkc6FmWQXHnTgpJxlcrXMxg9rNjcaLQgSUmaKKUwlGJ999EG6vR5JIyZMWWWeaQsRtKYYEzIONJUEN3WqjAUIbkIDkpQmlzwrRLFk/NIlELjlOAKy3EgkxlHJafAmckW1z3tDq5/xu2cvP12Tpw5i0rDhWYPgOXeHH/Ys5ockbtIGyZEAql1hoAPhv43ySHjlxcFdY7UWPnei8c7q2yIGK3RKPsOp+YIpTpzxAYSNpXnqKjRIms1xXlvWgbeQgKvzvYN5sRDcMTsGOt1R008KdfevTpHEbGMWhTxrqoF2HuXUsA5kqrhEJzSuID3Stc06Gxm21BMaYtcKxYO7z2NGMYiem8TEIMFgaUkhErNowaFNcjwqUBSWmel/+IcnW8pp09yY5+4MMLBwZzpwxdoxBNmmwz/Wrbz5/43RRrEnyFMb8OPh7T5iDw8wN7FAzwTVI5ougnOtZR0maa9RFndwryZIGI67VICY1HGYUXBgK/IecKghDCh8dEG4tCScmI8PCB6pXW7xMkOYWuKLnvc3kXGYZ/DxUWO+pbYnKTbvY7JiR1k1rEnAVklJOwRXYeXHQTHsMykcQX9w7C8hK56WrfDGCakMtDwIN7dwnl/O1EOacIRxc3A3caqwIO/+wBbch6Z3kB/6nYWZcLykd+luXCZyR3Ppw0nKGnK/uGKo0uHbF1/E1OfmcRtxA+Ml+bsLw7J7RZNFtJ8n/39Rzn3Ox9k+0MnyO11XHjoApce+hBHqzmhFaQ5S1ku2J60NLvb+OY6/Og5CGeRw54TN+1w6qbPY//cI7jlHo7E2E5p9ibMmwm0OzRlAc02k5s+mzw94jrdY/vENkeLFZf29rj44CHDajBq7e51nGk7Tq4ucXD5kKN9IcQJ27sTSh24NRWbtDmdKWe3HNvTTHPiLPPBk7PDe6WVCNMt4g03c+LiGfb2FJ9HYnAsizAJ0GTI44Lz+4XDVWLSLZB4gMORSmLMI62u0GafmyZzrj81sljMODrcxrf7jGOG5cbhfwxzFCnWL1WtKnDgsczRnA02Ba+W13MxIF6i4MQCgyFnA1ykQtZCTpahl5yQbL1nckYVhpyQktGsaM4MqkiCJhcGVaJ3RAJjdITSEtNA7jJdLrRUNPwwMPQ942pFGnqG1COaiGITnfIC0nJJPx6Ac0zPnubkzTdy4rqzbO+cIHRTJDu2p1PKiV20V3SxIu8tWc5m+CbRdDbZCRVqFd6ukQhFFaruvTgIanQ5devZdWL8ejEMQsmGti/FFP6cJcFIrZZI7XmrCK7OsJeKlShivXmcs1nZxdosdkyFQYXGYQh/HJQKBhRMTwHwxbAa6hzqrGJQig3ykVqiFAfqM0EjxVlP3zkxapSYrsGQExGT0nUipoon9doEjysOKYqqkqkVkLVOQBV0ChZJMlbA59RHxjMnWPQ9q488zOVHL5na43XXkD7mxh5n5y6MBH9AnCzoGmg5RHQf9ARKy5AGYpygeYLQWh+4b/GSiN0S7xLOK+MoZFmi7oCSE0WF1A8k7egmHUy28DLBuQhMycsJvhSka5C4hQszlJM0nGE37yJxl3DU47yj21Z819H5ji0PrSssFiNpuEQ3n9OEhsQWxA7i9RR3kjAZaTTjcEZJlhXtzmnC9vWg15GObmZcJGJQSlpwaRG4sHoYvXiZdB4WFz/MwQMfIrQzpguHX+7z0CMXmS/nbJ9u2N0NTN3IePZ2trsliw89xGU/QYuyHDMuTnCNZ9DCQx95L4f7wthENK5s7smyp99vmHihLDN75ZA+B0Yi5eA8k709uO5u9i81rI4u0+UFErbQcALfKU2coLMT5EsPsDp/GZc/xPXX3cANtz2XZRGOHryPRTlisXeIrg4pznRIkAYpJ2m6TFgd0cbIqVOnwDecmWbOnJ3Q50DIgVm7QBpPzwlW88BqOedE65hcv4tcvEjxOzztzHXcclJZjYp3jlWfWK1WjMMK8YlURhb9Act+QYgNU+fxUXGaUDUJcd/A6Si0E49rIk17hpU7yepQP9WPx7FdUw5fVcEV1NuirZptFKsahk8p6yQPzYW+JFOFKwWfMz02Ic9lE43RVBjHESlWKRA1PfxcRoIatx8p+Fz7/uLpvemxT3IgBK3T6bxN4qOwVMsuvVo5eBwSuR9Iq575/JBxuWBcrRiHJU5MrzvmTAgdU9cRZo7u1CnaMydod0/gp1OkeCiFpmuZ7e6QV8qhKmlIjIcrhumEtpviQrDeuVo5HtYKBVi5GsGJjfB1zlPEgI++mKMugvHvvcOrw3lIGZI3jr+IR50FVVL/tMTe8AFrh484EGogYGX+XAqiQosjCiSt35VY0OGytVZcHcVbxFdMhSeLIFmwYr/J7orl5jbCVxQnvtIEfeVFmoN3BXIwJb3sHCqKZLsm3nmyZqt+YIDErAUJDuftHNZKjW2tggQf2e1mpNNnyEdLDh89z+Ejl4h+Q8u7lm1xdC8hCD5HUnuBrclZJJymndxEMynkYUGsQK6sgjhT53AOKDY8ygWP8x25TEFmpGG0Pr3PiNvGx7PQ7JDrrROiMpGAS5eReIjEM0izS9M5dPsU2l6HdA/TdhcY+3NEtyCnSziJ4AvLcppl2gI6XBS0BEqc4GcTXLtF8NCISXL7PEA/QDGg7Im8IqvnQCa00iOL86TDQ8ZkwWteXGbx8Ae4/OhDXN67QBt2SHuJGOZWCTtzM/7sLfi9h9g+VfC33ES7OmQM78evelbzQppM8Y3gp6dZlo5lPiS1R8y2p/jBs3/wMHjrV/u4i4tLxqNDDh59iNxsU1xDzsree96NDx9ishsYx4z0v4N6Rxcc0rQs+yXl8Ii0uECTH2Ln1i+E7h72LmcOz2eG8w/jOSSEHtXC0bxHXURypF+M9EOP44jYXM+dd9zEs+84S56d4eCRBWn/QZITij9FE3fpJgOlLwgR/IRLe4+wtd3R+bPEopwsmeh6NBb2D3Z55Nwe5H1mUtlXRXGMBN8RgwNRNEMpjr2l45F5oHTBqg3tFgfjDvTXDsPnmnL4q3GgMCKqDKq4lMjJRiQWDH2Zi6nbGfK6kFNBVOlLsWy2VgesFVdlZlVNZ16FXCyDLC7gERpPpZE5WudpnVHRCgLR+r0O62urwkwLmow+5lKhHwaWqxV9XOAFViIsSsFLMd198YxpwMUJfjJloi1ue0roJhAiBZsDL87G3XazQDkNoCwXR6yGEVkM+G6khIbGBVP2c76q+ylEj8OR0cpKMActKpUSZ4BFcaZkmLCJhCoe8UqH8V4dWGBTRxQ7heIE57xlyUlxrgIoay9fslKcXfbgLNMuFFSMYilprBGaxQlOHODJzrQWfF1wVRSHx2ndGLH3EEVdnVOvNj9AKkjTIWSnFgioQ3CoKs4bjmAdFNnn2P6lKjSKE7LYfsUX2uBNKrnATD39zhbL68+wGnsWe0f485c+iU/Cxp6qzVcjW5MTNLqNdzOIdxB2bkfihEaWsLgAwxJ1U4KHMoIrGe8jSSf0dDTtlNjs0irgEsvViF8d0EiPb7dI/gS9TqEcoeMBbbpMLo5l6kmrkW1/iOuWtF1LFz19s0N0sJSR1dGK5WrJYr4EPzKMDXFrSt+epW09dCu8S8SQCdEmU7adYV4kg9BQJorkjIyrWk7OdHqEDvdzeOnDHB4lDvMWi6XQL45YXnqUiw89xOHhAmaZ1XgrJ86cZVvP0WZPuniRi0cDPkZ2z93HQd7lcnczafcC6BF5vsf8csa7j0B2iDbEMzch0xsY5wv06ABZzMmHlyk3nyHsnmIcHuJwEOjnbN9yEtdNkYv3QYBzlzsOLi3x/R6hXTBtpri4wG9PLdkQYaUjB4ePcuHdv8KlUVkcnSeEy5y+Ycr8YMry8BLTk47YTkiXR6Zhh1OnGnzbcOZUx/W33oC743lcvH9Fv/gtwmTOwPX0Y8cseram28yPlLzKpMkOfrJNNy2E2S7L+chikRlEuX4ncPZ0y6Rr2Lsk5OEQ1aUtQyhLMkKL9/ZdzXBkEnt9YKtrmV7fUeQEe/tKWmy09J/Q9o4O8WR8VuNr54QWGwiDq2VXhbGWrUsp6FqopxRcpeAZeMxG3pZalnbOMsJGbJZ8DAZWiT6aUI4zCplKzRalfkaViDWOeTY8QCjkNFJ8JjnPVGz8Ktmkc7s0MlAYNdHgWcUWnUT8pKHJhU4CrsA4ZoY00HmP8y3iHRKhwRGcx+95xuWKvBhYhAWoJ8xmRG8OcH1eyZm88DHqbi1SJBCxc8BVoB7Q4Mm1TE+wfr7zlsmvz33dV9faunbqSE5NqtiJ9fSdDTnK3hFRRCGLHgvseDVef1QTRvLZqgwizqoFAEnQ7IhAqtoL3tmxDfV8fCVWeueRdVYuYt+3WNBhfQPsmCsOxPZx1gJCcMHGJEtRowmKg5rpJZEK8FPG4Ghp2TpxgrEfOMzC/nL5SXwSNvZULW4/nW7rNO3kDHF6Cj+7nrh1ltBGZFywGlpk/gCxCRSJZDqSjHTBE/wM7XbI3TbeNfiQaVohzgbG5RSRhA+BnCKyAskr8njAIh3RLxv6AaJEynBAm+4l7HSUOCEobIUC04wUIaeGVU6IaxlKR849Xbek856YR6uqRROF8k4IqgQHiE3kTFmheFKYmqz3OJD6CywXD7JYnmc1B8SRcsuqP+KoP2KRC0uFJi+Zrj5CvCT0csRKDpkH6LaFh4fP5uH5s5lst6TJLv3iEm7ya+T9PfJyoNfRKmtb1+HjDq70lHTEGHbovWeWjjg6OGS+HBkWK7ZmM1Qd0zjQEBl3rqNPyvDwh8iLEVwk5sLQJ7roaEhoO8P507iyz6MPHHC06lnqAfPFinEYcOMRZWwYNNOVhtlsm62nOYblgpChjTCbNBweDOz9v0d49N77mXCeQQUfB6adMCz2WPTKwxeXrPqeIDN2hpHg4ZnXOR6Yz/jgvft85MFDPvjAwLNuOUkeMn0S+iFwOFdy6WnaBueFwRW2pkKMIxIDSXbJXaDpIs1Wi4qHPrK6htzstXMkgCwGHIUkoMnGuOaauWs2+pZTwWHOLIjRrIqDFutZF2+AstbSONpgsrrJQXTenJ0PRIdpprtAEMFVnrZRzgwA6IqQnY3VDWJKceIUkUwWR3KJqXMUZ6BASQMxteRVJOSIZItYGx/Zbab47SVxf8kkA6tEP1/SNhPyrIHgCL6xPnSIJC+MDmTvAE2FfNjTi6fzkTiLtC6QnLk2LxYklMpuEBWkBjdggDzvPKKGYE3e2ajh2j/X6JngWYphHaKIldHXDlWFEh0xGyVRxDStxQnqlUatMmAO29OVGoyJMnrHqOaw1wqIomrZtAouJ5zxLfGxDgMqNbsnI9HjciF7c/BBHYO38ZjRyhi4+v0CNeBz1urwvgYeBlII4klq1E3vTBLY6PWm818EVOxea11g2rS4U6eQopRNhn9N2/aJZ9HNdpjunKKbnqTrTjCZGs0ruxadbiFyhmYSyMxgMQArgh/xTpE2IrElFyWFkeAa2m4b1yrjOJiKZEqkcck43ycvH4a8oF/tkpKn6RwrVbrDzDCsaKcJcQGfF5RUGFNkLBnVgA8Ot+VR31jA7CBhszoKNsbWe39Mn3UKmn1te9mUykEdmcDALtnfTA6AO4K+p1vu0x+cJ1+8gC5HghbGcYnLv0M/BEaEMWZEJkxHx3VyH21eEdqRUTv6/hSubOG602xJZiAxugllcj1Iw/LwUcqli8TVOTRlXBhJB48gzCz5kIQojEeO4DOlnZJSpNu5gWl3AXzA5RZtHJPTd9CkB5FZRN0p9GDOucsfZrnYYbEckLDPVLZIZQvfBbxG5gd7zNpDHCcY5pHsM3mcc/niEeoT5eCQjvOcPTNlPp5AR8/ZM1N2dzuOjhb89gMXuP/99/OMkyPjaeG37g8MPMCD42keePQSbX/IYtXzofdf5oaTkUDPkpGLc9i7fET0jtPbHTuzlu2mYbrVQWhYqLLYg5Nj5GlbI9s7ga6f8LDfKO09obmcSaLHHOpYhOxMtCXFSqkST+OclWidIMFRnDCTgHpDhOPF1POLoGKiPdlZCdhVAJu6NWpdLLAIphLn1KRyvdpkOptLp/iKNl8PyTE+QDHnUjxdiOTYMoae2Hb0aWRMI64IjWuQGJjFltxkyzDHRDckZEiMjTlZHwMEG5azHQKNi+wh9AdzhjEj/cByHGi0ozQtwThw9QgNAR+ylcqdU9TVbFocyjrzNcEddWqCOGqBRRJHLJCc0e9wtTIgDo8/prsE8fhao8+V7uizMhaTOPZ4E/upmba9lWX8qmqCP9VBt+KNL1sU9YKYHA/ZcZylK5Vi6Iy+ZywOjxfFhdp2IOOxHjxizAQj6xi1MNeqxFgDExG1McBYOwi8Ua/E0VNqcFRovUe2ppwViPGaelQ29tHWzpD2LK45QxNbuphpw4BmRfKcad4ndgVtWwYCmldI6hFdUsaMX66I3Ra9d6Q+06cFQxbG3NKnjpJBV4f0832O9i8wHF2g5APyKuG9omWEccnoT7CanGGSDgiyRS5CHpScGnwsTB2E1hGD4NoArcPHiLgWjQUlUPrEWEZUG4LLeB3Bm+5GloCIo/PKGCJDdwLPSMzCmM8j40P04xE5L20yZcw002ml4gIywWlk1glaOsaVcOHRDxPu+22cZLZ3O7ZnT8PHKfnMTeiodBmaqKTsmM8vscg9vUac32XWLJF2h6JT3NgT8OQ4IzoQP2U5DIyrfQINO9MJZecW1E/Q/UcJ04AEhy8t4hztzg1oPIF3ymS6YGfcodddJi4yu+FpuNTzgQ+cozQjQ5yyvDRysrvE9NQN0DeEcERKnpHIzM3IQ6CNnuI65kdiYOWm5YZbznB0/zl2uZ/iOy5e2Oa9yw9y+egil8+d50QzsH1yYNI05Dyh6YSpCMs0suh7XCksh54bUsNkskWbMlvtDo1veGRvYG85orMld0yFna3IsHXt4H+urVUsWhaHdzTOoqKJczQIJVjJ3bsq2eoc2VnJVlSMkuethxurY6CYI3S1NL9u8Te+CscgFKe4YnS0TLGSs/P4ogQxhV+jr5mcrzEGHCUowUVchpQzjQ8MsbGBMy7SSEDxphvvAiG0dLMtxtgR24bgA8GbtG3nbY574z3qA8U5Ig1BHKOa2qDMV8SslFXPYrLCdw1dbMytqVaMQaGENQ/V9KSDc4gzp7d2wGgNYAxxR1ALB3Culs+VaH9QvJjYTqmsCCdIzscOWbQGQpqJFdynCEENK+CRWsJXtNTAxjkCBUrB+4h6681rse2wK0fB6HxAdej23q5S8rKojVCuI4IT1F5+IUmxPr6AqGetMqACOKMmriWUXa18rOmJXoWVM8rgxDvCjlD8hpZ3LZsWu087GW0tyBlZDqbpMczRlFkJlJzIumSZIeUJnQwwZrSsGA8vIr4jjXObcCYwz4FlgcQI/RFp/zyHlx6kX11i6JV+cRHne2LXUkpP6wfiNDOdOqbdLu1kC2EBMhK9ID6hGkjLSBOUBrVA10VC2MK5SM4Ggu3V7tNQeqQcEhsHoaO4gIrDFaF1hYKna7ZIs4IsMiUIY1gh3ZypCuo82kxpp4EYG8oYcH6FiGccFdUO1zgiQtMFfBOYndhiGhvUecbSkMZDjhaFhdxF13q6yTk07+HoIUwYjgqZizS6h+bCmAJDKXh1oIExBNoGZu0Ow6JnMVxCXSDqFPSAzh2x2434yVnGk59PiZ7l+fs4uPARpu2C089+NsOlOWceuo8VE5JGwmTkxA230J2+DRYDq72H0TKy2yxoOM3Mn2Oyez8l38Zq2KWf79MP55iWkafd4pCFYzkmNAm5t+FFN+0msjb0y8SsG9HQsBo8s87ztNOR1aHHjyNJRx49TDSN4Bro+yOm3Tb9qnA4HzgQ+M39R2lmI7dp86l+PI7tmnL4s50tQ4R7h/Pe9PB9XZxFSE6qkzFHogrqjIclIsdSu9YKFtSZky5imSwYaCsJeDAAGAY0S2TUqsgkcUiogK91iV+lAtZMQ9/pWMVrGiSCxkLTRPKks/nyKdGKkPJIdiMDgu8mtEFoxeMnU7SbEEKkbYLJ53qP99FaB6J0kwmlOsYFoKuekjJlOZKbgeI80yZamVw8EViRrL9OxSTUaYOCM7ViNVS+FKMxqjhiUbKr0/2wEr74gGi26wom66uOUoWRfM3+AVKV3i3HARSgroom1f55bdWEqqIX63UvHoI6ek0EEUYKos4AdeKq1LK9r/XuLXsHc9hF1kDBBKoMFNp6xr4GAqXOIhCEBqMD1gk+RuegEAWK88Z5cIIvDhHrpcYYcdeQPObGHm8xXUZSQ8mB7BsGQPseJwNpmKPjiuInpMHkV1NoITToKCwXCQ1zPCtaHJIPEU24eECRJXnwjP0+eTjHsH8fR5ceYjkeMYwdwypRNEFuidLSSabVPUopjKs92gG8jzg84jpyaa3U3wWiKM4VfKWsljo3RLygBAumKfSpQF6Rlj0xZAuOtaHkjpyVfjWyGhwjLa6Z0YSW6GY0PjNsDTa8qj1JM22YTCKjzhi1pe16tsae1RzEC9N2iXdKmJ5htuWYpJosJDh0M2ZxwpbOSLpAh0gatgjaEryy7w/pxZGykv2CXHYgzBjHzGocCF0gSkCX+6TlZZxbEf0ZQlPwfko7gRBGZjfs4k8+m9ViRVoo7eHA1vQi2z6xjLucvuEMR/sPkVOimcxop8Ls5BZjhNXhnHF5P7F7H9s7Zzl9+jRbJ+4ETnK0iBweDuS8ZC6JU6cWyPZdHOwvWYyJrhPObAsnr/NcOug494hj/+EjTm4fsjXt2O4cN57wzK8/ycH5A2KbORwalkdweEFoTidwF5lFT5kIwwAnFktOx4tct3Xt6HJfU6vYZLaFq0CxdUaHQCwG1IvZyv1ZTUffC0bkckLRYjryhVpOtmytOBvJ6iuMq85+scCg9m5VC0EcrULvIGI9fKkz1LNWAaC1FO1ac96Zrr9qxHWFyTqQ8NCGQBpWDOOIpsSQBryDpgs04inB005mhOkMYkcTG5N8bTxNMWeJE2bdtKoOKsO+g6SM/chyscL7QPSR0ETDH0igU3NkGQgu4o3MSF+vUVscRbNJ9aqQBAgQMQfn1KLyLIIGKutB0DoQSBAStXpQUfRBMNU8FVy20mFx5mBNyEfAe6I4Us2ym+KsWqDKaO7Z2iTOevKNWIkh8JgqTf0+1JtksCgkKXbcQFRPj+EWWzV6X5KCeMvguwwaPIJj4AoVU1VJUql8EuxcRSDYpzfew6akf02bW55jVGV0kdg4NAbGnNAxo0MxUR0EJ60FfmWOyyvyYknpPUUcohMGTehYOBzOAf8XxpE8BObLJf1ij+XRksWipx9Hsu6CnMK5KZ5MJDOODmVJKT1jiCz7DueVtmsJjcNJS2givg2Ir2O0JSOyQFKPZpAgOAKagdxTVj0lLRnHBfN0Gc1HBBqK32FVtlmkGYsR8jii2lsbNDi6Lto8jNigYUZ0gmtaiLcQ/RlOuAdxaY9ueh3SNXTNIUrE+x2aKXgpeE24pGyNheAzmkdSUnTYQpcNSVZMZMm0a1ienDCsWtJqRtEJogPLJaTSMY6Fvr+A6gWmrSc3Z5mduJ7J1jYx7Bi4st2mxDOUS48wHF3ED5eYBJByiuX991FKZHvm8NxAv7xI1kNi3MIXU8cbZSQdZGK+xHYbwN+Gk1uYbE9ot7eJs46jy0ecPLWiue4sTODifdu0H/wgKnMOxoi7dMSXPKfj3mfcwH9718Oki3tcGgf63HH69BanThe2W0B6tpaOZe/ZW400B3ByKkymQicD50vkMI3c0SrPvnXj8J/QfIx1Xrot8munO0oxMRWRY273GjhmPskEVkSsp7+mdSW1bDOqtQrWZXkHdcqcVI19Kz+bPO9ala5SwEp1RRXBXrQYaNA1QMHHYqV4F0kOJDhCE0ntwJgGNGfj/qspALrgiN7hg5XXfLAJdrENxOhNgc7b2FmKvdeMKa4oi6SsFiub+Nf3rELAB880eqJv8OIp4iEnojgUoXeW6cYCWbgyCMdTAZBCkUKjQhIrixYneM0YBn9dGfFkpwRM6RCxNsl6KI7zAS1VL8EZVkDESq0Wr5gjF+dxYnRCEavSXNFEUDoJhjdQkxHW9fuIZRtUTfxSv0NrQggigb7OBPDeKhEFUyHUkqzO4GtLQoRWHUOx41RRshSiKRWRnE3fCjXAK66KDmzsmjWhZbEqjId7aGzo2oagNdjXWmMa9vC+M12NvKKkBatxZNUnWtnGt9bvPRoiBwctwzzglvdRxn2W4xbLldF6rRDvTewpHhJiQ/QNjkAKQmACuZAl4Jyxb6w/VigeIOF0iWZHHhKjBLwTvFb6sSsU11JKJA0jmgZK6snDijEtyfnAAgG5TCmnSG6HnBzjsETHOcEXQhfJOeJ0CRiDKTjBa8OuU3q/ZBg7Wk7TbXnaZsBJB36bEM/QzaZ0XSIwR/ueXJTgBxhHk+FddSzbgm8Cja5guUfYhjIU+vk+w6ikNKDtQB4zfnlEjFNK6ShDxjenabfO4GPA+UBopzDbIY8rhsvnWM33GfYeoOQV82GGukuI2LkEF6GJSBB8blmcv8RifsQwvw+v+yyHwN7hEf7yA+BmNLObyDKFeBr8Pl33EK65iUfmyuLygp1ZS3AtJ07s0jWHfPiRBR+ZNsTZjZTLc2RR6GXKQec4mmemOXLTzZHQzBhGq9LMjwKPHi24buI41bYwLLlcJrz3IwXw/H+fyofjMXZtOXy90mcVleM+miX6VsbVSgnT+rNTKFLMkVVp2FJL004NtIcYct8rjGIgsbaO3M21lx2L0cqKGPrboSQv+FzR287I5lnFRuWq4o553Y6Za+iDI8WANB3DZIRcJXw1E9QhqmRvuAPvan89eKKPqLeo3GvNfMVjON1CqINlSslkLZQ+oQrjMNCvvI3BnUS8xUOIW7cphFiz8eSq+INaab+sg591IEO58rso+ECoaPqCJ2ipgVCpEsYGapQqc7xW86sCuKbnXwfkGB3eWgLBOTvOKr9rIZZjqP15jzC6OoynyJUxus6+O7zNW/ACWVxF8ytJCxM8OFMPVGeTFdfDgNZVneAEV2GHXtacf8MMiMUcBO8JYtiA5CrwcM1P3Ng1adnPUJkxiONwuaIgdN7jtUdLD2VJHgf61T7qB7QkSsmkMTAmYDXQTAT1NzJfRZbLjjSeJOdbkHyCMWUGcWgw+WbnepxmQ/kLuODxrsOHgpLR5BEHPgyE0IFasCphwJNw6QhhD5JHUyD7iKqNtlWXUCZ430Lyxg4YelJakfJAynvkvCCnI6seckSfO1JaommO6+e4YYXkkaIJ76NhBfwU2KUUIZZDWkYiI1IWMHhK2KJtPF07Mm0KbXS0PqKSGIszmrIqnSp9o7jGW6trnCB+SsA0SrrplGU/UIowDivSvGcMM3KK9KnQewjdSSQECg4vgk89uvcIwzBnXF4mLees0pLiHE1QyCvEZbyLhDahTSbnwHJ+P0OC1TJR+ks4t6K4wjJH5suerTFxeLRiNXyE/X5CLhOWh3P61YP8zv5p/DhndiIwLI44cTrQXncbD/zOisUHPsiNoSA3Tbj57JSbb9rGh47lGHnk0X3giO2TgUm3g/gJDx5t89D9A3n1MENTCJqZkniwV975nsI3fGofj2O7phy+orUtbPhsKzlbWVcQ43ir9XxFYCTjneDWM5CNbm4CPVDbA2KSrmoZIZV+lVC8mPJcwjJVV7n3UIVkcnUHCqqlOiRnNLLamw41SBFvJf4QCj4qk6ZhkExRpa1ZhinNrkvJjgBGN1PDK2QxKV9fFFHr40fnqyhQx1ASTVZWOqekEfpMcgM5WPlQWsG5YHQ6lerc7SEN4mpAY8h6RzEqjwJYCR81p5cr/z3VUAugeAsLBMNSOITGCSuUooKzkQc4AeSKgM7a8RtgUI419I0KsFbeEzyhOmeshK4wekXUyuqCErwdozElLFAJ9b7xGB7AVaW+dg3cVHDqK++e+qcJN1lwYZegoY71Ldi4Xl8YRa36oEIj1w61ZmOPNxcCwdUqFpmiI6oFKSs0r0hlyVhGlv2SMS8oyUCuOR+xWvWgJoPtJttkOUV2QmmFWZcgCf3hCkktvUyIYQHFSv9ChwRjxQgrNDtEIiINLowmkOUMwyJuRROPCL5H88CQkhUtQ4uEnRpxzklpRSlC21iboZSWcSwM44phWDGOIz72qK4YB+iHA/rRM4yZMSVW45zVAkpJdm2GjI897QR8BznN0aMFOi4YUkEmHt9OoVOcBgKK+kxKGSVDXppKpRPIiSEVyIFJE5HgGL3S6ZSsDikjSTtkVEQCuZ8zTA8Ymn2G5YAbInHqcS7auasJiI2rA9JolMahX1BST9s0xElgMglEjTg3tZZuWTCOwjgeMaYFpQghJpvp4U7Sth3ddIb3M1Z9ZHFuxeJwj8VqAQQOlyOH5z/CuPoQ0+vvIbXPoMuPMCwWDPfPaXPmzMlCP14mraZQCnFnyontZzKKkOKcvUcusN0d8aznnObc0Vku3ae04QLb7SluuF7YOeV54IGB0+ePSGH1qX04HmPXlMMPIoy192qjzgQoNkmtZq+KUvlmqCtWtq1lAAsSzNmX4zL2mmNtAQQAlXblpVYA1k679uDtcxzZ8O4m1gJILUvn2pc251UR70AUh6gj+WJjdl0gaZW2pYrziLmvpCamgTO+f2WU2fnUDJqKRSgCjYt03ZQyFDRlhqWSs5JH03x2MSDRE6LDi6/ZOiCGrm9KLWuKSQI7saDAO0hg4EWEEZOidSaaX1sbIFIqst0zVopjcUqnjiR2Pt5UdWjABtLUa1oqudE5IdZgwtWaTTFWnJVfK25DsLZLW79XmwdgLZkg6/aMECgEhcEJ4q+o70GVANY17VIfUwkwfr7ND1CCmHxyqlUl56TiQuw+LFfNctjYNWslEyThSksZB/qSsCmaK8PaaEfWCeoMpyEEUlqSSyGlOWlYIRwQy0V8m5jOhMVyRUyHiMwZNNOEKUwcUVoYHcWNINnooSlQcsEFh7hoQl8BnHeIK9hTtoRyZNl5KZQymoKlC7h4EbxHZSQPNucjJ8W7bYq2VmEYRsZxJCdImlBWjCMMvTAmoe87xtKSJSJNhtTiirUPPSOun6PDedLSM47ZOP9ZaYZE27YwDizHDDoBbWm9PaWBQkgmSBVDRGOHdI62aptkKajrKBqgrEipMImOkpf0aYmXOSWOZI3MXIvqkjQsMfGNjiyeJB7NI1kVlQhecWJt3YCjaVuapsX7kTE1uNUpivaIWwEj4jKgODelbWdEP6GosFhdpi+Z5crRL3uG5TmyFparjv5on8vyO4TJkrMnPMNKmbZzzpyJhBufQR4u0h8tePR85t7f/Ag33zah765j2Wd6ER591NNtD5zTQAZuPrtNXAonT7ScuvUsy+2ThFPnyIf3fyqfjKvsmnL45l5dHQZzRU1tnRUrWJaHUqQQ1ShfV5yCORzjfsvxOxaxPnUQR1HTZvdah8S4K5ntupdfCiBKLkJwV+h7mu29xATt8c6CC+UxuuxiTiNXeVrB0zqPWt5BVZLF1SAmYO2DehLGd3emLjd6RQrHWWzbtpRpsfkBueCGAV+EPBbGRQ/e0wZHqO2NLOCLTc4zDYOCSq6l9Bp4OGiKZfNZMdEdrNftqSDFdXWk9u9FHK7YomSsCWic1GE9Ro0rKBFHKErvDNznXG3MiPVCRQzz0FRZYMGUAbWqBDo15b4WX1X2zbIWEG/Dd9SujQJtMb59Ruq+dh2LrO8sOx+vniymUxDE7oNc7wWLybS2bNYZIwxcO3rYG3u8iQ7oKJTcUJIDZzMl8An1gg8OHzyt7pJKA35h3PeiaCMsdcWyH8nuXsJyGz8IkT36colSRoo2iOwzcYUmOopLZOkN5eKDBefqDBvjB0SjYXZkRGWF6oCmFaWskJBNVdN1QKLoiBt7XPaIGxkGRy5KLIUQe2vfjcqYBnJOlBTJQ0DdipISpRg9N4YCZcBnaL2gvkV8JIYRIbFaZdKYSX1iSJmRHmHA9yNN7wkp0AwZzTPGfoc2tvgQaJzic6ZMItJ6Jn7NoMmQcm3djYjOkdwzjisYMgf9yKrP5BzxXmniSGYkjTad0lqtGScR0YiIjQ63lUMJwcC7USMxeGKUun63xLhFKSucTHGu4Hwy6W7fEGKE5Cil53B+iTEpg2wxlykpNLA8QtzD5DIie3tsz06S9WaiG6DZotvZQeIObXka3elD5nqJ1aUHePDeexnbi2iaQB44VOW9/2dBkX1m29t00xnNyW0Gv8WlvQ4ZYRJnuOmNn+rH49iuKYcvTtYNXysP17K5AcUrH5xM8Vbi9/XvUCU4T1ahR618pqVS9uzBL9UxBKRmd2tJWpuOpEDSKzS+UPvLvvLPwSFBkGzKWDhHKDWIcLUa4KSWgF11moVpsT4whVqmt2zZhsZUcZmqmNfUdsFj+eUWKKxHBju6SWcT6kph6ZSUMj5l0jASlgMuRFzryY1dL/WuguIKAXAl1CqDUpxR5ZJzZEyRTkWqVn2x1onzNjNeBK/WIhEy3rsKcqRy7U1YJ6lJ8jbirCfpa7lcS0XWm3PVWn4IDrRcmVWQa8WkRVhIrY6okrDzMSqffU/UYC5oRoqB7dQ5Qqk6/WjVX7DqRhRPcvadGtXQ7p/gTbM8azGQZmWKqK7H8EK8th6VjX2UNc6Rs5DzyqhtpZA0o8zph4STYEA+twsugU5x0eMJeNeiLjNf9Cznl1A9IKeRVFYoC0QgSGuR3/IQVY+UBk+DSIu4xsBnFs1W7EiCkhA3gC7JY9XxlICUiMRYHV1BJSFuxEuyGQ85MI6OlHvckIGElgYIVrFy2DqYFWSgnU4p6khpwI8GBrTJmYESHJIDJTly0xJCQX3CrQoxC1IGSlbG1UDK+4yrzLjaYbqdKZOOpmkgBPCBSS7Iqic1xbA4JAoFcQ0uZXIa0XFgGAb6sQ6aSYWC4nxDI7uoLFkVT3aBLA2qDtXRgh5paJoJ7cQTI8QIPhecJhsGlhzOn6QJ2WjUWlkXDpSCiw6RxNDvMaxWqI6sxgbo0CiU3rNaCG4QmhA5s5vomsx0J7EaEimv0ElHGrdZHS1pu4DIKXZ2lRgCy6MDWneEa4SkK/y0Y9qcYSeuKBywv0j0iwVLX3BNgJTw9IQT7afwybjarqlVrJQ1QM8cnC+F3hnADlwVVvGI2IKfqGkggBaCGvVrEMv4GnUMUsjY6FXgSh+39ncFSGTj1rMWb6nOWxwl19qC1uOSGlwUNQ44FqQEZ5mu1IxdfB1i48x5G+1bGJwSi2W0KkJQy0DtHLTKu5qTc9mAiV5N596hEIQ4aYhjR9bCqD3qipUGh4FxaTUDqfPhrehhFYkinlGK0Q7VgbMFx6173eLN+2rGBZOh9Zgynoqn1PaAECqwstIjncC6YlHnGSBKo54k5nSlmGKiQHWmUoVuKlCvth9UOP5ujEJog358Ha1rNRPIXo/pmo3aeSmOqK5y9m1bjoGd9ZrqesFcV1cMwyFeiLkOS6q6DEY5dGgVdtrYtWuiAzF6KM6kaaWgOjKuEloE7x1JMhL3UEmU4gGPc8kQ7BGm08gwbDH2BRd7pAgpdQgZcYZ0F2fAWe8jMTQ4N8HFxjJeL9WB2fhlcQpktEwAu+e11Js8gRPD2xSfDHEiCQmDBS5FKdmhBZxEguvwocW5hPge9RnnmjrcarCA3PU4ydgYDLu7c2nI0lCaQAwB7xxx7PGhpx8yZdXUoLYYcDeP5H5JCgWnLSm3uDglxgYlkjLo6FDvwY31cz2aYQRyEfriWBUY04jmYgmWK7gAGgJFJxTf4lOBkhhSAqLRjF0kBqGJ2WiAbqx+wePExMpUlhRGAgpq8tklO/LgUUaW8xWLowNy6cl5i9hcIi+OSOMUXzpCCAR3gnDiTnbahhg9Q3+enAeQxNHlyDguScuRZnaCZrLLxMFisoP4wmqxRJZHiDqm2yfZPX0dTnrcYsX8wiXKcJ7L55Yktiga0WGjtPeEJmtFPNaZr9QI2ErCWqoam1AFdBSPjXE1ppj1/dcL/GhoO1PKq+t1rnKqRdZSuyZMuwaUOYrx9ylk8bboIzXLr8wBpZZ/62fXTHetcBekqtiVKyAxUUPCN5UKiBrgPK21sp0eq9Q57HxFavCgBmaUYrK2JTQ0k1nlxXty36MIuQirYWTwQhexEbTO26KBIdZ9La9bk7uCG+vZr69JqdWTNT1NnLVBUOvNu2KtkWKyfTWCd8cBlNZ+/PofNOOxSoPJEWsVy7F2yGjSeTi177OGNuvoD1eHiFiuk2mqqkIWuz+Ox5XWuEmOvy+rPvhaeZHaCoKqx1B7/H7dTnGuBnL12qszkKfTqgC4sWvVUkk0YSD4Qs4JzfVmLBN7vkVBC2MaSWVkHLMpU6oB21SF4B2hdYyuY0ggpScEG0krPuO8VYPEFXxIBDfgnK9xZWM/q5AL5LxuD3mQiSlKqqeorwFvIZe13HUhOg8ooglPpsiI5oacBsOrtEoIHhcKqhPSOCUEqboixrBJoyNWCWqRBic75OwYSyRIi4sNwXsokZUr+EUkaUupo6JLTaCcrGDsQSYohVJFtRIOjZaA5FTXB+cIYtXZEoMJpUkmUCgqKD2FYhReV/AuEyaJJilDP5DGTKHFxa5WUmx/jwkGqQ9o6GzmCQADQ16RyxGqI0gDGizQKsqYliwWA8u5gvOUNCelA3LZwXula7DJpPEmup1tZtMGly8yXz6Aw5H6JciDaJlRnCe6kQjELtDqkl5OsKRDxXFw/mGa4UF2mruI23eB22Jn0qDlHNoviW5OSg2arh03e+0cCZXGhSGw18C4pq76ufLsiyhRrcesVWBmbUkMtCdqTsL49lV/XYRYp6pJseyy+MrfV6PjpQrwc9hc+R6lrUp+AUANBGiz5dcKWRhuQCCqZe42zc/42x7qZDrLhDWZ05ViE+lUC8nZHHmVynFnPVVOyCpocAxaKtrXdJ1919bMXei1IMnAdiQD8o2LgZWPtM5oeYixG0p983XAFDHQY1ajGhrSPuBU6JziXR01LCaM05QraHdfr39BDEhZgwBXHX2qAYsXb71wrQA4J3h7UxRqm4A6qdCG/CDFSuxY0LUmW3rEenVIfU8LXMJaLAixkaJiFRpUK26gzhbQtX6Ar20ewVXgnipG21NlXCuf1ePa2LVtuQgpD1bqTpmcsDK39BbSp3qvqSfnhpQHG5STtYKAQUrCSzbn0kzRMVSKawaX8b7gfRXFAitF5zqbQbABNxUv451QtAWigUSxsjhSFSlLpiTqXI+MBI9zmFP0CV8SOUxIaYFqom0dTevw3gMRbbo6LVopOlC09tKbSk0tHtVAKS0hN2QaU89EcCQkFHDK0Hak2Bl8SGpQK+AZsZw9mbiYdxAbXAwmdCYGarVnI1s7VgPeB0KAUApeFqRxtDWzSl5rUZyD4BUaTxOnTNyMJNFAeCXb9EsCTgIqAXUe7zKSl/bdjiMpJXJKFpgLeGkYsyH8BU9stxBJlDDUkn+Lc0LTjLiuo5ndzNb2Gbb8guFozmQyIW47SvGUkk3inQ6XIl4u4cMWuXEMRwdMiuNodpbtFGjkQZaLAzTfx3ac0p1uyHqaxZgQ5xhXShk3wjtPbHL8P9bqarlyqI0rbY56rLQqwZvDrDAPKevXqYI8hewMOueKZf2icuxgpPK7c7G8Gr3SGwZHU7crdSKbZcNS2QS1HFydO1TAH1VatnYbxmNaoYEBi6+6/Ij19AGpU9tEleTkCp+eGlxooa1YAE8NDmIgSYRccFVhqy+FrJkwQJaRtOqNeugbCFVyWOT4OhuvtioIeqnaB1VqR+yzEplGDIWasYc1SSaqlfKKFopYqdJTy5lYgCNWMjGgIlo/v47NxRgRa8edAWrFxNXzxkulFYLVXoyG55xQstbvWExTQKztUSqaf03PdCLHgEOPqwFNXfxri2YNqEQq11jWBBGj7QUq42Fj17AFSslVtCvYGOusJLX1I3vLOI0KZhwQ49OBaF0GJaGuID7inTkaTQU0WZUoFHPKYmOaSymktL4xUg1fzVl41xB8a/RTFw1XEAI+mGhPTsVob6o4pwRnoFYngCak9Djf44NAqeN6I8cMFScBkQZVIZdCzgXvBdRXTQ2Plh1ycbjgbR4JSsmFXARoCVHtfCu+yTurxpk0ecF7T4gtMTQEX89BHM6H43aEE8iS6pCzaPgFSUhMJkeugSKFXEZK0Vot8IgEmsbjnOK9J2WrJOSUyZoRcYTQ4EID4iiaUMkMOSBEvJui0tfnN1fGQyGGXaKH1A1IBV4rDucjjkjsItOd0zSzGbFR3DyjumI6CVVGuDVmhS+otsRugnOJrMKQW5xTcCt2/SXaMxMafz3kQGwbJrMpxOvIvqEp0SSSV0t0GD8lT8QT2TXl8LOoLeq6Lq2vZ5nXh0GFZXX8oZZhi7PSf9JCpErb1pvbHBlIsj6wYNn9uhTt1AByYOU3mwdvzi4DEaOclHKlXK8qjIDDUzQTyxXwYHKgui6PAVXpr1Tnsw6gs8NQ61TtbMOVVTqiq3PgC1HtOINY9m+AeeUoKE1xdCFCZ4pWuSRisuAFLZScyauB3hufuC3GHjBnbLRGhxCy1JKnIeu1Xj0nwlDL67kGHTgTR2rEG/gOJdYBH0XqcJvKb18Po7GBN4ZjKLXMbgI9dXFzQDHaXv23sjTW2v+KSKGIIxRXWzIWUKzBh6G2eca6CIsITQXuqVDvoSqj7ECK0fPCunQvzsCZVcvZCTRq33koWif1bexaNnGBdaAuTpBgZXyXazWv4k2EgM15SHXWQmO4FUBcrEhvj4pDxahdWkZzKlorgM7hfKl9byEnB+rqymSBvaznTeDxvqnOyxGj1dTGIOSUayepIGqYESfWDNNiKoEl2+FZR6JYpiyAC3gXUWnANfW+LpRM1eOPeDqgPgO5kMbEMCZwLcU5SphBHm18NAWHze3QNYPGN/jQEF1H8LGKnBiQ13l/RTzMRwvQRyAVIONkxHsTMlkzgJw4EA+0dvze46Tg3EiQTCrJpm7GKeKDBRveKi9aIBGgzIBADAMpLii6ZBgSPjhCnFklr65kqs4cfrHaoCOY5LB6JmWPYTXSr1JVW93Fi2e61ZjiokCRCH5Czi0lKdEngiukNMGLo2u2aEM0ts/kerLrDMDZeoLbtnklxeGba6cdeE05/MdmxkN1FGuoFtXpxjWSvTrgjJXk1zr7OJtLLThiKTQD5FJQb3PrWwfrYTuiNuzBggfL9EypzsrgYyk12KjOsD7sFAMCRvF1Kht4VUOHs249m1PxlSaIrKNhJRY7s+zqZxWTpVGURCYUmzo31ixbdP0zta9fgxfnaWNDnk7pAVmuKCkb0yErIyNlWfcRoXFUYKNtksRAgFpxBMfyu7Lux0vttVtqHp1jVD3ufcd1Zq1yTEn0NWtyWo6dvVAXSWyOfaiZAsXK9qH27Esp1gME1kR6qQyGIiC+0IgwilRxIvtMo0AqvmBCJyKMXAFLTtSjNXCkGGrYKIcVNHl8PQ1fEQyEYJF+seNLXDsP7cYebzF2FDKUgpQq0gS1dmO4m5SrQI4kc7Cl1MDR1apPpfiqWp/XN2SJ5DxQSIh4Y7OIVbrQgo/OEOTqalurMmDE2kX2zI5VXbMhoIg3AGAWfyxWZcvSiGpCsaqbSGPVPxVyhjzW9cVZdS0XrEzfdHgxKltO5uh8THg/GgYlR8robRy0c+QS8N7hY8swjqyTH/t8o9sGMQ1+QoOPLSGagw4hEEJ11lWrAqrGSCg4KWh2lNIQZGAkkUupz2rd5rgKonhJKEty/Vzvd/HtFImR6AXHaJlCFlwQo1aWCTkNjENDP9j477YJIJMK4jYic6HUlkMwKuJYwAUGVVj05LyiiNBOdomhJcaAbz1IIhUQH8F3pLE1YLcfEUbEe2h28NMtAh34hrbdAckEbOBWwcSeQlCi7z75D8THsGvK4acK2hJMPCZrnVIHsM4+BbKvmbpCLFYOL1Js3GkVeREUL44sxUp3AqH2nNflfNaOBq6MwMWcd6kiO04MqGexu+EGDNVat6/laGPL2IAXqW7Ou9rXpgJztZa54Rj1nUup2eOV8btqjeNKdbO+c3BV6a5qElB7zThhGlpKU+hzsuMDXLGeteaC9iMqzqbBBW/qg2vZoHXbTgvq6zCd4o6rCVAqVx3Ixlu3bN1AfBkLRAI26Y76bR0rHq5xEcUAf+ugKhRbEK1/QP1u7b3X5XYndR81GF+WbBWgLPUcMgFHplKhZC13rMcVICo+oxZLjh07FXm/HpqDA1+rIwgUb8AqHGSy6elv7Jq1EBqyJjLJxmWrBXSiJg6NqmnXl4RzI4INi7K70UrI3kdDipQMUtXgXIOIBRMWuILzBjcVBwGPCw0JR1FnGh7rGw0smKDgtCDFJnI6LTh1Ve/L1gtPAR1QTXX6pT3bRRpGiaj35GRrooGXCyEJsfFV4dOZA27WKZJDNUFRNA+WbVLwklEc3gXEVRErtXWiqL0mTmh8wMeI+GBc/BjNyXtPqD9bxdOeGclgzVUTz3E0KEqMahLGxf60BS1blQUQBmtdMsH5Fh87XJzgg9EctThEmjrbIz9GEtkzNoFm6BhHj0o4rkCoBpz3IAWtfiQNI0NKVAk1SD1+jDQCIXhC9LgQKK6xYWUxIDHacuAKIdj3LxKJsSFMJ4QIMlr1wxfFkcjZaIGpXxj2oJlaufAasWvK4eOd9eCLXnGCIiTluKydixKyVPEVU1Az8VXrH1Odfc0P6wO2jqArGLsGBuKMrqfFsvc1/16qExDM8SdVGrX56yioAXPx1WlpBbsVqfPWa1/aLu5a9a0yAZwh9lWrJgBGBQzOWSmqBjkF6uCg+lnqSFLV/Kj9eLVhNNoIjTaQOjQrpSRKLYPnUvBjInnBBU9xHpw3HYHas1/rHIg4gloGrM56g433dq2gZsm1beKqZG/mGCUvyLGUr5X5qY64AhRr9kTNqPMaTnDcUpDK45cqhFTZD5WlEDWgZc2thzW/TjDOf3BXaHyNCqnOA85iKoBKBRnV58+rKf8llETtz9aKijhByrrJYTMONnbtWnAYGLUG8Zate1zwx/iLkqy3jUYQ05eEfOybXQ0C8BVTQ8GLxwePVmllG/pk96QixkF3LTEYzkVrew7sF6eClkIi4UtGcrbAXVwtcdtKVYqV06nBgaHvPeo8LrREWsYUSEUtAM3FnLl6cnJGqcWBd/bgaEByQHNC82Bs22JVCOe8Va9EGIrJf2uxdMc5R/CeLkRzmt7VqZw2vluaiIve2gZ2QSy5GAeGrBSx6oHhZTMuW4tDNaLJ1iZkRMRGY0FAPAQ3IbgqU7weFe48qtZmCZpruyOTxoHkA00slC6Tizl8BYokk+p2DYKBkces5CbRqa0vpYykHmOBoTTR40ODhEhxnqJCaFuKE4beZJBL9jiFNnqa2BKCo5TBxiiXREkrch5Z9gPLxUXDKjVbxMYZg+kasWvK4btS++5SqVjY6FLr7VcAnHOMCC0WwY3V2Vq+Z9l20HWGaipy1P1zdf7Ve+Ewp3rF8dQeMob+zhRTlkNqr9+Afb5YVpsq33atMKf1aRdx+GPKnpXMC1LBanJcogZIYq2voMpaijZXlUGfLMvNTi2/UPvMNRDOuPAW0fsm4FOHS5bl+2zn4NShGcqYGZxRfIwPb2X148WJyoXHbopcsxcq2NGtaYhSVQfV5hpYQUDpig38sfdQcjFMhNTUej2pz6vRJa0WUBeDSrt0NQBaVzdEK4vAO9Ja2U/qWOTaT5VS7PuT2gWoa+i68lCOg78r4Mv0mNekVhJafA1+qiLgGnxYRZnWlZmNXZtWtFTH4kzIShyUOsLZECPgaxswG8gNpTJtbMjVqGKofG+gvJoLVtosgIFTVU05U5xAiODNOa4xMrouWWH3uGSbCaFlYMwDpaRKj7UgwvrNlkk4xWh2lUnkvKlaiow2Vc4FEp5iSOGKh7EhV9al9nV+ZEGBXFJ9RmvbQWxwl3NrJxSOn5F16y14T/TW75a186XuF4PJeD+mnK8pUZLUpErIlXHk3LpiEHChkJKSS6pHuwZje1xwOGksoKjHJj7Y+oFheEId3U0N7LVpqyLqSMoOi388RZKtH1qD/QKNdxQNFDxJlXFckYqgrsV5aIIlCuo8ODs/1mDuEEiq5CrNra4yulKPpgEvSipCyiN5zOi4RCSRxSE6EkKmu3Zo+NeWw1+j2tZ69QmjrUVcDQIcbYGlWy/VciV6r851vcivgXTrbHrNt65ulXVBSY5XcqmIT1jzboJ14LCo2xaVvBZywbJXsyvHgxg7wJYZ28atgW1YVEkxx7MuM8e1GpyuZWDr/PdKmW+pdDq18x1Ej6f/KSYYg4PYRigdvUAeEj5btSDVjDikRBJP70YaibXPaU553avXihFwlX9eKjYBdSQKWWxGAZgMroHhSg1gtL5uJXHqYqRqC5Mr62E31kpwNcJeX4e15r5Qqqpffb1Ye0acyepKroqFFEPmC0QqZ16kBnHWeqnyKgwCoQi9K3bcYiCnus5aQFgrNK7iOcQJIkKibDz+NW4qEZVi0rZkm4vg1xGgZfmlNsxNirVGiXZzkupibmVuc97UaqPYg1y9vwHqVAoSzDGJ94TgLbiH2tVag4RrO6EkCp6SDQ+UZaCKgNfWYG0z4SyzrZm/L+64+rhmmJi6J8dJjcnNWqRbUKTUSVYKIgHn7Giy8ybg5aw1YYI2V6C6Qm2reZvi6UOwKYDeQggvdm2C84j3VclUa9XNggkkW7CsVqFwVfdDyYjUJE461sPIRALeR4IPlsHXqiC1AuIkW1CuV0DTrjKvQrBmiPMWaBQcqg2W4QyghZRhpFQKbjG6tsvE6JAmgq+Jh5ZKAYxWLS7GWig+4TVUmncxHIQWSGPVDFFSzuQ8Yq2hTOMCRR2TEOh0ROd7n/Tn4WPZNeXwrV+uJLEbWA1We0z1KghJLABYa96XOr7Uc6UffaVPXHvS676yXin41yGWFvurBQJO1hU2W+jHkm1/sSl6rgLwilv3t+2B87LW1+O4J72m1FnIb6XuLKYa4MUenlL0quDEnkzBl4zDqhUNV+R1qWp+sSitOnpnN9zxBDwP0jSQlZRhpaOJ9qgio1GABkn43lc+seEM1udlGIjKq5d1W6Wui2IDeIyxYIuJ1kXHUYfeVBljVci5EHywrLqYLG/SzFgXTGf8o7p/rVzYCmlAvFJMDKWyK7TUzAdYc+dxVEaD1llLFiiYAmK9E8Qkcz228DSsR+ZWnQQr5xwzKdbI/nWpUhQbvnLtVOU29gTmXIuXZIhzVciWDa5DUFedpGJOX8xnsJ6uaI7Gnvvjap9aG4taJV8nAvZIV6cjdg/5VFBnDIF1q8mYOdaqS9UhFZqqgzHWSoBW3YnqcsXcijh7JrPa9D1cg3hT6RChspawAKQe01XHr/b8iJji55qVA1atEwfqHa5WUaVyZl0d2b3u1xvvv1YTnbtS8VKtz5RRp9WZjDfZBLTsSkmtaFppTvw6oF4H+oJ4jzhfr1tNIOSx34lf11jt+xS79mvgMgjB23kWyjG+oaga60YrRkPVVP9U8VpsraznY+9itMk1QNL7Brw5/STQp57SL+0YnM1liDFgjKi+tmNqw9gFm0mihdViQdFrZ9LmNeXw18p1TqTqRNdeL+YUvNM6SrU6eWc8fHukrGyuUsVdEJKnIueluvb1ZLgaya4BeGDlnJrZhcrzX/O10doOyNYLFl954zXTlvpgUUtzolrHONYydY1OEStNeyv0QT0WA+bZFDvEyvxGD7zSNnBr74plKsN6sVqXy6nlyBgJxZaAblUYKORs0sGqSkkZ1Z5BCnQdOULn41rt00CSRemlVGyELWRrMJy6mp1zpUwekCuIeda0uYp5yDUAEyFU9W0bbCTG9Vf7jp3D2hi1aoFoHcJjJlJYq/dlV2wiH4ZtcJjanpVGC6LrK1MDvFrmV2wBWFM2pYoIyRrpX6qTFwvqpAKaqPfAxq5dk9gQilDyYKX9WmJTLUBCNfAYzo89dHW9qLmmZb+P6bdKrUwB9fmV496fAmrRr93fRSFbQO/E4f2VjLvUTxXEQGWYY1E1EJurbIHinDmz6rhxVwZfeefwsTrHGhzrejvWGbUlJZRCqfe2unU9s7J8EGTd55e1o9Xj+SJrDMDaERbjGNpauAYqa/0vm16+UWErCFKSVSdqpm71UVBvzzsYG0tq+1acHdU63kfWIO0avIiv55ih9FcqJrLG74TaorRnWxnNaatnLAZQFPT4WtvkU6ksDF+rQOYdxDkEb4GLqLV6gLEI45jok2n3B+eqbLndb1qKiTORkWCtx5ShJIf3oLJB6T+hrYEwWr2s1kV6/ZBKsQhqFEOOq5aqtlbV65Qqp4sJShRIFRm+hogUtRuf9U0igor18xSOWwKFYjx8zHGrM3jP6GxE69p8peGIliuLiVQAYXVi63KSVCdT5IojsdqA6Q1QMQZBKwq9otOlyuNS8QxePEotbzurHkhVlXPi0DYyaoYScEOiJZBrJcR63oKOmdEZFznLFRCOVDrd+sETPEnqg19725YJy3HYIuvj0irJK1cWSxGIatUMpE6nq9sgRqts1KJ+rSXWgikkelFTVMQU/oZaOfFqJcscjCd/BZghVVjJFmFD7dv3mtEaTJgjP8YQSO3V1fdeBywlV962WruolII1KzZ2LVopFdkuYos2a/9s9b6iyQR2arXQ/qsBe8305EqJ7zH5o5nU+1aqM1u7cCPMW2lI1VpgWQpJnTnWun5pdaSumAS25IBgKHrnaljr1tiaei66DmJrz1u9jbSWAN4cMDXhgIoXQFEptR1KDZLXYtWmBOi9r4GHPahW6atrobuC61GtzrxeG3sGLNApUlul9QqVmuE6cWRXjgXQrJdeLEnTCuTVdQCu5JwrCBJCcFU8y667zScwx2r7RFvDc11rnRhYcV0Fzna+uQZWhuNcjza3L91ElxzOhRqUmXQXWGtEVVHfWnugFmvUKaFpLDgRj/ehCjCtdRRqZ6hUqGcdsem8ASSzXjvtwGvK4cdsADUqeMZKQXos2iDY2p5k3Z9XgnrLPGsmvObBOjVJW6NpWCAxSl7X4OuDfyWYcFA18M0JSBEbwlPRqx4xMY51NO0c6+ExDruQSZQR8OvmuzNgXNY6KUu19pzLcUa8LhI6TKo2UKPyuoAMXmjKWuGuct6hZscWLY/OlhWpUXjA0fqGRTROvogQamQsqCH5RRlGm+XtnVGSfL2mZd1yqOcmUKsltq1TA0taAFMqRZBjEKGh64sB9ESRbOVFy0RsEl5+DK7ChG1sFdCadR3XQHQ9ptZSNtV0HLD5Sq0bEaJa5WONkahLZW3R2jUvtfJgWV/97soaMAVZ1fp91P6/wlByLUFu7Jq2cqUXjThKMa18qRUpFY+WcpxQrDP340BR1JzoY97yONPW9VYghVqhsn2zs1adCV4BcuVOWSs2qkjl2tf5n+qRImjxVdrbytC2ZAhas0WrQCagAm9FcL6gviDa1HqXBStO64TQZFx+V9H3Tq+0AKSW5W2Snp2RjQnHrpOz87HLWa5cIzW5bnU25XNtV8JsZ6yBx1QZrPqhFBKQ1n0QjFFzBbScsRae8za4SuvnixhgULwdq2SHZl97K6VGc65K/NrzLm5ENOKKyQs7KRRnmimJZMDnXHDrGSMCRUyCWHU9ldVWPDuMYMFXLES6iuXwRBG82GjgTMH7QCnWitGcQbIJONXPcO7aGa0teuXO39jGNraxjW1sY39EbQNF2tjGNraxjW3sM8A2Dn9jG9vYxja2sc8A2zj8jW1sYxvb2MY+A2zj8De2sY1tbGMb+wywjcPf2MY2trGNbewzwDYOf2Mb29jGNraxzwDbOPyNbWxjG9vYxj4DbOPwN7axjW1sYxv7DLCNw9/Yxja2sY1t7DPANg5/Yxvb2MY2trHPANs4/I1tbGMb29jGPgNs4/A3trGNbWxjG/sMsE87h/9Lv/RLiAi/9Eu/9Kk+lI1dQ/apui9e+cpXcvvtt39SP3NjG/uDst/vc/OlX/qlPPe5z/2DPaiN/aHZp53Dv9ZssVjw2te+dhOAPIF913d9F//xP/7HT/VhbGxjG9vYxrAx7hv7fdhiseB1r3sdYNHuxq7Yd33Xd/HSl76Ur/mar/lD/6wv+ZIvYblc0jTNH/pnPdZ+5Ed+hFLKJ/UzN7axjW3sE7FPWoZfSmG1Wn2yPm5jn2HmnKPrOpz75BatYoy0bft7bpNSYhiGT9IRbewzwTbr6cY+EXtKq+NrX/taRIT3v//9vOxlL2NnZ4fTp0/zt//2337czScivOY1r+EnfuIneM5znkPbtvz8z/88AA8++CBf//Vfz/XXX0/btjznOc/hX//rf/24z3vggQf4mq/5GmazGddddx1/9+/+Xfq+f9x2i8WC97///Vy4cOHjnsOv/Mqv8Bf/4l/kaU97Gm3bcuutt/J3/+7fZblcXrXdK1/5Sra2tnjwwQf5mq/5Gra2tjh79izf8i3fQs4ZgHvvvZezZ88C8LrXvQ4RQUR47Wtf+6Su50fbj//4j/OCF7yAyWTCqVOnePnLX879999/1Tbrntn//b//lxe+8IVMp1PuuusufvqnfxqAX/7lX+bzPu/zmEwmPOtZz+Kd73znVfuvv8MPfOADfO3Xfi27u7ucPXuWb//2b0dVuf/++/nqr/5qdnZ2uOGGG/i+7/u+xx1n3/d8x3d8B3fdddfxNfzWb/3Wq74bEWE+n/NjP/Zjx9flla985VO+Jrfffjtf+ZVfyS/8wi/w/Oc/n67ruPvuu/mZn/mZq7Z7ol7kBz/4QV7ykpdwww030HUdt9xyCy9/+cvZ39+/6jhf85rX8FM/9VPcfffdTCYTPv/zP5/3vve9ALz1rW/lrrvuous6vvRLv5R77733qs/96B7+vffei4jwT//pP+VNb3oTd955J23b8lu/9VsAvP/97+elL30pp06dous6PvdzP5f//J//81O+Lhv79Lc/Cuvp2n7913+dL/iCL2AymXDHHXfwQz/0Q4/b5smsG2t7Mmvhxj4B06dg3/Ed36GA3nPPPfpVX/VV+uY3v1m/9mu/VgH9q3/1r161LaCf9VmfpWfPntXXve51+gM/8AP67ne/Wx955BG95ZZb9NZbb9Xv/M7v1B/8wR/UF7/4xQroP/tn/+x4/8Vioc985jO16zr91m/9Vn3Tm96kL3jBC/R5z3ueAvqud73reNt3vetdCuh3fMd3fNxz+KZv+ib9iq/4Cv2u7/oufetb36qvetWr1HuvL33pS6/a7hWveIV2XafPec5z9Ou//uv1B3/wB/UlL3mJAvqWt7xFVVWPjo70B3/wBxXQP//n/7y+7W1v07e97W36nve856lcVlVVff3rX68ion/pL/0lfctb3qKve93r9MyZM3r77bfr5cuXj7d74QtfqDfddJPeeuut+vf//t/X7//+79e7775bvff67//9v9cbbrhBX/va1+qb3vQmvfnmm3V3d1cPDg6O919/h89//vP1L//lv6xvectb9M/9uT+ngL7xjW/UZz3rWfo3/+bf1Le85S36hV/4hQroL//yLx/vn3PWP/2n/7ROp1P9O3/n7+hb3/pWfc1rXqMhBP3qr/7q4+3e9ra3adu2+sVf/MXH1+VXf/VXn/J1ue222/SZz3ymnjhxQv/hP/yH+sY3vlHvuecedc7pL/zCLxxvt74H1vdF3/d6xx136E033aSvf/3r9V/+y3+pr3vd6/SP/bE/pvfee+/xfoA+73nP01tvvVXf8IY36Bve8Abd3d3Vpz3tafrmN79Z7777bv2+7/s+/bZv+zZtmka/7Mu+7Krje8UrXqG33Xbb8e8f/vCHFdC7775bn/70p+sb3vAG/Wf/7J/pfffdp+973/t0d3dX7777bv2e7/keffOb36xf8iVfoiKiP/MzP/OUr83GPr3tj8J6ul6PrrvuOn3Na16j/+Jf/Av9oi/6IgX0X/2rf3W83ZNdN1Sf/Fq4sadun5DDf/GLX3zV63/rb/0tBa5ydIA65/Q3f/M3r9r2Va96ld5444164cKFq15/+ctfrru7u7pYLFRV9U1vepMC+pM/+ZPH28znc73rrrt+Xzfo+v0fa9/93d+tIqL33Xff8WuveMUrFNDv/M7vvGrbz/mcz9EXvOAFx7+fP3/+SX/2x7J7771Xvff6T/7JP7nq9fe+970aQrjq9Re+8IUK6L/7d//u+LX3v//9x9f7f/yP/3H8+jve8Q4F9Ed/9EePX1t/h9/wDd9w/FpKSW+55RYVEX3DG95w/Prly5d1MpnoK17xiuPX3va2t6lzTn/lV37lqmP9oR/6IQX0v//3/3782mw2u2rfT8Ruu+02BfTtb3/78Wv7+/t644036ud8zuccv/bRDv/d7363AvpTP/VTv+f7A9q2rX74wx8+fu2tb32rAnrDDTdcFSz9o3/0jxS4atuP5fB3dnb03LlzV33Wi170Ir3nnnt0tVodv1ZK0S/4gi/QZzzjGU/mcmzsj5D9UVhP1+vR933f9x2/1ve9Pv/5z9frrrtOh2FQ1Se/bjyVtXBjT90+oYbnN37jN171+zd90zcB8HM/93NXvf7CF76Qu++++/h3VeXtb387X/VVX4WqcuHCheP/vvzLv5z9/X1+4zd+4/i9brzxRl760pce7z+dTvmGb/iGxx3Pl37pl6KqT6qUPplMjn+ez+dcuHCBL/iCL0BVefe73/247f/G3/gbV/3+xV/8xfzu7/7ux/2cp2I/8zM/QymFl73sZVddkxtuuIFnPOMZvOtd77pq+62tLV7+8pcf//6sZz2LEydO8Fmf9Vl83ud93vHr65+f6Hj/2l/7a8c/e+/53M/9XFSVV73qVcevnzhxgmc961lX7f9TP/VTfNZnfRbPfvazrzrWP/En/gTA4471D8Juuukm/vyf//PHv+/s7PB1X/d1vPvd7+aRRx55wn12d3cBeMc73sFisfg93/9FL3rRVWX59XV7yUtewvb29uNefzLf/0te8pLjdg/ApUuX+MVf/EVe9rKXcXh4eHzdLl68yJd/+ZfzwQ9+kAcffPDjvu/G/ujZp/N6ChBC4K//9b9+/HvTNPz1v/7XOXfuHL/+678OPPl146muhRt7avYJofSf8YxnXPX7nXfeiXPucf3NO+6446rfz58/z97eHj/8wz/MD//wDz/he587dw6A++67j7vuugsRuervn/WsZ30ih3xsH/nIR/jH//gf85//83/m8uXLV/3dY3u7AF3XXbVoA5w8efJx+/1+7YMf/CCq+rjrurYY41W/33LLLY+7Lru7u9x6662Pew14wuN92tOe9rhtu67jzJkzj3v94sWLVx3r//t//+9x12Vt6+/vD9Ke6D545jOfCVjP/IYbbnjcPnfccQd/7+/9Pd74xjfyEz/xE3zxF38xL37xi49xC4+1J7oWwFO6nk/0+Y+1D33oQ6gq3/7t3863f/u3P+E+586d4+abb/64772xP1r26byeggXks9nsqtce+3z+8T/+x5/0uvFU18KNPTX7A6HlffRNtLbHZtPAMX3pa7/2a3nFK17xhPs873nP+4M4pCe0nDN/6k/9KS5dusQ/+Af/gGc/+9nMZjMefPBBXvnKVz6OXuW9/0M7lsdaKQUR4b/+1//6hJ+5tbX1pI7rY72uqk9q2yezfymFe+65hze+8Y1PuO1HO8lPpX3f930fr3zlK/lP/+k/8Qu/8At88zd/M9/93d/N//gf/4NbbrnleLs/iOv50fax7v1v+ZZv4cu//MufcJ+77rrr477vxv7o26fLevpU7MmuG091LdzYU7NPyOF/8IMfvCra/NCHPkQp5eMqjp09e5bt7W1yzvzJP/knf89tb7vtNt73vvehqlc9AL/927/9iRwyAO9973v5wAc+wI/92I/xdV/3dcev/7f/9t8+4ff8WA/nU7E777wTVeWOO+44joyvVbvzzjt5z3vew4te9KKPe+5/ENcGrmTHj32/D3zgAwAf95675557uOeee/i2b/s2fvVXf5Uv/MIv5Id+6Id4/etf/wdybE/Wnv70pwOWoXy8e39jn1n26bqeru2hhx5iPp9fleV/9PP5ZNeNT6e18NPRPqEe/g/8wA9c9fv3f//3A/Bn/+yf/T33897zkpe8hLe//e28733ve9zfnz9//vjnr/iKr+Chhx46ppuB0UWeqHT1ZGkk64jxsRmaqvLP//k//z33+71sOp0CsLe39wm/x1/4C38B7z2ve93rHpc9qupVJfVPtb3sZS/jwQcf5Ed+5Ece93fL5ZL5fH78+2w2+31dl7U99NBD/OzP/uzx7wcHB/zbf/tvef7zn/+E5fz1Nimlq1675557cM49IQ3oD9uuu+46vvRLv5S3vvWtPPzww4/7+8fe+xv7zLJP1/V0bSkl3vrWtx7/PgwDb33rWzl79iwveMELgCe/bnw6rYWfjvYJZfgf/vCHefGLX8yf+TN/hl/7tV/jx3/8x/krf+Wv8Nmf/dkfd983vOENvOtd7+LzPu/zePWrX83dd9/NpUuX+I3f+A3e+c53cunSJQBe/epX8+Y3v5mv+7qv49d//de58cYbedvb3nbsYB9r//N//k++7Mu+jO/4ju/4PYEmz372s7nzzjv5lm/5Fh588EF2dnZ4+9vf/vvqyU8mE+6++27+w3/4Dzzzmc/k1KlTPPe5z+W5z30u9957L3fccQeveMUr+Df/5t98zPe48847ef3rX88/+kf/iHvvvZev+ZqvYXt7mw9/+MP87M/+LN/wDd/At3zLt3zCx/gHaX/1r/5VfvInf5K/8Tf+Bu9617v4wi/8QnLOvP/97+cnf/Inecc73sHnfu7nAvCCF7yAd77znbzxjW/kpptu4o477jgGvokIL3zhC5+UJPEzn/lMXvWqV/G//tf/4vrrr+df/+t/zaOPPsqP/uiPfsx9fvEXf5HXvOY1/MW/+Bd55jOfSUqJt73tbceL5KfCfuAHfoAv+qIv4p577uHVr341T3/603n00Uf5tV/7NR544AHe8573fEqOa2OfWvt0XU/XdtNNN/E93/M93HvvvTzzmc/kP/yH/8D/+T//hx/+4R8+7rk/2XXj02kt/LS0pwLpX9NIfuu3fktf+tKX6vb2tp48eVJf85rX6HK5vGpbQL/xG7/xCd/n0Ucf1W/8xm/UW2+9VWOMesMNN+iLXvQi/eEf/uGrtrvvvvv0xS9+sU6nUz1z5oz+7b/9t/Xnf/7nf180kt/6rd/SP/kn/6RubW3pmTNn9NWvfrW+5z3veRx97RWveIXOZrOPeQ0ea7/6q7+qL3jBC7RpmquO473vfa8C+g//4T/8uMelqvr2t79dv+iLvkhns5nOZjN99rOfrd/4jd+ov/3bv328zQtf+EJ9znOe87h9b7vtNv1zf+7PPe71j/4e1sd//vz5q7b7WOf7RJ83DIN+z/d8jz7nOc/Rtm315MmT+oIXvEBf97rX6f7+/vF273//+/VLvuRLdDKZKHBM0Ts8PFRAX/7yl3/ca7I+r3e84x36vOc9T9u21Wc/+9mPo9t9NC3vd3/3d/Xrv/7r9c4779Su6/TUqVP6ZV/2ZfrOd77z97w+qleodd/7vd/7hJ/x2M/+WLS8j953bb/zO7+jX/d1X6c33HCDxhj15ptv1q/8yq/Un/7pn/6412Jjf7Tsj8J6ul4f/vf//t/6+Z//+dp1nd5222365je/+XHbPtl1Q/XJrYUbe+omqk8CgVTtta99La973es4f/7849DcG3u8veUtb+Fbv/Vb+Z3f+R2uv/76T/XhXDP2cz/3c3zlV34l73nPe7jnnnt+z21vv/12nvvc5/Jf/st/+SQd3cY29smxzXq6sU+2babl/SHau971Lr75m7954+w/yt71rnfx8pe//OM6+41tbGMb29gfnG2m5f0h2k/91E99qg/hmrTv/d7v/VQfwsY2trGNfcbZJsPf2MY2trGNbewzwJ5SD39jG9vYxja2sY19etomw9/Yxja2sY1t7DPANg5/Yxvb2MY2trHPALumQHt/5+/9WR788HmGvjDb3WHaJNJiYMxKCA7vAydONnz43vO03S633rDL//c5z+HZt08IYZe48zSWw4p7f/d9jHnGg3tHPHrpHLlMueeuG7n3oftYznvmy56JEy6ef5BZu4vr4HM++wV87nNv5eLFfe6/NKdrW86c2mKeYdpN6Vc9eThk/7Cn7XboZgFJA0kd7/3QQ4TmBEEGdEhc2LvAww9fZmfSECc7fMHnPpeUBz5y/8Ncd6qjpMzi8DLN7vWE7gSnTrR0Ttm/fImSBiS0THc6tDiK8xwuRnxJlHRI22xx4uQpzpw8gWASle9/3/9hKB4BVosFF/aOWCxXXDq3xzOefj3N1ja9KtvbU/rDA47mB5w7f4lbb9zmwkGmT5Gn33ELFx59AMIudz/raTgGLh+uONhf0jYeVxI3nNximRLnzj8CruXpd9xOv0rMtnd45MIl+vkB3rVcODzi7HUnObt7kt2ZY7GcM+RAaBrGQbh8cMTFyxc4POzxQRmkYdY1qAQ8if39BZcffpBnfdad9EUpKeG9p3hhMU90PiJO6XxmtRzJDt7zm4/wS+/4Ff7MV9zDX3nZi/nND9yP5pGmjcyiQygcLBdsb22xNTuJpsLlC4+S8wFnTk+547bbOeoHfvv9D+M54AXPew4PP3KO4Jfc/rTbUOmQ0PKM5/+13/sm3tinxL71JT9JKiO5jGQSMTi62ZQmRrwAklAZwSuKdTHVKVkyfVkxT5c4mJ9jPhygCOBwKogDFUXFITg8HicZFch4VBwOcOIQQFBUBQREM1BABNSeVSeCoKCmg6+iFBRXBNWCqIBqPQZYi9CqFqREVAFRlAIKrgDqEBUEQcTe5/hoVBCFepSoKAGH4kAK4sDJFttyPY3MGFdLFv1lVsMBRUbAESUSXSTElkkzoWtnNJOObtriYiBueWY3d6TccPlR5eDRjnwwY1yMrA4uoWOijS2rdMjB8lGWq8u0Cm1InDyzS9OdRLMyXx3w6OJ+FmkfVzyiIGnk1O4Z1DmODi9RRiW2E07ddYqxXOR97/n/cZj2aXyg7bY5ed0NQGYx32cy3aKJ22j2iAMflUuXHuHw4DLT6Ta33HwnO1snaYbM+YfvJ7fC7NRZTp4+g/M7dO429h5R5kcHTE4OtFtLhtU+MTgQ5fLeoxzunUdEWB3uM1x6FO+hmUzBefIqcXr7Ou54/hfy+n/1jz85D8LHsWvK4Z+/sCRlcM7RBU9kJPtMUMgJxDtcaPASCAg5K8vVQIinEedpgnB42HP50iGDy+RxxSMP77NzIrLsD7l8eUkTHKjQTTtU4GD/Etv+ek7vnmH/6CL7yzk5OQiZoV+yWjmkKCk5Do9WrMbE9m59ZILn6HBgGAvTqacMI05MWlLVs721y6AtSRM5e5xXDpcjkyhI8AxZyWNCdMqYlog6Ghdw3iPqCd6xGnvKOOJkBFU8SigZ0gotwjBkVuNA7CaselilgrgW5waa1hE6XxcJz2I1sEwjJXq2T0xZLkZm3YTd2Wm2Jg3ntNDEEREopQEdiF7oYosmTwwNR+PA0TJz8lTHmBTvoWghrY7QPOdgPufy3pLrrjtDEx2r5ZzVaqSZdHgJpFBIZMYMyTkihVm0QpNqwYfIMI4MYyKPA2N2OA9jAVSZNi0lzW1RxZERnIvMugmI0vcDQ7+kawKXjpZILoTZNpMmMhZhb39OUeXU9hbTrqEfOwTHol8x9Inrz57k4KAwX2XOLyJjf0hoL3HHrbeB30zqumZNAGfOvIiSvf2JgEoB9Pgf0OrElaJqDtk5zLt7zN0KtldBxBy5V3PYijuWfXXmZkHNp+taJ960cuqP9rMAqlLfXXDY+65PQHG2nch61yvHa6EE5u/XIYuQUbwqDkFVyevzWR+XKIqj1Gtkx6uUGoiICMrAslyi5ExeFYblSJ8LiOK9UiSTg8NpsWvpMsE7pn6LZtIRdx3751c8/MAR4/wEIU+QUfAqeCdkD5nBQhD1BBfsyqrSjwPzch992WOlS/JkwOUeKULjWlSEw3DA6d3ThJ3TLJcrVvtzFodHnL79LNOtXZZ7c3BQNDOsenzbEtsJy/kRqVX8ZIuIZ+obdrd2Cd5xND/iwrkHmLQTtqanmG2d4rDfIzph6Acms0SY9GztTumXsFrMGXUf0YE2zFis5iyWc3PuubAYR5z3xK7Bdx0pZWbdNrtnb6GdXPeHeec/JbumHL7giNKSpeBkRd8P9nD4SCqK9gXNmZwLq5wRApoKTeu4cP4RJrtnOTi6xOW9ORfnl5gvjrhwbsmZMyd49KH7uHD+kNBkxqSkoSUXxyIpu6FjZzdw34PnGLIyDg4pW4xlxbIXcLvkBItFYto25GHFclGYThsOFj3edUy7yCiOcZXp+0zbbdPOOrREknraBsQpKi1KQrUw9gMnTu4ym0yZz0d8dCwWmWmrqBZi25LU45lTysAsbhMksNU1aB4Z+4E+OUQiWmyNEdfQdCPDyqJQEYd34HJmGDKueEZtyQyM45Ld3YZue8IyJY4WIzuyYDVmGlcoZcVydcQkCmNWkkIkkgaHQ8lpoG0axlXP0dGSYXnE4QKyerYmHcErq0GIIVCA6AQpQs6FrmtZJRBWNEEYSTg8k2ZCo57VfAlFcAKoR5wjxgBJSXmEomQ6go+oF/BQRDjYW7JcLpjOOg5WS+arFbvTRIwNs9kOY1HyODI/2ifGiIunuHT5PJOtQ8YefIjM+4ELly+Ri1Kyo/gpvYIbx0/1I7Kxj2HF2TNTnD0HI4mGoWbm5nQVtT9dQcnHr6EFKeY0HVKz74I4T1FBqkM1x1tqIGAxhhTFUtF1vQ0QqQGph1IIYsGHHO9XEKVWApxl648JRoS6Ya3hKaDZAg57yJUMx9uqg6SKByJCqfvavxYYi4gdY6nhhgMRZwGAZlI6ZLVckVYNY0oUGQlOcUFxTile6WOm+JEVK5ZpReqVnbjL+NCUh+9z9EcTCiOtPyB6QVNBGcluxEnEOYiAcy0iBc1KE7bY2jrFfOnJ/UMMZc5qeZHF8ohUBpo4w/vA4VEkSMPW1km2bzlBiB4fJlx/4+3M53s0M2W57Dncv0i3tcvW1gxNhaFfEDSh0rDIHUEDZ3euYzrd4vyjj/CR+3+XchOcOXsWOV/oD/Y5cWKXYXXEvJxn2t1GO2nZX4ws0z5bWx04yKUnOAvQlvMDyjBnzANlpUxDCwma0HLmxjvRdOIP+e5/8nZNOfzT08DBCMpIP1+yGkaKOra2WkLj0AKxPlXTyZTJ1BMD5NWC/UtLTt8EbaPMhxWTuEtyymwaWF4eSAF2ZidZjgmHsFiu0FLomoYYAhcuXOD+R3u6GHjk4sPMJmdQydx0/Z3kHCkuMNuFjsKqh26yxaSLbJ/o2F5eJA09wUdWDnyccurkNluzDj8oQxo5dXLK9WdPcXi45Mazpzl3PuGnp4kxExrIC0eiQyYdW7snGJeXkQKz6YT/P3t/9iRJmmX3gb9v1cU238Jjy8zKWrrRG3oGBAcgezgiQ77w/5n/bR75SOHMEEIA3QP0XlW5Rnj4Yqtu3zoPn5pHlkg3iIawpuIhb0pIRpi7m6mZq+q999xzzk1R4R3kdKJtWsZhh9A1OQrGNOCmjhwCQixRViJExNQNLiqsLHD2bnLoRpNtJgwRpRq2+yeuX75FJri7f0LaNdpKUnA8jhNh8MSskUaztpqcQUrFqR+4diu89yAUXd8zTBE3evbHyMvP3nJ5seE07gEwWWKlIcZyk1XSYJRHK0mKGiU0Pidao1HZAZGkBdIocJAiNAtBiokUA1kajLZMMWK0xEvIutzgumEkpszN1SW7w0DXT9w/Hbm9FmxWV1ykFrJn7PfYpUaKhqxW5FzxdNwhOPC4O2Brxe6UqETi6dSTvv9AZTX/8JbuH+N3HVEKgpAQI+RMIuLyhMkCITRCFug8kRBEYgpkmUgkYgzkmGBO+TknhMiInFACchIkOffiIiIRqCzJGaLIyLmrF2f0PifyGRXg3NXLmTBVkIU4vxqpIAilyMjkTCky5u8tyVsCqaASlM67QP8CSIisiCIXoOIZMEgzinAuGeaHlSBniRKCJJjRBklKgSmeCGiSBm0i0kpElYkklMqgIlFAFpEUHClAf9KE/ZLsWkxyJOcJeKIqlVdKGSUtWhliiChlid4T8oA0EERABE1lbriSS9rwSCvueTL3jGGHNILjcctp9BhjeerfUT9VtOsrhvCKzdULrq5esT98Dz4Q00iMDd5XNM0K6yb8NOBCR3Se2tRcrNa8uH2BEprHD3f86uu/wt9+SWstp90Os3ukMi1aW6Sd0EajlMHFhK4Mx7Hj/u57msoyOc9w2EEK2LpFmopIQpO4vH1FvXrN4b7+bZ/+/9nxSSX8lD11VfHiwvK3f3/iNHg2yyVKCfLk6KeMc4ks5g4Uh0saVV2yWkeUAlNp+rTg81e32Ebwx//17/PyouF4eMe373esb17TVBVPT3v+7b//34hToq41GsVntzecpkw9NqwuXnN50fLTz14RObDbnwjBokSkNpSq3QcWlcSnQFUtkBmkzEhjaKqG1aLidHzktD+RVy2vN0v2D3e05gYlHU2dqWoDQlDZllW7IISJRaXYjR7MipQlMTqEULjJcxCByioWTUarRAiSoTvx5//hP/Kv/uWfYRcN3ZDQMpIRrFdrno4Hur5nbde4AFoqUhyZQmTZaE5TxxA8n725RbgdeTigVYOoaxppkCojNPSjI6ZMyJrN9Q1CRZIUjAGSUOw6z2L9mi9ev0ULgY4alxy6rlFakkNCKomtDKdOoLRimBSQaKoKKeHY92StiQJCTiB04W8IQcoRbS0pQ5KKGCPJebzIaCkx0tJPjliwQ7IUNHVNbTTDMJHjHevVihgUShq22z2LpWKxqFBosjAFMpwizifclKkryd2D43CKvL1a/a4vkR/jH4mC3osCqcsC4/vocEmByigEiUgmIlIk48soKmVijHOdIIhCkKVkxgBQCBRALmhZzKUfF6n045K5axYzAzqX9CpzSeyeDEIiMmX8JBIiz/iASKWrF2Lu7eUPe/qCQOT5MSFL8hcAcobtKe29kKhcjiHK8zwfQBSOQZrRBjFztM9UbTGvsI6KFDPEjMwCJSPGKnQlESY9f8BCFKRAJIlUJXX0fUSOEqsUQUiyViihkUKScwBZCoocPD4VxBYlIGV0rfE43DiVzyIHcoKGa97Ua5zsGNMehaGbThA95EBS0I1H3FMi5MDNm7ccp+NcxESkKJ19ItFWLWu7wQ0D49gz+YEH+UTQgsuLa1QI7A477p7ecX35ktXVLWM/YhcRGRPOdyShSGICkQneMfZHYvRI26JiJOWE9x4rDXW1IPqJ2tZcvvwZrl9xvE+/zVP/nxSfVMJ/3HmMSLy7O/Kw74lCl5NMlpNBC/BOURuDD5lxcESXaJZrlN6XijIr/tkvvqDRHV99c8fbL3/C5rLml9898W//w7f8i395TbswfP5yzfSHP8cPmlpGGhN52r3j0Lf85LOf8pMvXmCtwuQTj/s9IiuMkIjkcAHaTYNgILlATBEfJiBAzhitMUaRlQIlkFKjtaWtAmPfcRpHkuvI4w69uiGHRGMEpkoMx5Ff/vIv+fbrv+P3/+Bf0azfoOslOp447COyUVR2g60kpB5SRlLx1/9xy//4f7f0CYTLVFqhJHRDz+jSfOOQGKXJJOxigW00rt/Rj5JF01CZzDhAd+hY3i4YosPITEoJbVp6P3DsO6paYw2cTj2yijTWIEUgC8Of/OkvuFhU7LuRFBNKWerFEjf2CCnJKeOmgLUtvfOgIIYOZTKTg2HyGGvQSIyyxARaZ4bJYWxFTBljNafJQYaUQFtJu6iRRtGdBh4eH2ibJRerFqWBaeLd+0dSGvn5l2+RqmaMMPqMDQ6jEj5Kbi7WfD30RJ+IPjBNiS57hNNIqWmqf3gV74/xu4/zLbV017Ek/BRxUSJJZClARIiRLBMBT4qJGHMpLFN8nruLnFBJlJ9hzqnzzDkjEEKSBPPQfp7TF4AJhUTmTEI8w/yl84+keWJfZugZOXfkOcu5eJjzeC5PKLJ4pgLIXL6e5/l8QpQRYRbz18rrpfK/gkCcn2+eEYgzP2Ce75u5gMg5zoQ/iRSgjURrkHrmK5ChHDESgc4KnRRCCwIDxAMxJwSpkBelxMhyzXjvCcHjgyfEWF5bKqQGlGeKrvxeyOQUSpGQgAha1mxkQ11d0plHhvGJkzuAUZjKIhW8f/8riAlTWSIVKkdinBiHI3asyVVAr69ZX11wY284PjwxjQNP779l0a5ZNxtuNte8e3zP48N3XF++pNaGMDl6eaLOAyFVhDBR1QYRA/3xCSEh5cwwdqToscZircVnTy0V1xe3vPq9X/DwtULbT8fq5pNK+DEp9scTNmeapiZlw3KpZ4guERGMvUCKjJDQtBtsZUAYfPLEEMkpI6XmuHPs9yP/67/7S9b1T8kxk7VmDAKhWp723zAOI5erlraqEHbiV99+QFZvudUSHwLaRB4en9h2I81igxYeHxM5aqoGUgArKuLkCFmzXFlyGGkrhdICl2GcBnaHI/10ybLKpJjZHgJVvYIYiW5C1wKlM0oYgjZ89ast/8//6a/4f/zxv2a9sUyj47R3TD6ippG2dojcIsSGyvRcXm/YXNkyI8+CJBSZiaEbuX+/pb1qqGuDrWtCdPjgkFpgteHX375HmpY3P/mCU3dEqQqXTmgRkdnSWENjBGnqcKcTHx53aCUQ0rLv4dJU5ByZYsWr21c0BvrhCaEa6rom+EDyER8LZBlyRcyg84iWgYXNiJSphGKKHhcSQhqmBCFP5aYXLTJLvBMIFcjJIbNACo1PA61pqZRBW8tu+8i7d1v+9A//OSpl7t+/xzlHP060jWW77dHWU2mNsYoYPfv9gWVTgbTkbADJ6ANKaWJMSBHpp0w2nw4092P8ZiQyKQeSTMQUScmRVWYKiZw9SspCaRcRkROkQMqJmCGmREih3DvSnNhF4RSJnBGxIAZCZkRWwIymi5IxhSjJNeXzY4XclpHIrErBQSHbzVz9ksjlDNGTPsL9OZW5fhbMvFTOUH8sfy0UPjGTBks1QgIUoHImC1GOV6jC4pfl2cUM/ytxVgwUNCBmgZSgFSQi2kiUFsg8Iw8iUSqDkvATmZgTRkTaxYQXO8KoaWhRQiJQqCxmRCQhUkAKRWVqgo6kGMtYQE4kMSFEIIlMVhGyL+87g88gokKiWJpXrJbXNNMDe/+AP/VEmbBNg+89w/FIFhmhFCSFUpopTMS8A5EIccVabrh9+ZYqwIfHO7aPT4TVxHq95vMXb3n3eMe+2+LtEjsKFhGMGQGD1obFYoHEoesKiUUisCKjqpqYQWiNHCcqu+L65U8grRDC01zvf4tn/j8tPqmEn0XC+4Q2Cq1gHAMpZpQI1NqSUyaJhESSksflCMaQswelQGSsBm0yyVQgDe/f7zjsI0pUXFxd0yxqco7cPT5y6jOtlQQ8X90N7KeKtY0IAiFMTEPg1E8smjW1lYxOkqSmrsprSaGZ/JGcIq0FIzO9EGgpUMaiVU1Kku3hRPQFtl+uloQgaSpLEgJbL7DWIuKExKPw/Pxnt1z8mwuePtzx+ZvPSEJSm5ppmFhag++e8EZg2yUxZsiSZV3hYmKK4FMiDj0pgzEVISUQCqUSgwukFGiaBeTMvodXL2p0Shw7x/VFy9hLZEoYI+h9R5VrwjiRsyRMiaapiEmgtKW2imFyLOsMKaJyJriIqgPRw+PhieXqC5KoyNGhZGSxqDluBxqh6NxABLpxwHmJ1gqrNVpadDb4NJByIkmDkRktLT4FlCzqCWs0LkQSgaqq6XeJYfQIJQg+8bg9kZMrKIUCEDxu92yWLRfrFucTRhn6aUIasFYTRSakgK6W2Cy5uF4zZIWyPyb8TzZELombeS6fEwRPpBBCU1IlSYqIkAWGzbkk4jiz20ta+yhfK9K30hUL8czH5yyay7mw3MXc5YvMnNoTOmdyykSZSCIAGpUzECjyPjFXCB9lgufEXVR8+XlUwMwBOMP0Yu7WC+1ePI8WSkcvUchnFj+yjBOYvybmhJyFIMyvkSUYpZBWkJNCmAx6lgiSAfU8qki59PyIQg5UMiLVliE+cnSJLDQEQ5XXNPoCmw1WZxoDooGTj/ihQPdokEqQ599LphTzpIxIoMJHaWH0EikUa3uNEhVTPtGnPX3XYRYN7XJJOPbkMGFqg58MWUxIrcgiMQ4nlIA4JW4vbvji1U+5Gq+4O7xnu31Ev3zDq9ef8bR/Yvu0JTYr4jCw3ESCc4gkyRH6qcOnSN005HGi3+8ROaJNjTIakRKbq1suvvgJ/XBiSHcoffztnvv/hPikEv7lMnLaZmIEISUhJLybmKIrbOw4n2TKICtBdAmpEtlHFJm6tnSDhBxol4rlsuH9w57daLm+WvHG7WhrQddtOew845QZ3IB3kf1Rcnn1Gu92VCqSU2Loegr9p8hUcgwE78jK0GaBFuCc4OqiJbmO43bCxYQUmsqA1h6RIzFKqkaBVCyainfbD1x/viKg0daAKiQh5wIxKLTW/MHPP+Ph4YDIAQCrEo01iNCBl2x3TzTKkMgYbWhXVwQ/sKyveOJEyBQ4W4NTFUJHjoctVbUkZkNKAaMqmkoiJeSoaKzFaEkQmuAD3RiJaGStSdEw+J6hO3F1tSYyUdtyk727P/DLv/uGV7crpPRooRm7DoRBoKibJVM4IXPCuYGqqpiMZOwjAQk+IHFcrG7oRkcMnjh5jMp0GJKb0I0kikyOiZjOaMGAcwkRKoRSNG3DY8qcuhMpJ7SqkMrgkuPp1KN0mUMOfUDJadb+S5CCwSfc1NEuFihVs6obMA1TN1HViu3WMYyfzoX7Y/xmBGIh5OVYOn3hydkREyQ0CoVCIEQC5UmiEMo+EuXSDG1/1NEXZn2RyIk86+7O8+90ptV9ZOgXiR+AxIsioxM5IZKAWRkAkEhkWdADJc6Jv/T5AlEQCEqHL0R6lgCexwlSfCw7ymsU0qLMM9+AjBJFOlgEBPNo4IdCwEwpOnLGi4zSEi0MEoW04PUE3pFS+XzE/JqJVJJ+yqRUyg+lJEZ5xrzj5CbC5LAsWepXrNU1C1EjpYQYkDIidSLNCT+rhFDiWVaYU7n3Fp5OJs7wvswKESXeS/AaW19i1ALFnlN/QjVglitO+1JQWCVZLZdMPjCcBrTQtO0Ke73iIBzH457bzSWf1Z9xv7vnOOxRk0K7ch5ECVlbBhfY7/YoGxB+ZH94BBmJXtBvH0nTiDIG01SEyVHLmsX1K6St2N99R5oeUfPv/VOITyrhh8FBVEgp0SqzWAiUsowDeJEQUpAioCXW1ER3wk0TMRdyRiYXyV3QjMMDrTVcXq45jgNfbq7g3QfC1PG0e2QaA0oqXMiMKSEwLGtDkC0LLTjsPyDQxCQxMaOkJAB1VSO0QKXSWWdRkaUFKbFVS+d6jKpYtkuk9FS2xoqaiCbkiNaZ7rhHyTUxCRaLBhl6Ysz0Q8A0CxbrS37685c83T8ydfdkveE0nOj7Ex3w88sL9vuO1bXAiSJJa1vF4+M9V69X9FNPLRRKamotiDHip8DTaeDNK4tS5Wa1WC3xu0dOw8TnC4WsatqmQqYJFyZCtCwWLdPU4VLk7nGH95rN5gXBB5RWjNMAwjIGxaJZ4YOfJTdg25a2McjkaGSg9wGNLIP36MnBkaPDASIEZAhF+6sVPiiG0yPkCqEywQccEq0itRSkGAlYpIysGo0xFmOL3vibb+55d/eOm+U1xiq6LjMNkWkpcaHIPv3kubt/ommXXC4twUeGaUSiUXp+3lrw/Snh+pHdbuTh4f53d3H8GP/JCGIiCU/MnpQ9xe1hggg5JbJQc/c8J3Axa8rzLNkTaU7ys4FNKhB5loWJn8VZEw/kVMhwcwOcE2gEgZkPn89Q/dyTz4x9MUPk6XwMedbzz1r5Mx+AXOB3EKRcpKlCCGRWH30DmKcH5JmLf/6vPBIBJeb0LxMpyzLrzwIpZ+5+SuSUZ728wugKIQVCF+jfSV2Kpxzn9xrQ2cwFUhlPSgXaKlbrFUII0mlP5wJBTnTyCZV0+Rx7SwoeUSesUUihCEI8S2rFTGbMWeBTgpTKvT6lIsFNZaSQnCK4RDhFhIHFaoPylmnqcX7EKINtG0LviMFwc/GSOAZICno4/OoOYTLBTzx+eMfFesNmeYkVgfff/ZrgPC54sm5Zb9ZMLnEaBm6ul4yhJ+VMZSv84EjeIVXG2HL/F1KyaFa8/OnnbA/3fPjm72iMoPPut336/2fHJ5Xw90NGKgUi4iaPUIrgFYuFJSRPRrBsW6IEpTTOC4LXxOSQBGRK5cRRmpAq1kvLy2iIIdCdThy3HQLBbtsjDPgx0I8DDlisbqlWS5q0IIlIaxSjLyxvqRVTmMjCEP3ANAUWiyUkz9CNCBRBelCGSi2IwTDGSKMMxtqi95aFF2DsAi0e6KcJZGDVaIauaFKVKbB3U1UsVku+//47Hh+3vHh1TfACYxtCDmQfCT4w9Ae6yTNNntrWdL3mWgqQiiwlxkhMU6FCIKdyQdWNJUwOITVISUjQmoYYAikkgh9J3nHyCdvWLFvLfntCmZbRZdY3FzSN5H63n128BOtlhbGJplVIYYhEsgiY1vDy+oow9fSnQ+E/mKYQpaLGB6itJYYJJQzj0JNVKVRSCrjJYxYrUpzIOWOUwBhJDJnoI0iNlZoUPWEYCuEHwX7veHg6cbO+xUgIwRUHrLZhdblBdQIRPN6NSOcYx6KZtcoQY+Jq3dI2ibqSKCzDOBFDwH9CF+6P8ZuRsycJR8ojiYmUfGF9i9IiiqxB6gIXMyf8MxNelGRb9PY8G/JIydkgDyiJOiMQWSFFKRTkPE8vpMFCoHtW2ZUXKj31nNghFyqB+IgQiPl7pKAkuLkAKByCNM/rZ6OcH3TsZ7HfWQL4bNjzXJzkZzK/mI+FPCsZ8pkbmIlEhJgwskYKWz5HkUgpAb7YW2VZBI2icFqYx6pRFB2DUpqmboi+jCYnn0lpwscTTlQz8pBQRmIrjZAagUUoTzITIhVUJoqEiqmoBlSEVO6NOQJZkVMClREpECbHMD6RVWKxXnGxuWAaJ1L0iDpjTMvKXLJYrRFZE0ImhB7n92y7r5ncAY/nkBy3N7dsLq95/+5bxr5jc/2Sm8trxpPC2BGlIjpCXVcYrRmGHSIltGmQShFDj0Fx8+ozRhf4/m//ht3991RK8OL6xW/13P+nxCeV8DsPxhQ5hw6KhCbkQK0ThhqhLMiE9w5pWlarJTeXS4xUTN4Xsk0WrGtL39dI02DYopgYuwEfRg5HgXcBSPSTx7YNxIqb6xu+/PwV/eE90+i4vVnxuI8IoZlCREtDSiNumEhKFDc+AcJImDLDOLK4rBBjJibHoU8YtcKFyGqzpmoUQSiSkty8fkXdWDKKGAZCnBhHRxaCsRuQEtaLNT5kfv3Ve968+gU5BKq6Zto/4SO4kElTYhhOtEbQ1g2bq8+YXEbPWmSjJY229Bkq5XmaemoZ6WIgC4GyGpGgbhSnrkdIi8gJFzS2sixbS991TFNkbSGGwHqhmVwgp5okBYvGIP2IEcWJ72Jzw9N2h9ATtZaEYc/pdGKahiJLUnVx75IS3ViUCIisyOOIVRHVLhlcQV2O/YnL5RUZidJFvph8RIpMNaNALpXOPCZFXdWl+0qZyQeatsWqujCLlSZMjhx7tChjGqMhJkfEIKxhWW1wrqNtFMEn6qsGKU+QRy6XNTGF3/Ul8mP8IyFELqQ04cnCkaUnJzfz4U0BVc+mM4k5iZ4pc6nA46WNL3P4MqSfjWlAiqLgR2akKNB44d2XZB1lSfJn854zUn9W0hXJ4PP0/3keD8w8AWbkgedknokzfH/245sJgs/FAwWCkOkZ2i8SPub5e/kjzzJAfoMxAPN7TwS8iCgUWhQ5os+ZkD2kWGb2siCsIhcSZM6SHAMxFy8OZEbpirpaMCaHJjEMI0Pq0LJGC4FMmjQlkk7QBoxRCGWIWkIq99MoQAWJUBFiIsVClkRkcqDIFXQqMr4IKWQgMk09xix4+eJLGrsiTxGrGqxeUqkFFo1znt53dMMTQgge+l8zHe/oTk883X3Hoq6ZosPFQJg8KZQRT2Mj3f57TuN7qqom+oAbjiQ3sFxdFrfRENi0V1y8ec0333zF+3ffsKprGinJ/fDbOu3/yfFJJfxKZuLkUUpiW4UwlrYpVaw1EqsF0+QIKWNij8AQ8PjgqKsGERONXrBaTJAWPDIS3QKjHfv9B4xNpOCo6xbwhGSoF5ar+iW/95Nr4A6lPJWp8GGiH3qubq5IXuOnnvff/z3OeZrFDZ99YfA+IVSi90dCyvSDR6BwfiImSVgr2nbD9e0FUiWM1CyWN+hq5Hi442JV8/T4ffHMDxIXAkmamW2aWS6vud+OJDExJY+REh88T8ORKBd4GcghEUWgWjW8/OyKr95/TwDc4FhYzeA90+hJOTLFyP33O+p6QbNR3PcOLSVaCVxO1NqijGZTGfqp6E1d0EwpMEVHP0QWVUNMibqVGNtQSUPOEVNpqrqC5Aixo60qcvT0fYJkUTgykWKqKUgq4R0c+r7odpOiqgyV1oiUqasF3RC4nh3LhMz0sXiZW624aBJ5OnDcn4g5EZWmbStEUoQQ2O4ORD9SGYXRhsp6BAmlJF3IRJ+RIpC8Ii0gqlTg4Lk4GH1GCMvlqkGZCr/zfHj6cYb/qUaZAUtyVjAn3LPsvBSBaZaYleSdzzC9nHVsGcrcfJa3kWaEPYOUpBxL0pm/JmDuxOcRfGIWrWVSTiVRz+Y3xdLnTPIrr5PyGern4+vMx8DZCf/M+J/fU3mesxPf7PM/jxaftfuca4HzzL2EyD8kCMYfjDJmMl9W+OTwKZCkIOUikSugwCxpZLbiFZBSIhPKwxRlkEwSmRsqOeLziRwjLnS4yjIKMCyQAWIPsokonTHKIIwhywSpKGSyiqgUkAlizMQYQBWVVk4UcqEXGCFQqkKYirpdcbl6xc3yLavmhrpq0RhkUOioSQ6cCDSqR+uKLpwQ4QNJNhgVGN3Iw7bDSMtydY22C6RV1EriomB/PBYkJyXi5CAU4zBhBCEFLJbLl18g25bvv/uWQ/fEpr7mNEWO06ezo+7TSvgajoNAK4FWCmsT1gpkLtrxlDM+BqIXeBUZ9MDDY08/CZBmtpYVCCPIItANA82qRkjBbvvIi/YClyRRGg59wOiKy80Vb1/dEN0D28MRFRVhAQ/TiNQVOY1MQ2Q4feCv/vJbbt98wRefv0abot0WWdE7z3qxoDKWYRyZ3Ihtahrl8XWmMgKrLFZnrjYNk4O/v3PQCrYPBxarS6w2jKcjtmrxyTO5yNvPbrn/cEDbhiwzjkC1WHA49FxvrsheIqVgGDyLShKP3xG6HTGCn0aUiLgcsUYx+IifJE/HzH/zixuGcSK4hFSBdVvRB0ljJEIKQspMPhJij4uKMUgeH98jhOTiqmXXjSgBdtGScybYms/evKGuNPvDkRAn2rqClPAZpIgI5RHZIEjEJNFKFQmNMlTtkqnfE9CcxowxisWyRpqB2sJ0GoheI5UmBdC6phtGTqdEti05FLLWoi7ESB8T+/3E+7v3VJXieDrQ9x6pDIeTY/QTImfCBEYGrjZXJFXU0cvGYpTi+8cPDNOJVy+vePfhESVgf/zRWvdTDZU0efaC0BTppxCZTBnzFFb5M3e9zKHJxcdCzW31nChFAcpLUXDW2s++8+qM8edcdPAzi/1M7ivddUm1xTRXPDfyYobUz6x++AH0/kOzHDl36uWGNif4YumbcnG7zD94DpkL7z+TZq7BTOoTZx4Ac6F9HhWoUppI5rEBFDOf+cPh2dW/oAey8B0kkpjSfMRhLk0SIZXPTCWBwCKFQilQSpJTwoWuyPWkweaqjBWiBOnLfUAqohIzgiBISs5yPFBZkKIi+oiiGCN5kQriEBW10tRty3p1w83qJ1y0r2j0EisrdNIgM9M4WwUbCcpSyYamWZblSKEoNtrlBYu1RWSBXa7RVcXkTtR1Q5YTwiqUWRCGDrd/QsYAyuJ9REvBollycfuSD3cPdOPA0U98+3iHFYLVcvF/9On+XxyfVMIXaKQVRCJKa5TKOBdRRKwW9N2JkD0p2nnOq/A+0diGU78nS4WuAuE40Q9Fw+2SRkuDyxVm0nz2xRUPux37o+difcNqUbOoM/vtnugCPirqWrHdTWxWGoGmFo6HwweUbvmjP/xX/Ozzlt2wY9cNRDTRR7b9ibRsqeslUtYsm4ownTgcn7gNL1m2C4hHnO8xOXE8HdgsNgRG6mZiSpneRYzORARtu0bIhlV7gxCZYYI+SFAtOjmWLSSRUDIjFLSNZb/vmA4n+n7ksNvz9vYCox3DyeH7CKLwIZSCMUa0SiShWDYN4yEwI2f4OBICHPqJ2jZMk+fucc/Pv/wpq/Ulj4d3ZCHxk0PbBdeXF6wqTX965BBGtDFYZXAxohdLZCiOVUaZYkUqM0oo6kqRRUuaGb/g0XZF1TRYU6PUifVyUTzxg0MFjwiJ0Y1lZps0WM00ZbSx8wKySE6R43Hi2I+0iwtyvuDFC42tLUY3HI89jbGMIXL76oLVwrA/DricMXWF94kweFKaqOprpuiwCvDiP3n+/hi/u1CzaF1mU2BpofBZkURhXSeKxFfMXPkz4Q5mhv45wT/P28s3yPNAXuRZsCdJonT4hX1fiHRClO89z8WlmPkBz1S6hMpnrX4ZA+R5Y56YW/00Q/nMgoBnE575QCXMMrzZRGcuMNJsy8tsxKNyKdyLEVkh9aV5uAERlQVxTvJnr31EGTkIkeZxQhl2IOJc+KR51i8L8pAKcpezelY7pJyx82dkhEGrCe8yLnqEnDBENBmyIjlNwqONwGLw2RGAJOc9A0qghEVmiLJ4GaScUEIgVSaFOeE3Sy4uX3KzfsNl9ZJabzCyRmHIPuLiRMyBMU6EGPBhIrqATYbl4grsQNYZXbVIbagqiw8e7wYe7r5BqVLM2apGi0y326OCRxuDXKyIeMLkuPj8D/Ba83d/8xeEKLDVmshIljCM42/57P/Pj08q4WedkKMsG5ayJ8TziS1IMrHrunKKSgG5QkmwMhLzRI4ecqQ2S6zuGXxJokIoQlbUm0u+++ae29jy8PRI8pLNdUWtM49Pd+hsSFOHlwtsXTNMH1B4Xlzc0qeOnDUBzdWLl5z6d0w+40bJFAJa1jTLGiEii+WCPgXqOvHw4Y6vv7rn5atb/Lhk6rcFDkqQkiS5jJORUz8gCEjbkmRGi4ounqibmtZmHo87EplFuyZMA8cucupPtCuLc4F+yAwoGlbQaJZy4t/9xXuWiyt+Jix5POLGDqUyi2VLyOUz2yxqPvQd3jtMVdzxXr/YcHoozmPjBO2iwmRNZY78V3/8JZFQ1AWrJaqqWdQtl+sF3x86+j5QNRppJD4nfBLIUFjTMWkGN6DrBVZbVNijZ2gwioiuKpRwGCtIbqTS4A+BHBxVU6OCIcRIlIFj59FiomWk6wtRUKoWTIuQipwyInuGaeTt6yX/4l/8ISKd6NxAbWsm19LUS66u4fZqiZSK4AO97/Gho7YtWhqGoWN72uMGz3rRsqg+qcvlx/hB6KRQaJKAKCQai1YVgVDgaenxBGKOICIpx1mHL+fZ98d5O4CKxZo5nbvzOTV6UTT2grIet3jhz5lcnrn1BT04gwHFO6QUEoVgp0oN8REsmLXozLyBHzL2Z0nerPk/FwBnlWDJ1+fCoXALyAIRS1Ej5ucQoqAAxb4nF4XAXJQgBGpeCsSMTEQxyw9mxEDMRMAkZ79AKeb3rUuxE0sBFLNCK41OFinljMx6fPZMeUKgsEKjvUU6j6oySIVEgygoDWSkFLMCUqLkuWARJCVQocj0jGpZtddcX37G1eo1C7FBpwaZBDElXPa4ONBPJ479gJsKmTNmR3YZmWuEXhDVhI8R4QO5H9FSUcviB3Lx8gVKag5dR8yJ1LZ41xNDGaVoMrVZYTeX/P1Xf4cbB96+eAsq0fX3VDoif5Tl/cNRNxWnnUPVhmph0dmRfEIrgZqtGmNOyChQWgOC09ThXKCpLD4mjFGkHOhHTzcljInEXNY8Zi357sM9UgmqukLphNEWNwZOpz3HMdOuLb07sXt6Qq8ueP/+fYG3s+bq9pqqOvL+3Q5dL9Ba0HcjQiSkzKybllZLWhkR44kP9zve3+346tfv+aOffU6YMm7yhAAhw/3uyO2bC3wqkJnNnkSDEBElDN6PdFjSFMqWQL/HZ0kOI27oqZsLtIFmWZOHzLYfmdAkGcnCkHSDblZUG0ETLK+XNaYukFpF4t1xSwiRoe+QjaabHC4kpNYk54hZlXtQGHlxtWS9aviw3SOVZrHesFq1qCxxIeFCWWnb1IaL9QuCnyBmxm6LMTWTD+QgkFXR2kbARUdwGalBqgorwLseomGYHDIFlk2FXtU83O2Kx7lQVNahpWUYMq4/IczIlAN+3J+nnwB0vUepxKvXNcNBIcYMQrNcrNhcrlgYGIc9F4trXIiM48Q0CVRjMRY0FcfdiZg8CY89b276MT65sEkRZSHCqpmBr4hI4UhyNt6RnpA94Uxce0axSxd8hv4lkqwKLH9m7jOPylX+mNA5d/rzk+VcltwIClv/vI5X5o+LbUovXJJuGRN8JPCf19mWr/0g6TP/3Nmpb0YExFwgiLP+PxVr2+LRL57tfZ9n++IjRREp52MqHX6WZ4v7VBCMTJmrzwTAUizM6IKYmQFq9j89L/JJHikCQgm0kuhgUcNACJEUHVGNKFOhZIUIkuzFvKho5hFQpI7noy+Hn5/HDhJJlhKUwsiGtrpk075ms3hJay8xqSm/xxjxzjFOPX3f0fcnxmFgGsq9umy5zjRS4ydZNmFqidGFgd9qy8XFBVpI2sUFnRvo0pEYA0YZUl0VrpYbqYH1xTV+cpw+3HF7c83Lz94gFeyfauLUF0vWTyQ+qYSvjUbpCSFBS0F0mSlmtHOkCNPkyhytMkiTIHvGMeGiYrEUSAWTO5YrU+hiAanK7EiqyPX1BcOwJU+ZqCYkGR96gpvoo6BqGpqmRUuPNRVSSaYgcdGyWLbcvt6wvf+O3g1UulSwSgpWjaHWklcvX7FeS1px4rv3D+SYCM5xOriy9a7bsx9PHHc9Uz+hakXIiZTK3notDUqloiJIoWjVjWHsPTnDMERkBeMIy8qhtWEaM11fkmScF8xkKVBaEPLZ7COgjca0C6SSYC29iwi7Yr3sWa7XuCRpm4pEwlaW03bPGOHu/gAp8fmbV4QQeNjtWa+v2SyXWJnIKbDfH5FG0C5qunHPC/mmyCtzZBoCOU1MzqOVnpfeZKSUxCTIqrh/ta0k9gGRF2QtqCqF74s/t58KHyPJiuMoCVGyNImYRoQ0nLrAaTgg08yEjpHK1tS2gRxpqopQRxamIURD4wXLRcW42xGdw40DGYfVlpQGhrFH68joDUppun5CCYEQPzrtfaohKVyfJPSzS14UASHLzrokNFIU8qhAEEVR05wNdc5Wupyn13MHnQVnit4zGe5sQFeIeDMSMJPipCgroJ/HA5RFOmLu2j9a8hawPxcc//yvYmf7nPTmMcH8gmImzf0wnkmA4uypn2YqQpHRlXUAZz/8c8EApdMvy3XOI4I4J9lnksP8tXl70McvqdLd59kJsJjtelIWpDyRZLHttZUuzpV+NtOKA2n2JMkuwJSQMaOMLBsCxbzfb17UA4qzywDIZ59+qWpqc8HSvmBdv2RhLjF5gaRYZXs3MYwnuunIcTpxGE8450AGbKUL6VIoDJpNs8Jpi60b6naJNYaFrZDBM4XAcRx4uL9DxkhTGfqu3Lvr1bIgl8JgVkuG/sRmdcnnX/yCpCVPD++5/+Y7pv5A21a/hTP+vyw+qYRvrEKbiqH3HFXG6EBdW2pjebzfM46Zxkqaeknb1ownjzYV2lii35OyIUdJCAPbQ880gm0XoOK8VMIzxojvBK8/s3g/Mg6CrtsTgsKaBqOK5nSzvkJVLT//vS/JMuCHjsfte7bbzBQl9WJBAiopuXp5S90abjaCx/fv+fa7D/zP/+bXZNswTQO77Q6QVLUlBIkyBjeNhCTJ372nWV/yompZNILH/RZ8YLFsOY6e6XQi5ZJ4hHAYaWnaNSkH3OTQRLQwTBFS8GX/tiySO5kz/amnEoa6lihd4Pjd8cjRg6orpqGj7zucaJmGiScKO3b39ICpL0EYhBB89tktx6Fj1a64vr3B4hj7A4lEayushNgJHg+lWFHZo6VkTIIQEklklC6dSpo9CXJKGJlYNEuGqafvjqybhhxBpoCyhv3xRBgCR+dBaboAVlVsh5EwUBQR0mDaDaYuoOww9AgpaJcti+VlOT9CAp9QwdAsNSGMnMaOF+slTb2kMieC2xGyIISeFASDFDS1RmqDUoJl80ldLj/GD8LkusDiORFTycZSFne9JDRJJmQOhDzr1nNJyCnGOckUqF0JSRBxXkoFz8x9ABKBwvVXcyPOnOzFbOIj8pnFH+cZgSQKinue+MjYP3vnl2edrXJnT37ymfh3NvSbnfdgttvNpZDJzNa+89RdFkD+WUpYCAVAWUDG8/spT3w2680ilqSfy+c2H3YZA4hChIby7/SDIqhsCEwzIYLZpteRs0LKispqhkojpkiMmcmPDENPVBKdFHrQZD8hKwHCkkUsCMqMUMg0E7HzPDLIGpEttVqx1C9Y25e0+hoZFuSscd4xDifG2HEa93TDia4/MriBFBJaSoTRiBgIU2Y4BUwjWS8usOsFtqmp2wZF5uH995yOR5phxZvLW9qLlriwHKcDp8Mj42nPze0LVNBIBCnB+uKG93cPbPc77p++wR0fqITk8voXv52T/r8gPqk72OAiaInRguw8TiTC8YRea5YLzTRFbN2gtEDkiM8eP02MfY8yA+Mpz253jhAlUma01MT5wgpR0FRrdv07rLogk+n6gSQVpyHSLgIxjEwq0Cw0tZWE2NN1PY+PO4bRs9ASJWrWzRolIheV4uLCcNgfiIfSlWIW2Mqw64/kONEfdoxjwAVPN8VCkhs8i2rBxXpDu1qzaiqOxw6RDX7qcNITRlGWzliNqRR+G1isBBhFVUkqKzgFQT8OxGhoasvYj4zeQ05oBJXRIA19Hnk6nlitNhxHwWlKkB1CKwY3oaqavvf0k+fxccu7d0/80R9cgwQjDYvW4rxj00bCuAchOJ46jBRkMSGSIqXIopbE6YkQBNllgu/xsZj7aFmKElMVFnUkYY0sdqjJQDaI7NCiIiWBzZnd8URKkjGUm1aSkFVN1oLmqkU7R00Caen7gFCWECa0llxdvaBZNpAFWmSmYUAbxdXa8P037xmHAS5WhBQQeeBmXeFRgGF/2JOJxORoK4skMbhPh3zzY/xm6CwhURAjZiJbpiy8ERkpEiorZCr2ukIEsggEeCbvnSl2kkQUZWYsKQknzex1cf5OUVQdEjAws+bjLA3Mz5C7oHj1nzvxTPHtzzB70pfXLojCs1vPPGY4H9v5VXlWEJ6lfDnPvgDiNzkIZ16AeHYZYEYMZFlUI+dZP+XzyfN4otj+ltcsJMEztjA/n8joM14w/7sYG8zLiWQk5wgStDYobRDClT0COeHGEaUtQteISRAngWhlaWiQyFwTswChONMIZEgQE1JYar1k077gYn1FY29QYkX2AjdNuHHP6E50saN3R4bhhJuKaipPGaVrfB9IMdAdO3ZPTwi550qsqNYtIkO333N8esRNjqZd8MXL1zSXa/ow8O7DPUlo9GoFaSSFiEExdAdOPtGNHzh2A8PQMfgjKnvaekHoTv8Hn+3/5fFJJXwhNFN/wpDwXmJrSTc4ttsTRoHVLYvaQJqIkyTGRAip7LOe7SKNVYQQS9EQi24zyVKBBxcRwnM47dkfLknZcRoc1kI/LLi6ekFMCTH0KNnifeDhoWMYt9w9PJCTpllpDuPI71c/xSgN2SOGB/Q0cgo1B9fjyCxWC/7ub3/J3bsHDk+ex6dHTOoJ3jMZgTSKYRjpp0B7qfHZ4XIixGIROg0KqWt8ilQio1Picr2msZawXJKO9yR/oJaJ4CaatmYIEZSikoqmqjBVxXq95ul4YvSJY+9YtIm+j8QoqFFoY3CjQ+fAYX8gpIiImbpa8vrFmm/vt7x5/RLbGFTynLoBRCJIiONUTDukLnuhU0IpwcPjlraqscoQQoc1ljwFTl3EttcoW2O0wZi6rCsVkbItvFiU1kqSUmRygdMoSdoSdEBRWLpKJnRFIfTVGi8KW3q5qtGqwJAvX99weX3N5HrcEAhxIMeMbT33X3/FYXsgKY0SAZUiXbdjcbXGGkk3TNjKEkJgUS85jXuGcC4bf4xPMdSchnIuWmkpi7QuZIWck3cSspik5Dj/3gNJJGIOkH9A0PuB2c2z414+FwPnOfwZTo9zf17sbAvLvbDfsygLes4IOefZ9ywRLNvzzjx++RtmOWVWTvl7Ki2/QM5ywo9QfjH4+Qi3y1wQi/JUYk78kjMPMD8n/3m3/bz8J4v8rD5Q5w9VFOleOjv8zahIWetbRgBZzUVRiCQp50VBHikjSIVSBmQx5skJJiZsGqhTDUGQnEZEjUQhRIOUoISGLIuRWgqk6FEx08qG2/VrXr/9jGqxIqUG3wucG5mmI/24ZYgnxjQy+hOTH/CTIzoPDsLkyUGSZPEHGNyOnHew61AbS503eOcJKbNpVmwuLrCXS572T7z/8A5HBu/x0TEd9yiXMJsLHp8eOXQDCU2z3uCTIvmIsYqkBM1m9Vs88/9p8Ukl/FVb8y4+Fb0nmhq4Wi/4rvcoVWOaQsoQKqFNhlEw+ZH9fs/yZkldSaZpxGZQUiFUxoeBKQiahSDFnvfvnwjR8Of/4WtkBUKt+OLNBRdXr3j58iVJOPKoMKbiw/aeD48H3t/d0S5uMHbir3/1PW9e/SGvL5eMrscIxd13O7xTCLPCR02OoKoaKzXb/YQPe/7mr/6ez18t+e7dV0QP4zgxnEbevFWoHJFUpJAYJ4/3mVebFmlb5CRYLzeoPLGIASMhp8j333ZkrVgta5wPLEOHEYZBJkwqBU9IE5OfyNNYbjoF5ySHhBZljtnYiv7xyMJ6XAwoaclM2FrQtBZyxU8+f4OOE9vTE8lrQuiZyBAjRib2h0NZhwnkJHF9oLFlC12tl+Tk8C5jjOXm4oKkNFOtmJzmMDmmKaKrQoaptcCQ0VLihGXMkhebDXroISeG0TH5DjD4MaHUhFcSZQ1iJhkC/OzLL1DK0HUnsu/QIiHzxPFuR/IORMZPgaE7cFk1SGGpKo2QhrHvESKzaAzrtcbFNdPQo9SnM4v7MX4z8qxjkzOUnUulX5jiKaOkIMuEzAolqmIXKz0i+1kvX9jn81Z3MmUhV0mEc4edi2Lo7NUjsyDKMsEuPID8TKIr8r70PIPOcu7YUzHEZe6s50qAs/f+udiYXYHKmxNzz5/SnKTzPBqYu/QzoY2ZSDhj8mfmvyKVVpniFSylKAhHVjD77UtKwi/GQvLjGGOGGaQo7yOIMmYQFDX/GflI8+IboRTESMoeKQ1KGhTn7aKZlD1TOmFzi/EWMRhEMFhREdDzVkNV0JUIOTlC9FRB8/L6mi9+8hlXb29B1rgJjv5E708M45be7eliRxKBKMs6pRAyhBn1SQKGRBATITmSKYXVsetRH+648J62XbG5uGZ9uWGKI7989w37py21VVgpud9vSVOHFBByYHd4otufaJs1b774Gdeffcavvv01/m+P5DAx4fjq/t3/fy6C/4z4pBJ+yhKpzFwRezIVISbWy4pF2yBlJARN1ViSyFhjyGhIFUrWSBHoTxPjMCGUROBL1yiL7UTwnmECVVke7ndUruInX77k8rJF68Ru/y2r1QuE1qTs+O7D93x4L/nZL/4Zv/jyNdP0wLvvL/iTP/p9lDgQpx4pNe/u7tD2JTe3ZdlO8BGVMvVqzc2La4ZTz1//9S/ZbyP/8//rr3h6OrFs1khT8Wf//SWrugaRn3dzKzTaNFR1y831GqU1p6PH9XmGveDu7kRWFqE0GQOqQimB9ZFKw7pV9MeO8TgwnY70DqL3dMNIbSWH045FvUYYiFmwqCxCGbStGcNUtLp+4u2bG25vN7z79V9Dgu3pQK0kKJBSEEIm+kC7ucQKyf39e4wVKBkQ2hU+cgStLVVlMCIyxkgYD2Qf8dNAdI6bxYohRcIwEGQma0X2xZSj1BZFQhV9RrcNMmmkcChhmIaew+HEcXsoC5ZygjBw6AbcsGchwZ88H+4O4Ees1TSVRQoYp7HcjKVGSsXxNHEaehYLS0yK0QW6rqO1ivBjj//pxg9N7xFIUbphmUqyyTmjlETJjMqRjCJKTUKBCBA/ztLPSTrlhJxZ+olzki3nQCQRxA920M/oguAjkS3NXfhviLKe+QCzJG5GEvQzY+AZZnhGAPIZrD8vzSk9PDDzAWa4IcsZVZi5d3l+njAXLoo8+xWU5yuz+TRD8nl21ftITuTM0p+PMov0sUgRzNI9yqcjZDkQBeRSMikl0dpitCZLj8wKUsBnx0SHRKOdIvUaKoMQCiEMnNX8WZBjTRsiLy9aPvvslutXN1QXK1LOxDTiphOH0yPHfoeLJ2Ka9wCQEUphDCiXkVkVW29pyL4Ug3ZREbEM3RNdp7h6+5r1mxccHg88ff0NLk5oK7HK0G4u6LcfUDISxmGWPUbG7khrG9787Et+9of/jNX1Jd144v6rlhQlQoL9hG4bn1TCz0JRV0ty6gijApkIMRZDhNoSfc9xHGiWS3KSKF0IcLap0ZUlx+LI19RrjN7SJcc09pycYaMk0UtWywtO40S9dMTe8fJmjdSe//BXf8nNxUv+h//uFdnDaeg59po3X/4B//r/8kdsFomxl6yrile3kl//+j+yXL5lfXmFbK643NwQU8RIEDlSVw2vX75gGCa++SbwN3/zd/z5vz1w6COjV0yD4+Jqga0achoQoiHmgJZF3qONRUlD0zbsu27uQBVWwvH0Dd9vj/z0Z28BS8yBbd/Rri/mhBeZfMAHsFVFpyStEUwmkaYJKTPvvn3Pn/x+y+Q0wzThk6dtF0ilUCwI/sQwjty+uqTff2B/GqmEZ3t/x6sXS7zPVM0SJQXrdUu9rIijoxt7KrMijpHO7Zi8BiJN0xJyYHADLiv2pxEwVCKiq4AYD6Spp8+S65sWoxOTKMS+w2FH33tMZWclhyWOgdpqpLG0UjF6yWqtsFXLNHRM05FlTIxdpFlqxjBSW82UDNvTid1xT2WX3F5fMk2O9WpNXTfsTh1SKLSWHE4di7ZFG8UYHOnZq/XH+NTiDJvnMyidIkI+e+YVy/lc3O+zkMisUZgCORNmR72IzBKZ5Sycm5O7zKQUCzqWJeRiRsMPvi5zKsk0nxUCJVOqMxQ+Z9KzxS3nTn7muOeZaJeZv29GG2B+Y+TyvgQzi728tsglqURRXPYUZ/vejz7/pe6YRxR8TOxnm94yjy8Fw9kwSJ59AZgJhqJYDZ/dAz9uBZjlCgmEyqW6l2p+jyC1QeoKqTIqS/Lc/adYJHxMkjRCSBAk+DyPDCh7A6zQXC4Mrz6/5OL1hmpZo6QgDRP9444P79+xPW2LCsB7YhjmUUNxExRGkS1IX1HJFm0sQoE9LvC2Ias1bjpy6o/c338gC8MwDqXYU5KmbWjrJSEF9scHut0DaXJYpUluQCS4vH3J6598zuKiQpvMzc0119fX9N1+lh0+D0l+5/FJJfz16gJ3FZn2A9thQIS2SKzqGluVZTqdThhT2PrSSyqVEKIno0jSYK2hykXa0TQ1SiQ6J1C6wVY1ja7JaqRWhoPuWCwXjOEDbXXBmzc/YbNo6QeHjRe8eSX5+S9+j8/fbDg+/YpjN3J5teBp/8RXXyd+7w8rpFUs2yVtZdgPHucmfApUbcuymbc7JXjY7jE0bNaW6xqSN7RtRUyeQ9+xqCGNgXEcMTrTnXaYiwalKupakvSESYnkHSIKutNIziNZmOITnk2pqgX4mMvjMhNih61ashTsxwc2IlOtL5jGzFe/+nu+/PJzFosLhKpYtCWhKdsyDSN9n3h1teFpuyOTeNxuGU8d8maBmyaU0DRVTUJigVFIYigXtdE1IQ2MpyOLZUtMI0rK8vkIiTYL9qOnx3BdGQYfUGhi9PTHHd4NRBdxY0DL4hueyAijyvrKnKm0xbYNPk5cXLQoVqwuNwzDIw8PW25eeKRpSKKmm7acpol3d3ccu8RxyLx5KXh46Nm0C9rWoFRFiB2LRUNlNfHgUAhqK5mGYijyY3yakWePdyHLn+Ic90zBI6VYmPRzJysps2ItZnRN9ohQ3OierekpK28L/F7SW5qTMWe//FzGCWmWAuYZ6j9b7Z5RA4GYt+3l57W2gYRCooXgXHQUUx753HXnVFj2Z3c+QUakj6RASARRluwUXKIcfJEUfkQEFDO7XiTUc6ouED6ywPNn7oCcu3w5FwkFNcikWKRyQgXOMsKZGUmepXvymUwgkVKhZSodvrEYpVFSM04dPnqi8MhJ0neOGDXkRAhlw6EyhkXVcnO14O3bDVevlpi2IobIcL9l/+2WX//193x42pIq0CJj24wQjvHgEKGCqAipFHikhIgSJRRrs0BcvuEgBE494McTadqx3z7hJ8+qXVAvlzTNmrZZUVnJ3bfffRyNplzurT5ycfGKVzdfspQ1TJ7JOS5qy5s3b/n+G0+fIkr+mPD/waisQinPGEMx27EGlTV1Y0jeE3Mgx2JBW02ZJAzOO6Y+FncqYVit14yHR5QSdEPHZrlAaYe2mtsXV9wdPFlqVpcXGPuI1pJaVWwuFbcvLthcXuLFjou65Z9vVlxdGMZxz8O+dKTjOJG9RzeGlIvD5GK5xucRlwtc7VMCKdmfhmIGZCXHY8eLywpjBVVjuXxxRdVY1lUmeEs/uuc5ZKMlbnJUVU1KgejHwlSfRqy2uBDoBsdpmLhdLRAyIqVhGD1kRVVZjIJxGjidetCKlAzOw/1xy6ubFywvrnjcb7Hv7/jpF29JJBZt2YRXKUsi8OJmzaLVbHdQ64Z33UTShsoYmqzoxwltGlIWWC3xUwJRRjJCevCGpjZYI3FjT3t1SUhgakHTWPbjhBaKzjl8VvS5RdDTRUu7WHPaH6grwbquOcQJkT3ee0JI+AhVXZS6JIeUnotVi9GSnAS//vU7vvzyCWNaks589tlnDD6xXkWWK0t9OrK52OBj8XH41S/veHFrWDTXTCIitMVWKzrnmHpXTDfS/84J/GP8ziLNG/CKl3x4hsZLgi7nSZo77CQoxjqzN60UCiU0QXoCDghFbpYhplJEC0qyPG/AU/KZr8+5EEAUyLxo+4tMrgwP5MxSLz8rRf5Nf50zkW4mAZbCZJ7bi48d+tkAiPNjc+IWeV42dx4tPD/Zx9W7Ks+b8c5d+1x0pFnWd0YdijvfLBoU51FGWeCTs0VEi8gTmL4YjhUDg6KGmFURSSUiZamdyhojDFlErDBYaQCP9z0TPVIo8liW6GQJLkdimGjkiotFxYuXF6xerjFthUTgjiNP3zzy7m8febyfiL4i7QZy27P8csSYRA6Z0ElE0uQYnouY7Mtys1VTs9i8YWEqjlExDffISqLbltpUrDcblpdXyAS1kozdiXG3I3cDOgnaekFKES00rWlQh4n+b7ekRQUqIKzg1aIlvLjlbvtU7sufSHxSCT/myGJRsdtapCnQlNESqxN+jAxjxJoK8ExRU9VLtFnQrjYoY8CDtAYjJJWUOFc8pmNwiAiVNWgVUEZz+/kt1sLj7nte317x4rri+vqSpiq72LUS2AQydXx4LIsSUjohkyIkhRCRw/6BcLNCWs1ue2CYDFPwxOiRyjK40rVWleF0VNT1kuubFd3pwPX1iqtLTYqRuqnYbyeyaqkXEpEmqqpB1KJcyEJiTUU/nUAlppgYY+LUnbhwbXGJypkkNcE5pDGc+qkwVLPESIvVBglEu6BZL7laL4hZ8vX377h95WmXGgWMkyPikdIW6E8GyCClRcqKxiqaRcN+2DOOI7e3muCBHBjHnqYqZBkfM6vbn6CPR/y0wyhB8EdcbGgWLTFpUpBEF8gKpBKEnJmcQtUSlxKH00CMjiSXmNYwTBPWKJaNYnc8EscOoQMawbHzfBgdIXgk8P7DAa0Ei/UanyamcWCxWvHPb2/oDp5fffsrLleKYZSMQfF4ELx9u6EbPU+7E01TM7qE6YGUuHu453K1+R1eHT/GfyoCeU7Cs3Xs/PiZhocoiT7NLPczEi1FIsuMRBX4f95Qk87QumLW0JckLM/mOcyFgyhw/tmTXkCxrMuzGawoc/J0npvPx5XE2VUuzR438rnvPif4GT6Yu/m5dZbFjrok8o8yvvOUID8/9kO+vySKH/j9548SvrMnQZJFWifFTEL8YQ+fISWDYIUWKzQZzx2ZI/IZDZm5C0IghUZkQ05zU5QpI5GQsdmCauiiI4pIJBa+hQpoaxDSokTDZrHh+u0l7YsVsq1BGEIfOT1mtl8Jhm2N8YLQ9ZwOPbndkVYjKXiGB0EcPd6NpBhp2zV12yIbhXVwsVoi1hYZIkJP9Bdv0MdH0IrG1qw316yuNiyqmtP7J+J+JE0jMXrqdsPF9Qvc1ON2HfSO0/EBYRJULVIlkvSEVebm9gKtKr7+7tvf1mn/T45PKuHfvLjm/Vh2p5dMp8g5MfYdYSowkpGFDWp0i7YBHzr2pyOvbwwheoxcYdvCsm3qqnSftkZKTVaZi1XNm+ULbJ3pa83Xf3tHlWv++E8/Z7NeI1TC+4nsI3VTiF0aSe8O9NsHGlv2q7uu492Q+MXPayId3quyT8LPdxQpIJWbyXq9xg2J9dpw2N0zuZrPv3jDynS8u7vj93/+M7SO1G3F6XSijwkZI0Z44jRSWUuMZW4kQ4Qp4lzir375yOX6EmMstqnJuqJzHuUjCEsmUVctUUhScohYPPNj8lRy4u1NwzfhBcosMbJm2+3IWdFoWG9WLFZXHA8OFyNuGshIjJKQPUM/sViuC7NdRaJ3DOORdlERRw8+4fp73OAJIbNZLgjThKoFYRyINPgEQQpC8IhYKHF6ZupLoXh6PBFjYvIgawm5YRCCJCXKrqmWmlZnvHJUdcPd44l2UZFF4nQcGPodty9f8+7bHQ8P3/HTn35JzJbd4xO3Vy1G1uyn92x3DlslPnv7il99t8PYhv0x8Pi4p19numPi8X7PdCX+907hH+N3FCE4slSFTKoKARaZSLOpDOdVs7J0rAWs+eg8J2TZhGekIKaZdU6e5WRnIt6sX+cszcszEC6eZ+FpToBCnBN4KRTS3AGrLObnSyXpU9a8RnhemJN/uLnvOfF//JqYofYzh+B8bAX2PxcVM1NMyGft/sf5vZjHCsykRImcfQREFjPSSOE6UA4sZ4ESC4y8wkgNaGJ6h1R9KUIy5bOSmswCEZeIUENMgCk7M5JDYGl1RcxNSfbzgp+sJKZasmmuWW2uuL6+YHW5xNaW7KE7BManid33I9s78M6QR49whQFx2g88/G+PpChRzpJSz5QnslBUNys2Vy3NqqL2FdWyps+OMHkuP3/B+u2C6bBnSI40eV4sl2gf6P72G/rvv6VZL3n79uec+gPaVlT1ikZbXHNg3B9IPnCc9ng3IY1knDqmQ4Dde5JV3DY/yvL+wZASBucZx4xVCqUEOQaOx0Rd2eLDLCJKiXIeUWQ3Ic0dos+IGLBmQRCSmAJTMNi6pgsRU8S3nMsAAQAASURBVFsqa1lYxxAjFxdrvvzJlzw9HLnaLHlx2fDN3d8TZUtjNSKdeHrqkcrw7puvyKfA5suaFALBR1xyTGFiigmfBS6MpJCIMdO2Fe2iJbPj4mIJYeDx8QNCNty8WCOE4+HhA0/DI3/w+z8HAvvdE8EFxtMj6zdv0KlnjAktG6axsFyTTAwhMDnHwSliziSX0I1me+pA6PL5hGJwgVlxPBw5DIG+dzTW4L2jNgpjM6+/uKZeLfn63bd0h4G2sWw/DLx4/TNcyAyjpDYN/X5HZRXeT6RU9o5frVpSiDS2ph96lNTsHrZUTcViscHHgFYtWXi01RwOHZXKBJEIviP6E94lnA+YyiC0wKfANHrGPtCPEaGr4rEgBDnnMi5JkZwDU4AuV/gQEArWLy65uLoAoZjGxMP9I69vd+yetozHgXfffYfzktPpiJSZgZHx0LGfnrh6cYE1BqM1m9Ua7w4s2hX7LqC15fUrze999up3e4H8GP9ojGnAZINK8rx2rkjRZOK8Az7nyEfRXSwyNOatcDkV29ZU4Hk1r6M929mLs+XtbIgvZ4RA5TKbTznNLPaz7zvPMr2zvL7Y15b5r5jn/FkU2L806Wdr2VKfCHGW683vZeYm5R8kdTGjDczXx3nz3ryPb/50Zt5BeSeoZ0rg7AGQM4XVGAsXQWSYiwAxcwhEjiQcCIOmRWeDRxPTA1kekDKRkkCqGplbZKyJqQESRoy4LMnJgXQFsZQGnyWmUtjLhmpVkyeDnBoWekltGkSShN7jJ8fh3Ynt90dOu4EQc9maZxXN6pocKsJT4vBwRCiFbCpy7PG9Q+WmLOxqFzSNxa5qIpmpOzJGR+4EpknYusbmimqhqI8dw6/fEd8/0m0/IENP88VnLK9uqM0CIw39fkvPhF9oTLOClDn1Pf60ByYykW7fMc3bQz+V+KQSfkgKhCUIjSLj/IBVmbqx1FYR8VhhSdngp8BiVUGOxKgKaUVoiBNCJIw2SFmxbBuO40h/OtJWK5yfGA8KHxLXm4bLa8PQWx4e9ry6bdntB5rVFbLSHJ8m+tNIWxnG/YHt08Dnn10yjJn9MKCkIvjINEWkkoikitOUlJAiPhamfNUssEqzHySv3yy5WFsO2z0PH/Y8HCRdPxLjiXEQaKNply1trUgpkX3Pd99/zebllxi7xPmJb+6emFwiy8iH3YmrF28QMhODRyhJwuCJyBjpYjHzETNrlZxRWXO52eDDjqbzbO96/t//619SSU2lFeiaf/1nXxDiEXSDURpZt9STww8RlzxN0yClxmiLkhIRE0pZQsrcLlrWF1c87vZom9CyAhHJSpC1xNgFtQ80IhLjSMgBo2sGX2Zd9aJC56n8TudlGT5AzgGNJviA0aDRSKFwQTGOEy9vN9R1TY6ZmDzbwwlE5vpqg4gD49ExuESMjv3pyJAi/alAfz/98oq//o9/RRdhsbmkrmomn1AWFosNx0fH13d3v+Mr5Mf4x2LMAyEGrDKYczIlAmneileSfsyZICPkSCIRRCqk13MXndUzlP3cF4t5e9wz732e3Ytzyswz633+GfFRMpdn2F7kwokv2vgyWyhLfkrHfabblQJBzBvwEh+rhbMZzzmVn9GJkpyfPfbFbBZ0fh3mtXzP64M/Jv8fdv6SQm5LszagKOxEYZhnRRYwhQMDW5Q0LOQCIxu8WOLzHT4dy31AXKJYIUJDTBVKgBUjXlXkEIqNryhmRgZBtWmwLzb4KZM6xypOMI74XhKzgjFw/HDk+19/YHv/hBCC1fUNzXKBaSoqa7GDwPdrOlvThyMuDwR6Yhpo5ALpNakXYBsccHh4Yj/uMC89uInTw4F2s2ShBWy3vP/l3+D7DktNdfMCe7XBx2IH3rQVTkSeRI+TDr0wVBcr6naNkZr9/Qf6xyeEi9jhQBz3TD867f3DYSqDtRXrlaZGkR1QRyoj0bZC+IA0IKShspYweSrbIqQmCYHRimmaOE0DPkSkkUSRMJVmDBNDGBmGgLaKi9UFJzcynGr+7L/7r9jd/zW7J0NT1SwrVWxchSCKyHa3Y78fiAK6ocd7gZw9s0MEWyniKTD5sgmPGHEBatMgpUQIxdWLlsOpwzaKxVLz3fsnTn3ZTHc6TrR2SV5GQhzodxOnw4G+kdw9PPH0GPn5H1/TO4FQGnfypBBJI5y6iatrz27QaKPLWtrpRIq5EB+FRJiKxaKCymKyYbFash17nu479seBv/n77xm9pk8Tta7403/5czYtfPPdnr4beHGzRFuFtZLrTUOKkcvLNYvlAuczu/0OhEBrC3jaxjBNB7TV5H5PP3m6mElqzWmcUPJEFw0PJ7CmuFKJMHJp62fSkZpdqkxVPBSsFCQ0PoEnkJUkpITPDl0rlqkmDwdsLjf5EDO7Q0fMga4P7I8nmlry+ZsL6voNx+PA/W7LV27L19888XT3/+FnP/ucP/njP+Ju+4BKkd3+CWUUddXQjRMPuw+/y8vjx/hPxEiHxhCTxSeNVrIktnl9bUpFR57n7j5nT0qBLBMpzzB/KqY1SkgiYob1KWx5SgIXaSa+ifOim3n1bJ5lbTkici6LrESRBYrzvnqAOVmXjn4uF3ImzOtgVU6cc3Xp2NXzHL8cy9zdz978P3D8n5O/mBn7PP9cmkcM+Tz1z+cRRTkeSXGfy5yd90RZMz0/v8BiZFEf9MM9ewSIt6z1BmMqBmHI8ojKYNMSYgM0iGxARURlCb7FJ09O6aPLoJSMuWL7VUT2sFkuEA2MeUJPGdlnDtuOD98/8t23X3M6PlEvFwhqQJHGhOg9EsmyuuZy9Qa6d3g8IKmWG66bt1y112jZkqOh2+7ZnnbExREhRmppqV5uWEaFejzx+N07Ukzoiw2mbblYGtTFBbHS3H/1DXdPX7M/PTD5CSMtlWgJhy3b3a5wSCIEYVm2l7x49adEH+i2Pyb8fzB0BmMr6rpGTq7oQaXCWoHSkhgiLkm0AlsV9qxLE0oKrLEIJYgT+FNZLtE0NVk6YiqOS0objBHUizWvr9fs9or2ZsFnbxZsHxz3jyeWq4ZpPKCkIPiA7x3HPnJzdYN3AzkUb/mmaemGhEsTOQey8CTvsMYglEZZSbsozmwpZxaV4fJqPcv0EklpdLXEEPnwcODnv7iBPPBwt0UrQT/25HiN1BvaRUbEHWMHIQi6sRgKiSQZh5HgJqRURSMsa0KayomXfHHVUorgHJPz3GxWxAxOlAUXbQVhkrSLGm1qVouK//pf/BwrPTEngvcc9h3LusGhcG5iepi4vDEcDoneJbwbMLZCxYTIAhc6QtQIoRnRdKMjJYGoBN5nau8J3uKixAfBaYwkY4kplc1jcSp64BhJWbCyFiU9IiWyD+jaFKGRL+qLq1bhuoGHx0canedlOZ6HDwfu379H6Su0VuQs6I57hEiM3nHs9zjXkdC8f8z82b+6oLGSl5sFv/7mPSlkqqrBTwNP+xOu/91eHz/GPx5B9KSkyMkSoyUJhRbiuYstxjilW085EIjkmAqbnkRhx88Lb7KcE2SA2SHvvNpWzo6SnOftoizTyTPzXmTxA4O8jz72WXyc3RcuQFGTyDwn6/xRHz8f8Bxxzsw/XPx81uGXJC/Es9p+Pu455ln/GQko5MOzi9684U8Ikvy4qU9QXCuVqtCqXMMEhYiFFxGmzDic2KYPRDLrZklVX2JlDVkik0IIizI1RhcJnxU1eazJakDMn3vOZeV4HjJZW9pFy3LRoJUluMjxsccdHdttz9O+4xR6nHIoLOPYIbMkmxpdrVhWLZe6whrN5dUbgoVpnJC9Z1VfsFleo1pFqiJjnGDjkFVHTgPRN9SqIbw7cNh+IJrIxfItsbKkjaFaaraPT+RjIGZPd9rSn7aILAgqQQzQ96QsEShMbtFxQRYtWV5hVYOtfpTl/YMRQiB4z7IyPB47lMpU2ZZlDFkyTSe809jW0JhMlgFVtShKcZBzWdggpEBRgfDkpCBbTBMwukEtFa9fXnHZgp9allcbkh/JCb69e+L3mluSkIQscQ4OncMaRU4eryQ+e1oDqFRO6OBLIeKKyiBmR8qCnBSbzQaly67lzcUttmqJUTDFkbqu2SxbdILH7T2/4JqYwIeRMElWy2uOg+Pi4pr10mHNgrH7hq6PpOSKvzcJ7yN9P9DotsB4RiCELahHyBAjIQSmaSxbolLGWEOME0HANHmslmiZubq8ZH2xpq4CY8wYW5GGxPF44mq1REpBUg3BJ7rTgFQKaSRog7UtImcW7ZrF+hJBhescISh8kGSjyqzeVihVld8XkTAFVouKqm5RStFPE2lKhFh2eze2JmZF9CMhCGIS1FoQfWSxsLxcNlR5T2cCY2OIHIsPSIKu82y3O16+vCQlMLVFiUjwgd2u5/37jt1uYFXVhHbJMHUsW82FvmC3H0HU9NNUnAKrhpcXL37Xl8iP8Y9EUp6UHCl5dAol8as8S87UDFmHYrk7z7RTjiQCiAiiFANZCBBqBthVeTzPevVZpgYUHt1sTnOe9Z897LOIzx12nvfsyvRMsfsBe/4MpvNs2Zt/8J7ErMPL+dx1U45nTuPnEUJ+fs65bZ95A4UxX8jDz3TTLJ5/CkoZkefv1zJR6RV1tcToQuQVKDyRkMoMX5PQKdOPByafmLzjIi5ZtheFVJwBLdFWYpRCJoWMNaO2OCFJIpJkJmnIWSBRrOqG9aKlqioygmmIjIeex/sTh7FnCEeGMIHIRJGYxh6RBUYZsAoaiRGGZX3DSt1Arei3R5SKNFVLqsGLgWE64eoDsu1QKqAri9sdCN/c44aOaANq0dIuWrRUxMayfHtLFzxf/8X/l83rW+Soi6VwSqTsccmDrBDCkLNEZkstbrDpBfK4JGdLUp+OnveTSviempgXNK2kGycQikWTeNoOrNpYJBsShtORSSveHUfevl6y6xwZUKIubnfagBAYW+HiwKZd0yrFZ69vub3c4Lffsn04oKgYDoFTcwFJ8s1397x6dU0rIn0SuJgRMpJTYnA92QvCYJFy4LTfsbp4jQsjtmrxLiBisYBVVKisqLWmqipkclSV4su3X7A/TXx3vyP4zGrVsLKS3e6R4ANdv0PLBZOf8GFCmiVuusckgwgTwXes1y8ZxiMhQ8iRnBTDMFEv4ZQSlS5++0Yb+nEko0g5U5mKlBN6ljmmqWc49cSckUaRUKw2F/zf/q//Lafj91SXis2i4Zv9PY9P96w2C9r1FT5NGA1jDFy1hjGWjYRZCWIMVMsVxz5y3+9Kx640fdDFuz+X1b3DmBBCl+1+OSAEBDch6gpjTJE52YxtKqpaM6FQqiIFT8oVE7BcWq4ai4on+iExjAotG17dlsJKYEAoumFktaoxTY0Qns264ekYiszQ1KQ0IuTAcqF5PI4QHXePTzxunwjS0NYtm82azfVn6Kr9HV8hP8Y/GiaXIja7MrvPgZhmsZ0o8tY8e9uT0vMs+YewfTpb2YqIyKCFIqUCPScE4rwIR8y2spLnbXqz3Q0wQ/HMM/w0z/rFD8SC+ZysC1duFs4B89j9WRtPQe1muV0+owHz8cIzmX/eUHsm7X2c+5fa4Jzuy8ReFPH//G1nLoBEy5ZKXFKzwYglltK151jGlWmcIJUxoU4TQ5jYnj4Q3UByNyyqBUZrrBWzJS3EHAgiEMn4kIrDnoKsFSkmKgvLWtMoVey8XWI4Ofb7nqd+wIWxLBTLxZDH54CPIzqameA7opIg27Ia2GpDhSGTsY1GtopdfKI7fWDI75GXR3SbCT7gHo7oLhJcOT4hG5Kq6BaaumkYQ8fXf/5vuXzxktXtK7rTnmHsS5EmFTlDDBkpM0ordF7Q5heseMEiX2JzRZKCJH7U4f+DkWVisdbcPzhCgtWyQleGNmR8DChtyGLC+8TpFLC2QSrN5MW8ktGDklSNpa4lISjSqJFC8/J6zWcbi0wHfv31rzgMhqurmr/5u0es/H2IRTby9bc7/vnvvWD33QMxC4KfCB6Sk2QRqFYrpIksFo512yCDwugKnyQxgbYtOSdcmNAyY4whpsxhbHilLFE4vvjsLc5H1psL6irw/t13jL1j/3Ri9AE/7eHDxO3VgsmPvLi+ZfIj/+bf/Xv6PvNw94BIgRB7nJfsDicWVxGURWZRTsrgyQRy9hz7nk27YQyBkBwieLRPZALWZIxV6HrJem15fWv4939xojsMfP76DY+7I99898TPPv+c5mbJ+95TWUM/jKxXEz4V3y43OHL2+JjxnSckgVEWlxVJRMLkUcowjcMMb0qkppCTlAahIFFWEqfiyy2FIhLROlGrhsllrNRctBVLE+kPe7aHAdMajoPHeccwTfNNLyCF5Otv3vMnf3TgF198yfbpe7rBcf+wI6TMOBwZ/IjMCWkqxlFw6kZCbsmy4XF/JIUDf/p//j1e3XyJjz9a636qIY0kqkwKHp8DORf4N2dVWOmzO1ohuM0z+xzPvHnmsfr8rxmmnx8r8//C0JcIZMrFra4MxIu0jFlWBzyr4XOe3erOOP3c24tiCCTFeapefqa83vlJ8nM3XhD9QuA7S+vOM/2PRUB5/jy/7nNlMs8f8sz6L1K/H9QA4uO2Pp0XqLBGck1ODYNQ5ChwYyL0gTi0pChQUVFnh3c7gh84DDtcP1DbBVW1YN20rFc1CFUW2QhXkM+Yys9bhdIQ4oTSYJQuv5MQmbrA067nOHhCziRZiNyaFTF1hOgZ/QmlFMNYI6PAV44uTCihaGJNjBYtJaI1eDHSHd9xDL9GrQekDPjHntEdUTERg0SpjBAaZSz1eo1aNowic5oCY3fifTew3FyxH+5J3iGlQmRJJM9WxAKRaqy4YMkVS7HByKZwJkTCq9/YpvA7jU8r4adIyJEYy3pJ4oSbQKgC0WsBUwikJDl1Eakz+WVgcCd8FBiRELIQT1wokpIX61fc3q4Q+Ynx2PH+4Z6n/R5hrng6dHRHzzhFIpI3L6/pTnum8YKcM7ZaMx62NLXhFCZyVGhVsbxY8jpk6tqitcLPNw+0RuaEd5mh97SVQCmFFA2Xq1suX7wmqnv2hycqW3F5cU1TTawur+mmifWy5s//l3/PeHKsVxojHa9ef05tLYfDA4/vdvz5X7zjuOvwPjMNE+L6gm5MBCHRUqB0Jk6JfuyZpsjxuIcEzvVE5/CnkRwTIUQe77fc3r5AKkurNW0l2O++429++fdY7zFK83D/xDRJhnHi1O+xVcMYAtZs6LqepBpyTiivSJay4nfMaFOhTE0TIREJRCRlnFBVLf2+I7mJYeppaIlaoLPFuQhIjFAYbQGF1QY/TZAj15sVawOn/Y6u6xHR4U6RbnQ87k50fSDlRAyRlOHXXx347rtvef3i53z17TuUsEw+EFGYeoVWgeg7Qphwk6L3keXFkvD9lvv7Pf+nP/4pb16/RiuJCZ9Opf5j/GYYpcjKgEzEOOGFmzX4/z/2/qvJsizL78R+W59zrnIVMjNLdrVGN5Q1wUea0cgHfgzyo44NIQacwaAVuruqUobycPcrj9iSD/tcj+ieaoAwAKx4iFWWVZFefmXce9Ze//UXmoz+0ENL9divjT7NP8w1VpcyZ7Cfp35R8+1JH/Xg2c2v1AAbmauj3pmI99ECfW64Hyx35MzAr4N1OefbUaRAzaP6BztcKlmwpA/T+Bni55xPf4byPzw3cdYRFqhxvI9gPh+z+R8Ff2dOApoYLT41kDtSMISYKUniA4RgIFTIXYqCjOCyg+iJ6cSpHziKA8Z1+NWKlC/wk0GbBCHRaUdZrCgyYzpdWfRTQhuJlNWHIPrI8TRxDBFPQSj9qOPXskUVQYhHhnyCWMBn1KLGo8t+RAuJNw2x6bhcXiAsTNOBobyH6yN6KcljpKgIUhClIpOwWiOlZjfuuHFrXCPZ3z/wcPuOaRjQ2rA7PTD2BwQZqSp8rwCpJCRDw5KVuGLJFZ1YU4QhMiHERFHjf7fP+X9rfVIN32oJKSBNtWcNQaKEQ5sMKWFbyXGv0dqxWWtyluSSicmTcqKRGoohhR0+Zr54+ZQna8Wwf8P3b98xTAdCcExjQqsABaacKFngmjUXxpHyO/7ib7/l6mKBIdA0hs1qhdCeoQ+0eiB5gSdR4sRSZrTRCFXIcWDqM1rNLlol07SOP/0nf8iySSALy6Xj3/0vr/hX/+r/zLMX1yzlxNP/2/+db77+S7RpOGx7vv9hSxGB77498v/8f/0L2rZh/3bix8+e8m/+Pz/ww7sTi0WDKobj4GkclOmExOLHapahtasaUOXYNIK79w/EkNjvHvj661/xanfiOBleuBWLziPIpOD57rtb3r898rMvvwJ7xfWLRGP3dG6DUp6L6xd8/eY9N5sVh/tX2CYitGEIgc4sEUqRgkeKQtMYwuRZth3b40RjDLZb0ifBfhwRGZ5snqJcRy6+ynrQUBJ9OqJkQTuFazVJCNbLBbLA63dvKBmGIaCypJhSvQmEQrYtwhhSTBW6jYW37+4J6Zrv7xJaeXIJXF20PH1+zfa+Z386sFx1mMUVuEsWizXOrvizf/bP+LN/+QtCLIz7H6qN7+f6JKu6TRqkKERdM+5LmUgiUUREFD3bbzM37KrJL+XvT/oZZtlcJbWdDXPE46Q8775FRdWric1HxL5SkJVgMxMFawiMnOVuhTrdn2F/OLPmRd2982E4Lx8x8+dHr/8+W/cy+1Oc70jMrw8+OgScCX6zEZiooRScvflBUmYzoBiBSRAnifQGkoCUESmiUz3kkCqfIRaJwtKKhiAFFE/KEyUcGXvoi0L7DtmAVRrXrFi1HbpTTGLk/SnQuEsMkhh7SnaMQ2bMGaEkzHtymYFU+RYIgZYtfRgY+/dM+kDOAddsMMqhkDg/0hiNag1FRSa95djeM3JiY1sKEtVe4g87rDPs7l5BrNfw+4e3HMPI4vSMw/GBdNoTtj3rm2fEOBJjX8FIaaBoKJKcJaYsWOUnbHhCp1c1RpgqQUxyHgY/kfqkGr62lhJhmKBr23oKllBKIOZCGTxRKZbLBdfXK3wciCFDTJSkENISvCd6jyiFJwtg2rG9e8CPiRCqfWvOEus0ox9RSqFUhcT8MEKG27stjVMYVQgh4Kcjm7YhJll17lmxHzLOJRanROsg5IhQgnGMLDtLzrmyySUsLySHu9e83T3QtYaf/eyP+dM//jGtGvjmb/937OoZr17/wMPDibZb8wd/+Jz9dsv19RN6f+Rv/27PbrtHG8HVhUU7jVQSozSjr2zg4+7A4tlzcohYo1gsW378oxf85MU128OO4BNXqw6lNc495ec/fsr1+orRn1CisFxfcn3zJeurJS9fnrh8fsnTL64xbcNr+XcsN5qSJpoWjNNsNi3v3kRsYyhCsXpyxWrV4UbPdtfTdZbGaA77bY0nVpKpCMI4IRcN68s1Yg1WW7JUpBRQSjOGTPCeXBzONnTG0LqWEHtU6nm7HfCjQJqEbhqsMCQyTVH89GbBFCSr1Qp/GpEiobRmfxx48UywXLa8eXfH05sLfvTjn7DfnSg5oJVksV7y/Pk1X7x8xtPrF/xfr16QskfLzOvXb5gO96zbT8dA43P9/ZJ67oNJYrAoICVPIZPnYKlYqq+DKCBnu9xMmn8nzV77+ZEArM6dlzpYzJk8ZFGPB2Imzp337bXri8e1+TlAp9rVzp72AAUizJDv+T5kpfCdmz9nZcDsuz/v4x+XSvLD5H622BXzauJRc/eI6KtHxKDu8B+fLLPRcNUplAGRR8gjSmikrCuRkqtZVqU71ueos6AIQ1aOoiqaqrMkyYQoCR8OnLInFcNy1dE6w2q5QnQwniZISzSKPERO+3tG70hyAdpALmg0ZTY0SllXTk7OxBzJiWphniPleM/Cexq7xJol3WpBt+wwjeZUDtwdX3F79yvCYuTQCzara7KP+OHA7s0d426PDJGhP+Bch3MtLngkMI0RoS1owRROZAlS6kpWLhaiRhfHQlyzkk9YlBVGmnoom8OahIyE8unIez6phl+QuMahdGXUWq3mE3gl2+QcQWgSmdVqyTBFDsfAwkHKAusMo59o3Yq1PRLCiI2Zkx8ZIqSoGeLE6D0cTyQkxih8GCH2xABN29I0A8NUuNoYYsqcTif0ymFUJhRFRhBpWHabmewDp2OiMw2NrVI2P0yMKqK1QhtNEfDtd69Zr1b84e+/BD+w273ir/76a7rNxO37wu6ouPniK/7pH33Jf/jf/oo//KNfoMOO4yGgTcPm6oaFs7SNhVx9C3RrQWpSMaAtndvQLFqM/o5//gdfMO7fczweiKXQjxN/9md/xD/5Jz9nt3vHG9Pz7//iNRHDv/yzf8Uf//4F+/0B8Sc/YvIDh+0d+73n+ze3bBYaZwXX0kA4ctjVw8aTZoVbXmAbC8WDHwhhQKmmXoVTQtumEuSkJiewQlVosCRiLKA9qQSsdrSyqgMuLjsuL9qa4Z0Tp3Fgd/fAICRmljjFHCvrPikMYHSmMQZNYcx1f2qd4+5+4vDsgbevXzH5zC9+8cc8XTX88m9+xel0qGgJmpurlifX1ygjcQTubrekGPEFJr3k1e7TgeY+1z8oWXtgnd4lFE0RkZQyhUAmIlCQVU2tK6ky9gWPO/zHGNqPpHIKzlY0NcWNj5j6MxFQzBr8LKoQry7+Z5McAaKkR1HdGfqfyfR14i8CyCBrEkCdbz+W1M2v7byb5yPY/5Ea8DF0f64ZU5jld4+mQfOPHw13KBSZKPpEah6QqqF4jUwSoqaG3MtqWjS/NoUgYymioHNGIkgIEgNT6YnxRMoOMXXVJMtuSEriy4hPCWMsIUTGcc9uf8sQLE33c5r2Ei00MgpKSPiYiSWilEVIQRSR7AVZFpIAHyMKj8gBpwpm2WBWLZMIbPfvePX273hz/AadBKt2STIth/2Wh7vXnA53ON1SvEeWgmuWoBT3d+9QJZFjxrUdaIEfJkSpia1GtajcUo8lSxp5TccVTVkipAKqEiGQCIx4cfrv+1n/b6hPquG3StK2ksbWE3VJhX5KXLQGXxJKtrQ2IkoihhNGFTbrFVPqOQ49V0HX07sqmJLZnhLLBoZ+YrcPtE1bDXz0RH8KLFdLtEr4kOkfHvBlToKSinHqGSbQpqVtEuDxodSfVSdORIbddOJ5cVyslwifcE5xnDJIwe7ocdbRWItSLSVrQgRU4O3+hPEC212jRcO//LOf0x/3/PJXb3j+4x/zl3/3t/zqP/0Fa1v4/Z//hDFphrBntVqzWbQ87A+MYeDaXHCxXvHk+Q1N67jYXOGnI2mIyOj5m28fCFrQT57dvufuzRumP/yKd/dbfnh9y3F/4vL6Bf/8T75g2L/jm199z+5wzzBNGO348qsvuLx+xvev91g5cXN5je8n4rLhyRc/xeoBpwXDfiCXyMP7e0IxdKYGDBmjidSLWLdsiUSUVRxuJ8bTCYHANYqYM4fBU2JGaVAqIONAGHo2ixUlZlCCZbvEn05oKfFTpsiMlJlUYDjteffuxLu3b9C6uqh1rYZUuL27I6WE1UAY+Mv/dMvX37wlBo8wS5qF4xc/+4J2uWF/OHL35g2DH4HM2I9kCjdPXv42vx6f6z9TUladeM1kl8gsgXPWfSaliCixhrrMjPty3nGXmQU/h+zMqVFn6l39R5xNeOt0q8SZbqcQaobvZyb+ea9e9/Lz3n7O3BVi1ubPMjtZzv26du48P5OKDFTYviBmghh/v8vPB5SK7n90CHh8FnOvl4+xPo+HjMf/njMDpBCge4S5A6soTSLFFSq2qNEgvaYkTc6anOp7F1NdH6aaQI8SAVsSfR4YcwA1YnLBJI04ClLriSkyxEhMgf7+wPbhW7b+O0KJLFOi1f8CJbvqs65BKVkHpjwRwhGRLEZZBG2d8FFot8KKNa1ds1iswWn24wO3D79i23/PlA/ESbM2S/rtlve333I8blm5KsnOcWK5uOTpix9jVytu797ijz0OiTaWEDKpT8hsEbJDlTWGDoml5YJ1uWEpLnDSEUlkkZCyzLK9I326/x/3wf+vrE+q4VujWXUd7cIhlSSGjEgG07TkceJw9FzfPCH4noftns3GkPNEShe01jCMCddafIos1gtCEZx6zzAUkjSkkmhtSzCOKRtC9PNJvsJTlIJRjtZq7m7fomXmerlECEt/CoQEYwRBT06xGsSUJQ+HI9PxAecWSJkhg7MOGTVHleoOR7oq+1ENp8NAY1uU1CwXHVOGMPXsd/f86te/5vd++Bn9Yccv//od//yPvuTQHzkNB3ZjpFu2dJ3leDIMfZUgri86LpYNL59dE4Ln69evyQUe9iMBVeHLlEgh8fr2gft9zxgEvmgGH9kIyfv717x7/Ypfv7ml0YX7h5FYJrSt7od/9Z++ZdNltrstQmgWa42LgtP9PVPoicWhUqKzBi0kJUYymRQDfsxoK/HjQAGUDAQf8DHip4SPDqElJkWckyw3K6zPLLqGNB15eOfZ7SJtuwClyA6WTQtIYhjQuk5t+37km6/fcDwcWa8bInDx5CmtErSd4l/86c8IUjF6yd/+8q/Y7440iyXKWl6+aLm6XtH3Pd9/+x0PD7f000SImbEPSJNJU/wtf0M+1z9eszf8o51sQRY1u+vV/z+WDPOkL2bd/WObLOKRBPexyh2qWuRMhM/kx4AcZlZ9luepvcbQftjDV6e6s+zvbMebqIz/Mi/plaj+9mfoXsgz3048evk/yvoeIf/zvr9yBOoQX/UFokgeZYZCPTb1x/ubPf7P79c5n0eSkWICfUeWHmF6iGuEWiJNgwgZGS0ERVYS6QWRGp0rJEQCBV/zTkQlUkYSY55IMTEe6tozhpH96Y67u9ec/A94sUeKxGn431EY1t0f4tpLrLKoUSB6QT8GQpaUItGqpZDIYqIITUoS061oFhco55jKyO74iofTd/R5Dwq0sIgiOey2nA7b+vchQJSMFpJmdUGzXlOUhpIRIiGUIyE57neMwxGkxaFAWqxsaMSGVtzQyouqCBFQzPz3JjMh9Qx5T5+3/2M/+v8V9Uk1fB89hzESMFilCD7ROImxCoQiRME4nDAWhGkxxpHjARIopwjjgLGWLAVxmojasWpbitSoLLFGoI1CS8FYBEiNNQKhwFhDmTxN52CYaJYtt+8nfvLVM2KAIDSmVaQCndWcGClScOqPqOYanwrF9xQSMQQa29TITu1QxtQYyVQIIVY7XqURaqJxcNhGpglCVlit+bf/9j+y3Y0chkjI0A+B13cHQjZYK1ksLXavCT7QDxMhwspIujzxzcOBwXvGyVOEoV0YBh8I05Fp8pz6zKGfEFLzsOuZkuBHL1+w3e549f6A95mxP7Lz8PL5V6w2a5bZcPjiyPs333J/v+Xy4goZj0x94dAHWh0ojSGGwDRFklDEDEo7UkpoqUBLfExYa9HWkGe/c60NOQmkAmctl1ctF60m5YHgB+4eDrj2glA0TYI49liraoCPkCir6E8ju9PE/d0DD9sdpdRwja5dcP30hi+fPOdiGfnxT37O6TTy53/xn7g/JoTV/OirL1ldXPDy2TUax93bt3z97ffsjw8UpdGyJSdBydCfPp1d3Of6ByUeSewUkWs+PQIhNSWnOa+9zENxqtN5VrVRi/yRpK762nOWvYnaCBVqDpWZyffnFcBjDn2ef1bF+efbfpDk1d+v+fGVRyA4k+nOnvz1seauzNk2l8Jj7vzZPe8M4cs5r68SCusk/4gwiFlVwAcnvg8bAEHOBcQc7SvqK6+POFXWvAho2aPMGsQaqTp0KCAsOUlEFuQiUVkRYyKLQFKBIhKoiHSGxli0VSAFqUTyPnIKe+4evmXbf0/SA0oGpBII8cBh+tdM6Y5L+U9R7RcIpRFSkFKhRIGUCp0NRbYIadHSVf6DAGEsRcIYjhz7d/TxliQ8ShisahBIprGnpIg0muBPqJwwxoFxTH4gFch+RISA1JLBn9gf3leHTtlw1SzBZKyyLNWahbygTTXop6iIkpkkC0F6xnxkKHt8/nRWgZ9Uw9/vIrEklBVoDagWlQVhzNX3OidSiZiuxTWa5WKJGHrud+/xfU+z7BBoRAmEmLGNJaYRowUmRWI0qAxSKfrtkfbJS3ST0FIwCYmztjpDGcuXL57xt6c3OLNC5p5iNFN2NO2SHPb4JBhDwI8TXbrGi4bd+1doveJiuWLqDzhryWlAxPqFyinip4nRR4QsMyNVM017wjRh3JKnz57w7/7df8S6Kg86DAOjbylCs7m8Qpkj11cdr948ME0jKSY0msPdO/a7O4LbQIZxGvChx1jHAU9IhUINJTJaMR5HdMlcXl7z9EnL9z+84zQE7g8HusWS3/ndn/GLn7xgbQu73YEvv7hhe/+GsT/ysx89I+fAYXekCMvbd294+uWKKcEQEyVHIh6ZFMOUaVbgTydigtY9qT7lMTKFSCmStZMobXh21XGxXLB7/5ZcFG/e91zfTEjtWS83yFwoSmBNgxAWH/YM/T3v3u643w/s7vb4cXzMG7m6WHNzueL6puF67ThNgX/9b/4D33z3A7/4nZ/xi1/8iJ//9EvGU8/Y7+iPBx7e3fGw3RJjxnWGkDM5DAghca37bX9FPtc/UmJurnVqnq1MxdwokRSpkKWQ8rzTPv8CzJ71ILN4pN+fTXHlPHmnGZovj2z681Qv5sOG/DBFM9/fLLXLuYL/QlZpXu26s9xPzJG98+8++vCJ+TXNBwMxm/cIePTC/z/u7ctjvrx83PWfYf+51YvKti+PjL4zk1/wQUAIRWbIgVxO9X7lhNJrEB4pFxSvIUtETPPz1eSs8DGBSwgDkwks24RtBAlFEhJiIaeRY7hnYI8QGSkgSYOTisSOvf9f6N9/x7D8P9G2X1Xdu4lVdRNAWEOjGuKUUEVinCWbgo+BNGWEsmTXYKzFhJoJIKXAjyfGcER3DVZZyjShlGK1vsK5juNxRwwjohRct0Fay373ju3DPeNwQooeQ0PbtRh1w1o4Om0pQtYDj0pkFYmlp89bdumWfXjPcfwM6f/GKlKgjUVIULpFNBndVFKdFgahJE1biHnED5DDihfXT/j+7QPHw4mbq6sam4hkCgEtBIYqSxHGIHImpIQU9cTurCXEibEPNEYTp5GSE7ZZIMVIuxR8+3rHs43kNIwsrp7QWM00KayR9MeBfkqsU+HkI19/c8/Ti8TLlwqPR8REjh6UpEiBbR1BJHwIjFPl6TpjySkTyDRNy9ObNU3TcDycqhWsaZhytYE0RtG0HevlgpdPL/n2+/eQIjmOPBwHfGxZP11SQiIViZ9gCFO1/83VhKfkGrNJEnSNoV0vGMYdb9/t2Q0R113z+7//c754toEcGEfwPrBYNXTrS/7ib3/NV1895+biCVYMZAm3u4EvfmwYYiCHyJv7B15++ZT+9B7tLLLA1A8gBMfTDpVy9TcwmpgVUoEsA0yZ+/HI4XSkbRdI6yhFctr1EBMqF7qra3YPeyY/8ctffYcRhYfTyHY3ocyCZtFg9AMxF64uG758/pyQPbcPPbvjey5uNjx9ccPPf/4Vi8UCFSdO2x2n056diAxDP/MFClIBJKStDeQjl/LP9anVuTkKQc7nqTc9euFTZPVfL6k2vFLgkV0iH6V1mUIWeYbO5xlbnDfgzNa885/J82FhZuwX+Tj9y3Oq3UeRtXUlUPt0PR58eNzalj+E3tQzQb2zGqV75gicp/T5wDH74z+2/nmq/8A+kJx9+ucdRPXQP//6uemXD4eVc9uXqLouEBlEpNCTcgIiQjVIpRCqUESgyIjEYnKHHycCE1J6DlailX28R2kVwmeEDcTs0XURQsoJKzUoh9Q9PvzA9vhvGKb3NPoGpTqkHqmmBw5RqgJKSU1RkIyg957lUeDUguXyBUN6yi6+JaYRmQpjf6zkYLdAZ0AEjG4w7Yqs6jSfxiNOOErTcvA9++MOH/xjAmHMJ/y0I7uBKCd8GasLIJmcE7EEek4cy56xHAl5oCT/3+9z/t9Yn1TDn/IIWdCajmc3V6Qwcto9IGVLUQmNRIqE7zNZHfnV8dcsVgZhWg79hFECbQRKRISIlJzRTuBLNeAIMSNCAh/wISF0wQhVw28oTCFjTdVMWt3y5PKS27s7nl7eIKzGWcjhxPFYk7n6KaB1R5ECQa5JbkXz/vYOZVuWTYSSkVohSiHnRFES7yvrXpSCbluyiEgjqpRNP+XFF1f89V8NtFoyhUwREiEcqUg0gpV1bJZVhzT1PeM4MJiaKZBioBhXDX90YehHnIGYCloajLM4rXlz7Ek5smjh6x/eENKaH/30x1xfX7DpGvzYo5ViGieiH0jDwGJpuWfJ//Rv/5r/x/9lSZbwF3/+l/S95+72NUMxjKeR43bL9t1rhikzhBF/OtAfA6U42k3EuSMLZVksVxQ06IwVmeMhc9hvgViJjlYilOY4Rl693bHQgt/fXHE6Hrl9d8f9wwHbOkzjuGk2nIYBL0OVd4qRy9WCNB3QxnKcEtM48eLFDatli1WePAluX7/m+6+/BymQcUA4g3RLlPJobSilkDIIEmH6dKC5z/V/rLPcTJwp8EKQZza6mIlyVRF/vsFZH/3Yzjk73KvHHj1r7EVFD+o3/ax+m+1rzvD+3HirzK7+7MwgEGdUX57Jdh/F4ZRK5vuwl59/t3wA48v58HHe2YuPFHgfWeiKmUvAWXgwCwL//n1+RNwTZ67C/MrmxylnwmFdDtTnWgCRSVS5YyrgSySVXBMIRcJKhWHNkAxjf+DOv2MqI5fLp+jS1AAhVWOn9TQjKKLGm8eSUcrMNtonfPmBQqSIN2i9wroG/CVl36GTQ2kDVK+NMEAQhUFGhJR0NxtWV89o/Xf4Q0+JEe/7uhIoihwGFNAtNyw2V4QcMFph3BKZKiPk0O8Z/QBCIo3F2RanHSoHJn/HyTSENFKSA+HAaMYycio7jmFHCFM9fIpPBxn8pBp+jBHbOnSz4We/91N++O7fs98WXFOtF0VMKOFwKpEFNIsV7UIzxcRhdJh2TSkebQo5BaZpQhXPdj9iGkMRnhIlThmmMqJ0JXMoGRlTYsoZIQSuZJxzLKzgvSycxohVHafTPXEM9GGkTIEkC42pUZtSUj3i3ZpukymHPQvTYp1G5FQ/6AVKgtEHCgFjFAiJMxojBUppNtcr/uRP/oBvvn4LRbHbTZgfX1CkYfIJf9giBLTOkhKEOJFyJBZ4srJEXcHFGBP7fkQkgbSaEGYdsKwHkO3phJSSccwk+4x/+ie/z3oBWinaruO4CygUndIcfeA0jqisaRvJqzd7/sNf/ZKQFb/65ZbFZs3//K//I8vVirZteP92x68XDxjrmGLiNBouF9f8zu//Dt1iTYyemKdqZJMCPkwUYj15S0sYT9y+uqd4T384cL/vMbpBCsHf/M3f8N3tlozgRz/7kp989VOMge9fveHP/+aXLJsFUhkkASM1x+PAxap+zNdLy9OLNSFCipH9Ycub2x1DkoTTgVQMpu1IIeFUqcQpFMkPKCnw/tM5qX+uf1AzJA4foHDOMaylVFmNFAiqG2b10K/NTqKgxLrPzhk1o4IfUANmNnotQY3FzTObX5V6mCji0RrnMXmuiA8e+HMPfSTJ1cqPO/+zl+8ZEDjv48/xvJmPrXrFjDidd/MfEQ4fWYPnN0b8/ft/PHR88Ax4DNzhTGas+/3a8md9wiM3IlJKIaZCTKEekkQkUd9DLTsWqkNnwyHccv/+Daf9e5arC5p2CXpCWUtjHaXUhFTIjwcpqQxSWWLyxLzFqoLqBEIklN1g3AXaL8hBEJOHIlBZIZRhihkzRpwQXFxsGMYbwrhlHO/IwqOsrRLHEJBGYdoWZR39/oA/HeiaBauLp+zjxJg8IHCuQSnN0i1Zmo5GWfqwJ/gJmRxKXNHaJxiW9OXEsWzx4kgmIYXGqs8N/zfWOI4slxewhNVFx/jLkZgK+/2Jy5VEKEBmlMhI67CdIhNpbMdXX/0E7RxhHDFqQbtYUKxFmEijNUlqSiw402I1OCdJpQbE9P0dShhCqAl4xUBB0XULVNmxPU48vzbkbHCdJRwnDqFnueloWsfUe4xp6qSvHZjEdohouYciOB4Gcg6kIpBa0S0aYhgpIUGMtI3FOkOmMA4jVxeXdMuG0zEzhkQqhUZndkNgShltJNZZhK4zgjWSrjW0HSQruUseSsEqjTYG0zZotUdJhdAGSCyXG9zTS1y34erqBdeXGyQTVht8mBC5cNi/Y+h7xmlEiEgqA0kXLjYX/M3Xd2xWK6y19McT1jgGrzDLBX/8L3+HP/2T30WlAioTckLlxHLZEYVg8I7DYeR07JFhJMUKo/rJ47NAuiUbs0SZ9xjp+PKrpyyXDa0S7HoP7SUXl46LiwXkPf0xsN3dY5TCKoUzFYo0rUPpFqkVF4slU5+xWjGOIxToY6RbXzKMnjAp3t9vWV5m2kazaC2+CEJK84U9gbS/3S/I5/rH6zxlP+6r6w/FvE+vEPfc5ISou/MzCkCZde7y703HZ6i+QvFpvrWqa4E5Drd20llyx3ldwNzEyyMBsN7n2c4WOBvuICtfQMgPMAD1wHDew5/d/8TjoeYDsP84u59Jio/QvHg8ODwiB7O64MP7wwc44Xx4ON8dNRRGznyF6i2USCWjtCKHXD0OSiKJQsrVmEhKi1JVn+/0mqKqZj9x4nB6B3pEF4fRButa4my4JYAQQs0YUAZjO0iSXMCniIkBYxvMItJmg44deVDEIdbHjgVhDUVVh0TjNG7Zsmo6DJJDnKqkEkH2U32PTUuWmpAjuQiMsWhjyAr6YazXXeNolcEaR2c7WuXQQJwmjsMOHyLLxqPcEqkcUQViCcQcQYARBvWRSdJvuz6phi9x2Kala1c4M3Kz7Hg1vMXnzGK1QmpQFKKRpFJT7IrUWKu4uV6gnMIPieQzVgqkbSAfMVIitaDIFgrEJBn7QIo1EOc0TRhR0NYAkiQKxiimKXK/3XMcMy+vF0RAlEBOnvXFkpgztlmhDEBCyUq0M1Jj1tcofWIxBEIBmSNSCXTTcbNa4n3AqIzVjoVTyBwYTifk+gnGKa4urxmGexKJ7aln3QlScWhtSSFSpODqosPqhpuLa6SS3N8/0C4kWUqkyDXpry6i64VBCoxxSL3kj/74T7FOMIwnNm3D/vCeNBxZr1ZMWpNF4jD2DIeBGEcygtPYE1LELBoSGqElz55f8PB+z+r6hi+fP+UP/9nv8pOvvsCS2d7fczzdMQUBYeL9/S1X1xccvcA2DUoY4iDx0aNMg5CSJgaUMShlefnlC9Zd5PrpJUVEsi8s0CwWG9pOcjh6/DQyec9+39M6g2lXoDWiCLSzTDnilguMdsRJcBqOnE4B1xhKLvTB0y7aqg/2EyHAar3AjwOx1DhhJRWKiJKfw3M+1aoM/HMTf+zEH+DuQs1Mpja+Su6rm3QhIvm8Yz+b7wgB5TzhVli7yv3q4U+WsxkOdS/+0UdDPE76f39Xfnbmg+ogl8/EPlFRuQ/7dFGDfkp97EdmfX2hwEfrgLPt7tlYZyYFivODckYXKjFQCDHD+uLDbSggz429RpEXISmo+jxEhCxruyyCHJmJiOnx+RhUPaQojZDVDyUVQaNWKFHwQjOUxDANWF1QSqK0IyPn71iFUXLJKFGnf5s7YvTkkghxwNiGVLak/B5jllhZm28Y54RDXQOSIgGpC0bV9zTN9yGtJaeETAGlDa5ZIaQmhAkpoWmWGLfgMPQ8bO+QudC4llY7GtPS6hYtoMSESImYBsYSWMiI0AJpBLLOU5AUtVvNss5PpD6phn9zfcWTqwuy79l999fcvd/TLhbILNBGk8JAjAahLIaIlKbqxBuDFQPDbkCrjiJO9PsdkiPXGwtk0jiROTFEjxWJ/cMRHwJTSIS+0Me6o05+ItkFOU6MwdM2V0QKhzFyveqIqZpo9Mce225YrRoetnvImc26JcaAEguMNIy6RdiCTIf6AstA9pZSCuN4pFldYqzA50zxEWM72qZmcf/+7/2E2/cPTGNmf3I8u1pzCh6rG8a0xyqLM5YnT55xfXPFd6/e4CIsLgxCVSJPzhq3apGpKhOEVDSd5ebmikXjeP3mOx7u3vBqfIUQhXUjGKcDbnPN/cMDh4cTIib6cSTlyG67pwRFlqCt5KEfkSnw5RdPWGwu+PnPnvPzH79k1Rru33zPN9/8mn2fsF3DYlWDM45T4jRGGuOQuuH29MBud0TqA1JDZxqEAq0aDrs9MvSsrzbU7JxEkRJjM1k5MoUiNDEGRAHjqrtfzhklwZnK4DWmYRgj41SYvEToSuqcQkbm6oolhMS2K6RRJAGLVUs+DdzvdyytJoTAsN//Vr8fn+sfr7qrn2l3ZU7Am/fMWcwN7byXzpWoVxPxct3lz41enrfmj4Y0ZyhezimPeb6AF0rKj031EcJnZvPP03Y9b3zU+KES8cgIKR8PFFU+Vw8k6TzNi+rLL2buQCURyoo41ZfCWSVwVhqI89Q/IwrlccyfZ/35No/6e8F8/+cNwDwolPpuFKkfjykKjSwaiiYTKbJQdO351a9I12hwJImMzIJYQMoOCyQZmKY9MR6hxBm5qH95Jdfbi1LDcIo2tLoDbZjSQCo18liRyfxAmBqUfo5wHUbmagaU5jWMKQiXKDKSfaDEiNO2SvH6AxTQzuJcg5aqmjClSJYCoTQh9Vgh6ewSJzWNMjTKoqWikPECJpkJMiOlxXYbbNchW4NUEh0EMlLVEvw94Oa3Xp9Uw7callrglOe7u3fsdp7lwhGPfc1RlpIUI862UDQhBayHYhWCQE4QZYNxDev1Bbe+oIpg1cApTkQMIVUTJykKTkpk8ZwODxz7gJ8MbbNgvZEIaZHJcHl9iVKCPkR+tOx48+4duRisKficiVmijGXRNRhVmxIlAZkpWg5xIvrMRWMQWaOkIGZBKy1KFIqQaNOSSyH6kf3+nkXbslm13NysuX84MY2eQsT7EaMMy+WSPmwRSvH8+QYpAiEljFJMcURIi7KaEApliKQc6IcT3vcoY5iCZ/vwhh9e/cCpn5i8wLUKJxXeTxgcD7sB7z1l6Pnu+7f0w0hIcLHaIFWF2pR0KGv5+Y8uuH72hJvnl6xaV3fjdwcSLX3YoiQ0iyUpFIS0CCXxwbM/Hfnm1Xum0WMbhbKaaOuezbmIQHLahUrq0QohI0UppM0VjZH1dU8xIYtEiMQ4nsgxgs2zqqFarMYwsWot1mq8j0zes9semcJI8JHRR9r1iq61jIeBYRzIWbBo1jgr2I5TDVz6XJ9kndXm5Wy8M7Ppxfxz5n/Lj17ymsqVz5VfMzdFOfu318aoanOeWftn4J4CUujZNz7PmvoZTTg78JWPIfbzc/xQat7AZ2Zvf2SF4kX5YMIz30idm7iQiDMj/1FBML+yR/Jd4Syze9TszyURFPnRUuCjs0A93NT7yCXOJj6KjEdi0MKihaFKakTVzSuqp/4sitDoaj+bCzkHIEKcuQHa4vIaUTK93JKZqmJIFIRRCKFQUqIT5BTIM+eis0tsshzzSMxTNVpT95Rc+T/O/hznHDkZ0lAPbEokMgMlTwzDgT4eKVbhpKaUjDYO2y6RbVudg3NACYnRDoFBCMHStugiMFLipEZTsw5CzoQYGXMgSkWrL1i4SxbNEtE0eOlp1IqEAkLlhfjPkP5vrEVjKNlzOr7jV7/ecpocq6Vj7LdsFi3MJ92SI/04sXKWVCJSbyhpIEWL0JGULN1iybJp6MMOikBLzcXa0XiBk4HTswtWXSH5nt3+gLYdL55+idWCtql69alEmtWadSe5e/+aCBgkUoIfJb4k+qEw+YzSjtYtMa7BGInTMKWMwGLaNePuOxoLuQQ0BXLAT5kgO6RSaG3JFKzRNNZwdb3hyy+e8vDwK0LI9EMg5IK14IpHC4nSmp/+5Bm2U6RckFbTaEdTFFJmhMw0rvrsL9oOKR3aGg77e/a7gVwEJav5ogf9GEgFHt7c8uqH1/THA4eHHcIsSMnw/IvnLDpHSRNxSmSZWF8sGMaBTgrWFqIfuDv6qmMdArq3XK42GGMZwkBHJgyePhsOpypZvLjYcBr3kAtpdkyTJXO5bhmOA1Y6cBCGSI4J2zhyghIFKReWiwbx5Jox1HyEcylZkCUw9oLWWaZx4HB44HjIbI8HjsORxXIJwpBUpnGGGDNCKaRccbE2uHEkToFufcWy+e19Nz7Xf76qp32ZzXHOe/NKA3uMjJUgsyTPTnRiTsIryPq74jxxyzr1IT6S4Z1XBOWxGdcmrDib01dvepDijDDwgVvwQQhQw3YEiDJD+UgU5wPKh/XbuRGrOeFOCChCVAWBmO/so7Cf89J+dsx/XEZU86DKDPxgsVs+ph/Mr+XDxr8eW1JtWEUh0Y+vE1WQVqCLrm6aUEkSuaIR1WUzEMpIItS/mwBKOYxYYJJnlB6Br+syaSqnQUkaYyjRknNCIWhti5Adpd8y5Uj0I8pJkrqvw0nRCPUVSi3RsiqhUhM5Dffk42se7l8z+RNWWkrKiJRouhWL1SXCNUwlkgdPaxfYbkMqCl0kzjYoaoNUuaoJapBPIgVPjBNCGhbNNStzRadXFK3wKlVDsLLBE2qwkPrc8H9j6ZKwZIYi6E8Z70e++f7Ium3p9ydMZzHG0rjC6BMpGUxT99UIhRQFKw1JDWitMUUQk2UKkkFIplOk6BYREvsBchKkKbHZPOXJ1RLFkX2fMW7B8dTTdivWdgFpYm0b7h8OTN4j1BLTeabDhNSK6BMxZ5ZXFxx3I629ZFKJabyjMx3SGPaDwvvI9bNnrK5aVDGM/ZYmj0gUzrWotqVrG5TIWGf54sUVX3//mlQiQjmuL1eoMlGOAp8El9eXXD+55OFwRClJSJK7+z2hbdG6Ih2SgrKKREQqg5KScZjIyeP9QCwCqwUP23saJXm439L3mRDh4vJHdJ2n2TiG48jzpxe0nea414hOsd8dyT7y5rDnavGOF1/ckMZ7WlW4Dycu1mukkFytV6TkabViCAFRFMdhonGazXLJMByQotB1bd2HpkyOGikjp+OBHHtGX7eoQlamcvDnC4bGNAaGWH0XikUZTY6wfdhzealxtkFm6I89KQdy1oSYcO2Cpluii0QOiSH0OKNYX3Ykn2g0OGfIHuy4h/h5wv9U6wOfrZAFc6NOj5rzx9U+89Qv6w6/kuTrvv68sz//FjPDnrOWXX3YqZ9jb8VHTbb23DmQp+QKVTOjAuf75IwG1IOpRiFlNcoRAoTUSKEQQqCkmh9n5iGc1xKiUEjzHj2SS4TzGuKRuJirRW85owz1fZGVNIDMNfBHSFnXFDPecEY5xKzBl1QzsjNWIGRBaaBIslEoClqUqs9PQA41PCxFQg7kMtV4YlWRDGUUTi4pJTOVPan4x3dICYFRgsZ0hBzxsy/92qzAJu6mB0r2lXtlNNkGRv4GmRJG/ggpHZlA0TtO+x847r5lu3sDUqDQpOAR2iJdC01DmFchxjZYtyAhGOOIVQqjDRqByiBSIadcc1ZyxKeJWAoLu2HjnrHWl9jSEaOgywKTHVFA1oWcC8V+OtyfT6rhN41CF4+zC4TRj65Ji6WlP0ViFshUKDliDaSYMFozjBWnV0DOES0zIp4Q2dE6h3EtQ8iQIIvEvvf4JAiTJ4yezdow+ZEhFGIS7E8jT66WNFajnCFMgcVmwe3dHiEE603L7e2O3kukVOy3JzyF037PaVDYbkG826PNgsZKCgGlJNjqLxCHgW61oltdsF5E7rcjppFkJTkNPWvjMELTOMNmtWR/DCyXS4QGkQz7fo81gT/4nR+zWDjuDp7GGhSSEAKdNChRPaGlVOz3h5k9XNciRjmyTjO87skx4SdBJOHaC376sy9om46uzdw97IixkK8gFw+xShavNwsKgS+eP0ekESEGcjiCLugEUz9xebXgdEz4eKKkzDQOYCzTnKndKonIGVA42SJK3YkFP7EfA8dTTz9O+Knn2Es6p5BWE0dPygktMm3bkmR18gpBsFx2dMsVp7s9/TBxc11QJPanidMwEHJhHCO2NSg9S3RiBJFQxrLsGqwqCNOgKUiRGXKqnuSfzkH9c/2DyjnPkH55hPOro1z5sCeHuVmeBWB1z0phJqlVOV7d1T/O+vP0z6Oxz3kvf96Vz1t8pKxxs3XOj5Q5B73M07UQdXI/r9XFfBspJRJZY1fFTHpDV1VNnpsy4jGdj1lhUESkEAl5qs+nRHKOCMqc3Mf8zGoVWR7zAuq2or5X8iM2P6IghUFJh6RBlbaa3MwER2TlPipZEcYyv+JEJPpIDJ7ASM4TJQdiihRCJdFScwKcqxLbkgu+7IlhqpHDIiMVaGVxukHlUBUMCpa2ZQo9fZhIpFlxBFE8EP2fQ94i5VMSHhkfEOmW/eEdU+lxxqJKPZS5xRK32FRENU7oUjBNhzFtdQlMHqstThqMUIgMMQay8ESf8XFiKgGpOpb2KSvzBCPX5ElTxjyn+lmMFJQsKKlQymfS3m+s4/2veLvN/N4f/IKbp1ccpx3rdomUW8YYWbSWIhXONuS84zRlgh/wNITRg5H0wx4pGsgJYwyZASMFTiTeb7csL56SS8A1lq51CLvi9es7xqTpFo6SC2OYaNsblATbOmI4gXb0w4gzLa4xJKFYrFuWncMoSQqRVMA4Rx8neu/rTj0EfKoTQomZ3bFnmSxPrg2XnUYr2B23aF0T4yYPujUgMgjNs5s1/bDFNAuuLhoeDgfWVzdk0dJdXHAcagJVYxXjlLDGolRBKBhGT9M4xqlnmjwiV7WCMZrTaSRGiKGQQqwnbNvw059cYYAijjxsHxiHxKFPPLt5SszVtmTpHEoXbp5c84vf/Sn/27//f7Nwirfvbvnyi8plaBpN0zoaozluD2z3D/Qh42LHm7f3PH35jCQsOQqs1gxpIo0BI6dKfDGGpukoqq5XmBm3zlpKok4lRlNC4bjvCd7jzIInNyvWqzWH99+ByDhj6Y8H9n3i9q6nWTi6zTVaF4ZhwETIJWGUwVlJ0zqcEmjlGPuenEYWqyVFFSSfw3M+1colzJNwqdr3/NESfD6ofQzOVzHeR6lycsa28weWvsjlcV9eRPpg7IOe/7fSBMlnK980T+CqZtkLQS41H55zQM4ZWhcCKeT8E4mSGinNPN2b2vCFqaQ9WfkGj2EBZHKpoVwiR5RsZrc3T5aeWCJn7bx4dA0EVc46gbP9r+CDy36aOQIGIxdIOmS2yOIQ2VCSpOQ5mCcLZFH1sKQ/cBFKyUwxMZWJmAZimHfxokYIJVGqO16U2FYjWXJMhQmBSCPESJSCqBKNa7FJ4tPIcdpxZTZcNhfAA0MOiChRQpGlZ8o9Y3hA0iClxiCJ9JziDqFE5cqnESMFbbvEuhZrLCGO6AIL2yKURpfMwhiMcFhh0QgSkaRSDf/xE2MMZKFZdU+4aJ/SyiUxFHo/EmMCpRBGo5VESDUfEj83/N9Yf/W//jnJ/oQ/+CPL5bOOoRe82Bj+17/4JabZEBBYbZhSYHec6L3kYmMxDo6Tp3MFHxxt51CqQSvJECMoDSJgbIfRBRs1ywZsZ7l9e+Av/+4Hvvjix3RLWRECITHWkMnIFJn6CZ8LPkLbGYZTT04KIyU+9rRWobQhXl5ClCxk4PmmYXfosTi0SDRdR7GGpATCthhdOOx3XF0/w5jCuH0gGgesMLYh58Lm6gaE5Nvvt5ymkZ9dvOTV6x9YGEm3cBgZSNNAYwQpJZqmw0/bGqVpHSlXqG/RXSDUADITxkApVYY0hZGQJCUpBIoYAvd3B8gJZxWyGKQ0CHlCyYLQjnXr6LPn4vIpP13XZMIvfvYHHN/9GmUaCpph+55WNtzfvuJ0CmhRmDz4MWFVRiTB6XBCrSVT9BThySWji8Zqh9YSXybQltMpUEKk00v6wWO7QiYQQ0QoxThWfe3l1YZCxtpC43RNKBQK5xzjNOKUY7lc8uTpJQFJf+rJORKyY9G25Gxp2oKQGmUkOUxMfkLhuV45Fl3LMHwOz/lUK5cPdrgVzp9ta2ZiWHWmqw75SIHMFa6vbHTBY1LueXo/k+6Y2eozsU8ICUI9gvMVMp+pcukj+H1GBGrNwT1U+90za05Qp/dKxNOPLHhVTP0zFoRGZgNFIcrsRVIB/Yo4yUzOnoQnCU8WIyIHIh4hIiJHikyPB5yzYZD4mLEHUAwCgxYOLdq52Ve//ByhxFKNCZOsGkRZ/1FCIrQiz2S9cRqAyBRHYpge04bzHBCUBSiVUUljbcdaW05C05ctOY/4yXOiByXZaIdOMPiRQbVcrtYIK3h9fAc5IpLCh8gQT6QwIX1BKUEjVpVY508oaVEItICuWWKaFVFQnQu1QgvHursAYQhlROSOJOsqJlEIRdTEzBAIMVKUZr284GbxIy70c3Re0PeRw36oSICSKNNUYrmSFJmQn3f4v7nuD5nLJw5lBE8uOsaLka9/+J4+GDpXXd8MkVM/IYTB54iPCqknUkgkJIWq0Y+ANgqVBCEVYoJD39MsLjByIobI1AeiT/gpMk4RpWT1TFYOpTU6e3wYKwlDwrLruFyv2R8ekErTLSwIQz95Io5hGmmFoe8n+mlPmjy62dBqSyFilaIUzeX1U9q1YX96IPoT/Wmga1oWpuUYFBmB9yeUUnjvWS8s0xSqvlwkTLPi7tU3NM0GbRPWGDICowtTL1hrycVqgZ98Da0oiZgyxmm0FkijUbrDuonMhNGC4/FUEwOVQRnLsT+xWTT4cainepkQRaKtYa0avnixoT+8p+8Dx2nkzd3I2Pc8efqSu/sDbgHBC0LICCuYUqEfIoulwjkLSKKHlAJCC7SyWFWzxVOOGKOQskqotBaM48T+1LO6bHBaE0UmxABSYq1l2bSMfkQrh9aVSJVTRCpL23ZECoulouSJKQq8D8QYWG0WdI1mGicECS0kk090VrPZrBlPGZIneU/M+b/0Ef5cv6WqzPL8OGHPQ/ojIz2Xc1Dt3PhgJukxowEfNu3n9NsPm/nyiB7kUvfgQpQ5hbdOtoL5zxKSqNRRISWq1MOInDvfGVU4S+KqFK6y1GWWKKFRopLkZNYIWad9iamHi1zZ/JWzV8l1URoEDomH7MhqxOeBRAA1ETjzBqtu/oOyPz/6EQih0Gg0BlEUomhkVpUcmzM5JogJYiAkiTbgjEBqjRaKlAReabTWSCnqoTyPNc5ASGSWSKHISkCWpFgbobMO7JJCZIyZEHr8OGCEZFooGmWgwMRIadas9ZrDdOI49Hjv8cUzDCf82EMqNLaiwLGkil6UGhM85/VWP7+SKNFjlGbdXbJs1pSYiUoQdaoMfHL9T85EH0kpIlvN0m24Xv2Iq+YLTFzjj3CaeqZpJOZELgqZIeYCpiqKKm/i06hPquFfPnlO4wxkybMnX3H/Zse6VXxXFGSJLZCD56HvaVSD0zBOmZwG3t/f8fT6CyienCzjOKI6icoFpxVeW5x2tI1jPNxjmgU+RKKvbtenYY+PSzabFdPUk7KklZnkqZIco2mXDj/uGIYTRS0RouBDIIlKCCpBIjrHwykyFksmc5o8MRfGlNGAbQytVTjdcHX1hPdvv8XZNddPLnnzw/c8HCNKJYYwEcLEru9ZXy5ZLloOu3u2d/ekqfD1q3tePjVcXq2QRZJFxCnoujVLCwsn2b69Z9dqlleXSC1BSJadBZGZxp6QA0MIBKbKpg2ZHBYoq1DSoZUmF3BNR7fsICg6Z3n51QsO21u2x4H90TMmiTQdqLHu5jvHeuk43Q087HZIKUiBWR9d08GEkmSRaVqDUhIpbYVjSXVai5mUA9Ypuq5lkSU73UOOZGqOgMj1wt44XeHaXFDSYZsFoiiijxgtoHEc+kCYAq019ENgHAaWy2VdEeSAIBK8RzqNswotYEgjwzSx2/madCbb3+r343P9Z6pUT/uz372UAkR8jLSVczRtEZJS4tzcq898PsfewkxUm9PxyFV2R5XeFVkbrKRC2vMDfjQtM4fhyLpPn6VzqdQjhCzVGKZQ6mFjluMpIdGlQtSiyFm/LZGiTt0ChUKjioKs5gmdmW+QoRgUkYwhF0dCY4RClpFczq/0fIj50HxEllDqaxFyfoys5tcnKoQfBWLKFF8P4ilkUgQVPaJ0aKHmkB0wUqBlldchMqkEUq7ERF0UJQlkkohYyKqQZEaohLUtawU6CE4lE+PIyR8RWqLaFVJqMp4+blnoNdZa8nBgCj0+eIbxiO9PACi1JqZI1hnjWmTKhLFHu2qhm3JCl4qnLNsVV4tLOmFIaaKXgqzqoUgVjUiSVDzCZIx0tKsb1u0TrrqXLMwl4wmO/aFeJ4gUqdDSIaVB4UBIpE1I++msAj+phv/85Y+5ulghG43xLd/c7nj/+oCTGhEyMWWyCKyaSoxI/UjJhRgL41QoOeFjTyyWKRa0r6Q+KPzw6hu2fcYur3DSslo2pDCy208IDX4c6E8D1yvHNFaYrOSJ02lPygJitW0N0SNouby4wqrE6TCitWMaRy5WC6ZcndrKQ2JfPA/HE32AY8kUaXh6taHpHFpqsmuwmx8h4hHTbBjjO3L0qDywdC1bP9E5RaNW3N3e8nd/87fsjwf+7J/9hM36kpiq9/84DYw9eDFh7ZK3b2+JEfbHEb0/0l5cUkJGK01rDc4tkWqLEgktgWzQusWPkXXXEnMka8fz5y8YQ+biasN62dK2Sy42LdOwZz9MCNmyXFued443MfDD97/mb375PcUfuNgscE4SYzXSyFISYkRrwbvbLc9/ZIipQSuFLJGYPCl5mtaSI6QEUlSrS20qOziFjBIKZRzKFKTKhAS2VXif6BYWZwAdKSSEFBirGcceSsD7wuQnss90raRtGogBKSNd6/BjRJeBVq94uNuhZ98HtOZ0GijyM0v/U61SKmEOIc69v06vQjwy29MsPpezAxr8fQJfmVnutSGfIfA0T9MCcqn6c3iMs4XyqJPPpTzm3AshKfLME6i3L2Lepc+7A1kUKqp536vrVC1UhdeFIRdZJ/sqDkMWTWbWwReQpdrZRplJKAyaOHMZlDDkYgnFIoQhMZLxNWZcVp8Q8gc2vkLVxk8dDM6qxpQEIUEMiRQLPlWnPVXmY4gwGGNRQqKQKGWQpiabJpFJOSKERgIhCUQSCF9AKYqR5CKQQmNMx0JBJrAdPCWOjMfCAWhtRwmR+/ie0UWmKXH0A4MfyDEzhMQUC1JJDBmTAyIUkhbIHHHG0ixW6GZJloKcAyUnGtNhdENIkb5MBFnIVqFyHTAjAaxCuY7LpmNz8Yx1e00j16RBUtJEyoJQary6VBotLUI0KNlhG4taRIr9dDI4PqmGb5xjtXBIIVGNAdUylIa2zRgSOResM/VEKQVjiCxES4iJECxt10Hx7I4BLRMET1aC3fFEEi1KRiRV9rU9DMRh4vb9gWW74Gq1QaXE/ft3DN5x3B9pLwWH/T0xC1RzgdCWUGqSm7OFMSYsVRlgjEZK2D0cmbxBIlgsHOxPZCVYth0xbNHWsVh1TGlgGEYykbe370nFI03GtY7GtgylygbrRcCzP/Qce8/T65csuoaYI1fLNcM4EoVBa0eWhtWqIw6azhai1IQx0uqZsZwLIczxljnQaMXtYUdOqsJwIuDjiDQty0WDbQ3r1ZLGKVzT0K0Mp/2Rw2GLW92Q4sR0CkQx4UNE2RX3ux0iBR52e25ubhC/fIuPkW69YCHXDIOHArZZUEImJVBakgOQBcl7tFFo03A8HCvxSTicFRRpsK1FSjlbaQoa2zCNBSkz68WalNJMRlLEWBnHfoTRe079EW0XICyImqxYRMFaQWM0woMi4YceJRM5eUQRTDHiQ2K13vx2vyCf6x+tXMQH33pxnt011YQn1rYr8kymOzf4s/Y8U3KapW9nBn7957wAmFVx5DNr/izxm1315JnRL8oH2b2QlFwPiRKIszSwiHl3Px808pziJ4QCoSpnoDyqwCvkXgVw9c+5Hm6yEBVdzBI12+GK2Zq2zJp5LWXl9IhK4mM2Gsq5Ih5CqPmwVF9TUZKSK5JArjv8lAJTqSZXOQooioxhihEdI1olWuGQ5LrTR1bXPaFBxgqhF0XJEFMGBanUYBmhNIhMTKGSEKWi0S2hCMY0EU5bfEw1RjcMbMWAj5nheMCXUpU2SiOMQildiYQ5I51C5kzyHrVe4toO6SyxJFKUGGkwwhC8f1QJWGkopaI7PkfQBaUM1i5YdWtWizXLtpq+jcfIFBIxKZAWpSLVqEiShSIWicbUY9gnlMHxSTX8ECLGuJpTkgOtUzTG4GyuHyJpaDqJSILoA1pBChHrNEkYjJEMosaZGiUZZyMOazRGWRZdx3KhuH19YLdT6I1mfzjRrRak1PPD9ycmH/nZz3+Hu4c9F+slTdNwOgXe3r7n8uoZp36klZHk64fXWpBSzc5bnhIiWoKXGq2piXhZsGoWJJHRxjBOA8UfGE4TY5/ovWZ3GNgdHmi7p3XnlCVt1xF8QhrBctFwf/+ely8v2T3c8vD+PVerFVo0TP6A0ZkiDD4MxFBDcIzRhFxYthYpwSrJ5c0FYTqilSZhiF5gW4cQER3lbF2r6BrNOHjalePJzSWyFOJYoW3XrlivLnh4ePcBwpSCZrlgf3+LJoHQbDrLsrWMMbPoWkLqSamwWq8R0pBLIMVAyNC4ln4S+OjpTL0QSK1QpiFKx7KrF7BEQStFzpm2qdyFlDPOOpzT7Lb97IkFp3GcyTe5ciBE5Q8oliiRUVLWkA0rEaGQkey2RxZtprEtu/FIzoXdcaAxEiHTf+ET/Ll+WyULCOpkhjjD8JXTU7Pq5y48y/Iq+a7up5l38Jzjbmeo/jH8Bnhk+5e6g65WuvnR0OYsfhNz0xcUZM7kUuWCiIKeyX/IShKsTP1z9p2Y2fvzlF3EHEorK4m4KEqq8Pnj3p1ZQqiq0kTOTHkh6uuuPb9iBLlIkpDEIkk5IPPs1FfqY2UpiUhEkuiikMkhk2XKmZAnfJyIKSCzqqiDqi6FMXlCclhpELlgkLTC4KTDCwP4ylooVYdPrnvxVAROV2I1xeP9ltE/UGQdnpRsat7JNHAMAa0cMkX64YgPEzELbLema1fY1OBUi5QCO/sEWNOiw4iwlkW3wXTLilwGjxGS1rUgJb3vkSmwMB1aGbLInMJYNfQJnG5om5ZFs6Qkyb4fKSUzToIpZGKukk6lLGVu9kkIpCxMMZIGheEzS/83Vs4Dr99tWV9cIWPApjjDuQIfIo2OIDVIg1EG0ysmL1h0De2inWNiE22jeHsbwGqUHCAHGi04TAOHoyYGz3FfmPpIlpIpTOz6xJhbrp5e8+Tpmq5pGAZYdiuGcU/wgdj35JzJBl6/fkW3uuHlFy1T8LTKcBwLm8WKplmBhqVI3Pc79tsd7SpwOPVsd7eI7GlM5DSNdOunPO06OlPTlfaHe97EzPOXT0gpgRYcdwGzsLhGcnO94ptvj1w/eYJtBavlCv8wcN16pJ4ISbG6usKlAZkzsWQWrSGkEd06ri5X+CGw7weOfURay6I1NS2KNet2iWkb1os1pzHw9PoSkOScKEajpcQqy2ncUbJgHAeGwwMpVTg0qYZNm/n21Ws6W5u0UJDCxDScELahaQyqqHoBUXXakTLQdYYQBTHVvOyutbiFw7qO4bBnivVinKNHzAYiRI8oCq00XWvYbWHZWYSU9KeRGCN+HEhZ0LWLSoScRmyjiSmy0hpy5DgOHMfIlDNrbeiHnn5MhDyRQsHIiI+n3+r343P941WKnONvP1Ql6gnOISZi3mdLoWfSXv7IW+Fx3q42zUU8svvzOVaWKudLM6Vfl8dWPU+19cIuSz1m1tlfUUiP0rjHcNssKEoQK6iOyokkNCWfp/FALhojqhcIWVRpXJn9/HONlK3rgtk2F5BYkgj1Vc8mQKpkZGnqKkJUO/FCqJp9KQENuUFgKRgKhowipUyYfA3PKrm67VEREHVm35cq6/UyojLIrLGyxZmWSTvw83pNzINRrjHnOWRySEgHIXl8OLE73qKtpGs3NLpBNEuOQ8JPPT7XXHofI2NIIA0ru2C9uK7XEX1ElkzJA0UlJImcI13XsVpfElRVMokMtnEYt5w5S1Nda8qCl4mAIMkqlzaurg8NjhgU2UN/GjmderI3xFHhQ6FkM9sL18+ClAnkRM7xEV38VOqTaviLpeWH1z/w5NkTLtaOrEDbFU1b2L26xWiF0ZnFskEbgdB1XxdiZBh6cn5Cu3qGOPVoIRFWkZOkSEe7ULzdvub4zcjVWjActqTWUADfR7pmyYuXz/jxj59ytbIYo/A+c3l5wavX79i+37NYrGit4nQ8MPSBZy8XLJzjcr0ixYnD3QBoNhdXdNOB+HBge/uexJJn1wtyzOz3ns6MNbddeqyWGK3RUvPmdoefRlbLht3DgXbRYduO/uRRMbJoq0GEdku6diCEwpQyU4RTcixyYnWxwIeE9z2nGLlwDa2xLJwD1RCzQxtFmiRhmOiMwkgFytBuNNYYnLO4pkPZiFCGMYxomdnf97RdjbbcPdS43fv7E7IcwawIRaCbFp8HjHF8/eqOZxdrAoXgA5erNdJYhK8xlZM2nMaMYIRi6BaKHAspeQSGyQdyLkwZlIhY47BmwTAGUhaPbmV1l+jZb4+cxqEePHJmHCaKn2aWdqYQIRsKiZQVSgpEihxOu3oIPI50KtGPW6Yhk7EkoXGNIEvB9Ol8bz/XP6jHAR4ec+lrepygTt9n21weGfwg5mlYzFN+mSHZMhO35ol5hnnrcTMis0AWR4Xb1YcJf7bAPWvlz6eEGscrKCoBH6JmBQVSqs6SMiIIhHDEqoaSCrIccKYlliWGFSpJciyomUlfsqwIQazcBSHEY8pdVnWdVZ9LJbqmXNn+GlFttYWYY/4cohjAUXAUUZGzquxPCOohP1PIKc3axWqAVrIiCk+cswmg1J28aLC6I+tATnHmBcwEwiwgBsbxhLQZ8kQcPTJnhA8oPWBFDb1yQjGqhtM0MJWMyAKpK1egtQs6s2IQA8L3kEekKhhhEKHacBepCCKTZz8mrTRaNeRSuU8lBaJShDSBqj77xrZIUyXJpWgYJeMIfspMoyEMhTJJCBKTBcIWdClzGOOElhOJTCgB73umtPv/x1fg/6f6pBo+Ek7DkfEYMDeOIUps1xHDPYWE1C3atPzoi+f44Z7tPRyHyoy9f9iDbsnTPfuDRzvJw/FE52r86ul0IvSe99sRPzZoKxEy431A5sh0OiEuIpdLzbJtWC0Nu+MIRO6OPbEUDseBZ0+WTKPHtAvWm1VNYmocD4fa7I1VLBvB337zLdvbHcPgUaeRZvECISvUHKaMNg05brl99x1usWYaJh4Omc1yjTWFYZx4fnNR06qS4PDgMUZzuz9yGnq8n5DSEXP9UC8WV6TxwBQKU1aV9CYVrluQtWLZtdw8e4EmUdKEUQE/nlguW4xTrDZrmsailWF9uWKzWfH23R3DOFGvkAqhDdM4EVVif9jTtQ3b/YHNqhBDQGmBnyIegz8e+dXtK/7VP/1jOms5jhNN25BCgpTxecIPtaEb1WBti9QCSyZOmeEw4MPIFAL7caL0J1IKHEaPMw4xeWKOSGcYth5nJO3KVJXAskFqxTBN7A8HToOvU4lRKFmw1kIKiJSZRs3ptEVbS4oBoRW6GEYiXWvQKaKSZvQePw2/3e/H5/rP1Af9e23055/Pxi9lbnLzrzIT+EQ+J+TNro9CzEQ98Rhfm8T5BvN9z5KtM8p/hv0lYl4m1ZIfpdsUCsyyPTkfHVKaCCESY67ae6k5Hk4zYa5gbUPrOkRuWZmnaHlFCZJWrrDS1FwAwWzvq5CS6rtf6Qo12lbqyt4nP+7qpah7+ELd31dSXZX2kW1VAcgZi1AarRsohSRSZcDns63w7LKXM0kkVJ6PXEKipcXolmjHWaEjEXMGcS6FEiL5VL0DnCyUUHA4JAERqiS3cQaahskucF0gUsihMJ5OjHFk3mxUy+1cQ8IabWoIzngCCtIYitQgFFoErBIYYRn7gTD1CEAbh3MOqy22W9E0HUiNLJIU6+dKZGoanxZIR9Xq20JOEZULIUdCnMjpOH86IqewY4g7pvDwP/Bz/19Xn1TDPxwD19dXvH245erpUy4uNrx//5bdbuRiccFPfvoT/uB3f8pPXgr+5//pGyBxmnoWPtNPhdMw8e7NLcI+xbYD0y6xaBWtFryLheX6kncPb9keIqv1AilgOPaYpkVbw8P2ll/9Ev7VP/899vev+NV3R756cUG3eskfPnVU4K5n5TSpJFpniSVjpMZqy89//Jy/+I9/y5vXLe93PftDj2sUIcQal1ig73uCg1zWxBCxzQLXrDgdA5vNJUpmUBaYGMeR02EgoyhasVwu8OPEMEUur5+ye7gj+Mg0ghADUmqs6RApE2xDozVN0zLFesFpTeH2/T1X12sOYeTZ8xucEQhpaBcdF5cXTMOR50+fcPtwz/F4pO0s67arcqc8cTjsAY0UhmmKOK1xjWM8BqZhQkpDorDbF45Hw1/+6jXPn93gupbTOGCEYJgyy/UCWRKNMQhdyCIz9hEpKnNXKYeUkcY2rDcr3u4ekCYScsSWBiEFUQjClNCmcHm5RqSCsw6Z5axt1gQMWUhuLi27g0cLGHMhx4DKEVUMy7blOAZurlaUlIkpYV1Td78IYvYYLTgNn46e9nP9gxLV0rbAh6CZc/Odm/tsLj/nwZ+NdeouuibozmS4Asg63Wcxw+Uz81+Uc+Le7OxHdcRkDm6tMEOq5lazGx6qcokq8lBtnAFC9hz6E37yOGUpObHbbokxgBCs1xumpDjsPcvuCQvzBCfXRPUFXQFTGpSoRDWpBEXV6GqUqpbRpZoQSWFma1kIAkKuxMIMiKIRWKChSPth/58VpIwujiSqDFaJBCbW969UZr+YcwZSTIQiIFePPy00SlmktNX5sBKzzhgAMUHxgXiciFahkGjZoUiYZFBTQZWIaBVKa5zSCF1XDUfZ8HB8T4wjw3RE5IggIsmzX0KCkmm6Fa5bo40jzZ4d2i6IWXCYjkzDnoXraNwF3fKCbrWhde3MTyiPm/esM1kndCtQTqBCZgq+JriG6tFR4oRIAyUdCSkQ8kRmRDNB/nQGhU+q4S/Xl4i85/XtwGmK3N1t2W1HWrfiD3/xgj/+gxd8+ULysH3Ldu+ZpkCIAh8S48mTiuXm6XNutzt2+xMlO4oAKwvGKITs0NKircE4R5hOlOShWFIR5DDyl3/11zir6Fzi+9vEi5/+mOfPNcs2sz94xl6S9XmXLZFGIUzD885xerjl7vaWi67jNBYympwkRgt88CALw+hJsUUicJ1FCJjGUyXd6IxIsFxskIxs93usa5BSEUKLDNUS+NnNJSkm4tCiGk3TtYwh0DrDGD22aZj8EmMbjNHErACFNpZTVnRJIt2GkgtTEWy6jmXX0FhF9DAMB/zkUWRCf2IUCqstafaU9ymjtUAJRbtyHPojjemQzZppGskp47Rm0h3Hw8Dipy1WWkLyhJiJRJRuUDqQU2YYe5yW5JyxRuCcZPABpTU5jpyOW6SROJ2x1qFUzQnQ0hFKYGENk8+k4JlC4jhWYg0l0y0su0Pd4qZY07QkihBHlF4itCCFyDANbNYtw1DwJdEqgVQN4QQpTzjnEOIzae9TrlIyRdRmx+y4d85yPxPpzoeAPDf9mn43w+xzPn1NyTuT2gqinIl887a+VFQxE4GMkGd5n5j32lX2Vj0n4kwSBCEiyETN4cvEGAjRV8RO9JQUCNnjmjnG1SZMZxDBsxu/I8R7Vu0Vy6pfI4WWkhtEthhZJ3ehCrN7LlIqiqweFYoPyEJVHuiqTMgaIRqkcJUjgKzpeMVUCV0qlFQtfHOOSKFBpfpas0YmgciJanWmUKWu2IzQOGFJ0pJUJAox//0khMiIXMgl1aAdMo2V2JnZLwATQZcMaSIbCabiJ8Y4ojFopZhSYBqPCGJV1IjKu8hpxBhNs1pjXUdJiZwDbdPh7AX9OJJImK7FdWts16FNQymC3o/V24CCTIUcC9FHQsxIZZAKIJHzhE8DqfR4MRFkjQPOosoedQyzV0JDMZ9Z+r+xUsw4ocg5ELLCNQus7WmXa16+fMbFquX99i1//lffcbfzpFIdm0ou+DiQckGUkdtXt/hUQBZynsj5RGskX9/e4boFrrUUBbv9nlwMyjiK1NWHWSfuHwZ+9me/R9QHuq7hZtOS/RGy5nSInKaAswusbZCmbuOsTHy/fyDnQh8mjDKMGWIWlfBGg5aaMEW0XdA2Hff5nhgS29MWIwROp/qFk5mH+yOlBG6cwxqFVoLUtCADw9AT/cgwHMjpKTENlSDiJT5FpGsQWtI0XY3VLRbhHElJpvEEpSWmia7doJyl2zi6peM4Hni/G0n6hEBwcXlDno6UUgl3oR9JceR0HLlcbSgycPKRHDJLpxinQPAR66qOmFxlK3d3e15+8ZRb7xknMKZKcVCJWBJS6irNm2ooiFQKZQSMkIskTJGQ5HyhrbNZKCAzpJJZdA4lG/owVBZwqhNbmCK+P+HHESUVp3HEiohdbGYSlSaFxDh4GueYfCTljEThc6YRhf1+h7MFkR1KiP/iZ/hz/XZKiLpbLufd9BlKL1WgV84M+1JB+XKW552T4WZooJTzz2ocqiiCLCrkj5Dzv+f5GHH2iE+VmEbNuT9b7Z4Jq5n86OJ5lgLGlIkhIYvAKkGOnlQiRYPtGtbrBcvVgvVqzdXNBV9/8y27wy2+9Dy5WmF1RpQVUi7RqsNajVISbRRFFrQS5JQZfaEkSJMnpUDMEUrV9CMKUktE1nX/X1QNyknV5Y8sECkjoqTyDudY4RIraXbmStR4wqqGkegaA0KNu1VKkXP1t88kwuwdIOd1QM6RHAOjUhRtUKoaGgUJjRDoLMgBQk7kEJjz+9CFak7GCSFqQ5daQgooBF3T0bQtQsMUBhCFxnUYaejFSGMsbbNi0awRSIbhxGk4ViQix/l5g/eVfJdSQUo9h+l4fE6EMBH8SBGpBhDlhBKimjWVjFCiyg75dBw6P6mGL1RtOpSMH0aWS4FPmdXFBtc27PY77nYHUgItVWXqlIBPgRxEhXiGA83yAjVuGbzHKYfVAunvkHni2fMnnA4B2xhAsrl8guvAh4I2mvXFC3wM7A47kIlVt6Ixie1u5P3bO477RLu64MmTC4TOGGUgZcbTgTAm7nc7njy/xph60TBKzWx7jzKJvu9prCMEjx8jISkWywvWneXh8MBxe6QfAnaxIkbPqY9c2xbfT0hrkWrF3cP3iByZplIz36VEK0Uosmr4jwOLpcE4wcPuyOgH1ssGP05gWtLsopVLoPjAor3g/b5nv+1pmhYpHEtnOO13NEoxeM9xjEhsXZG0SygFPwwstCZiGIcjp6ESfIRLTMMRayVFKfrDgda8YPT1oty1DQpNFJJQBmIuTEGhBEy5oDJoYxHMk31rCQkQktNxR1kZUIqQMlMA1zUc+0g/JqSIGNeShWCcEg/bnjQFjmKPFAVnHOtWY+gQKKYxME6FphWM4whFkoTACcHu2LPdHnn2pCXGQuIza+9TrSyq9bNAVDhazNv18sFKV87wc53E57z7+VpczXSqFK6QOSfa54+5AaXeRolqsJPFvOeXs49/OYfLz2iCqCiTlBVtSLHuwFNJpBSZxoFpODEOAyVPZJGZoifFAS0Sm3WHdoJ12/Glf8a3MXB3f89fv/pLnqxf8/TiJVfXLyluyTHDvt+y2z2QS6az1Slz0a5ZdQs2WrJQEIPguzeeXDSbdcOyNaQomLwmeE0KChE0JE3JiuQLYozkIimx5nCUJGvkrcyULJDZzUhKIc5OhMw591rVbPskEjIVTKm2t0V+OJgVJWuUbkqgHY1xjAiMlHRSo4tAlkREombCIBRKGOv/6hpTLEum+IkkFGK5IksoKSJywTUtkoqAhuyxtkWZhj4mxtOWEHqUkBit0FrjtKtxvgXIkpA9UzrV1YUfCTlScp7VEDXxsEhBzhmHxLYNRWoipUo/P5H6pBr+3cN7Wqu4vFzyfnuHUJZm0WEXltvtHZsfPUeoIzlWZmulyNTwiXHccto/YE1GKoc2jnCKoBwxJRKC58+ecXW14vWrI3Qdz198wfPnX3E4vGO3H7DOgC74KXD79sQXL7/kyeWSuzevUaZhs7ni8rohiEgugZhg0TpSGtkfjowhkBIYbQlxQpER837LT5GSJbbRuK4FKShCUSSsFgZEIs651pvNBc2m4f79HQ/3d7DLKCnoFkvGKYC2rNoVl4vqyiXzQMmZFDJTglgiLghIiTB5hnHCuQ7jHFk3hFhg9gdrV0uSyExT4vL6GpUTIgxEEruH94xGE1Lm4TiQAhgZkEpjlGS1cFxcSaahYTwdWSwFWglKSXVyEA1Ga3wMbLdHbjaO+52vYRI5QBE42ZLLUPeDiBl+69Ba4azG2RogolQmhkKMASk1fsrENLJaLohoDv0dwlR54XoN2lSyZhCQRNUXW2cJKVav/VnLP6QIWiKlZjgz/ElY2RCmiBIFjGOcJj6hWOvP9Q+qCFGJ8nMzeOzSUNGk8+99dBsh5pz7AlkUsszoXJfbRXxImyvzhCZmG10hxbzzr5OqFGKOo63TXRHneN5ISRFEYRwnRl8DmXyY8H4gRs/xWL0eNqsNq+WC+/t3+H7kzfiG/rjn5uaKbtFSisAoSSmF9/f3TH7i7X6HeP1rpgQqGzZmQaM1WUiOosZdj/FrUJknN9f8wY+eYpTgxKFeV9cFYycUgjKBSZYUHCJ05JgpSZJCQY2ZOEW0z8QgSF6R0xz9OqsOSq7SuxpJLEBWc5soLVHGumJTFSVRRaFFNRYqQlMIde+eIj7Vw36WllFRbbezQKe5sRZDyB4dAyYFhBRkqTDOoqVAhlRjza0mkhEp0AhFq1pE0fg0oZRBZc3hsONw2jEcd0hg062xpkM7iYUq/daWogQTiZQjJSSSicScSGRSmd2UZn+HnGL19zCOUhLJj6T0ueH/xjKq5eqm5dSPvHl7gFRNJbTWJGtRreHt9oHdYTwf8mqjy4kQIzkXYlI0RvLmwePDggCcUkNSEusUF1drlHV0yxumrwRXlwv+w5/fE9KS9bLBNpqyKTTLjt/5xc+429+xGyUvXlxg9JH+sOf7b79nc/MSKQzSWPx+S4gZZTpWqzWuNUy7E0UV8sRs1CFxxqKdJpZERqNl4TC9Y7tvKbFKbZxUiDQwHUZ2D3eMw0SYBsJux+ZiQ6CQw8RQMl89eUYCppAqM70YvA8suwsap5FNg/cPSKp3vSTTLFtSyWjXzqqIkStx/f9l789+LD3SNE/sZ+u3nsWX2MlkkkxmZdbS1VMtaTQjQICgG0F3utC/OwOM0ICgVmta3V3dteTCJIOMxZezfputurATzGoosy4r44IvEGDA3SPCefzYZ2bv+zy/hxDP1Bqmw5kpRc55YR4HZq2oRc1pv+CC48mqo2oaclxYQuI0ziyLZDgfaVYrmr4nzQN13WItLEvmtATevH/gyy8/J8QRT2LVbVgGwTGcsdYSQ0Iri9GWFBKYjJE1Eo1GYUx5+MyTIxFxbgKp6fuaaZrxi8daxbzMJD9jVRFZbtZrooPzdGK17iDKCxfflElqztS1JkVYXCKJCLIIj1yKYBwpwbKMbFY/svQ/1io3zDIjhnJ4LEr0D0i8Yssqrf3L57nI8C+erQ+QpnzRAJSvyz/Y7j7gdOXlhs+FBSGSKBZP4EMiXxKJ5BaW8URWmdktDMvM7Bcyl5t+8CwuoLXBdD2YhiXIggWvNMIJ2M80UyTncnjvmpolCO73JxZ3h9U11/0TbtY33HTPsBQ0dRYQc2YRgXEZePzNkf/0MFF1DS4EqnpmOB6pDGitSFmilSnPtB8sh5YsJdlIogC0hKCQSpEGQZozSEVWqdzr0wfWnEIiiSiUMGihCjETEKp0TBSKeEH5CmQ5MOWICA4fFqbKkIxFaovKChEiVhmUNii/oHJE5UhOvnRvhCjMAtvQtA1t3RC0xIWFJEz52SuJ1hU2NSzzwuF05HC4Iy0jfV0haBAqEVVmZsGLSKUKXCmkWA55quSGyMvFoKQtlkOhiJ54sXnmJMgpErzDLR8Pkvuj2vBfvnyKtROKim/HmSwVUpRscm1rzuPI8TRznmaWmIkCYkqkBDFDTAIrLVLA/cOeczK4s8ULSzCZ0+DI74dyMq9g3Wtev3kDOdO1Nb/48hUujMxLgWcs44CqLJtNaTdnmTgcHklzYqUMYZmR5ppxGjidPIfzTGUkRiiMqJCiQqqAbcrsPmaJnzyTX0hBcTzvOO3PyKuazdUGLQRvxq95vzuyhIBQmu264nyaykwue4ZxZBwddqXJRLQqwrSMwFYXxrdM5bYaACFYEoRlZH31hCgULoO2ktGdMXKF8wJrOozw3B0PBBnLTSVlptNAvW4gOJLzZArJMIma+TzgfIAU0VIRneN0nsjjyDiMkBWTlxhZEYLndDyw7jumCFY4Prtd87t5x2E4I3QFErQut5gQEknBnAKLH8i54uQW4hFeKIsxFdoYTG2YTgljDNM08/h4QmRDEoVI5nyx8yitqGvNbrfwuLtHmlt8XDgPC20VSaIqQkIJc3A8PpwZ55nb62tCFBhjfhTtfcRVaHbisnGXTT6L37PtyZcOwIfPXWx1P3zkMncufcOISBdNAGWmLES+tI1LwyBeVPBSJLIosbfiIvbSEjKOw3JkmAfG6cASI7q6ZD1EAdKQjUQmfwl82vDlV3/ONC64eeT22Usqa+m7liwSu90jS3zA4FAiopoGvdly++QVTbMFb8ipgmmF8hqSwmZBlQOt8ETriS4QXUSKSBgFQUomVUYVIQM6I7RDyBlrNFoYZGrKbf4SPGTQqFwRg8Q7gZaRrEuCX3EI5GJ1zBAEBSF8SYsTF42FQJbUYiEv2QGaSBHzZQnZgKwU7WrDev2EWhlsXGi1RiaJP2puekEbb5jDwhgWSEXwbI3Btg3SGowqFkUpDNIahDV0GFS2hHRCVy3d6opsFJvG0Pctdd3RVevi15cF10vKsEzMbsFHz5JGwsW28QHlDUXcm1MoIUeXA0BaPGle/gVXwj9fH9WGn+LEPC6FDS0AqS+YRUNtLZNbcEmXRRkDMRXcLoALgZAT1hS04ewHJr9CqB6ioNaSYGqEblg3mv1wYnaZ3/z6LTfbHpEdw/mMzxnnZk77E5998oqnm55xXvDJMQwjXduw7gPztKPuW+bDI+fziJCGeRjwMZNzJKQRKSN1W1Ot1swxYo1lPzr8mHj3fsdwdgzDGVltWV9H3LwQU7HNvHz1E7RIDMOR8+HM6CKH40Lfb3jx6jksrugbjEXJhRQDbkkYo0kXBT0KhNTlASQUdVMz5IjRJSM+REFnFDl72hrctBCSJ8SIlJIYAylmTsOJECemxTNMDaiKpi8PleAVWSaMsZcFAb1SeO+xynKaQGhHU2m8L618aVf4qAgpsTiPzolGCzCCSulL7nzALYLKNCUgR5fbwnm8R4nysKhqzXlyDGNBIvuYsEaz7jfoyhCSQ2uNMQo/LQzDmXHxaJm56gwyQd9JSCOzj7jF0fcN491A02iePblB5AUQhCQI7uNZuD/Wf1vin4BufriU53JA+70nXxQP+u+N+EWk96Etm4r4Dspt7gNgRogPHQB5YduVj2uRf/jHPvD3lc4YHWmbYlvdHU8EFsbphIkNz263KBLT5EEr2ramqnuiP/EP/+X/RdM1fPqTnzKfjhgp+OnPf8HTl68w2nJ6uOfrX/093/72VwynA+v1iqfPb2m6FdMUCYdcwqCSpU0rclaE7NExkqInKEcU4eJI0AihLnRAgUUQl3wZbQSCzJA1IlhiTGQdkCajpSItmjhlRBAofbnN6mINEB8OQaq4HJQUZTaeBSmDpxAHs5TkbBAio2UqhzOjadct26sndN2Gvlux7jdUxqBTQOdMdIlt3fKi/aqIkseB96d33O1fMx/eoim3eClLdG9temq7weoVSViCm1nCGfLM835NqBvmdEVfaRpzSbpTGmMsRmoaW6OFxsoapwpi+OxnfCr23TEW6ya5KPpNksiUybGIEXOMl/jlj6M+qg0/pwIxuDTSsNYgRfFYG5N4/e6RYfSkXBjVOWZiLEQtlMBogzWa+8Mj52HCy8tcSSRyBKMlUimklSxnx6ZridGzPy+8ePqcp09vGMczd3cjmYbVakXbNDSrK4bxnqatSWEGdbG3xALxUdqATkhVTrpBZbQ1SBWojMJayWbT0taW/WnGZY+LAV0bpGsQwhP9wjQcqbSh0gk37RgWx+5wJPqZFDMhltbhs9tbHt6/Q5mGFB1+HqnqivOwUDWSrm+AGecXUiy2IqUNwQXOy8iT2w4rLW0VqHLExMhwOjPFBeeL8tg0DZ5IlgmPYPKZGAKeUMYsxrKME4rMaZhIORUutjA0q5rVdgGRafuKZXYIoUhCM88zq/6Gd/ePKGMYQsDYlqOHSpQHd5aStjFMbqHvKoRQGBloK8s3j44YIz5kvA+Ms8PNZ6YgUFLTrTqeXLWs+p6YzoQYSXEhpsTpfMJWPdYYhtMRW1tMZfCTAspsdZ4yQiWePLuhUpL9bipt/uQZ5/FPuj5+rH+uxEVh/+HhevHai8t9/7KB5w+fyemHEf+HdLwkCjJFkMnp0poVFwP7JXs+i7LGS8TNBS0rM0KC0oLV2tK3Cq0CIfdM7oHdPuMrw3k6cB4FV+seU2WmZUYIyf74iLU1r159wvNPvuCb1+/JquL5T77i6sVX3L58SWUlTbtiHD3v3tzz3TfvOO4m5tnz6U9eYq3B6TNOD9RJEKgwQSJcyRcQ+hK/KwA0IlskVYHtZFnEqlEQM+WAQyyvwSIhOpJaoMpELUlOk0NGqUvjpKgbQRWh8ofX+cOtPypROggffg5CorIuLACZkLqhtQ1Vp1lfb7i5eULTtCglUSIR4oi2Fjd7zsOe4DOpvqVpbtnYHmk7KmHYp4SPE1JCjL6IBm0NsThwTsue3e4dKS5IW5wJRmpW3Yam7YnJ4aNj9DPMoGRh7lfSkuNF/KkEVlUYJIsLCGnRRhODY8mCFEubP6ZYCKMEvPxxhv8Ha9NekeXIYV44u8BNDY1piWlEqAZSoOluUGrh3XHA5RKPmoSi1iUBbQkBITVXV0/ZTxXTFEgp4dGEDG1rmccTfnb40JBzQGfDZ588Yd3XDKf37I5n2uaKxpY3NqaQqKSQPA4zNC1da/HuSMxbUJnjMFBbS1MZ/AQiKZRSBQITRdks/YyUGSkU2+sNRhuatmMaPOSM1Jp5Pl08r4m27dHacJh3qKbBNC3TeCpjA63IMZUFqyRCWdpVdQneyUTvCh9faZqqYWICI8mDpK5rnl11xMMdv/n+kdV6zXm/AwGTS6zb8rYQtibGgFSaftVibLEZxhipq5ZZnhE2oYaENgrvAkJ6Yqip6obpfGJJI8PoubOSZ82EtB0hQ0iR035gHBe6VSYJhcmCcZ4JwTOHCBGkkYTkQLa0fU2OoHXCSEeaR25XLQ9BMyeJD5G+Fvh5wU0nUp1/IHYF33McF7zzLKcjpjJs5IamviKoiiZCbCqSkHz6k1vImsWN5CRxS2A8DzTmT7s+fqw/Xj/E3eYftvTLRz6kxH3w2OcfNp3y22KTQ3zw5hd824e8vPK15b9Zlpv+B4dfyiCMpN3UPH9+y6uX1/St4npbYXHs98/5j/9R8r/+f/6e035gmRdev37PdD2xWbfkFFGqwmqJn2ckguF4ZJxmfvaLv+bLX/45t09uWLUtKQZOh5Gvv3nNb7/5msfDHSLB4XTk4X7HT149RVnJ7hhY5YiuK5RsyYsih6IBIBcwjZACIUvbXQiJQBfkdJYQS6pdmdvlwvZPAmIB8SRVwrKkKvZhRCSLS0BQTqQsiEmgPyQCqouBUX54rU3p3pIgS5TR2L6m6ix1q1ivG6q6wlqJNRbhPON0RkZDrWuklJxP99zv79nf79mubpAkWlWh+ucM/pGQJrLPROc5DSecH3ABdtMjcdnTSsOtfoaQkvN0ZudGYtwUYbPWaGNASlJOnKaBx+WhdH8E+PKyYJQluIQPgaapybKIhAnlWb64hZQSStds1ld/iiXxB+uj2vDrrmYcDoyHAZFr6s6SZWKaAkY23N5csTsdkaY08HKGGAMhSmJMpSXeVlTKoCuLGwNLCsxLwDQbrvuKHAPnacG7hKkqNtstL598QlUtLGPJmG7blu264827b7i+ekpvG7TteL/ckWXHy2dX1Glgd3jAxwVBZp4LqlUIhSBhbcMwTqQYMDmTc4mfXJbAPM5smhuyq5HJU1VdUXUGSUonstSst1tM3eNj4J0/sV4/RSrwM1iV2XY9SRiELByAcQlIXdpz42kkxYh3EUSktyClwVYWqRO9SdQi8vX795xOEySY3YzRLa2WqGJ4Z7O9IswLlgxdw+gdWlu894ynka62HKeZulHMLpNy8RtPs2eYRuZpQqqaEDKTV6QMEsXu4Y6cFCImcnAo1SATaGHIIhAIP4TxaCWJPmGuW3x6oK4UXVeztC3ECSUF151Ga0FImVWjOe3P+GUmBE0WmaruGKeAlIoEnJZIF2EvDuibBmkqyGDxtF3Fqq/57dfvWLcgtWI4nAg5Uasfffgfa0XiDx39Dy36D2E55QbPD7+XQl4OBvABxpMv4TY58UNwjriIr4roXl781T8Q45FGsblt+eWff84vf/FTPnmxgjhBXhDCs90JknvJsH+L0Qvzr46c58w4TvS1ojIVPni0UHgpuN/tke0Nn331S7rVhrZusbYmZmi7ns8+/4zgZnJceP31hvN5xLmZeXb86us35bKRAr5JPN08oVJrlJSoyRTr8uXsIy5M2sL4v4w0Lrd0mQU5S1JWiBz5EC0scrm4IPIlHVQVWZ4UBTajMj+82jJfAocyQgnUBxGfKO19ISRZQWMrqkZTdw261ShVonRlzoiY8dkh3UwjFa1tUFLRtQ1xs6YeZhqt6ZuWFBb8cKaWmiAtIjmyj3gXSDgQBjeN6Bzpuy3ezxznPU/6a541Wx7GHd/ff09X9Wz7LdvrZ9R9S85w8A9Mw0gKHh8TPoaLQLy09HNOTHPh7scQLocpibSGxl6x6a64Xb/4l10M/0x9VBv+b3/3LW0N69UKpR+RlHSjnBNVo5iHhbCM9EKQgiRFj5GynERjJifPMid8huM4QZLM44KxNVrDNExka/AOlhCJIdN2LU2vOJ0D9br80Po6MZzu+cczfPUzRZYGh2fOFS+eb7i56XGn+2Llsw15PJKiAwpfep5m2togjGSaAzksOO9Y9Za3j0eG8wDuzPlwj65r3DDjxol2ZWnqpjxklMSHmdpqlsnTtTW7h3uO+wM/efWSl89uOexPKHmxpiRP9gqhIuRIEkWoJmWirhQvP32CrmuOu9fExXEYJt4/7FCqZlrOSJ2oOsU4lQUSciadT8VKo2A+OqKLCO2Z53QRqGRCWJAIVMosi6fVHZM/k1MBjjRW4YMlJAHZslmv2P3uDabVBK2IKV8AJ5IYAjEXCyKyMBhiTLR9R0qK4JYyQ5WawXty1DgNtZHUvaZvOtZ1x+16xZ//+Z/z/u5rckwk79EiY2QmG0vVbZiGE9PxxHp9TV1blBGcTyP7eccw9izjiVkIEjWLj2gFi/9xw/9YS4mLwv4DROeftPcF8gegDhcR3oetXohLq/5iMfsgwCpCPnGR9H24wZaNXlmFsYqr2zX//f/4S37+81fcXHX0LeQkiL780nSkz18yjg+8enmFFJ67faKuWhpbDulL9uzHgXnKnEZH0iv++rM/Y3t9Tde3KJkY9vd8/90ZaypuX7zk//p/+79zOuz5+uvf8ttf/T3f/uq/cjzseTweUdLQr+A79zvu3QOf9l/yZPucYdaczyNh+TBXlmXUKX3Z0C8W4pRLxzRLLmOPDxu5LGMLKcpYVF6MvQKkLpZGLsAr+cNYpbAQstBEpcqrKEWxrbUNthJUbcbUiSxmYkr4yZXY4BTx04iNkfX2FoHE+ZnNekXf9Qz7A6ZZc/N0i1sW9tOReRyJfiIlB3MAL6isoaktp2nP6I40q6fc9j3j+ch+2vNk9YRPbj9BDvd8//iG/e49L5eZpy8/pVptUHVD7dbM04jIHlnUmkUsHkrXxMeMqgWmrmnrFVbXVGrNqrphXW9ZVT/e8P9gbbcVyxKpa5iTZ54FzjnqpisCCQdZCVICZTRu9KQoCK5EOcaUefv2nvXNU6I0bLYrfAqs+zLfnkJGa0sWCm0ybb9h1RrO40LVbohK0lQ1d4977nYOR09/7diN79luGlLMVFWNWzLC1DSdpdWWlbVs1xoVBQ8i4H1C1hfqliyozSXAtCzUypBiZPShbK6xMOSfPnlKZRVNHcgxMp1P1Ks1yzwgpCSlDCFgbc3ueKSpLHXbILE0+sjRBVQslpmIpKtrKq04oTDW0nc1p92J4D2784QfT6AMISROpzNCFWmC0JrJgfMDXdtSVZacJdEINlcNtYH9wyN3D3dcdQ217Vn8mRhnNAqhJQaFT57GdLSVZp4WYvJ4NApP1zYch0ekXKGkQURfdBB4pDGQCh9cxYwjk4zivJxQShJdScxK6ZJrniURQ0yqCAFjRCjF9XXPm3eJ4APPrrY8poBbPI/7R2rbIjRUTcfsHFVdsJkowzItuOgRMjLOkdM4F1Ki0DwMpz/1Evmx/liJhCRdBFLywq3/fXxsJhXkbv6QeV+6YSmnC1v/cr3/YOe73PhL1+D3oTtCwvq653//P/4Fr16u+Olnt9xc12iVQV5sWkoTVUDJ/uKuCewfH2k7wz/+w/d892ZkGAtmepgXnI8sPpIXx7e//nuquuOX//p/w3a7pWssXWswsuX1d9/z93//9xwOB/7ir/4VL169JCXP99/8Iz4WvoV3M6+//x2j/wc606O/knzy6hlr/ZTDaeF0ODAczsTAD77xnOUFGlSARR9olrE04ZFSXvbu0kKR8jK4V1y4/RmVCwAshTKvLvkDxQcp0EWArSxJgzKKppOgAnOa8Etx+mRj6JqqMPbHibWuELpmmBdYJtZtR7da4f2MokFZCU3A+YnzcmA83LG4Q/HaR4H8Yb4uYBkx88I0DHQ3HVc3T9kNO+7nPc+bhk9e/hQsfPPt3/Pr13/LFEaevfi8RPVunzHWR/bHhyJKpDAGFqGJOaCEpK3XbFe33Kxuaaoeq3o0LSKrAhz7SOqj2vBzSLx+vefLz9f4OUBuS9ADMyHUGKVpa8N551GitIgTvsyeskQbwzLMxFVACIk2DY0dOS1lZhyCQAtARKK2bG+v2b1+4Lg7s11fFXSiFkxeUmvY39/x9e8s2+ue8WR48uwJOXmGObFdGbqqRWrJeYksLvCktmw7Q20SQsx475j9wqruSC6QYgIh6fquiOjIdMbiJaA1QgmatmZaHFlB26yYHnfU1mKNxlhJJvLt63fUyvDVTzsep4TSFSs0LihM1bDExHC4JwxHFJnaQA28Gw8cTwdsbUpAh6k4DUfqydF3hhQD82lg8AtaGapYk0R5iMw+0GiN0oX/H6PENg0uZXxMRKBqKyKReImk9d7jgsDFSNsaXAocT0e2Vxu++/6O9bWnaiwuemrblJv+JaPchUDMJQp0HpbLv63xzl/APgXGExFsuzUmZ2KaiUEwzIHJLXjnWJaJJK+xdY0+BV4+3WLqNeNwJKfMcRqLlUdplCzdHCk1tt0wjQPOH9BGlU7Nx7Vcfqx/UjlfbuiXW2m5w18Y+EJcIF2CC1WHDyp9LnYwsvxBjQ8fxvofDgJlzi9UpG4NT551fPrphp9/9YKuERjzQbWWSga9yihpQAo6KflEf0rdWpQWVNqwe/wvvLmbeRwd59kzT+7SRVKEdObX//CfqauKvunoG8u6b6hry5eff8qLZ9e8ffeWf/zVr/h//tv/icPje+7fvmY8HoEShEVUiAjRTuzya74Lv+aT6s+4vbqhtg3W7jidB9ySwEMKnhQzYPhgWihEQUDISypdvuQBXFr1ErKMpUMiS9RviflNl1ft93AioSVVVWMbg9YJLxY8xwuKN0LMWGtoux6pJXoM1FkhlWUSkewntqai1pp5GQhhRMviekrBM58OHB7fMOy/I4WRlDNKWpR25FBYLil5gvfIaeZwPNLdPuPl0xd88+5bvjm+4ZnJrFdXvHzxBW/e/I5vv/sHhtOeTz75GevtE5TV1HVp8+M8eFCiKqMPUb5/UxtUpcimiBG9czDDMv644f/Bmk4PCFkxLZG+qZDSI4XEWIUWgnmcGM8jzgv0JWYyuEBKmpgC3hdfZPKO4BLUCXWZT/kEIXi00GBr7PoaNz7y69/+jhRW6M9VyYnPkspU5OWMm888Pu4JccavN3z6E4m0mjxHptlTdRswmeAdpyHh54RSmkpriCOVCBgEfVsTKbPpxTsqq8ujJiaapuP+cWSZPdpHUggEF7l5dsMyDXz99Tc0Tc2SErpqWdKA85GQJUoqhEgMLtEKgdWBSKZpK8Z3B2qdyF1L23U8vn/H6TxzOkZWq5mmlpxJl86IJ4sWT8KFzHQ+UjXXKG2BjGFBxYy0gXkcUSKz+HL7resKlEFVgpwcWmZ8SJjGMpwGhCouhrqpEVkwjjNRdUhtWIahJJTlimUqkZOtySiRi1hQSJSSHB4G1FWPj5phXDifRxAZhURZydNVw939G8bF0fVbQki0rSUGgfdwOi+8e7PjdD5grcVYj5CJxlRcdS2b9YppLMxsJQVWqfKgSxErFMvkEBK69uMJwfix/ttKOV82msvdNBcYS3HWl1jY33PyKSI28g+evSzy5faf4cLl/3CpBWhazfWzDV/9/Bn/5l//GS9frFm1YMwH4UD56wSAKPY/YwykTNU0rLY3+JBRUvLdm7fc7U8ssSQw5kv87jQvSBEhPfKr//L/vVDbMpt1wWyv+zWrruPl8xek4Ni9+57fvHvD4/0jMhWBqm4zxtToVHN1XVIqv777R/K55rOrnr7tMEaia83xeMYNF9RvihBLi7r8V4HQ5fW6bPSCXCJ/JSX5TkSyiEQZSrZAgpRDQdxiyqavS1vd9Iqq88R8wvkzs3Nkqei1pVYGIQRhnslhxqgGoStOl65er2u6bs2sMmE+o1Uu6YBSkxbPMpyZjg+Mw44YF9AaaxIqOnASa3u0qUhquIxvIgcf6bZXbLpr3j285S1v2V7dcH3zHB8C7+++Y5jOvHn9K4bdHavNLav+hko3TMvEOJ1JbkFhy3srZabpRI4OKRXWNKzMFTpUpcPxkdTH850A7x4ekeo5VdOxXa3QYWSzWhX7iAi8343cPr1h9/rIe+8ILhIiJKmJORNiurSLB4yxNDbjwkKMM1Io+pVlmhxPr66x1pBcIibBNI9M80Tb2pIMlQOPh4FMZIkzu32irlvkRZ26+Jn1ZovVEEgYPJ3KeCGp25o5ZvwCwQUao2lrSVhmrNIIFrJITD5ilS1q+hRLJKPRpBTIGYbDA6f9mRQzT58+5e480LdN0TMYVcYEPqAvIuNRaFoWtl1iGY7cH3fUjSJljbWWd28G5sURw8g4tTS2oVE9Sg4o1VDVKxoDx0kSjkdaIWnritMwYXWFkY7zcKaTBWAyDzMpGNr6iuQC58MD4zwjZUAqwTKeQUgW5zBKMUyR8bxjW0tWq0SSghAj23XHsESyVggPIkiEiChlEEqQVKYxLaAQyRN8xLNgmxYrM1eV4uH9HafTmdlLbF1AKDEJYvIoowlLoZqFCOfHA0pbTuPAsyfXYA3V+oamXbPaXOGmATefuN8P4CdS8Ayz43pVsV2v/9RL5Mf6IyUuIJ0sLpoQxAX5Kn+IsRWUNnXR5BXm++XO/0/+ntLHz5eQHFNpttuav/pXr/ibv/6C58/WXG0rjM4I6UFZMiXhrcS0QpalK6BEJuuCv26qmvVmjTGSX/zln6Hrjn/373/D4TgTtYJ0cbrMgeAd9w/fc/x/79g/3rFarzmdDlTa8vTpEzabFdN4wg0Hbq+3jOcT59O5qBOWgFIJowVCFTJcDoHfvvkH/Lzw05efsW6ugQ6yZO+PLLMnhYKOJQdIBjBIAllqlJQX73wma0FWCSkDSZXI2w/cgkxC5lAgRAh0XWM6AWYiVDNLOjL5MyEtaKOxxtLZmpw843iCEFmZFt9kluQIw5FV3YJtOLgRISVW5NJllIqAwo8T8+OOvHiMtJcUvov7QBSxYC0k23bN6CZcjsiUmMYTDztJs9qwDTPvH98Rc6DvSgdwfZUR3pPcwnk44ueJ6XyivXpG362xomKWI1aUAZEjkVNgHJeiZUgwNTu2zVNq8+MM/w/Wpm2QVYtVieNwZIqepqmZhiPTquL+sHD74gk7ERhOC/v9jFQ12iiEEoS0UNWCGGq6zmJNZh4zWRiUkpgKRK64vVnx61/9HfXqKau+Yp4njIQwLYzDxH53ROoGWyemsBAkGNOilCLME+fDkWdPn5K1IMWZeREoqUh5RlA2emk7oo0Y1SGUpraWvqlReuG4P6M/vWHVt2SRqW2NMgZtDU2lWeYHfFI07Qol76i7FnkaMAqsEsVmtsxM44BuVujK4pdI19/Q+Af0/AhZMg8TZtWjrSREj/eZFNwP1sCqLYlayxJQumWJJ1J0dKtrjCp58nWtcVPxLNdNgwwRITXII4nE8bAnuIllCUihaK3lPJ1Lq210LD7hfaJuLdrWnKTlWRyo25bj/UjfDFSmIJDBEZxGGYE1JYY3hYisSgtx1TVIoWgaw+bmCj0vnO7fsSwOZSzOR8Z5QgrNNDliToXxnWKxC2WDNAGfJVBIXKc5snt8x/ObW4S6oV8pUtcyx3dYBW19Rgya1soSrvNjfZT1wWGfBEV9nimxt/BDqz9z0fGJ9EMv4IOJT1xCdRASkQo1z1jDp59d83/8P/ycv/zFM7ZrjVYJIc6I9AGvWxXLsJCgCt0xf1C951gcL0bSNBXQo7Xmy5//nK7fctgNdK3lcT/w628OLEkwCsE0TiQvicHzq7/7T1hbkXJRxP/ua0PbNjRVRVtXtLXlk+e37CvLcZhYfGAeZkYxcBx3KB15dn3N6+OvOaffsn76Nzzf/A1KbImDxenIJD2JS9poCpACMJOlIUlDkgItFUIpogKpckmVkZdfOZNTxGcPRIw0mMZSb0D0njEeGZcdkz+TssTYGistlkyaRqZlQBpN024gC5bk0EnSyoYMTMLhxiPVAsJa/KJANSjhSMOMGGZu1RWi3uDCRMKjjUFog9ENtVpjNZyqM+/HA8s8oozieMoI84Tr62dM04nH/QPLsrDZ3tD1a9I0E5xDlxRk9u+/43R4pLt9QW3WNFWDubR3bPbEHFnSyJIcPkSm3Ynzcc92/epffD38sfqoNnyRQclMTq7AYkxNpSsqFQhjwsUFvyy8e/Oe/ancIK0t1pIQitXu5ZMtxxl2biqhNTHTtC0IT1UZZNUhcmA4HZlYc91vWVxiHI6MZ8d8ODOMC7quWHVrwmnEC01VVez3Rw4HwXQ+YbXGXWbe1hgGl1AG0jJhckbIjFYVMWumJdCvWpLWZTZua4ytyATSDKuuRmfPaX9i0zUM55Enz5+RwoBSCR/P1LYqm3ZKVG1FTgK/LAhtUEbz7MkLVlLx7T98T5MDjVXEwaASWGkRqkA3UkqAZl5KhyCEzOlwRIiAjxmExFhN8OESNGPJNhFzCegRsVjlal0RXdFXtNYyaEPyCzlIjvd7nPO8+f4RaSpe/uQFT55ccxpmnr34BK0jV71k917z7m7Hy+cd0xRou8IJF/qDiGgmLAFfRbbrhoMKhKywIiF94PvX3zNNI63VdG2LGKbSmsdxOJaQpegDuVaYpmXbbFnckWV2SCEJQC0VisS8jCwB1v2KOUqCz8weEBqBp20rYvjRiP+xVrgozcvI/kMu/YWSRwnH+SDgE5ev+QBAk5fNvzy6SwdLK8nPvrjif/gfPuev/+oZ131NZilWtZRASrIyZGEu+N5I0QeEC6Anl3lALN53rTRV1ZCypFtlNleOv/7vfsEXxxP/+W9/xd39kXf7BcgYq3BLLGeGPOH8jBRFYyCdYl4mamMZq4qubWlsxfZqg20aTuPENM7FdqwEtdEorTgOe1JeoBrp1p7lEEm+pTEbmjoQLgCv6At8J2Z/ya9XaG2pZIeWLVqbEjuuIsiAUImUSjytExPKCKpKoTee3HsGTpzcjjmc0WRqY9FCM09HTsuADpHatFR1gyfhlwGVA0GAabdgJbOfSMtcDmVywZHwsqLBUI+eLljM6hUmWZJz5DSjtEboiigVUmgaHRm6wG4Z8W5Gph7vPPvdI6xa1v2aw3TmcN5ja8NqdUPMDf58JPuAkQppK3bjmftv/4F1u+Zq84SqXlHbltp25JRRaFRSzH7CpczkZqb5t//i6+GP1Ue14W83N/TbLapa6PoN4AhTwkjPMAZkVMVOcXQkUbPqEz4ojLZIITidjpgXK/wwokKHbi3DqLnd9Ex6z2H3yHG3J6xXBAer1qDkzBIcp/NM1ypmv1CZkvs8x0yKGa0zIjnuHw7ouuHTJ8+QyeOmheg8/bqlriwBOPoFtcw0ZFSacCGhZQPCMU4jLi40fUVIC1JohEpMywiDoKobhmlgGmeU8OwPB7q+ZXCB4DM+TaRU1MTTcmA/1mzrlk9unrCyhtdff0OyHe8eHtGVRpuFYRiZzkdGl3DJswQ4HEfkuqBnE5EEzPNMArybAcM0D2hzi8yGJZwJbgSpiD6QpGI6nZCpob26JvhygFLS4qVndAEfIkFWrNc92+sVuqq4qlpunj9leXwgzUeUkcjYQvJs+paoSgSnc46UAoiIDxppBaaxtI2kazqWw4HJOybvsHWFtRXno2P/uGe1WZGELCyCULK367qmDws+C6zZgDzj5kJNb7uOum5JGPaHHVILVv2Wbr0FoVmcwh0csxdo9SdcHD/WP1ulEV8c8h/geMVM94GZ/2FDz8QskD/48MXvt/uLlU/pzFdf3PB/+T//kp99cUvXGVKeAX9JhRMgVQHL5KIZgIxInpwdxExWqiSpIYvL46IXUIAVkuvtlvbPW77/7i339wNffTHSvjty/zgxLIlTnhhnX3zvQpa/K12E8VKRVSb4ksJZVw2rdYfVczlcSIUcMi9evuKzT77g+7evGU8TAhjGI/O4cN47wlBhleWq2yBMII1jyfcIA6M/EdWCkYFKWHr9CbV+hVI9SQiyKlHUgkjOASUDuspUFqpmITQTQ45My0SKkVWzwiZBXAbGwzuWaUJlgakbdFOD1LjxTA6J2tZMaeQQR8yikCFhAZcCYwpMy4wRI63uMN7QqDVNf41VFWmeCdNQWPi6JUpBlJ6VzQSrefRn7k937PbvyVrSdVvQcH214amtePPuLQ8P9wW01WzwUkElmaMA2WLrijTsGO/fcnr7HVFrbp9+yl/+d39D1XS8+9171NSw1h1JR5KA3en4L7gS/vn6qDb89XpNW1vO/sRySZqqVM/jPtHVCneaOD5kQgiQLEJJhCz5xTkLhnHheMpMcyRlg0LjXEKFyPHxRIqC/eOROWomDJ9YyXiccHNgmhJNU3jQSwyImJnHCSU0Mjnevf0eaQQ/+emX6LrFpYAQmZQS8zCgBCQhkUazeANRoPSKzkiMcigsIiuUUFTWomXhSEeREElhbEXX9xjdMs6BeYmczw6dLFJYghiRUZCjp7KW8+nEOI48f3JFX1nu399zOp/JusaubhgOj9SN53E3cDwfybl8r1FKjLWY2uCnhRgFSqlCtSPRtx3TvFDbMmI4PByZh5EUIiH4whuoKkbnCcGhZEZrw2rds4SMsoZKG9arlv76BV1nWW82dBrOS8ktD9qy+IW6NfhhYfBwtRI8njySiNGClDUhK7SVWKu5XfeoeaYysL9/ZHP7E85S4b1jygI3z+SUaLuWaU7My0LKucBGhICQCc7jZaY2NYuasRqatkJKMEpDSMzLzPUGrK3IvSM8erRMCKUI/scb/kdb4sNs/sM9PVOy8i4Z9Xxo51+EeJkfELD5ItBDlJn3k9ua/+3ffMpXX9zQrQxCZkQMpOjJKZOVLlGtQpHV5RGaPDmXz/8e61+EgikXYV85JwikLGBelwS66fjiz75AWcH6t2/4za/v+fq7R5SiWPrSpX3+ARCUQOUCsFFSlo6DD2ibkdqQrMXERCtXhOnE//rv/y3JzfQaOg3GeY7vTzw+1oS5rC1jNJuqJUnDEE8s7j3nNCKFRAtZaHp6j7QbMpqYFSEmlAwoURLrhPDYCpRNBJMIEUIW1MKgjCKnBb+cccOJ6GeaypKlIUrFOYzIuNAECcrgjUDLGlUMDwXcI8F5x7w4jucjrZSoTV/stBfdpNAV1KVDTJYgJQXTkpBKcNNe8dQ/ZfRlLu+cY8rHyx/O2GZFv7rFKxiPO8bhTGValOkRVYUPhTIoVEsUC246k7xD2BuG391jn0rCIeIHQ988ZXO9pb/dcHr344b/B+vqpsdNHqJgUylGDFYI7h/f8+v3D5xOCZMrjruF2SXW6wZpS8tKKsF+f+I3vzV8+flPCecARlMbRYoTMRX16f505tPbZ0hbvLPT4pmnM9+fX9N2r1guvtz3d3e8/u49L5/egirq8M3tDRLBMhxZNu2F7qVxKaKsQkvDeDoVqI+weAp6spOCGBakKIt2veoJwRMSiCSpjGA8n9l0K1IaIXmmMbNZ1cxzotENoUrFViIto3dY21LVmt5oGg2n456kLFoJfOwwa41e7hjnHTHLAopA0SiD0hKhEt4FvM8opfB+wZqSYhUXR86ZN6/fQ3LsH0+M55Ele7xPmNNADJlmXXGeBm42W6xWCCVRVaZqLFVTUWtL21Y0ShOzQ1UWJTU5G2IMdLbmvGiklLh5ImVJIhX9QA5YpUvKlTAYqXj37oFpHHm4u+f66StcuLRYs0LKzHpdojGHccD7gNaaqq5IMTDOE2iF1RofwNTqEm7UMIyOxIQQkRwdx+OZtqmwuiXMHqMV3kXq+scr/sdapXP/oTGff0/Xy7+f6X8w632Y7fNPDggfoDtXm5p/9Rcv+dlPN9Q2/zdAmhiBLJBGFeZ+9vBDWtqFs68MQsrS2s+BD2jfnBJKa+LFfkdOhGWia2uUfML+/ogS7wvMKkkkhkpLfCxiOiFKLyLljPflPalNwXcLLX/4/61sTWtqiAuHwx3TstBITdSGVjSc7iT/dbdjPgqIZ5RuaKuaupEY1dHYllXdUIkOKVuMarF1xlgJyjOnBR80goyJAaETWnmCOIAIGLtC6JYqC6zIkBzzdGQaD9gUS/JdozHWMiE5nh8Z/YhVBtM8xZiWICIGg0qpRPYqQUiOOZWY4Xk6lVGjd4QoyXiEsCTK4STJ4oDKaSk01hzIXtL0Da82zzhMZ2Y/43wsIT4xMz7uoC+kkHp1S1uvcecz03Jg8geCuUIai82CIDVZSOpmzbOnn3Jd/4zp14r5G0dlPqUJPW1cIV2LOq15Vv04w/+DdX214d1yJIie62c3qHtoreDxbuL+3YAPkfm0Z3b+8jA3l83ewoVt/dMvPuP5iydM399zco5KOM6Dwy0j0+TICOZljz+O3L3LvP/uLQ/v78jJ8OSmJ84D54d73ny3Ly3qpKjbDmkb2rqjEgKZi5Je65rRj9TNms3asD+eUMKg1ELKC0rVKK2pzIppPoHUvHjxjL6v2e3fAwayJ4nEcB6Z12d0a0EYVo1h//ieSrc83u1K4nJK6MYSg6erK+bxzDIdWV2XnPa67VFCsISBYQi8vntkf5rKQymUdqQ1CglYBQZFt6oRIhEzqKqF48Q8zJzGmf3jI13T8vbtPSlEVldrpIW2NdRVg8axuEy76WmUZp4dDo+ta9q+YhwSTVuDBKMkfdMhU0YkgU8Go+A8BbZXktMw0a1XuKgIMVBbw9XzJ8zniWkcUEpwmgvm87g/UVfFOhd8RFqNrQRaSOZpZp4mtMy0taRvO7LzNHV7yTrXBBkxugQzESTzFBCMTJNnDpl58rx8+RTnSqDKqu1JMrPabP6k6+PH+uOVAeTF+53EhXX/QZYnSxrbpXkvCyGWD6z8spkWMuVXP7vl3/zVC7YrRXADKSsQmRg9ImeUspQgnVzm9pe2fUaQpQE0pXGvkOEyXkglyjvl/MO/3VaW1DcsLhGWxPbJU376xZlpWFhC5OEwc54cPmpitoWbkQRCSJQsgkRSIudU7If5w0SiKNOJASkalFUkqdGmgm7Fb88HiP8ZZTRV1SFVQ62v6PMtNYrGbFlbidcGq1dY1SJqSdADgYifEz4UmmdWERI0bc3T9QqXj0xhRiRPAqIbGc93zMMRIwTCGJIyoDWx0TS15OrpLVfXDcPkeftuJIlMnTQqi3I7T4HFL0QEc0gM80zwDiUrwsmRhAShmNKZkCLZZKTQWED5Ys9MKpKiRIaFZ5tbphhZ8MTDHZHIEEbOw5HVcKS2Hf1qS1U39LcbUn7OeDpxXs44N5TsluAIyaNsx9Xmr7kOn2PWDbKqWIIrGhBqrKrJWJb88WyzH893AkxzSVx79WzL4fSeGBYOoyfMEWn0JY1NYpuWtGS0ECitLkKSwLOnt1xvOkSWSDLH/cg4l81LRRBSs+rADWfev33N/euE1plvfvcOoysaGzk8ntgfJ7xXPP+kxzYNWhucX4gh0XY1LoJQtsAcQiTFXDZQJVASCMVbK3RJbSKDspJpCWz6iuPuAecWusoSs8CNiRgjIQpqYWmqDiUcx6Pn2dOKYRpIIqK1Zp4CZIjOcds1rLcrDqdIADKe49ExuwkfIkZVTNPM/eOJvlaM3oGWpbXtHXXTYMYjwzhRHSfmabl45zUxBLp1xbx4bm6vUVrQmKrw+I0g+RGFRcpMZRVtXaME2ChwvUEbRVNlrGkwqkA5jvsD6AphBUFatEjEFJmXRMLSWQFBEIxie93yfNvz2/dnHt7fEX7xJZWWaK3IwlE3CqUlKptLOhaEmInujNGJGIq1ahxPWCHwoQittFVYbdFZEL3HuwGfHMkHhDL4AFEWd0BYAmFZ0FoSo2fVVX/qJfJj/ZFKJQkGBUWpz+Vm/4GSx+VjOSN+IOsB8sPnJW1nePWiY92DkhFSJPmCns05FwuetOUQkGW57YuLx/+H2N0IqYgFC8Euldz3y2QhxIQLnkxGKYE20HaWq9AzHG6o69eARGtNWwl8FOTLH3bOXyyHINXlfawMMRYbm3eR8zhwu13z7JMr5ntFQrG+uuFme4s0isNhz+n+gTEuHPx76rqiVi37WbPub2nXW7SpUV7TVBkpR5QSJOEgO6JaCJUi+Fy8+pTvEXvLOrfE028I8T3Oz5xOj7g8ULWWpl0hUw26wifP4bzjWd3ws0+f8urTz3h3d+Th/T8SvQOlEVIhEMSYCKnQS6dp5DTswI2IuidER5IWbSzCZJIKhJxRIkIGnUr8b8gBkUA5jZXw/PYZD+nMOS0sbmS5uIqmVBJB3Tyw3t7wtH2FD5JNe4VVlkUMjNMJrzPZGSxr/FkRG4V3jjprZjcUKJipCTqzjEfk/PEguT+qDX93Aqsjw/GBr7974PH9kYrE4XTGzwHdGhCKrgPVV0hVuOpSCZSpePX0KatuhZQZt0SeP3tBZTS2HojOc54D1ytBCoGmajktJ9abls31iuPjiYe7HeMUmafE09uOWieCH7BGkkPk8fGBEB3nKbF4T9dKIgmhBG2tWCbLWZ1RKrE4QZVlyZ9OmZQkyQmErJiDZw4zxLJx+gVIgnmeEO5ESonBTYxjCcFp24rjOOG8xxqDtvD0ZoUVlrs3d+j1E1SGcRiLhU4WS4+QJXN+mB1TOGBsi4gzMWWcizzc3XM6Dpi+4+HxkbpSNFrTdhYhVnR9zcNupO9a2rqmrgRNXTHHgB8yRmm6LGnaK5RMGBVIKXJzdU3Mmb07YbRAy8RpmAhocgpMUyCnREoBay0hRZSRjMOJ1dWW1dUz2kZxuHvk8WHHu/ePHI8lwEYZxTJ4ZJ7RQpDkJdlPCoJPSKFYlolxnFh1qqA1l4gxkkAmpYBQisVNWJGZfXmNZwdZRLStyKbm8XguVqTsGKeZ6Af83P5pF8iP9UdLXGiv8gLS+9Dclz/Q9fIFGXvZecWHub0gZ7CV4ssvrvn80w1aZNw8ESjt+oRAmxpb1UhbFcW8FJcOwsUSmBKIQtKUSEi+tJe5JPjFQKaApAT5oodJTNPCNEeih7Zpef7iKV9/+57Hw0QCmloDAqE0trIEXw78Rhms1ogM8zxx9oFlCSQ8st3QPHvF85ufoo3h1atPWK02HO4faeuOTbNm9p5pGojBE1Mih5nD8TW+2bFZ36CQ3D++w7kFVZWk0PpyyJBGUulMrWpu+qc8Xz+llU9JSfDESo6Hr9nt76DW6KsnPH/xnM7W/O71PW8eHzkNA3JxhGPm4f53bL/9hjxJ3Ana+oLbFqUtH7PER8noR07jgXkZMSmSsyCSykXIGkzVglXEMBWefoSYNFpYokikGEmLQEwD9XbDp9ev2C1njuMj0Xtq43FhYZjOEALHac/u+Ig1Fp0Uc/SAZ5knwjJy03/Ci6s/Yz543j1+Q14EVjZEMikJ+vXCRm5ZbzvG4/KnWRR/oD6qDd+dPe1Wcj+deXicWRbPcX/P4XFGSIMxBf1YrVts2zEdvqPSHUIEJBVSC97vJ642lsk7or/j69+9oW2LW3L38Jbm6ic0KrJa1ZxHR9+vePE0soyeLDVVU7j8SnpU1Czzif3i8S6w3ir2j488uapJSRC8Z5onbG2YD+MlkSqjRWbwE+iaECIIA8GjlGAadhx3nqaznM9Hsj8xuMQyBKpKcHYLQlsEnsZalIC2r3HTyJTh2fUVYdI8f3rDr//xOzbrNc+sxrWaSniELwcSK8D5eGFmK85LjdvtuKpqDn7gdAj0XcUwOiqjcKNntVnT9EUM1KwsVrc4l+n7jmcvrnFzCQWqY+DgwTQSqQzbzZpldszKk6NHa4OJmuF0z/YmMM+O8zDTP7lmSb4E6UhFyCUXAQNd1yOio28MvRJ8/8179rsjp2XGz7FY6QCrLXOQZLdQ6fJzrqxiWTwQCMkyjp5xHthuKow2uCVR2cL4D7HiMBwLE1sLEBYlHdYafJqpFIgkOJxn1rXCu0zyiZwFjw+HP+0C+bH+aKkP7HupyETEh/l9TuQP4TeiiHLFpSGfL4p+KTK31zV/+YvnPL0to6roXDkMpghItOlBVEgyymhSyCijkEqW1noqWOgkBDFn5Ie+gSwZ6UKAIlIZ0H2FcxD8gkgBJTJXa0Nfbznstqz7FVfbxOQiyWWEUiAFlamI2pAuQjYtLqG/OeG84zCeyDkQwjVdq7l/c+T9/szh/XtevHxF3615+vQF21/8ktpYYowIBD54Tscdb1//Bj/s+PlnLc+3a/7Df7rj3/6n34GuqOsWIw1SgZaaSkKlNF3rePasQlWex2EiOk8UE2OaQfWo1ZaDrBndwOxOxHGmEZZkNHe7O7791dcY9Xc8v3rOqyc/p281lbLknJnyiPOByU/sz3dM05Hgh2KoMBJhNNkXDr8UsszwQ2JwIyF6mmioskVohUsJJzNqGVGh5naz5qfjC/52PkPdoHJCnCNCqML3n/ecxyK2kwk2qyv2+z1tVXG7fcVf/+X/iXq+IohAmgTzecZNnrrR+ByIbsSdKx6ngLA/3vD/YL1+/8jt7Q3TVGhZMXiO+zP7wVE3BiUlWSkqW2ObmuloUUYhhKNrrjEi8vU3d7S//AzInE8jb+8PPHu+LZGLqsREjqcTx/2RcZzZ3R9IfkJJhdIthImmbam6mizB+6WgEXPGSIhZ8uT6llpZusZws14zhoEsNN4nYsyETMHCSoHBsLgzKQZSztw/7Oj6Bq09x90ZN3vavqFuQciEqGqMlUS/oKsaU1Xl5hJnblY3/OSTF5wPitN5YLc/0K5blpz5/mEA7yBHDAsCR2cC1hjmkKnajrv7A7dXiZWyrDZXpDTTz5GqtiigaTTZBXQrAU3OcHu9pes7pLZYnTC6BTnTdAkpM32/wtqa027PsozISyJZSB7nF9w8klJm9oFqnDGqJiVJTglPxNiSwFX3PTetJriZ/e6O3W5gnjyg8QQyAaE0aMXxuCCUoq4Nbim2QiEMQiw454hhJgXIKaG1wMvCuyZpEp6ubokx4J2jMoKu65GqKmCd4IhxwnvHOQjSslDbTEwVzqV/7u37Y/0p60PPnIjkAtbJ5Y5fKv+eoseHGXyRzve95auvXvD8aU9Onsmd8OOZZZlJGepmBdrgQkSGWNTfQpDCBewkLjLBlEqwTM6X/PlcEL+U7hFZohUIoVC6Lt9J8ORaUrVrlnlmu17x+RcvuXm+5c2bR7777sCweEBilSjPFKmpdYsVGuc9h+GR8zyXkBkBX3/7jp99ec+XL7b8z//wd/z2eOLv/st/YLXa0jYd66sNfbcqNNK2pWlrjJY0bY/RoE3Ls6dbfv7lJ/y7//orfBIQPEJDWzdEfzngqIDOBySPBLng3I7DyfNwWnj7eE9MEbO7p7GSRktE6Fn1twSZOZ6OLAmmlGi6Lav+GcY0RFnEhzEUa68nMvmZeZxguUSQVxWibRG2wU+OWEcmt0f4CuETuITLjhgWliCx2hK1IEuPdBo5aXorefXsGY/hTNjlIv6NkbbqCX7DPJ0Yl9MPmOElOLbbW17efMEXn/w1t5uf4lLG9rCkgcnNSF1j1x0iO9SiWT/reHjzyGm3+xMtiv//+qg2/MNxxMrnGB1plebsPftjQqDROmOkYA4enzJVXZVNWBuayrDuOmodef/wyFfhC5SI+Jhp1j3icvrb3m75/vHMsjtx2p/wS+T+0Rc7V10RFofUiqprWK17pnmhWtX0qzXWaKq2Y7PasukqsshoUyNlaS9JUyGrlqwnlKmY3cyNFngfUFgyJcf6dDjy+FiRXIV3I22/Zd1rhOlRMrO/P/H801uW3ZllmHB+QmpL3bR8/tNP2KxXDKc94zKhu64ojlNmXATBXzy6oiqugWpG60wtM+s28EYbRhRXlaFVgSlJINA1K2xVY01LEiN1XZMAiaWuNW3XMi8RXdXEWNRBlVVMw0TUA8fgOB/fk5JlGA+Iui0kMmF5uD+TReZ09jSVp+sLlbC03x3WCpSCq02DQfL29TuiPwMGVRtWKlIbizGKxmiEEOz3M/NSeOdalSCgxTvatmOcxgtlS2CMpG06puOOHCPOeeY40DdbZqVh0TQWIgYXMioLjuPMMjmWFHAKKiUxlcLHiJv9n3iF/Fh/rJKQKMqNN2V+CHwhFeDO72/8H2R8AAIpBS9fXfMXv/yEZ7cNMhxZlonT4ZHxfAJVo6orVv0Nuiri4JAyWl4U/okSyyMhpUx0vnQTcuHgIwUxgVICIXPpDFzU9m1jaT99USxsSXOUme1Vx5/94hXH0xmVPcN5YL6fOY2ZIDPG6KJdkYHKWipdoaeakEZ89BitGbzjf/p//Ds+eXKLSgEjJedxYJoH6qbl7u57xIX1L4SkriuaxtLaBmsg7B/Y6IH7uzsUksq2KBIqJta6wgtHmBNbX9GNDe9/FTmEwPEsGUICLHKo8H4mVIKh0uSqwUhLMLLoHlJkXAZU3fDlT/6KTzafko1CymKLXsLCtDiGeGKczhAyKXhyDAgssx85m4qrbUu1XXO6v2d4eEBnzYLDC8ccQ0HueoOtOiIRr09IYdBZU19f8fPnP8EnzzfDiK0ztel5ev2c5D3LMOCcJ4dMYxuePf8Jz25/yvyQCUeF0YrZDbx/f0+zqrGiQmZD9hljVkhbXSLHfwzP+YM1hUSqKqxpefXkhuP77/A50tU1TdVSWUV0HlLm/LgnArOf8dmQjEZVivPhLe8fZsgBqS2VkYyTwyfYasNwODDtznjniD6zKEPbtrRWMJ2Kj32zXlF1HabfoqqaT5+9wOcRrXp+9rOfslkFTing3UKIETcXYI0WsGpbjsOMUWXuZbRmWY4cD++Y5xLecDgMKCRNJbm9XaOtxCiBTAsP85l185z9SXI+7OjrT3j14pa7N4VmdXd3zxQgC43VmWHJrFBEEXg8nWnqmspqgoTRlwdMv16h1UTXwqqpCgJXOqYUIWuQGSMl/aoGNKYyGKVQsqFpK5SW1DXE4Pj+2+9Y9w3DcOZ0OjONkig0OsPsBqJbMHXF4TiwP+yofI1UF+sdGqEsIgaMVSip6NqGWimW/cBunBnngsHddAaX4RxE8fE6j5Eaq0pQhc6gUaQ0XSgrATcLgpuw2pRWvi+mqKquSXFGK010iZgcIQjmyTHJhcmNHMfIEhzH41TmpkrgXQYt6Pqa027ELz+29D/WEqIosst4vvjf5QWkI8g/bPQfyHsfOvx1I/nyyydc9TCfHljGR4bjPfv9jtPDjpQr5rhBNc/ZmAotFDIHUozEEFBKkXPEu6V0DUQmBk/OAUlESBC6QSuDMWBkacELmUCC1KZkagQQcsvPf/Eznh7OvL97oDKWTz55zu++fcd//Ls3vHszEpO5iGV7kiiXGcVC9PekCFSaBJzHmYf3B1pr2NQWKRPTMrHMMxJdxhzzVHDhccH7itws2H7DzdUVy9jz/bdHnvd/Ti0N5iJR0HuB8jW1XbPKG/Jdy/dvFaclcDjfcU4Zeodd3bLeVAQRCCmTQyQqRS2KoFoIj4tHPn/+OV+8/At8jsS4lM6bd+QQGNzI6E4kNyGIhBQA8GHiYf89Mcxk8xKbPGMODHlEJkEUmTkFQlwge7R31EqShWU8OWIIBOdZyUS33vD5+hn37+9wYSLnkbPf07c3XHcbGtnh54TImkqsUEvLVduRBg9BsgxLcV3YNTlbTscjKXlmlXn3bSDmES8+HiT3R7Xh51Ru5X4cceN7huFMV1lWfTktylTxIZlIyEBlNLUxWNOSo2e3O3D35oF//M33/OWXW05+JqdIiAGf4ThEpvOZx4c9WYCtLFoLlBBII9GVQhlFVUmEKhn0f/MXv+Srnz7j/cMdp1FQ1ZokFDIujMcTOc5MiyNkTQ4zKTmShNYUX/4UEo/nHadzZFkCWcmCjlWJputJiwdd0XUV113FvLsnnU9UjaXd1Dx/sqWzmp1MLD6gdGLxM6m40pAZ/DyWk7gUNHUNyoOwgMKHzLh4jotjiRKbBJ2C4ziRs6C2irqyVJVFEOm6mpwVbVsTlgTRcVociw+MuxOnYaapLeNUgmyi93jheHH9hMHt8DGxe/PA6XjGec+mu6JtOozWbJ88o+lWhDTQ1BVd09FWht2b3zFOkZg8PoOmvPZKG9w8ExaHT0WtXLoqAnBoXV1SzwSp5JnSrhrSDFpKlmlmnuci4nSR0/lYIpWFYhoHlnlkyJrDOHF2gZwz67XlattxPGdEKHQyKQRaKXxyf9L18WP98VIffPYXtrtAULrrhYIjPkj4SrrNBYQjubld89knN/SNxs+OaR45HE8cjkce9xNLgOpWUdeW2khUjhADIXhiLD57pYonPuciRF3cQs4RqyUyJlRaSFbhEhixoGVJ0xNSlgh6VUA6daMQskKb9gLiWvF42BFS4N3bR3CZjEXpCrImJYtVPcYsxPQaSGglEFmiVUXVbakVhOgJCKSukKqmam5LZgDF9iZkpm9q+qahNzec313zd39riefPuVKSRklUrEhBUeWeTq9o1Ko4ZsjkOiD1kZQMy6AY9w+E2SGjpFr3ZOFw2Rebs4uM8z2j29OvOl48/YQle8bxgEieeLEWTmFh8kMRFAqIyV1e6/KzXKaBY4pURqNlQ2Mb2hsNMRGjJ84zOQh8yozBkWOBiTnn2PvA4jxzXpjnEVuv+OzmJdO7kckNLI/vOJ0OtKpjra7wo6YWDbIqpNGb5zccxx373T3nYaRf37C5uUWJispWHMZ7wjyDg+A9wX08ncGPasN3yaNMz/Xthr//uxEjNJ99siHFiceTxmrFHCNKC7TUeKFRtsJUNdM08vXrhYf9xJMp0nTX2MMbHt6+Q0nNwzgW8M7DSIzQbzoWnxhPC21jiHPAuYjKkbqtMVqzsh2ff3KFNpK+rXlzd880HPFnkNoiVEm2UmFB2pbDUma8zjsOjw9snq45nR+4f9ijlMGdZnTTERbHNAg2XUVQnpXqueo7kjtiteL7715TtWu++PQVN0+u2T2+wwtLY2um8cwwjPTdmjenGbs2+GXAKkWzKlZBQQRp8SEgtYQkyFKhmoYlGYzUVLZhVVW8e/iamoZVXXEaTpAnVusnBO/5/s07rBYclgkfEzYkHk8Tu+OJ4BxSOCqViQnusUznkRwjp2FEqJrbpx1GS9qmpl01KAv7/Y45CNpOcbNasX888fbdPfU2I60lXSI2Dz6zvdJoGWjqCmxFzGeQkNG4xaMrXYAm0mKMQdsKW/cc3ZmqqQjuzDx5LALvHc55qqYBLEYtVOuO0zCzG06s+hZBw3ZVoaWgVp4MeB/xy1RcFvnjEd/8WP9tfZjJFz96MbvnC/IuZ8giIZGFqifK17ed5mdfPOdmZZiHOw6P33HYP3LYH7h7f+D1dyeEzdzOFwV8yiSZiT4wzzPLspAyNF1DI1Xp0mlF37ZE74nR4X0os/zoIXowqWRq6Lpk16cAQhXxqNFUUqK1oetaNps1zXvD+bMjQhe9yvnkubt33L/PjEPEpwWfPSlH8sUqaIxh3fa0fYeIkjyPpDgQp4V+e80vfvbfY+WKOGaG04EljUib6E1P43qWQ0M8WWyukd4grCRLQWUbtmLLuuro1hahBc4HXJqLOl1IdFVznre4cKRyCjNL5jwRZMDWK5w84+M9Qxi56VbUMTMc3hVbbQoIJDF7hmUoB6qUL+LKQjcoKYglXTAHSE4RkcxNwyIVlVAo7xBSo0Nh6ft0ICVPYwyNMpymM+PxniGOrIYj/dUNV1dP+SRnfvX930GYECyc54W2tfjZEuZAu17hzUKFoLcND0vCSMVqs0HVmhwySI2VLVovzNERiaj88RA6P6oNv9IlUa3uej579oS77x/RQrEfIlJqvA9lXq4USEXykdll3OJACK6f/4L/3b9xmB7u799x//jIxmrmEFDLwvnkaC7KbCMFC+UElpaFpCTL5BFL5u2bt/zVn/+Mz15tGM735LBlGY5M3nN/95rvvt/z6idf8tPPblj8G9rKMIQFaw3DztMYxWN0LG5Ck5mHSEojw9nRqIoWjUrl4aBsAVBEN/Hd67e8/m7H8bznz3/5KX/zr3/BN292fPfteyrbcBy+I3rHuDjmYcK7wOJGhkmjtSAdAz56UIqUBpbZU1cGZXtq2zLFO3JU1P0aS2LyHmVb1ps1t9fXvHv4LdMkWffg/Mz79/e0fcP+cMQvgVVb8/67O7JUXF11KCupmoZlSlRVUxS/s+TGNFzfbtFCIZjpr24Yl5mwzCU8yNToCG/eP7B7PJGSpGsbHmaBEh7hItJEljGw7ioedoJKgdFl4cScmWZP1ytsu0bgUUDXNOz2I7Mf0EaxLKC1QLqEuUSFlshQjcQUMWGYsZWm7yrmudCbYsxUBiYXcX5hmDQpBnT1oy3voy1RuPYfNnP4sPGXmyy5HMbF5cwmlWR73fP0WU/KJ8bTHYfHtzw87DkeBr7//sCvvz9x9eQKoS3DPKNPYBS4cWQYR4QsXULlI9oktCkxuTmXnAari2As+AXnPX5ZUIvEWktdC6wsdlIwl3x5kEahjEX6QAbWVxu+/LOf8ckXnzOejvz9337L4/vvkFFghCUCbjnh/UgSgCpx2E1tUaYwAmzTs1GaVa/QeoP1W541nxMj+DxxTif8EjDLBVUuDbrWZF8CwOrOoozBRkMtK1QlmO3EklyJsSUijaSpOppVy9P8BO8XUp5JKjLGkUM4spwnkp6IdkQnTW1XuNlTKVMOaCESkmdKE4lYnA7xEoAV/cV3qUpMLwJtK4Su8VEwjwtz8ATT4JbEPCVkNmh1Rb3pWKZHFjmzXm0xcmE4Hjkdz4zLitQ0KODpzXOmcObN238seQGADwubzTX784nD7pHnNy84xwP784kQHMZY5jCzvz9R6w6jK1LOuBRpmxqnDWPIfCz1UW34GZDSoJXiPHuSruk7ybdvJ5ZgaZQhxgUVJbISpDQR84oQF4yqiMnRrzvqvrxdutWGz36amGOkXz0wjJLoSypVbQQhZ3zjCC6SrSixjVozHBzJzTy++5p37x949ckrunXLtk2cj0fe7088+4lAqI6UBFVlGfYPLD6jtSWqiaqpMLqhqj0+BWaXEFZSW03TrEFLBBWLC4SYORyO/O7773ncT1gpefrsljfv3vHtmx27xxN9PRFSAmXY7R2VDZxnj9UaW0lidqRUuNhJRgyJSkmW4EgpItC0VcPsE8HDMI0E4Zld4GFfWpjHw4RSiczM/cOO42nicB4YhpG6bmiV5upqi7yMWdx8JMXC/G5aizGSAUffNWw3PXfvHmlqQQiR8zhz03WkLBnPA26eSmaA8zgfMXjiEmhXFiUr1quW83Ckq2BefBEkGWhrS4qZGCJSCnxKECLWaiY3k0gsi0cryZIpPAKl0UnTNYaoJFqKspGfR5KPGCGomxqlDUqUlLBhWljmgPOJeTmTU6Tq7J96ifxYf6wyxAt4R+TC1SdfYnIRF0jMB/JeuTRsNivaWuPmE9M4ME0zx8OZd2/3vH53YoqGn2w2kCU+CVwoKn+fJdPicSGylQ1VZYghMw5jyZYQiRQWSL7AY1JkmBb84jBKXdj6RcQnQkRqiagUgnA5ABS3gdaw2azpVit8gmla2Fw9YbN9yn/49++4f+953J04DI/MsXA3lCggMqUVgkxMCZ0s1l6z2r7k+fpn3KTPUMeOZYwoXZGTYvEOoS4H21rDJpPO0OqaddMjlWCZHedw5CQSfplZ/IxImUpXGKsxjcFHBymjjUEJA1WNDBVhBOSA6Byf2A1fyGumSXM8e6LU+OwJUrJER4j+oqq8AI9SJJFIApQsn9JSU1drbLVGyLoIMkPg5Gb86MhzhhCJ2tCvrqESDOGMFgHZGrRXjGlmMYmBgBx3SCxXastobxnCiJCJcZnoqkDVV+x3e94e79g+vyoR3l2LCzOeEyFHztEhFoupFaavOB882iiqfvWnWRN/oD6qDf+zF88IwWMknMeFtt1yeyPx+XuImSQyMUeCiqQUSDFhRSA62N484fntU4Y60VcNs/cYGfESOq2ZWs1uWNh0NccxcPNkw3I3kGJDzIrbpxt2D4/4xaOMYRiONMrwX77+jj/76itaW7OyA9PosbYp+doiU3UbcAfevbtDqIZN13I8JbrVmlpmJlECLrRSrPoNVVOj2kRIgkRZ2t553rx/z+QEL1++pKtgu2n45rdfs4ylLXiIl+Q9bVldXTEeJ+q2QWuN0iCdQ+qMVOmSaueJ0SOjQIqAD4FhnFhcAKEJMZASLN6zO3vm04Hz6US3veZhPzMvnrptqKqe25vMel2TRWSzXlN3Kxa3IIVHy4zRNaY1tKJFigUfI/O8oIRinhbGsCfmYpXyPjHsD9RVQeJmygwev9CYqthuTBEOqrln8RNKQFP3pOCpW0uIifE8080zwc/oXKxQ53MgxqLel1IQUipK4awRwaGrnmWemaYTi5tK6pZKWNsW605IKAXTMJNiYPERP3m0BWMrmqr+E6+QH+uPlZSU4BpxGdHny8cuaj1x8eDnC0q3biyf/eSGm2vDcjgyTiPz7DkOM28fB+72C9fPXvDzr77gyy8/5fZmjVKKaRy5u9tx2h0JKZNi4ebrdU1TgRYZSYmMDb7AdUIuEJkQioXPCkkWkhwTOTpSEChZoStTAoAQaClBS5S2ZKHwWdE0Gzbbp7z45Et+/tdnfv0Pb/m7v/0Nr/d/z91RlJjfFAi+YLS9n1GiQmOpzBM25hWr/BQVqtImN4UlYEONUBphVZmRNxl8IhlBVDCnBbcsjMtEDDPCJYTMDO5IiKEcqrSkiZbZjWQXsdJSq4a66vFEIkeunmb2eSKKyLZ/go8eYVP5XpImCv8DPAv4Pd8gelQGKVS5eQvQtqVutqzqLcI0iBSIlMhvlRVRQcwRlyLH84wyBqUqQojYrqGVt2Q7ITEM45Hd8Q6RBa3eUmM5+SOH+cDKbBjcia65oosJlya++c17Xm1f0K5KjHFvJcsScHpkt7wjTB3NqiN3MJzOiCT/2ffuv2R9VBv+s+db7g8PbE1JR5qGE2l7VWhVCZSUVFrh44KUFcqWNDwhDEhYb1fM8x2784g1khDBGMXj/sw8TRz2I+unNdGDlgYpNbppeHr9CT/7ouVv/6Nj1DM5Z+7vRv78Z1/x7uEtIifm8QCX2991V7KPs8ys+g6dFNvNjnnxhGUgjAPj6cTVVct8OlPXLTIa+tWGlGcOuxM5SqZh5Gq74nx6ZPEzX3z+Fa+e9wyPe6T0HOaJhGQ4O+ra0q0qxtkRU8KHiWlYuOquqJuWVizUeB6ngIvykgOQQCeUlBxOBw6HAVPXqLpiXAbisrB/PNK2LcEpXMr0ecKHjE8l+KbualKYqWtD1awRQuOTwM8DVWVJ3hO9QwowUiCyYplndCPBWI53d9w8uaLSNY+7EzEKlmUoBwDdU9cNi6wZXUQUrxMiZ5z3VI3l8eFM3Td0bc9pOOBzRMlyYOlqi8pHslA475hcLAliShYhphRYa6jkmv3hDXgPOTLPjpg8ImaaqkLaiuE0Mi8LsdKMs6exGlfDqm9JAZboaO2PN/yPtQRgxD9h6IsLZS+XmyH83omvlOT6ZsX1VYvKieAD0zxwHgeOw8Lu7BnmyDp4lDYsfubuzUyWkpgiVdOy2mzQSpaRkQik6HBzIOR4EQvGkv0xT8QQCbGEVTl/mUELSY5QaYWtgByJOSJNg5AWgUQnTw4zMWeMkChryUJiWsWn9RXtasv6pmOMD9j/JfHt63ukkGU0IBWyTMSpOs3L68/YiJ8QzxVeCmQWOO+orGaRkSQFnREEm0hZonJRBE9uYIqR07gjJo82MC8TUsEw7fHeQRIYo9kvmTkMZMA6Q516mqqj7yte/ULx6V+94O4kuH84cBpt8bGbjpAALVAuXOiZCpFTudUnTyKUcZyAJDJaSFb1ir7ZUpmGbEuHxRpdcMYyIp0HcUGW50COiSgjYwooaek310gzcz4cGI73hKGIjI/qHdvuKSvR45nIKeDCwLq5Jp5gGgbMPDGIxHbbY9OIzyNGSlTaY69OJBRzSki7wtQ9Pv3Y0v+D9fjwPTY9Z3sFYDBNRZCq0KSUQGiJTgKUQgtNEJGQItpIcgzELHjzeGBddzgfmbzgfJhRpkVXA4mR2eWSTy3AGI3Umu2qpbPlDVY3zYWsBrZu+PJnNySRUEi6yuKXhel0ZF5uCLFAX5ag+eLLz7l/+57T+YSR0NsKHT1pnugqjR9BakEMitrUqNqyWltur7ccHt6y7nv6vi7JbGHi/eMB5zMyJ7pOU9UNbVshTUXdGuaDIBMIwSHIPEwloz6T2HQ1+6lQtMRlFpWQaCOQWuOmqaArs0BrS7fqMFcbTnvFphV0ciZWApUEm76h7zY0bc1uf2QYRpxz5JCIISJyJuRIThIXA8HHopjPGS0lbd9gjOU8OHwo+d3zfOS2e0LIEu9mYvaEpNAkrNYYoxAYQgoobRjmBWkhZ0VjWqRWLD5Ra4lIMEdH29VondCXFDGpyi0qCYmsLe4x0FUQqViGgEyqBKpIzTzPhYIeHUY31JXGSEtTF2qalorkJZVp/qTr48f645XF71PvED8E5f1+hs8HwVfh0F9vVnStIcUzOXhEkgSfOJ8cp1MAUfH02UvatqHvejabDu88x92eaThjVIOqGozV1LZCC4/CFa1AigTncD4RomSaAy4Ejrsjw3mk6zc8ff6E2+u2xOzoiL1oS7JQZGEv33tC/P/Y+69mSbMsTQ97tvyUi6PiRERmpKisaj09MwSGg4GBMCNoFLglfyzBexoNPTAYurtaVnVVpQp5lKtPbsmL7VmNGVaTd6y4yJV3mZER4X7887X3Wu/7vFKfKYIlTAapUdRkNBdXFucTf/gnf8bbDweG5W857g9opdDWonWFigKjJTJHvPPgGoJOCOWI1nNyDqpI0om5UYgMy+xJKrCkkSVOSCFZ9IySgihg1++ZzoEzIiZUKhkXlbVICSJLsjec/MwSPdvn1zz7fEV9UfPCXiB8yz/u9ywhoWVVWP14sivfV0IIUi7ppjnGH/yUxcaYEspU2GaNrVqE1aBAZoE0pvARvC92QAmESI4eRySnhTRHrFVopRBoQgqEEIiiTEd8OHDSls3qJdvqht3yRO/2rOpLVt01bkyIRpKSIC6Wtb3ku91vmMae5AyfXv0Zz59/ileZ5RSoL1akzcdzUfioGj7SE4NnWRZ8Tux7hzXFK+rmQEQTvKBua7LISJ8gnj22aWboB1KUFOBUzzBm5gApJZZYIxTcH3oml+kPCySJETPL8MDUW5QqH5ikQAvL9fPPefFpz+HgqFeXzP0jc/RIrRinidN+wSjF4k6cxhltG2yTuL69Ze/vkWlh1UiehoGq2qAFTM5TV5q6rbi6WjEcDtRtzZ/84RdcXmw47Q9Y27I4R20kswdrLUv0NKLl8y9ecdo90tUGaJnnmXmZubs7IqREKo0KGhWPWBkRWjP7GW0MujJURpPCQt3UzOPM1dUlV5cbQo7MoyUrwTjM4Bxffv4Z7faGEGaOxyP748jYzzRVjUsSfAIZCTGW27Op0FZjhKeqLLYyNDpyGiNu8Yyjw/mF1WqFMDU5gZHFjoTUaF1Uvt7PZBGJfsZIU25saUHICmsqhJCMw0Kliz4iePAhYqUk5oBC4WOABEbIoub1GXJkmQXZe3yMhCAKaz8GYhSFgKgaJODmkbAsLCHQNmusAW1+jMf9mCufd/Tity2+9Imc5dmbX5qJUsV6q2XAz0fG0yNTv+NwOPF0GOhnT5YKW2m0OS+Nc6ZbtVRG4OaCfhVaFhscmRgTzi2QAjmnYgUOiX50PD0dcUtg8vD+w8h2sqwuCvJaaEXOmewdWUdQJZkPMiKHYjPLmSRKbHfBB0vqqkUrSVOvUFrTdB0XV1t+/pd/y9RPNNbS2ApNw+XqE1bVVfkODYGUE6YS1G3FMIwoKWi2DYfxgBtnopvRK4VdafwplWAuqTjMA94vHI5HFjfilhEtJLW0yCzLoaRqWOs1OUtm1yPUwNVPbrh4oTjNO8Iw0C+JKcyFaSA1QSSUKFPdkmkUSdEjoydFV05vWpFJSCFpqhWtXVPZDmUrshQYBFImRIh4mYiUA5IQiSwDiUg+M/VxAbcEfASlVMnzyBkRyqUkZo/zjq66IKXMcX7gWO34yeVPOQSH0BVJKBafub16xRx6vh9+gaLD5Fusec7FszUncSDuPdvV9e/hafjd9VE1fJUkXVUxxbkAMpNiCWC0IoWAzKn8UHMiZY0QgRhmyrpLEP2EUoLjHLA5UTWWcbYs04KtrlDqiegFQgUWF9lcXqGERqEZ5+GMwNTUJtOtVtw8u2A8epSNGBWQMUBItG3FME/F5gM451DKsshIDEXw9dSPvHz1Gamp2U0Zs73msHtimmb8HGjaBh8ii/fcPnvOJ7ef8HQ88dhPLAt0wheCl1asVzULAqEU28uOeZyIwtCfnni2WpP8QhKJ02Fie9nifcJlyzA7KmOoTUtWgXGWIBWdNvTjjNEWLTwxBQ67E+MwkpOhaTpWbc31VrKbez487MhR4sZMCCDWFUYEZh8RWWKQ9Mc9q+0NbdeSJtAxEmPicDjx4X6PNBo3eWwFwm6ZlhmRKhAKlwRTyIiU0VmVrX6OKCUxSqOxvH/zjvX6M66ui0d5mRwgkVqQQ2QZI9bUZAqNC84cdSkJqUA8GA3zUPLJpZRcbDe0dcXwtGBrTaUrQswsKWG0pTKRtluRskRS0hZ/rI+0RP7trf4H133JtjmPU0UhvAkym03FixcrmioSRo+PC/PiiNkQsCxppqkaLrZbRPLcvX1LurmlNhkpI0qB0gqlMkqGoi8KDu89wS9lbx8D4zSy2x15++7I0zGTVA2skdPCcDqxzBWVrRFSIKNDuRGZRfHZc9YfkFGyjCzi+dWVC6/AVjXatHz105amXfP+3T3/8Nf/gDQNNita3dJWz9jWN2Qn8MGhKkdScJwSagRlPXOaWbxmjAOmFgiTCFLgo2d3OuGTQCnN8TThZk9YDMQKnTNKCBCaTJmotXbLxr5gCCcSd7z4bOa/+NcVV1tBdXA8hczTvkfGQkkNpDLBTRkhJEJKkkvkEPGhTP+kyKjzgU0ZS9NuaZsttmrRtQKRkEHjM0QVCLIkHApRopCJGZ2LWkilMh02STC7uaDIz6u6mD0igDIGIRRCrLnqbpi94/74gevVEwsGkyL3/sQ2Zj5bf8JP2n/Fixdf4o6B7BRKWWLIqHXLdDzw7ldvgM/+//k0/Iv1UX2DKSGpZYk51ZUhxBlDR9XUkA8kMkhTYBUpEcgo8cMpXpHcQAiRtu5QMqKj5W65J6bENOwZhiMvb5/x7t2OKBQXV5c0xrDfJ5qmoWsfkNbQnuMNtVZsL2/R1ZH37x8RUqCVABG5vVoh0gQ64VNCKAg5gbKEDPsxsnn2jHXc0HaXvH0aePduZBomuq7jsDvhvWNVG7qmJkvBblx4d3diCYkvXm0w2qCQBO+wumLVrpmPA1Pfc3zc471HVhWnyVNVikM6iwGVJAfJshRriFUZozx9SjjlGJYCollmR4gLw3HkuHvi5nqLkoaMKcE/TjCfjozDRIySUz+gJXhXDkciJeqqwS0z4+io6pElRhqrCfNIcI79/sTT4URbV1xsWnRlWEJGakWMZZymMKSQkCiSD5S8sXgOLo9UpuXN9+/52ZcdfpnIQD/MxASt6hjiiUAgiSKw05VFmaoElmSoa0MMkml0ZdyoMl1dglBSdAgRkaomxEROmehmNtsNy+LOYkON847x9CNp72Mtcc67L8K1H5r+D81Rnr8kElpLrq5X3FzVpHBiOD2UvIcM4+IZxoAPAuUiQz/RdSuev9xQGYHRmhjyOYO+tN4U428DbBAZFzzTMDANE+/eP/H1N0/0vsNuXhJ9Rro9F883uKA5nTymMmhryIhy+05TwccqjVKaLARZFLscSGIqBwGtgZwQ5652cXnBn/7pH/Mf/59/wfH+hNU1Wjas2xcQOk7HiRxrUJEUBMHPLGkkp4kpTDR5S20r+mlidB7nIuM4cdyfENmgdYnhTUEXUSwZIRIGiUaRkQhZsa4vsaajH4+oeuDV5yu8WPj6YaBNiVopdExoaUFKcvYIEjEHUo784KTI6UzWE7k4FwQIylqtbTfU7QrT1CirgYTQiZQ92ieCAJEyMmU0qTzDqTAZyqqvpA9qY8jZIlLEqwhao5RCyhqEISVFVd3wyfZf8+sPf8H949dc13+Im0cELWPURA/r1Zq23RJXkeNhJGvNMHmGsadfek6n/e/vwfjP6qNq+KazuOlAFhGpKmJM5Kyw3RZtRgSGmBMZhdGZHAUhCY6nmVWzJabI2M9oA9cXa9wyIVKgrivevX7DPCu2Vx1393uEllirCCERBSRlePn5pzSVwk0RZI2WEmsUb18fyKKh6WZ0PxKiY+n3fPut5OWrS5Zpods0tJXERUfVVaxaTV3XHPpM3SSuu5HaCh6J+JCRfiIfA1erGy4v1oScCVkQQ+T68hntZoWxNcMwFASuKF7UFAzTPGJryzIWMIzUGqUFpq7Pp2DNcupZfEALyTDsWV12+BTR/kDKFWTJPI/4WHQL19cXXN9ccvf+wLz0VLZicJLHp0dWumMSAqkjCkWc99S2wwvNb8nkMuGmE1pq3DKTCHgPfT/SakutFS4s9HMgpZntzTWOzLwsWK0gJubZsTaKhCTkgBGalBN1XUJHjBKsuxYpFMexJJkJqUpjl5KAJyOJGWKKxfYkM1bXWNkS/IKpDE3XgWk5HU/IDHXVEdEoVTJWRVQ4n1h8RKREU5XmMfTH3/cj8mP9CyVz4dOLM3ExFzk++Xx8hNLzba25uFxjDOTgyDGSymYQpMSHRApweXvB1UVLjjNWX7JeVQUKpYsNVQlJjJ7gFnxcWLxjHgb6/ZH94z1394/85rsTh3DN7U/+Fa++/Cl+OvLN3/8lx1NEfb7FRYlziRASSefS+AiQiqUu5POtVyuksChhSEkjhKbsGQqxT1FAZNdX1/z0iy/5ZvwOHS1q7tDNGi07Yt6DDEQWpmkpmF8VOS0LWirmZWT3tOc4BCaXiM4ToifHjMwRQjlwkAvOKFE4AFIIBAopBLVZsbE3KGEJYqG7TlzeNHy3m3nz9EiXZm67S1qtGKQ8C/A0MQVCCsQUiSmUoLFzBgJSgpQkCj2zbTpW7QVtt8E0FVmX94ickTGjcGXyEIp/HxFJ5+mPEhKtNUKXvb/OkhwtSXtEFoik0FiM6FDJFqGgUVw2X/LJNPI4/wp7lVnpFfMplFWmrmi3a6LIuJioosTHgl3/8PTE04cPjH3/e3kmfld9VA0/xR6fG1LO1E3FcR4Yk+fisgMBzgcgYWSFthEfAkEUIppQCh9mhnGibTVKa9a1om63KCLTHKk3N2y2LVlJtuuG8TSiTSTGAsPQtiUuJ7bbFdZWgCL4iTdv73n28gvW2w27pwlrFG4YeZxrrrygv3/H5y8+oZeRVVdz+vCB4AP7hx27/cS7t+9xbmY6nZApFuJWViVVT9dcX1whRM3FxSXmjwSX2w5rDeOp4mG/xwV3zn8+8vrDjv3JoY3m6vYZ280a0XX4+x2bdUYkQ1xK9E1GooUuY+0Ih5PD6kjXWVTlCV3Ch4osBBdXN+xPI/t9T46Ji21HkBtMvWE57RDGEF0mhIHV2nI6HXEuFJKdbKlry9g7YjjRVBXHfuAff/WaYXRs10XcpK1E6Kqczo3Eu0CIAZkpt/kMSjikbSFKcogIHbjcGm6uL9BVRc4LfgkMQ8RHVSYrQF01xGWBlElhom4MByzL5Fk1EaUtSUSqrkaaln6YC/o0l0yCCFRVQ2UE/VlRXdeShELKjEDiwo9peR9r/eC3z6kE5xSEbjpv81M5lypB3VqurtcYrWBJCCmpmo5qTkh6tLFUaw06U7UV11drRF4IS6brOjLlEHr+DclSEV1mGRcOuwOP9/f84h+/45dfPzCEjmdf3XLxyU+4/vQr8tIz7B45vvlHnh7vuby4RYhMjuAjyJyIqmiShBIofY7fzSWBsvjsKxI1UtaIXNhzSoIxhsvLS372R39MOGqWR4NKV+C3yKom5p6YA5KRJfeMw4LwC70vNjmXEs5pou/IsSYGRUSQRVmLIQpLH3L5d0BEIbI4ryFqNu0LbLPBLSNCDdzeGk5h4eEu8PBwZO6f+PQqosQFIjdnRLo6B3WVr4CYE1kklMxFk5QtWRV9hrEV7eaKZnOBWXWo2hBzBJFQZHTKuBhJKWEoiv4EGGFIRoBUWKPR2qJQGDxJZdAZESW6alG06LRC5xXWdpjaoJXk1fM/Iz8m9tM965tPsaIi+UxqwV5pTgdPyAKtG2JYSNPCcIj0U0Spj0fs+1E1fDcsJKXJeeFwfyAtBXaxkaKocBPEkFlSROSEkgqpSopeTh4hBDdXG9ruoqg56wrTGt6/fcBUkkvTYpUiRoGSmaf9gaYVSGHRuYyqpjnw5RcvWJaBx31Pq04ELM1qjZSZpttiKrg7fqDVIIUumfMxoI1geNjzq3/6lnGOv8VramN42D0Rl4RMknlxSGOwSO7efeC7776l26xRzRarTAHDTAtGJS4uLlmc4XCYiLLsn/w0sL5okcKyuViThOSRggVWpsJaBWPJhrYbjTYN01BohC4b0hxZNbCyFmc0Kjr8kjjsduTkmaeJ6EemeWS/v8MNE1ebjkYFHk8nvJcYrRAxYWtJVRfAx+Nhj8gRnzN3uwPTvOB98cI2uubi2QWr9SWrVYPIEjFP9AIqk6jbFVOKTFOiNRlbWfrpSKstTa2Yh4lfD+9L/jWZxReNgHcj9rxGWWKksnC5vcBWAzEFXCjCPms1xApyIKZE1xoWL0kpoIWCLKmtwKqGXZzw7lTGpzrio4Yzm/3H+jgrn8V6+SzdK8P9M0u/8HIAgXOOU98To0QjEEmcb5YJhMTWLZucub7YYFViv3uka54hGsgkjC3am5ACCYGPJSMjhkRIkX4ceHu34+v7kc2zW5rLG6pug6pqqkazvXnOdHiPi7FwAgQsISJiEaTl9AM3QJRmKhVKW4SyIGpy1ojz5xXUOQGwNGCpFFK1bLovaatnDH1idhnvIylEpmVi1bYIEdmND8xTjw8LIS7F+iy3WFFjpCYrcf67hHPktSwdOZ1H7rK8XhBoqWjsik11iZIS72dE07O+0rw/jNyPiaHvkQn2U6BWAZ1CGemL8nuGnIlCos7/5AwxlwN9FmVl2rRrutUl9WqNaWrQghQ9KUZSjsTkSGmBGJA5UwlF1g1Z5XIgVAphFEJKIokkBUkW9b8ylkrU6LxCxhWWlqpeoYwl5YyxNS+2f8B++pbZHaiamna9IVtIbUSGzLyHvCRO+wk3OaxWXF48o86r39NT8f9ZH1XDn4PAinTeWzliyMRQFNjGKmJ0pJyRQhdxFme7yfBIvFhR2ZrbC8VyBi6M+wWVFM+ub8i6oq7XkCLynJg9DD3WdqR4YhhXXHQtF5sNjUk8PAxcXLzk9HjHEgW2aVAktpst87zHBWjWlq5bo2uLaWum00DfjwihSclhGkU6BpCRyS1MMSBVQxDloTHa0A9H/urvf8WLT27Q6p6f/uQVwzQyh5qbizXpFBDa06wVIUa0j2xaS13pciPWgnGc8dPM4gSVEhhdkfMBR0brH5ToGa1VGZ3FxOQ1h/3E9rJCkxj7Hj9MZL9w6kfevH3EKo2bFqZ54SHCi9tLhnEkJ4mWkiwzbdPQdhZEhx5HGmtoWkM7Vqy2a5oQ+OLzT1jVlu75LcJUqCyZ+pElOIQSXHSS9YVBSMthvxDGAdNlUspobZBJUhnNuCgeH/Ysy4zOhY0vKkWVEyGWdUiMEqEFOUa0KA1gHCa0zNi6JpLxMRZql5Y0uiEnRf904HJVYpSlFmSXyEmWiNMsMFXN/BHFXP5Y/2klkZHnli+FgJQKo57zmJ/SRNuuZruqqa3+gdRafn2OxBhYrVpurjuuN5LrVebZhQUmQsikZItWQEtULs+fRCBkwtgSxzyOjrvHnsln+jfv6Yf/yP27geeffE5tBcz34BdiNsSw4FOhxIlYOBY5QyIUbzuyxG5jEGgQNUbXRSB3TgL8ATKlVcH4ruoLfvLJ51Rs+MU3/8hpeMD7QJ8ODOGEyAuP4wd24xMiekLyhFSEhsJIlF6jWRXgTypNF1leZ7EzF/5IToKcBUlAkorarjGmK7jqdGB9sSBqyf1hZneYcMuRy6ZGJIEPgSgdUlTIFEm48w0dQi5HtiwFQgmSBJETRlesmnVxJxgDstzkcwqEMBOSI6VyCayVIqeKaBRJU7IMskcIVfDcEjyRjIQzmdDYDik2qFijUouSLZVpsKouAV0islldUWnLfnyAeGB7+RxVKURXtEL9NJBPgeNw4rScEFVmVa9Rrvt9Phr/SX1UDb/eXNIoxZI8UgmmZSbGyDAsmKrCzx5dVUW8lwtWNSVHzjVSS5K0TP5U8qp1xTh5YkxstzcMIbOylrvHO7oGyBKRAiEnlsOJQ9vRrlc8v7lmP/YM48S7t1+jlObF82sEjuPoMFIw9iMZqGyiqltevbjBSEWYe2KcqapyQrbVmuPyiDCGfiw2MKEdpqoRSTEOR5Zp4TTOCFvzsy86ulbxcH/i+uULhnmPFIqqu2CZHpjH4kro1hoVRpLYoOoKf5gRwjJNPUZrBLbs3WIq4pe5KNfnocc2HSF5EoosDdMCu8PI1B8gRUiB6Cf605Gxv6Kxa7TtaNsaFyI/++IF7x972rVlmQKbiw1VLVkmweISX/3kU6b+gFGWm+stm03N5bojJUlTNWSlUKZYAkNSaG1x0XGcMmnpkdFhREWeR3IUpGVhGCbyakXMgbcPB5ZlwubAskyk0ACBnMu05dCPbGxiGgdShn6aUXJE4EipwicFUiCtpLEV0c+4FJCV5vrqGcfTTG0kYwafMmGJrFYNs0vUqx9Jex9rJQo7Q3LOwT3f9NOZXCfPIKbtuuZqrSGciHFGKY0xlra2fPrsmm67IubM7YWgs7D0e+pqg0ya4CeCUdiqISVF0GCswXtJchGZAnmZCONEdIbHwwPff/2aX/z8L7m8uuanX7zg1bOa59ctJq2JcU2ItjhTkiT5oizPIiFNIiFISRKzOA8pftDp2zNfo9zsUyx0vRgSa3PN5uIZr9+85t3hNwx5R93UiNzz9Pg9u0EQZgf8sD4IJbtHZmI64dMOqTq0MGhRo4CcAxAocUTytwp4IXJZm0iBtRWygiU5vHrg2VbQ+4VxdoRlKoCjkIkuseSJTKZGIVQi5MLjTyKgUiywnBTI5+dakKiVobIN1toSXR09IS7E4AjBAwkRBTpLkIasFVkUi2TOCYU50xYViXA+MCUwCqlqrL3AiDUyGvAGkati0Q2CjC4rDBFwKJa5RYySVCeiXEiyIQvJMB94evvE7GcmFUhCILMjxR/T8n5nGW0JQNMo5vl8IjOSJcFms+bD+EiKER0j2pTdj18yzgpCUAhticKjhaHWlhhjGSvHA5fbllpYvn2z4Fwkp0xd11hdMwpVAnkELG4saEulGd3IxcUz1l1NfxgYT0emZSKFSLfuiho4axCGp90jy2liOD5SyUBOkX63Z+xPHE4DtTWE6MuNW2gS8eyvhegWhv3E6s86cvAYpQhzYQpsLi7x/kR7u+HD/YEYSvxl8pnDEAhRFvCNVsQcCCIyB1fGjD4Sp1B48jLQrSzTEhBGEZxHSYVbAns348YeqyuMMbRNQ84JbTKb9YbTNLJeNZz6kaZrqU4zXd0Sg2e1vUATyc5R2xoZM/gZQaCtBFJopmFAmBbjFi6vrxDaMtpysh7ygkuKcQrkEKmtgRBpK0srErXS7OZA1UWkbLBWs1pXxCWxLDPTBLoyKFkmGSm19NPMNDlyDoSw4PyEy6nEpgqojGGKBUTi5xltBKu2wkWJDwIXMzGWfXDbNlTVhvv9e7767Or3/Yj8WP9ClTZUbr2JIuJD/KDVL6NzpTV1YxEiEtyMjBGpwLZr1smQ8ojxkpgllfLUjaaqFMl75smRskXqiKkExiiySMRDIPiMD5kUMxfPLri97Xh77FESthdX3FxuePVswx98dcFNJ2hMwtqi8A8+4nVhjEitQGRiXJA+0akWiyRT9umSjMyBnN1ZrFrCcaSSJF/WnevmGYdHxdv798zhxJJ3nPqF4TQy+SdiBFDYpqKrOobjnuiKGh48nkdEahG6wag1IpmyBosRWM7v9vkWrhIZSSATdNm/T/5AVE9MYuLxGFjmhFtmIGKNZA6RJRQol5SqaG5IpJzPjIBQxvQp/pahr6Qo9EFpCAgW74luJsb5DDrKqPzPGgN5FvoJYxC5TPlSKs9/4Jx6GIsrqG066nZNYy6wdAXGs0jCAmnJ5DiTk0RImHyinyI2dhgMEUM/RA5Pkc5otE3s5z1ee7KG6GpMAiU+Hn7HR9Xwc5oRsiH4hegWcpKEkLDrNVcXkoeHI95nQiVIqUIyk6QmKMuSJEq3rLqOEMsYV6bI4XDk8spQmwYjEv2QcKrmOHq2m466Etz7wHxcEM/zedeXMDqzYLAi4/xCiDD6hXFaEMKS5hkTJ3KWDH7GDwMrY8hCsl01pLQnxhlDIPgFawXWS2Iqo2pFwuoKu64J88Q49dzf7/nqs2cs45FxOLFME/HiugTuyAapDmQkjYpMCayO+MVhjSHnSJYGMPgZvFsQIiKsRMhcIjynjv1xx2a1JUXHNE8YNKYu8I9+6rm+vGK96chSUdcGY0EvCikU1iiSF4SY0ARuNg11ZQluxlQCpQXWJGJdU9UGP3vaxpDnhK0KjtgvnjjOaF32c0YpwpIQdcJozRIjUkhM1WA1RF+ab1NJumrN8brl7eWWx/cD8zRjjS2rTJ9Z5pHxNBHChEL+VsHsl1DSEVNGpIUsVYmyrCVWlSAOFwR3j/dkB4fDglABqwyXlysGXzzGl+vN7/kJ+bH+xRKq3H7P4j0hxA+m/PKfRcZYyaazVFXJcU+Z82RIYuqWjayoYrHcVVqhTYXSFlNVCKWIMRGjwLtMNuksMgWrFFlrpK5Zba959elLvvnua+ZKcXH7Bf/t//F/TxeOXFc9VTgxDwdUyPSnkSUKqiVgm0SzMhhbkWQsMJ6zgK3cUguuN+HKS8rFq8/5NSgpMapCYzju75n7EZksORkWv2cMPbKRPLt8yTQ4lhB48fwL+uaS43jCGEPMDmMbRNoQZ4mJGrzEJ1PG3+f3FxKRRJagRKYyDUoqvHOM81vyas/BTcxBsUwl9EtoRWUsCEkKGVFlYi7xtyhVXkQo/vsUAjn7QsKk5FjYugNKRsg09YXCmWOB/YjCQ5FClO8VqdFSlQXPeUUQUiSHyA8JPEkolKlouzVtvaZWHUo0iKyYCUQ349xE8KFYeaVimCTZtWhtC2ZZSg6nGfvBo593dDcdorMYpfHSIyJIKbEfEb/j4/mbAKSANZnFu3JjVwIpFNu2Zb02CJMhG6SEJALx3OSaVUNOM84NzIvDqLJD90Q26w3D5JDHiUqNHI8Dygj2hx5bK07HB6bJsZsHni/wSb3CmMAy9wSvqKsG72acC4yzQ+mq8LOrNTELFjfStC2NXgiL4PbZC3a7X7JeWypTcXt1w+gT3z++xbvIsnjqS4k2FpUdOUrqxlBVivf3e4QyrNc1LBE39iy9QK87dGtp22tsa2iE58P3r5EshGmHmxN+cYSwAC0uTihT3j9rDXVTFZpU0LRdS86SsHiSj/gMMpcVSls1WK3Yrlue+ohQhrrr6E8jvt+jlGL3+EA/9FxftxgV8eNMZRWYiuwj2miGMbBMnvXFltbIoubVxTKpVPHNKmEKPMRYxv6AsSvmIEiq7N4lkcuLC/aHmdnlgsnNkU9u13x3fcH9u3se7o9cvbxmCoKw9MgU6CycRkEKDiUkImWsFQipSXPx+fajQ+nIPFt0CoSUiVkxDZ7GSlQd+PzmBfsxoHSF6xdWJtLWP8bjfqxVoqh+UOdzvt3/M4RHApvVirq2RDfhWJA5oayl1hKTFJ2wkGWhvBHQShBQhKyxuqZZX1J3K6w2iBwhZTZdi5aRlBPra0PSK16+CvzBHwg2DyPtpqH/cMezz55jrGJVtwhmclgQWlI3LU3TYRpb7KkklCgKdSFysQ5SUh+jrhEyIUXJx5DSlERAIRFCoanJPnN6OqCSprUbVJNZb9dch4V5DHz54o+RYUV/nLndPKf5vCHXGVPVpMmR54jCsD9NzCGwH2b6neN4mpnnzJIDIhvKoLtkm6yqNTJrTtMTS/6eup44xiLEm/yM1Za2rmhsi3eiwM5UhdCyiCpTghgQ56CjJPJv3b5CKKq6Q5sWsmJ2nuAdiYgVEqMkwlqS1mht0EKjVZmWCATJx3O+QcECF5gR1EIhpUUljQwFyEQOhBCZTkcOxz2n4Yh3HktN4hI3b9FUBKUQas0wgtg/QI5s1ituPrnh+WdHPrzeo3ODlAmTVXGEfCT1UTX8NA/IyhZEpTLYxmAbjVEFkBJcJCWFtQ3WQJwSxISbE3SZ/rhnGSe8bYiYMgrLEzHC3cMTLzaJq4sS5nAYAlVd8e7dwOI83eI4HU7ML1YgEtY0hL5Y8FIQkDzGaJq6oV1JZLScXCaExGp1UYSDfuF03GErg5Vwd/eEtgXw4EMsfP/ZcaHLCiGkhRRgs75Aq4p+N/D6u3te3CiM75nnid1jT3Wq0Z9+UpIEc401Du8GjK4RQqNEQhtDU7fYymIEDMqRcjmwVMaQcqSuak5uQqSJVauIoeX+cY938AdfXeNEQ22rIkKa9oW372caoxDKMswjIQvqqgZR40noONCfItNcgDfeR6KfEDnTtTV4hxUJN4/s9xPdtqHpWgiFkOeWQJQRgmNxkdVmjTGS2XnuHvdYVZOE5DhHxuxZ+qI6Xlzi737xjv/d7Q3BSYwIyORZYkCoQGUkQWsEhmGYSyPQhsVHZh+43tRIExChfKmEQBkjYlnXa3aHBaEU8xx5/HDPT76wqI8n9OrH+s9KwNneJUD88w9KnNc4CXDeF5G5VKSUy6pLCISy2KpGGVtumDGSY+Gt20qXJDmhEVmilcJoQS4wRzwZnyVBWbA1upZIW2GqhvVKcvO84cWtYO7fczydeH5ZoXXFtD+yfh5RZKrKorQlxgApIZEIFNM4k6JEW5D6vKM2EmPs+WRTwEI5B3KW5Gx4evzAbn8AI9iut5jVJW5x5NnQXG14fv2KSq4QUaFSJsyB9rJBpAKkmtKInx3rbovTCxftyMk4dvbEwz6zi46YBDJLrKqorAYrmP1CCB8w3YEgI0lYYiypgbrSNFWD1BUsqYjkjC2WuCwRqQjwiobII0koIUjiTNarO6yqyFIx+oXZT4U+aCuULJMOJUo0cKak5KUkzoe3ErwWfcHlhlh0XSWhz+NjJvnMwEDOiuAzp+MDh9OeYT4S3UgtttRsMfoCW3d07QU6rljCwmF/5DB8S91avvrZJ1w/X/Pwdo9KZT2jpULqj8fe81E1/OgKcz2EBCLS1BZri3fSuZEYAkKUsTkig9KkuJCjJGeYl4RtLPM0M02S4CLel3Hu/tDzLivqRtMfjuxO8Pmr51zcPuPbX37P4TCyOxx5/b7m9voKa2s++3TDEjxZNSgTuVmtmN2Inz1xWrjf93i14o9/coVMBejjYsJqw6mfSanEZVbKsEwT0+AQRjGNE52tkKKiWVtEFrhlQirJ//g//c/8N//VH5CWgX/6xffMEb762Wec9kOZek2OmYQ1K8Qy0TQlTVCqJ2xdUefEPM8Upnhh6wslycGR0owQCQl0dcthmGi7mnmc+Ox2zfePAaUL1369XiNkZqUNXskCCpo83ic22wuqti7K3hSw2jF4T2tEIXqlIsRpm4aUPF3b8vphJAZHOj/cIZWRapAwz4mVFURZVPFtU+MiZB+xViKUJnqHNjA5h4vFafCb7x/5kw/fl9ewaMbHI7PQFGlvWT3040yMis5mYjTElJFKUDcdzjmGyZHzjLYWU7csMVEpOA4zV9uau/3E/nSgkTdMw4/gnY+10tkXn34QuJXIOlBlrCtyLvnoPpKiKBjtCKObMFZStTVSGoRV5FzcLlZJtJFIfUatkhAxlJhuvxDCwjhORS8iLEIrhMlEXbE/DewPAyEmrG745Isv4PKS3L9j6Uf6/kgKCWQZbVd1+fMR5e8WQiDmgI8R7xasFFhZhIfkTDrb2sprlSW8ynke73fMp4n6qkKvdHE+xS2r6pJGXVBhECEy9iMigfQKEQxVrpFaUdnM6WlCI7F1xUqCXmuIjmWSzBZcghwLJ0AoRUiJKR6RvEepgeMc0as18/IIRISQGGvpVhco4UkLRawrPIhIjLm83ugI2ZOFKG4ZCVXTYao1UtdEIZjcwjgcMEqgxBZtLCIkFIEQIQpJsU8EBJLoy0rVRc98TvbMKRFTSQgU+kRlq/JrU8LNM3N/ZJknlrmHeUTomm59SVNfYZsG224RSSGdZ5pnBnfH6lBjHwzT7IvlOJW50g9QoI+lPqqGj7TEmMv4fFzKqS56JqdZlgkpTLFchUgyqjyEplhqZu8YveGT2xXRv6EnIFUudDpp2GzWDH4pRCYRkBhUBiNrNjcvuboSfPv9G+4+PPB/+T//t9w8u8EvPclJbC0xzRVEx/3De7bdijGncubIgmwUQQour69YXESkyDTNtK0hLD1WCowS+NpgGkPKgfG0R8uKqmqIIbDMI6vtBf1S8un3j/f44Lm9ucJqT386MHqAA1998RzbbqmqCtNVPIwjfi64MKM0b5522KZCAW6esXXNeBgYpwWyxccBowUilf3get1SrW5Y9U9EEfBCY41C6zVvPwysmwwx8u7NO5qLC64rg9YNVSORYWHZ92gN67qC5EEaJBXTNFPLDMIwOl/yEcoRnGUYEbLk+M3jRNPVKCNpVzUJsMYgdDkUzNMCqqPtLMNJUdUtOcO4BIZh5t/9+U/YT2s69ZavH56Y/UIWuWQKeIc0a7qtJETD4AQiZf7y777lcpXpuhVta+i6mv2YQHiy1LiQ6CrBPJVRY6Mktal+n0/Hj/X/pcT5O7XIyM5YXVnm+/k853cpEjIgLCmb334evV/Iy0QCmtZSWUttFG1tC2pVaYQqAjDCSHQJt4xM88TgQpkmZkUUNYfhxGmODB4Os+Nwes8//Oobbm5+xX/4D/+arz67Zjk5VpWg70fqYabpNhAdZM/kAiEKlLKkrIg50q1apDCQJd5HMAGpDJmyX46x9JSYPA+7J/bDidWm5UVzgz5JlkWTlOTbu2/xoWe96qjrCiPLxEsMYLLGp8TN7ZakPM4lKiWJ05EQFmQYMHHGsKBxRBlJSiOEJYfAFPeY6onRH/CmxY4jx+OermlQ2mCrNderT/BtZL87kNL50JVCSbFL7qzML16ALDNS69+O87M0+FSC1SbniVqgpwmBJFlHULqsW4VGkPA5Eki4FFmWkeA8wfviQsqBEDzRe0jpjNNVxORJwZOWRA6Z6ANxDlRdhanWGFvTNBu6do0LDlE37IQnuoxqK07jkftvd/ilrCkyBX2cRPi9PRf/eX1UDV9IQ9O2DK7HNBVJDgQBKUdEEghZxGPOzShdlw+6gJBK8pGtLJUJVEKyWWlEzoy9IACVgfcfDoyz5NObjjWW25uOrCFLzfZC83R34mnfY6VEC0mS0DUGoTxzSOxPR8YZLtYVWkUq7cnZgTBkYdj3Hpclx3FmngPHY+TmqmXwIzGFEuYgQQvJPI3IGsISih+8sWRRaFIfHkYq2fHJ55dcryU+lQSnxS+s65ZlHou9zV7iUqLvB6bFI6Ngr8uNuIhcA8u8YG3NMiVSzEgBLloEmuP+xHEauL0ulL3aVMwhcBo8m6amP2n+4i9+zb/9kw3Pr1YsLmKWgEwC7ybWq0uySPikyD6QU6AfB4QpqXbj2PPsxYplgWVJ1KrwzJGaREmq0lKSRcIYSVVtUKotNylRo3QmpgxGYqsagaCymu3VFoQgxczdk+M0BN7ePbGETJiBWMxDSglM9c9Ro5mMmz0fdguPhz1/+ic/YdNcI0UkxgmLoGkMw5KpK0XTNNTNgq00UoP4MS3vo610ZttLfojDTWV7n88HAKEw2lLXNVXTgR9JSybGiPOZ2iRqrQkpk11ACEGVJTn9oAEoiW5CCEKKjC4wjI7DMNFdPsPYFYfdwi9/+T3ffv+A92AReJmZY2Tfn/jw+nu6fGK9yVzdXtCuWmJ0ODfRpKbcMueJaU5k1XBxdUu7WpOyLKCgLM8TOEFMJVQnCUVOghQEj2/3vPv+Lfe7R97Pkn7IyMkQpGO1lTzsXiOFIOF4fCyJllo1CA1KRobjzP74AREr1l3LyivGMHA4HtmfThzmIyEOSOEgJVIWxKRJ0RPlgagHxuhY6WuGsWdZZpqqRSmLyh0yr1nVmrkODNOp/LySIEZIIZFjQevmHIsYUlfUui6uJiEIKeNDIkeIwIwnsyBQRBUhzsiYkalMTnwqOSfBTSWGO0Ry8CSR8ClCDOgM0kWkOFMLsyRnWf5/kUhKI2WFUE2JZTcaqw1ZSaR1dBqGJ8vpeMS93xEfAsml4jTIgZg9Uf1oy/udFbJBSs0yOzIGhEEoidASU8miRM+SmBTzMiERGDSJYlGxOiLdTNNYpiHSDwujj5iqLpnrwYFasb1+xrNuzfW2ZncceHZ5CXpmtV5hlSUpQdvUGLVw3B8QWrM4QRKyTAyATVdjZCIpiFERQ+Du4UBYPMfDQCYjY2CZAo/390xjoGqbgoxNZZyYgZQDRtRIZairDisSdw8nvvriiquLLSpNZOdpjMb5hFEC/BGlLhBW45eAxXO1NkxTYH/sef/mgYvrDmUkSmumecFaizQaEQIxZ4ZpoT8cyZSD0TINTMeZ4ziz5Irn11/yt7/Y8/rdDs2J6l+9YNXUhMUxjhMyOdaVwShJxJDyhK0kTaiKfUkN+GFk3b3kcf9IEoJVZ5BSIgT4QuxAKk3CgNKoqgRqBB+AGa3M2YebEUIXy2Bt6DYbQhZUQjCcZrLWGKv4+T98xzgc2Fy05CyorWFlDcpogiujvYennuPugf/m33/F7cvPSIOjrlqmSRPiEeccKQWaWrFESWXg+bPn6KbjNH08J/Uf6z8rcd7Xk34r1iOnM2a3fOYqa0kxMwwTlcoIbVG5RasK23RI22Cswaiy9nI+ohVoLcuzozQxZ5aUcVTMaabvF3Tt2XQN+6d7/vJ/+hvefthxu+nYXq+5O4BmwpAY+hPjoHj27IK2rWnbGlPVSCEY+wFrauqqQ1pYgqAfR3TVsN5elXCwnMt6gsKFl+fXlZHMY+Dx7kh/6plOO06PR+7fvGF7ccHLVy/YHx1zHKhsxRgOLPPIFDS2WlPnmnnumY8LrWpQqkYZyINhdo7ZecbpyMJIMh5DwoWxKNlNKO9zdSSJAakNSgjG4UDOsZACZU1YDO/ve26uthihEDkickaf5ZaShMqJlCFSXpfRNTLrcz5CIf8ll0v4T4io6FEZphSQokRhi5yRqQDJBAqdRTkdnHtLlpqcPEpE0AYJ6PMhMeQEWRFzLpx8UdYKShWyqNEVyiqkEQgJ1Xbkk+ua2QfksGCfAsyOKcyQA1l6Mh74seH/ztJ1TZQZ5zNNawnRlZ19SkSf0EqWOEYiRsjiqSQRU8ZIyaq5ZHvh+NXjt8XGQkYZixSasCzUynL97JY//pOvGFwkCMc4jlxuL9gfEm1tmPqBv/uHb3jx/AKZStylNpIkdIF6CIHQlpwnIonDYeHTMDMvZy6z8bgUyt68sxzGE49PO4xWaF1u2VY7+sXhdYVsAxfrjEsZkQLCmDLGdhrvFwSCZ9c3vHn7LT//6+/58tUtzRcrajUgpUBmS9dY/ARTmhiHwK9+c8cf6hfcPLsgi8Q4nHCuR+DJweFd5NB7xnEGpfHRIXJinGbef39Hc3XN4jrevv+a6Bz3jzNPR8/68hPudu94+/CBi23LtlJ4W+HITC5yPI40TWRYZuYYuNloYpT0p5nKWqpujVQKJUq8qLaWadcjs8ToikZbRM7kmFDW4N2MUoJVu6KuaxSaEGCeffk5KKjaNVI2pBR4etihbGaaHF2jWSbNNCdavbDdPufb7+95/d0b/sO/2fJf/+nn/OYe5rEnR8NxGAnesaoMSnrGRbHrPUYFfIrc7WcW/yNp72OuLM+j/CTOU52CVC2AmsxpHDiME8+yAc4345SxVqF1VdTdCrSxGKkQOZDIhPP3jwGE1PiUiUKThcQ0DTHD8dDTD46HDweOdzusyoR5Yd/P+LMHvDKa25fXvPj0lm5VkRVIXUS9bdcSErgciUkVkI3uWFxC9CNV1aGUZp4d0gWMiRjbIpVCoAjB8w9/9/e8efMbSAsxjfiUOc096jggQkQaw+p2hV9m3HJgzpa1USAXng6PHHZ72nbFqtowPvUYa1BKMPcDKSVkpc+CAACbX0lEQVREJahVRZ4TwZ84+TtivsNuKvTW44JGV4Zp7FnmCasVGqhMRw6Wfp7pjEbIhRzmQhg8Bx5lQckoiIWlUA4KhhQzzgeigmkccYsn+VQIgW4hJE1MFVZZdNWU9L6z5bdY8goNUCWBjJmkFFFKSOFMXS2fk5gzElUOL+SzkFBijMI2CggYrc+oY4FZDVy/yqzkhvCNJ58EKkNsI8QKLwRel8Nm5OOZDH5UDZ+4IGjRuvDplZakGIheUGmLsZLoi+8yuEj4QfARHTGWH64UgeM4o+kgCdqqcIx1sjRtzeX1BoyBcWE3LVxernHTCSU1VzdX2Krm05c3vLt75NlW0baW/XBkdhIRAzFKcgp474hRo63CVIaQHFIlKtEglCUh6AdHWxnqpqayI1obFpHwqgiJw+w4Pe35859tmINmSTCngE+JZrXi1acb8rTjeHjiH775wDDPqAbmySH1ieQjiS1GW8bTgaOv+PDhDf3U8/79jqquePnyCr8MxBDZWsNv7o/sDie8VcQMShUA0RJUQfQuE+544n/4H/4f/OpX/0ReBqaV4f2na9Y/e8W6a5BaYckMpwmPJ8RcxlzC87QbGBePadZIrXncD5z6AwHD2l7il4CqQaGZfWD2M6tNQ9d0RJHxzlPZBmkU3i8gK5TKDKNDi5EUPMvcY7UoSV7aMs8z3aqIjkQOXF62bLcvOZz+hnZl+NM//yl3T5YPu6+5voT/03/370m5wfd3DIcHZNdiRaYxgtPhyNWFYj95gnNoqQluJgbD0A+/5wfkx/qX6reJeJy5+rKw6M9i9jOyNhbRaOYs/BWkLJjmiSwMKEOtDEomJGXHLBHELFBnDKuQGmUzOEkIGdu0tOsNDw89Dw+7s/XL0x8H+nFhXjw5FOqfqRXXLy5ZX26QMqGt5pwJR4w/5LBr4gxj79G1R9eWeXIMw1LMB1mgtaZuWlatwpgKkRSn04nd6XuW8IZKtGitWPzIaTjRv76j61YkBLvxAWtqvJvR2bI/fCiK+UUQpCewEJQr++tkCgtlWog5F+V5jogcMYBkwXSC60+3dNfPSPkFIQW+/+ZX5Tk2BmNqarWGpSIsAT/2VNpjU1mZJiDlMq1AlqjfnMvtPOdETJB8ws8D03jC+zNdL85IGZGLpcoa1cgy9cCcMwUk5XKfIMQzjj2WvXpUZBJRJNQ5RjfKXJgAKRPiQoiBlDPd6hmX2yu0TMSc8S6whAXTei4uNPnOsRo0UltElUiixQFBeLz0BJnwfDwXhY+q4RtdkUnlFBcStbVIWXZFQQhSEoScaAA/z8hGI2VCiYiSgpADIUn6YaCzHVrB7jiTc2ClBdubG24ur5BSlQxsVVPVibv7R7rGMrvMulvz069e8atf/5ptdQWNZuwXEobFBbTS4GYEkkppPn1xw3jsaaqK1mpU1GjdIJXk1Htury/o5+/xoth5ZFI4lxDCAJkYHILI5drw4WFkdIl6tcFoi4uGynTMBF68fMVXP2l59dkNzCfm/ogICy4eWVzkl7/8Df/45sB0OhC8Y384sabsNqdhZtNUPNw90Z8mlBAsw8LVZUPWFRaJIDD6gE+B/bsPvHt7wKaIsZIlLDw9Hfjum6+5uZG8f9iXlD4WUoTd4XB2Uyi+//p7phB49fmnfPriM07TQgig6sQ8LghdDjoSwTJFalszTK6ocrMmxgWvPDZmamPxLmCNRklBbStOSSFJSClwY8RHj4uwahtsVZPzxM3NBV13jbaWZzeXXF685M3dkfE08sdfXXB58YLXdwMXl1u0iIQ44ueZb37zhJY96+4V85BITCi1wuqAmz05Lf8/PsE/1u+r8hmpe87IOSujf7CuCaTWNHVNZSqU0MQsiaqiXtW0bYc2DcooMr6ApZQp6vczxjvnXFTgPpSUzphAQls3NE3LNDzwj3/zN6SlZ7M29MPIeJqRSmKqmvXFhqtnV6xWa2xdkUWmaqrz37Fke4QAPmTGOdKfJvxuYLW9pmpqjJI8PdwxzwvrzSXPbl8hhaeKpWm9f7/j7fs7tOmozCWVMtTuxOAe2Lk7Dg9v8CmgRcuquqZWFq2qsg6NCoVG5uLxj2lmHBYavUJLTZKSmo6YAqf5iWl+xKcnVJeQnWE3HTjcD1xtt6xXHZt1x+5Q8Lc5KvwC2S8oFYm5RFC3tiYLwZQCJhpqZYmiUPt+gOnElHBhIcXINM84t5CSZ4kT3o0ombEZMhqMITn524wfkeMZxpQgFKFuRhAEZS6cYnETxRL2S4wEX5C7IThCzBjbcX39BS8vvmSaLUGU5cPJ77lVA1WwvH89UdctprOQykEzCkOSGU8kKXA/NvzfXdZqlCzjL0Uih8TcB9YXDY937znsetpVA6lENKaQkDlhpUfiGfuRR+uxtiFJmILHTRPSNqRqxc1aFFRmXHDLE3cHy/XWcnP1DM3EtBRbWgyem+sL/unbN3z58pbgEh/unzj5QFNXNEYSRKRSI/uHiFPXXF4pWisJKBojUVITc6RSguAjwSdizIissVqCj2ekpCDMkdVa8MvTnpAluerIOWKrBkGNbeHVpxuEMBht0OuSf63Dwv0x8DB7Lj+55vLDA9oYnFdUWmJEeXh2u4GlH/jN45HLqwuOhwNGZaTVzLHQ7tZrS1dr1O0Vje55tumoRcP+27e8CxP3bz4wn3r8eMuHXc/TasQ0lsoYlBVM3nP/sGPwnqapaE0kLI6v39zj/cLLyxccxwFrPYJEEGuMlpxGzzKNLG5CqoUQI5VtyTERzmEaDw8feP7yS7SqESqisyie2VTUsA9PB/43//oVle6Yw4hRiUO/p7YCpQTLmBgOT9ha8fz5LafTSD8szMNCzpamTvRHx8W6pl8Cp/0JtyhGP3J9YdmuMsYWpfaP9XGWEAmRz5YsUUJofoDwCAHGarpVR86ZaQ7oTiOFJWRBkAZjDEpJlLZApHy0EloJtFL4GIjJF1V6ligtsXVD1bQlUrfSDPt7auFwRObJoaShsgqpNevtitXlNbpdY+sOqcq4N4RAOKOcETAunu++u+fN63fk5AgxkpUhBklXVQgRsHXHH/yR5PlzwbopN/D3r1/jcfgucD9/QAtN1yaC7FmWPcOyJ0SHVoqUH4nNNRfNC6xsyHnGJwMxkRKMp57TMONUwNaG2jZYbQvTIkYO4Y5TvCsJg0eBXAx10yCSo1aRP3z1Ca3KfNj1VFVXrLtqpqmgtYbGlOcoSwkhEJIr/lwhSKRzLK4gLpEkZyKiWLLP4/aUYqEPRogy4RFngFih7QlR4tFyDKTzv485kVNC5VCEgckTQizW4pwIOZBi2d2XdLyGy+sv+PTyD7i9+Ixdr/hwmjGhhmrg8ioxPizk2bJ5uSkxxk6Q5kDyGpQiKY0TES1/bPi/s2KW1FKcPagRSBz3J55vVxyGI+GMvTyeenRtMDkiomS1rktSUvSEceH6+jnJw+4RYszYSrHatmy2kqqK6JzZVjXvlglJRb1ueXx3R3JgtWKeHTFKEvAXf/V3bFvFt6/v2B/h6mbDMs1crC3VpuJ0TIiuY54UWSi0LFYaUqY/9sR4RYqApOwWNWgJnH//HGG9XlPXFqskTdMwzD3RZ06Do60FXV2DigyDRwqDG8otxHQrtJ/I90dMbfj3/8WfsPv1O97GBp8NLiSmfubxaY/55DlRaPx0pFKRHBLmjLNaNxaVFUoIVk3D+rO6rE7envA1XK2vOI0jw+KYF4cgny0sioxm1awZTx9ABv783/wRIke2qxqXFm5ur7FKUqmO3o+0bYWtDMvg0CkyTxO1UTR1xbTM5JwxQuKzK9CjpFivOqqmRlp48fI5u2MoNM6UcD7z8LRnHvagMrWQXG639HdFvEOOHPsT43jgxfMNtbni4eGRfpK4ZeRwXBj7J06nJ1bdBcg103DiYtXR33vcdKS6XoPQLO7jEd/8WP9Z5X/W5qcfbvpAkvnc+BPROcZ+oK+LO8RaUbDUkQJoUQIpDcZUyFQU1imWEa6QGmkS5JLREKMHqTG2Q+mKzcUFX375CWHoefPQo7sNtVIIAXVlC/41KxZXUkErXQJZ+tmxLCW5zccFlyS7w8LPf/5rDk8PXGxbHnc7Hp8GvI/cPrvi+vYZ37858Od/+r/li+uKNAb+/m9/ydWLKz598RW/+aev+fbdL8haoXRg01S01XVJn5QSYwSmUii7FDCOtBg2RSyXIosP5HjG+EYNQTAzE8KJh/FrdvE9XjkqUVHVDU3X0dQNVmmCj9DA589fUpsRIVfUQtIKQaUlVmkK/78k2UkRUaGsXkKKxb2DLC6A5JCquLOslFhRvqOEaJklBD/jk2MOI9mBJzBHj86GWmk4Q/wKnCiTY8aRCTERUrHuFbVHIuRUVj5KUlVrtutn3Fx8yra7pm3WuJzIx5E5Cj69gioLTu9nrFxRmxqtDVFLFhGJIhfyoTIgAgn3+3gifmd9VA0/o8i4El+aMsF5ximzvqwR3xUb13pdEeKC1A1KBJYckMJymgOji3zxsmXeeaY8QYjYuuL6ek1TGy7XF3z75j0XF2tC0iUQoZ94+rDjsNshVIWpLc5PPDz1zEHy9t0dfafRsvD9h16Sr1rWFy2IwO60sG05i0zaIs6xFVrD065nHmd8dBA8YFHCgIpUlUDIzDgtxBiQaNqzEl9nOBweqLuK5sUNnsztRY3OR5qqYgkL8bgUC1t2iOSpZOayblDCs/3qE3799sjp/gk/G4Rc8803b9ntDgymKGQVgU8/ec7u7sAvf/UtcboiLZnj6YgfJ667Dt0/0VrN2yWSYqCymnZTMQsQSmN1gfoc+j1X2wuaSvLpl59ic0Qqy7NLw/4oGPqeEKEyK2xtiTKX0JKmYnO5wsiGqqqJgJ8iSWS6VUtKApUzzarFx0xbKYI3bLotUmpCSozLgvOJZfGsVxa8pa5blJiJXrD0A4s9Ed2EFYLTaULLgEsVT4cj333/gbuHB756WWOqijBOmBpubi74sOu5fdbR2orTOHE8/rjD/1iroJcT4rdQM3GOzJWoJMghsdvvcXFhfdFxbTq0CiijC2VPKlJK+FAwuTJ7hMwoqRCiBON4V4BWmUyKAWsrlCo8kM3FJV98+SXf/Oo7jsMOU9cYIXDO4QQcdkcWn8m24TREEpoQIt9//4DzmapbcXd/5MPdnnlJzC7y/ds7Hp4sJPhwt2dZPG/ePrJavWb982/57uc9/7f/w1fs+if+41/8jzzNb+jMS372/L/icn3i5L7Baku92hbHjhTUdYOxFmIuQVcZUBptBEFJltOMkIJKG0xQyDkh88Lj8prX09cMeU+zbmnsBtsqrq+uaeoGN09ImUky4WPistLcrDvGwVCp0rCFLCN1JyNSg8rpHHkeidETz/hCUXpwScNRGS3B6JIzEDMM3jHOmmkSEErQTgweJ0Th9keHk4XGEFMqzgYhiTnhOUN3UkTGSEyRTEYrTVOtMFXLqr3hsn3BxeolTb3B1C1b6fnk2jFxZCtqjr/KpNliG41GY7Bl/ZMis5uKoNMYoowsYv69PBO/qz6qhq+tIYaBFBwyQYyRutlQtQ2ttTSdwdoaqwZCdASp8FIy+2K98lHSdCvk4z05JTaXa9aq5dntBfvDgYene/72737D5198wrbxDP2J427m4e5E8DNKVTSrNQ8f3vPhwyPL0jPtByq9Zr3e8m//dcNhTjy/WWFtRb+fuXsceP6zFSknkvBlpGQktqpIvihjM4XepYQqaVBZIEVGkmisQtaKLItMqDIKkSXLsCe4Sx53e1IK1JVivW0ZDydm5zmehkKRcxPzsacJDjVO1FawZFFuIZREqsvLNd/+1a+JXnDKgdoq6srw+t1j2RlGR+9XLMvC4bTwdH9A31r+9Cc/5eHrX8Hsub1+hk8LN8+u6Mf3eKGQWeL9QtNVbDYXbFc115sN1xfXvH7znnXb0k8epTR123D/dEB3CoGhqhemPrMsEZcdx37Es1AZTT87PJosMloVVOb+eOLZ7QuSdlRVha0sZJinwNA7/u7X3/Li+SXDacTaipwGvAfvPdEv5BgIy4TzNcNcaH/DlFico6lrXj7bkrsV4+OIXSL9HLh5vkXZC0KauLra8HT4cYf/0VaK52afCmFS8M/K77M1q24ML58/p607Zg/K1gV61U9UWlBVlkpwBu1ojCmuoOg9UnDW+idAoKWlbVZIVVw1Uld019fo7SXVZi5ulCxprGZynlM/Y3TNZvuMw+Mdj0+PDKcTf/3zf2C373nx6St2u56/+et/oO/nEtvtAynLYkcTinEZSTmTjokqr0h7y+FN4K+/+yu+e/8bbBNYxtfcqVfo3OFcIMZMVdUIU2OUQZgajEFZaKoKkRPTMOGTp9nUlM1z+Q6y0kKK3LlveB++IVaJ5y9e0XYdVdNycXlFW1fs7t+TsyfFjBAN6Wy7u920fDd5fI5kKVFSoYQvU7wzja5Q7xaWMBKTKybr6AsF0GiMVlhraasa27SAoF4mGp0ZlWB2ZUohlCLL4qUXJUAPcQ5ICjERs8PnSGnvJXkwCYlQkqqq6doN6/qSttpi5YqVvaRTW2JSuMVhhOGziy1TGkjvI2EUmNZiVi0Yg0+SxXlO88RhPpXJidVknXFp+r0+Gv/r+qgavtUSt0SsrYhpom4qlvO4R9kKqTzOF8GbxJOQGKNxaGohmGdPlhV1u8bHTFhtqeuGeex5ursDWSG0wCfIWbLb7VhCoJ8cVlsgsfiFaVb4aYQcUJWiaS95+eKam8ua7TBws7XcH3smnzH1qvh0SbhlodUJaxXtqkbGiLICKTPSlJ2+z1BFRU4BrRUuC0CjdIG7mMYSp4XT/sCxec8yTHSXW0LMSNmwhANhHrGVwCXJsR+YYyDMA6+uP2Xc7fE5sV411MeZzaZj3a1Zr9f0x4nsF4SqCKkAgGytyEmy3XZsqorduLDZrFmOE8fas1sSF1eXvHz+ksf7t6yqXEI9ssenqtiKtMUtjssvXuDHSJAPbIxgGiJdZTiGSNIRvwTmcUG2hoBinAuedNMqRC4M7aZtC/s+B4TSRe3bGC5aQ6OKqvrp7g5B4YdH70FJHu53fPpSc/3pZbnhqyNoUNWaxUWGaYeIPUasAHuGaBRR4+VastqsuZ8cOSfcoghxQkvJ92/vuVktbJxCV/b3/IT8WP9SqfPNPp2T8sTZ8pURCCmom5rVegMIptmBKD/LtrNs1iuMUZAD5ILe5Rw968/cDSGL3z2lErOrlUGaqugAYlkjxCSpmg1t1/Ps2S2Nsbz+7mv2xxOqqslZ03SXPH6455e/+A1vXr/jr//2H3h67NHmVySRGfqBqmrxPpVkzny+qfqEEJIcPEvKLC7z4vorlKn55u2vuH96S2Vh01yzLAdccLy7+4CSqcRWW4tShrbuMKZCKYGyRdneaItIMOSRylZc3NTEaWY5eu4PT7xzX1Ndtzx//gkX1zdcXV2itOS42/P6wxum4YjREiklw3BiOD7RbluqlcKlTJblPaxF4YJorbCiZIkEHFMYWZaRHMP5lCbIKSBFTSU1ra5o9PnAIkDlCoPACs2sK1wMBDJZqLMrQyKyOvMWI1lmUlIlcOjMYsxItC79o6071vUlq+aSrtqiRYsVLWTBdJpglLSmwSiDpia5REyBZYwIvZC7CgPEBcICMcCcPOAQMRAYfx+PxO+sj6rhn/aPCBOZpoXDcUYIjdSarimUoyQ8wUdEVVGphhwTUnhEDBA00+IYXWJG0qzW6Mqzf3ogLAsk2B17tpcbtquWnHu0zCwhoWX58i/7NsP7xydef/OGJCSm66i7Na+++pL79+9JCaxVSBshKlaqQuWA9wtSKKQUGNuCVFTWorWiqS19HwghEXKm1RplDVJk8JnkZt69HzgOnjrN9M7x9DDQNjW3lUGkDmk0Hx6OCCxZG/KyoIis1ltuouY37554Cgv3h4UPfUTUiuBBm5rjfsJPJV9aCYsQCqUUyUtCgOttzU3XYrYlqzq8rHj/y6/5xd//Le0Xr4iNxrQzn3z+EjfNTKMjGo1UESM0437G5xPfIBiHic9fZK4vL/nmG4duVxwOOy4vr3h8OrGEieumIfhMzpHL2xWdNSwuYXSBKxkjUSojsyGqyM2F4bLb4I4nHj6847Dfn2NBEz56KqX48rNL/t2/+zccn+5xGbQpHWAJkdW6iB1zLmsAOc0sWZ2tVxohEgiFjBrvI/1x5L/88y/55s3A/cMdy2Gg+bIlxx8b/sdaAYEU/DZUBs67W4pgf1lmDoeEUuUAcDwdePnimqZrGEeHlAKtwauEySW4Jfui9I8pY61Bn9XX1lYIbUAoFp9wLhKTZA6J2Xkqa3n+8lM26zV3j/f49x9IlWByAR/gOAZ++cvXfPP6Ne/vTnifYBlREnxImEoilUHqTBaWyljqIAkl7JEYIlZfcHP1OXMKfDi+KZOHeUGLBeESLqUSJY4j+EAcirBBKQUUG5xSpUnXVYMUmugcf/izP+Lf/3f/lv379/zl//I1czhwdfucq2e3PHt+gzUViw+c7h64e/c9MTo26xWbzYa+P+G946KqUbrj7jCRY0OlK2pV0dgGrSVKgEgZYiCFmbSMCO/QWSCTQAFWFUFwJSU1ikoodMplzSJkES8ngUad2QWFqhc1JCRJlIODzAIbJSqWCN0kIItyKNRSY6ylrtbUtsPqCilLVPiSHD7kshIgg0pYoVHSkpQhZkmYEqdlwPdgVANRkJ1E5uIuC3kuaPgfb/i/u+4fD9guELMDlWiMxO16+qeFZRoROWOqFc+fPSPHIx/e78hSI4WkNoocA8djj1E1VaOY7+9JQSOlJyR42O15uX5JtmBjQ9sY5iWjlUPmSFwih90T7968p1uv2Vx3RLXi6sUtd7sTuyHz8uqK1XaFOD2yeMflypAFuBgK1ctYSEcqW3P3sOM0LGRh8fHpfGsotK7KWlJY8Mnz/sMRcmQMmWWaiTGWyN5+5OUnC9PpkW9/HbG14cXLW2z7jOAcaegRosbPI9VmxS/+6m9YjOT6J5/z9sMdk3ccjgeeLQ3RlZSoBAUQ4ROL74GKk0z84jfv+PzVM54/W/H+XqNooWn45GevOA09N7c39KeRGk2OEaGqkmqYFvCZRSZ++c33qBSpZYuQLW/3J/RxIsWJlDJ+PqIvn1FpzeAWkpIgNELXLP2ErSxRKKIWCGuIKfDsouOTy5bDnSfII11TMbm5RGcCRhrmQ89GX/L6nef9d+/56qcVUoA+QzKaaoMWFUFOuNkhasksAjkl5n5mZSICxfF04sO7R/79f/kCY284THtiUrx+e+LV7YD/iNS2P9Z/WlGU27zKP3juAZHPTT8VVgQGpRSn08B206KUwrkSslJV5bCehcCFhFDl7FCaS1MawflAL3WFUAbnEz4kjseZJCSHQ8/79/e8/u4dQhjW2zW7p12ZMgj48OEdf/fzv+Vv/uZv+Pu//xW744HZLYjziq8yFaTE09MTGUEWkma9Zn1xhTJHnPfEFKmN5JNnP6XZbtnNDwzzPU2tqbxk1X4BesPu6ZdEIkqX9UStJfm8u07JF095KE6BeRoRUvDy1Rd8/sU1tzctqv6U5q7n1ae3dO2KulaE4Hja3dHWNbvH90xTj5KK4BPjuJASXG42PFttGcbE5DLNakVXb6i0RUsKqTR4Ui5o2+wDyXs0haYntaTCUqmWWtZUWGTWpZkSyURkTlRCIKVEKYND4XPhl3hZIGYhhrNDQ2GMRevC9ElZIKQEEc6kBUESiagSc14Iiyd7SfIGHRuibMkyE0VEZY3MCqksgeIgylkx+ohWDpNNcXdIBcqiRCSLiOTjuSh8VA2/7iy2krTNGlM7/DwxLY6YEtcXF+zmhRcvnvPHP3vJ198deE9Z1qS8EHPZm3ebmmVS1EZw9+EdOa/JKTC5wOE48qno6BqD8WVMVshvATdO5cT2ABeXl/zRn/wMJaFfJG2lubm5oms6nl2sUNohpKSpOzabLdGXyNZGShZf7B8xBMZhKGI1a9BCnoV95YSefEQJQVNpyIlpWtj3kWdXWyIQksBqiTaK3dOO/dzz6tVtQdOqmigsGcm0jOi6pWk19XaPMAqhMkoUt8P+qef+6YRPAaUExXaaC0YSRc6RQ98zTz1z0nz1+TOm90fyHJHtBW9/9T2mE/SnDXVd4Y+elAXE/FuICUJgjSaQS9PcKQ7uEVUVO8ziPJsLy83tDV3TIHMgxQkIGGOYXMaHSFaKflpAZi7qhlVteL7d4sYe50pcr5At1liUknD26g4+8NgvPCzf8/23H/js8xeE6MkiU9uq/HlJMIwBqzMuzWAV3hVOQ2PLqmh/OPDVl2tePX/Fd2/eMo4nEp5pTkzziFr9GJ7zsZYW8rcBOkLkM1y/7PLJ5zVUSjw9HajqiqvLDT6A8uDdBJRbbs4ZXWmEqEhCYIzEWEWKJeExJ4HUBj9FjqcRUBxPE/0w8t23b3n79g4XEz4s7HaOaVmKLk7C22+/4f/+cM+3373m7u6h6H2Q52lVZJim38bkLotHKolMgk+efcGjfst+v6MSNY265JPbPyAJwRQGKp2xwrBefcLl5Z8QhGdyH/DLkWXOhQWgqxJWdU5uK7wCQRYZJcAoQ2MyL15ess8VJ9Fw9fIz7t6/5fHDe6bxgHMT81KiaWMI5TmURXUfPaxXG9ZtW9YqpqM1Dau6o9ENiozwC97N5OQQMZHCjPKRRhiQFUEZpDDU0lKJCiMMMquitvceXEbkBGewEkpgBRgki9BIUQ4RMUZidOQsgJLoJ6VGCU0SFESTLKLOTHFvDOkJKQtLX+UKEWtUmskigAKPI8eyA5RSEbViIRITiFAOKJYGQ4XQGiMNyExWEOWP8bi/s14+u8ClCSGfaNsGR0brmXa15bMvXxH1zB/90Qv6wyP3H3bIlDG6KL6FzGijiCkRkyC5heRHpKnJBNwycTr2ZBJViixjj5SZGB3CJ5TUqDrTbVr+7M/+Fc9eXPL9d6+53XQo69ApsG416Jr73cDl9pIYjozjieZiQ1WBFoKn/Yl5WdCq6AHcMmOsYLVumWfHqqvxsZwqlTJ0leL6puXuLqH6mXGccEh0VXF1vcWYNf3Scxx6snhZJEkJYlbE7DmeZqY54B92LMPIGBIzgajKe7E79dR3DTFElJZoXdTBOQVMlkSdWW9ajNfcvX7gcHdAvr6nVYLdqSeuDeuXl+xPA6/Wz1CNo7GGwbsyNlM1yKJfiFmThKPZrkgpc3N1yXBwYCT1eovSku1mQ0SxuBmri61vyUWWK5XEykTT1jy/WXFtK8bdnsf3H9AqEvQKgUOR8dFDzsQUiUngk6A2inHJzJND63IrCM7j3JEgBKfRc7UJTHOkURXznPApFFiGD7x60fH89iX9uLA7nkhhobIGqcFayeXl5vf9iPxY/0LJs9+6IHUhF2ZqUdwjSDkznSFQQkqeng7EWAKWrq82/5zbIFRBcQeBMlXRkcRIKBdGvA/YSrPfH/lw94C1Nd9//4a/+9t/5Oc//3t2ux0xwuF0oKkb5rk0fHLm+9dvOM4L01SU6UqVtVIGgo8sISJlIqcf1hKaT27+FCMuif57mqrcOn9y+2/Ydi/47t03XF1f8wdf/Nccnx7o7CcEZTiM/0h7oUDfMAwHQvA4N5NTOnd6iZLyBy4RUSpCzNx/eM/h4Q3Xt5f85h/e8It/+g2PD3cEX2K1hRAYbYiU9ykjMHXNarVivVrTGEOOGUSHrddYUbEShiokSI5lOhHmEz45tCw7dqsqmqqDDCkJTLZUwqAp3w0pK4IXpFCIeIhcIr6FQGpAFHCyAGRKpBSIcSKkgBRVWV1ki4wVQhhizpATKgmcLrwXlRMuxuLhkwEhI1ZFjArkmIiLw+QKUkVKZc4grCVqQYiZhELJBptXdHmFNRWVqFBKI4RBi49nMvhRNfybqw1vn0a8jyhRFOuN0ShrWdcrbq8a/DLy9PY9fixMfTfOkBSdFRAcnZGIODL1A127ImXP7HzJlK4qpJIEP6AFZOeZTwtWwcV6RXCZP//jr3hx27Hf71n6hT/+8oK//6cPZOeRSTCueh4PnudXCjf2pKZl1a2IcyL4QLvq8N5T15KYSjxv3dpClVo8zcpQW02ShraxaBX47Muf8vj0t2QhkKZiUzUYDS9fPEeIyOZyg1xJLi4vcZPD1hptNclabp9p/Cz4p19/jYwTflqwcoVbIpXVZ0eAOE8NMiklKiN59cktOTjW22tCMrz9/i1LcpwOPQ2epCrGWrO+vaBPmWrKPD70XNiAsoo8B7ICIWKBkvjAMp/oqoqr7ZYkIutVhXeJetVSdQ3eBZxPzC6glUJoVQKBrMKLxKrR1KuO66trrpuKp/t3PD31KAJdazmNHoRkOJwQMZGzIISSpRBiZBpPRDT/r//51xjdISXE6HHLRAwTy+wIbmGYAqvLC9IhUQvFce/4y7//Nf/X//4/8ItvRqbpiMiwuIRfFqqu5frFS37y5We/70fkx/oXKqVEFsX6lXNECIFKkGTZ3f6A2c0p4tzM6SQwRnN9dYmtaqRQaGNxfqHfHSHDerthqQ1unqnq+reH5tNx5NtvvufXv/6Wumn5/vUb/up/+Vtef/uanEEb9f9m70+ebU2v9D7st97ua/bep7l9NgCyUKwii6REMaSwJh44rIEn/k898ciOcIQ9kOUIRYgSRSpYZKEKQCKRzW1Ps5uveVsP1ncTJF2gPBLuIF9EAom8Fwfn7rO/vd611vP8Hs6nE5fThXWNXI09OSUeponzHGlF4WIl1w2EA9OSwBj81r06bzANDmHPk91nfOi+5YsXe27Gr/ji2T/i6+/+Hb/+7l/x51/+c149/UtG+4oPD+/49sP/RJZHxm6kDyPDsCelZbOu6bS0Vkg5UkvRMbq1WCucl8j/6//zrzheMp3vKJc7apx0dea9xoEbIXQDzgW6fmToR64OV0BmXjOd2dP7a/ZmwOdGSAkTIy1eKPM9bZ0gNMz1Ld6NmAZkTxdGagFbtYv++GynUhXwUyq+aYytMQ0L2KqNnv58Gy0ri19Kw2JwNmAJmNoT2o5WrCr3a6Gais+CRRBbaWhConUWKGAaNQjZZHI7QT7TqkHKNpHJjmICVpQQ2OrKSiSTCGWHk4W93zHYoGLQT+R8UgX/eJ6IUwSxOBp0Dh8MrSp3+vt3r3le9ww3e9zDI94KbVLPfiOwpsJu2PPmzQ/kajR3/hyJMTP4jnHoSLWwYElLJOeGGOiHwNPbgbg0nlxd8c3vX3N3/8D11XO++d3veHOXcKFjsCuPr1/j+8+5zEmTXjGUnEkpKRMaowCh/Yg1gKgiNOw6DlSqCKXqmHB8sue637Hb3ZCbw3cdz188ZVoi3dXAlz//gjw/8LgUnl/tMaaBKYhVy1pO8PLZz1nXxhf/9ET/dE97f8TtD9iHhek8s6bCyxdXvP9wz/E0b5Ylh3M9YnY8HBfeP75GasX6kcP1wCVACZ69jCQxLDny/fevud4/ZVkdOWcdW/lKrpGcBdMswXmub/csccZ1I9M0k0viurvGG68eWArWgTGOikNcoayRZirdzvPy2RM6N/DNd284nU6QKrZ1fHio1HQh+JVmkgJTZENjNkOMM33o2Y2et69PPL2Z+PkXN6zrCbqezlliNkwXVUEv04QLHu/g+9dH/syp8vfD8R7XKq1mJbRWw2Hf0+8O9OPtn/oR+en8kaPj8W1cLYqrxerff4zJFRq0QloLF2C333OZLvSXnqurK3JK3N3d8fbtW25vb7EucH/3ABR2uz2X85lh6Dmdz/z3//2/4JvffYcPgV//+rfcfziRUqbve4ZhZLff8/DwSCmFYA21JNY1UXKmFiW8GYRsDLVBNZruJ6LhL9RGLcJjfM1f7v4z/slX/w1VKqYK3/3wG/76m/+W0/QD57/9hm/f/JxSC6fpSJWV4DxLLeQCKVVyEkIY6IaeUgvGGEpTtoYx2x4b0TjxFf72777n+dOX3Bw+J4RblqSiZOeEYejpBwXNiFh1Gs0zKUVMdeqEmBNFGjZV6hxhjeQy0WqkWcGGDud7nB90xWAMrfSUUqEactEI4lRmYi1Uzb2jNrU6hmYpGJwI3pgt/rhhWBkkYk0mVt2dS+kQOmwLlKSwLqUla1yy9x6RRretPC0OcRasrj2bE5oUis2UtJJNouWC1IDUERFw9LgUqLmx2pVqHD4XyrSwmj1efgrP+XtPqwaPIDWTcsQ7ixXNtZ6miYf7e1ITvnzR8+LFLd+/+YAYQy6JlBZSbVymC3leWExH53sutVFbxjhLFzypVaZFYxSXquIc6zus37OrZy7nR07nM8Y5rq8D331zwnBFXoXDFzfUD3eEUbibJxDPOi1AZYoJ7zw5nmmlcRj2DIMnp0SpmcPVFV2zXNYVxLLfPeHq5oYvX77gPC/cPntKuIZf/vxLUiu8+vkvuL7ZczKNVy8qbnyCCx2SV87ziqkJSYXH999TMdjO0D27haVyviSmZWGulYrw2WcvCcEQ5x+IOUEz/Pa7bwkuYJ2jtIRDA206cVzEEoYdwVmmOWPaSq0rL14cuN09ZTGeb757R8mwpoTUldIsxlpcGEjLDK1xzA1re31dcmZZVqwTWi54Z1iaUHJl11meXD3h5uqG8rDwzemRDw+P9AFMM6w5cj4uOCZe3lyzPwRNCRMQZ2jOcpkTVwdL3zlun+0IMhOC53h/4eWTZ+wHhzGG+8dHbp8OxCUz7HuGXUDeN3a7EXBIKTRXaSIsa2KOkRc3nqEP2DD+iZ+Qn84fOzrK/7i3t+rxRvfTqqbdfrl9XPR7jqeJVgpPbp8xTzNSI8vlQi2Vx8cTb96853R85MXz54T+yHQ+45zlN7/9mv/pX/4vvHn9AecMd/f3tKIOkzQpljelxPky4aynC56HhxPzNJNK+9E54J3DWAu10kpCgFpV2OuMIxXhb379P/PDm2/o/TO87Tlf3nOa3hHjUXfp9cK7x9/8iL41YpVXXwwl66XCGkctQrNe7a4VTQO0AWccGB3xO2sQ5ylzRz2+5Mubl1zcI5e8cF4eMV2k3zmsA+csLakOKMfE2PYMbsDNhWZmYm2kNRFjwtZK9YUaBAk9LQRoRpkC0jQCHfNjul2rSvKMpZGaoWIpzWGaYKvBSMCaoPRDMdvPU+hMpraKk0YwhVYBHLVaWnWUAjmlTarfiK5BNXSdx1lL8F7z7oNGsidJxLJSpICvRJkxRagmQlFAkKkNlx1dGZDWkatyAIRGSSuXy4SEn0R7f+9ZEjgnvLjpIPbk0lDRBbTUlFc9r/jhKU/KHd9skblxrcSUaDXz4e4tc4ycEoQamZcVEwzNCOMgpEuijJbRFd3R9gf66yfEArtxx+vHIwXH9X5kjg80CYy7Hvoe6Qaub3Z8uD+yzmeGm8+JJbNOJ2yD3dhTljNQaLngXGB/GDD7gfu7hdWfaNGAN4QhkBdN0zqdM5///CtaKzy5PtCMYzSWb7/5gbE3HPqB4xox4uhcjy+VNx8m1laJ0yNlWXn/8MiSOjrr6K4Nx7ujekIp/Lt/+3f0vSZRGd/R7UZEEt4bjZh1AzVWWoVqwYeOatULe301MNSeGGc6cThT+ezVS/rxwPTwyG/f3JNiZlkjoXPcPUz0wXB9nbnZXbM77PDBIqZgnY4yU85UArVErg6B6/0OM5+5/81vSTmRzIF1TUhpdNaSYwWpPMyZ5fUDXjq8C9BUZ+tFkGYxVqg49gOsa2KOhZgq1hpqjvTe8N23J3a7A1fXI7uh19jdaikxktaJWgoxF06PjcvlTC6CNXvOp5X7u3d/4ifkp/PHjvLXlcEuYjYxmtU1tSr3ts5fsCYQjKXGRO175nkheBg7jaWdpte8+eFvuZwmmtHR+ut37xDjWaYTv/vt7/n66++4f3zAW0uwHqGAwDAMKmRrQi0N33m8NSxr1G61Kc66NcW9Wutx3iBLI2Vl57vaMMbgHaR14v1yprWvlRBXwIhBjCGJkK3jauzYDQeCeNZUWYtg6w6PoZgVawqtFEz5mEQH3jic6fBmR2tCsJ4+eIIfeXr4nK+e/Wfc7J/w7duvIf/AsL+iuRWRSGZRBn3MBAl0EjAlIWWlNOGcj0jJmFLUOme9ugW8wwSPDYEsUEsmUjRJEAfiSCIssrLUzFIVc0QtUAIGizRPkwFsh6FT1byAEaciPKthOLlVam4kEUqppFKwuWCKoZSq0ce5EQtQKqYfsX4g+I4hOP1clEySXnn9NRKsp7SONUdqEiT2mLzD5QFpHmc6zWIQoy6CriO6C1N9/BM9Ff+/55Mq+LkUlnnl7njhMAY6F/n115F0uiObRBWF73bdgCwWSY3WMilBozBdLjw+eJaoqUdGDCYlsB2hG7D+rJGWnef44YLxHbvdjq7zDEGYL/c8XuDp0+dkDLXumdsFkxbinef5ixusBNZ4p+lKcaFU4XRZubkaWJeMNYK4RmoNR6MZT8Dg/MTVOLCsK74PSFlJk7BOE8E7WkpYMjUtnKYFWmS5PGCb0uqmXBmHgr+9orVKeHpFfkwcz0dkVu78acnMMQEOMwyImyALFbhMK7VB6FRM0lrFiMEFh6meeVlAMsZ73ODpe0jZsuaIUHlydYU1AdfOTJdCTY39fuQ2F747XcA0hrH7EXlbq2HX7aF1xFToTMBbhzXQDT3rlNiNni+fP2d5nLic7pDljHWeyh4rCes8TipucDwcE3nNPP/8hiV1+F+/A4R5WslVsNYxjp4cL5SYiblw//CBv/zzp+Ss2M6+D5TsMVK4vd7RnEWM3r4PV4IxSiY7zQtLhBIrsTb2h8D790c+//LVn/Lx+On8J87H+FpEMytk+3uF8BhEzDbeb0gTcqo0scxz5nKZ6TtH7yzL8YE3P3zH17/5HW+/f8fTVy84nU68fXfH7e0zPrx7w++/+T2n04WaK81ZClXHvAChMvSBaZ416yFHHi8XLsv67/H+t1S1pkExILQtGra2StnogCKa0ldb+zEyVvS3AyDGqu7HObp+R28GjfaNFQg0sTQ8UpXq2XIFyhZx6zB2h3O3GBsIzdEzcB2e8/nhK667F9RFrWjOWAgVnKU1g6mOUjN53dDmziM5ssYIKW8hNkaFdcbgbcHQsOJwVHyrSF00pE4aIkorrE0tdbGspJKR2pCqACSDwWKQphOAmpranF0Ao6+NNRqZrX68RpaGaZVaE6QNv9eavqYIhawvRzSIifS+IdVSmk6ABYO1DmMb0pom+VWHmHELV3LY2oMEmrU0swVsiaVawYyF4OWjMeKTOJ9WwSdRmtCNz5nnB6bTTO806vAweKzZcq6lsRZDLgVrHdBYY8bUhikzTiA1h9gKnaNgiLni/Z5qwBpLEMf14YZu3GO8x9qM9Zb9sKc0wAjzNHNZEiE4ao08PMz0xtA5z7ROuBYpsZGz+uYL4MUhDZqxNO8U6uEtIsJ+3PHmw0xcEpM5c7jZ02ohNUOoiwZ8rPqmvf/wXj259xFjAmNnuLt/jzGNm9tbbm+fcTt27PuOy7u3tJyY40q68tzfH8kAptH3luuba46PJ4xf6LseI5XW0Jzv6jAYQqc7uefPVJH/8nZgnhMPx0jvGq4lWkl03rAPiXfvF94/Tiy54vod1hm6LihiuFR2u56u65jWAtYQfKMmwBucMex7x/V+wMfG2/sHaspUE7geLNE0Ppx0RdKssEToOsPzmyeMY8/xfdUUsdZIuWJspR81W3y9rNSaCV3HhznRd5ZxHNSNYVbC4FnWxOMlY909+yEgFp482eOHHYjHOKvBIcYwDIaShfcfjqT06ahtfzr/4WliECxGRIuS2M1lrWN+EfNjdK7OhQAMKQnzZWXuA7ZmpscH3r7+ga9/93viZcEPHd9+9y1ZHCnD99/+wMPpRC6Nzge89XhriWnZ3o+6PlRgXMNbQ8uVkrcELVTtbtSLR8orIXS6h25a8FsuSHDUUn8cczsXAHWlUPX5zbnQBWUFWBswDOxswIfK0hqpiHb1bAr2nKkkWi3kVRHCLThM6BA8vf2MZ7tfcDu8pJXK+/NbHpcfqOaMQYt2bZm6LqzrRI5ncikYE7YJS0U2Lj0oYVQkk6RimsHVRsiQa9UQHPSibo2lFEPa/PMllS1cTMFYtglGHOCx1WnR3f5cikA01CbbZIWNN6IXHCkNWxxWKk2aWvdENlBDRUoBKZAqqWR9zatR+5/opKWZijUWcNul0VCNUI1DzEBrAxiPkR4ngdqEVAvGFfoOvDF8KueTKvjOBGo3IDYTpZHMgB33PH/WcX58h5NKqlrgj/NMo1KrpZaFmj+O7xrLEokuMFqPeEda9KYfuoHoHPMS6a2w3w9clggV/CD0Xc95WrC+Z8q67zFuwIaeOK3M88xUTjw+XrjqHC9G+OYc2XWeVBrWJFLRsXstJ1KFtVT6ADlWvZ0TqfQsMRH6wHk+I9mR10DoRlynQRDT8UQ3DmQRDraypj3n8yNd59j1jjAeWIuOsFOupNNRU69E2dC9jxjg6ZMDX/3ic371N9/q5MMKth/ppIKpmvJUDDI4mhRunz7F73Z88WTP8eFIqRdyzYyj4/r5LaVlarpwPM/M04L4HpGC844+WHJ1nOeT7g1BC20sLK0pGW+tdN3AIBlXV9aWOV8uxPmM66+ZzheevgiwCeemlJmXuK06CvOq47lSMo1Kq43Bg65CBW801jYmyHmh5hUjHc4GbveNX6XMX//qjvtzx8vnjlcvXhHGEWcNtXpiTHTOE7pGFfj89orTOVPSA8vx/k/5ePx0/hPHNLs1vqJTNlFx6o8F36iVTLS0siXdQ4W4NuJSWaru3Y8Pj5wfjzjrefPuLZfzheAGXLZQhVq0eGIdYz9Sc9SiVRsxJs7TRbt31KpbaiI3HV1/ZACK0T2ytZZx3FFLIZeMbQ1rDc441hJV9b/hq61Bi9IW8NNq1Q7aG4IVxubwricKSFHtkHarbmt6ixazLQu+tEoyGesUyfvqyZc8e/Ic8fC4vOWYf0M17+m9UJtqcNZyYV7OnM4PLOsJQfBmwPkO7zzGex2xY36cLFCKCuNyoPiAc57BdxjrwTgEhzUOB2pzK0Krup4xqqPX/r45jHFINbonr6rIp1owTvUbTbZJhhbzlgWh4qx27wkwrZDRi2GjanhPbcScWXPBF4dY0YkRDaFixUBTpZNIoIhDjEfMAKanicWKx+JoRqcJVgy7XYeVn9Ly/t7jg0ISbq86TOn5u7dHvN3xcDrx8Dhx1Qs/fDjRFrWUiGnKzDZCawuGQnAeaTM7byBlpstKbDoKswFMTvTdwGh62uMRQ+N8vpBXx3VvSKVRKKRlwdRKCA5jHYdrj3OG4/3CfJ756tXn7HeBoSt0wXI5LRwOjiYG6z19P26e94oRD7bpKKoYjIOcNLkt3OwoC5jRYcmIBKRFXOdYlpX+cEXYe+Jc2O123D888urpM7xzJGPxztCPO+bjiRIzFkMIDu+UW22txzrPz352i/m+Ur1HjME7RzOCNIezhpIbYT8QcVxf37DmSsyVodMkvpfPr3n+8kt+/avf0o8HvLsQbCW1DAjWehXRAKWqmleohGAI3hFz/RHSkUuiCyqKEylAIgQV4X1YC/HDkb2FkgzrGrm97cmp0fWGZUmUojtJqYrXFQMFnVj048C8nMkrdCFwmS+seeLpsyv+7PqW//nffOD197/jsnzN+zeOq/965Ha/Azp++8071unM1Y1XXGop7IeOu7vILnhK/ike91M9Wsgthq0Ta1axyWzFvxlsc1QMSMOKqvYbjZIMy1ywrTDPmWWOxFKoqVInVdWf14LzPSH023VBd9ApRWouKl7dFOPzPKv4XCzOOVqJ5FppAvbj+B4I3cAv/+Kf8k//yT/j3/7bf8Xf/Nv/hZJWamu6J24F49Qu6KwioIN4ihi9HBihDx1j1+HdltmRG0PWhL0kihymOSyi4l4aVRJiqvr/TaH2Dn9zIDzpaGPmEo8cy9dke4c1mVwal3jiHI9clhPLfCIumjXibUeTlZgzqVla3pC9WKwYdCDfCMYiwaqf3zQdc1uDcVrwRZyixmvR8i4NEQfGQhNaEkzbvpo4xDiEgKDCPcGqqK8ZjBXyR42E14wD1wxIwedMk45WGqXo50eh6oWstW21oGhlY3Q1gJjt1wTZsvFEOqoZEBNom4CQZigiepkoeqELIeDl05npf1IFfzceqLVgI/z23/0dP3x3x+dPXjCdV5Ldk2tk3DWCyzrKahXE4IIoxrBlmnc4b2m50SST84pxgZh0hxes4E0gVcPjJVJrZplXjnNH3++2HObKftyRlok1F6Q0dldXLOuZXBtPDiPBOQ77kdsrkJYxrRD8LbEurOuRWhvWNmIsYDylVYJRH2uthtISU24M1lOrx3UDh5sdmIbYAl53WF3nmeaED44ueabjAzHPhHUkrpGaMzZ0PHv1En858v7DmWVdaXXWfXnnGfvA6A+qKXAdr+8nast4LGId1RiWZWVveo4PFw4vbpmPDzgjPL0a6Zzh9tkT7u4n3j088vLZjr/4+VO+vzP88G6hZANSMcYiRgjGsx/2dN5jTMY7p/s9a2nSaHrpJ8UVRHBBiLMwXSbWtDCaW0qesWbhMPZQhXXVD9ZuGFnWmYyjbnvCJ0927IYDOSW63R53eiRn+OyzJ3z52Zc8vTrw7t0jKZptBGs2sIchzkf+y3/2giXBf/cvfo2UI1+8/DmOBxqZ29uB9x+iOjzip3NT/+n8R6epFc/g9X3BFqBi3Lb7tWq5MkL9MWhH8xxKEuap0mImTmCaxxol3rWiX9qIEGNmt9/pxdboaL7WjBgLRrCif7VSKaVhrANryau+52VDxBgxGlzlHOs6czw98tXPf8m3v/st51PadvcqAsRYxKnDxBpLPw6cTifqqgr8vu/wIdAMZEmELuOMIZTGUIRiHWXjyBvRPXNjC4nHKKDICdnDuU3UNBPX16T6ltwmUl6Yp4lLOjOvF9I6E+MMNLzrVOVvt4JYEq028ra7d2gkrzWCtTswGuBjjAXnEecVRU5PK46WMrSs4/umcenSnF7mrIZ8tarFXUT/onkVzInfdByC3Tb0xhZSLSAVacppCNLrz81ocl7JH90COjWh/IFGiMjm7NhcHqoA0X9uLMZ4rBsBr3oJEU3ja5kGrBdLzQbbfzrunk+q4Ne64qWj5DvuTnfktJIlI26krZX3d0fm3PPN94+0rLe8UjM1VcRVGnrra5sa3eREsJ6lJJY10fsDwRumZcXExDRnhtHoLa81YmoEY7BGo3Y7b7i/W3CdYb2cGDpLGAyH0HF9c0NzI9msBOtwZmE9vueH9xM2CNZknIHj+czN9cDgAobE7c2Obz+c2Dmr++IGQ+cJDqY5EcuFaZkRLK3BvBRC3+NDj5zvcCFwfJyQGmi2MS/Kpm4ID1Oj7w+8u39gmVbGvsd7o4xxLM44apypcSWViu0auAYSKCkSLxcGa1lOiSejI9tAt7vl9Ljyt988gr9wc33Lq+fPmXaFt6fC1WCYHu8InWNaVmJWEM+0RKwTHDrW9EEBI+qZLdS0ArLhTCtxbbx9f+HJM93/rQSuXUNMZV51VOqHQFoKyxx1v0bDiOVnP3vJON5gJPL4OOG9ZU2F58+vefLZz/ntD2cejpH/2//jv+X4cKQfevrB47vGF5/f8stf/pL/8a9/oFrIqTH2gf3QMQ69jgpd5bPPrxi6/k/8hPx0/thpVZXrDYMtTouBsYDFVAdiKcZsAB7BNUWrViwtF5apkSzU2NOqB7O5PmqjlYxxQi4ZHwIhBCXXebc9pxXvHN64DXKlHm5rFdKypKwr4+3/24hsK4bG+XRPjJF+2KmYFtmIf4Jznmbspj9Q1f8SI91uoNaMt46u69SiaoVSI0uudHRghOAsfYPVqcBMo3aVW9FE/e9iDV4cpgbqpKmSzb6lsZBqZs4rc1kUrlULlYIYfW69DUqTM1Z1T00nJrmpyE0ETNVLdme9vh7W6mezs6reNx2eDjBQG3GLrG1WmQOaFqhoXC30DtN0gtNkK/Bicc1hjaGZtl0UjE4GjMGIwntMbdtMB/qqwshIUrAPm4vJbojmutnuRJsZfT/pWiS3rGN+Y7DWquhQ7Ga3LNt6SWAyXB6F3v+E1v17z9s3P7Db7zhNGWMCcOE0LTh7Q1oW4rowJ1VRtzWxrJVWGrW0rWsWjNFxOl2gzQZHpaTMPK+E3TViLUuqhJxpUnTM3vShrlnoDgPLtBDGwmEfELnDWMFJx2evXvLmTWSaM8dp4e7+xLxUxt3I43TCMNPvPdNS6boAuVJNU2FhE+02XKCkTKnqIjjcXHE+XUjZUIrGzpaaKSmSqqFZeO697v5KoaTK/d0D+3Fk6DtOGvVNjpk1VaZWGXZXiLvwdLdj138kR4E1iTCCeyys84zsbnBGSCXS2gox4rjmsN/jXGWunl13RTcm+jGzzBOtNf7ud28oRegt1DFwOXieHjzHpdJbz/00s1yOCDt2Q0dne0LnuEwTtYGrlhAGlqIZ5q044po5nWde3N7iSsV2nkupdCWx33XU6rb9W0Zcg6yjvyYVZwxPr/d8//oNN7tAWkY+HB+4OnQM4xP+xb/8a9Zz5jLNPLkNNPEqcrKNly+f0YzHesvz2yse7hPn+cx+CFwf9ixRuLkd+PKLn9H3P7H0P9XTmoGq9lu2cb20rctvquDX/wSzAbM0XMVgm1BKo1jDGgNr9rSt4BqBvESkNhKRXAt91zNNJ5xx2s1TCV1HiVlZFM6y5gU+gn7qxxR2nUKog8DSquB9x82z56ynMymVTZym35cYTbP7GAJYayPPC65z9ENPZwO+GwihV1FZrTSTiFW1OikL4h3edETxNCzksiFoy6aBEWiZvjpCDqSPiw5TlCPf2CJrC5VKEfSiIBZnO5zz1E2AJ01JntQMNN2xC4h1OBtwbNwBa3WcbxyddBhUWF2piAS61qhSqbbRClp8N2CYyFbsmyUbu60OtMPfUIr6H1v4UUBV+WUTPIqgMbnF/MhmiLUg4nDOYz++3tv/rqGY5rb1RqkVSlFdkDWb48Fs4r5m9YLTNHGv5cry0JiGn0b6f+9pm7DFBMsSK5fzSj80bGjM88R5zsziwIp2haVRmyq2izVUMTQRqi0E6zTz3VZaBmc6LnGhZ4+IXg6sdUg2emOUypIj1014uD/x1fVXjGPPzdNI55W/XHLhMA7cL/f8+te/IRXhF1/+nP1hR3c+EFxHSJHHuOrqqWm0ojGJVDI+9JR2obascZoxU/P25qJS6qoQDNdz//hI1+lefEkrtgRqKfzqb7/m9ukTXrx6wV4GStXkO+/V67pUGK521MpmbbSU+aJRtt6RqmDlTM4XRjcwhIGHY+JqMOw6T8Xw/fsLT65H3KA311hO1FRoObIshdMU6bqeGmeueuGzf/IZh3HH7757w77r+XU5cTXo6LRtawypanHxxmFbpRqHbZvTwjtwWwBPjPibPeelkOuK7UdKhF0YWOKZRmBZol7UpFKL1ctLK7x5/Z5//PNXnM4LLd2x33U8u71hmirn0z3X1z1/9Q+e8eTpFesKd/d37Hc7YikEb7m5dXz27Oc8Pr7HG0fwlpgbh6sd+8MI7dMBaPx0/qPTNJNeaD/Gn9rNk6/do8FiqVK34BTZSJnyI+ueAmk1UDzSzEah03G66m8KyzJr+pq16uWvFbEa3DUtSmGz3mGtJr/FFFniQt063ipt667BWMsw7HGh5835W2Ja+WglaKK11hmzFftKk6pFumR6N7DbjfS+xzeht45YI7VUaKpSxxqkRaxZseIpxiMZpFSklh9FfT48Zdgbgg3UtadK+NEm11rW3fp2SUGMEv+cpW5JfHaLFmy1gSR1AVX9c+pdy+hrblErrLVgHdZ0WBQYVARoBqmGZhxI214D0U69fXQ16rTS4rbAhG2dYKAZnWZW22imYYzTgAUUuOTatn6IQsts0wADJWoQj1fHAKJuCbXmaaevrgsoVVNHc1NwkWn2R7KjERUPFirSUApoTKTLp1NmP53vBChFGIaR+7ffcTkt+sYpBpHAZb2wxIQNe7rguf+w/OhnbSXTqrCWhgTH2PYgHtt7BfOUrChFCXjn2O0sl3PGd5aWE80YghHIQo4rxhiG0bKshdAHghFarZwuZ56OPXLT8fbNa5CRrjNghbETYulwriBt5f5+ods5Uix03YDvCk0MNWVVs0rlMp948+6O/ehoFJzpCIOjlcTpcmEYb5inC8H3HL+bOT4+0gXDZ6+u1ccbF2yNXNbIPgz0/cD5OHO47ul9xxoBU7mcJw6Hj6lxjr5zvLjaczt0lGbJUvlHf/4lT/YQcTyeM/1uRMRyvPuA60buT28ZBWJJGK9j+S4Enl47PvvyGd+/z/z5L7/ien9gvwMfPD+8X+izUFLES6AYoQ8OaZU1LdCEZdHJhkEIXtP1LjFymiJ+gEJmKQZX31MTvH43cffmLcZljAh1iz4tKZKWzHFZOV8WlhVcZzjsLf/wzz7nN1/PLOczr15d81d/9Q9ISZjmM847LhdN06tAtx+I7x1WTgRTuT50pCysqf6B0vbT+fRO07GrNLT7E7dBc0TH/G2D7mCpVXey1KqkN9HqWpohV6FU5a3nWrCAMRq/ao1hmc+EbsRYTzNNm4eNUx+CI8dMTpnWRBPanKZhhqDRus7qXrpU6LoO7wPHe3UFtJo1v0Un1fr7g6fvB06PjwgN7x1D37Hf7RjGEWccYhvYQu8c58f5x6Q96yxSMjVXOuPxwUNntpfLsi6FNQt+cDy7HXAyUI8jtIHSDK2qkr2ogU795RsPwIhTX74oFZCmRVa29KJSI4aCMz2dBJwx6koUQazQuR5nexXeNUurGZMbJjVMFUprmpRnVGuzVXTAquVZ1N5rPkIJtm5cNi1FFVR0Z5Xk55tQN1eDOIOJAlG/nikW4xVtbs22qRe9CNbW8FswU7HqVsjtQm1R3QP2o9+jYtoKpkOaoW36otYay/wTS//vPSEMuGB5uHugZDDiWZfEdL5QUqQ1oxYL08gt6Y1PLPiOzusDpXvhxjyfaeuK0AgeQLgsCzlValYfpjUW7yuLdRgbsbbQ7fbsraHWTFpWwqAFf1oTLTUgYUXFMqUFxt2OnDJVdOeXtoANg+EcBRMbJRf93rbYRksjpYQvhZQK3ltibQydpTOOZbOmXKYF7waum+H+8UirhevdSCsOJ3B/98B6PmOBlDUrO64ztey5vt7zeInsQod4FQG10ihxpu8s1VwRs3CcZ+Ypc1zhyc3I013H7W3PWix3DxdSnMmKOkTCHpMKXTNM54ldSNRiELvj4fFrot+Ras/z57/AucSUHlUfEVdSXfRm3zytVZwJrHEhp0xpMAyBq+uBNUVGApCgaAwntqMWS44Lp+OJw84zVQ+btamJxYhhHAPihDU25iWyLImaG8ENdF3H4eaG8zTz6tXn5Nzxze+/ZVrP/PDmyHp5oDGT047ucMV8LtzeWq73HWt2XOZE/cmH/8me2nREb9C3xTbd/fG/VBHyRtqTj6FxTT+sjQhSlYBXsiMVsOLwBryzaqvTDyTNr7ee0PXY1mj1o91OqZWlaYhWQ9+TPlgOu44lapdqncW5jpwbfdfT9QM5Rc7nE9D0c0K0OzXWUUohp5XgVdMTQofvekI/MoReufhkKlWxs0aINVJypbMeaZVMxUrFGZ3yWeuoVe16VXqc6xmvBhwjU1qIS6eNFmzCNUMxirW1oq+piNOJl1U7HJsAshmN4M5U3LYWsS7o7t35rbN3BDNgbEeplpYrKSbSmlQ0V9CZPBbwOqUxBqqmzzXxeglodpvgWGozlLJdqCwYUzEGFUEb0bdB00kDBjIN1zYYUxV8H+h9hxPLNidQeNvG8fkYYKhdfiTlhSSNTKXnFtsKBk+TdYMdBYpAYSVePh13zydV8MUYTVSLmZgTc0oYt5Kq3jGFQhc8nTT8xiuuYvVGlwVJM10tnJJRlWaD509uMePIm3PkeHciUvFhYHGN6+sBWwxrUUBGaZY5JuIKLXek5Qx2j3UeYw33d3eY6DlNF3o/kKRhg1W/nw3kmNl5x9kJ4jONTM7CfuygLJznRsqJSiKvEWngJBDcqOO9zlJoTPOKMZVlzUhwuK6n5UegkuvC+fxITTd89/uvGW3lcH1LK40cGwYN6XBOyHGGtdFqQmpWmIRFPeolU8QTl8gyrawx8/Y+82efv+T1Q+F8OnK5TBz6nuO8YJqGAqWc2Q8dl3phHAfEGJZUqEvinCYep4n3oefV7Y7dOHA+r6SikZY1N3JOOGNIa9bRnYlYXzmMB+4eElOKvHQeUwq+WZZlZeiENRo+3B3JRP78q8/48G8fdM5WGgWz7VYNY/Ccl8gUF379q2/55h//htdv3vPD2zekJZBbJYQ9r+9mfvPdIz7f8/q7R/Zj5PmzW14/Lny4zNzuRn7x+TNStvjB0Cgcj5c/9SPy0/kjp9WP62aNwm26oOdHNF1tVNP0QlBF3VZb14/I1qU1pBgtAMbhrArrjOjvjzECDmrRjrVkMArKoelumFp+FNw5Y3jx7IZffL7j7u6Bh/sTVQQbekq1CB0ijcvlyDpfsNaq9ofGNpnX1eWyst+PSGtYH+i7niF0KhT0jmVOVEXqkJsmzCmYSkWzrVaqSeAb3mrgTClNEdV2TxdGXG+xxmHOjraqGr1ul6X2712aEIO0oml0WLboGr1gbZMSaVWR18bT2w5vHd46nPe44LW7x2GLUEsi5rhd/hMkaE1o1ar+gs22h9C2kblpurtvYinbqkHEgjr6lfJnGkjZfq7aBCq0TTHLvjn9iduKL5W+D/TOYyoq0gPVXWyrlY8EBWlNJzFtRUphlZUqC75dYRgRGWhNRaOFRikF4acO/+89zhgcYKzGuYbR040j2IqIelJLKsRaSSlS1kilUUqj1sx+t2PcH3jzcMG0RkmFfJ7YjB9c9R5vhJIbzTo6uyOfM+fzPT4Y9rsdeVqxYaBJwYWOUjVspiyZ4+VMXPV7/erlgavbg1K1SoZU6Q1EJxijD8zt7VPmNvF41gIclxnnHV48S1owppFKIfiA7wMPp4lu2DPPK6VGTo8rNuxZ45lpOhL6jnHYc3u9o+90d2ZdRUhYK9zfv8eHDlMLy3ym0uicYdje6NkJ54u2N1Ib8zxTG/R9x+nxHXt74FL+nPvja1XlxplVHGmpXO0dcV6QEllnw9g7yIL0jnXJhKFnXRqGwuPDBy6P7/jsyy9IAr5VGoa0VlwoWAtiNSAjOIgtqB5ju9ZlCs6y5W4L5/OEUPjiiye8fXPm558/5+9+P+vIDLYPksA4GsZdj7OWq9Hy1S9uuRwfef36DZe1cTxfCP0tv/nte/7u2++4nM+cTg989vnAz774in/3t69Za6bEytPPR4bxmunhA701tNizTNOf7Nn46fyvnKaWt9oqlc1K13Q0b42oJ37bBecNXWvaVsA2QZ0IyObZLjVTW8Fb7XRbU9Gc8UJuRf3sFGJcEcymmvc4K4jXNZ8Y0XjqztGPnut24PEyEbzHhxExHZ1rxMs9Oye8/OoLfH+F7we8s/zq3/0NS4wYp0V16AMh9HR9TxcCIXRgGp0LpKSWUYfBG0dKlWYUKJeo1KTiZovgOkdultI6xv6W58+f0e0H1lQodqXISpWyiQz1yGZLY6PMiRicgPuoQNKkGhX8tbxZgntc32G93Rj6moFhjYcCsa7EkolpYVknUsqY4jES9DKEAntEZ/U/KvWp+hNrWxKpbPkATowKMsVtDAB1FbTGttrZRvW2EbbvF6lY3+i7QGcDrRaFJsnHsf4GaBIVLraaiK1SqmBqozBRJJHaGRiwbQetAxwNo1RDlv+NHoL/9fNJFfxUNqsFjaud36RsekPLWcdvKa/cPTxwmldKEcTprgpTydXy8HDh8XikCz2uGS4lc1kj1Mbnt3uSLVCVa3334ZHj4z3T5cKOjmICTbKKOeyI79SriVhaW7i9OZCL+um7odeRmhWcsSzLib4bOU0Ta6wIHSF0zGZirZnXHyb2neFq1/PwYaLUhtRKlazgh2Wh1oiURE0rDQVROLeRpoJn2I90FK7GAap2HsEEbIG1ZHwwxBh5zJ7KQD9aXry4pe93iClc8sKyruyu97RVmE53GLfdjkvj1dMb/ubvNBSk9wZnhLfvH/Fd4HhszNOMtYVWMs9uBsgZ7wwlJaZ5wVtPo2IahGA4Pk64YOj6nlwTJleESmlCqZWSEtI8y7oSnEWsIaXKskaSNJwz5DVCTYzB0g8Dxq6kOWpuuECphWlaCJ1ntwtUCj/72S23B6GVjv/+X/6aJInbmz1NVtYU+NXvfs/leOLt2+8ZB8/nP/+cm9uf0b9eKNMDh9FirGeNhdoMwWv0Zaufzk39p/MfntLyBpfRtYtsvneotKbxr0VgS0fFNqFWtchp97p1gE07y1wzoMlqOSf90C+Qa8JtSvNSm0a2Clu6p46tU8mIceRSiTlhxNN3VgWCAsYEuq7X8barDKHnly9+zp//gy+x7oDbHWgpsTze8ebuHtnEZN5avdBbpwx962mm4kfBroJrjRzjJvLToJ3OOWiOS9UpADlRjaFVJWHudweePXvGYb8jPk4gF4yZcdL09UEvRk0+goo1o6AaRzFb7GvbGPWCPv8oQdB5j/EO4z0SAsapD78apxG4ObHklWlRj39NBSeDJqYCvlbsNoFxRrUCLW+FvilzwaJocCPq0KhGMK3pSsBYNgyOrmw03k4FnU4zTgxQrVOmgDVKMGwfu/o/+PGbNCXy5QIJTLF89ORTC6WeKVyg3AMdpg20aikm09qnw+/4pAr+4zmyOxhqbez2IzkZzmvhck5IGPBypiCsaSUWFUQYwJRCrpU5nbicfkDaTKmBLvRcSuPD1HBBaDFhZcG4QlxOvH09Uyk462nNM80n+t7TUtYELFM5fphZw8DV9S3Px46vv/1OUY4IHx5XPrvVTOfd0G/jelXzIpBSpgp4Cx/uHoi7jr3RX/dWb8hm8616tMDOy4xpQmd77M6wv9khtuCCUGKiH4TDYaDvPL1AWiaoieauiNWz1oxzsESlBOqD50CE4EesJHI0ZBH6fhuL5cSwu2Iu8PXvfk/OM1e31+yGUTMKTOPycEQEXr14QXCNw/We+XSitExKiRwj/ejJUhAqYgdSA6aVKGys6+3D1hhybTgTmNdCjBnvAyKWlBKGTC4ZWYXeWq6fXNGKoYrwj/7JX3J6+4HLmjdlcGVZImI9qTjev71jvx9w1vL+PvLN99/zD//iJWHc0ezEr7++5z//p3vmlJnXTD94/va3D9wdA914Ta4NyyMPj5maTlzfBPpuz1wjzvs/7QPy0/nj56OFqmlAjNRN6S4WqQVpouKzZqAKdlsVF7Y1v9HRt6lC5wJGLLVmYs7k2qgu0FlDzJkYExIgZb0ASgOpVT3hTqN5xTv63vPLn7/kZ19cc7x7y7c/vFEBqzc01/BO51nO9wxXV4gNDH3Pk8++oCwL/8V//k/51//mr3mcZ2qpDOPIsDuAMdSiY39rjJLsuh4nhVg7TjEiTnflfRcotTGVpAWrwJqzqvc3Tr8xRpn35QMlv8G7iwbZGKjbtUk2Zp5FVeluYwOA/Gjdk6ZNjAGCC5o3b5T06ZzXrlsM1EopkTUvLOuFy3JUCFf+qF9oGBy5BWgNJwYkbKI8sBU2Y+UmwhddJxj5cRLTjPIGjLhNbFupUvTPK3azO+oUBoFgFMpkEGxRoWdlsxkKVDK5RlKJ+l7bwEUaw7w5zFqjthmYqJxpbPY8Ph3tzydV8GNcadWRY2WeKsZaLucjD8eR68MOI28pFC5njXANRkf/qxWsc+QSWVLSVCZrsK3QUmUpFV875guEFOnFYkqiC5EoioFc5sztIdB1Fh8CQ99xfJyJNWJr43QuLKkhpWJdT1xWjvNM9+6Rl0/39KEj+ML1MHD/uPBwuid0PcEvBJOZzzNxqdynmVLhZjcwDI5x2BFcR4mJmNFCZj1X+8wxFp4cdlzO9wzWk9JCN9xQUmN1je7qgK+Bx8vKYR9I6YQ0wXo4no88vT1wd6n4tnI7NKQFntxck1vi7rxwdb3n4bTiPAy7K1IrGJcJ28OQ0sR+7PXhbI5ntz1Xo2Pcd1R6gstU41lyRqSR20qTDtcXainM84mba12PeNOBs1RriHmlIuRaN/KZsKTCZT7TeWEcR46nC22duHn1jFrUV305V7747Jrvf/+eeV222FNFFKdasP2OmlZyLooVNYbDzchwGMgZrq8O/PrNa07HByq9UtCq8O7dPd+//oGXL18yDgMSbnnzzdd88Y+eMoYDWf1R+PATeOdTPZW6AbREV1xSqc0p+76Cq39wWJj2kcW2Da1FFebQqAW8BKyoPSxXFWalmrFVbaSIkIum47Va8b5j8IHOBXJrlFzxLvDi9oYvXt7Sd4Z0GHmxXDEGjbBdcqK1pGNvG/nw8Ih3jttbS3+aCP3In//jv+KyXvjX/+bvmGIkl8rt7Quev/iMt+/ecj49UorGQ3vn6IPHWViWREoqYHO9x7dGyCuXZcHR8BQwEVykpMyHdx8Y+pXz+XfU9D2uzhRjEae6m2YsUpMqIq3GBVuzCd4+itqqvv6KPDfYMGBDwARLCB7vVQelUbewppU5rUxJ6Zo5x03hX6BERAyB/VYwHbSeVgtuY+t/3Kmr3VFXCm1z7UiDkgSsxVsNvGFb8TQMxglIxYsKEUXQ6aRUSvuRRKAaMdMorUBOxJxIuVDaH+ycVQfQfFxo0KC2QpO0rSMUx/upnE/nO0F3+CquqMxzoZrEcT7y7u4pf/WXt9hgWJfCfBH6MIBbSbmBNfTdjjEMCI28JGwQvNOdnjeGEAziHOuSOC2Ztx9mdvsDB2eRuvC7xyMrT7jd9XivEa/68/OsecXEyDTN6gG1lWAN0hLUirUd1lfqUhl7j0GIS6bf7ahNNJGvNo6PZ/I6Y73F+g5szzh0rNNMLYWSVLAYOo9rnkCgpoXj/YVcYOz3eN9xWRJ+B91uz93riRIjLUdayyxr5mrc4Z1HTKO4ikkRv++4LBd2h1seHxuSC9djz+lhYnc4EJwlxoJzBjGBWiulCFBpJTJ0wtPDgaGz7Hc77h8m1VRUWOYZi+EyZ/ajo+TE8TizO/R0frvltkazaLrdZn3KMSG20+SsKupblco6R6zA9fWOcdyzzjOnaWZKovtXqx7cjw/duqykdcEiHONKrA0IGAd+6KnFsMaMmEq/2/H63cSrz64JzjCEQK7Ceq6UdSI6S06eZy+vefX5c6aL4fH+AQkO/KeTevXT+Q9P3YRRgsMg5NqUp18r1EhqOtY1NSha9d8rHHWT7TcaOVdaUUhWa2WzVxlcA++UwDLu9ux2e6bpwnR63IRd6kN3omNhYyz9uCMXsMbzxWdf8Oxqx7u3bzmfZy5zYk2FECo+VFpx5JSINfH7r39Lt79hfxi4evqM/eENqWq09zytjOMVrz4L/G5ZmJcJDHhrqMZhbNDsDgqlCakKTizeBWpbtViJ4ExDjGpl5uOFd+8eKOV7anlACvo6UbeCJWoz3JCyqprftvetbJZIZZWYVrf1w8a4d/oXThkENWekqCg75kStCZGMMfp1VBDnsMbTJFPJlBaRuiJ42iYVFNFJjdkEFnrZ0JCkWjJFKoh+j9a6j+kHij7eJgJlox6aLQFPu3S1amps78ZHyVVTTUuhlgZlEzX+SBeEUvW1kKocCMUxs0Uz2z/Zc/Efn0+q4Nda9UM6ePb7YYu8rZymiAsO33nqkhHTGHeONCWWVFR80wpj6Oks1Ja3EY+OzOtamJ3hdgx0wbMLjuWbSonw2atryqIwhXkuWDdw1Qc8hUJGJLMfO/K6UivEVPBiSNs0QupKqp5UVnJTK2FrSo0qLZNKJC4FaRUrhatnB/ww8PqHe+bfP/D0xWc6qUiJaZo5T5GYC14ar56/YD2fNPO+CU+fvMSFno+3W+sDx+nMwTuO84LFYW3B+a3jEct+9NQtCtOazOPDmfuHE33fY7uBJzeF5jolgs0R6xq5NlpZ8SLUFNmNjoquBYwz9MGxVr30kBs1KUu8pMqu75hTph8N3naUarDeMc9JI3gtQCM4IceCdQ6RRC1R93SDp+aJF692GAmkmICK2MA6zyw5Yzz4zuGCJ64rNesaIeVMwWOtEGuhSsVaQUwF00i1MRwC3/3wgcNV5OZqx83uwP15JpfKZTkjznA+C4OrWN+RpaPVO+LUeDyf/4RPx0/nP32yduoUteOJp21aEYvFtUbdrHdUdB1YHSLKk9cntlErepmsUGsGMtIKTqxS5RrMy8zh+oYXLz/jbx8+IFIx1UAyeAMxLogzXI5Hnt7+Y/7yH/2MWlemU8d+7Pjw4QOn4xERq+ut1rAukErgcnpgOiXm9Jrx6grnDbfPntPEk5twWWfefHjH4fqGbrdnSSspzkTTuKya/+76DjtrA9EAHxwHOs7TRQVsxtA5C1IIfaTVM8eHOypvwFRM0T8n1WC3/XmxQTtXo5olayybPFJ35DpL3z4jrE5YrUGMxXqPcx2lNgqZ3DKrRBJZL/BilK9ft6/18Wdok2qqSLq2bAmMagVoKtZrtA2aVDUXwQib1B7TlNZnRbY4W9UjGP0C2/tmC0ve8u7Vybmx9prRyPVWiU0TEiXpBEF5BCoKFQFbK1XSptEwmyPAK9TNfDpl9tP5ToC+H/UhzZXaVioFg+GyrFzWRK2CE+H2cOD5reeb40QtSsCLaWVZF86TozRw1pC2XGTrLUjlNCWe3eypeSLmCjZSaESq0rGccNg/4fkTS0wrpZTNXtOxuwmsc6a1gusGqkSaOIyzHKeFkhKtwpoTyxxpTTg9LJScKXXlcO2Z3kX2+z372+ecT4llPvKb33zLZ8+vuD4YEMf98R2nh5XPv3jG9XVgmVeIlXG35/rqmlgWduOg/OraSBjmIuyrobSMOKteXSvU4hiHAx8e7nk4Jr59e2RZI75rHG6fUFNm7DqmUliLwbWJdJmx3R5XdM/27OmB6jrOp4htQtd1xBIoSyR1gdOqgj1aoXO6q7LG0u8HSIlWK8fjTF5mDtIR9tcbfQu87xTC0fXEZSbmyvVeL2WhWWIWpnymDyr6yzlT8qLBHM5gNpFNXFfqHLF1xRkQ54nV0EoDq+E9LnrmdaF3lnXNnB/e8sUvf8HjeWZazjjnVZxZKqfThaXNxOVLPjxG+j7QUsF/Ohf1n85/fLZdqWD+ANUp8cfpElU2a2nSsWvZlNtNYS3uY0tWREVgFc1SN4ZkLLlooXDes84X3r79gV/84s/o+pHL+ZGK4abvf6T4xZzoh44nz58QnMW5kf3hS0LfUVrmcD1iReE8YjzNdqyL55tv7jjmmcfHB75984Zh3DHsdvSHa/rdgdB7lrLilpX94Rpa4/HuPfM0U3LSkJ2+J3SJbAziLOKtsiiOgVgrOI/xBmsiyb7hUk8wPyKcCN4jGHKDUjZK4UfCnbgNM66Tv1LUfSMUaklQMs4aggvb3n6z49ktY6B+DAQyVBxVNGPAGYtxKsQTB6ZUattskpvC3iG4Cpp3aLbLXaVhMU0vCjQ2QXDDGjXNC4VKQpzu7U37aKpXcWGTP7x/hIqIeoWy6FTjD8Af89GPoC6BVreLx7bT2P531rgtlvljwI9RO+Encj6pgm+dhQxd6NjtO+Kj7nnKvHB/P5GWwuEw8uVnV6zLQiNjqPrBj+H944U//+oVt0/3XF3dMD2+wxmPtJWWEqc18+Sqp0TdnfXjQMuNcRgwdub6+opnT28Zh8Z0F9nvHMFBSivzanj1/AprjyzLRN9ZnIWutzTJhM5SUmNujbgVusvxTBDDZco8eXHN67cX4jKT28rusGcYLTkljCl89upzfnjzHskWHyy7fuAynTgeJ5Zp4qtfvOJ0PALg+h5TIcWJWuD+fOH57YgxiZr14bq9HVlmzbB+PJ+YY+T+4YHxsKPrBpa8MHYW8QFTDS1HSq3kCnVZQRZubnbM2WyXn0hrml/9/t170rpSnMc7T99X8pqwNOZ1wUpRy85uZN2wtc72uGAxUvA26GskjpgKOUXm6cTVGBl2B7q+Y42FZTlvwquyYZeFmJT/XUojb7ngCNrtHxtDb7hUvVAMweNqAKvTlr6HZg3joWdNOs0oNdKkEdNMlwZa70i14m3lPM1Y4ObmCW/uJrru0xHf/HT+w7Otaf8w3t0U9LSMVI1IbcVgSD8CVVrRrlQzGQxYRemK0bAd8UrlS3HZdvP5R9fMuq788MP3dF0g5xFEEyd77/6w3xWY5hkrt1zt91zSQhj2fP6zr8hppdVKCIFGoGBZ10pqjsc5UT488nj3gePDI924Z3+44unzz3j5+SudfIpn7Doexx0gXI53yt2g4M0WqmMK3nuwKr7rx5G6LgqxcRYxjdTuOKUHnK100kgxY2xR0VrTjrcotOBHRwNGx98fR/60RmnKJHDBY7uAbFY8570K9Zpmh6iXfxOytYp3FlstHoEWaK1QU9KC2tQmKdUgaMSxRuSCjmk2dLKp+nW3EBwxjVaFaoW8rXoMysLXxl4vFLUpZ0ERu1XXB5QtYElH9LXo92mqYJpHTIdI1nUuH1kPou8X43+0BFb0dQK78R4+jfNJFfzONWgL3vcM48Dbu/uNdBV5/+Ee6zz/4Jc/w5Yjv/vhTq/pm5qylcoaE7vdFf3Y8L5xFxfOy8QUE8MAea147ygJhv6K0BmmlGjzytU48OLZjtqOHB+h7zqGcAWSeXy843QGnKJhLZndMCDOY6vHVfXlWu+hCs56ut4Sxo7zhzPLZSWMPeJ0DOQBEcv++pr58YGYFTBlrCZsPXl2jd/gUu8/PLDrdGoxT0durp4iVVhmDaLJpfB4mmkt4WyPSZkCjN3AfDmS00LNjVhXxgC9HzidF+7uVr7657/k4RRpZiFYQ14tlcaug9F35GJ4OM7cPh2pteFs4niOvL57JNfKi6B8f2nCnA21BcWIdo6WM7Z65suZsRsJQ6DrRmJJ2ArewRIN05yYHj8QbOHq+RPWWLBSuZREzAbrHLWqLdM5T62VlOtmn1Hc7bom1pxINLx4Dl1PxVFyIVWhVA+msAs7jpeV/X7gMl04XlYVHhGoZYYCU6yUAt3VQM4NMXBeIjFGun740z4gP50/etTNq11ZbXXj6m8KegSD5txKZTPVo+S2pvjuYpTQLhis8XhjGfBk14hmoZVCTJFSNCHNWss8TYzDwDjuWJaV3FSM55zBd555nkg50e12LFltck9vXiJSKbVgTaM01QDlUokJitnxux8eKe212tbWC7lWai6827/j6vYpu8OO4D23t8+5ef6U3fUV9x/ecffmB9blQs6ZYRigzbr6sorJHQ97qrRNQKfEPUTV5a1WqoNqlZdvZfO7iyrZjYbZYTawjbIOlO1ft0Lrjce7gLNOBYPG4K3dMgk+omx0l/8xbc7Y/ke0TqsFWiYJyvtvBlMNthhM3TpmNdLpD9183M0rw4PalK+w7eW3pp9K0UufVMRYWtHOXqo6AqCRRVX80raERKk/vi60qjZovXao/Q8NO7ZGMMbSsLq+EH7UPYBRPHD93/pp+OPnkyr4V1d7jE1kKlIah7HDe8PDeeLuvuPLL7/gZy/2/PY33xFT0nGLmB+FG84a+nHP9aFwni5Ml4m0RnoD3hVSbfTjwP10T78fMbJyfHcPFL747AVXoydfzgqmyZ40JfrDNddX13Rdofeek0SePdvT1ok8Jy7LhRfOkteJWoXLfKFIoVUDNRO8YV1Wbp7e0nlht3PE8xHrPE+fPOH6Fy85Pj7yq9++59mTQL/zPLndq8p9gafXB54+vWItkdsn1xjbgQjj0NGWBef1tr2kQklF0wAXNKLSeqw4atVt227ouX+88HiZgJV392c6P+Kaw+TEODjW6Hny9Ib5fGY5z6TUqGVHyYm+u+Hr7+54OM/YAH/WW06nyGevrvn6m8S0TAzdFeuaoEYiFtMMa46Y4jT0xjTOrDwZd8gy0cqF65ue4FSMaI2QYiEnw7JGBhvAGMWdGo347Zyl95pH3USY1kaulRAG4jpzGBvjfsd378/kubJMC85VWsukOTIMhmly3J/P7IMGDFEbl8uZzm7MAPYsa2Zdg2JPXWbwuz/tA/LT+aNHxCDiaDhabSr+pejnQ1MLLx+LPlvePGhRaAql0V1uxQJYYc5q/Rx3V+RSSI8Pf4CwbJ8565a9UXJSsVa1eB8gC2laWU8T83lWm97Q47qgWR3W4r0hlUQIA4Il5kKh58svv+D7b9/x9vV7SirU2liWhXffvya4gRefv+Lq6S37Vrm+ueXq6pqHZ885HK54vLvj4e4DaV3oRxWw1dpozTD0vba6tRKC32iBmhZqxGxF2qtXvm6Z9s1QWuVjcyzCtibZgAZVXzcxBu8DXRh1pG10f2980EsDiv21rSnmVxzOW4yxm8UPalEtlZWAqwZTB0wOODwO/X1WjF7SKhjZkvE2Tb1eJFBXTVNEbmsqJqw100TTDbHbhKKBUeUdRRJNtrwMKeTNZqd/xEquWZM+xeCsWvgKbbtcKABILX9szAL0AirbRuATOZ9UwcdAjJnTecFv6u667WPiWtkdrkAa3kJwjaVASolawZuAFY83AWrEWcu0JIaxZxBHk8phdBohmQ3YoqS5smpnbIS0JkpMHHaBOE9cpoVuTdweem5uR2op9EF9tt1+R18vzMtCKqpAH1wjrpF1LeRcGLpAnKyOF/OiYA4s1lRuho7eduy7a9yTwP/wP/4rrHnB9dVOCWHANBd+9sUzPhwXzkvl9gruHt8x7m7ABuZ5xjrNfl+i8PL5yOu7yPl0JqfK4VqJXa4TnIGSGkssiBWMBI4zyHTi+fVIiZnOVPajYZ4n1qyeUxFHjAu1Vo6XRFwvpNr42auX7MLAqZ6xYrBe6Irncj7RUtkY4St5czLsqictlSIJ3wulDlBX+hBwCCUKj8cHXr3ac3xIpJxxvYDN5NYwxhJrIZZMNY0weOV6i0UfeU9umVQb7+9nrq8s42A4HWFdTvTjXlHITgWUT5494e7hkRe/vOU4XaCph9oHq+6CPhCGnvvjRGkLV7sdJX86AI2fzn981DvNlkSn3b4FjAqshA2ss3m1TUPKx73tJgCr2vlVY0G2XXat0DKlfCwu/CgU07VBJgRNzwPBWEsVQ0aIrfLm/Qc+3J958vQJ3vVK67MWMRrH2sTgXEetUGrFdyO3T2558uSaEByhCyDqNhHvePv2DfMy8ez0AnIlWMv11Q0vXr7i+uqKd2/e0O/2PN7fsU5nptOJtWRdHzhDFwJGHF3QdUeJehHSZLgt8W1zMGgSvTIGWv24ztpG3bVo94u+Ptag7iNrCeYjBdSrqt+oX92KoUnBUuhMhzEaEGRlI9I1jcetzuj4PAWMDDjp8DbgWsDjqMnQzNbpty2R70ckwNbZt0L5uF/fRu/SGqYpo0HQtU5VbyWNvLk1Ks1k/dpVhYB/mBg5XRegIUxGof/a3LFheD+inDdhteoLfxrp/72nVVjmhHOBhYU1Nh09+UIpiffvzvzy84H9eGDvT1zmgvU6rrKm4KRh2kROOtrzwVJiYJr1BgqZmKJmXK9Z0+ZEfxgPj0dsq1AiQxAShdAF5jUz7CqP55mx99QKl3XmcXYMXc9g9QecqmHEUmqhlkw/jqRlptRKE0PoleSUYuH6aWDNheP5jAsd1hUOh4HffvOG/91/8RWlFB6OmfN8RswV61rp+4H9YU9aEqVkpX+J4bIUlthw4lhzxNvGsqysS+J2UJBH6DxPdzt+/8M7rDUMoaNJY0mFz5+OfPZyz+s3mZt9j/OWuFaWOdNq1oI/X+hsYE4r4iyD7bg+XBNzJaWVzjduxsA39xecqA0px4q0TG7C4BylwLKuuM5jstGsbSPk1pQy2AzTWkjFsuZMaU1V/aZRa8SbwNqK7usaOOc1L7w1StGs+9IWjBWM63iMlZvRcWeF82lVxX0TbvcdKQqLsfz62zM3+3eUGgij4JrGnZpmsD6ocrgIlUQpQsvrn/T5+On88dNaxZgK6Ai51o8fwGwXABVfbYGnbCaebd+ukBa2z+q2Udqc1c+RmIvuoI3Z8t4/Qn4apVakZILzqEOtMZeVEDrWnHnz7p43b99xfXPD3f0JoXLYjyzSyH3AWENOhZIbj+eJywohBIaxxwZHN6h/PYwj10+fk2Pm/uGBuw/vefPmB6Zl4R/+1T/mcHXF1fUNzuklYbfb8+H1D1iEfDqx5EgugNFO30rF1KI+8yrbHUnZBHYr/h85A5WsWpkN8F+a4mqlbQWfDWdrvQrjgsF3bosRdpoxwPbDqFWLtdNdd2NLokPtfNZoQI7UTkf2rsPnni6PCD1SnNritnWefpcfRXF1u/R9HONb9ehL3iJ8LW0r9NKaWi4BWtkIjeoIakXlgJVCaX/QCYk12OooAqEqyjmhryEAH1P6Pr7lqtoHLT8V/L/3CJUYI6VVliwY73TsVCsxJe7efyClL/HjXqFMRdOlnFR8MIy95j/n4liLPpg1FeaYuRoCDqcf/jUzHWesdVibKWXhdMp88fSGJZ55e7fy1RdPWWpjvV8QU4lrZDrPlCXR5MIPP5w4PLnmH/6DrxBruJwyaYnUFig10yTg+xHMI8aNHK6vubruOR0j1zcvef3uTLpMPLqOmyfX9P0Nz57cUqow9D3ff/89VSypGWznFTphAj6oCGboR87HI6lE1gYP04XeCrt+4Pw4EXMEOQC6h3bDjigfECn03YCIY/Cerz5/xmefPSGllcPQkY4N7yzvHheMS/RBUwCDdcznTM6FNc6kZWXOgu8scc18+/2J777/wBevrki1knNVVOaupw8drkIthVo39WsuiO1JbaVUhSWtsXJKFRMMNVlSqvSDpWJwxjI2R3A9lYsKPLcdYspV9bM5YsyOlhPRVqYJeqlkUxl6i/M9O2f4zdcPdMNC3+948/qBL798gQs3pMuj5gesGWP2xJgoErm5OrC7umK+/BSe86keRZ9qyyZbR9+M9qimqpdeRMfMGswi2gS0bf+PwlsEcFhsbdhWKKXRYt5S2f592AuKaBVLLZUmRUNbPsJoSiJFy6/+9jc4A/d3H7i5ObDbjRz2e/phoFHouo7LZSKVgnU97z48UKpQy8o4dJRSMcYjpdK7juHqCb3r+PD97/n2N3/L6x9+z937N/zzf/5f8erVZ/Rdx6uXnzH2I9bA7+NMuXuP1IYb+q1bVWudsQ5jsj5/xmigzfY6NayS+Wqh5EopGSmqnSmbYM6iwj1jDCF0OG8Rb5DQ4bqBEAaCBGzT0X1tTWN7G7SA3hVKpUmjbW4o5zuc62glINkiOeDzgE09ZK/iSbMR9pr+zNW6p/WjYkD0glaxpKLvB1cqYhImOKw16uXfID2QN8+/6hGqKIa4biIAIzqJoDbatjZoRidCgmBFrxrbPF/fkBtPRkcPn85M/5Mq+LYD5wWpqxb5ZcZIo5VEKpaSZ7777jXPbw14kFCoGTABnMWFgZi30JnBkEsiV/VENmsoddXoylLAZFLNmz1FM4trazx7NpLMgXHnMcsZudYdfFkjbz/MXB8cgwVLIseZJWaKGMY+UOMZ5wxONKxmXRO0Ss6JacpYN2AlkqpwOs/shkpbT1xOjq4LfPmzL9n1C8fHBw57IZUO7x21OXKbyWmh85Z1E+utKTOEjs73jIen7OxCnArv7+559WSkloyVRmc8qSSGcUdeH7jdC8taCDZzc3NNszc8e3JNXCfG3Z73b+80lCMETN8To1BaIaOip5w390QRehe4e1x5dzxzuOk0mrJk1lxZ8sIVPaVkvIPUKlIzVTplabeGNQUTBh2vpoy3hlgzg9eLUy0N5w21QEOTDaFQKptQpyqJTzxiAik2JCfs0HGOCuAYd57rMWCC583bE5f1gvU9IcDlCM5ZqJZsglLZbMRtD2voeowXus6y5PFP/Yj8dP7IkS0hr9E25bhy33UM67e/3xLYtmS1j0VcQ3S0YxUaVjSzPifd46a0/ge6K9mU5nzEutZGyoW+c5tHXTMwOus5nk78m1/9huPpyM8+e87t7YGrm2u6vlNBX9czz0qHHMc9y3SG1rjtMz97NvIdjVQdtRXSPHN9c8Pt86cgGV433r35nv/3//P/zoc37/jf/x/+j3zx5Rd45+mGjtD3KiRcJoJ1XN3cElMkx1XtysZuWHHVMbZNgGbE0oqoOr8VhRe1QqoJKeilCa3XYgzOd4R+pPMB7xzBOjrbEcTg0csF2/Wg1aZAsFa2Ubwa54x1+nVCjxUPxWOyx+Qel3rlKmCoMeO2WFodn1flrmyCQMjbqqWCxE2Ua8hNsFYwyWFxeDzOGrIoYLnpHENZA2gB//iv+pE1QGUzHKi1U9TqibRtpaATC9ka/ma3DVPjkzmfVMHvfcA78FaY5zOgoBeorGui1sL944XPXj7nZj/w+DDRTMOZHm+glcTlfGRJHX1nuTvO1KhpdpdFGMbA4dBzuT8x9h3T1PC+5/rG8ObtI7tDz5PDnrvHmXnd0Q87aplZl4XjeSGTcc4xr/qsW1PwnZBTpeaKJXO967keLW/fnRCrgrlUEm/fvmedE9fXI5fLCZoQvCOmC1wcu/2BLvS8/PwVc/0t6d09z28Gai7kuDDPC7HAuAuYJMqbtqr2P+x7jA+8efuO07nS1syr25EPl2kbR1aswGHfMd9V4nlRxnhZsNYTW+bp05d88/tvccaw5kLfe3xzrM2Ta9Lup1RSLJSYmc8naj+QY2Hod/RD4Gq/oxPHdDozjj3Lw6qXn9LwnSAtY2yna4JUfrw9T3EhViXZOWNZyAzdyJoyNVu8Dcyx0kwjrREnGmNpxaowt0EuKtBJNRKcwXvP0mBpnk4KNSlZbJ5nutBYs6HrLHd55e7xxPXhhoYjUzDGsxs2lbGD29sbdsMN03L/J35Cfjp/7IjIJuRSoRRiMWhstcFtABSjtMRitnhc1V1L1vGuNdqde9PhTKfOFGcZuo5zXPT3bpf5j/7+j10+rVBq2nQ6mls/hI6SFubThbfBQWtcppnbOWGcpes8yFGLXiqs04U/++wpnTc8uMzQnuCd4fXDwhwNcTkR4y3j/orD9Q3GwrpMfPu7r/mf/4f/jvPxgf/m//R/5vmL5zQayzITY9SIX+sZDleEXChxJUVNoOwkEDotkLUUxDpa3cR0VvClUJojR710K+Bu63wRgrF0PmC9Q7bwIB/8tr83GGvxxvGRO1+r22xubMwC/Qywncf5QdP0msNkT5OAocPgt7z7ijiQ3H5MQmwfaXgtU0V9982gfBbRi4CUbUEhgi2WrgWM7WjGqrhToJB1YwG0WmhSlID/UX/X/lD86xamo7+8Cce3Xy+yTYD+FA/B/x/nkyr402ViXSIxNdZ5pQsqzGrFYI0So9YUKa3n9nbg29cWm3XXzBY528wNw2Ekx3uaEcLOk2dNiNqNB3YhcC6V6bKQa8UEsJ3FhkBp+gYdBkOMZx6PjeA8Q2/xZqQ+zFgJTPGIsR19v6cL+w3qs2BtZT4/sPOWwQmzEQ67nppm5nPi4eHC+PkT3n+YKFJxXWM5F0pcmObAtFyI04HOjQz7A09fPN92gHc8u+rZ7QZKEyyNZPQhjgl8N+AMxGll+vBAyws1rdrTeIf3BueE/d6xHBxpXnjyxPPVL77gzXHhH9w8xSHEZtm5xON0wvuB2rSTOfQwLdoJnS6R4AWSsJaZ5hvruqlj00qwwpIzh2HAGU8wgqBxknZrjDrviSmyFoMhkGpSr711SCkM/QBYJOn1eIkrawlAosSq2FRvdXLT+NG+1AclLq4p0rGnD57ONC7HzLv7iSc3hmAbOOX45wpSCu/fHvXnJIKplnkp3B56cinYlunDyJJf8nD/4U/8hPx0/tgpHz9kN8W55qZr1KoVixGn9LaiFeYj8wMxP6bkGRGoDmt6HaOLmqus8dQyY4ymwIkon7+JMkD0w113x7GsVDHMpyM5Z3XqSKOtifsPR42GlbMKRK2l1oKxhttdUAFrnQgmMPaOeej47OkeEeH7h8wlJT68e0PoB7rdjlIrL3/+FbGsSMv87tf/hv/r/+WRf/Zf/tc8ffac4+Md715/R84RHwaGYUeMK4fnz3j48JbpdGYYe4wxpHUiZtXoWDzedPhWKUawa8Y4YXGWmssmsmtITYTgcJ3HBg3Icb7Dh4HgAsF1dKbDNbf573X8b1ygbV24WHAhELoOFzo62+Oah2hBHB+5CKlo322kkiWrlkBU69Oq+ud1BF/A6Ni/No04rpszgGqwLYBrNCvYJrhqsdIU+0v5Q1ffUBFfg+3ftmL/8Xfw44RItrZftlVP2iJ9pW2hPp/Q+aQKfpNCjJXHKeE6Q46Gw1XghzcXQhjwQYVvznX0+54+eKaL4h79ZvHwYcC6wOPZcrULnJcINEwB6xpCptbEaVoQA+ID0ywcxpGx86xrBARvHUlOWNvh/I5d75nWmWlSBXsXvFKyvFo+amtYG7ikwt3pwjRPmDBw6A1TqoyhUmoiVshi8L7nanfF+fyeUiN5eiBeBo6nHcFYnj15SaVnmt/z/OUtXzy9wvmO8+PC4dCR0wK1kIsuIedlpRkLZuXJlSP4Sp0MJTW867i+3nNaCz//M+H2EPTBwPDb373lMD5j6BpJAv144Pjwd+x3BRMMuS7sOsslR04XJQ2K7ZhT1BQycZSW6Jxj8CA5k1qh7yBYffPXaje0GQTvKTRKa1gUmLEsiWHvkaT64PPSKGVm12u85rJEjMkqqZVKShnv1eYj0raxosE6DcbIsVHaSnDardytIINhzYlD3/GYKqVVpvNCqRMf7it/8ReCt8JpiYhRFOi0aMrit7//ng+n71nmn9C6n+6R7UNYMOhIXqEn5kdvtsaZ2k2wZ7S1RMeyH73TCotV6uXHrs577f5Rm/dm8VN/f6tlS04TRcm2ilgDWfnqQYRd32EtiFRyXHm8yxhrGYNj7B2hc+wxDFU43z9yejTMsfB4qZwuM61WOtuYl0JdV+7e/MAXX/4MGQZq2vPnf/FX0DL/+vgv+PZ3v+JyvudnP/8l0Hj3+jtqSTx9ZjC5MfQjfb+n5jd4Gxh2e2rNXOYzqTZaAbHaeYs1OB+oxhJMxQYPWbn5rWY8Hc4HjPNYt4n0+g7vPdZqfj1it9dUi7GGZVmqaHm13tL1vdJBXSCYDlsc4uyGKLeUBq1laknUHGlVQ2xizRv+eCvYRnfsbC6MJoVWCo1MokDT+HXTHNIitap4uDn11vPx0rh59NlS7lpTLA9bOiuowLFtq6CGRu627YIYNjteRdMZzY+iwj/9+aQKvoagJGqpaFCCIfQOYx3DsKPvR/pxpFTYuwO9c7SmnV5thq7rGTrPtCwsaWFDqFNyJISA7wa60dMNgZTP+BDoe89yKfQ9WMmcpsRhN5JjIi7CmhZqE4axstt5bJ441UJuTsdQtsMZQ+j0pr9MQsQxi+OAJdbM8TTzi89fYJyqXvvBM4x7whjovHabjcgaJ44PHxjHkbJUwr7S7wcGeg7Xe1pNIBnj9pAirYrS5oxhzprONF4duN57dvs9+5wgRTrXyDhM6Ll6eo0Ry/n+Hqlw9/CBf/nXf8PN4ZbnL654jA3fe87HI+5qz25wdCGwrCeWtNL3A6kpPW+aEywrlJVxv+d8jtweAjkZbIXdLpBKRXwgR5iWQj8kCj2u60gx6hitGUpsm2AGSk3kMhPLiA9w2PXEkjlfCjknkEYIFreF6Fgx7IZB/fO+aFqiCLTKu4cTpa6I61m3kJx5tfzw9pFcEy1XPrx7pOXMuL/mshY6ozbAUhOmwZv3dyzrwtD/tMP/tM9HDb7ujLdPY1Xn163wIzqXtvrRrZIq0RFzaRvGVUf9yIbebYI3lpjSho/ZVgibcKwZ7fBKyTTTqMUQ+pEquhLY9z0Ygxt6Oqt0zZoSUTy9h3UpfLuuBGspLWuaZIFmdjxcFmKGtQg0r0FAlzN3b17z9OVL5HCgtpF5OfPi5RdYMVyO9/zqr/8VPgRaLjhTuTjPPF/4xZd/SYyRy2XiMO7p+h3zfCIX5deLMYqml8pHTLGC5HQiIVaRM4Jh3/cEE5RhJIJYTwgDxrpN36g7cTYOgjFaFKsC/XHWEnwgdL1G6doOS8CKAzGqkM+FVDKpLEQSsa2sZSGXTCqZXDPNyo+NuBgBY6lGaGixV0jXR6qiOuYbebPt6euq06CPGGGQplOEzBaP2+ofQnZoG1jn425f/r1/wtbVy6bO/xgj/GmcT6rgT5NGnqZcOc2FJpXHS+Xqqielxrwm/Jp4fHxk2BfWtFLylvDWDJ3vaMWxpCNLXPXhzwWxhlQbpajyX8RCLpiggq/Hy8zLF5+xzJmUIsnv+HA6E0xhnhtiDXWpDL7j6jZwKR9Ia6HkgneB1nSfPKWF47poAEN1FGt5mBN355n7U8H6wLDrCJ2nMzusOK6vD8xzZC16a80p8v7NkcN+xHc3XF8dSDGz33f0feCyqGJ2WSMxL4TOUGPD2YCMhtI6xmHPulacyYwD3OEpfWDX9Vx3Hd/9/i0pV/YuEKeZo7zlNJ35+S/+K77+9luS9eAWrCtUPJfzSsqFmycHnt++5NXLpxyGG/7N3/0NIWRa6vCDZy0Xnr3YcY4r0gqdt8xTgVY47ANnK4CHYmkeYjLkXMmtYrOyC87TGe881gxqe1kq4hq263F2JgTHmiZiyttGDZDNS208nYNSI2VZuaTKuq5KOBRIeJZSePvukdPxnmVpXN14/uwvXvF4nvjFs1ec14wt6wbsgJgya4zsRuHJbfiTPh8/nf/Eka2QNy0osu2YRT56y/VsH/GAbKp6had87PDa/7e989pxJE3T8/PbiCCZZJpy3T3TmB2HEVYaHen+b0IQpDWzO6a3fBqSYX6vgy+yegRoDoUpqPkWCihkFVCZIBmfe03NUhRUL9urmlBK47wj5kwtFbSY0sgGQIqZWb9Gq7RS2VxtoBScA2c7cqs4LdyTmgutNmItnMeJWivjGIhJzMFQSlIm9SNLatI8NIXzG2yRUnIez5S38Prbn1FUxTjNzc0N1ljmeeL0+EDNCes6tBap6rQslFrph0HinodObvZtLfS5oowBo3CdR5VG1ZpcJXGuiW0hqj6fOqW4q7WAK9NJMt0agtNYM+ibkildGzBGZHEKtLXodUMghDdFM4rapFhXXUgkYg3MdWQpC6EshDIRUyDnKAW5GVFcKYttHkUn2v/WhCOwFvxGA9Ootq5yvbK6tGY0GaudhJ8pRdaNUtqP3Mx1+/MczKOUTPzKPJs6tS+bHnnfGWk0Lyz9vw3jenKpPDyc6TY9LS8c9ltajnz4sBDjSKte8omtxvoOTCHXJs5QBsY40TnPNC2UpsnK4gdDCpDSTE5bamv4VZ4xTaPc+Kzl/jgxbBz3T088zYHv3tzSaUsoCdVkfa2c5s3rG969u2fTOYbdFedxIqVIDIFxSsyxkVRj32m2/YZcP/M//vk9v/rNa+5urxhHMZYpsWG7rcRRBkurmq2DOs9sd3d8fjjRWyfdpR0o2vyVFrhK924dpETXW6bR0Rmx4TzPE7c3Fm8VY6zc+g5i5fHpAVMjOMfD45nTcWS33xBD4oe377h/OpGK43r/mmwrKUwMTvMPP3vD7YvvMcbz/etX5Nrx8u7MHB549zjyZr+hKSHXeKUptXF3ONBdGT5++CRmFdqJkQWR1npayahW0aaAUZKJUBTeKarKKPWcNy3mKVVlsJrBb/gcHsRApYk9qdUKg0elQs4QYpaH+GqqUqqQP3NVzFOE3LjZKn723YFf/Oa3fPg8kfPK9NVigmJM5ekk0qlXrzb0w8Vp72uFFHK1pp219YEs3h5tXdX+tUueWUNyeGZ3P6+BdZUwHeUwKJkEs9xmtREjHtVYk/fW2W21li2r85tTcNj20BRlOQnvaA2OqUaJDXTOGGOJtaxJjZnTEiR+FY11Bm80pVlCnsT9rmjUoDAtkWNgQfHxwwduX71gt9+zv7kl1YbpPFdXV8R5YplnrPN8+/NfcD4+8Zc//BO/+8f/ys1+L2Y3uZCyGN6UmkHLKr7zw49EtxgxUW7aWbU1VEisZK1VuGeXOe+km5JaKAVSrWt2JD0PbWhqVc4bjbFGGhIlrPpSJTKtAbktzGkhxIk5jMxpJuaFOU/EvNBKWh0WHcr0aDrQhrXWysSvmtzxWU8VplKNvIbi3SCMf10rhSK251ZTdSOvcbeqth8JhqzHI8WX2z3ru6gpaXO+nO2N4jnV72vBV1Xwa46kLJnUVheMdWy84/j5XuQSpWKtZbPZomzk+nbHf3xcsJ0ioSmIKYuzljg3XOfQrtJZQ44LrUq2sbOOzntCLZj1RhRS5Hga2V2/5uH0mW/f3LG52lJKZVCyzqNYcq503YGXrzX9MKCMolRJZCtjoaRGTordYYOxnch8gPOY+fl3v2CzVZxP/4G2jf6qIz6csdoSTcN1PW41qjA0jo/3zMrhd3uW4tBLwjhLc4ZSVvtcb9ARlHYka/nm53c8fH7g6sWBly92NG2oOWLjxPx4RhtZP4U5czwFUiqUEHl4OJKna05PJx4/nagbz8sXPZut5/e/+yWH4Q1/eR9ROrPfvuLdccJaT5sUSznh3R1hjQy2zvPq5Ut2mwGfet5/fGCcCro0Yow072gtoJWwgquBECrOyl0+pIQyZn1AixxwYy1aGeISKKESVsmUWpnVOUeMchJb3IoQdHSTRgE5fZgKS5qhh3ks/Jdfv+Tl7QvG44xzWx4fF/reY4plmRvLkmlK8+rVjtvDgTl8RfqaC/4PiPf7s5WOPOifPShYHfSkg1zXvopViyYPfqUaurQv8juHE9OUpkgl0JSs9QsFlBIp5zo51i9Z6AZMo1A4nh+46q+IIXLOAIUYZryzoA1LzChTmVOiNJHJhlDEqKspTCpka2iqYKtoyGtpQowzhpzOlFJBW8q7xOtXL7i5viaEQEqR3jqWaWKZJ4bNlrsXL7h/+2fe/fs9V/s9nfccnx6Zx4l5nsm1EHLGZ+RWbz3d0EvITi3ksFBrJswjrVS8ddjO0XvNoBVzkRhsqyw1QypCuWmUVckga3qUOOtpLa6ETob+VWVRKevWtubCMp6ZzyNhnAnzRAgLIc+ENpNVoJGxyqJUL4kqDUBcU5vWNIRzVFY5kNKrJZ8Sr3z1bKUL5NVtrwG6WAqiumglC9m4qlVSr79YBaOe/88m77e2ZhAAGWl0nq2evhZ8VQVfW0XMEq3acqVoy3ajV5MHRd913Fxtub3uiePC0ynTKLjOSW9fFJ3rqLWypMhmf02OmTkUqrI43zHPo/gq64karRgwKIXzHYc7xdV1zzJ3bLc7DrueTw9HXOfYGM3DcYZcefv+nsNuJ/a+IWCd5fHxzPE0cprPYAeM9yx55Y6ub/zTEvHDFmrj5c2OnCvTdMTbDucGjNP0W2htS55HWp4ozVCi4/Fsubk+0MoINaARFzvnOoyJNK043NxQ+2uKP/HmzTVWN5bUSKeIWfO9G5Z5bozLwqf7R1KIjNPItETOjydOn94T5pGnbPnm2+/5/hev+O2vfk86Zv745z+hneGPbx/4+PSROZ5Z4oymMi5QQmHoPdsrzfXta8bzE3N4oqFJWRzzSnGUEHGdomnDeazk3LBO4Xv5PKZUyaHRD4beeXHPpRDXMButxSvdrgxp7yzeNHIJNJVJJdFIOHOFtgmDBqsZp5newfXhhrgY5ug4HUfOacTubmhVM3hLIxOCkCJtB9uN43yeJRzogq8Szw9jhZKVMcjDvGVpDJtdi3uj5rpS/FZZ3lr8xW+tkg00Lb784gtfiE204trZNVBGTHvMqi8vtaz+8lIMTqcz83HCGM2sKlYZjFKEUmi6ElOhtEjMGbQl5kYukvLXaqXpKlsmrVeve0VJFWPcOplaal1oTZND4FMr7K8P3N29kqY4BXLM3NxuePPtd8QYuLnqeDw+8of/9d/Z7PfEEIghE0JgnmaR1HU9nd6J9MxY7DDgjEPljFaFNI/QMsZahs7RqUQPxKmCUVirwVtKWP3t2zruU9DNrO2YTPpS5AupFcDQKuSUSMtCnEeW85kwT6RlJuWFUhOFSGMBleR3c+imUNWidAeqUk2jGmn0W1lllIhtrhDri/i7tCJeAzxr6w2pRpH2qUwraVUDyCRvtKwvmtLSyKxnofVy/6NUr8kGQ95hzyeBrwNfVcGPy0QuEd875iXQ+4FhK9r8EgqtKnbDAHni8emeaSx4K2xcrREiiLcsYSJXg7IbUjyL37UxFGCcJ+Yp8/Jux+f7zLRkur4jxkTnHao23ry+k7tbtWy2G1TJhDnijWNJIzHPTDN0gyWEAEWTaySXivMelMVZ0Zs6ZXHOkBaYUqJ8euL6Zst+v+OHP/2A1Y7DoactlpubA67zlAzn0wmjZIW4s4YcZ8bRsOu2tDJSS11vXUrIJ8bQuR3LErg+7MF4ch5pxrKsulVvPQ+nIzk2jsfItCRCgYf7hbb6zL++cQzbPVo5DocDv//df2NnrpnMI9TIOBrm5T0Px0fQJ2pplNjIYSTHEWVecfPq51wdXjKFSpvvpeuvM4UiEqdY0bYRiiGmEWdkY1GLBuPBzhjVJGnLyj1siZHmNdpZehSd8+LTrcD7ns57TCnMsRe5zmrIk5rCa0daopCHKpATw9byz//2mf/0q1usd4TpiO82lFy4Pmzor7a0cSSXwNZ67uf5i6HGBV8f5OFa+XG5v5rpqNVGpa1rfgVUcX1r1DW7XGhXGIVuja41nJLmobaGodFyoVmLtZaUMzlnce8zCm2dEGhLQtfVstVJmE/I4gSpVRXr7yTTZ8ki85JVf1vNb0TbL0aURTzs9Ur9MoZcAyFMOKcIMdB1EghF6Yldx8Pjmd1ux3a3JUVDzAlvO3zXM58eudrueDqfiUtgiR8w2kiTUUTaqo1Yg4cQ1r8rWC9BOr0R7//ee3pv8N7iTMOkM2UJmCVhtJDwSnXYDDEncq5/NQ2LMkjp1eAmV0qpNMT3vqwbwDSPxGmSepAXco3UltaCXTDrM6G1imkN3TK6ZZQSr46msnACVktcraBpaf5qTTQrZa+pZy99eDbWaSRai9RaRAGwOulopWlN0VRdz4zPXBC5869dozAX1jjh1irlmVD4leCrKvi1dKRc6f0gpgo0UoJaZf1iNGwGQ62BHBKtyYuvW8Nai/GO7XZLihNLyrI2I4Nq5JLpdMN0G6ZT4HBzw9sPf+A0V7ZXlo233NzuuTsMErWbG4/HR0rOXG9la+CcYw7QdxblFEGCl5hDZIqB45QBjfcacJgW2WwcnXecTCOHijGJq9sDnXFsdjt6p/G2MMWMrZpSgZZZlhmUZ3+1oesMNVXCHKlRcbXzQIfXPXPJNBTDsIEWeXPVkR5mXEocj4XPYyDVxpwqxilKSJRWOZ6ONFXwXlM0HK4Gbm57tl1h/iFQErTY8e6Pj+iDZrPz7HZb7t8/8vH+kdN8z6+/v+NhXNhuevaHK06na1rR7K8PvHz1Decp8P7jR3KJEvfbdSjjaKoSlsiUrNzxS2VZMvPTicPNQMrywZpIOCvBP6qKF/puY7GLoWV5ANRWMQ52hx3jcYIqE5OyHmXlIYIt5CXR9Y6aKjlLyuAUFT+8f+L1qz1usFgNLRfe3N5wuLthnDOdVvhuQD1VxnH5+35ALvibUGuyjfryZ3kcyx0ZjG5Qf0xA08iEXtcwFftFay06bmOgaiVSNYQLUHLBWAmGSSVTNbTSxKpVC/9E7F4l/lYracJRiTmcCbWs8kApcEqvc2Atq6fEc2lY41uRZ1+pMqVqI01FyomUkjjiGQDNEka2nSGEM6p1bHc7KUy18XD/jkHHdZIG5zXTEplToOs6WpNsCtkW2rW5KORxwkVDUIrsO5y1DIOncxu6bU+vMy5lnlYzLG+Ead+ayBiNKqQsDPem2vozKqoqq4WvTP+1FnKJxBxJMRGXmRQDpUbZZighxyllaFpSM9t6rispklVG2YayjaYSpizrKr3RWJ30VJHJfSVZVrMG3vB8f0e+FyWx27WKh75aG7/2xdlPfUnoo7G68K3yzi8c/grPQU6tfU2cva+r4J/TzIdPJ1ICcFivUQaWKLf73nkqhXmZeTqdqFXWKrUoaoPtZmDrBx51JaXKw+ORrjPkkJhC5RAVQ2foNz2+98whUqqEKlzfbLEqE8awhjloQojsD1dgLdYtzPOEKQ2nPC1V5vPINAdyglot1npAs9l0NF3Y7Duc6tlsDOUhs0yRN98dGPqOw37HcRp5ceW5/3yPa5F0fMe/v2+8voWN12Tt8d0gVp26YM1CU5Wc10zpmvC24SxcX/W8ur6mb4q/PH1mCfD0cOT95xOxKo5jZG/X8Bk0m43n6XwiVbjqOu5evuTm7o6nWvFm4uG08K//+pa93+CVZhM6Sqq8ffueT0+f+fa7b6h2y2Zb+f7lt5wmcRm7Gnq+e/mKTXeNMYaUEjlNIqOzW8k4sDD4jimO6ySj2Gw2hHOQ/AQ057DglCVnjXayQjUtYiscxxltNdpaIc7UhsLifI+2ibZ4lqWwNWCVwwKhGXxvwYA5ZWpS8uDLhdIU+162OTEtvHxzTed22GGgFs05aM5LFtLgBV815LndVj0+smY1snKlrVO7ktW5biszX0FiLda6kE1BWY3TsmZGWaxb0/gQ8pfkpksctUGKENaS1zWwasIVut2/oTXDp/N7nsbPxBTX2z/PhvAS4LM2K89nB6WfSYbrtK+U+MSnhJ4XlFLknOTrsaAnyeDQ1hOXSMkn+t7je8uHtx/xG8uSFpy2pFLW01nAGLEg7rZbMefpBjpj+TLClkxTimWpTLWRYg8oBm85DJreeY44rAHnOpR2tFrRGlEuKMilUlRDtfSFKFebRmlpvVoV+/GSEyUGKFlW7mhQBq0r+llp2TQ1Q1WFUiypJqoG4xXGgjIFdJRmR2tqyyhdxAdkfT+UVkE5IQ42WcWLx5im8hwOtLoH1CLkTa1JaPlV62qb32jKIIHK0hx8ceRTUvQ1FXXR4f/foZr45+daUXrgsPEcn07k0lbrRsh5IaYg+dMpEWLG+hOUHm8VrYxi5WhhSZlhcPit3Klik+YgF8UcAlppnLOSCBULTQWmlAhJyDAhRNhrma5D5Hg8o9DMS6LpTKc19/ePXO0OlJLItaE1WCVMX1Ua1gqpzKMYw0hsV0y1YkzjsN9SUkYrwzffvuD9x3tSmrDVUG3PmBVdreiWUGis6WlGUchoI/nP236gpMLdds/eNT6+f+J//tsTV7uep+OZzlY+PAjhUemM1j01Bpz3eO9Y5oTSmlgLHx4i73+YRXrUCqenD5zn1zyMIx8/f2LYKMJ8wvYWt3vB4+Mn/vG337G/uuKf/vAHXr3a46znX/7lj3z7prAdNkxToLOOoXdULTdy1w/QMvvWkZvmFAve9PSdk41OsVAd3vUUA713DMpzShXVLCFkUhT5E02RC5ynSIiyxeg3FlQPpaC1kKBSyWg7YP2GyidCqhyuO67v9vRXHS9eHliCoe86WvMoLBs/kJojVznf0Ia/90fkgr8Fpb5MY4BEtzZ55D6b74BGa3HPUWVtAtY5UDqFStXr5tAovPJk2Qdjldya6zp6m2cW+DrpaTTOdkQlWRNUsNpwfXVLZ3cM/cDGDzxOn5mWM3nl94jZi1r5g+p5g4x6PgAjwS3PkbwpJSjQ9R2lSLqb1oqoFDEsWGtX4lkkLhO7/RV9NxDzTFoyMWSKrA6gZpYQcK5nYyzX+2u22x3OuS+24C1n4Q8UIcClFFgmyzRq8trkGOVwTvgGOQpxWSmFrXIKyXmVxa3KG6Ma1hkJ0tFGSJU505KQ5GrKqNpwKOpq4GOtQ1VNK0VyBaqiOI/SCmccxjiUrhjdaFpRTEO1iq3i2/9M4qwqi/QniQSxKZHurd2NNACtyEC1NoMNhAdUDVU36qrveN4QPNPyWxVbX5T5kqHz44v5deCrKvimCddeq4r3YgerlKL3nmUSZqx1llzlwxJrJOUiOlWgtcjpfM/DwxPLGHC95XjS3B06DttEnk/k/oqn80Tn5X7ljEIbIYShIKSJ3DSqBRoarSxLGBlzYE4FZxTKa8Kc6TcKZfwXyU5KEddbUtV0WqF0QiEs0aJkxTOGyo0WFmyqZ+LpTDEDGY/WjuvrA8pkUjTMKXKtelSDVDLExNXtFVoZllTot57bzYbrXqNOR/78duZPf/kzD4+P1LpjHAu7w0Crj/RWg3PYQaHjSGc1sWSMM6ScGUPkh7/cM0/3bPoN+23P2RViScQcaLrS9JbNYaDGzNPjW765cdze/YzPx4mH48R2u+HTaeLd27ccj5/41a//M6f7J3aD+P57DdVVfNcxbDf8x/uRmCO2WYwG2wuxipq53vYop+m9o7eGPBemeeHl3Q1VaVIRp7xaKjFnplh4eJxkPdgKvfMsaULZRE2gaqLEiDYiHdS68Pqba375m18SguE8ndGmcdgNzCFg3VYIUtqQS8Ztd9Qy/70/Ihf8DYjhiaY18dlQWvgaiuf0vPWYqyRaVXb9bf036+1fC7mstYI2hmYNJVTJTV+vvA0x2KlrkyARuYWsFZ31oL0YZK3xK8Ya+s2Aca/ofc/uuOH+/JHTcmKJC6lmyYOoawOi1JcCIsrCZ7cJ+d5YNxe1WnJN5JKw1tGIxGXGalErOddBUzw8RDqrqQ1ihSVnkaflgsNQpdqRc8Z1Hj8M+K7DW0/OSb6u9HpKkGnZe0NLMy0r5uKJLeGcwVGZQyJnUAZKk2fHFAK5CHnSKIU1hq5YrDFUbeUnbJFWZdunsaAaxgqt0qwphqpBSYXYkizSrcHRobAoJPRHNi0OjCLVvL6e7UfVxpqQ1yoUVaTY6+dy36T5aNI0NAXkvG5aNPa5+lN5pn3K6T6hlVsX+WY9x6xJeeor2ucDqv14OLrgggsuuOCCC/4/xdfVflxwwQUXXHDBBf9PcCn4F1xwwQUXXPATwKXgX3DBBRdccMFPAJeCf8EFF1xwwQU/AVwK/gUXXHDBBRf8BHAp+BdccMEFF1zwE8Cl4F9wwQUXXHDBTwCXgn/BBRdccMEFPwFcCv4FF1xwwQUX/ATwvwE7p1uN+x6TJwAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"visualize_predictions(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter03/image_captioning_pytorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## download data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.2.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\",\n      \"Requirement already satisfied: pycocotools==2.0.7 in /opt/conda/lib/python3.10/site-packages (2.0.7)\\n\",\n      \"Requirement already satisfied: matplotlib>=2.1.0 in /opt/conda/lib/python3.10/site-packages (from pycocotools==2.0.7) (3.7.3)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from pycocotools==2.0.7) (1.26.2)\\n\",\n      \"Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (1.2.0)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (1.4.5)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=2.1.0->pycocotools==2.0.7) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->pycocotools==2.0.7) (1.16.0)\\r\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2.2.0+cu121\\n\",\n      \"0.17.0+cu121\\n\",\n      \"3.7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\\n\",\n    \"!pip install pycocotools==2.0.7\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import nltk\\n\",\n    \"import pycocotools\\n\",\n    \"\\n\",\n    \"print(torch.__version__)\\n\",\n    \"print(torchvision.__version__)\\n\",\n    \"print(nltk.__version__)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\\n\",\n      \"E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\\n\",\n      \"--2024-02-15 02:19:19--  http://images.cocodataset.org/annotations/annotations_trainval2014.zip\\n\",\n      \"Resolving images.cocodataset.org (images.cocodataset.org)... 3.5.29.220, 52.217.166.89, 52.217.114.113, ...\\n\",\n      \"Connecting to images.cocodataset.org (images.cocodataset.org)|3.5.29.220|:80... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 252872794 (241M) [application/zip]\\n\",\n      \"Saving to: ‘./data_dir/annotations_trainval2014.zip’\\n\",\n      \"\\n\",\n      \"annotations_trainva 100%[===================>] 241.16M  35.8MB/s    in 13s     \\n\",\n      \"\\n\",\n      \"2024-02-15 02:19:32 (18.9 MB/s) - ‘./data_dir/annotations_trainval2014.zip’ saved [252872794/252872794]\\n\",\n      \"\\n\",\n      \"--2024-02-15 02:19:32--  http://images.cocodataset.org/zips/train2014.zip\\n\",\n      \"Resolving images.cocodataset.org (images.cocodataset.org)... 3.5.25.18, 52.216.147.124, 3.5.29.78, ...\\n\",\n      \"Connecting to images.cocodataset.org (images.cocodataset.org)|3.5.25.18|:80... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 13510573713 (13G) [application/zip]\\n\",\n      \"Saving to: ‘./data_dir/train2014.zip’\\n\",\n      \"\\n\",\n      \"train2014.zip       100%[===================>]  12.58G  28.0MB/s    in 6m 12s  \\n\",\n      \"\\n\",\n      \"2024-02-15 02:25:44 (34.7 MB/s) - ‘./data_dir/train2014.zip’ saved [13510573713/13510573713]\\n\",\n      \"\\n\",\n      \"--2024-02-15 02:25:44--  http://images.cocodataset.org/zips/val2014.zip\\n\",\n      \"Resolving images.cocodataset.org (images.cocodataset.org)... 16.182.71.17, 3.5.25.41, 54.231.167.81, ...\\n\",\n      \"Connecting to images.cocodataset.org (images.cocodataset.org)|16.182.71.17|:80... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 6645013297 (6.2G) [application/zip]\\n\",\n      \"Saving to: ‘./data_dir/val2014.zip’\\n\",\n      \"\\n\",\n      \"val2014.zip         100%[===================>]   6.19G  35.3MB/s    in 3m 2s   \\n\",\n      \"\\n\",\n      \"2024-02-15 02:28:46 (34.9 MB/s) - ‘./data_dir/val2014.zip’ saved [6645013297/6645013297]\\n\",\n      \"\\n\",\n      \"Archive:  ./data_dir/annotations_trainval2014.zip\\n\",\n      \"  inflating: ./data_dir/annotations/instances_train2014.json  \\n\",\n      \"  inflating: ./data_dir/annotations/instances_val2014.json  \\n\",\n      \"  inflating: ./data_dir/annotations/person_keypoints_train2014.json  \\n\",\n      \"  inflating: ./data_dir/annotations/person_keypoints_val2014.json  \\n\",\n      \"  inflating: ./data_dir/annotations/captions_train2014.json  \\n\",\n      \"  inflating: ./data_dir/annotations/captions_val2014.json  \\n\",\n      \"Archive:  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\" extracting: ./data_dir/train2014/COCO_train2014_000000244928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312220.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168569.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004981.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071856.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515917.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475185.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476430.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182933.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566495.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484273.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573533.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255419.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281327.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529788.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538821.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216954.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365319.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360376.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105633.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000047192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324977.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106513.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093272.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571774.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568432.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536127.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505888.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000434118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494391.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530832.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000120018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475317.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557374.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088210.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346653.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417720.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286490.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333841.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024296.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580785.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543028.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269183.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310300.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347989.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000339501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500321.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416111.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040244.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123074.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533042.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518552.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130132.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091721.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533941.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001090.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538747.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000543769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317401.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565765.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525813.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252038.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316091.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354761.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175347.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393178.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312939.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000262603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000370748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276354.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474024.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454681.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386043.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452479.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463172.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484019.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003080.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063266.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411478.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466196.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141756.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000097450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577240.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000260116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046634.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408576.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157822.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443018.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486628.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100312.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354094.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000148246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575511.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040603.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000277165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000066667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487539.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560627.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554561.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363671.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173948.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454107.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486994.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275793.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412704.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000463803.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256748.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471500.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032846.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335551.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208829.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424705.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393438.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438805.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000200008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183286.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345216.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336966.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396499.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505642.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197044.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341341.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294466.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363947.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000179961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319795.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462872.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525636.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213826.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356068.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123083.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000312338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000570416.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562608.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000035717.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117532.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021447.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387226.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123949.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331919.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366153.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247098.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000275245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285285.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000257102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476504.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386542.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090744.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000527970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461786.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324912.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526165.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289333.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251758.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000438145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580149.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526082.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000111694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386884.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578588.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000568880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500764.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199131.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004189.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415378.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377854.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000252956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000437904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374999.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028802.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447980.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000388575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239791.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000299924.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421775.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416372.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390693.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261858.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476848.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514407.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060810.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571797.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193690.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087000.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000529745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180335.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165404.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000280409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107783.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168527.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433331.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000144937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314904.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390243.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494309.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000352524.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548772.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348951.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259393.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074722.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255239.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436596.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146469.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000202667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432417.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450996.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557388.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486035.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000043477.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484814.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425150.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394866.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000192390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187878.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387606.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222572.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152429.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366061.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341737.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422206.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025755.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483817.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564796.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185394.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000119129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000117834.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498263.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000084594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011373.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320763.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100930.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010818.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464928.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227451.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011402.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255550.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193281.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203940.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000534735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000581860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197759.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355022.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484506.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027008.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384595.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167498.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156709.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000294491.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000115862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424666.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186794.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224199.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141664.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196245.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469124.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000131570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476032.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109106.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313385.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560396.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096684.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000080691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274509.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480457.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315964.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000077137.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000528452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569525.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098190.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034579.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521899.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314202.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060056.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239269.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237622.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561484.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466294.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096925.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324521.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000243421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000282557.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018743.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194161.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128029.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070812.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564861.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474780.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000531049.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413400.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000003538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307751.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227612.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000387225.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000368564.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195039.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298152.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291262.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572615.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101891.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000470754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000566175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000450194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145249.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431922.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222078.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000176918.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389133.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562741.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248610.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000109324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285013.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321182.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289987.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000371360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504699.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398270.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292440.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000555088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190617.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146760.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503961.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000310526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024972.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000173475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464937.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000094118.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028876.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477497.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000544913.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000075754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244293.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000484208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466591.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190117.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145792.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000146586.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329799.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464800.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189156.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477104.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283093.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000105708.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000364169.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072573.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569062.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000536112.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000054065.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334953.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000446900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000519489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369369.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380474.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425433.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266914.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270224.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255271.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334441.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522100.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158334.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000539371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082411.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000269983.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116141.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000314626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071719.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244839.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325915.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259514.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000496195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328144.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000435360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082679.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000088735.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353301.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000431480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306342.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381808.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315322.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102316.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337842.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380070.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291194.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000366016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508567.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000480200.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000343159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306483.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000102718.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000503623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056896.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510665.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220077.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205707.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068041.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014611.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000225036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028307.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402726.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000099649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036282.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196460.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000545314.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000526682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067004.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181857.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000032729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064114.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259906.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000082475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000565740.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239811.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000462454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514697.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000221346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000051434.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296700.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013258.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408163.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367742.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138351.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000303723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000121237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000288021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395326.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000027089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001670.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247597.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181208.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461815.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000018819.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372480.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000412575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355582.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230515.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104290.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563739.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000207048.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386467.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000273716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266228.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000401583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000488538.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000395284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264365.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474268.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000029180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000009408.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569050.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000217306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020193.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013616.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000239350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332135.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229494.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000042415.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134893.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000474026.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308652.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000563545.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000153154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000031157.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000554587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000569079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228907.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000149682.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533770.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000441727.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000147520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542745.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000458673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108620.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000481885.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305703.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000471191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000289238.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313517.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265364.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453779.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546847.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000321403.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000134079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000124167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136757.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052348.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000011931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000001298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511031.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000472990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000490938.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000551344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000155746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382069.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019489.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375580.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085085.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306191.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000108589.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000069284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000074126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000152782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000137767.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123711.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242036.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076122.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000091025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000361255.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000267059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000228209.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000542304.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420045.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160232.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000333302.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537002.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019487.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313921.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205223.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492352.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022142.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000301508.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000577762.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367724.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000557188.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204962.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298203.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000156823.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000292101.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000509023.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281632.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114692.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205887.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342086.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232292.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000579752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204275.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071371.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408806.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420221.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000223571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546959.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123382.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556638.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012422.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348604.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068340.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358492.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000374502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000415659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000504158.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258753.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058565.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000145444.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122303.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337752.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078336.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323970.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178645.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414320.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181021.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254512.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096931.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000133257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000332943.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065769.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000213207.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515853.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098529.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000331992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540882.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567337.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548010.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000198880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000067997.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000245713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048630.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000034625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000070159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000222929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000353306.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000482298.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461350.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000436837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263974.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000571455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552254.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174677.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000211955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127560.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575164.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284831.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000390287.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107138.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550134.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206412.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000286973.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344284.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006464.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351418.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502058.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215428.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000068420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000307033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210932.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000553548.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329784.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495956.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208659.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237716.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007455.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000007510.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479081.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304605.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000495776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175546.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000253395.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000572544.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376668.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000048130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000430731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575252.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000163125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000508456.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564211.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040283.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000309541.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000421602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313880.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000404849.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000475485.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357247.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234357.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455660.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000479687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024259.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000424658.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000318462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000456648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000209890.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107454.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000512816.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414648.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000205631.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328995.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000023673.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400593.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000058619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000085339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000501571.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000246706.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000537649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271590.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000053236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000165945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000409674.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000079578.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000214676.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000408217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000453397.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061830.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423647.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000517869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000413046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000296867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000167695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498218.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126392.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425437.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185177.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000065345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052219.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000575176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000170730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000096496.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347274.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000210205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242984.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000448609.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547594.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000319449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000126978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114139.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190881.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000423231.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000550655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118386.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000562960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000489978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000336503.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181128.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460279.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000342353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185368.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521452.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000201151.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279264.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072898.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000386619.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110251.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050096.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000188175.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182801.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000406420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000057820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266910.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403197.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000396927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000515710.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297640.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000193034.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429613.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073889.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000022624.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206680.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000497384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000393577.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000494155.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081903.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191734.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000182629.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000157435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000256057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358828.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000164234.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360125.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000186537.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000002377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024990.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013901.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000410356.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498204.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063235.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000244425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326442.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000454253.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000160420.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000326723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000506011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000530781.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500323.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000340971.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030328.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000558600.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000358462.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000428809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106644.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283863.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000306095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422894.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215879.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000439621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264712.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000469431.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516233.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548723.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242807.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000502409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055641.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000020384.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033698.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389184.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000427399.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000185986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317186.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000486730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059012.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000242827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000317587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000150427.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154073.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398214.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194845.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000505033.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000046950.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000143198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000220991.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000561488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546730.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106003.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541746.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000140278.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000081205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171729.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000215625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399488.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000461967.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298017.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171736.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000376958.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206166.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000078656.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090310.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425288.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564217.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000076992.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000098405.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000363621.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000440097.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000158299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259960.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355902.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000520986.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012501.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000547367.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000468011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216110.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000063689.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000323475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229250.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000574360.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000040398.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231835.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000281176.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159963.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000050827.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024516.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000237344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000196639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093014.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442771.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142824.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000033732.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290449.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546655.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000521222.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000507257.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154686.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000276694.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000159768.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000422343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141993.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254637.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270330.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523599.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000224168.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000297092.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000036725.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227909.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425123.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000030787.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000180470.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000045299.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000451066.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000229833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000459978.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000316089.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000095651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026900.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000169179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189071.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533568.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000101837.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000511179.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000064009.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000151534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000178840.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567410.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000129865.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000028942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005046.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000227198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000378864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329502.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465975.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000365295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000338383.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000142969.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000141766.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000264526.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136332.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000432461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291574.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313738.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000199237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000218201.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000247346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000052626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452905.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000284192.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000573260.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060602.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000429353.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000114229.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000279946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000492519.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000360672.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000510059.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357877.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000433563.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000347313.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000268159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000059934.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000525212.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000403291.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000308121.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139173.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000006295.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444749.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000350040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265625.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000016683.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000056695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181661.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000300534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000513105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230867.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000195195.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000445965.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000233102.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295007.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411581.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357471.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442886.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000369859.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000106804.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000302945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000258649.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382929.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315387.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090145.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000235543.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104343.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362108.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000071678.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000162390.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000293520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000425439.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420079.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000135305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460833.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000194159.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464675.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000391976.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420862.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103230.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000251860.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010276.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334944.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291140.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000259338.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000127067.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000123180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000103575.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000487528.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000060547.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000418536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000417261.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000357435.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008733.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000478982.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000208731.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000559358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000204181.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000315728.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327895.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500646.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000072790.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000295315.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000263450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341942.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231087.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000335713.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000477040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230916.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000354318.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385265.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000825.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232227.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000232198.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000457667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122776.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086820.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397414.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000231280.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367523.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236389.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000405897.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000112358.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000349750.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000541957.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000266696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491084.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000466714.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447016.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000442473.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184908.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000516570.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000203054.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000382643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000154614.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000465507.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000236425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373099.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270873.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000394115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000522362.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000362654.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161051.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000348180.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000377011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328113.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000181344.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000005459.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000238187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000449103.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000019146.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000580345.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420308.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000285639.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000290952.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000325935.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037076.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000359147.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000460030.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000191754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000514060.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000473955.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190554.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000261702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000576850.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000327702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187037.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136651.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062053.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000183329.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000017171.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000535160.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000265361.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000272297.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305450.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000118851.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000483241.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000420425.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219445.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021095.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000122923.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166522.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000013482.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000249688.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000010015.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107011.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000367809.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024380.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012421.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000240685.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444566.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000255486.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381667.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000538116.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328381.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000014844.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578663.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000107691.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426143.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000444945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337883.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000024289.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000092553.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041311.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166998.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000044702.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000355236.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000015843.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000397205.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000038375.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000128215.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000250424.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328242.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313481.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000337448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398936.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000320911.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000464237.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000172040.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000313556.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000130598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000346562.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000455601.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379136.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000567836.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000234536.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000278549.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000426988.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055534.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000372213.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402120.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518355.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000037868.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000012696.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000083875.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000174623.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000039531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000523468.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000062715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000548662.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000549626.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000416119.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000384643.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000392167.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055493.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000560754.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000491277.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000389088.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000407926.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380115.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000329892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000206530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000518075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041475.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138370.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000087871.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000171126.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499920.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000385075.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100535.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000212359.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000113598.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000004377.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000546695.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000498409.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000402174.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000400443.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139945.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000190530.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000324339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000168453.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000271870.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556001.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000311476.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000139109.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000184518.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000399687.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000330426.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379558.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000532583.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000026635.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000380715.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000373148.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000274170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000187025.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000049363.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000344669.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000476874.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000230129.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000305170.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419798.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000452005.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000089838.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000055607.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000447130.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000100777.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287105.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125063.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000270379.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000116006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000552472.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000540864.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000556855.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000398540.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000287324.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493187.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000226555.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000197584.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000411505.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000086927.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000351852.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000248979.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000414047.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000379448.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104248.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000216413.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000493618.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000241325.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000138782.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000499985.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000356064.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000443650.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000021968.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000304587.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000132778.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000136346.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000345585.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000485465.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000041461.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000166592.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000419406.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000125305.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000322892.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000578154.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000189520.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000524773.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000104559.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000110423.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000383446.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000161436.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000090006.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000061072.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000381256.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000008458.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000375027.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000073339.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000564020.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000175057.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000328246.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000298657.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000219869.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000025162.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000341052.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000093789.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000254701.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000177349.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000283267.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000334463.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000500946.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000000531.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000291366.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000533055.jpg  \\n\",\n      \" extracting: ./data_dir/train2014/COCO_train2014_000000467840.jpg  \\n\",\n      \"Archive:  ./data_dir/val2014.zip\\n\",\n      \"   creating: ./data_dir/val2014/\\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000324670.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000464263.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000526418.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000230593.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000186147.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000037149.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000284743.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000550691.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000515126.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000539419.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000401240.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000185240.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000002592.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000189203.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000169539.jpg  \\n\",\n      \" extracting: 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\\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000105220.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000052982.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000035368.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000050422.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000008473.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000537069.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000561491.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000463722.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000352507.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000518974.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000091909.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000322122.jpg  \\n\",\n      \" extracting: 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\\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000332877.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000514294.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000070945.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000199927.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000206751.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000036077.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000201045.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000079490.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000079566.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000086988.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000560266.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000254834.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000190783.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000524575.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000433156.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000532620.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000533171.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000074462.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000386584.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000575625.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000000599.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000316123.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000442206.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000411665.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000112698.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000395462.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000368663.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000399049.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000479939.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000132860.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000281315.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000014526.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000154892.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000535313.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000008483.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000259087.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000030667.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000132288.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000155617.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000049682.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000382438.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000488693.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000324492.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000543836.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000551804.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000045516.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000347233.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000154202.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000038210.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000113113.jpg  \\n\",\n      \" extracting: ./data_dir/val2014/COCO_val2014_000000441814.jpg  \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# linux\\n\",\n    \"!apt-get install wget\\n\",\n    \"# mac\\n\",\n    \"# !brew install wget\\n\",\n    \" \\n\",\n    \"# create a data directory\\n\",\n    \"!mkdir data_dir\\n\",\n    \" \\n\",\n    \"# download images and annotations to the data directory\\n\",\n    \"!wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip -P ./data_dir/\\n\",\n    \"!wget http://images.cocodataset.org/zips/train2014.zip -P ./data_dir/\\n\",\n    \"!wget http://images.cocodataset.org/zips/val2014.zip -P ./data_dir/\\n\",\n    \"    \\n\",\n    \"# extract zipped images and annotations and remove the zip files\\n\",\n    \"!unzip ./data_dir/annotations_trainval2014.zip -d ./data_dir/\\n\",\n    \"!rm ./data_dir/annotations_trainval2014.zip\\n\",\n    \"!unzip ./data_dir/train2014.zip -d ./data_dir/\\n\",\n    \"!rm ./data_dir/train2014.zip \\n\",\n    \"!unzip ./data_dir/val2014.zip -d ./data_dir/ \\n\",\n    \"!rm ./data_dir/val2014.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import dependencies\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import nltk\\n\",\n    \"import pickle\\n\",\n    \"import numpy as np\\n\",\n    \"from PIL import Image\\n\",\n    \"from collections import Counter\\n\",\n    \"from pycocotools.coco import COCO\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \" \\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.utils.data as data\\n\",\n    \"from torchvision import transforms\\n\",\n    \"import torchvision.models as models\\n\",\n    \"import torchvision.transforms as transforms\\n\",\n    \"from torch.nn.utils.rnn import pack_padded_sequence\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[nltk_data] Downloading package punkt to /home/ashish.jha/nltk_data...\\n\",\n      \"[nltk_data]   Unzipping tokenizers/punkt.zip.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"nltk.download('punkt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## build vocab\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loading annotations into memory...\\n\",\n      \"Done (t=1.10s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\",\n      \"[1000/414113] Tokenized the captions.\\n\",\n      \"[2000/414113] Tokenized the captions.\\n\",\n      \"[3000/414113] Tokenized the captions.\\n\",\n      \"[4000/414113] Tokenized the captions.\\n\",\n      \"[5000/414113] Tokenized the captions.\\n\",\n      \"[6000/414113] Tokenized the captions.\\n\",\n      \"[7000/414113] Tokenized the captions.\\n\",\n      \"[8000/414113] Tokenized the captions.\\n\",\n      \"[9000/414113] Tokenized the captions.\\n\",\n      \"[10000/414113] Tokenized the captions.\\n\",\n      \"[11000/414113] Tokenized the captions.\\n\",\n      \"[12000/414113] Tokenized the captions.\\n\",\n      \"[13000/414113] Tokenized the captions.\\n\",\n      \"[14000/414113] Tokenized the captions.\\n\",\n      \"[15000/414113] Tokenized the captions.\\n\",\n      \"[16000/414113] Tokenized the captions.\\n\",\n      \"[17000/414113] Tokenized the captions.\\n\",\n      \"[18000/414113] Tokenized the captions.\\n\",\n      \"[19000/414113] Tokenized the captions.\\n\",\n      \"[20000/414113] Tokenized the captions.\\n\",\n      \"[21000/414113] Tokenized the captions.\\n\",\n      \"[22000/414113] Tokenized the captions.\\n\",\n      \"[23000/414113] Tokenized the captions.\\n\",\n      \"[24000/414113] Tokenized the captions.\\n\",\n      \"[25000/414113] Tokenized the captions.\\n\",\n      \"[26000/414113] Tokenized the captions.\\n\",\n      \"[27000/414113] Tokenized the captions.\\n\",\n      \"[28000/414113] Tokenized the captions.\\n\",\n      \"[29000/414113] Tokenized the captions.\\n\",\n      \"[30000/414113] Tokenized the captions.\\n\",\n      \"[31000/414113] Tokenized the captions.\\n\",\n      \"[32000/414113] Tokenized the captions.\\n\",\n      \"[33000/414113] Tokenized the captions.\\n\",\n      \"[34000/414113] Tokenized the captions.\\n\",\n      \"[35000/414113] Tokenized the captions.\\n\",\n      \"[36000/414113] Tokenized the captions.\\n\",\n      \"[37000/414113] Tokenized the captions.\\n\",\n      \"[38000/414113] Tokenized the captions.\\n\",\n      \"[39000/414113] Tokenized the captions.\\n\",\n      \"[40000/414113] Tokenized the captions.\\n\",\n      \"[41000/414113] Tokenized the captions.\\n\",\n      \"[42000/414113] Tokenized the captions.\\n\",\n      \"[43000/414113] Tokenized the captions.\\n\",\n      \"[44000/414113] Tokenized the captions.\\n\",\n      \"[45000/414113] Tokenized the captions.\\n\",\n      \"[46000/414113] Tokenized the captions.\\n\",\n      \"[47000/414113] Tokenized the captions.\\n\",\n      \"[48000/414113] Tokenized the captions.\\n\",\n      \"[49000/414113] Tokenized the captions.\\n\",\n      \"[50000/414113] Tokenized the captions.\\n\",\n      \"[51000/414113] Tokenized the captions.\\n\",\n      \"[52000/414113] Tokenized the captions.\\n\",\n      \"[53000/414113] Tokenized the captions.\\n\",\n      \"[54000/414113] Tokenized the captions.\\n\",\n      \"[55000/414113] Tokenized the captions.\\n\",\n      \"[56000/414113] Tokenized the captions.\\n\",\n      \"[57000/414113] Tokenized the captions.\\n\",\n      \"[58000/414113] Tokenized the captions.\\n\",\n      \"[59000/414113] Tokenized the captions.\\n\",\n      \"[60000/414113] Tokenized the captions.\\n\",\n      \"[61000/414113] Tokenized the captions.\\n\",\n      \"[62000/414113] Tokenized the captions.\\n\",\n      \"[63000/414113] Tokenized the captions.\\n\",\n      \"[64000/414113] Tokenized the captions.\\n\",\n      \"[65000/414113] Tokenized the captions.\\n\",\n      \"[66000/414113] Tokenized the captions.\\n\",\n      \"[67000/414113] Tokenized the captions.\\n\",\n      \"[68000/414113] Tokenized the captions.\\n\",\n      \"[69000/414113] Tokenized the captions.\\n\",\n      \"[70000/414113] Tokenized the captions.\\n\",\n      \"[71000/414113] Tokenized the captions.\\n\",\n      \"[72000/414113] Tokenized the captions.\\n\",\n      \"[73000/414113] Tokenized the captions.\\n\",\n      \"[74000/414113] Tokenized the captions.\\n\",\n      \"[75000/414113] Tokenized the captions.\\n\",\n      \"[76000/414113] Tokenized the captions.\\n\",\n      \"[77000/414113] Tokenized the captions.\\n\",\n      \"[78000/414113] Tokenized the captions.\\n\",\n      \"[79000/414113] Tokenized the captions.\\n\",\n      \"[80000/414113] Tokenized the captions.\\n\",\n      \"[81000/414113] Tokenized the captions.\\n\",\n      \"[82000/414113] Tokenized the captions.\\n\",\n      \"[83000/414113] Tokenized the captions.\\n\",\n      \"[84000/414113] Tokenized the captions.\\n\",\n      \"[85000/414113] Tokenized the captions.\\n\",\n      \"[86000/414113] Tokenized the captions.\\n\",\n      \"[87000/414113] Tokenized the captions.\\n\",\n      \"[88000/414113] Tokenized the captions.\\n\",\n      \"[89000/414113] Tokenized the captions.\\n\",\n      \"[90000/414113] Tokenized the captions.\\n\",\n      \"[91000/414113] Tokenized the captions.\\n\",\n      \"[92000/414113] Tokenized the captions.\\n\",\n      \"[93000/414113] Tokenized the captions.\\n\",\n      \"[94000/414113] Tokenized the captions.\\n\",\n      \"[95000/414113] Tokenized the captions.\\n\",\n      \"[96000/414113] Tokenized the captions.\\n\",\n      \"[97000/414113] Tokenized the captions.\\n\",\n      \"[98000/414113] Tokenized the captions.\\n\",\n      \"[99000/414113] Tokenized the captions.\\n\",\n      \"[100000/414113] Tokenized the captions.\\n\",\n      \"[101000/414113] Tokenized the captions.\\n\",\n      \"[102000/414113] Tokenized the captions.\\n\",\n      \"[103000/414113] Tokenized the captions.\\n\",\n      \"[104000/414113] Tokenized the captions.\\n\",\n      \"[105000/414113] Tokenized the captions.\\n\",\n      \"[106000/414113] Tokenized the captions.\\n\",\n      \"[107000/414113] Tokenized the captions.\\n\",\n      \"[108000/414113] Tokenized the captions.\\n\",\n      \"[109000/414113] Tokenized the captions.\\n\",\n      \"[110000/414113] Tokenized the captions.\\n\",\n      \"[111000/414113] Tokenized the captions.\\n\",\n      \"[112000/414113] Tokenized the captions.\\n\",\n      \"[113000/414113] Tokenized the captions.\\n\",\n      \"[114000/414113] Tokenized the captions.\\n\",\n      \"[115000/414113] Tokenized the captions.\\n\",\n      \"[116000/414113] Tokenized the captions.\\n\",\n      \"[117000/414113] Tokenized the captions.\\n\",\n      \"[118000/414113] Tokenized the captions.\\n\",\n      \"[119000/414113] Tokenized the captions.\\n\",\n      \"[120000/414113] Tokenized the captions.\\n\",\n      \"[121000/414113] Tokenized the captions.\\n\",\n      \"[122000/414113] Tokenized the captions.\\n\",\n      \"[123000/414113] Tokenized the captions.\\n\",\n      \"[124000/414113] Tokenized the captions.\\n\",\n      \"[125000/414113] Tokenized the captions.\\n\",\n      \"[126000/414113] Tokenized the captions.\\n\",\n      \"[127000/414113] Tokenized the captions.\\n\",\n      \"[128000/414113] Tokenized the captions.\\n\",\n      \"[129000/414113] Tokenized the captions.\\n\",\n      \"[130000/414113] Tokenized the captions.\\n\",\n      \"[131000/414113] Tokenized the captions.\\n\",\n      \"[132000/414113] Tokenized the captions.\\n\",\n      \"[133000/414113] Tokenized the captions.\\n\",\n      \"[134000/414113] Tokenized the captions.\\n\",\n      \"[135000/414113] Tokenized the captions.\\n\",\n      \"[136000/414113] Tokenized the captions.\\n\",\n      \"[137000/414113] Tokenized the captions.\\n\",\n      \"[138000/414113] Tokenized the captions.\\n\",\n      \"[139000/414113] Tokenized the captions.\\n\",\n      \"[140000/414113] Tokenized the captions.\\n\",\n      \"[141000/414113] Tokenized the captions.\\n\",\n      \"[142000/414113] Tokenized the captions.\\n\",\n      \"[143000/414113] Tokenized the captions.\\n\",\n      \"[144000/414113] Tokenized the captions.\\n\",\n      \"[145000/414113] Tokenized the captions.\\n\",\n      \"[146000/414113] Tokenized the captions.\\n\",\n      \"[147000/414113] Tokenized the captions.\\n\",\n      \"[148000/414113] Tokenized the captions.\\n\",\n      \"[149000/414113] Tokenized the captions.\\n\",\n      \"[150000/414113] Tokenized the captions.\\n\",\n      \"[151000/414113] Tokenized the captions.\\n\",\n      \"[152000/414113] Tokenized the captions.\\n\",\n      \"[153000/414113] Tokenized the captions.\\n\",\n      \"[154000/414113] Tokenized the captions.\\n\",\n      \"[155000/414113] Tokenized the captions.\\n\",\n      \"[156000/414113] Tokenized the captions.\\n\",\n      \"[157000/414113] Tokenized the captions.\\n\",\n      \"[158000/414113] Tokenized the captions.\\n\",\n      \"[159000/414113] Tokenized the captions.\\n\",\n      \"[160000/414113] Tokenized the captions.\\n\",\n      \"[161000/414113] Tokenized the captions.\\n\",\n      \"[162000/414113] Tokenized the captions.\\n\",\n      \"[163000/414113] Tokenized the captions.\\n\",\n      \"[164000/414113] Tokenized the captions.\\n\",\n      \"[165000/414113] Tokenized the captions.\\n\",\n      \"[166000/414113] Tokenized the captions.\\n\",\n      \"[167000/414113] Tokenized the captions.\\n\",\n      \"[168000/414113] Tokenized the captions.\\n\",\n      \"[169000/414113] Tokenized the captions.\\n\",\n      \"[170000/414113] Tokenized the captions.\\n\",\n      \"[171000/414113] Tokenized the captions.\\n\",\n      \"[172000/414113] Tokenized the captions.\\n\",\n      \"[173000/414113] Tokenized the captions.\\n\",\n      \"[174000/414113] Tokenized the captions.\\n\",\n      \"[175000/414113] Tokenized the captions.\\n\",\n      \"[176000/414113] Tokenized the captions.\\n\",\n      \"[177000/414113] Tokenized the captions.\\n\",\n      \"[178000/414113] Tokenized the captions.\\n\",\n      \"[179000/414113] Tokenized the captions.\\n\",\n      \"[180000/414113] Tokenized the captions.\\n\",\n      \"[181000/414113] Tokenized the captions.\\n\",\n      \"[182000/414113] Tokenized the captions.\\n\",\n      \"[183000/414113] Tokenized the captions.\\n\",\n      \"[184000/414113] Tokenized the captions.\\n\",\n      \"[185000/414113] Tokenized the captions.\\n\",\n      \"[186000/414113] Tokenized the captions.\\n\",\n      \"[187000/414113] Tokenized the captions.\\n\",\n      \"[188000/414113] Tokenized the captions.\\n\",\n      \"[189000/414113] Tokenized the captions.\\n\",\n      \"[190000/414113] Tokenized the captions.\\n\",\n      \"[191000/414113] Tokenized the captions.\\n\",\n      \"[192000/414113] Tokenized the captions.\\n\",\n      \"[193000/414113] Tokenized the captions.\\n\",\n      \"[194000/414113] Tokenized the captions.\\n\",\n      \"[195000/414113] Tokenized the captions.\\n\",\n      \"[196000/414113] Tokenized the captions.\\n\",\n      \"[197000/414113] Tokenized the captions.\\n\",\n      \"[198000/414113] Tokenized the captions.\\n\",\n      \"[199000/414113] Tokenized the captions.\\n\",\n      \"[200000/414113] Tokenized the captions.\\n\",\n      \"[201000/414113] Tokenized the captions.\\n\",\n      \"[202000/414113] Tokenized the captions.\\n\",\n      \"[203000/414113] Tokenized the captions.\\n\",\n      \"[204000/414113] Tokenized the captions.\\n\",\n      \"[205000/414113] Tokenized the captions.\\n\",\n      \"[206000/414113] Tokenized the captions.\\n\",\n      \"[207000/414113] Tokenized the captions.\\n\",\n      \"[208000/414113] Tokenized the captions.\\n\",\n      \"[209000/414113] Tokenized the captions.\\n\",\n      \"[210000/414113] Tokenized the captions.\\n\",\n      \"[211000/414113] Tokenized the captions.\\n\",\n      \"[212000/414113] Tokenized the captions.\\n\",\n      \"[213000/414113] Tokenized the captions.\\n\",\n      \"[214000/414113] Tokenized the captions.\\n\",\n      \"[215000/414113] Tokenized the captions.\\n\",\n      \"[216000/414113] Tokenized the captions.\\n\",\n      \"[217000/414113] Tokenized the captions.\\n\",\n      \"[218000/414113] Tokenized the captions.\\n\",\n      \"[219000/414113] Tokenized the captions.\\n\",\n      \"[220000/414113] Tokenized the captions.\\n\",\n      \"[221000/414113] Tokenized the captions.\\n\",\n      \"[222000/414113] Tokenized the captions.\\n\",\n      \"[223000/414113] Tokenized the captions.\\n\",\n      \"[224000/414113] Tokenized the captions.\\n\",\n      \"[225000/414113] Tokenized the captions.\\n\",\n      \"[226000/414113] Tokenized the captions.\\n\",\n      \"[227000/414113] Tokenized the captions.\\n\",\n      \"[228000/414113] Tokenized the captions.\\n\",\n      \"[229000/414113] Tokenized the captions.\\n\",\n      \"[230000/414113] Tokenized the captions.\\n\",\n      \"[231000/414113] Tokenized the captions.\\n\",\n      \"[232000/414113] Tokenized the captions.\\n\",\n      \"[233000/414113] Tokenized the captions.\\n\",\n      \"[234000/414113] Tokenized the captions.\\n\",\n      \"[235000/414113] Tokenized the captions.\\n\",\n      \"[236000/414113] Tokenized the captions.\\n\",\n      \"[237000/414113] Tokenized the captions.\\n\",\n      \"[238000/414113] Tokenized the captions.\\n\",\n      \"[239000/414113] Tokenized the captions.\\n\",\n      \"[240000/414113] Tokenized the captions.\\n\",\n      \"[241000/414113] Tokenized the captions.\\n\",\n      \"[242000/414113] Tokenized the captions.\\n\",\n      \"[243000/414113] Tokenized the captions.\\n\",\n      \"[244000/414113] Tokenized the captions.\\n\",\n      \"[245000/414113] Tokenized the captions.\\n\",\n      \"[246000/414113] Tokenized the captions.\\n\",\n      \"[247000/414113] Tokenized the captions.\\n\",\n      \"[248000/414113] Tokenized the captions.\\n\",\n      \"[249000/414113] Tokenized the captions.\\n\",\n      \"[250000/414113] Tokenized the captions.\\n\",\n      \"[251000/414113] Tokenized the captions.\\n\",\n      \"[252000/414113] Tokenized the captions.\\n\",\n      \"[253000/414113] Tokenized the captions.\\n\",\n      \"[254000/414113] Tokenized the captions.\\n\",\n      \"[255000/414113] Tokenized the captions.\\n\",\n      \"[256000/414113] Tokenized the captions.\\n\",\n      \"[257000/414113] Tokenized the captions.\\n\",\n      \"[258000/414113] Tokenized the captions.\\n\",\n      \"[259000/414113] Tokenized the captions.\\n\",\n      \"[260000/414113] Tokenized the captions.\\n\",\n      \"[261000/414113] Tokenized the captions.\\n\",\n      \"[262000/414113] Tokenized the captions.\\n\",\n      \"[263000/414113] Tokenized the captions.\\n\",\n      \"[264000/414113] Tokenized the captions.\\n\",\n      \"[265000/414113] Tokenized the captions.\\n\",\n      \"[266000/414113] Tokenized the captions.\\n\",\n      \"[267000/414113] Tokenized the captions.\\n\",\n      \"[268000/414113] Tokenized the captions.\\n\",\n      \"[269000/414113] Tokenized the captions.\\n\",\n      \"[270000/414113] Tokenized the captions.\\n\",\n      \"[271000/414113] Tokenized the captions.\\n\",\n      \"[272000/414113] Tokenized the captions.\\n\",\n      \"[273000/414113] Tokenized the captions.\\n\",\n      \"[274000/414113] Tokenized the captions.\\n\",\n      \"[275000/414113] Tokenized the captions.\\n\",\n      \"[276000/414113] Tokenized the captions.\\n\",\n      \"[277000/414113] Tokenized the captions.\\n\",\n      \"[278000/414113] Tokenized the captions.\\n\",\n      \"[279000/414113] Tokenized the captions.\\n\",\n      \"[280000/414113] Tokenized the captions.\\n\",\n      \"[281000/414113] Tokenized the captions.\\n\",\n      \"[282000/414113] Tokenized the captions.\\n\",\n      \"[283000/414113] Tokenized the captions.\\n\",\n      \"[284000/414113] Tokenized the captions.\\n\",\n      \"[285000/414113] Tokenized the captions.\\n\",\n      \"[286000/414113] Tokenized the captions.\\n\",\n      \"[287000/414113] Tokenized the captions.\\n\",\n      \"[288000/414113] Tokenized the captions.\\n\",\n      \"[289000/414113] Tokenized the captions.\\n\",\n      \"[290000/414113] Tokenized the captions.\\n\",\n      \"[291000/414113] Tokenized the captions.\\n\",\n      \"[292000/414113] Tokenized the captions.\\n\",\n      \"[293000/414113] Tokenized the captions.\\n\",\n      \"[294000/414113] Tokenized the captions.\\n\",\n      \"[295000/414113] Tokenized the captions.\\n\",\n      \"[296000/414113] Tokenized the captions.\\n\",\n      \"[297000/414113] Tokenized the captions.\\n\",\n      \"[298000/414113] Tokenized the captions.\\n\",\n      \"[299000/414113] Tokenized the captions.\\n\",\n      \"[300000/414113] Tokenized the captions.\\n\",\n      \"[301000/414113] Tokenized the captions.\\n\",\n      \"[302000/414113] Tokenized the captions.\\n\",\n      \"[303000/414113] Tokenized the captions.\\n\",\n      \"[304000/414113] Tokenized the captions.\\n\",\n      \"[305000/414113] Tokenized the captions.\\n\",\n      \"[306000/414113] Tokenized the captions.\\n\",\n      \"[307000/414113] Tokenized the captions.\\n\",\n      \"[308000/414113] Tokenized the captions.\\n\",\n      \"[309000/414113] Tokenized the captions.\\n\",\n      \"[310000/414113] Tokenized the captions.\\n\",\n      \"[311000/414113] Tokenized the captions.\\n\",\n      \"[312000/414113] Tokenized the captions.\\n\",\n      \"[313000/414113] Tokenized the captions.\\n\",\n      \"[314000/414113] Tokenized the captions.\\n\",\n      \"[315000/414113] Tokenized the captions.\\n\",\n      \"[316000/414113] Tokenized the captions.\\n\",\n      \"[317000/414113] Tokenized the captions.\\n\",\n      \"[318000/414113] Tokenized the captions.\\n\",\n      \"[319000/414113] Tokenized the captions.\\n\",\n      \"[320000/414113] Tokenized the captions.\\n\",\n      \"[321000/414113] Tokenized the captions.\\n\",\n      \"[322000/414113] Tokenized the captions.\\n\",\n      \"[323000/414113] Tokenized the captions.\\n\",\n      \"[324000/414113] Tokenized the captions.\\n\",\n      \"[325000/414113] Tokenized the captions.\\n\",\n      \"[326000/414113] Tokenized the captions.\\n\",\n      \"[327000/414113] Tokenized the captions.\\n\",\n      \"[328000/414113] Tokenized the captions.\\n\",\n      \"[329000/414113] Tokenized the captions.\\n\",\n      \"[330000/414113] Tokenized the captions.\\n\",\n      \"[331000/414113] Tokenized the captions.\\n\",\n      \"[332000/414113] Tokenized the captions.\\n\",\n      \"[333000/414113] Tokenized the captions.\\n\",\n      \"[334000/414113] Tokenized the captions.\\n\",\n      \"[335000/414113] Tokenized the captions.\\n\",\n      \"[336000/414113] Tokenized the captions.\\n\",\n      \"[337000/414113] Tokenized the captions.\\n\",\n      \"[338000/414113] Tokenized the captions.\\n\",\n      \"[339000/414113] Tokenized the captions.\\n\",\n      \"[340000/414113] Tokenized the captions.\\n\",\n      \"[341000/414113] Tokenized the captions.\\n\",\n      \"[342000/414113] Tokenized the captions.\\n\",\n      \"[343000/414113] Tokenized the captions.\\n\",\n      \"[344000/414113] Tokenized the captions.\\n\",\n      \"[345000/414113] Tokenized the captions.\\n\",\n      \"[346000/414113] Tokenized the captions.\\n\",\n      \"[347000/414113] Tokenized the captions.\\n\",\n      \"[348000/414113] Tokenized the captions.\\n\",\n      \"[349000/414113] Tokenized the captions.\\n\",\n      \"[350000/414113] Tokenized the captions.\\n\",\n      \"[351000/414113] Tokenized the captions.\\n\",\n      \"[352000/414113] Tokenized the captions.\\n\",\n      \"[353000/414113] Tokenized the captions.\\n\",\n      \"[354000/414113] Tokenized the captions.\\n\",\n      \"[355000/414113] Tokenized the captions.\\n\",\n      \"[356000/414113] Tokenized the captions.\\n\",\n      \"[357000/414113] Tokenized the captions.\\n\",\n      \"[358000/414113] Tokenized the captions.\\n\",\n      \"[359000/414113] Tokenized the captions.\\n\",\n      \"[360000/414113] Tokenized the captions.\\n\",\n      \"[361000/414113] Tokenized the captions.\\n\",\n      \"[362000/414113] Tokenized the captions.\\n\",\n      \"[363000/414113] Tokenized the captions.\\n\",\n      \"[364000/414113] Tokenized the captions.\\n\",\n      \"[365000/414113] Tokenized the captions.\\n\",\n      \"[366000/414113] Tokenized the captions.\\n\",\n      \"[367000/414113] Tokenized the captions.\\n\",\n      \"[368000/414113] Tokenized the captions.\\n\",\n      \"[369000/414113] Tokenized the captions.\\n\",\n      \"[370000/414113] Tokenized the captions.\\n\",\n      \"[371000/414113] Tokenized the captions.\\n\",\n      \"[372000/414113] Tokenized the captions.\\n\",\n      \"[373000/414113] Tokenized the captions.\\n\",\n      \"[374000/414113] Tokenized the captions.\\n\",\n      \"[375000/414113] Tokenized the captions.\\n\",\n      \"[376000/414113] Tokenized the captions.\\n\",\n      \"[377000/414113] Tokenized the captions.\\n\",\n      \"[378000/414113] Tokenized the captions.\\n\",\n      \"[379000/414113] Tokenized the captions.\\n\",\n      \"[380000/414113] Tokenized the captions.\\n\",\n      \"[381000/414113] Tokenized the captions.\\n\",\n      \"[382000/414113] Tokenized the captions.\\n\",\n      \"[383000/414113] Tokenized the captions.\\n\",\n      \"[384000/414113] Tokenized the captions.\\n\",\n      \"[385000/414113] Tokenized the captions.\\n\",\n      \"[386000/414113] Tokenized the captions.\\n\",\n      \"[387000/414113] Tokenized the captions.\\n\",\n      \"[388000/414113] Tokenized the captions.\\n\",\n      \"[389000/414113] Tokenized the captions.\\n\",\n      \"[390000/414113] Tokenized the captions.\\n\",\n      \"[391000/414113] Tokenized the captions.\\n\",\n      \"[392000/414113] Tokenized the captions.\\n\",\n      \"[393000/414113] Tokenized the captions.\\n\",\n      \"[394000/414113] Tokenized the captions.\\n\",\n      \"[395000/414113] Tokenized the captions.\\n\",\n      \"[396000/414113] Tokenized the captions.\\n\",\n      \"[397000/414113] Tokenized the captions.\\n\",\n      \"[398000/414113] Tokenized the captions.\\n\",\n      \"[399000/414113] Tokenized the captions.\\n\",\n      \"[400000/414113] Tokenized the captions.\\n\",\n      \"[401000/414113] Tokenized the captions.\\n\",\n      \"[402000/414113] Tokenized the captions.\\n\",\n      \"[403000/414113] Tokenized the captions.\\n\",\n      \"[404000/414113] Tokenized the captions.\\n\",\n      \"[405000/414113] Tokenized the captions.\\n\",\n      \"[406000/414113] Tokenized the captions.\\n\",\n      \"[407000/414113] Tokenized the captions.\\n\",\n      \"[408000/414113] Tokenized the captions.\\n\",\n      \"[409000/414113] Tokenized the captions.\\n\",\n      \"[410000/414113] Tokenized the captions.\\n\",\n      \"[411000/414113] Tokenized the captions.\\n\",\n      \"[412000/414113] Tokenized the captions.\\n\",\n      \"[413000/414113] Tokenized the captions.\\n\",\n      \"[414000/414113] Tokenized the captions.\\n\",\n      \"Total vocabulary size: 9948\\n\",\n      \"Saved the vocabulary wrapper to './data_dir/vocabulary.pkl'\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class Vocab(object):\\n\",\n    \"    \\\"\\\"\\\"Simple vocabulary wrapper.\\\"\\\"\\\"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self.w2i = {}\\n\",\n    \"        self.i2w = {}\\n\",\n    \"        self.index = 0\\n\",\n    \" \\n\",\n    \"    def __call__(self, token):\\n\",\n    \"        if not token in self.w2i:\\n\",\n    \"            return self.w2i['<unk>']\\n\",\n    \"        return self.w2i[token]\\n\",\n    \" \\n\",\n    \"    def __len__(self):\\n\",\n    \"        return len(self.w2i)\\n\",\n    \"    def add_token(self, token):\\n\",\n    \"        if not token in self.w2i:\\n\",\n    \"            self.w2i[token] = self.index\\n\",\n    \"            self.i2w[self.index] = token\\n\",\n    \"            self.index += 1\\n\",\n    \"\\n\",\n    \"def build_vocabulary(json, threshold):\\n\",\n    \"    \\\"\\\"\\\"Build a simple vocabulary wrapper.\\\"\\\"\\\"\\n\",\n    \"    coco = COCO(json)\\n\",\n    \"    counter = Counter()\\n\",\n    \"    ids = coco.anns.keys()\\n\",\n    \"    for i, id in enumerate(ids):\\n\",\n    \"        caption = str(coco.anns[id]['caption'])\\n\",\n    \"        tokens = nltk.tokenize.word_tokenize(caption.lower())\\n\",\n    \"        counter.update(tokens)\\n\",\n    \" \\n\",\n    \"        if (i+1) % 1000 == 0:\\n\",\n    \"            print(\\\"[{}/{}] Tokenized the captions.\\\".format(i+1, len(ids)))\\n\",\n    \" \\n\",\n    \"    # If the word frequency is less than 'threshold', then the word is discarded.\\n\",\n    \"    tokens = [token for token, cnt in counter.items() if cnt >= threshold]\\n\",\n    \" \\n\",\n    \"    # Create a vocab wrapper and add some special tokens.\\n\",\n    \"    vocab = Vocab()\\n\",\n    \"    vocab.add_token('<pad>')\\n\",\n    \"    vocab.add_token('<start>')\\n\",\n    \"    vocab.add_token('<end>')\\n\",\n    \"    vocab.add_token('<unk>')\\n\",\n    \" \\n\",\n    \"    # Add the words to the vocabulary.\\n\",\n    \"    for i, token in enumerate(tokens):\\n\",\n    \"        vocab.add_token(token)\\n\",\n    \"    return vocab\\n\",\n    \" \\n\",\n    \"vocab = build_vocabulary(json='data_dir/annotations/captions_train2014.json', threshold=4)\\n\",\n    \"vocab_path = './data_dir/vocabulary.pkl'\\n\",\n    \"with open(vocab_path, 'wb') as f:\\n\",\n    \"    pickle.dump(vocab, f)\\n\",\n    \"print(\\\"Total vocabulary size: {}\\\".format(len(vocab)))\\n\",\n    \"print(\\\"Saved the vocabulary wrapper to '{}'\\\".format(vocab_path))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## resize images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[1900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[2900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[3900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[4900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[5900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[6900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[7900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[8900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[9900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[10900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[11900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[12900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[13900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[14900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[15900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[16900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[17900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[18900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[19900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[20900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[21900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[22900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[23900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[24900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[25900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[26900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[27900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[28900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[29900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[30900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[31900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[32900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[33900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[34900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[35900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[36900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[37900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[38900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[39900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[40900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[41900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[42900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[43900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[44900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[45900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[46900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[47900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[48900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[49900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[50900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[51900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[52900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[53900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[54900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[55900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[56900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[57900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[58900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[59900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[60900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[61900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[62900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[63900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[64900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[65900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[66900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[67900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[68900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[69900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[70900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[71900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[72900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[73900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[74900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[75900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[76900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[77900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[78900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[79900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[80900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81800/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[81900/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82000/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82100/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82200/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82300/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82400/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82500/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82600/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\",\n      \"[82700/82783] Resized the images and saved into './data_dir/resized_images/'.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def reshape_image(image, shape):\\n\",\n    \"    \\\"\\\"\\\"Resize an image to the given shape.\\\"\\\"\\\"\\n\",\n    \"    return image.resize(shape, Image.LANCZOS)\\n\",\n    \" \\n\",\n    \"def reshape_images(image_path, output_path, shape):\\n\",\n    \"    \\\"\\\"\\\"Reshape the images in 'image_path' and save into 'output_path'.\\\"\\\"\\\"\\n\",\n    \"    if not os.path.exists(output_path):\\n\",\n    \"        os.makedirs(output_path)\\n\",\n    \" \\n\",\n    \"    images = os.listdir(image_path)\\n\",\n    \"    num_im = len(images)\\n\",\n    \"    for i, im in enumerate(images):\\n\",\n    \"        with open(os.path.join(image_path, im), 'r+b') as f:\\n\",\n    \"            with Image.open(f) as image:\\n\",\n    \"                image = reshape_image(image, shape)\\n\",\n    \"                image.save(os.path.join(output_path, im), image.format)\\n\",\n    \"        if (i+1) % 100 == 0:\\n\",\n    \"            print (\\\"[{}/{}] Resized the images and saved into '{}'.\\\"\\n\",\n    \"                   .format(i+1, num_im, output_path))\\n\",\n    \"\\n\",\n    \"image_path = './data_dir/train2014/'\\n\",\n    \"output_path = './data_dir/resized_images/'\\n\",\n    \"image_shape = [256, 256]\\n\",\n    \"reshape_images(image_path, output_path, image_shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## instantiate data loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomCocoDataset(data.Dataset):\\n\",\n    \"    \\\"\\\"\\\"COCO Custom Dataset compatible with torch.utils.data.DataLoader.\\\"\\\"\\\"\\n\",\n    \"    def __init__(self, data_path, coco_json_path, vocabulary, transform=None):\\n\",\n    \"        \\\"\\\"\\\"Set the path for images, captions and vocabulary wrapper.\\n\",\n    \"        \\n\",\n    \"        Args:\\n\",\n    \"            root: image directory.\\n\",\n    \"            json: coco annotation file path.\\n\",\n    \"            vocab: vocabulary wrapper.\\n\",\n    \"            transform: image transformer.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        self.root = data_path\\n\",\n    \"        self.coco_data = COCO(coco_json_path)\\n\",\n    \"        self.indices = list(self.coco_data.anns.keys())\\n\",\n    \"        self.vocabulary = vocabulary\\n\",\n    \"        self.transform = transform\\n\",\n    \" \\n\",\n    \"    def __getitem__(self, idx):\\n\",\n    \"        \\\"\\\"\\\"Returns one data pair (image and caption).\\\"\\\"\\\"\\n\",\n    \"        coco_data = self.coco_data\\n\",\n    \"        vocabulary = self.vocabulary\\n\",\n    \"        annotation_id = self.indices[idx]\\n\",\n    \"        caption = coco_data.anns[annotation_id]['caption']\\n\",\n    \"        image_id = coco_data.anns[annotation_id]['image_id']\\n\",\n    \"        image_path = coco_data.loadImgs(image_id)[0]['file_name']\\n\",\n    \" \\n\",\n    \"        image = Image.open(os.path.join(self.root, image_path)).convert('RGB')\\n\",\n    \"        if self.transform is not None:\\n\",\n    \"            image = self.transform(image)\\n\",\n    \" \\n\",\n    \"        # Convert caption (string) to word ids.\\n\",\n    \"        word_tokens = nltk.tokenize.word_tokenize(str(caption).lower())\\n\",\n    \"        caption = []\\n\",\n    \"        caption.append(vocabulary('<start>'))\\n\",\n    \"        caption.extend([vocabulary(token) for token in word_tokens])\\n\",\n    \"        caption.append(vocabulary('<end>'))\\n\",\n    \"        ground_truth = torch.Tensor(caption)\\n\",\n    \"        return image, ground_truth\\n\",\n    \" \\n\",\n    \"    def __len__(self):\\n\",\n    \"        return len(self.indices)\\n\",\n    \" \\n\",\n    \" \\n\",\n    \"def collate_function(data_batch):\\n\",\n    \"    \\\"\\\"\\\"Creates mini-batch tensors from the list of tuples (image, caption).\\n\",\n    \"    \\n\",\n    \"    We should build custom collate_fn rather than using default collate_fn, \\n\",\n    \"    because merging caption (including padding) is not supported in default.\\n\",\n    \"    Args:\\n\",\n    \"        data: list of tuple (image, caption). \\n\",\n    \"            - image: torch tensor of shape (3, 256, 256).\\n\",\n    \"            - caption: torch tensor of shape (?); variable length.\\n\",\n    \"    Returns:\\n\",\n    \"        images: torch tensor of shape (batch_size, 3, 256, 256).\\n\",\n    \"        targets: torch tensor of shape (batch_size, padded_length).\\n\",\n    \"        lengths: list; valid length for each padded caption.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # Sort a data list by caption length (descending order).\\n\",\n    \"    data_batch.sort(key=lambda d: len(d[1]), reverse=True)\\n\",\n    \"    imgs, caps = zip(*data_batch)\\n\",\n    \" \\n\",\n    \"    # Merge images (from list of 3D tensors to 4D tensor).\\n\",\n    \"    # Originally, imgs is a list of <batch_size> number of RGB images with dimensions (3, 256, 256)\\n\",\n    \"    # This line of code turns it into a single tensor of dimensions (<batch_size>, 3, 256, 256)\\n\",\n    \"    imgs = torch.stack(imgs, 0)\\n\",\n    \" \\n\",\n    \"    # Merge captions (from list of 1D tensors to 2D tensor), similar to merging of images donw above.\\n\",\n    \"    cap_lens = [len(cap) for cap in caps]\\n\",\n    \"    tgts = torch.zeros(len(caps), max(cap_lens)).long()\\n\",\n    \"    for i, cap in enumerate(caps):\\n\",\n    \"        end = cap_lens[i]\\n\",\n    \"        tgts[i, :end] = cap[:end]        \\n\",\n    \"    return imgs, tgts, cap_lens\\n\",\n    \" \\n\",\n    \"def get_loader(data_path, coco_json_path, vocabulary, transform, batch_size, shuffle):\\n\",\n    \"    \\\"\\\"\\\"Returns torch.utils.data.DataLoader for custom coco dataset.\\\"\\\"\\\"\\n\",\n    \"    # COCO caption dataset\\n\",\n    \"    coco_dataser = CustomCocoDataset(data_path=data_path,\\n\",\n    \"                       coco_json_path=coco_json_path,\\n\",\n    \"                       vocabulary=vocabulary,\\n\",\n    \"                       transform=transform)\\n\",\n    \"    \\n\",\n    \"    # Data loader for COCO dataset\\n\",\n    \"    # This will return (images, captions, lengths) for each iteration.\\n\",\n    \"    # images: a tensor of shape (batch_size, 3, 224, 224).\\n\",\n    \"    # captions: a tensor of shape (batch_size, padded_length).\\n\",\n    \"    # lengths: a list indicating valid length for each caption. length is (batch_size).\\n\",\n    \"    custom_data_loader = torch.utils.data.DataLoader(dataset=coco_dataser, \\n\",\n    \"                                              batch_size=batch_size,\\n\",\n    \"                                              shuffle=shuffle,\\n\",\n    \"                                              collate_fn=collate_function)\\n\",\n    \"    return custom_data_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model definition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class CNNModel(nn.Module):\\n\",\n    \"    def __init__(self, embedding_size):\\n\",\n    \"        \\\"\\\"\\\"Load the pretrained ResNet-152 and replace top fc layer.\\\"\\\"\\\"\\n\",\n    \"        super(CNNModel, self).__init__()\\n\",\n    \"        resnet = models.resnet152(weights=True)\\n\",\n    \"        module_list = list(resnet.children())[:-1]      # delete the last fc layer.\\n\",\n    \"        self.resnet_module = nn.Sequential(*module_list)\\n\",\n    \"        self.linear_layer = nn.Linear(resnet.fc.in_features, embedding_size)\\n\",\n    \"        self.batch_norm = nn.BatchNorm1d(embedding_size, momentum=0.01)\\n\",\n    \"        \\n\",\n    \"    def forward(self, input_images):\\n\",\n    \"        \\\"\\\"\\\"Extract feature vectors from input images.\\\"\\\"\\\"\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            resnet_features = self.resnet_module(input_images)\\n\",\n    \"        resnet_features = resnet_features.reshape(resnet_features.size(0), -1)\\n\",\n    \"        final_features = self.batch_norm(self.linear_layer(resnet_features))\\n\",\n    \"        return final_features\\n\",\n    \" \\n\",\n    \" \\n\",\n    \"class LSTMModel(nn.Module):\\n\",\n    \"    def __init__(self, embedding_size, hidden_layer_size, vocabulary_size, num_layers, max_seq_len=20):\\n\",\n    \"        \\\"\\\"\\\"Set the hyper-parameters and build the layers.\\\"\\\"\\\"\\n\",\n    \"        super(LSTMModel, self).__init__()\\n\",\n    \"        self.embedding_layer = nn.Embedding(vocabulary_size, embedding_size)\\n\",\n    \"        self.lstm_layer = nn.LSTM(embedding_size, hidden_layer_size, num_layers, batch_first=True)\\n\",\n    \"        self.linear_layer = nn.Linear(hidden_layer_size, vocabulary_size)\\n\",\n    \"        self.max_seq_len = max_seq_len\\n\",\n    \"        \\n\",\n    \"    def forward(self, input_features, capts, lens):\\n\",\n    \"        \\\"\\\"\\\"Decode image feature vectors and generates captions.\\\"\\\"\\\"\\n\",\n    \"        embeddings = self.embedding_layer(caps)\\n\",\n    \"        embeddings = torch.cat((input_features.unsqueeze(1), embeddings), 1)\\n\",\n    \"        lstm_input = pack_padded_sequence(embeddings, lens, batch_first=True) \\n\",\n    \"        hidden_variables, _ = self.lstm_layer(lstm_input)\\n\",\n    \"        model_outputs = self.linear_layer(hidden_variables[0])\\n\",\n    \"        return model_outputs\\n\",\n    \"    \\n\",\n    \"    def sample(self, input_features, lstm_states=None):\\n\",\n    \"        \\\"\\\"\\\"Generate captions for given image features using greedy search.\\\"\\\"\\\"\\n\",\n    \"        sampled_indices = []\\n\",\n    \"        lstm_inputs = input_features.unsqueeze(1)\\n\",\n    \"        for i in range(self.max_seq_len):\\n\",\n    \"            hidden_variables, lstm_states = self.lstm_layer(lstm_inputs, lstm_states)          # hiddens: (batch_size, 1, hidden_size)\\n\",\n    \"            model_outputs = self.linear_layer(hidden_variables.squeeze(1))            # outputs:  (batch_size, vocab_size)\\n\",\n    \"            _, predicted_outputs = model_outputs.max(1)                        # predicted: (batch_size)\\n\",\n    \"            sampled_indices.append(predicted_outputs)\\n\",\n    \"            lstm_inputs = self.embedding_layer(predicted_outputs)                       # inputs: (batch_size, embed_size)\\n\",\n    \"            lstm_inputs = lstm_inputs.unsqueeze(1)                         # inputs: (batch_size, 1, embed_size)\\n\",\n    \"        sampled_indices = torch.stack(sampled_indices, 1)                # sampled_ids: (batch_size, max_seq_length)\\n\",\n    \"        return sampled_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loading annotations into memory...\\n\",\n      \"Done (t=2.63s)\\n\",\n      \"creating index...\\n\",\n      \"index created!\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet152_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet152_Weights.DEFAULT` to get the most up-to-date weights.\\n\",\n      \"  warnings.warn(msg)\\n\",\n      \"Downloading: \\\"https://download.pytorch.org/models/resnet152-394f9c45.pth\\\" to /home/ashish.jha/.cache/torch/hub/checkpoints/resnet152-394f9c45.pth\\n\",\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 230M/230M [00:03<00:00, 78.2MB/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch [0/5], Step [0/3236], Loss: 9.2069, Perplexity: 9965.7374\\n\",\n      \"Epoch [0/5], Step [10/3236], Loss: 5.7262, Perplexity: 306.8135\\n\",\n      \"Epoch [0/5], Step [20/3236], Loss: 5.3792, Perplexity: 216.8478\\n\",\n      \"Epoch [0/5], Step [30/3236], Loss: 4.9030, Perplexity: 134.6947\\n\",\n      \"Epoch [0/5], Step [40/3236], Loss: 4.5123, Perplexity: 91.1312\\n\",\n      \"Epoch [0/5], Step [50/3236], Loss: 4.2817, Perplexity: 72.3634\\n\",\n      \"Epoch [0/5], Step [60/3236], Loss: 4.1911, Perplexity: 66.0933\\n\",\n      \"Epoch [0/5], Step [70/3236], Loss: 4.0287, Perplexity: 56.1861\\n\",\n      \"Epoch [0/5], Step [80/3236], Loss: 4.0219, Perplexity: 55.8064\\n\",\n      \"Epoch [0/5], Step [90/3236], Loss: 3.9482, Perplexity: 51.8401\\n\",\n      \"Epoch [0/5], Step [100/3236], Loss: 3.8957, Perplexity: 49.1896\\n\",\n      \"Epoch [0/5], Step [110/3236], Loss: 3.6707, Perplexity: 39.2806\\n\",\n      \"Epoch [0/5], Step [120/3236], Loss: 3.9360, Perplexity: 51.2143\\n\",\n      \"Epoch [0/5], Step [130/3236], Loss: 3.7325, Perplexity: 41.7841\\n\",\n      \"Epoch [0/5], Step [140/3236], Loss: 3.6715, Perplexity: 39.3091\\n\",\n      \"Epoch [0/5], Step [150/3236], Loss: 3.6253, Perplexity: 37.5369\\n\",\n      \"Epoch [0/5], Step [160/3236], Loss: 3.7145, Perplexity: 41.0363\\n\",\n      \"Epoch [0/5], Step [170/3236], Loss: 3.4528, Perplexity: 31.5899\\n\",\n      \"Epoch [0/5], Step [180/3236], Loss: 3.7393, Perplexity: 42.0696\\n\",\n      \"Epoch [0/5], Step [190/3236], Loss: 3.4098, Perplexity: 30.2579\\n\",\n      \"Epoch [0/5], Step [200/3236], Loss: 3.3617, Perplexity: 28.8368\\n\",\n      \"Epoch [0/5], Step [210/3236], Loss: 3.4434, Perplexity: 31.2936\\n\",\n      \"Epoch [0/5], Step [220/3236], Loss: 3.2682, Perplexity: 26.2653\\n\",\n      \"Epoch [0/5], Step [230/3236], Loss: 3.4921, Perplexity: 32.8546\\n\",\n      \"Epoch [0/5], Step [240/3236], Loss: 3.3675, Perplexity: 29.0047\\n\",\n      \"Epoch [0/5], Step [250/3236], Loss: 3.2467, Perplexity: 25.7049\\n\",\n      \"Epoch [0/5], Step [260/3236], Loss: 3.3711, Perplexity: 29.1110\\n\",\n      \"Epoch [0/5], Step [270/3236], Loss: 3.1751, Perplexity: 23.9300\\n\",\n      \"Epoch [0/5], Step [280/3236], Loss: 3.2855, Perplexity: 26.7219\\n\",\n      \"Epoch [0/5], Step [290/3236], Loss: 3.1541, Perplexity: 23.4315\\n\",\n      \"Epoch [0/5], Step [300/3236], Loss: 3.0611, Perplexity: 21.3517\\n\",\n      \"Epoch [0/5], Step [310/3236], Loss: 3.0888, Perplexity: 21.9512\\n\",\n      \"Epoch [0/5], Step [320/3236], Loss: 3.3238, Perplexity: 27.7651\\n\",\n      \"Epoch [0/5], Step [330/3236], Loss: 3.1083, Perplexity: 22.3829\\n\",\n      \"Epoch [0/5], Step [340/3236], Loss: 3.0567, Perplexity: 21.2576\\n\",\n      \"Epoch [0/5], Step [350/3236], Loss: 3.1392, Perplexity: 23.0855\\n\",\n      \"Epoch [0/5], Step [360/3236], Loss: 3.0306, Perplexity: 20.7104\\n\",\n      \"Epoch [0/5], Step [370/3236], Loss: 2.9587, Perplexity: 19.2735\\n\",\n      \"Epoch [0/5], Step [380/3236], Loss: 3.1526, Perplexity: 23.3969\\n\",\n      \"Epoch [0/5], Step [390/3236], Loss: 3.0189, Perplexity: 20.4682\\n\",\n      \"Epoch [0/5], Step [400/3236], Loss: 3.2267, Perplexity: 25.1973\\n\",\n      \"Epoch [0/5], Step [410/3236], Loss: 2.9869, Perplexity: 19.8235\\n\",\n      \"Epoch [0/5], Step [420/3236], Loss: 3.0411, Perplexity: 20.9274\\n\",\n      \"Epoch [0/5], Step [430/3236], Loss: 3.0526, Perplexity: 21.1699\\n\",\n      \"Epoch [0/5], Step [440/3236], Loss: 2.9519, Perplexity: 19.1423\\n\",\n      \"Epoch [0/5], Step [450/3236], Loss: 3.0161, Perplexity: 20.4106\\n\",\n      \"Epoch [0/5], Step [460/3236], Loss: 2.8997, Perplexity: 18.1693\\n\",\n      \"Epoch [0/5], Step [470/3236], Loss: 2.9183, Perplexity: 18.5094\\n\",\n      \"Epoch [0/5], Step [480/3236], Loss: 3.0405, Perplexity: 20.9150\\n\",\n      \"Epoch [0/5], Step [490/3236], Loss: 2.8397, Perplexity: 17.1109\\n\",\n      \"Epoch [0/5], Step [500/3236], Loss: 2.7942, Perplexity: 16.3500\\n\",\n      \"Epoch [0/5], Step [510/3236], Loss: 2.9035, Perplexity: 18.2386\\n\",\n      \"Epoch [0/5], Step [520/3236], Loss: 2.8997, Perplexity: 18.1685\\n\",\n      \"Epoch [0/5], Step [530/3236], Loss: 2.9761, Perplexity: 19.6110\\n\",\n      \"Epoch [0/5], Step [540/3236], Loss: 2.7572, Perplexity: 15.7563\\n\",\n      \"Epoch [0/5], Step [550/3236], Loss: 2.7760, Perplexity: 16.0546\\n\",\n      \"Epoch [0/5], Step [560/3236], Loss: 2.8672, Perplexity: 17.5885\\n\",\n      \"Epoch [0/5], Step [570/3236], Loss: 2.9223, Perplexity: 18.5834\\n\",\n      \"Epoch [0/5], Step [580/3236], Loss: 2.7741, Perplexity: 16.0243\\n\",\n      \"Epoch [0/5], Step [590/3236], Loss: 2.7487, Perplexity: 15.6220\\n\",\n      \"Epoch [0/5], Step [600/3236], Loss: 2.8975, Perplexity: 18.1290\\n\",\n      \"Epoch [0/5], Step [610/3236], Loss: 2.8309, Perplexity: 16.9609\\n\",\n      \"Epoch [0/5], Step [620/3236], Loss: 2.6752, Perplexity: 14.5153\\n\",\n      \"Epoch [0/5], Step [630/3236], Loss: 2.7685, Perplexity: 15.9352\\n\",\n      \"Epoch [0/5], Step [640/3236], Loss: 2.8315, Perplexity: 16.9704\\n\",\n      \"Epoch [0/5], Step [650/3236], Loss: 2.8286, Perplexity: 16.9223\\n\",\n      \"Epoch [0/5], Step [660/3236], Loss: 2.6818, Perplexity: 14.6119\\n\",\n      \"Epoch [0/5], Step [670/3236], Loss: 2.7726, Perplexity: 16.0009\\n\",\n      \"Epoch [0/5], Step [680/3236], Loss: 2.7618, Perplexity: 15.8279\\n\",\n      \"Epoch [0/5], Step [690/3236], Loss: 2.7021, Perplexity: 14.9117\\n\",\n      \"Epoch [0/5], Step [700/3236], Loss: 2.6316, Perplexity: 13.8959\\n\",\n      \"Epoch [0/5], Step [710/3236], Loss: 2.6607, Perplexity: 14.3060\\n\",\n      \"Epoch [0/5], Step [720/3236], Loss: 2.6992, Perplexity: 14.8679\\n\",\n      \"Epoch [0/5], Step [730/3236], Loss: 2.6481, Perplexity: 14.1266\\n\",\n      \"Epoch [0/5], Step [740/3236], Loss: 2.9093, Perplexity: 18.3440\\n\",\n      \"Epoch [0/5], Step [750/3236], Loss: 2.6741, Perplexity: 14.4991\\n\",\n      \"Epoch [0/5], Step [760/3236], Loss: 2.6633, Perplexity: 14.3430\\n\",\n      \"Epoch [0/5], Step [770/3236], Loss: 2.5315, Perplexity: 12.5724\\n\",\n      \"Epoch [0/5], Step [780/3236], Loss: 2.7306, Perplexity: 15.3418\\n\",\n      \"Epoch [0/5], Step [790/3236], Loss: 2.6162, Perplexity: 13.6843\\n\",\n      \"Epoch [0/5], Step [800/3236], Loss: 2.6153, Perplexity: 13.6709\\n\",\n      \"Epoch [0/5], Step [810/3236], Loss: 2.6016, Perplexity: 13.4851\\n\",\n      \"Epoch [0/5], Step [820/3236], Loss: 2.6704, Perplexity: 14.4463\\n\",\n      \"Epoch [0/5], Step [830/3236], Loss: 2.6956, Perplexity: 14.8137\\n\",\n      \"Epoch [0/5], Step [840/3236], Loss: 2.6190, Perplexity: 13.7214\\n\",\n      \"Epoch [0/5], Step [850/3236], Loss: 2.5938, Perplexity: 13.3805\\n\",\n      \"Epoch [0/5], Step [860/3236], Loss: 2.7854, Perplexity: 16.2058\\n\",\n      \"Epoch [0/5], Step [870/3236], Loss: 2.7389, Perplexity: 15.4702\\n\",\n      \"Epoch [0/5], Step [880/3236], Loss: 2.6662, Perplexity: 14.3848\\n\",\n      \"Epoch [0/5], Step [890/3236], Loss: 2.6687, Perplexity: 14.4217\\n\",\n      \"Epoch [0/5], Step [900/3236], Loss: 2.6823, Perplexity: 14.6188\\n\",\n      \"Epoch [0/5], Step [910/3236], Loss: 2.6794, Perplexity: 14.5760\\n\",\n      \"Epoch [0/5], Step [920/3236], Loss: 2.7020, Perplexity: 14.9098\\n\",\n      \"Epoch [0/5], Step [930/3236], Loss: 2.5750, Perplexity: 13.1310\\n\",\n      \"Epoch [0/5], Step [940/3236], Loss: 2.6618, Perplexity: 14.3221\\n\",\n      \"Epoch [0/5], Step [950/3236], Loss: 2.6162, Perplexity: 13.6839\\n\",\n      \"Epoch [0/5], Step [960/3236], Loss: 2.6711, Perplexity: 14.4552\\n\",\n      \"Epoch [0/5], Step [970/3236], Loss: 2.5928, Perplexity: 13.3670\\n\",\n      \"Epoch [0/5], Step [980/3236], Loss: 2.5044, Perplexity: 12.2362\\n\",\n      \"Epoch [0/5], Step [990/3236], Loss: 2.3921, Perplexity: 10.9365\\n\",\n      \"Epoch [0/5], Step [1000/3236], Loss: 2.5621, Perplexity: 12.9637\\n\",\n      \"Epoch [0/5], Step [1010/3236], Loss: 2.6762, Perplexity: 14.5298\\n\",\n      \"Epoch [0/5], Step [1020/3236], Loss: 2.6081, Perplexity: 13.5730\\n\",\n      \"Epoch [0/5], Step [1030/3236], Loss: 2.5806, Perplexity: 13.2046\\n\",\n      \"Epoch [0/5], Step [1040/3236], Loss: 2.5939, Perplexity: 13.3822\\n\",\n      \"Epoch [0/5], Step [1050/3236], Loss: 2.4203, Perplexity: 11.2493\\n\",\n      \"Epoch [0/5], Step [1060/3236], Loss: 2.5424, Perplexity: 12.7098\\n\",\n      \"Epoch [0/5], Step [1070/3236], Loss: 2.5928, Perplexity: 13.3676\\n\",\n      \"Epoch [0/5], Step [1080/3236], Loss: 2.5891, Perplexity: 13.3179\\n\",\n      \"Epoch [0/5], Step [1090/3236], Loss: 2.6104, Perplexity: 13.6041\\n\",\n      \"Epoch [0/5], Step [1100/3236], Loss: 2.4810, Perplexity: 11.9529\\n\",\n      \"Epoch [0/5], Step [1110/3236], Loss: 2.6095, Perplexity: 13.5926\\n\",\n      \"Epoch [0/5], Step [1120/3236], Loss: 2.4402, Perplexity: 11.4751\\n\",\n      \"Epoch [0/5], Step [1130/3236], Loss: 2.7735, Perplexity: 16.0151\\n\",\n      \"Epoch [0/5], Step [1140/3236], Loss: 2.5786, Perplexity: 13.1784\\n\",\n      \"Epoch [0/5], Step [1150/3236], Loss: 2.4131, Perplexity: 11.1684\\n\",\n      \"Epoch [0/5], Step [1160/3236], Loss: 2.5141, Perplexity: 12.3554\\n\",\n      \"Epoch [0/5], Step [1170/3236], Loss: 2.5356, Perplexity: 12.6245\\n\",\n      \"Epoch [0/5], Step [1180/3236], Loss: 2.4224, Perplexity: 11.2724\\n\",\n      \"Epoch [0/5], Step [1190/3236], Loss: 2.6044, Perplexity: 13.5228\\n\",\n      \"Epoch [0/5], Step [1200/3236], Loss: 2.3866, Perplexity: 10.8765\\n\",\n      \"Epoch [0/5], Step [1210/3236], Loss: 2.5744, Perplexity: 13.1229\\n\",\n      \"Epoch [0/5], Step [1220/3236], Loss: 2.4944, Perplexity: 12.1143\\n\",\n      \"Epoch [0/5], Step [1230/3236], Loss: 2.5563, Perplexity: 12.8879\\n\",\n      \"Epoch [0/5], Step [1240/3236], Loss: 2.6160, Perplexity: 13.6803\\n\",\n      \"Epoch [0/5], Step [1250/3236], Loss: 2.5510, Perplexity: 12.8204\\n\",\n      \"Epoch [0/5], Step [1260/3236], Loss: 2.5823, Perplexity: 13.2280\\n\",\n      \"Epoch [0/5], Step [1270/3236], Loss: 2.5798, Perplexity: 13.1949\\n\",\n      \"Epoch [0/5], Step [1280/3236], Loss: 2.5163, Perplexity: 12.3827\\n\",\n      \"Epoch [0/5], Step [1290/3236], Loss: 2.4050, Perplexity: 11.0788\\n\",\n      \"Epoch [0/5], Step [1300/3236], Loss: 2.6245, Perplexity: 13.7970\\n\",\n      \"Epoch [0/5], Step [1310/3236], Loss: 2.4695, Perplexity: 11.8167\\n\",\n      \"Epoch [0/5], Step [1320/3236], Loss: 2.4542, Perplexity: 11.6370\\n\",\n      \"Epoch [0/5], Step [1330/3236], Loss: 2.5962, Perplexity: 13.4125\\n\",\n      \"Epoch [0/5], Step [1340/3236], Loss: 2.4930, Perplexity: 12.0979\\n\",\n      \"Epoch [0/5], Step [1350/3236], Loss: 2.5497, Perplexity: 12.8039\\n\",\n      \"Epoch [0/5], Step [1360/3236], Loss: 2.4798, Perplexity: 11.9393\\n\",\n      \"Epoch [0/5], Step [1370/3236], Loss: 2.3664, Perplexity: 10.6586\\n\",\n      \"Epoch [0/5], Step [1380/3236], Loss: 2.5087, Perplexity: 12.2884\\n\",\n      \"Epoch [0/5], Step [1390/3236], Loss: 2.4681, Perplexity: 11.8001\\n\",\n      \"Epoch [0/5], Step [1400/3236], Loss: 2.3510, Perplexity: 10.4963\\n\",\n      \"Epoch [0/5], Step [1410/3236], Loss: 2.5976, Perplexity: 13.4313\\n\",\n      \"Epoch [0/5], Step [1420/3236], Loss: 2.4271, Perplexity: 11.3256\\n\",\n      \"Epoch [0/5], Step [1430/3236], Loss: 2.5205, Perplexity: 12.4343\\n\",\n      \"Epoch [0/5], Step [1440/3236], Loss: 2.6245, Perplexity: 13.7971\\n\",\n      \"Epoch [0/5], Step [1450/3236], Loss: 2.5147, Perplexity: 12.3633\\n\",\n      \"Epoch [0/5], Step [1460/3236], Loss: 2.5549, Perplexity: 12.8705\\n\",\n      \"Epoch [0/5], Step [1470/3236], Loss: 2.3336, Perplexity: 10.3153\\n\",\n      \"Epoch [0/5], Step [1480/3236], Loss: 2.5071, Perplexity: 12.2695\\n\",\n      \"Epoch [0/5], Step [1490/3236], Loss: 2.7078, Perplexity: 14.9967\\n\",\n      \"Epoch [0/5], Step [1500/3236], Loss: 2.4325, Perplexity: 11.3877\\n\",\n      \"Epoch [0/5], Step [1510/3236], Loss: 2.4629, Perplexity: 11.7391\\n\",\n      \"Epoch [0/5], Step [1520/3236], Loss: 2.5521, Perplexity: 12.8334\\n\",\n      \"Epoch [0/5], Step [1530/3236], Loss: 2.3820, Perplexity: 10.8265\\n\",\n      \"Epoch [0/5], Step [1540/3236], Loss: 2.4758, Perplexity: 11.8909\\n\",\n      \"Epoch [0/5], Step [1550/3236], Loss: 2.4287, Perplexity: 11.3443\\n\",\n      \"Epoch [0/5], Step [1560/3236], Loss: 2.3822, Perplexity: 10.8284\\n\",\n      \"Epoch [0/5], Step [1570/3236], Loss: 2.4933, Perplexity: 12.1010\\n\",\n      \"Epoch [0/5], Step [1580/3236], Loss: 2.4147, Perplexity: 11.1866\\n\",\n      \"Epoch [0/5], Step [1590/3236], Loss: 2.4632, Perplexity: 11.7420\\n\",\n      \"Epoch [0/5], Step [1600/3236], Loss: 2.3434, Perplexity: 10.4164\\n\",\n      \"Epoch [0/5], Step [1610/3236], Loss: 2.5619, Perplexity: 12.9610\\n\",\n      \"Epoch [0/5], Step [1620/3236], Loss: 2.3669, Perplexity: 10.6641\\n\",\n      \"Epoch [0/5], Step [1630/3236], Loss: 2.6467, Perplexity: 14.1069\\n\",\n      \"Epoch [0/5], Step [1640/3236], Loss: 2.3954, Perplexity: 10.9721\\n\",\n      \"Epoch [0/5], Step [1650/3236], Loss: 2.4984, Perplexity: 12.1629\\n\",\n      \"Epoch [0/5], Step [1660/3236], Loss: 2.3422, Perplexity: 10.4038\\n\",\n      \"Epoch [0/5], Step [1670/3236], Loss: 2.2464, Perplexity: 9.4532\\n\",\n      \"Epoch [0/5], Step [1680/3236], Loss: 2.3294, Perplexity: 10.2719\\n\",\n      \"Epoch [0/5], Step [1690/3236], Loss: 2.2904, Perplexity: 9.8794\\n\",\n      \"Epoch [0/5], Step [1700/3236], Loss: 2.3689, Perplexity: 10.6854\\n\",\n      \"Epoch [0/5], Step [1710/3236], Loss: 2.3950, Perplexity: 10.9681\\n\",\n      \"Epoch [0/5], Step [1720/3236], Loss: 2.4263, Perplexity: 11.3165\\n\",\n      \"Epoch [0/5], Step [1730/3236], Loss: 2.5233, Perplexity: 12.4703\\n\",\n      \"Epoch [0/5], Step [1740/3236], Loss: 2.4657, Perplexity: 11.7712\\n\",\n      \"Epoch [0/5], Step [1750/3236], Loss: 2.3167, Perplexity: 10.1425\\n\",\n      \"Epoch [0/5], Step [1760/3236], Loss: 2.3225, Perplexity: 10.2012\\n\",\n      \"Epoch [0/5], Step [1770/3236], Loss: 2.3368, Perplexity: 10.3481\\n\",\n      \"Epoch [0/5], Step [1780/3236], Loss: 2.4584, Perplexity: 11.6864\\n\",\n      \"Epoch [0/5], Step [1790/3236], Loss: 2.4053, Perplexity: 11.0822\\n\",\n      \"Epoch [0/5], Step [1800/3236], Loss: 2.3909, Perplexity: 10.9236\\n\",\n      \"Epoch [0/5], Step [1810/3236], Loss: 2.6055, Perplexity: 13.5376\\n\",\n      \"Epoch [0/5], Step [1820/3236], Loss: 2.4160, Perplexity: 11.2009\\n\",\n      \"Epoch [0/5], Step [1830/3236], Loss: 2.4035, Perplexity: 11.0614\\n\",\n      \"Epoch [0/5], Step [1840/3236], Loss: 2.4381, Perplexity: 11.4511\\n\",\n      \"Epoch [0/5], Step [1850/3236], Loss: 2.3440, Perplexity: 10.4234\\n\",\n      \"Epoch [0/5], Step [1860/3236], Loss: 2.4234, Perplexity: 11.2846\\n\",\n      \"Epoch [0/5], Step [1870/3236], Loss: 2.4560, Perplexity: 11.6576\\n\",\n      \"Epoch [0/5], Step [1880/3236], Loss: 2.4599, Perplexity: 11.7039\\n\",\n      \"Epoch [0/5], Step [1890/3236], Loss: 2.6406, Perplexity: 14.0214\\n\",\n      \"Epoch [0/5], Step [1900/3236], Loss: 2.3546, Perplexity: 10.5342\\n\",\n      \"Epoch [0/5], Step [1910/3236], Loss: 2.3254, Perplexity: 10.2307\\n\",\n      \"Epoch [0/5], Step [1920/3236], Loss: 2.3638, Perplexity: 10.6311\\n\",\n      \"Epoch [0/5], Step [1930/3236], Loss: 2.3568, Perplexity: 10.5572\\n\",\n      \"Epoch [0/5], Step [1940/3236], Loss: 2.4926, Perplexity: 12.0923\\n\",\n      \"Epoch [0/5], Step [1950/3236], Loss: 2.2796, Perplexity: 9.7725\\n\",\n      \"Epoch [0/5], Step [1960/3236], Loss: 2.4177, Perplexity: 11.2204\\n\",\n      \"Epoch [0/5], Step [1970/3236], Loss: 2.3727, Perplexity: 10.7264\\n\",\n      \"Epoch [0/5], Step [1980/3236], Loss: 2.4195, Perplexity: 11.2405\\n\",\n      \"Epoch [0/5], Step [1990/3236], Loss: 2.3340, Perplexity: 10.3193\\n\",\n      \"Epoch [0/5], Step [2000/3236], Loss: 2.3190, Perplexity: 10.1650\\n\",\n      \"Epoch [0/5], Step [2010/3236], Loss: 2.3446, Perplexity: 10.4292\\n\",\n      \"Epoch [0/5], Step [2020/3236], Loss: 2.4156, Perplexity: 11.1961\\n\",\n      \"Epoch [0/5], Step [2030/3236], Loss: 2.3031, Perplexity: 10.0050\\n\",\n      \"Epoch [0/5], Step [2040/3236], Loss: 2.2843, Perplexity: 9.8189\\n\",\n      \"Epoch [0/5], Step [2050/3236], Loss: 2.2733, Perplexity: 9.7114\\n\",\n      \"Epoch [0/5], Step [2060/3236], Loss: 2.3786, Perplexity: 10.7896\\n\",\n      \"Epoch [0/5], Step [2070/3236], Loss: 2.3644, Perplexity: 10.6373\\n\",\n      \"Epoch [0/5], Step [2080/3236], Loss: 2.2348, Perplexity: 9.3444\\n\",\n      \"Epoch [0/5], Step [2090/3236], Loss: 2.3407, Perplexity: 10.3882\\n\",\n      \"Epoch [0/5], Step [2100/3236], Loss: 2.1914, Perplexity: 8.9478\\n\",\n      \"Epoch [0/5], Step [2110/3236], Loss: 2.4547, Perplexity: 11.6426\\n\",\n      \"Epoch [0/5], Step [2120/3236], Loss: 2.2734, Perplexity: 9.7121\\n\",\n      \"Epoch [0/5], Step [2130/3236], Loss: 2.3491, Perplexity: 10.4757\\n\",\n      \"Epoch [0/5], Step [2140/3236], Loss: 2.3338, Perplexity: 10.3170\\n\",\n      \"Epoch [0/5], Step [2150/3236], Loss: 2.1638, Perplexity: 8.7039\\n\",\n      \"Epoch [0/5], Step [2160/3236], Loss: 2.2446, Perplexity: 9.4370\\n\",\n      \"Epoch [0/5], Step [2170/3236], Loss: 2.3085, Perplexity: 10.0590\\n\",\n      \"Epoch [0/5], Step [2180/3236], Loss: 2.3734, Perplexity: 10.7343\\n\",\n      \"Epoch [0/5], Step [2190/3236], Loss: 2.1157, Perplexity: 8.2956\\n\",\n      \"Epoch [0/5], Step [2200/3236], Loss: 2.2525, Perplexity: 9.5118\\n\",\n      \"Epoch [0/5], Step [2210/3236], Loss: 2.4623, Perplexity: 11.7321\\n\",\n      \"Epoch [0/5], Step [2220/3236], Loss: 2.1785, Perplexity: 8.8327\\n\",\n      \"Epoch [0/5], Step [2230/3236], Loss: 2.3641, Perplexity: 10.6345\\n\",\n      \"Epoch [0/5], Step [2240/3236], Loss: 2.3032, Perplexity: 10.0060\\n\",\n      \"Epoch [0/5], Step [2250/3236], Loss: 2.4026, Perplexity: 11.0517\\n\",\n      \"Epoch [0/5], Step [2260/3236], Loss: 2.3514, Perplexity: 10.4998\\n\",\n      \"Epoch [0/5], Step [2270/3236], Loss: 2.2651, Perplexity: 9.6324\\n\",\n      \"Epoch [0/5], Step [2280/3236], Loss: 2.4690, Perplexity: 11.8101\\n\",\n      \"Epoch [0/5], Step [2290/3236], Loss: 2.4466, Perplexity: 11.5491\\n\",\n      \"Epoch [0/5], Step [2300/3236], Loss: 2.4042, Perplexity: 11.0698\\n\",\n      \"Epoch [0/5], Step [2310/3236], Loss: 2.3743, Perplexity: 10.7440\\n\",\n      \"Epoch [0/5], Step [2320/3236], Loss: 2.2305, Perplexity: 9.3045\\n\",\n      \"Epoch [0/5], Step [2330/3236], Loss: 2.3442, Perplexity: 10.4253\\n\",\n      \"Epoch [0/5], Step [2340/3236], Loss: 2.3592, Perplexity: 10.5825\\n\",\n      \"Epoch [0/5], Step [2350/3236], Loss: 2.2826, Perplexity: 9.8022\\n\",\n      \"Epoch [0/5], Step [2360/3236], Loss: 2.2497, Perplexity: 9.4849\\n\",\n      \"Epoch [0/5], Step [2370/3236], Loss: 2.2066, Perplexity: 9.0847\\n\",\n      \"Epoch [0/5], Step [2380/3236], Loss: 2.3090, Perplexity: 10.0645\\n\",\n      \"Epoch [0/5], Step [2390/3236], Loss: 2.3440, Perplexity: 10.4230\\n\",\n      \"Epoch [0/5], Step [2400/3236], Loss: 2.2924, Perplexity: 9.8982\\n\",\n      \"Epoch [0/5], Step [2410/3236], Loss: 2.3870, Perplexity: 10.8810\\n\",\n      \"Epoch [0/5], Step [2420/3236], Loss: 2.3919, Perplexity: 10.9347\\n\",\n      \"Epoch [0/5], Step [2430/3236], Loss: 2.0715, Perplexity: 7.9367\\n\",\n      \"Epoch [0/5], Step [2440/3236], Loss: 2.2738, Perplexity: 9.7159\\n\",\n      \"Epoch [0/5], Step [2450/3236], Loss: 2.3411, Perplexity: 10.3923\\n\",\n      \"Epoch [0/5], Step [2460/3236], Loss: 2.2894, Perplexity: 9.8687\\n\",\n      \"Epoch [0/5], Step [2470/3236], Loss: 2.3325, Perplexity: 10.3039\\n\",\n      \"Epoch [0/5], Step [2480/3236], Loss: 2.3614, Perplexity: 10.6058\\n\",\n      \"Epoch [0/5], Step [2490/3236], Loss: 2.3796, Perplexity: 10.8001\\n\",\n      \"Epoch [0/5], Step [2500/3236], Loss: 2.3464, Perplexity: 10.4481\\n\",\n      \"Epoch [0/5], Step [2510/3236], Loss: 2.4417, Perplexity: 11.4926\\n\",\n      \"Epoch [0/5], Step [2520/3236], Loss: 2.2462, Perplexity: 9.4514\\n\",\n      \"Epoch [0/5], Step [2530/3236], Loss: 2.3440, Perplexity: 10.4224\\n\",\n      \"Epoch [0/5], Step [2540/3236], Loss: 2.2825, Perplexity: 9.8015\\n\",\n      \"Epoch [0/5], Step [2550/3236], Loss: 2.2538, Perplexity: 9.5238\\n\",\n      \"Epoch [0/5], Step [2560/3236], Loss: 2.3661, Perplexity: 10.6562\\n\",\n      \"Epoch [0/5], Step [2570/3236], Loss: 2.2919, Perplexity: 9.8933\\n\",\n      \"Epoch [0/5], Step [2580/3236], Loss: 2.3439, Perplexity: 10.4215\\n\",\n      \"Epoch [0/5], Step [2590/3236], Loss: 2.1885, Perplexity: 8.9215\\n\",\n      \"Epoch [0/5], Step [2600/3236], Loss: 2.2882, Perplexity: 9.8569\\n\",\n      \"Epoch [0/5], Step [2610/3236], Loss: 2.4895, Perplexity: 12.0554\\n\",\n      \"Epoch [0/5], Step [2620/3236], Loss: 2.3098, Perplexity: 10.0722\\n\",\n      \"Epoch [0/5], Step [2630/3236], Loss: 2.2582, Perplexity: 9.5656\\n\",\n      \"Epoch [0/5], Step [2640/3236], Loss: 2.1383, Perplexity: 8.4847\\n\",\n      \"Epoch [0/5], Step [2650/3236], Loss: 2.3447, Perplexity: 10.4299\\n\",\n      \"Epoch [0/5], Step [2660/3236], Loss: 2.3293, Perplexity: 10.2710\\n\",\n      \"Epoch [0/5], Step [2670/3236], Loss: 2.3196, Perplexity: 10.1718\\n\",\n      \"Epoch [0/5], Step [2680/3236], Loss: 2.2376, Perplexity: 9.3709\\n\",\n      \"Epoch [0/5], Step [2690/3236], Loss: 2.2633, Perplexity: 9.6147\\n\",\n      \"Epoch [0/5], Step [2700/3236], Loss: 2.4018, Perplexity: 11.0432\\n\",\n      \"Epoch [0/5], Step [2710/3236], Loss: 2.2385, Perplexity: 9.3793\\n\",\n      \"Epoch [0/5], Step [2720/3236], Loss: 2.3810, Perplexity: 10.8158\\n\",\n      \"Epoch [0/5], Step [2730/3236], Loss: 2.3666, Perplexity: 10.6608\\n\",\n      \"Epoch [0/5], Step [2740/3236], Loss: 2.2942, Perplexity: 9.9170\\n\",\n      \"Epoch [0/5], Step [2750/3236], Loss: 2.1058, Perplexity: 8.2133\\n\",\n      \"Epoch [0/5], Step [2760/3236], Loss: 2.3379, Perplexity: 10.3591\\n\",\n      \"Epoch [0/5], Step [2770/3236], Loss: 2.3033, Perplexity: 10.0076\\n\",\n      \"Epoch [0/5], Step [2780/3236], Loss: 2.2257, Perplexity: 9.2599\\n\",\n      \"Epoch [0/5], Step [2790/3236], Loss: 2.3080, Perplexity: 10.0543\\n\",\n      \"Epoch [0/5], Step [2800/3236], Loss: 2.2811, Perplexity: 9.7879\\n\",\n      \"Epoch [0/5], Step [2810/3236], Loss: 2.3120, Perplexity: 10.0942\\n\",\n      \"Epoch [0/5], Step [2820/3236], Loss: 2.3427, Perplexity: 10.4090\\n\",\n      \"Epoch [0/5], Step [2830/3236], Loss: 2.1916, Perplexity: 8.9499\\n\",\n      \"Epoch [0/5], Step [2840/3236], Loss: 2.3450, Perplexity: 10.4333\\n\",\n      \"Epoch [0/5], Step [2850/3236], Loss: 2.3422, Perplexity: 10.4041\\n\",\n      \"Epoch [0/5], Step [2860/3236], Loss: 2.3109, Perplexity: 10.0833\\n\",\n      \"Epoch [0/5], Step [2870/3236], Loss: 2.2526, Perplexity: 9.5121\\n\",\n      \"Epoch [0/5], Step [2880/3236], Loss: 2.2988, Perplexity: 9.9621\\n\",\n      \"Epoch [0/5], Step [2890/3236], Loss: 2.1767, Perplexity: 8.8173\\n\",\n      \"Epoch [0/5], Step [2900/3236], Loss: 2.3893, Perplexity: 10.9055\\n\",\n      \"Epoch [0/5], Step [2910/3236], Loss: 2.2602, Perplexity: 9.5851\\n\",\n      \"Epoch [0/5], Step [2920/3236], Loss: 2.3303, Perplexity: 10.2806\\n\",\n      \"Epoch [0/5], Step [2930/3236], Loss: 2.2212, Perplexity: 9.2182\\n\",\n      \"Epoch [0/5], Step [2940/3236], Loss: 2.2398, Perplexity: 9.3910\\n\",\n      \"Epoch [0/5], Step [2950/3236], Loss: 2.3727, Perplexity: 10.7265\\n\",\n      \"Epoch [0/5], Step [2960/3236], Loss: 2.2045, Perplexity: 9.0659\\n\",\n      \"Epoch [0/5], Step [2970/3236], Loss: 2.1940, Perplexity: 8.9711\\n\",\n      \"Epoch [0/5], Step [2980/3236], Loss: 2.3615, Perplexity: 10.6073\\n\",\n      \"Epoch [0/5], Step [2990/3236], Loss: 2.3597, Perplexity: 10.5877\\n\",\n      \"Epoch [0/5], Step [3000/3236], Loss: 2.4384, Perplexity: 11.4549\\n\",\n      \"Epoch [0/5], Step [3010/3236], Loss: 2.3974, Perplexity: 10.9940\\n\",\n      \"Epoch [0/5], Step [3020/3236], Loss: 2.2113, Perplexity: 9.1273\\n\",\n      \"Epoch [0/5], Step [3030/3236], Loss: 2.1477, Perplexity: 8.5652\\n\",\n      \"Epoch [0/5], Step [3040/3236], Loss: 2.2928, Perplexity: 9.9031\\n\",\n      \"Epoch [0/5], Step [3050/3236], Loss: 2.0298, Perplexity: 7.6128\\n\",\n      \"Epoch [0/5], Step [3060/3236], Loss: 2.5319, Perplexity: 12.5774\\n\",\n      \"Epoch [0/5], Step [3070/3236], Loss: 2.1799, Perplexity: 8.8458\\n\",\n      \"Epoch [0/5], Step [3080/3236], Loss: 2.1757, Perplexity: 8.8086\\n\",\n      \"Epoch [0/5], Step [3090/3236], Loss: 2.4465, Perplexity: 11.5478\\n\",\n      \"Epoch [0/5], Step [3100/3236], Loss: 2.2651, Perplexity: 9.6324\\n\",\n      \"Epoch [0/5], Step [3110/3236], Loss: 2.4766, Perplexity: 11.9005\\n\",\n      \"Epoch [0/5], Step [3120/3236], Loss: 2.3208, Perplexity: 10.1835\\n\",\n      \"Epoch [0/5], Step [3130/3236], Loss: 2.2222, Perplexity: 9.2275\\n\",\n      \"Epoch [0/5], Step [3140/3236], Loss: 2.2172, Perplexity: 9.1812\\n\",\n      \"Epoch [0/5], Step [3150/3236], Loss: 2.2080, Perplexity: 9.0977\\n\",\n      \"Epoch [0/5], Step [3160/3236], Loss: 2.1846, Perplexity: 8.8874\\n\",\n      \"Epoch [0/5], Step [3170/3236], Loss: 2.3927, Perplexity: 10.9429\\n\",\n      \"Epoch [0/5], Step [3180/3236], Loss: 2.2257, Perplexity: 9.2601\\n\",\n      \"Epoch [0/5], Step [3190/3236], Loss: 2.1482, Perplexity: 8.5693\\n\",\n      \"Epoch [0/5], Step [3200/3236], Loss: 2.2989, Perplexity: 9.9633\\n\",\n      \"Epoch [0/5], Step [3210/3236], Loss: 2.3445, Perplexity: 10.4280\\n\",\n      \"Epoch [0/5], Step [3220/3236], Loss: 2.2069, Perplexity: 9.0875\\n\",\n      \"Epoch [0/5], Step [3230/3236], Loss: 2.2073, Perplexity: 9.0909\\n\",\n      \"Epoch [1/5], Step [0/3236], Loss: 2.1442, Perplexity: 8.5356\\n\",\n      \"Epoch [1/5], Step [10/3236], Loss: 2.1191, Perplexity: 8.3237\\n\",\n      \"Epoch [1/5], Step [20/3236], Loss: 2.1891, Perplexity: 8.9269\\n\",\n      \"Epoch [1/5], Step [30/3236], Loss: 2.2781, Perplexity: 9.7578\\n\",\n      \"Epoch [1/5], Step [40/3236], Loss: 2.2312, Perplexity: 9.3107\\n\",\n      \"Epoch [1/5], Step [50/3236], Loss: 2.1883, Perplexity: 8.9197\\n\",\n      \"Epoch [1/5], Step [60/3236], Loss: 2.1372, Perplexity: 8.4758\\n\",\n      \"Epoch [1/5], Step [70/3236], Loss: 2.2231, Perplexity: 9.2358\\n\",\n      \"Epoch [1/5], Step [80/3236], Loss: 2.2610, Perplexity: 9.5930\\n\",\n      \"Epoch [1/5], Step [90/3236], Loss: 2.0789, Perplexity: 7.9956\\n\",\n      \"Epoch [1/5], Step [100/3236], Loss: 2.1383, Perplexity: 8.4851\\n\",\n      \"Epoch [1/5], Step [110/3236], Loss: 2.2308, Perplexity: 9.3072\\n\",\n      \"Epoch [1/5], Step [120/3236], Loss: 2.2150, Perplexity: 9.1610\\n\",\n      \"Epoch [1/5], Step [130/3236], Loss: 2.1894, Perplexity: 8.9297\\n\",\n      \"Epoch [1/5], Step [140/3236], Loss: 2.3766, Perplexity: 10.7678\\n\",\n      \"Epoch [1/5], Step [150/3236], Loss: 2.2585, Perplexity: 9.5684\\n\",\n      \"Epoch [1/5], Step [160/3236], Loss: 2.1671, Perplexity: 8.7330\\n\",\n      \"Epoch [1/5], Step [170/3236], Loss: 2.2366, Perplexity: 9.3616\\n\",\n      \"Epoch [1/5], Step [180/3236], Loss: 2.1328, Perplexity: 8.4387\\n\",\n      \"Epoch [1/5], Step [190/3236], Loss: 2.2164, Perplexity: 9.1747\\n\",\n      \"Epoch [1/5], Step [200/3236], Loss: 2.1136, Perplexity: 8.2782\\n\",\n      \"Epoch [1/5], Step [210/3236], Loss: 2.1654, Perplexity: 8.7183\\n\",\n      \"Epoch [1/5], Step [220/3236], Loss: 2.1593, Perplexity: 8.6651\\n\",\n      \"Epoch [1/5], Step [230/3236], Loss: 2.2169, Perplexity: 9.1787\\n\",\n      \"Epoch [1/5], Step [240/3236], Loss: 2.0737, Perplexity: 7.9543\\n\",\n      \"Epoch [1/5], Step [250/3236], Loss: 2.2480, Perplexity: 9.4690\\n\",\n      \"Epoch [1/5], Step [260/3236], Loss: 2.2498, Perplexity: 9.4861\\n\",\n      \"Epoch [1/5], Step [270/3236], Loss: 2.1532, Perplexity: 8.6120\\n\",\n      \"Epoch [1/5], Step [280/3236], Loss: 2.2681, Perplexity: 9.6614\\n\",\n      \"Epoch [1/5], Step [290/3236], Loss: 2.3705, Perplexity: 10.7023\\n\",\n      \"Epoch [1/5], Step [300/3236], Loss: 2.1797, Perplexity: 8.8436\\n\",\n      \"Epoch [1/5], Step [310/3236], Loss: 2.2499, Perplexity: 9.4867\\n\",\n      \"Epoch [1/5], Step [320/3236], Loss: 2.2875, Perplexity: 9.8500\\n\",\n      \"Epoch [1/5], Step [330/3236], Loss: 2.0883, Perplexity: 8.0712\\n\",\n      \"Epoch [1/5], Step [340/3236], Loss: 2.1740, Perplexity: 8.7935\\n\",\n      \"Epoch [1/5], Step [350/3236], Loss: 2.1653, Perplexity: 8.7173\\n\",\n      \"Epoch [1/5], Step [360/3236], Loss: 2.2059, Perplexity: 9.0788\\n\",\n      \"Epoch [1/5], Step [370/3236], Loss: 2.1086, Perplexity: 8.2364\\n\",\n      \"Epoch [1/5], Step [380/3236], Loss: 1.9087, Perplexity: 6.7445\\n\",\n      \"Epoch [1/5], Step [390/3236], Loss: 2.1093, Perplexity: 8.2423\\n\",\n      \"Epoch [1/5], Step [400/3236], Loss: 2.0928, Perplexity: 8.1076\\n\",\n      \"Epoch [1/5], Step [410/3236], Loss: 2.1916, Perplexity: 8.9495\\n\",\n      \"Epoch [1/5], Step [420/3236], Loss: 2.1718, Perplexity: 8.7743\\n\",\n      \"Epoch [1/5], Step [430/3236], Loss: 2.1547, Perplexity: 8.6254\\n\",\n      \"Epoch [1/5], Step [440/3236], Loss: 2.1091, Perplexity: 8.2412\\n\",\n      \"Epoch [1/5], Step [450/3236], Loss: 2.1182, Perplexity: 8.3165\\n\",\n      \"Epoch [1/5], Step [460/3236], Loss: 2.2972, Perplexity: 9.9467\\n\",\n      \"Epoch [1/5], Step [470/3236], Loss: 2.1173, Perplexity: 8.3090\\n\",\n      \"Epoch [1/5], Step [480/3236], Loss: 2.2033, Perplexity: 9.0552\\n\",\n      \"Epoch [1/5], Step [490/3236], Loss: 2.2366, Perplexity: 9.3619\\n\",\n      \"Epoch [1/5], Step [500/3236], Loss: 2.1093, Perplexity: 8.2427\\n\",\n      \"Epoch [1/5], Step [510/3236], Loss: 2.2139, Perplexity: 9.1515\\n\",\n      \"Epoch [1/5], Step [520/3236], Loss: 2.1621, Perplexity: 8.6890\\n\",\n      \"Epoch [1/5], Step [530/3236], Loss: 2.1472, Perplexity: 8.5608\\n\",\n      \"Epoch [1/5], Step [540/3236], Loss: 2.1187, Perplexity: 8.3204\\n\",\n      \"Epoch [1/5], Step [550/3236], Loss: 2.0926, Perplexity: 8.1056\\n\",\n      \"Epoch [1/5], Step [560/3236], Loss: 2.1177, Perplexity: 8.3119\\n\",\n      \"Epoch [1/5], Step [570/3236], Loss: 2.0573, Perplexity: 7.8245\\n\",\n      \"Epoch [1/5], Step [580/3236], Loss: 2.1293, Perplexity: 8.4086\\n\",\n      \"Epoch [1/5], Step [590/3236], Loss: 2.1138, Perplexity: 8.2798\\n\",\n      \"Epoch [1/5], Step [600/3236], Loss: 2.3088, Perplexity: 10.0622\\n\",\n      \"Epoch [1/5], Step [610/3236], Loss: 2.3185, Perplexity: 10.1600\\n\",\n      \"Epoch [1/5], Step [620/3236], Loss: 2.1527, Perplexity: 8.6081\\n\",\n      \"Epoch [1/5], Step [630/3236], Loss: 2.2893, Perplexity: 9.8683\\n\",\n      \"Epoch [1/5], Step [640/3236], Loss: 2.2038, Perplexity: 9.0593\\n\",\n      \"Epoch [1/5], Step [650/3236], Loss: 2.1812, Perplexity: 8.8574\\n\",\n      \"Epoch [1/5], Step [660/3236], Loss: 2.1932, Perplexity: 8.9641\\n\",\n      \"Epoch [1/5], Step [670/3236], Loss: 2.2225, Perplexity: 9.2302\\n\",\n      \"Epoch [1/5], Step [680/3236], Loss: 2.1392, Perplexity: 8.4929\\n\",\n      \"Epoch [1/5], Step [690/3236], Loss: 2.0883, Perplexity: 8.0711\\n\",\n      \"Epoch [1/5], Step [700/3236], Loss: 2.1012, Perplexity: 8.1759\\n\",\n      \"Epoch [1/5], Step [710/3236], Loss: 1.9748, Perplexity: 7.2050\\n\",\n      \"Epoch [1/5], Step [720/3236], Loss: 2.0855, Perplexity: 8.0482\\n\",\n      \"Epoch [1/5], Step [730/3236], Loss: 2.2463, Perplexity: 9.4530\\n\",\n      \"Epoch [1/5], Step [740/3236], Loss: 2.1602, Perplexity: 8.6731\\n\",\n      \"Epoch [1/5], Step [750/3236], Loss: 2.1597, Perplexity: 8.6686\\n\",\n      \"Epoch [1/5], Step [760/3236], Loss: 2.1212, Perplexity: 8.3409\\n\",\n      \"Epoch [1/5], Step [770/3236], Loss: 2.1630, Perplexity: 8.6976\\n\",\n      \"Epoch [1/5], Step [780/3236], Loss: 2.1350, Perplexity: 8.4573\\n\",\n      \"Epoch [1/5], Step [790/3236], Loss: 2.1429, Perplexity: 8.5238\\n\",\n      \"Epoch [1/5], Step [800/3236], Loss: 2.2323, Perplexity: 9.3217\\n\",\n      \"Epoch [1/5], Step [810/3236], Loss: 2.2239, Perplexity: 9.2431\\n\",\n      \"Epoch [1/5], Step [820/3236], Loss: 2.1563, Perplexity: 8.6388\\n\",\n      \"Epoch [1/5], Step [830/3236], Loss: 2.2622, Perplexity: 9.6040\\n\",\n      \"Epoch [1/5], Step [840/3236], Loss: 2.2719, Perplexity: 9.6982\\n\",\n      \"Epoch [1/5], Step [850/3236], Loss: 2.0834, Perplexity: 8.0321\\n\",\n      \"Epoch [1/5], Step [860/3236], Loss: 2.0836, Perplexity: 8.0330\\n\",\n      \"Epoch [1/5], Step [870/3236], Loss: 2.1833, Perplexity: 8.8751\\n\",\n      \"Epoch [1/5], Step [880/3236], Loss: 1.9680, Perplexity: 7.1560\\n\",\n      \"Epoch [1/5], Step [890/3236], Loss: 2.0259, Perplexity: 7.5831\\n\",\n      \"Epoch [1/5], Step [900/3236], Loss: 2.1477, Perplexity: 8.5650\\n\",\n      \"Epoch [1/5], Step [910/3236], Loss: 2.1876, Perplexity: 8.9137\\n\",\n      \"Epoch [1/5], Step [920/3236], Loss: 2.0601, Perplexity: 7.8470\\n\",\n      \"Epoch [1/5], Step [930/3236], Loss: 2.0136, Perplexity: 7.4904\\n\",\n      \"Epoch [1/5], Step [940/3236], Loss: 2.1357, Perplexity: 8.4628\\n\",\n      \"Epoch [1/5], Step [950/3236], Loss: 2.1663, Perplexity: 8.7258\\n\",\n      \"Epoch [1/5], Step [960/3236], Loss: 2.0537, Perplexity: 7.7970\\n\",\n      \"Epoch [1/5], Step [970/3236], Loss: 2.0459, Perplexity: 7.7361\\n\",\n      \"Epoch [1/5], Step [980/3236], Loss: 2.2095, Perplexity: 9.1113\\n\",\n      \"Epoch [1/5], Step [990/3236], Loss: 2.1617, Perplexity: 8.6856\\n\",\n      \"Epoch [1/5], Step [1000/3236], Loss: 2.1542, Perplexity: 8.6208\\n\",\n      \"Epoch [1/5], Step [1010/3236], Loss: 2.0422, Perplexity: 7.7076\\n\",\n      \"Epoch [1/5], Step [1020/3236], Loss: 2.1892, Perplexity: 8.9280\\n\",\n      \"Epoch [1/5], Step [1030/3236], Loss: 2.1699, Perplexity: 8.7571\\n\",\n      \"Epoch [1/5], Step [1040/3236], Loss: 2.1326, Perplexity: 8.4366\\n\",\n      \"Epoch [1/5], Step [1050/3236], Loss: 2.1117, Perplexity: 8.2626\\n\",\n      \"Epoch [1/5], Step [1060/3236], Loss: 2.0999, Perplexity: 8.1653\\n\",\n      \"Epoch [1/5], Step [1070/3236], Loss: 1.9380, Perplexity: 6.9445\\n\",\n      \"Epoch [1/5], Step [1080/3236], Loss: 2.1156, Perplexity: 8.2949\\n\",\n      \"Epoch [1/5], Step [1090/3236], Loss: 2.1923, Perplexity: 8.9561\\n\",\n      \"Epoch [1/5], Step [1100/3236], Loss: 2.2008, Perplexity: 9.0322\\n\",\n      \"Epoch [1/5], Step [1110/3236], Loss: 2.1727, Perplexity: 8.7819\\n\",\n      \"Epoch [1/5], Step [1120/3236], Loss: 2.2367, Perplexity: 9.3625\\n\",\n      \"Epoch [1/5], Step [1130/3236], Loss: 2.3724, Perplexity: 10.7230\\n\",\n      \"Epoch [1/5], Step [1140/3236], Loss: 2.0653, Perplexity: 7.8874\\n\",\n      \"Epoch [1/5], Step [1150/3236], Loss: 2.1360, Perplexity: 8.4659\\n\",\n      \"Epoch [1/5], Step [1160/3236], Loss: 2.1585, Perplexity: 8.6579\\n\",\n      \"Epoch [1/5], Step [1170/3236], Loss: 2.2024, Perplexity: 9.0471\\n\",\n      \"Epoch [1/5], Step [1180/3236], Loss: 2.1657, Perplexity: 8.7207\\n\",\n      \"Epoch [1/5], Step [1190/3236], Loss: 2.2590, Perplexity: 9.5735\\n\",\n      \"Epoch [1/5], Step [1200/3236], Loss: 2.0869, Perplexity: 8.0597\\n\",\n      \"Epoch [1/5], Step [1210/3236], Loss: 2.1778, Perplexity: 8.8267\\n\",\n      \"Epoch [1/5], Step [1220/3236], Loss: 2.1159, Perplexity: 8.2970\\n\",\n      \"Epoch [1/5], Step [1230/3236], Loss: 2.1627, Perplexity: 8.6948\\n\",\n      \"Epoch [1/5], Step [1240/3236], Loss: 2.2172, Perplexity: 9.1816\\n\",\n      \"Epoch [1/5], Step [1250/3236], Loss: 2.3553, Perplexity: 10.5417\\n\",\n      \"Epoch [1/5], Step [1260/3236], Loss: 2.0628, Perplexity: 7.8682\\n\",\n      \"Epoch [1/5], Step [1270/3236], Loss: 2.2034, Perplexity: 9.0561\\n\",\n      \"Epoch [1/5], Step [1280/3236], Loss: 2.2095, Perplexity: 9.1115\\n\",\n      \"Epoch [1/5], Step [1290/3236], Loss: 2.1066, Perplexity: 8.2200\\n\",\n      \"Epoch [1/5], Step [1300/3236], Loss: 2.1120, Perplexity: 8.2651\\n\",\n      \"Epoch [1/5], Step [1310/3236], Loss: 2.2324, Perplexity: 9.3224\\n\",\n      \"Epoch [1/5], Step [1320/3236], Loss: 2.2913, Perplexity: 9.8876\\n\",\n      \"Epoch [1/5], Step [1330/3236], Loss: 2.2980, Perplexity: 9.9541\\n\",\n      \"Epoch [1/5], Step [1340/3236], Loss: 2.0836, Perplexity: 8.0336\\n\",\n      \"Epoch [1/5], Step [1350/3236], Loss: 2.1349, Perplexity: 8.4558\\n\",\n      \"Epoch [1/5], Step [1360/3236], Loss: 2.1410, Perplexity: 8.5080\\n\",\n      \"Epoch [1/5], Step [1370/3236], Loss: 2.1204, Perplexity: 8.3348\\n\",\n      \"Epoch [1/5], Step [1380/3236], Loss: 2.1590, Perplexity: 8.6625\\n\",\n      \"Epoch [1/5], Step [1390/3236], Loss: 2.1113, Perplexity: 8.2588\\n\",\n      \"Epoch [1/5], Step [1400/3236], Loss: 2.0477, Perplexity: 7.7499\\n\",\n      \"Epoch [1/5], Step [1410/3236], Loss: 2.2444, Perplexity: 9.4344\\n\",\n      \"Epoch [1/5], Step [1420/3236], Loss: 2.1803, Perplexity: 8.8491\\n\",\n      \"Epoch [1/5], Step [1430/3236], Loss: 2.2038, Perplexity: 9.0594\\n\",\n      \"Epoch [1/5], Step [1440/3236], Loss: 2.1393, Perplexity: 8.4932\\n\",\n      \"Epoch [1/5], Step [1450/3236], Loss: 2.1527, Perplexity: 8.6081\\n\",\n      \"Epoch [1/5], Step [1460/3236], Loss: 2.1755, Perplexity: 8.8070\\n\",\n      \"Epoch [1/5], Step [1470/3236], Loss: 2.1719, Perplexity: 8.7750\\n\",\n      \"Epoch [1/5], Step [1480/3236], Loss: 1.9647, Perplexity: 7.1330\\n\",\n      \"Epoch [1/5], Step [1490/3236], Loss: 2.1633, Perplexity: 8.6996\\n\",\n      \"Epoch [1/5], Step [1500/3236], Loss: 2.1515, Perplexity: 8.5975\\n\",\n      \"Epoch [1/5], Step [1510/3236], Loss: 2.1841, Perplexity: 8.8824\\n\",\n      \"Epoch [1/5], Step [1520/3236], Loss: 2.0912, Perplexity: 8.0950\\n\",\n      \"Epoch [1/5], Step [1530/3236], Loss: 2.2794, Perplexity: 9.7705\\n\",\n      \"Epoch [1/5], Step [1540/3236], Loss: 2.0968, Perplexity: 8.1398\\n\",\n      \"Epoch [1/5], Step [1550/3236], Loss: 2.1595, Perplexity: 8.6667\\n\",\n      \"Epoch [1/5], Step [1560/3236], Loss: 2.1649, Perplexity: 8.7136\\n\",\n      \"Epoch [1/5], Step [1570/3236], Loss: 2.0524, Perplexity: 7.7864\\n\",\n      \"Epoch [1/5], Step [1580/3236], Loss: 2.0679, Perplexity: 7.9082\\n\",\n      \"Epoch [1/5], Step [1590/3236], Loss: 2.2441, Perplexity: 9.4315\\n\",\n      \"Epoch [1/5], Step [1600/3236], Loss: 2.0884, Perplexity: 8.0724\\n\",\n      \"Epoch [1/5], Step [1610/3236], Loss: 2.0586, Perplexity: 7.8348\\n\",\n      \"Epoch [1/5], Step [1620/3236], Loss: 2.0254, Perplexity: 7.5789\\n\",\n      \"Epoch [1/5], Step [1630/3236], Loss: 2.0041, Perplexity: 7.4198\\n\",\n      \"Epoch [1/5], Step [1640/3236], Loss: 2.0881, Perplexity: 8.0699\\n\",\n      \"Epoch [1/5], Step [1650/3236], Loss: 2.1617, Perplexity: 8.6857\\n\",\n      \"Epoch [1/5], Step [1660/3236], Loss: 2.1348, Perplexity: 8.4553\\n\",\n      \"Epoch [1/5], Step [1670/3236], Loss: 2.1136, Perplexity: 8.2779\\n\",\n      \"Epoch [1/5], Step [1680/3236], Loss: 2.2307, Perplexity: 9.3066\\n\",\n      \"Epoch [1/5], Step [1690/3236], Loss: 2.1442, Perplexity: 8.5356\\n\",\n      \"Epoch [1/5], Step [1700/3236], Loss: 2.1768, Perplexity: 8.8179\\n\",\n      \"Epoch [1/5], Step [1710/3236], Loss: 2.2308, Perplexity: 9.3077\\n\",\n      \"Epoch [1/5], Step [1720/3236], Loss: 2.2072, Perplexity: 9.0903\\n\",\n      \"Epoch [1/5], Step [1730/3236], Loss: 2.1928, Perplexity: 8.9602\\n\",\n      \"Epoch [1/5], Step [1740/3236], Loss: 2.0623, Perplexity: 7.8639\\n\",\n      \"Epoch [1/5], Step [1750/3236], Loss: 2.2527, Perplexity: 9.5136\\n\",\n      \"Epoch [1/5], Step [1760/3236], Loss: 2.1156, Perplexity: 8.2949\\n\",\n      \"Epoch [1/5], Step [1770/3236], Loss: 2.1719, Perplexity: 8.7746\\n\",\n      \"Epoch [1/5], Step [1780/3236], Loss: 2.1943, Perplexity: 8.9735\\n\",\n      \"Epoch [1/5], Step [1790/3236], Loss: 2.2538, Perplexity: 9.5234\\n\",\n      \"Epoch [1/5], Step [1800/3236], Loss: 2.1658, Perplexity: 8.7213\\n\",\n      \"Epoch [1/5], Step [1810/3236], Loss: 2.1826, Perplexity: 8.8696\\n\",\n      \"Epoch [1/5], Step [1820/3236], Loss: 2.0682, Perplexity: 7.9107\\n\",\n      \"Epoch [1/5], Step [1830/3236], Loss: 2.1442, Perplexity: 8.5349\\n\",\n      \"Epoch [1/5], Step [1840/3236], Loss: 1.9848, Perplexity: 7.2778\\n\",\n      \"Epoch [1/5], Step [1850/3236], Loss: 2.2563, Perplexity: 9.5481\\n\",\n      \"Epoch [1/5], Step [1860/3236], Loss: 2.0558, Perplexity: 7.8128\\n\",\n      \"Epoch [1/5], Step [1870/3236], Loss: 2.0563, Perplexity: 7.8167\\n\",\n      \"Epoch [1/5], Step [1880/3236], Loss: 2.3317, Perplexity: 10.2959\\n\",\n      \"Epoch [1/5], Step [1890/3236], Loss: 2.1540, Perplexity: 8.6195\\n\",\n      \"Epoch [1/5], Step [1900/3236], Loss: 2.1050, Perplexity: 8.2070\\n\",\n      \"Epoch [1/5], Step [1910/3236], Loss: 2.1055, Perplexity: 8.2114\\n\",\n      \"Epoch [1/5], Step [1920/3236], Loss: 2.1561, Perplexity: 8.6375\\n\",\n      \"Epoch [1/5], Step [1930/3236], Loss: 2.2437, Perplexity: 9.4282\\n\",\n      \"Epoch [1/5], Step [1940/3236], Loss: 2.0709, Perplexity: 7.9316\\n\",\n      \"Epoch [1/5], Step [1950/3236], Loss: 2.2351, Perplexity: 9.3478\\n\",\n      \"Epoch [1/5], Step [1960/3236], Loss: 2.1453, Perplexity: 8.5450\\n\",\n      \"Epoch [1/5], Step [1970/3236], Loss: 2.1193, Perplexity: 8.3254\\n\",\n      \"Epoch [1/5], Step [1980/3236], Loss: 2.1086, Perplexity: 8.2370\\n\",\n      \"Epoch [1/5], Step [1990/3236], Loss: 2.1156, Perplexity: 8.2943\\n\",\n      \"Epoch [1/5], Step [2000/3236], Loss: 2.0211, Perplexity: 7.5468\\n\",\n      \"Epoch [1/5], Step [2010/3236], Loss: 2.0930, Perplexity: 8.1088\\n\",\n      \"Epoch [1/5], Step [2020/3236], Loss: 2.1497, Perplexity: 8.5823\\n\",\n      \"Epoch [1/5], Step [2030/3236], Loss: 2.0736, Perplexity: 7.9534\\n\",\n      \"Epoch [1/5], Step [2040/3236], Loss: 1.9467, Perplexity: 7.0055\\n\",\n      \"Epoch [1/5], Step [2050/3236], Loss: 2.1151, Perplexity: 8.2908\\n\",\n      \"Epoch [1/5], Step [2060/3236], Loss: 2.0660, Perplexity: 7.8930\\n\",\n      \"Epoch [1/5], Step [2070/3236], Loss: 2.0995, Perplexity: 8.1623\\n\",\n      \"Epoch [1/5], Step [2080/3236], Loss: 2.0574, Perplexity: 7.8253\\n\",\n      \"Epoch [1/5], Step [2090/3236], Loss: 2.1619, Perplexity: 8.6880\\n\",\n      \"Epoch [1/5], Step [2100/3236], Loss: 2.1338, Perplexity: 8.4467\\n\",\n      \"Epoch [1/5], Step [2110/3236], Loss: 2.1301, Perplexity: 8.4160\\n\",\n      \"Epoch [1/5], Step [2120/3236], Loss: 2.1823, Perplexity: 8.8669\\n\",\n      \"Epoch [1/5], Step [2130/3236], Loss: 2.1105, Perplexity: 8.2526\\n\",\n      \"Epoch [1/5], Step [2140/3236], Loss: 2.2433, Perplexity: 9.4245\\n\",\n      \"Epoch [1/5], Step [2150/3236], Loss: 2.1114, Perplexity: 8.2597\\n\",\n      \"Epoch [1/5], Step [2160/3236], Loss: 2.0552, Perplexity: 7.8081\\n\",\n      \"Epoch [1/5], Step [2170/3236], Loss: 2.1554, Perplexity: 8.6316\\n\",\n      \"Epoch [1/5], Step [2180/3236], Loss: 2.0368, Perplexity: 7.6661\\n\",\n      \"Epoch [1/5], Step [2190/3236], Loss: 2.0618, Perplexity: 7.8604\\n\",\n      \"Epoch [1/5], Step [2200/3236], Loss: 2.1665, Perplexity: 8.7275\\n\",\n      \"Epoch [1/5], Step [2210/3236], Loss: 2.0116, Perplexity: 7.4756\\n\",\n      \"Epoch [1/5], Step [2220/3236], Loss: 2.3003, Perplexity: 9.9767\\n\",\n      \"Epoch [1/5], Step [2230/3236], Loss: 1.9847, Perplexity: 7.2772\\n\",\n      \"Epoch [1/5], Step [2240/3236], Loss: 2.0384, Perplexity: 7.6780\\n\",\n      \"Epoch [1/5], Step [2250/3236], Loss: 2.2563, Perplexity: 9.5476\\n\",\n      \"Epoch [1/5], Step [2260/3236], Loss: 2.2402, Perplexity: 9.3953\\n\",\n      \"Epoch [1/5], Step [2270/3236], Loss: 2.1042, Perplexity: 8.2003\\n\",\n      \"Epoch [1/5], Step [2280/3236], Loss: 2.1559, Perplexity: 8.6356\\n\",\n      \"Epoch [1/5], Step [2290/3236], Loss: 2.2839, Perplexity: 9.8146\\n\",\n      \"Epoch [1/5], Step [2300/3236], Loss: 2.1419, Perplexity: 8.5156\\n\",\n      \"Epoch [1/5], Step [2310/3236], Loss: 2.0976, Perplexity: 8.1470\\n\",\n      \"Epoch [1/5], Step [2320/3236], Loss: 2.0368, Perplexity: 7.6659\\n\",\n      \"Epoch [1/5], Step [2330/3236], Loss: 2.0859, Perplexity: 8.0518\\n\",\n      \"Epoch [1/5], Step [2340/3236], Loss: 2.2351, Perplexity: 9.3478\\n\",\n      \"Epoch [1/5], Step [2350/3236], Loss: 2.1369, Perplexity: 8.4731\\n\",\n      \"Epoch [1/5], Step [2360/3236], Loss: 2.0908, Perplexity: 8.0914\\n\",\n      \"Epoch [1/5], Step [2370/3236], Loss: 2.1435, Perplexity: 8.5292\\n\",\n      \"Epoch [1/5], Step [2380/3236], Loss: 2.1417, Perplexity: 8.5136\\n\",\n      \"Epoch [1/5], Step [2390/3236], Loss: 2.0984, Perplexity: 8.1534\\n\",\n      \"Epoch [1/5], Step [2400/3236], Loss: 2.0378, Perplexity: 7.6737\\n\",\n      \"Epoch [1/5], Step [2410/3236], Loss: 1.9260, Perplexity: 6.8619\\n\",\n      \"Epoch [1/5], Step [2420/3236], Loss: 2.0991, Perplexity: 8.1589\\n\",\n      \"Epoch [1/5], Step [2430/3236], Loss: 1.9822, Perplexity: 7.2589\\n\",\n      \"Epoch [1/5], Step [2440/3236], Loss: 1.9360, Perplexity: 6.9310\\n\",\n      \"Epoch [1/5], Step [2450/3236], Loss: 2.1250, Perplexity: 8.3729\\n\",\n      \"Epoch [1/5], Step [2460/3236], Loss: 2.0957, Perplexity: 8.1308\\n\",\n      \"Epoch [1/5], Step [2470/3236], Loss: 2.1376, Perplexity: 8.4788\\n\",\n      \"Epoch [1/5], Step [2480/3236], Loss: 2.2403, Perplexity: 9.3962\\n\",\n      \"Epoch [1/5], Step [2490/3236], Loss: 2.0818, Perplexity: 8.0192\\n\",\n      \"Epoch [1/5], Step [2500/3236], Loss: 2.1297, Perplexity: 8.4127\\n\",\n      \"Epoch [1/5], Step [2510/3236], Loss: 2.0998, Perplexity: 8.1647\\n\",\n      \"Epoch [1/5], Step [2520/3236], Loss: 1.9356, Perplexity: 6.9283\\n\",\n      \"Epoch [1/5], Step [2530/3236], Loss: 2.1427, Perplexity: 8.5227\\n\",\n      \"Epoch [1/5], Step [2540/3236], Loss: 2.1330, Perplexity: 8.4405\\n\",\n      \"Epoch [1/5], Step [2550/3236], Loss: 1.9982, Perplexity: 7.3755\\n\",\n      \"Epoch [1/5], Step [2560/3236], Loss: 2.1704, Perplexity: 8.7616\\n\",\n      \"Epoch [1/5], Step [2570/3236], Loss: 2.0963, Perplexity: 8.1358\\n\",\n      \"Epoch [1/5], Step [2580/3236], Loss: 2.1125, Perplexity: 8.2692\\n\",\n      \"Epoch [1/5], Step [2590/3236], Loss: 2.2239, Perplexity: 9.2431\\n\",\n      \"Epoch [1/5], Step [2600/3236], Loss: 2.2494, Perplexity: 9.4822\\n\",\n      \"Epoch [1/5], Step [2610/3236], Loss: 2.1887, Perplexity: 8.9235\\n\",\n      \"Epoch [1/5], Step [2620/3236], Loss: 2.0317, Perplexity: 7.6272\\n\",\n      \"Epoch [1/5], Step [2630/3236], Loss: 2.1827, Perplexity: 8.8701\\n\",\n      \"Epoch [1/5], Step [2640/3236], Loss: 2.1738, Perplexity: 8.7913\\n\",\n      \"Epoch [1/5], Step [2650/3236], Loss: 2.0096, Perplexity: 7.4607\\n\",\n      \"Epoch [1/5], Step [2660/3236], Loss: 1.9863, Perplexity: 7.2887\\n\",\n      \"Epoch [1/5], Step [2670/3236], Loss: 2.1266, Perplexity: 8.3860\\n\",\n      \"Epoch [1/5], Step [2680/3236], Loss: 2.0609, Perplexity: 7.8533\\n\",\n      \"Epoch [1/5], Step [2690/3236], Loss: 2.1312, Perplexity: 8.4251\\n\",\n      \"Epoch [1/5], Step [2700/3236], Loss: 2.1779, Perplexity: 8.8280\\n\",\n      \"Epoch [1/5], Step [2710/3236], Loss: 2.0335, Perplexity: 7.6408\\n\",\n      \"Epoch [1/5], Step [2720/3236], Loss: 2.0115, Perplexity: 7.4745\\n\",\n      \"Epoch [1/5], Step [2730/3236], Loss: 2.2344, Perplexity: 9.3410\\n\",\n      \"Epoch [1/5], Step [2740/3236], Loss: 2.0422, Perplexity: 7.7079\\n\",\n      \"Epoch [1/5], Step [2750/3236], Loss: 2.0567, Perplexity: 7.8203\\n\",\n      \"Epoch [1/5], Step [2760/3236], Loss: 2.0705, Perplexity: 7.9284\\n\",\n      \"Epoch [1/5], Step [2770/3236], Loss: 2.0768, Perplexity: 7.9787\\n\",\n      \"Epoch [1/5], Step [2780/3236], Loss: 2.1501, Perplexity: 8.5858\\n\",\n      \"Epoch [1/5], Step [2790/3236], Loss: 2.1607, Perplexity: 8.6772\\n\",\n      \"Epoch [1/5], Step [2800/3236], Loss: 2.0509, Perplexity: 7.7749\\n\",\n      \"Epoch [1/5], Step [2810/3236], Loss: 2.1714, Perplexity: 8.7708\\n\",\n      \"Epoch [1/5], Step [2820/3236], Loss: 2.2534, Perplexity: 9.5203\\n\",\n      \"Epoch [1/5], Step [2830/3236], Loss: 2.1158, Perplexity: 8.2960\\n\",\n      \"Epoch [1/5], Step [2840/3236], Loss: 2.1870, Perplexity: 8.9088\\n\",\n      \"Epoch [1/5], Step [2850/3236], Loss: 2.0478, Perplexity: 7.7509\\n\",\n      \"Epoch [1/5], Step [2860/3236], Loss: 2.2462, Perplexity: 9.4515\\n\",\n      \"Epoch [1/5], Step [2870/3236], Loss: 2.1998, Perplexity: 9.0231\\n\",\n      \"Epoch [1/5], Step [2880/3236], Loss: 2.1156, Perplexity: 8.2944\\n\",\n      \"Epoch [1/5], Step [2890/3236], Loss: 2.1369, Perplexity: 8.4732\\n\",\n      \"Epoch [1/5], Step [2900/3236], Loss: 2.0931, Perplexity: 8.1104\\n\",\n      \"Epoch [1/5], Step [2910/3236], Loss: 2.0578, Perplexity: 7.8287\\n\",\n      \"Epoch [1/5], Step [2920/3236], Loss: 2.0967, Perplexity: 8.1395\\n\",\n      \"Epoch [1/5], Step [2930/3236], Loss: 1.9693, Perplexity: 7.1654\\n\",\n      \"Epoch [1/5], Step [2940/3236], Loss: 2.0966, Perplexity: 8.1383\\n\",\n      \"Epoch [1/5], Step [2950/3236], Loss: 2.0820, Perplexity: 8.0207\\n\",\n      \"Epoch [1/5], Step [2960/3236], Loss: 2.0940, Perplexity: 8.1177\\n\",\n      \"Epoch [1/5], Step [2970/3236], Loss: 2.1171, Perplexity: 8.3068\\n\",\n      \"Epoch [1/5], Step [2980/3236], Loss: 2.0420, Perplexity: 7.7057\\n\",\n      \"Epoch [1/5], Step [2990/3236], Loss: 2.0642, Perplexity: 7.8787\\n\",\n      \"Epoch [1/5], Step [3000/3236], Loss: 2.1407, Perplexity: 8.5056\\n\",\n      \"Epoch [1/5], Step [3010/3236], Loss: 2.2105, Perplexity: 9.1202\\n\",\n      \"Epoch [1/5], Step [3020/3236], Loss: 2.1529, Perplexity: 8.6097\\n\",\n      \"Epoch [1/5], Step [3030/3236], Loss: 2.2517, Perplexity: 9.5040\\n\",\n      \"Epoch [1/5], Step [3040/3236], Loss: 1.9146, Perplexity: 6.7842\\n\",\n      \"Epoch [1/5], Step [3050/3236], Loss: 2.1614, Perplexity: 8.6836\\n\",\n      \"Epoch [1/5], Step [3060/3236], Loss: 1.9738, Perplexity: 7.1980\\n\",\n      \"Epoch [1/5], Step [3070/3236], Loss: 2.1987, Perplexity: 9.0131\\n\",\n      \"Epoch [1/5], Step [3080/3236], Loss: 2.1664, Perplexity: 8.7268\\n\",\n      \"Epoch [1/5], Step [3090/3236], Loss: 2.0991, Perplexity: 8.1590\\n\",\n      \"Epoch [1/5], Step [3100/3236], Loss: 2.1277, Perplexity: 8.3952\\n\",\n      \"Epoch [1/5], Step [3110/3236], Loss: 2.0108, Perplexity: 7.4696\\n\",\n      \"Epoch [1/5], Step [3120/3236], Loss: 2.0818, Perplexity: 8.0192\\n\",\n      \"Epoch [1/5], Step [3130/3236], Loss: 2.0922, Perplexity: 8.1024\\n\",\n      \"Epoch [1/5], Step [3140/3236], Loss: 2.1674, Perplexity: 8.7357\\n\",\n      \"Epoch [1/5], Step [3150/3236], Loss: 2.0824, Perplexity: 8.0241\\n\",\n      \"Epoch [1/5], Step [3160/3236], Loss: 2.2528, Perplexity: 9.5146\\n\",\n      \"Epoch [1/5], Step [3170/3236], Loss: 2.1422, Perplexity: 8.5185\\n\",\n      \"Epoch [1/5], Step [3180/3236], Loss: 2.1179, Perplexity: 8.3133\\n\",\n      \"Epoch [1/5], Step [3190/3236], Loss: 2.0235, Perplexity: 7.5650\\n\",\n      \"Epoch [1/5], Step [3200/3236], Loss: 2.0888, Perplexity: 8.0754\\n\",\n      \"Epoch [1/5], Step [3210/3236], Loss: 1.9576, Perplexity: 7.0822\\n\",\n      \"Epoch [1/5], Step [3220/3236], Loss: 2.0453, Perplexity: 7.7311\\n\",\n      \"Epoch [1/5], Step [3230/3236], Loss: 2.0739, Perplexity: 7.9554\\n\",\n      \"Epoch [2/5], Step [0/3236], Loss: 1.9296, Perplexity: 6.8868\\n\",\n      \"Epoch [2/5], Step [10/3236], Loss: 1.9732, Perplexity: 7.1937\\n\",\n      \"Epoch [2/5], Step [20/3236], Loss: 1.9518, Perplexity: 7.0417\\n\",\n      \"Epoch [2/5], Step [30/3236], Loss: 1.9884, Perplexity: 7.3040\\n\",\n      \"Epoch [2/5], Step [40/3236], Loss: 2.0336, Perplexity: 7.6418\\n\",\n      \"Epoch [2/5], Step [50/3236], Loss: 1.9785, Perplexity: 7.2320\\n\",\n      \"Epoch [2/5], Step [60/3236], Loss: 1.9558, Perplexity: 7.0696\\n\",\n      \"Epoch [2/5], Step [70/3236], Loss: 2.1200, Perplexity: 8.3309\\n\",\n      \"Epoch [2/5], Step [80/3236], Loss: 2.2138, Perplexity: 9.1507\\n\",\n      \"Epoch [2/5], Step [90/3236], Loss: 2.0110, Perplexity: 7.4707\\n\",\n      \"Epoch [2/5], Step [100/3236], Loss: 1.9568, Perplexity: 7.0767\\n\",\n      \"Epoch [2/5], Step [110/3236], Loss: 1.9280, Perplexity: 6.8760\\n\",\n      \"Epoch [2/5], Step [120/3236], Loss: 1.9410, Perplexity: 6.9659\\n\",\n      \"Epoch [2/5], Step [130/3236], Loss: 1.9941, Perplexity: 7.3460\\n\",\n      \"Epoch [2/5], Step [140/3236], Loss: 2.0146, Perplexity: 7.4975\\n\",\n      \"Epoch [2/5], Step [150/3236], Loss: 2.0203, Perplexity: 7.5407\\n\",\n      \"Epoch [2/5], Step [160/3236], Loss: 1.9898, Perplexity: 7.3139\\n\",\n      \"Epoch [2/5], Step [170/3236], Loss: 1.8744, Perplexity: 6.5170\\n\",\n      \"Epoch [2/5], Step [180/3236], Loss: 1.9964, Perplexity: 7.3628\\n\",\n      \"Epoch [2/5], Step [190/3236], Loss: 1.8389, Perplexity: 6.2894\\n\",\n      \"Epoch [2/5], Step [200/3236], Loss: 1.9867, Perplexity: 7.2912\\n\",\n      \"Epoch [2/5], Step [210/3236], Loss: 1.9437, Perplexity: 6.9843\\n\",\n      \"Epoch [2/5], Step [220/3236], Loss: 1.9993, Perplexity: 7.3840\\n\",\n      \"Epoch [2/5], Step [230/3236], Loss: 1.9483, Perplexity: 7.0164\\n\",\n      \"Epoch [2/5], Step [240/3236], Loss: 1.9745, Perplexity: 7.2028\\n\",\n      \"Epoch [2/5], Step [250/3236], Loss: 2.0163, Perplexity: 7.5107\\n\",\n      \"Epoch [2/5], Step [260/3236], Loss: 1.9392, Perplexity: 6.9535\\n\",\n      \"Epoch [2/5], Step [270/3236], Loss: 2.0132, Perplexity: 7.4875\\n\",\n      \"Epoch [2/5], Step [280/3236], Loss: 2.0057, Perplexity: 7.4316\\n\",\n      \"Epoch [2/5], Step [290/3236], Loss: 1.9861, Perplexity: 7.2873\\n\",\n      \"Epoch [2/5], Step [300/3236], Loss: 1.9175, Perplexity: 6.8036\\n\",\n      \"Epoch [2/5], Step [310/3236], Loss: 2.0381, Perplexity: 7.6763\\n\",\n      \"Epoch [2/5], Step [320/3236], Loss: 2.0001, Perplexity: 7.3895\\n\",\n      \"Epoch [2/5], Step [330/3236], Loss: 2.0648, Perplexity: 7.8841\\n\",\n      \"Epoch [2/5], Step [340/3236], Loss: 2.0217, Perplexity: 7.5515\\n\",\n      \"Epoch [2/5], Step [350/3236], Loss: 1.9962, Perplexity: 7.3608\\n\",\n      \"Epoch [2/5], Step [360/3236], Loss: 2.0276, Perplexity: 7.5958\\n\",\n      \"Epoch [2/5], Step [370/3236], Loss: 1.8972, Perplexity: 6.6669\\n\",\n      \"Epoch [2/5], Step [380/3236], Loss: 1.8784, Perplexity: 6.5429\\n\",\n      \"Epoch [2/5], Step [390/3236], Loss: 1.9471, Perplexity: 7.0081\\n\",\n      \"Epoch [2/5], Step [400/3236], Loss: 1.9797, Perplexity: 7.2406\\n\",\n      \"Epoch [2/5], Step [410/3236], Loss: 1.9755, Perplexity: 7.2102\\n\",\n      \"Epoch [2/5], Step [420/3236], Loss: 1.9417, Perplexity: 6.9706\\n\",\n      \"Epoch [2/5], Step [430/3236], Loss: 2.1504, Perplexity: 8.5881\\n\",\n      \"Epoch [2/5], Step [440/3236], Loss: 2.0245, Perplexity: 7.5725\\n\",\n      \"Epoch [2/5], Step [450/3236], Loss: 2.1107, Perplexity: 8.2537\\n\",\n      \"Epoch [2/5], Step [460/3236], Loss: 1.9828, Perplexity: 7.2629\\n\",\n      \"Epoch [2/5], Step [470/3236], Loss: 2.0781, Perplexity: 7.9890\\n\",\n      \"Epoch [2/5], Step [480/3236], Loss: 1.9687, Perplexity: 7.1610\\n\",\n      \"Epoch [2/5], Step [490/3236], Loss: 1.9928, Perplexity: 7.3362\\n\",\n      \"Epoch [2/5], Step [500/3236], Loss: 2.0398, Perplexity: 7.6893\\n\",\n      \"Epoch [2/5], Step [510/3236], Loss: 1.9720, Perplexity: 7.1851\\n\",\n      \"Epoch [2/5], Step [520/3236], Loss: 1.9637, Perplexity: 7.1255\\n\",\n      \"Epoch [2/5], Step [530/3236], Loss: 1.9349, Perplexity: 6.9232\\n\",\n      \"Epoch [2/5], Step [540/3236], Loss: 2.0463, Perplexity: 7.7394\\n\",\n      \"Epoch [2/5], Step [550/3236], Loss: 1.9969, Perplexity: 7.3660\\n\",\n      \"Epoch [2/5], Step [560/3236], Loss: 1.9851, Perplexity: 7.2799\\n\",\n      \"Epoch [2/5], Step [570/3236], Loss: 2.0530, Perplexity: 7.7912\\n\",\n      \"Epoch [2/5], Step [580/3236], Loss: 2.0611, Perplexity: 7.8548\\n\",\n      \"Epoch [2/5], Step [590/3236], Loss: 2.0283, Perplexity: 7.6008\\n\",\n      \"Epoch [2/5], Step [600/3236], Loss: 2.0927, Perplexity: 8.1071\\n\",\n      \"Epoch [2/5], Step [610/3236], Loss: 2.0518, Perplexity: 7.7820\\n\",\n      \"Epoch [2/5], Step [620/3236], Loss: 1.9344, Perplexity: 6.9196\\n\",\n      \"Epoch [2/5], Step [630/3236], Loss: 2.1396, Perplexity: 8.4963\\n\",\n      \"Epoch [2/5], Step [640/3236], Loss: 1.9983, Perplexity: 7.3765\\n\",\n      \"Epoch [2/5], Step [650/3236], Loss: 2.0543, Perplexity: 7.8013\\n\",\n      \"Epoch [2/5], Step [660/3236], Loss: 1.9542, Perplexity: 7.0581\\n\",\n      \"Epoch [2/5], Step [670/3236], Loss: 2.0646, Perplexity: 7.8820\\n\",\n      \"Epoch [2/5], Step [680/3236], Loss: 1.9470, Perplexity: 7.0080\\n\",\n      \"Epoch [2/5], Step [690/3236], Loss: 2.0377, Perplexity: 7.6728\\n\",\n      \"Epoch [2/5], Step [700/3236], Loss: 2.0120, Perplexity: 7.4784\\n\",\n      \"Epoch [2/5], Step [710/3236], Loss: 2.0175, Perplexity: 7.5197\\n\",\n      \"Epoch [2/5], Step [720/3236], Loss: 1.9448, Perplexity: 6.9921\\n\",\n      \"Epoch [2/5], Step [730/3236], Loss: 2.0104, Perplexity: 7.4666\\n\",\n      \"Epoch [2/5], Step [740/3236], Loss: 1.9953, Perplexity: 7.3546\\n\",\n      \"Epoch [2/5], Step [750/3236], Loss: 1.9154, Perplexity: 6.7894\\n\",\n      \"Epoch [2/5], Step [760/3236], Loss: 2.0233, Perplexity: 7.5630\\n\",\n      \"Epoch [2/5], Step [770/3236], Loss: 2.0656, Perplexity: 7.8897\\n\",\n      \"Epoch [2/5], Step [780/3236], Loss: 2.0402, Perplexity: 7.6920\\n\",\n      \"Epoch [2/5], Step [790/3236], Loss: 1.9448, Perplexity: 6.9925\\n\",\n      \"Epoch [2/5], Step [800/3236], Loss: 2.0931, Perplexity: 8.1102\\n\",\n      \"Epoch [2/5], Step [810/3236], Loss: 2.1863, Perplexity: 8.9024\\n\",\n      \"Epoch [2/5], Step [820/3236], Loss: 1.9640, Perplexity: 7.1279\\n\",\n      \"Epoch [2/5], Step [830/3236], Loss: 2.1826, Perplexity: 8.8692\\n\",\n      \"Epoch [2/5], Step [840/3236], Loss: 1.9959, Perplexity: 7.3585\\n\",\n      \"Epoch [2/5], Step [850/3236], Loss: 2.0701, Perplexity: 7.9255\\n\",\n      \"Epoch [2/5], Step [860/3236], Loss: 1.8880, Perplexity: 6.6062\\n\",\n      \"Epoch [2/5], Step [870/3236], Loss: 2.1205, Perplexity: 8.3349\\n\",\n      \"Epoch [2/5], Step [880/3236], Loss: 1.9288, Perplexity: 6.8809\\n\",\n      \"Epoch [2/5], Step [890/3236], Loss: 1.9621, Perplexity: 7.1144\\n\",\n      \"Epoch [2/5], Step [900/3236], Loss: 2.0690, Perplexity: 7.9168\\n\",\n      \"Epoch [2/5], Step [910/3236], Loss: 2.0439, Perplexity: 7.7205\\n\",\n      \"Epoch [2/5], Step [920/3236], Loss: 1.9429, Perplexity: 6.9787\\n\",\n      \"Epoch [2/5], Step [930/3236], Loss: 1.9056, Perplexity: 6.7234\\n\",\n      \"Epoch [2/5], Step [940/3236], Loss: 2.1050, Perplexity: 8.2073\\n\",\n      \"Epoch [2/5], Step [950/3236], Loss: 1.9443, Perplexity: 6.9885\\n\",\n      \"Epoch [2/5], Step [960/3236], Loss: 1.9926, Perplexity: 7.3347\\n\",\n      \"Epoch [2/5], Step [970/3236], Loss: 1.9288, Perplexity: 6.8814\\n\",\n      \"Epoch [2/5], Step [980/3236], Loss: 2.0614, Perplexity: 7.8569\\n\",\n      \"Epoch [2/5], Step [990/3236], Loss: 2.0678, Perplexity: 7.9078\\n\",\n      \"Epoch [2/5], Step [1000/3236], Loss: 2.0815, Perplexity: 8.0167\\n\",\n      \"Epoch [2/5], Step [1010/3236], Loss: 1.9288, Perplexity: 6.8809\\n\",\n      \"Epoch [2/5], Step [1020/3236], Loss: 1.9818, Perplexity: 7.2561\\n\",\n      \"Epoch [2/5], Step [1030/3236], Loss: 1.9386, Perplexity: 6.9489\\n\",\n      \"Epoch [2/5], Step [1040/3236], Loss: 2.0300, Perplexity: 7.6144\\n\",\n      \"Epoch [2/5], Step [1050/3236], Loss: 2.0266, Perplexity: 7.5886\\n\",\n      \"Epoch [2/5], Step [1060/3236], Loss: 2.0584, Perplexity: 7.8335\\n\",\n      \"Epoch [2/5], Step [1070/3236], Loss: 1.9542, Perplexity: 7.0583\\n\",\n      \"Epoch [2/5], Step [1080/3236], Loss: 1.9307, Perplexity: 6.8944\\n\",\n      \"Epoch [2/5], Step [1090/3236], Loss: 1.9811, Perplexity: 7.2505\\n\",\n      \"Epoch [2/5], Step [1100/3236], Loss: 1.9963, Perplexity: 7.3615\\n\",\n      \"Epoch [2/5], Step [1110/3236], Loss: 1.9637, Perplexity: 7.1257\\n\",\n      \"Epoch [2/5], Step [1120/3236], Loss: 2.0168, Perplexity: 7.5142\\n\",\n      \"Epoch [2/5], Step [1130/3236], Loss: 1.9325, Perplexity: 6.9069\\n\",\n      \"Epoch [2/5], Step [1140/3236], Loss: 1.9663, Perplexity: 7.1443\\n\",\n      \"Epoch [2/5], Step [1150/3236], Loss: 2.0662, Perplexity: 7.8951\\n\",\n      \"Epoch [2/5], Step [1160/3236], Loss: 1.9771, Perplexity: 7.2217\\n\",\n      \"Epoch [2/5], Step [1170/3236], Loss: 2.0037, Perplexity: 7.4164\\n\",\n      \"Epoch [2/5], Step [1180/3236], Loss: 2.0104, Perplexity: 7.4661\\n\",\n      \"Epoch [2/5], Step [1190/3236], Loss: 2.1270, Perplexity: 8.3898\\n\",\n      \"Epoch [2/5], Step [1200/3236], Loss: 1.8596, Perplexity: 6.4209\\n\",\n      \"Epoch [2/5], Step [1210/3236], Loss: 1.9837, Perplexity: 7.2696\\n\",\n      \"Epoch [2/5], Step [1220/3236], Loss: 1.9231, Perplexity: 6.8421\\n\",\n      \"Epoch [2/5], Step [1230/3236], Loss: 2.0476, Perplexity: 7.7490\\n\",\n      \"Epoch [2/5], Step [1240/3236], Loss: 2.0752, Perplexity: 7.9661\\n\",\n      \"Epoch [2/5], Step [1250/3236], Loss: 1.8302, Perplexity: 6.2350\\n\",\n      \"Epoch [2/5], Step [1260/3236], Loss: 1.8636, Perplexity: 6.4466\\n\",\n      \"Epoch [2/5], Step [1270/3236], Loss: 2.0347, Perplexity: 7.6500\\n\",\n      \"Epoch [2/5], Step [1280/3236], Loss: 1.9836, Perplexity: 7.2690\\n\",\n      \"Epoch [2/5], Step [1290/3236], Loss: 2.0091, Perplexity: 7.4569\\n\",\n      \"Epoch [2/5], Step [1300/3236], Loss: 1.9655, Perplexity: 7.1384\\n\",\n      \"Epoch [2/5], Step [1310/3236], Loss: 2.0148, Perplexity: 7.4995\\n\",\n      \"Epoch [2/5], Step [1320/3236], Loss: 1.9597, Perplexity: 7.0972\\n\",\n      \"Epoch [2/5], Step [1330/3236], Loss: 2.0825, Perplexity: 8.0249\\n\",\n      \"Epoch [2/5], Step [1340/3236], Loss: 2.1692, Perplexity: 8.7513\\n\",\n      \"Epoch [2/5], Step [1350/3236], Loss: 2.0815, Perplexity: 8.0166\\n\",\n      \"Epoch [2/5], Step [1360/3236], Loss: 1.9186, Perplexity: 6.8117\\n\",\n      \"Epoch [2/5], Step [1370/3236], Loss: 1.9937, Perplexity: 7.3428\\n\",\n      \"Epoch [2/5], Step [1380/3236], Loss: 1.8581, Perplexity: 6.4117\\n\",\n      \"Epoch [2/5], Step [1390/3236], Loss: 1.9605, Perplexity: 7.1027\\n\",\n      \"Epoch [2/5], Step [1400/3236], Loss: 1.9382, Perplexity: 6.9459\\n\",\n      \"Epoch [2/5], Step [1410/3236], Loss: 2.1045, Perplexity: 8.2027\\n\",\n      \"Epoch [2/5], Step [1420/3236], Loss: 2.0320, Perplexity: 7.6293\\n\",\n      \"Epoch [2/5], Step [1430/3236], Loss: 1.8753, Perplexity: 6.5227\\n\",\n      \"Epoch [2/5], Step [1440/3236], Loss: 1.9954, Perplexity: 7.3553\\n\",\n      \"Epoch [2/5], Step [1450/3236], Loss: 1.9446, Perplexity: 6.9912\\n\",\n      \"Epoch [2/5], Step [1460/3236], Loss: 1.8894, Perplexity: 6.6152\\n\",\n      \"Epoch [2/5], Step [1470/3236], Loss: 2.0673, Perplexity: 7.9037\\n\",\n      \"Epoch [2/5], Step [1480/3236], Loss: 2.0868, Perplexity: 8.0589\\n\",\n      \"Epoch [2/5], Step [1490/3236], Loss: 1.9411, Perplexity: 6.9667\\n\",\n      \"Epoch [2/5], Step [1500/3236], Loss: 1.8887, Perplexity: 6.6109\\n\",\n      \"Epoch [2/5], Step [1510/3236], Loss: 1.9938, Perplexity: 7.3431\\n\",\n      \"Epoch [2/5], Step [1520/3236], Loss: 1.9735, Perplexity: 7.1958\\n\",\n      \"Epoch [2/5], Step [1530/3236], Loss: 1.9694, Perplexity: 7.1662\\n\",\n      \"Epoch [2/5], Step [1540/3236], Loss: 2.0956, Perplexity: 8.1300\\n\",\n      \"Epoch [2/5], Step [1550/3236], Loss: 1.9837, Perplexity: 7.2698\\n\",\n      \"Epoch [2/5], Step [1560/3236], Loss: 2.1255, Perplexity: 8.3772\\n\",\n      \"Epoch [2/5], Step [1570/3236], Loss: 1.9405, Perplexity: 6.9622\\n\",\n      \"Epoch [2/5], Step [1580/3236], Loss: 2.0307, Perplexity: 7.6197\\n\",\n      \"Epoch [2/5], Step [1590/3236], Loss: 1.8375, Perplexity: 6.2806\\n\",\n      \"Epoch [2/5], Step [1600/3236], Loss: 2.0255, Perplexity: 7.5796\\n\",\n      \"Epoch [2/5], Step [1610/3236], Loss: 2.0441, Perplexity: 7.7219\\n\",\n      \"Epoch [2/5], Step [1620/3236], Loss: 2.0487, Perplexity: 7.7582\\n\",\n      \"Epoch [2/5], Step [1630/3236], Loss: 1.9270, Perplexity: 6.8690\\n\",\n      \"Epoch [2/5], Step [1640/3236], Loss: 2.0604, Perplexity: 7.8492\\n\",\n      \"Epoch [2/5], Step [1650/3236], Loss: 2.0975, Perplexity: 8.1457\\n\",\n      \"Epoch [2/5], Step [1660/3236], Loss: 1.8796, Perplexity: 6.5506\\n\",\n      \"Epoch [2/5], Step [1670/3236], Loss: 2.1158, Perplexity: 8.2961\\n\",\n      \"Epoch [2/5], Step [1680/3236], Loss: 2.0689, Perplexity: 7.9164\\n\",\n      \"Epoch [2/5], Step [1690/3236], Loss: 2.0429, Perplexity: 7.7130\\n\",\n      \"Epoch [2/5], Step [1700/3236], Loss: 1.9708, Perplexity: 7.1764\\n\",\n      \"Epoch [2/5], Step [1710/3236], Loss: 1.9838, Perplexity: 7.2704\\n\",\n      \"Epoch [2/5], Step [1720/3236], Loss: 2.0142, Perplexity: 7.4947\\n\",\n      \"Epoch [2/5], Step [1730/3236], Loss: 1.9270, Perplexity: 6.8687\\n\",\n      \"Epoch [2/5], Step [1740/3236], Loss: 2.0134, Perplexity: 7.4889\\n\",\n      \"Epoch [2/5], Step [1750/3236], Loss: 2.0098, Perplexity: 7.4617\\n\",\n      \"Epoch [2/5], Step [1760/3236], Loss: 1.9433, Perplexity: 6.9820\\n\",\n      \"Epoch [2/5], Step [1770/3236], Loss: 1.9822, Perplexity: 7.2590\\n\",\n      \"Epoch [2/5], Step [1780/3236], Loss: 1.9357, Perplexity: 6.9289\\n\",\n      \"Epoch [2/5], Step [1790/3236], Loss: 1.8858, Perplexity: 6.5916\\n\",\n      \"Epoch [2/5], Step [1800/3236], Loss: 2.0343, Perplexity: 7.6470\\n\",\n      \"Epoch [2/5], Step [1810/3236], Loss: 2.1373, Perplexity: 8.4768\\n\",\n      \"Epoch [2/5], Step [1820/3236], Loss: 2.0986, Perplexity: 8.1549\\n\",\n      \"Epoch [2/5], Step [1830/3236], Loss: 1.9873, Perplexity: 7.2957\\n\",\n      \"Epoch [2/5], Step [1840/3236], Loss: 1.9148, Perplexity: 6.7856\\n\",\n      \"Epoch [2/5], Step [1850/3236], Loss: 1.9976, Perplexity: 7.3715\\n\",\n      \"Epoch [2/5], Step [1860/3236], Loss: 2.0528, Perplexity: 7.7898\\n\",\n      \"Epoch [2/5], Step [1870/3236], Loss: 1.9620, Perplexity: 7.1132\\n\",\n      \"Epoch [2/5], Step [1880/3236], Loss: 1.9075, Perplexity: 6.7365\\n\",\n      \"Epoch [2/5], Step [1890/3236], Loss: 1.8767, Perplexity: 6.5319\\n\",\n      \"Epoch [2/5], Step [1900/3236], Loss: 1.9821, Perplexity: 7.2576\\n\",\n      \"Epoch [2/5], Step [1910/3236], Loss: 2.0929, Perplexity: 8.1082\\n\",\n      \"Epoch [2/5], Step [1920/3236], Loss: 2.0061, Perplexity: 7.4344\\n\",\n      \"Epoch [2/5], Step [1930/3236], Loss: 2.0404, Perplexity: 7.6935\\n\",\n      \"Epoch [2/5], Step [1940/3236], Loss: 1.9986, Perplexity: 7.3786\\n\",\n      \"Epoch [2/5], Step [1950/3236], Loss: 1.8217, Perplexity: 6.1826\\n\",\n      \"Epoch [2/5], Step [1960/3236], Loss: 2.0452, Perplexity: 7.7309\\n\",\n      \"Epoch [2/5], Step [1970/3236], Loss: 2.0754, Perplexity: 7.9674\\n\",\n      \"Epoch [2/5], Step [1980/3236], Loss: 1.8874, Perplexity: 6.6020\\n\",\n      \"Epoch [2/5], Step [1990/3236], Loss: 1.9037, Perplexity: 6.7107\\n\",\n      \"Epoch [2/5], Step [2000/3236], Loss: 2.0985, Perplexity: 8.1539\\n\",\n      \"Epoch [2/5], Step [2010/3236], Loss: 1.9334, Perplexity: 6.9130\\n\",\n      \"Epoch [2/5], Step [2020/3236], Loss: 2.0067, Perplexity: 7.4390\\n\",\n      \"Epoch [2/5], Step [2030/3236], Loss: 2.0938, Perplexity: 8.1155\\n\",\n      \"Epoch [2/5], Step [2040/3236], Loss: 2.0538, Perplexity: 7.7978\\n\",\n      \"Epoch [2/5], Step [2050/3236], Loss: 1.9887, Perplexity: 7.3060\\n\",\n      \"Epoch [2/5], Step [2060/3236], Loss: 1.9560, Perplexity: 7.0707\\n\",\n      \"Epoch [2/5], Step [2070/3236], Loss: 2.0546, Perplexity: 7.8038\\n\",\n      \"Epoch [2/5], Step [2080/3236], Loss: 1.9898, Perplexity: 7.3140\\n\",\n      \"Epoch [2/5], Step [2090/3236], Loss: 1.9616, Perplexity: 7.1104\\n\",\n      \"Epoch [2/5], Step [2100/3236], Loss: 1.8724, Perplexity: 6.5040\\n\",\n      \"Epoch [2/5], Step [2110/3236], Loss: 2.0281, Perplexity: 7.5993\\n\",\n      \"Epoch [2/5], Step [2120/3236], Loss: 2.1063, Perplexity: 8.2175\\n\",\n      \"Epoch [2/5], Step [2130/3236], Loss: 2.0273, Perplexity: 7.5938\\n\",\n      \"Epoch [2/5], Step [2140/3236], Loss: 2.0344, Perplexity: 7.6479\\n\",\n      \"Epoch [2/5], Step [2150/3236], Loss: 1.9500, Perplexity: 7.0288\\n\",\n      \"Epoch [2/5], Step [2160/3236], Loss: 2.0880, Perplexity: 8.0689\\n\",\n      \"Epoch [2/5], Step [2170/3236], Loss: 1.9666, Perplexity: 7.1464\\n\",\n      \"Epoch [2/5], Step [2180/3236], Loss: 2.0209, Perplexity: 7.5455\\n\",\n      \"Epoch [2/5], Step [2190/3236], Loss: 2.0098, Perplexity: 7.4615\\n\",\n      \"Epoch [2/5], Step [2200/3236], Loss: 1.9578, Perplexity: 7.0838\\n\",\n      \"Epoch [2/5], Step [2210/3236], Loss: 2.0830, Perplexity: 8.0286\\n\",\n      \"Epoch [2/5], Step [2220/3236], Loss: 2.0869, Perplexity: 8.0596\\n\",\n      \"Epoch [2/5], Step [2230/3236], Loss: 2.0993, Perplexity: 8.1608\\n\",\n      \"Epoch [2/5], Step [2240/3236], Loss: 1.9784, Perplexity: 7.2313\\n\",\n      \"Epoch [2/5], Step [2250/3236], Loss: 2.0150, Perplexity: 7.5006\\n\",\n      \"Epoch [2/5], Step [2260/3236], Loss: 1.9987, Perplexity: 7.3791\\n\",\n      \"Epoch [2/5], Step [2270/3236], Loss: 2.0162, Perplexity: 7.5098\\n\",\n      \"Epoch [2/5], Step [2280/3236], Loss: 1.9295, Perplexity: 6.8860\\n\",\n      \"Epoch [2/5], Step [2290/3236], Loss: 2.0727, Perplexity: 7.9463\\n\",\n      \"Epoch [2/5], Step [2300/3236], Loss: 2.0486, Perplexity: 7.7572\\n\",\n      \"Epoch [2/5], Step [2310/3236], Loss: 1.9613, Perplexity: 7.1086\\n\",\n      \"Epoch [2/5], Step [2320/3236], Loss: 2.0282, Perplexity: 7.6006\\n\",\n      \"Epoch [2/5], Step [2330/3236], Loss: 1.9985, Perplexity: 7.3779\\n\",\n      \"Epoch [2/5], Step [2340/3236], Loss: 2.0453, Perplexity: 7.7319\\n\",\n      \"Epoch [2/5], Step [2350/3236], Loss: 2.0755, Perplexity: 7.9682\\n\",\n      \"Epoch [2/5], Step [2360/3236], Loss: 1.9370, Perplexity: 6.9376\\n\",\n      \"Epoch [2/5], Step [2370/3236], Loss: 1.9752, Perplexity: 7.2078\\n\",\n      \"Epoch [2/5], Step [2380/3236], Loss: 2.0004, Perplexity: 7.3920\\n\",\n      \"Epoch [2/5], Step [2390/3236], Loss: 2.0626, Perplexity: 7.8664\\n\",\n      \"Epoch [2/5], Step [2400/3236], Loss: 1.9629, Perplexity: 7.1200\\n\",\n      \"Epoch [2/5], Step [2410/3236], Loss: 2.1889, Perplexity: 8.9258\\n\",\n      \"Epoch [2/5], Step [2420/3236], Loss: 2.0326, Perplexity: 7.6337\\n\",\n      \"Epoch [2/5], Step [2430/3236], Loss: 1.9996, Perplexity: 7.3862\\n\",\n      \"Epoch [2/5], Step [2440/3236], Loss: 2.0351, Perplexity: 7.6532\\n\",\n      \"Epoch [2/5], Step [2450/3236], Loss: 2.0036, Perplexity: 7.4157\\n\",\n      \"Epoch [2/5], Step [2460/3236], Loss: 2.1292, Perplexity: 8.4077\\n\",\n      \"Epoch [2/5], Step [2470/3236], Loss: 2.1130, Perplexity: 8.2733\\n\",\n      \"Epoch [2/5], Step [2480/3236], Loss: 1.9464, Perplexity: 7.0031\\n\",\n      \"Epoch [2/5], Step [2490/3236], Loss: 2.1127, Perplexity: 8.2709\\n\",\n      \"Epoch [2/5], Step [2500/3236], Loss: 1.9589, Perplexity: 7.0914\\n\",\n      \"Epoch [2/5], Step [2510/3236], Loss: 1.9934, Perplexity: 7.3403\\n\",\n      \"Epoch [2/5], Step [2520/3236], Loss: 2.1411, Perplexity: 8.5089\\n\",\n      \"Epoch [2/5], Step [2530/3236], Loss: 2.0825, Perplexity: 8.0244\\n\",\n      \"Epoch [2/5], Step [2540/3236], Loss: 2.0754, Perplexity: 7.9676\\n\",\n      \"Epoch [2/5], Step [2550/3236], Loss: 2.0570, Perplexity: 7.8226\\n\",\n      \"Epoch [2/5], Step [2560/3236], Loss: 2.0369, Perplexity: 7.6668\\n\",\n      \"Epoch [2/5], Step [2570/3236], Loss: 2.1269, Perplexity: 8.3885\\n\",\n      \"Epoch [2/5], Step [2580/3236], Loss: 1.9847, Perplexity: 7.2770\\n\",\n      \"Epoch [2/5], Step [2590/3236], Loss: 2.1726, Perplexity: 8.7809\\n\",\n      \"Epoch [2/5], Step [2600/3236], Loss: 2.0916, Perplexity: 8.0978\\n\",\n      \"Epoch [2/5], Step [2610/3236], Loss: 1.9762, Perplexity: 7.2156\\n\",\n      \"Epoch [2/5], Step [2620/3236], Loss: 1.8216, Perplexity: 6.1816\\n\",\n      \"Epoch [2/5], Step [2630/3236], Loss: 2.0402, Perplexity: 7.6919\\n\",\n      \"Epoch [2/5], Step [2640/3236], Loss: 1.9441, Perplexity: 6.9872\\n\",\n      \"Epoch [2/5], Step [2650/3236], Loss: 2.0518, Perplexity: 7.7816\\n\",\n      \"Epoch [2/5], Step [2660/3236], Loss: 2.0366, Perplexity: 7.6646\\n\",\n      \"Epoch [2/5], Step [2670/3236], Loss: 1.8560, Perplexity: 6.3980\\n\",\n      \"Epoch [2/5], Step [2680/3236], Loss: 1.9726, Perplexity: 7.1893\\n\",\n      \"Epoch [2/5], Step [2690/3236], Loss: 2.0106, Perplexity: 7.4677\\n\",\n      \"Epoch [2/5], Step [2700/3236], Loss: 2.0161, Perplexity: 7.5091\\n\",\n      \"Epoch [2/5], Step [2710/3236], Loss: 2.0793, Perplexity: 7.9990\\n\",\n      \"Epoch [2/5], Step [2720/3236], Loss: 2.1073, Perplexity: 8.2259\\n\",\n      \"Epoch [2/5], Step [2730/3236], Loss: 1.9766, Perplexity: 7.2181\\n\",\n      \"Epoch [2/5], Step [2740/3236], Loss: 1.9307, Perplexity: 6.8946\\n\",\n      \"Epoch [2/5], Step [2750/3236], Loss: 1.9611, Perplexity: 7.1069\\n\",\n      \"Epoch [2/5], Step [2760/3236], Loss: 2.0680, Perplexity: 7.9088\\n\",\n      \"Epoch [2/5], Step [2770/3236], Loss: 2.0344, Perplexity: 7.6480\\n\",\n      \"Epoch [2/5], Step [2780/3236], Loss: 2.0609, Perplexity: 7.8529\\n\",\n      \"Epoch [2/5], Step [2790/3236], Loss: 2.0730, Perplexity: 7.9485\\n\",\n      \"Epoch [2/5], Step [2800/3236], Loss: 2.0004, Perplexity: 7.3918\\n\",\n      \"Epoch [2/5], Step [2810/3236], Loss: 1.9284, Perplexity: 6.8786\\n\",\n      \"Epoch [2/5], Step [2820/3236], Loss: 2.0960, Perplexity: 8.1333\\n\",\n      \"Epoch [2/5], Step [2830/3236], Loss: 1.8936, Perplexity: 6.6430\\n\",\n      \"Epoch [2/5], Step [2840/3236], Loss: 2.1400, Perplexity: 8.4992\\n\",\n      \"Epoch [2/5], Step [2850/3236], Loss: 2.0279, Perplexity: 7.5981\\n\",\n      \"Epoch [2/5], Step [2860/3236], Loss: 1.8818, Perplexity: 6.5656\\n\",\n      \"Epoch [2/5], Step [2870/3236], Loss: 1.8519, Perplexity: 6.3720\\n\",\n      \"Epoch [2/5], Step [2880/3236], Loss: 2.0678, Perplexity: 7.9073\\n\",\n      \"Epoch [2/5], Step [2890/3236], Loss: 2.0556, Perplexity: 7.8115\\n\",\n      \"Epoch [2/5], Step [2900/3236], Loss: 1.9235, Perplexity: 6.8446\\n\",\n      \"Epoch [2/5], Step [2910/3236], Loss: 2.0535, Perplexity: 7.7953\\n\",\n      \"Epoch [2/5], Step [2920/3236], Loss: 2.0395, Perplexity: 7.6869\\n\",\n      \"Epoch [2/5], Step [2930/3236], Loss: 1.9599, Perplexity: 7.0984\\n\",\n      \"Epoch [2/5], Step [2940/3236], Loss: 1.8731, Perplexity: 6.5083\\n\",\n      \"Epoch [2/5], Step [2950/3236], Loss: 1.8774, Perplexity: 6.5365\\n\",\n      \"Epoch [2/5], Step [2960/3236], Loss: 2.0858, Perplexity: 8.0506\\n\",\n      \"Epoch [2/5], Step [2970/3236], Loss: 1.9348, Perplexity: 6.9225\\n\",\n      \"Epoch [2/5], Step [2980/3236], Loss: 1.8589, Perplexity: 6.4165\\n\",\n      \"Epoch [2/5], Step [2990/3236], Loss: 2.1142, Perplexity: 8.2831\\n\",\n      \"Epoch [2/5], Step [3000/3236], Loss: 1.9384, Perplexity: 6.9478\\n\",\n      \"Epoch [2/5], Step [3010/3236], Loss: 2.0717, Perplexity: 7.9382\\n\",\n      \"Epoch [2/5], Step [3020/3236], Loss: 2.0195, Perplexity: 7.5343\\n\",\n      \"Epoch [2/5], Step [3030/3236], Loss: 1.8144, Perplexity: 6.1372\\n\",\n      \"Epoch [2/5], Step [3040/3236], Loss: 2.0655, Perplexity: 7.8890\\n\",\n      \"Epoch [2/5], Step [3050/3236], Loss: 1.9800, Perplexity: 7.2425\\n\",\n      \"Epoch [2/5], Step [3060/3236], Loss: 2.1541, Perplexity: 8.6202\\n\",\n      \"Epoch [2/5], Step [3070/3236], Loss: 1.9388, Perplexity: 6.9504\\n\",\n      \"Epoch [2/5], Step [3080/3236], Loss: 1.9874, Perplexity: 7.2967\\n\",\n      \"Epoch [2/5], Step [3090/3236], Loss: 2.0041, Perplexity: 7.4196\\n\",\n      \"Epoch [2/5], Step [3100/3236], Loss: 2.0770, Perplexity: 7.9804\\n\",\n      \"Epoch [2/5], Step [3110/3236], Loss: 2.0163, Perplexity: 7.5103\\n\",\n      \"Epoch [2/5], Step [3120/3236], Loss: 1.9498, Perplexity: 7.0274\\n\",\n      \"Epoch [2/5], Step [3130/3236], Loss: 2.0842, Perplexity: 8.0384\\n\",\n      \"Epoch [2/5], Step [3140/3236], Loss: 2.0356, Perplexity: 7.6571\\n\",\n      \"Epoch [2/5], Step [3150/3236], Loss: 1.8549, Perplexity: 6.3911\\n\",\n      \"Epoch [2/5], Step [3160/3236], Loss: 1.9376, Perplexity: 6.9418\\n\",\n      \"Epoch [2/5], Step [3170/3236], Loss: 2.0018, Perplexity: 7.4027\\n\",\n      \"Epoch [2/5], Step [3180/3236], Loss: 2.1172, Perplexity: 8.3074\\n\",\n      \"Epoch [2/5], Step [3190/3236], Loss: 1.9293, Perplexity: 6.8844\\n\",\n      \"Epoch [2/5], Step [3200/3236], Loss: 1.9038, Perplexity: 6.7113\\n\",\n      \"Epoch [2/5], Step [3210/3236], Loss: 2.0234, Perplexity: 7.5638\\n\",\n      \"Epoch [2/5], Step [3220/3236], Loss: 2.0261, Perplexity: 7.5845\\n\",\n      \"Epoch [2/5], Step [3230/3236], Loss: 2.1442, Perplexity: 8.5352\\n\",\n      \"Epoch [3/5], Step [0/3236], Loss: 1.7492, Perplexity: 5.7497\\n\",\n      \"Epoch [3/5], Step [10/3236], Loss: 1.9319, Perplexity: 6.9027\\n\",\n      \"Epoch [3/5], Step [20/3236], Loss: 1.9721, Perplexity: 7.1859\\n\",\n      \"Epoch [3/5], Step [30/3236], Loss: 1.8225, Perplexity: 6.1876\\n\",\n      \"Epoch [3/5], Step [40/3236], Loss: 1.8524, Perplexity: 6.3748\\n\",\n      \"Epoch [3/5], Step [50/3236], Loss: 1.9074, Perplexity: 6.7354\\n\",\n      \"Epoch [3/5], Step [60/3236], Loss: 1.8917, Perplexity: 6.6307\\n\",\n      \"Epoch [3/5], Step [70/3236], Loss: 1.9340, Perplexity: 6.9169\\n\",\n      \"Epoch [3/5], Step [80/3236], Loss: 1.8611, Perplexity: 6.4311\\n\",\n      \"Epoch [3/5], Step [90/3236], Loss: 1.9982, Perplexity: 7.3756\\n\",\n      \"Epoch [3/5], Step [100/3236], Loss: 1.8487, Perplexity: 6.3514\\n\",\n      \"Epoch [3/5], Step [110/3236], Loss: 1.9943, Perplexity: 7.3468\\n\",\n      \"Epoch [3/5], Step [120/3236], Loss: 1.8818, Perplexity: 6.5654\\n\",\n      \"Epoch [3/5], Step [130/3236], Loss: 1.8625, Perplexity: 6.4400\\n\",\n      \"Epoch [3/5], Step [140/3236], Loss: 1.8753, Perplexity: 6.5226\\n\",\n      \"Epoch [3/5], Step [150/3236], Loss: 1.9712, Perplexity: 7.1790\\n\",\n      \"Epoch [3/5], Step [160/3236], Loss: 1.9811, Perplexity: 7.2508\\n\",\n      \"Epoch [3/5], Step [170/3236], Loss: 1.8456, Perplexity: 6.3319\\n\",\n      \"Epoch [3/5], Step [180/3236], Loss: 1.7978, Perplexity: 6.0362\\n\",\n      \"Epoch [3/5], Step [190/3236], Loss: 1.8965, Perplexity: 6.6627\\n\",\n      \"Epoch [3/5], Step [200/3236], Loss: 2.0164, Perplexity: 7.5111\\n\",\n      \"Epoch [3/5], Step [210/3236], Loss: 1.9698, Perplexity: 7.1694\\n\",\n      \"Epoch [3/5], Step [220/3236], Loss: 1.9905, Perplexity: 7.3191\\n\",\n      \"Epoch [3/5], Step [230/3236], Loss: 1.9969, Perplexity: 7.3660\\n\",\n      \"Epoch [3/5], Step [240/3236], Loss: 1.9510, Perplexity: 7.0355\\n\",\n      \"Epoch [3/5], Step [250/3236], Loss: 1.8351, Perplexity: 6.2661\\n\",\n      \"Epoch [3/5], Step [260/3236], Loss: 1.9500, Perplexity: 7.0285\\n\",\n      \"Epoch [3/5], Step [270/3236], Loss: 1.9069, Perplexity: 6.7320\\n\",\n      \"Epoch [3/5], Step [280/3236], Loss: 1.8963, Perplexity: 6.6611\\n\",\n      \"Epoch [3/5], Step [290/3236], Loss: 1.8911, Perplexity: 6.6268\\n\",\n      \"Epoch [3/5], Step [300/3236], Loss: 1.7987, Perplexity: 6.0418\\n\",\n      \"Epoch [3/5], Step [310/3236], Loss: 2.1118, Perplexity: 8.2634\\n\",\n      \"Epoch [3/5], Step [320/3236], Loss: 1.8672, Perplexity: 6.4701\\n\",\n      \"Epoch [3/5], Step [330/3236], Loss: 1.8815, Perplexity: 6.5631\\n\",\n      \"Epoch [3/5], Step [340/3236], Loss: 1.9926, Perplexity: 7.3347\\n\",\n      \"Epoch [3/5], Step [350/3236], Loss: 1.8596, Perplexity: 6.4211\\n\",\n      \"Epoch [3/5], Step [360/3236], Loss: 1.8670, Perplexity: 6.4689\\n\",\n      \"Epoch [3/5], Step [370/3236], Loss: 1.9404, Perplexity: 6.9615\\n\",\n      \"Epoch [3/5], Step [380/3236], Loss: 1.9465, Perplexity: 7.0040\\n\",\n      \"Epoch [3/5], Step [390/3236], Loss: 1.8302, Perplexity: 6.2352\\n\",\n      \"Epoch [3/5], Step [400/3236], Loss: 1.8861, Perplexity: 6.5935\\n\",\n      \"Epoch [3/5], Step [410/3236], Loss: 1.9647, Perplexity: 7.1330\\n\",\n      \"Epoch [3/5], Step [420/3236], Loss: 1.7652, Perplexity: 5.8429\\n\",\n      \"Epoch [3/5], Step [430/3236], Loss: 1.9369, Perplexity: 6.9373\\n\",\n      \"Epoch [3/5], Step [440/3236], Loss: 1.9004, Perplexity: 6.6883\\n\",\n      \"Epoch [3/5], Step [450/3236], Loss: 2.0364, Perplexity: 7.6627\\n\",\n      \"Epoch [3/5], Step [460/3236], Loss: 1.9474, Perplexity: 7.0107\\n\",\n      \"Epoch [3/5], Step [470/3236], Loss: 1.9934, Perplexity: 7.3407\\n\",\n      \"Epoch [3/5], Step [480/3236], Loss: 1.9234, Perplexity: 6.8441\\n\",\n      \"Epoch [3/5], Step [490/3236], Loss: 1.8825, Perplexity: 6.5702\\n\",\n      \"Epoch [3/5], Step [500/3236], Loss: 1.9340, Perplexity: 6.9168\\n\",\n      \"Epoch [3/5], Step [510/3236], Loss: 1.8286, Perplexity: 6.2253\\n\",\n      \"Epoch [3/5], Step [520/3236], Loss: 1.8047, Perplexity: 6.0779\\n\",\n      \"Epoch [3/5], Step [530/3236], Loss: 1.8249, Perplexity: 6.2022\\n\",\n      \"Epoch [3/5], Step [540/3236], Loss: 1.8755, Perplexity: 6.5241\\n\",\n      \"Epoch [3/5], Step [550/3236], Loss: 1.7515, Perplexity: 5.7631\\n\",\n      \"Epoch [3/5], Step [560/3236], Loss: 2.0085, Perplexity: 7.4522\\n\",\n      \"Epoch [3/5], Step [570/3236], Loss: 1.8615, Perplexity: 6.4333\\n\",\n      \"Epoch [3/5], Step [580/3236], Loss: 1.9251, Perplexity: 6.8557\\n\",\n      \"Epoch [3/5], Step [590/3236], Loss: 1.9723, Perplexity: 7.1869\\n\",\n      \"Epoch [3/5], Step [600/3236], Loss: 1.9372, Perplexity: 6.9394\\n\",\n      \"Epoch [3/5], Step [610/3236], Loss: 1.9143, Perplexity: 6.7822\\n\",\n      \"Epoch [3/5], Step [620/3236], Loss: 1.9563, Perplexity: 7.0728\\n\",\n      \"Epoch [3/5], Step [630/3236], Loss: 1.9522, Perplexity: 7.0439\\n\",\n      \"Epoch [3/5], Step [640/3236], Loss: 1.9680, Perplexity: 7.1565\\n\",\n      \"Epoch [3/5], Step [650/3236], Loss: 1.8971, Perplexity: 6.6664\\n\",\n      \"Epoch [3/5], Step [660/3236], Loss: 1.8562, Perplexity: 6.3996\\n\",\n      \"Epoch [3/5], Step [670/3236], Loss: 2.0065, Perplexity: 7.4370\\n\",\n      \"Epoch [3/5], Step [680/3236], Loss: 1.9395, Perplexity: 6.9555\\n\",\n      \"Epoch [3/5], Step [690/3236], Loss: 1.9262, Perplexity: 6.8637\\n\",\n      \"Epoch [3/5], Step [700/3236], Loss: 1.9695, Perplexity: 7.1674\\n\",\n      \"Epoch [3/5], Step [710/3236], Loss: 1.8004, Perplexity: 6.0518\\n\",\n      \"Epoch [3/5], Step [720/3236], Loss: 2.0418, Perplexity: 7.7048\\n\",\n      \"Epoch [3/5], Step [730/3236], Loss: 2.0081, Perplexity: 7.4491\\n\",\n      \"Epoch [3/5], Step [740/3236], Loss: 1.9629, Perplexity: 7.1199\\n\",\n      \"Epoch [3/5], Step [750/3236], Loss: 1.7909, Perplexity: 5.9949\\n\",\n      \"Epoch [3/5], Step [760/3236], Loss: 1.8729, Perplexity: 6.5070\\n\",\n      \"Epoch [3/5], Step [770/3236], Loss: 1.9221, Perplexity: 6.8351\\n\",\n      \"Epoch [3/5], Step [780/3236], Loss: 1.9367, Perplexity: 6.9358\\n\",\n      \"Epoch [3/5], Step [790/3236], Loss: 1.8855, Perplexity: 6.5895\\n\",\n      \"Epoch [3/5], Step [800/3236], Loss: 1.8930, Perplexity: 6.6391\\n\",\n      \"Epoch [3/5], Step [810/3236], Loss: 1.9700, Perplexity: 7.1707\\n\",\n      \"Epoch [3/5], Step [820/3236], Loss: 2.0238, Perplexity: 7.5672\\n\",\n      \"Epoch [3/5], Step [830/3236], Loss: 1.8949, Perplexity: 6.6518\\n\",\n      \"Epoch [3/5], Step [840/3236], Loss: 1.9171, Perplexity: 6.8009\\n\",\n      \"Epoch [3/5], Step [850/3236], Loss: 1.8787, Perplexity: 6.5449\\n\",\n      \"Epoch [3/5], Step [860/3236], Loss: 1.9354, Perplexity: 6.9268\\n\",\n      \"Epoch [3/5], Step [870/3236], Loss: 1.9619, Perplexity: 7.1126\\n\",\n      \"Epoch [3/5], Step [880/3236], Loss: 1.8544, Perplexity: 6.3880\\n\",\n      \"Epoch [3/5], Step [890/3236], Loss: 1.9664, Perplexity: 7.1452\\n\",\n      \"Epoch [3/5], Step [900/3236], Loss: 1.9302, Perplexity: 6.8912\\n\",\n      \"Epoch [3/5], Step [910/3236], Loss: 1.8888, Perplexity: 6.6111\\n\",\n      \"Epoch [3/5], Step [920/3236], Loss: 1.8536, Perplexity: 6.3826\\n\",\n      \"Epoch [3/5], Step [930/3236], Loss: 1.8445, Perplexity: 6.3247\\n\",\n      \"Epoch [3/5], Step [940/3236], Loss: 2.0035, Perplexity: 7.4149\\n\",\n      \"Epoch [3/5], Step [950/3236], Loss: 1.8969, Perplexity: 6.6654\\n\",\n      \"Epoch [3/5], Step [960/3236], Loss: 1.9297, Perplexity: 6.8873\\n\",\n      \"Epoch [3/5], Step [970/3236], Loss: 1.9694, Perplexity: 7.1666\\n\",\n      \"Epoch [3/5], Step [980/3236], Loss: 1.8545, Perplexity: 6.3883\\n\",\n      \"Epoch [3/5], Step [990/3236], Loss: 1.8857, Perplexity: 6.5911\\n\",\n      \"Epoch [3/5], Step [1000/3236], Loss: 1.9008, Perplexity: 6.6912\\n\",\n      \"Epoch [3/5], Step [1010/3236], Loss: 1.8816, Perplexity: 6.5643\\n\",\n      \"Epoch [3/5], Step [1020/3236], Loss: 2.0801, Perplexity: 8.0056\\n\",\n      \"Epoch [3/5], Step [1030/3236], Loss: 1.8855, Perplexity: 6.5895\\n\",\n      \"Epoch [3/5], Step [1040/3236], Loss: 1.9814, Perplexity: 7.2529\\n\",\n      \"Epoch [3/5], Step [1050/3236], Loss: 1.7594, Perplexity: 5.8091\\n\",\n      \"Epoch [3/5], Step [1060/3236], Loss: 1.8354, Perplexity: 6.2676\\n\",\n      \"Epoch [3/5], Step [1070/3236], Loss: 1.8610, Perplexity: 6.4302\\n\",\n      \"Epoch [3/5], Step [1080/3236], Loss: 1.9260, Perplexity: 6.8622\\n\",\n      \"Epoch [3/5], Step [1090/3236], Loss: 1.9853, Perplexity: 7.2812\\n\",\n      \"Epoch [3/5], Step [1100/3236], Loss: 1.8713, Perplexity: 6.4966\\n\",\n      \"Epoch [3/5], Step [1110/3236], Loss: 1.8651, Perplexity: 6.4568\\n\",\n      \"Epoch [3/5], Step [1120/3236], Loss: 1.9195, Perplexity: 6.8172\\n\",\n      \"Epoch [3/5], Step [1130/3236], Loss: 1.9551, Perplexity: 7.0647\\n\",\n      \"Epoch [3/5], Step [1140/3236], Loss: 1.8997, Perplexity: 6.6838\\n\",\n      \"Epoch [3/5], Step [1150/3236], Loss: 1.8725, Perplexity: 6.5044\\n\",\n      \"Epoch [3/5], Step [1160/3236], Loss: 1.9318, Perplexity: 6.9019\\n\",\n      \"Epoch [3/5], Step [1170/3236], Loss: 1.9339, Perplexity: 6.9168\\n\",\n      \"Epoch [3/5], Step [1180/3236], Loss: 1.9401, Perplexity: 6.9597\\n\",\n      \"Epoch [3/5], Step [1190/3236], Loss: 1.8558, Perplexity: 6.3969\\n\",\n      \"Epoch [3/5], Step [1200/3236], Loss: 1.9070, Perplexity: 6.7325\\n\",\n      \"Epoch [3/5], Step [1210/3236], Loss: 2.0068, Perplexity: 7.4397\\n\",\n      \"Epoch [3/5], Step [1220/3236], Loss: 1.9432, Perplexity: 6.9810\\n\",\n      \"Epoch [3/5], Step [1230/3236], Loss: 1.8573, Perplexity: 6.4064\\n\",\n      \"Epoch [3/5], Step [1240/3236], Loss: 1.9902, Perplexity: 7.3168\\n\",\n      \"Epoch [3/5], Step [1250/3236], Loss: 1.9943, Perplexity: 7.3471\\n\",\n      \"Epoch [3/5], Step [1260/3236], Loss: 1.8998, Perplexity: 6.6844\\n\",\n      \"Epoch [3/5], Step [1270/3236], Loss: 1.9438, Perplexity: 6.9851\\n\",\n      \"Epoch [3/5], Step [1280/3236], Loss: 1.8232, Perplexity: 6.1918\\n\",\n      \"Epoch [3/5], Step [1290/3236], Loss: 1.8964, Perplexity: 6.6620\\n\",\n      \"Epoch [3/5], Step [1300/3236], Loss: 1.9324, Perplexity: 6.9064\\n\",\n      \"Epoch [3/5], Step [1310/3236], Loss: 1.9820, Perplexity: 7.2571\\n\",\n      \"Epoch [3/5], Step [1320/3236], Loss: 1.8941, Perplexity: 6.6468\\n\",\n      \"Epoch [3/5], Step [1330/3236], Loss: 1.9021, Perplexity: 6.6997\\n\",\n      \"Epoch [3/5], Step [1340/3236], Loss: 1.9618, Perplexity: 7.1124\\n\",\n      \"Epoch [3/5], Step [1350/3236], Loss: 1.8677, Perplexity: 6.4732\\n\",\n      \"Epoch [3/5], Step [1360/3236], Loss: 1.8616, Perplexity: 6.4341\\n\",\n      \"Epoch [3/5], Step [1370/3236], Loss: 1.8963, Perplexity: 6.6614\\n\",\n      \"Epoch [3/5], Step [1380/3236], Loss: 1.8087, Perplexity: 6.1026\\n\",\n      \"Epoch [3/5], Step [1390/3236], Loss: 1.8802, Perplexity: 6.5547\\n\",\n      \"Epoch [3/5], Step [1400/3236], Loss: 1.9636, Perplexity: 7.1247\\n\",\n      \"Epoch [3/5], Step [1410/3236], Loss: 1.7988, Perplexity: 6.0424\\n\",\n      \"Epoch [3/5], Step [1420/3236], Loss: 1.9127, Perplexity: 6.7711\\n\",\n      \"Epoch [3/5], Step [1430/3236], Loss: 1.7855, Perplexity: 5.9628\\n\",\n      \"Epoch [3/5], Step [1440/3236], Loss: 1.8910, Perplexity: 6.6263\\n\",\n      \"Epoch [3/5], Step [1450/3236], Loss: 1.8346, Perplexity: 6.2629\\n\",\n      \"Epoch [3/5], Step [1460/3236], Loss: 1.9889, Perplexity: 7.3075\\n\",\n      \"Epoch [3/5], Step [1470/3236], Loss: 1.7753, Perplexity: 5.9021\\n\",\n      \"Epoch [3/5], Step [1480/3236], Loss: 1.9163, Perplexity: 6.7960\\n\",\n      \"Epoch [3/5], Step [1490/3236], Loss: 1.9829, Perplexity: 7.2637\\n\",\n      \"Epoch [3/5], Step [1500/3236], Loss: 1.9075, Perplexity: 6.7365\\n\",\n      \"Epoch [3/5], Step [1510/3236], Loss: 1.8168, Perplexity: 6.1520\\n\",\n      \"Epoch [3/5], Step [1520/3236], Loss: 1.7687, Perplexity: 5.8632\\n\",\n      \"Epoch [3/5], Step [1530/3236], Loss: 1.9210, Perplexity: 6.8281\\n\",\n      \"Epoch [3/5], Step [1540/3236], Loss: 1.9195, Perplexity: 6.8176\\n\",\n      \"Epoch [3/5], Step [1550/3236], Loss: 1.9726, Perplexity: 7.1890\\n\",\n      \"Epoch [3/5], Step [1560/3236], Loss: 1.8440, Perplexity: 6.3217\\n\",\n      \"Epoch [3/5], Step [1570/3236], Loss: 1.9463, Perplexity: 7.0024\\n\",\n      \"Epoch [3/5], Step [1580/3236], Loss: 1.8773, Perplexity: 6.5361\\n\",\n      \"Epoch [3/5], Step [1590/3236], Loss: 2.0021, Perplexity: 7.4049\\n\",\n      \"Epoch [3/5], Step [1600/3236], Loss: 1.9563, Perplexity: 7.0731\\n\",\n      \"Epoch [3/5], Step [1610/3236], Loss: 1.9702, Perplexity: 7.1720\\n\",\n      \"Epoch [3/5], Step [1620/3236], Loss: 1.9735, Perplexity: 7.1961\\n\",\n      \"Epoch [3/5], Step [1630/3236], Loss: 1.9722, Perplexity: 7.1861\\n\",\n      \"Epoch [3/5], Step [1640/3236], Loss: 1.8502, Perplexity: 6.3610\\n\",\n      \"Epoch [3/5], Step [1650/3236], Loss: 1.9959, Perplexity: 7.3585\\n\",\n      \"Epoch [3/5], Step [1660/3236], Loss: 1.9570, Perplexity: 7.0783\\n\",\n      \"Epoch [3/5], Step [1670/3236], Loss: 1.9169, Perplexity: 6.8002\\n\",\n      \"Epoch [3/5], Step [1680/3236], Loss: 1.9511, Perplexity: 7.0367\\n\",\n      \"Epoch [3/5], Step [1690/3236], Loss: 1.9985, Perplexity: 7.3779\\n\",\n      \"Epoch [3/5], Step [1700/3236], Loss: 1.8671, Perplexity: 6.4697\\n\",\n      \"Epoch [3/5], Step [1710/3236], Loss: 1.9696, Perplexity: 7.1678\\n\",\n      \"Epoch [3/5], Step [1720/3236], Loss: 1.9235, Perplexity: 6.8447\\n\",\n      \"Epoch [3/5], Step [1730/3236], Loss: 1.8904, Perplexity: 6.6222\\n\",\n      \"Epoch [3/5], Step [1740/3236], Loss: 1.9116, Perplexity: 6.7642\\n\",\n      \"Epoch [3/5], Step [1750/3236], Loss: 1.9102, Perplexity: 6.7542\\n\",\n      \"Epoch [3/5], Step [1760/3236], Loss: 1.8684, Perplexity: 6.4780\\n\",\n      \"Epoch [3/5], Step [1770/3236], Loss: 1.9128, Perplexity: 6.7721\\n\",\n      \"Epoch [3/5], Step [1780/3236], Loss: 1.9524, Perplexity: 7.0454\\n\",\n      \"Epoch [3/5], Step [1790/3236], Loss: 1.8839, Perplexity: 6.5794\\n\",\n      \"Epoch [3/5], Step [1800/3236], Loss: 1.9500, Perplexity: 7.0285\\n\",\n      \"Epoch [3/5], Step [1810/3236], Loss: 1.9682, Perplexity: 7.1580\\n\",\n      \"Epoch [3/5], Step [1820/3236], Loss: 1.8387, Perplexity: 6.2883\\n\",\n      \"Epoch [3/5], Step [1830/3236], Loss: 1.9000, Perplexity: 6.6859\\n\",\n      \"Epoch [3/5], Step [1840/3236], Loss: 1.9122, Perplexity: 6.7677\\n\",\n      \"Epoch [3/5], Step [1850/3236], Loss: 1.9662, Perplexity: 7.1435\\n\",\n      \"Epoch [3/5], Step [1860/3236], Loss: 1.9360, Perplexity: 6.9309\\n\",\n      \"Epoch [3/5], Step [1870/3236], Loss: 1.9335, Perplexity: 6.9134\\n\",\n      \"Epoch [3/5], Step [1880/3236], Loss: 1.8979, Perplexity: 6.6718\\n\",\n      \"Epoch [3/5], Step [1890/3236], Loss: 1.8656, Perplexity: 6.4601\\n\",\n      \"Epoch [3/5], Step [1900/3236], Loss: 2.0578, Perplexity: 7.8284\\n\",\n      \"Epoch [3/5], Step [1910/3236], Loss: 2.0171, Perplexity: 7.5167\\n\",\n      \"Epoch [3/5], Step [1920/3236], Loss: 1.8394, Perplexity: 6.2929\\n\",\n      \"Epoch [3/5], Step [1930/3236], Loss: 1.8065, Perplexity: 6.0890\\n\",\n      \"Epoch [3/5], Step [1940/3236], Loss: 1.8782, Perplexity: 6.5418\\n\",\n      \"Epoch [3/5], Step [1950/3236], Loss: 1.9676, Perplexity: 7.1534\\n\",\n      \"Epoch [3/5], Step [1960/3236], Loss: 2.0112, Perplexity: 7.4724\\n\",\n      \"Epoch [3/5], Step [1970/3236], Loss: 1.9685, Perplexity: 7.1602\\n\",\n      \"Epoch [3/5], Step [1980/3236], Loss: 1.9811, Perplexity: 7.2506\\n\",\n      \"Epoch [3/5], Step [1990/3236], Loss: 1.9269, Perplexity: 6.8679\\n\",\n      \"Epoch [3/5], Step [2000/3236], Loss: 1.8670, Perplexity: 6.4686\\n\",\n      \"Epoch [3/5], Step [2010/3236], Loss: 2.0100, Perplexity: 7.4630\\n\",\n      \"Epoch [3/5], Step [2020/3236], Loss: 1.9647, Perplexity: 7.1326\\n\",\n      \"Epoch [3/5], Step [2030/3236], Loss: 1.9347, Perplexity: 6.9220\\n\",\n      \"Epoch [3/5], Step [2040/3236], Loss: 1.9775, Perplexity: 7.2245\\n\",\n      \"Epoch [3/5], Step [2050/3236], Loss: 1.8920, Perplexity: 6.6328\\n\",\n      \"Epoch [3/5], Step [2060/3236], Loss: 1.9552, Perplexity: 7.0657\\n\",\n      \"Epoch [3/5], Step [2070/3236], Loss: 1.8441, Perplexity: 6.3222\\n\",\n      \"Epoch [3/5], Step [2080/3236], Loss: 1.8576, Perplexity: 6.4082\\n\",\n      \"Epoch [3/5], Step [2090/3236], Loss: 1.9172, Perplexity: 6.8017\\n\",\n      \"Epoch [3/5], Step [2100/3236], Loss: 1.8667, Perplexity: 6.4669\\n\",\n      \"Epoch [3/5], Step [2110/3236], Loss: 1.9684, Perplexity: 7.1591\\n\",\n      \"Epoch [3/5], Step [2120/3236], Loss: 1.9225, Perplexity: 6.8381\\n\",\n      \"Epoch [3/5], Step [2130/3236], Loss: 1.9073, Perplexity: 6.7351\\n\",\n      \"Epoch [3/5], Step [2140/3236], Loss: 1.9106, Perplexity: 6.7573\\n\",\n      \"Epoch [3/5], Step [2150/3236], Loss: 1.8750, Perplexity: 6.5210\\n\",\n      \"Epoch [3/5], Step [2160/3236], Loss: 1.8635, Perplexity: 6.4465\\n\",\n      \"Epoch [3/5], Step [2170/3236], Loss: 2.0280, Perplexity: 7.5992\\n\",\n      \"Epoch [3/5], Step [2180/3236], Loss: 1.9967, Perplexity: 7.3645\\n\",\n      \"Epoch [3/5], Step [2190/3236], Loss: 1.9275, Perplexity: 6.8724\\n\",\n      \"Epoch [3/5], Step [2200/3236], Loss: 1.9807, Perplexity: 7.2480\\n\",\n      \"Epoch [3/5], Step [2210/3236], Loss: 1.9055, Perplexity: 6.7229\\n\",\n      \"Epoch [3/5], Step [2220/3236], Loss: 1.7534, Perplexity: 5.7741\\n\",\n      \"Epoch [3/5], Step [2230/3236], Loss: 1.9518, Perplexity: 7.0417\\n\",\n      \"Epoch [3/5], Step [2240/3236], Loss: 1.9304, Perplexity: 6.8925\\n\",\n      \"Epoch [3/5], Step [2250/3236], Loss: 2.0022, Perplexity: 7.4057\\n\",\n      \"Epoch [3/5], Step [2260/3236], Loss: 1.7481, Perplexity: 5.7435\\n\",\n      \"Epoch [3/5], Step [2270/3236], Loss: 1.9831, Perplexity: 7.2651\\n\",\n      \"Epoch [3/5], Step [2280/3236], Loss: 1.9080, Perplexity: 6.7395\\n\",\n      \"Epoch [3/5], Step [2290/3236], Loss: 1.9138, Perplexity: 6.7790\\n\",\n      \"Epoch [3/5], Step [2300/3236], Loss: 1.9708, Perplexity: 7.1766\\n\",\n      \"Epoch [3/5], Step [2310/3236], Loss: 1.9238, Perplexity: 6.8472\\n\",\n      \"Epoch [3/5], Step [2320/3236], Loss: 1.9192, Perplexity: 6.8154\\n\",\n      \"Epoch [3/5], Step [2330/3236], Loss: 1.8609, Perplexity: 6.4297\\n\",\n      \"Epoch [3/5], Step [2340/3236], Loss: 1.9892, Perplexity: 7.3099\\n\",\n      \"Epoch [3/5], Step [2350/3236], Loss: 1.9472, Perplexity: 7.0092\\n\",\n      \"Epoch [3/5], Step [2360/3236], Loss: 2.0454, Perplexity: 7.7324\\n\",\n      \"Epoch [3/5], Step [2370/3236], Loss: 1.8481, Perplexity: 6.3476\\n\",\n      \"Epoch [3/5], Step [2380/3236], Loss: 2.0085, Perplexity: 7.4522\\n\",\n      \"Epoch [3/5], Step [2390/3236], Loss: 1.8417, Perplexity: 6.3070\\n\",\n      \"Epoch [3/5], Step [2400/3236], Loss: 1.9203, Perplexity: 6.8230\\n\",\n      \"Epoch [3/5], Step [2410/3236], Loss: 1.9083, Perplexity: 6.7417\\n\",\n      \"Epoch [3/5], Step [2420/3236], Loss: 1.9398, Perplexity: 6.9576\\n\",\n      \"Epoch [3/5], Step [2430/3236], Loss: 1.9753, Perplexity: 7.2085\\n\",\n      \"Epoch [3/5], Step [2440/3236], Loss: 1.9229, Perplexity: 6.8404\\n\",\n      \"Epoch [3/5], Step [2450/3236], Loss: 1.8797, Perplexity: 6.5516\\n\",\n      \"Epoch [3/5], Step [2460/3236], Loss: 1.8876, Perplexity: 6.6033\\n\",\n      \"Epoch [3/5], Step [2470/3236], Loss: 1.8918, Perplexity: 6.6314\\n\",\n      \"Epoch [3/5], Step [2480/3236], Loss: 1.9612, Perplexity: 7.1079\\n\",\n      \"Epoch [3/5], Step [2490/3236], Loss: 1.9159, Perplexity: 6.7932\\n\",\n      \"Epoch [3/5], Step [2500/3236], Loss: 1.9145, Perplexity: 6.7835\\n\",\n      \"Epoch [3/5], Step [2510/3236], Loss: 1.8899, Perplexity: 6.6184\\n\",\n      \"Epoch [3/5], Step [2520/3236], Loss: 1.8191, Perplexity: 6.1665\\n\",\n      \"Epoch [3/5], Step [2530/3236], Loss: 1.9693, Perplexity: 7.1656\\n\",\n      \"Epoch [3/5], Step [2540/3236], Loss: 1.8025, Perplexity: 6.0648\\n\",\n      \"Epoch [3/5], Step [2550/3236], Loss: 1.8157, Perplexity: 6.1456\\n\",\n      \"Epoch [3/5], Step [2560/3236], Loss: 1.8856, Perplexity: 6.5903\\n\",\n      \"Epoch [3/5], Step [2570/3236], Loss: 1.8543, Perplexity: 6.3872\\n\",\n      \"Epoch [3/5], Step [2580/3236], Loss: 1.9042, Perplexity: 6.7143\\n\",\n      \"Epoch [3/5], Step [2590/3236], Loss: 1.9867, Perplexity: 7.2911\\n\",\n      \"Epoch [3/5], Step [2600/3236], Loss: 1.9400, Perplexity: 6.9588\\n\",\n      \"Epoch [3/5], Step [2610/3236], Loss: 2.0853, Perplexity: 8.0469\\n\",\n      \"Epoch [3/5], Step [2620/3236], Loss: 1.9540, Perplexity: 7.0566\\n\",\n      \"Epoch [3/5], Step [2630/3236], Loss: 1.8633, Perplexity: 6.4450\\n\",\n      \"Epoch [3/5], Step [2640/3236], Loss: 1.9827, Perplexity: 7.2627\\n\",\n      \"Epoch [3/5], Step [2650/3236], Loss: 2.0351, Perplexity: 7.6529\\n\",\n      \"Epoch [3/5], Step [2660/3236], Loss: 1.8449, Perplexity: 6.3275\\n\",\n      \"Epoch [3/5], Step [2670/3236], Loss: 1.8583, Perplexity: 6.4128\\n\",\n      \"Epoch [3/5], Step [2680/3236], Loss: 1.9873, Perplexity: 7.2956\\n\",\n      \"Epoch [3/5], Step [2690/3236], Loss: 2.0292, Perplexity: 7.6082\\n\",\n      \"Epoch [3/5], Step [2700/3236], Loss: 1.9297, Perplexity: 6.8875\\n\",\n      \"Epoch [3/5], Step [2710/3236], Loss: 1.8835, Perplexity: 6.5764\\n\",\n      \"Epoch [3/5], Step [2720/3236], Loss: 1.9404, Perplexity: 6.9617\\n\",\n      \"Epoch [3/5], Step [2730/3236], Loss: 1.8420, Perplexity: 6.3090\\n\",\n      \"Epoch [3/5], Step [2740/3236], Loss: 1.8953, Perplexity: 6.6543\\n\",\n      \"Epoch [3/5], Step [2750/3236], Loss: 1.8794, Perplexity: 6.5494\\n\",\n      \"Epoch [3/5], Step [2760/3236], Loss: 1.7639, Perplexity: 5.8354\\n\",\n      \"Epoch [3/5], Step [2770/3236], Loss: 2.0747, Perplexity: 7.9625\\n\",\n      \"Epoch [3/5], Step [2780/3236], Loss: 2.0180, Perplexity: 7.5234\\n\",\n      \"Epoch [3/5], Step [2790/3236], Loss: 1.9382, Perplexity: 6.9459\\n\",\n      \"Epoch [3/5], Step [2800/3236], Loss: 1.9657, Perplexity: 7.1402\\n\",\n      \"Epoch [3/5], Step [2810/3236], Loss: 1.8877, Perplexity: 6.6041\\n\",\n      \"Epoch [3/5], Step [2820/3236], Loss: 1.9531, Perplexity: 7.0504\\n\",\n      \"Epoch [3/5], Step [2830/3236], Loss: 1.9896, Perplexity: 7.3125\\n\",\n      \"Epoch [3/5], Step [2840/3236], Loss: 1.9933, Perplexity: 7.3398\\n\",\n      \"Epoch [3/5], Step [2850/3236], Loss: 2.0044, Perplexity: 7.4213\\n\",\n      \"Epoch [3/5], Step [2860/3236], Loss: 1.8581, Perplexity: 6.4116\\n\",\n      \"Epoch [3/5], Step [2870/3236], Loss: 1.9911, Perplexity: 7.3237\\n\",\n      \"Epoch [3/5], Step [2880/3236], Loss: 1.8945, Perplexity: 6.6495\\n\",\n      \"Epoch [3/5], Step [2890/3236], Loss: 1.9283, Perplexity: 6.8781\\n\",\n      \"Epoch [3/5], Step [2900/3236], Loss: 1.9225, Perplexity: 6.8383\\n\",\n      \"Epoch [3/5], Step [2910/3236], Loss: 1.9630, Perplexity: 7.1203\\n\",\n      \"Epoch [3/5], Step [2920/3236], Loss: 1.9021, Perplexity: 6.7001\\n\",\n      \"Epoch [3/5], Step [2930/3236], Loss: 1.9184, Perplexity: 6.8100\\n\",\n      \"Epoch [3/5], Step [2940/3236], Loss: 1.9303, Perplexity: 6.8913\\n\",\n      \"Epoch [3/5], Step [2950/3236], Loss: 1.9876, Perplexity: 7.2979\\n\",\n      \"Epoch [3/5], Step [2960/3236], Loss: 1.9145, Perplexity: 6.7835\\n\",\n      \"Epoch [3/5], Step [2970/3236], Loss: 2.0211, Perplexity: 7.5464\\n\",\n      \"Epoch [3/5], Step [2980/3236], Loss: 1.7664, Perplexity: 5.8495\\n\",\n      \"Epoch [3/5], Step [2990/3236], Loss: 1.8808, Perplexity: 6.5585\\n\",\n      \"Epoch [3/5], Step [3000/3236], Loss: 1.8676, Perplexity: 6.4729\\n\",\n      \"Epoch [3/5], Step [3010/3236], Loss: 1.9553, Perplexity: 7.0661\\n\",\n      \"Epoch [3/5], Step [3020/3236], Loss: 2.0681, Perplexity: 7.9094\\n\",\n      \"Epoch [3/5], Step [3030/3236], Loss: 2.0121, Perplexity: 7.4787\\n\",\n      \"Epoch [3/5], Step [3040/3236], Loss: 1.8979, Perplexity: 6.6721\\n\",\n      \"Epoch [3/5], Step [3050/3236], Loss: 1.9583, Perplexity: 7.0871\\n\",\n      \"Epoch [3/5], Step [3060/3236], Loss: 2.0468, Perplexity: 7.7431\\n\",\n      \"Epoch [3/5], Step [3070/3236], Loss: 1.8637, Perplexity: 6.4476\\n\",\n      \"Epoch [3/5], Step [3080/3236], Loss: 1.9119, Perplexity: 6.7656\\n\",\n      \"Epoch [3/5], Step [3090/3236], Loss: 1.9247, Perplexity: 6.8530\\n\",\n      \"Epoch [3/5], Step [3100/3236], Loss: 1.9662, Perplexity: 7.1435\\n\",\n      \"Epoch [3/5], Step [3110/3236], Loss: 1.8912, Perplexity: 6.6273\\n\",\n      \"Epoch [3/5], Step [3120/3236], Loss: 1.9680, Perplexity: 7.1564\\n\",\n      \"Epoch [3/5], Step [3130/3236], Loss: 1.8972, Perplexity: 6.6670\\n\",\n      \"Epoch [3/5], Step [3140/3236], Loss: 1.7720, Perplexity: 5.8828\\n\",\n      \"Epoch [3/5], Step [3150/3236], Loss: 1.9999, Perplexity: 7.3880\\n\",\n      \"Epoch [3/5], Step [3160/3236], Loss: 2.0334, Perplexity: 7.6402\\n\",\n      \"Epoch [3/5], Step [3170/3236], Loss: 1.9205, Perplexity: 6.8247\\n\",\n      \"Epoch [3/5], Step [3180/3236], Loss: 2.0503, Perplexity: 7.7702\\n\",\n      \"Epoch [3/5], Step [3190/3236], Loss: 2.0182, Perplexity: 7.5247\\n\",\n      \"Epoch [3/5], Step [3200/3236], Loss: 1.9327, Perplexity: 6.9082\\n\",\n      \"Epoch [3/5], Step [3210/3236], Loss: 2.0177, Perplexity: 7.5210\\n\",\n      \"Epoch [3/5], Step [3220/3236], Loss: 2.0231, Perplexity: 7.5614\\n\",\n      \"Epoch [3/5], Step [3230/3236], Loss: 1.9389, Perplexity: 6.9513\\n\",\n      \"Epoch [4/5], Step [0/3236], Loss: 1.7669, Perplexity: 5.8524\\n\",\n      \"Epoch [4/5], Step [10/3236], Loss: 1.7634, Perplexity: 5.8322\\n\",\n      \"Epoch [4/5], Step [20/3236], Loss: 1.8713, Perplexity: 6.4965\\n\",\n      \"Epoch [4/5], Step [30/3236], Loss: 1.7330, Perplexity: 5.6577\\n\",\n      \"Epoch [4/5], Step [40/3236], Loss: 1.7862, Perplexity: 5.9666\\n\",\n      \"Epoch [4/5], Step [50/3236], Loss: 1.7676, Perplexity: 5.8570\\n\",\n      \"Epoch [4/5], Step [60/3236], Loss: 1.7414, Perplexity: 5.7056\\n\",\n      \"Epoch [4/5], Step [70/3236], Loss: 1.8360, Perplexity: 6.2715\\n\",\n      \"Epoch [4/5], Step [80/3236], Loss: 1.8556, Perplexity: 6.3955\\n\",\n      \"Epoch [4/5], Step [90/3236], Loss: 1.8696, Perplexity: 6.4859\\n\",\n      \"Epoch [4/5], Step [100/3236], Loss: 1.7975, Perplexity: 6.0345\\n\",\n      \"Epoch [4/5], Step [110/3236], Loss: 1.7818, Perplexity: 5.9408\\n\",\n      \"Epoch [4/5], Step [120/3236], Loss: 1.8882, Perplexity: 6.6075\\n\",\n      \"Epoch [4/5], Step [130/3236], Loss: 1.7720, Perplexity: 5.8824\\n\",\n      \"Epoch [4/5], Step [140/3236], Loss: 1.9268, Perplexity: 6.8672\\n\",\n      \"Epoch [4/5], Step [150/3236], Loss: 1.8920, Perplexity: 6.6325\\n\",\n      \"Epoch [4/5], Step [160/3236], Loss: 1.7635, Perplexity: 5.8331\\n\",\n      \"Epoch [4/5], Step [170/3236], Loss: 1.8577, Perplexity: 6.4090\\n\",\n      \"Epoch [4/5], Step [180/3236], Loss: 1.7829, Perplexity: 5.9470\\n\",\n      \"Epoch [4/5], Step [190/3236], Loss: 1.8487, Perplexity: 6.3515\\n\",\n      \"Epoch [4/5], Step [200/3236], Loss: 1.8758, Perplexity: 6.5261\\n\",\n      \"Epoch [4/5], Step [210/3236], Loss: 1.8355, Perplexity: 6.2684\\n\",\n      \"Epoch [4/5], Step [220/3236], Loss: 1.8553, Perplexity: 6.3939\\n\",\n      \"Epoch [4/5], Step [230/3236], Loss: 1.8020, Perplexity: 6.0616\\n\",\n      \"Epoch [4/5], Step [240/3236], Loss: 1.8032, Perplexity: 6.0689\\n\",\n      \"Epoch [4/5], Step [250/3236], Loss: 1.8671, Perplexity: 6.4695\\n\",\n      \"Epoch [4/5], Step [260/3236], Loss: 1.7141, Perplexity: 5.5514\\n\",\n      \"Epoch [4/5], Step [270/3236], Loss: 1.9770, Perplexity: 7.2214\\n\",\n      \"Epoch [4/5], Step [280/3236], Loss: 1.7963, Perplexity: 6.0276\\n\",\n      \"Epoch [4/5], Step [290/3236], Loss: 1.8566, Perplexity: 6.4019\\n\",\n      \"Epoch [4/5], Step [300/3236], Loss: 1.7993, Perplexity: 6.0455\\n\",\n      \"Epoch [4/5], Step [310/3236], Loss: 1.8563, Perplexity: 6.3997\\n\",\n      \"Epoch [4/5], Step [320/3236], Loss: 1.8000, Perplexity: 6.0495\\n\",\n      \"Epoch [4/5], Step [330/3236], Loss: 1.7979, Perplexity: 6.0369\\n\",\n      \"Epoch [4/5], Step [340/3236], Loss: 1.8795, Perplexity: 6.5501\\n\",\n      \"Epoch [4/5], Step [350/3236], Loss: 1.8587, Perplexity: 6.4152\\n\",\n      \"Epoch [4/5], Step [360/3236], Loss: 1.7370, Perplexity: 5.6802\\n\",\n      \"Epoch [4/5], Step [370/3236], Loss: 1.7993, Perplexity: 6.0452\\n\",\n      \"Epoch [4/5], Step [380/3236], Loss: 1.8741, Perplexity: 6.5147\\n\",\n      \"Epoch [4/5], Step [390/3236], Loss: 1.7996, Perplexity: 6.0473\\n\",\n      \"Epoch [4/5], Step [400/3236], Loss: 1.8007, Perplexity: 6.0537\\n\",\n      \"Epoch [4/5], Step [410/3236], Loss: 1.8694, Perplexity: 6.4844\\n\",\n      \"Epoch [4/5], Step [420/3236], Loss: 1.8282, Perplexity: 6.2226\\n\",\n      \"Epoch [4/5], Step [430/3236], Loss: 1.8091, Perplexity: 6.1048\\n\",\n      \"Epoch [4/5], Step [440/3236], Loss: 2.0117, Perplexity: 7.4757\\n\",\n      \"Epoch [4/5], Step [450/3236], Loss: 1.8917, Perplexity: 6.6310\\n\",\n      \"Epoch [4/5], Step [460/3236], Loss: 1.7763, Perplexity: 5.9080\\n\",\n      \"Epoch [4/5], Step [470/3236], Loss: 1.8613, Perplexity: 6.4319\\n\",\n      \"Epoch [4/5], Step [480/3236], Loss: 1.8126, Perplexity: 6.1261\\n\",\n      \"Epoch [4/5], Step [490/3236], Loss: 1.8841, Perplexity: 6.5803\\n\",\n      \"Epoch [4/5], Step [500/3236], Loss: 1.8763, Perplexity: 6.5292\\n\",\n      \"Epoch [4/5], Step [510/3236], Loss: 1.8853, Perplexity: 6.5881\\n\",\n      \"Epoch [4/5], Step [520/3236], Loss: 1.8586, Perplexity: 6.4144\\n\",\n      \"Epoch [4/5], Step [530/3236], Loss: 1.8506, Perplexity: 6.3638\\n\",\n      \"Epoch [4/5], Step [540/3236], Loss: 1.7980, Perplexity: 6.0374\\n\",\n      \"Epoch [4/5], Step [550/3236], Loss: 1.8559, Perplexity: 6.3972\\n\",\n      \"Epoch [4/5], Step [560/3236], Loss: 2.0034, Perplexity: 7.4141\\n\",\n      \"Epoch [4/5], Step [570/3236], Loss: 1.8733, Perplexity: 6.5098\\n\",\n      \"Epoch [4/5], Step [580/3236], Loss: 1.8133, Perplexity: 6.1309\\n\",\n      \"Epoch [4/5], Step [590/3236], Loss: 1.8900, Perplexity: 6.6191\\n\",\n      \"Epoch [4/5], Step [600/3236], Loss: 1.8796, Perplexity: 6.5508\\n\",\n      \"Epoch [4/5], Step [610/3236], Loss: 1.9203, Perplexity: 6.8230\\n\",\n      \"Epoch [4/5], Step [620/3236], Loss: 1.8851, Perplexity: 6.5870\\n\",\n      \"Epoch [4/5], Step [630/3236], Loss: 1.8895, Perplexity: 6.6158\\n\",\n      \"Epoch [4/5], Step [640/3236], Loss: 1.9741, Perplexity: 7.1999\\n\",\n      \"Epoch [4/5], Step [650/3236], Loss: 1.7713, Perplexity: 5.8788\\n\",\n      \"Epoch [4/5], Step [660/3236], Loss: 1.7779, Perplexity: 5.9172\\n\",\n      \"Epoch [4/5], Step [670/3236], Loss: 1.8073, Perplexity: 6.0941\\n\",\n      \"Epoch [4/5], Step [680/3236], Loss: 1.8566, Perplexity: 6.4018\\n\",\n      \"Epoch [4/5], Step [690/3236], Loss: 1.8547, Perplexity: 6.3896\\n\",\n      \"Epoch [4/5], Step [700/3236], Loss: 1.8263, Perplexity: 6.2106\\n\",\n      \"Epoch [4/5], Step [710/3236], Loss: 1.8729, Perplexity: 6.5071\\n\",\n      \"Epoch [4/5], Step [720/3236], Loss: 1.9379, Perplexity: 6.9442\\n\",\n      \"Epoch [4/5], Step [730/3236], Loss: 1.8378, Perplexity: 6.2827\\n\",\n      \"Epoch [4/5], Step [740/3236], Loss: 1.9125, Perplexity: 6.7699\\n\",\n      \"Epoch [4/5], Step [750/3236], Loss: 1.9379, Perplexity: 6.9438\\n\",\n      \"Epoch [4/5], Step [760/3236], Loss: 1.8164, Perplexity: 6.1499\\n\",\n      \"Epoch [4/5], Step [770/3236], Loss: 1.8939, Perplexity: 6.6450\\n\",\n      \"Epoch [4/5], Step [780/3236], Loss: 1.7362, Perplexity: 5.6758\\n\",\n      \"Epoch [4/5], Step [790/3236], Loss: 1.9961, Perplexity: 7.3605\\n\",\n      \"Epoch [4/5], Step [800/3236], Loss: 1.7799, Perplexity: 5.9290\\n\",\n      \"Epoch [4/5], Step [810/3236], Loss: 2.0057, Perplexity: 7.4315\\n\",\n      \"Epoch [4/5], Step [820/3236], Loss: 1.8369, Perplexity: 6.2767\\n\",\n      \"Epoch [4/5], Step [830/3236], Loss: 1.8662, Perplexity: 6.4639\\n\",\n      \"Epoch [4/5], Step [840/3236], Loss: 1.7461, Perplexity: 5.7321\\n\",\n      \"Epoch [4/5], Step [850/3236], Loss: 1.8055, Perplexity: 6.0831\\n\",\n      \"Epoch [4/5], Step [860/3236], Loss: 1.9280, Perplexity: 6.8757\\n\",\n      \"Epoch [4/5], Step [870/3236], Loss: 1.9999, Perplexity: 7.3884\\n\",\n      \"Epoch [4/5], Step [880/3236], Loss: 1.8389, Perplexity: 6.2898\\n\",\n      \"Epoch [4/5], Step [890/3236], Loss: 1.8695, Perplexity: 6.4848\\n\",\n      \"Epoch [4/5], Step [900/3236], Loss: 1.8431, Perplexity: 6.3159\\n\",\n      \"Epoch [4/5], Step [910/3236], Loss: 1.7514, Perplexity: 5.7626\\n\",\n      \"Epoch [4/5], Step [920/3236], Loss: 1.7154, Perplexity: 5.5589\\n\",\n      \"Epoch [4/5], Step [930/3236], Loss: 1.9403, Perplexity: 6.9608\\n\",\n      \"Epoch [4/5], Step [940/3236], Loss: 1.8644, Perplexity: 6.4519\\n\",\n      \"Epoch [4/5], Step [950/3236], Loss: 1.8031, Perplexity: 6.0686\\n\",\n      \"Epoch [4/5], Step [960/3236], Loss: 1.8786, Perplexity: 6.5447\\n\",\n      \"Epoch [4/5], Step [970/3236], Loss: 1.8518, Perplexity: 6.3711\\n\",\n      \"Epoch [4/5], Step [980/3236], Loss: 2.0308, Perplexity: 7.6202\\n\",\n      \"Epoch [4/5], Step [990/3236], Loss: 1.8629, Perplexity: 6.4426\\n\",\n      \"Epoch [4/5], Step [1000/3236], Loss: 1.8867, Perplexity: 6.5974\\n\",\n      \"Epoch [4/5], Step [1010/3236], Loss: 1.9093, Perplexity: 6.7485\\n\",\n      \"Epoch [4/5], Step [1020/3236], Loss: 1.7932, Perplexity: 6.0088\\n\",\n      \"Epoch [4/5], Step [1030/3236], Loss: 1.7659, Perplexity: 5.8470\\n\",\n      \"Epoch [4/5], Step [1040/3236], Loss: 1.7675, Perplexity: 5.8563\\n\",\n      \"Epoch [4/5], Step [1050/3236], Loss: 1.8750, Perplexity: 6.5205\\n\",\n      \"Epoch [4/5], Step [1060/3236], Loss: 1.8209, Perplexity: 6.1776\\n\",\n      \"Epoch [4/5], Step [1070/3236], Loss: 1.9492, Perplexity: 7.0228\\n\",\n      \"Epoch [4/5], Step [1080/3236], Loss: 1.9085, Perplexity: 6.7431\\n\",\n      \"Epoch [4/5], Step [1090/3236], Loss: 1.9576, Perplexity: 7.0823\\n\",\n      \"Epoch [4/5], Step [1100/3236], Loss: 1.8609, Perplexity: 6.4297\\n\",\n      \"Epoch [4/5], Step [1110/3236], Loss: 1.8266, Perplexity: 6.2127\\n\",\n      \"Epoch [4/5], Step [1120/3236], Loss: 1.9183, Perplexity: 6.8094\\n\",\n      \"Epoch [4/5], Step [1130/3236], Loss: 1.8419, Perplexity: 6.3087\\n\",\n      \"Epoch [4/5], Step [1140/3236], Loss: 1.8288, Perplexity: 6.2265\\n\",\n      \"Epoch [4/5], Step [1150/3236], Loss: 1.8556, Perplexity: 6.3955\\n\",\n      \"Epoch [4/5], Step [1160/3236], Loss: 1.8181, Perplexity: 6.1602\\n\",\n      \"Epoch [4/5], Step [1170/3236], Loss: 1.9768, Perplexity: 7.2198\\n\",\n      \"Epoch [4/5], Step [1180/3236], Loss: 1.8330, Perplexity: 6.2525\\n\",\n      \"Epoch [4/5], Step [1190/3236], Loss: 1.7441, Perplexity: 5.7207\\n\",\n      \"Epoch [4/5], Step [1200/3236], Loss: 1.9221, Perplexity: 6.8351\\n\",\n      \"Epoch [4/5], Step [1210/3236], Loss: 1.8829, Perplexity: 6.5724\\n\",\n      \"Epoch [4/5], Step [1220/3236], Loss: 1.9335, Perplexity: 6.9134\\n\",\n      \"Epoch [4/5], Step [1230/3236], Loss: 1.9123, Perplexity: 6.7686\\n\",\n      \"Epoch [4/5], Step [1240/3236], Loss: 1.8921, Perplexity: 6.6332\\n\",\n      \"Epoch [4/5], Step [1250/3236], Loss: 1.8529, Perplexity: 6.3783\\n\",\n      \"Epoch [4/5], Step [1260/3236], Loss: 1.8664, Perplexity: 6.4650\\n\",\n      \"Epoch [4/5], Step [1270/3236], Loss: 1.8413, Perplexity: 6.3049\\n\",\n      \"Epoch [4/5], Step [1280/3236], Loss: 1.8260, Perplexity: 6.2088\\n\",\n      \"Epoch [4/5], Step [1290/3236], Loss: 1.8707, Perplexity: 6.4928\\n\",\n      \"Epoch [4/5], Step [1300/3236], Loss: 1.8271, Perplexity: 6.2156\\n\",\n      \"Epoch [4/5], Step [1310/3236], Loss: 1.8470, Perplexity: 6.3408\\n\",\n      \"Epoch [4/5], Step [1320/3236], Loss: 1.7104, Perplexity: 5.5313\\n\",\n      \"Epoch [4/5], Step [1330/3236], Loss: 1.9046, Perplexity: 6.7167\\n\",\n      \"Epoch [4/5], Step [1340/3236], Loss: 1.9618, Perplexity: 7.1123\\n\",\n      \"Epoch [4/5], Step [1350/3236], Loss: 1.7861, Perplexity: 5.9659\\n\",\n      \"Epoch [4/5], Step [1360/3236], Loss: 1.8005, Perplexity: 6.0526\\n\",\n      \"Epoch [4/5], Step [1370/3236], Loss: 1.7292, Perplexity: 5.6362\\n\",\n      \"Epoch [4/5], Step [1380/3236], Loss: 1.8441, Perplexity: 6.3225\\n\",\n      \"Epoch [4/5], Step [1390/3236], Loss: 1.8175, Perplexity: 6.1563\\n\",\n      \"Epoch [4/5], Step [1400/3236], Loss: 1.8766, Perplexity: 6.5314\\n\",\n      \"Epoch [4/5], Step [1410/3236], Loss: 1.7880, Perplexity: 5.9774\\n\",\n      \"Epoch [4/5], Step [1420/3236], Loss: 1.8743, Perplexity: 6.5159\\n\",\n      \"Epoch [4/5], Step [1430/3236], Loss: 1.7609, Perplexity: 5.8176\\n\",\n      \"Epoch [4/5], Step [1440/3236], Loss: 1.8994, Perplexity: 6.6822\\n\",\n      \"Epoch [4/5], Step [1450/3236], Loss: 1.8810, Perplexity: 6.5599\\n\",\n      \"Epoch [4/5], Step [1460/3236], Loss: 1.8313, Perplexity: 6.2422\\n\",\n      \"Epoch [4/5], Step [1470/3236], Loss: 1.7733, Perplexity: 5.8903\\n\",\n      \"Epoch [4/5], Step [1480/3236], Loss: 1.8771, Perplexity: 6.5348\\n\",\n      \"Epoch [4/5], Step [1490/3236], Loss: 1.8759, Perplexity: 6.5269\\n\",\n      \"Epoch [4/5], Step [1500/3236], Loss: 1.8159, Perplexity: 6.1466\\n\",\n      \"Epoch [4/5], Step [1510/3236], Loss: 1.9122, Perplexity: 6.7678\\n\",\n      \"Epoch [4/5], Step [1520/3236], Loss: 1.8356, Perplexity: 6.2690\\n\",\n      \"Epoch [4/5], Step [1530/3236], Loss: 1.9213, Perplexity: 6.8301\\n\",\n      \"Epoch [4/5], Step [1540/3236], Loss: 1.8294, Perplexity: 6.2299\\n\",\n      \"Epoch [4/5], Step [1550/3236], Loss: 1.9354, Perplexity: 6.9265\\n\",\n      \"Epoch [4/5], Step [1560/3236], Loss: 1.8817, Perplexity: 6.5648\\n\",\n      \"Epoch [4/5], Step [1570/3236], Loss: 1.7943, Perplexity: 6.0154\\n\",\n      \"Epoch [4/5], Step [1580/3236], Loss: 1.9585, Perplexity: 7.0886\\n\",\n      \"Epoch [4/5], Step [1590/3236], Loss: 1.8456, Perplexity: 6.3319\\n\",\n      \"Epoch [4/5], Step [1600/3236], Loss: 1.7967, Perplexity: 6.0294\\n\",\n      \"Epoch [4/5], Step [1610/3236], Loss: 1.9781, Perplexity: 7.2293\\n\",\n      \"Epoch [4/5], Step [1620/3236], Loss: 1.8672, Perplexity: 6.4704\\n\",\n      \"Epoch [4/5], Step [1630/3236], Loss: 1.9283, Perplexity: 6.8779\\n\",\n      \"Epoch [4/5], Step [1640/3236], Loss: 1.7380, Perplexity: 5.6860\\n\",\n      \"Epoch [4/5], Step [1650/3236], Loss: 1.9353, Perplexity: 6.9259\\n\",\n      \"Epoch [4/5], Step [1660/3236], Loss: 1.8438, Perplexity: 6.3207\\n\",\n      \"Epoch [4/5], Step [1670/3236], Loss: 1.9049, Perplexity: 6.7186\\n\",\n      \"Epoch [4/5], Step [1680/3236], Loss: 1.8245, Perplexity: 6.1998\\n\",\n      \"Epoch [4/5], Step [1690/3236], Loss: 1.8670, Perplexity: 6.4691\\n\",\n      \"Epoch [4/5], Step [1700/3236], Loss: 1.8445, Perplexity: 6.3251\\n\",\n      \"Epoch [4/5], Step [1710/3236], Loss: 1.9448, Perplexity: 6.9924\\n\",\n      \"Epoch [4/5], Step [1720/3236], Loss: 1.7676, Perplexity: 5.8568\\n\",\n      \"Epoch [4/5], Step [1730/3236], Loss: 1.9023, Perplexity: 6.7010\\n\",\n      \"Epoch [4/5], Step [1740/3236], Loss: 1.9037, Perplexity: 6.7104\\n\",\n      \"Epoch [4/5], Step [1750/3236], Loss: 1.8411, Perplexity: 6.3035\\n\",\n      \"Epoch [4/5], Step [1760/3236], Loss: 1.8264, Perplexity: 6.2112\\n\",\n      \"Epoch [4/5], Step [1770/3236], Loss: 1.8923, Perplexity: 6.6348\\n\",\n      \"Epoch [4/5], Step [1780/3236], Loss: 1.8900, Perplexity: 6.6197\\n\",\n      \"Epoch [4/5], Step [1790/3236], Loss: 2.0277, Perplexity: 7.5962\\n\",\n      \"Epoch [4/5], Step [1800/3236], Loss: 1.8600, Perplexity: 6.4240\\n\",\n      \"Epoch [4/5], Step [1810/3236], Loss: 1.8780, Perplexity: 6.5404\\n\",\n      \"Epoch [4/5], Step [1820/3236], Loss: 2.0066, Perplexity: 7.4382\\n\",\n      \"Epoch [4/5], Step [1830/3236], Loss: 1.8006, Perplexity: 6.0533\\n\",\n      \"Epoch [4/5], Step [1840/3236], Loss: 1.8846, Perplexity: 6.5835\\n\",\n      \"Epoch [4/5], Step [1850/3236], Loss: 1.9052, Perplexity: 6.7206\\n\",\n      \"Epoch [4/5], Step [1860/3236], Loss: 1.8989, Perplexity: 6.6785\\n\",\n      \"Epoch [4/5], Step [1870/3236], Loss: 1.9321, Perplexity: 6.9042\\n\",\n      \"Epoch [4/5], Step [1880/3236], Loss: 1.7301, Perplexity: 5.6414\\n\",\n      \"Epoch [4/5], Step [1890/3236], Loss: 1.9148, Perplexity: 6.7852\\n\",\n      \"Epoch [4/5], Step [1900/3236], Loss: 1.8596, Perplexity: 6.4213\\n\",\n      \"Epoch [4/5], Step [1910/3236], Loss: 1.8353, Perplexity: 6.2671\\n\",\n      \"Epoch [4/5], Step [1920/3236], Loss: 1.8921, Perplexity: 6.6335\\n\",\n      \"Epoch [4/5], Step [1930/3236], Loss: 1.8728, Perplexity: 6.5067\\n\",\n      \"Epoch [4/5], Step [1940/3236], Loss: 1.8762, Perplexity: 6.5288\\n\",\n      \"Epoch [4/5], Step [1950/3236], Loss: 1.7786, Perplexity: 5.9218\\n\",\n      \"Epoch [4/5], Step [1960/3236], Loss: 1.8012, Perplexity: 6.0571\\n\",\n      \"Epoch [4/5], Step [1970/3236], Loss: 1.8588, Perplexity: 6.4161\\n\",\n      \"Epoch [4/5], Step [1980/3236], Loss: 1.9184, Perplexity: 6.8104\\n\",\n      \"Epoch [4/5], Step [1990/3236], Loss: 1.9299, Perplexity: 6.8889\\n\",\n      \"Epoch [4/5], Step [2000/3236], Loss: 1.8817, Perplexity: 6.5648\\n\",\n      \"Epoch [4/5], Step [2010/3236], Loss: 1.8652, Perplexity: 6.4569\\n\",\n      \"Epoch [4/5], Step [2020/3236], Loss: 1.9078, Perplexity: 6.7381\\n\",\n      \"Epoch [4/5], Step [2030/3236], Loss: 1.7688, Perplexity: 5.8639\\n\",\n      \"Epoch [4/5], Step [2040/3236], Loss: 1.8859, Perplexity: 6.5925\\n\",\n      \"Epoch [4/5], Step [2050/3236], Loss: 1.9103, Perplexity: 6.7554\\n\",\n      \"Epoch [4/5], Step [2060/3236], Loss: 1.7938, Perplexity: 6.0125\\n\",\n      \"Epoch [4/5], Step [2070/3236], Loss: 1.9524, Perplexity: 7.0458\\n\",\n      \"Epoch [4/5], Step [2080/3236], Loss: 1.9411, Perplexity: 6.9661\\n\",\n      \"Epoch [4/5], Step [2090/3236], Loss: 1.8557, Perplexity: 6.3962\\n\",\n      \"Epoch [4/5], Step [2100/3236], Loss: 2.0271, Perplexity: 7.5918\\n\",\n      \"Epoch [4/5], Step [2110/3236], Loss: 1.8947, Perplexity: 6.6508\\n\",\n      \"Epoch [4/5], Step [2120/3236], Loss: 1.8696, Perplexity: 6.4856\\n\",\n      \"Epoch [4/5], Step [2130/3236], Loss: 1.7196, Perplexity: 5.5823\\n\",\n      \"Epoch [4/5], Step [2140/3236], Loss: 1.8689, Perplexity: 6.4813\\n\",\n      \"Epoch [4/5], Step [2150/3236], Loss: 1.7496, Perplexity: 5.7525\\n\",\n      \"Epoch [4/5], Step [2160/3236], Loss: 1.9196, Perplexity: 6.8180\\n\",\n      \"Epoch [4/5], Step [2170/3236], Loss: 2.0177, Perplexity: 7.5207\\n\",\n      \"Epoch [4/5], Step [2180/3236], Loss: 2.0350, Perplexity: 7.6521\\n\",\n      \"Epoch [4/5], Step [2190/3236], Loss: 1.9981, Perplexity: 7.3748\\n\",\n      \"Epoch [4/5], Step [2200/3236], Loss: 1.8558, Perplexity: 6.3966\\n\",\n      \"Epoch [4/5], Step [2210/3236], Loss: 1.9080, Perplexity: 6.7398\\n\",\n      \"Epoch [4/5], Step [2220/3236], Loss: 1.8697, Perplexity: 6.4861\\n\",\n      \"Epoch [4/5], Step [2230/3236], Loss: 1.7363, Perplexity: 5.6765\\n\",\n      \"Epoch [4/5], Step [2240/3236], Loss: 1.9190, Perplexity: 6.8144\\n\",\n      \"Epoch [4/5], Step [2250/3236], Loss: 1.9572, Perplexity: 7.0792\\n\",\n      \"Epoch [4/5], Step [2260/3236], Loss: 1.9858, Perplexity: 7.2848\\n\",\n      \"Epoch [4/5], Step [2270/3236], Loss: 1.9272, Perplexity: 6.8701\\n\",\n      \"Epoch [4/5], Step [2280/3236], Loss: 1.7591, Perplexity: 5.8073\\n\",\n      \"Epoch [4/5], Step [2290/3236], Loss: 1.7913, Perplexity: 5.9971\\n\",\n      \"Epoch [4/5], Step [2300/3236], Loss: 1.9193, Perplexity: 6.8162\\n\",\n      \"Epoch [4/5], Step [2310/3236], Loss: 1.7211, Perplexity: 5.5907\\n\",\n      \"Epoch [4/5], Step [2320/3236], Loss: 1.8098, Perplexity: 6.1092\\n\",\n      \"Epoch [4/5], Step [2330/3236], Loss: 1.9743, Perplexity: 7.2015\\n\",\n      \"Epoch [4/5], Step [2340/3236], Loss: 1.9863, Perplexity: 7.2886\\n\",\n      \"Epoch [4/5], Step [2350/3236], Loss: 1.9311, Perplexity: 6.8974\\n\",\n      \"Epoch [4/5], Step [2360/3236], Loss: 1.8630, Perplexity: 6.4429\\n\",\n      \"Epoch [4/5], Step [2370/3236], Loss: 1.9275, Perplexity: 6.8725\\n\",\n      \"Epoch [4/5], Step [2380/3236], Loss: 1.8097, Perplexity: 6.1086\\n\",\n      \"Epoch [4/5], Step [2390/3236], Loss: 1.8390, Perplexity: 6.2902\\n\",\n      \"Epoch [4/5], Step [2400/3236], Loss: 1.9342, Perplexity: 6.9182\\n\",\n      \"Epoch [4/5], Step [2410/3236], Loss: 1.8266, Perplexity: 6.2124\\n\",\n      \"Epoch [4/5], Step [2420/3236], Loss: 1.8876, Perplexity: 6.6038\\n\",\n      \"Epoch [4/5], Step [2430/3236], Loss: 1.8932, Perplexity: 6.6404\\n\",\n      \"Epoch [4/5], Step [2440/3236], Loss: 1.8467, Perplexity: 6.3390\\n\",\n      \"Epoch [4/5], Step [2450/3236], Loss: 1.8287, Perplexity: 6.2260\\n\",\n      \"Epoch [4/5], Step [2460/3236], Loss: 1.8784, Perplexity: 6.5429\\n\",\n      \"Epoch [4/5], Step [2470/3236], Loss: 1.8069, Perplexity: 6.0918\\n\",\n      \"Epoch [4/5], Step [2480/3236], Loss: 1.9340, Perplexity: 6.9174\\n\",\n      \"Epoch [4/5], Step [2490/3236], Loss: 1.9557, Perplexity: 7.0687\\n\",\n      \"Epoch [4/5], Step [2500/3236], Loss: 1.8975, Perplexity: 6.6693\\n\",\n      \"Epoch [4/5], Step [2510/3236], Loss: 1.8307, Perplexity: 6.2383\\n\",\n      \"Epoch [4/5], Step [2520/3236], Loss: 1.8125, Perplexity: 6.1257\\n\",\n      \"Epoch [4/5], Step [2530/3236], Loss: 1.8314, Perplexity: 6.2426\\n\",\n      \"Epoch [4/5], Step [2540/3236], Loss: 1.9300, Perplexity: 6.8897\\n\",\n      \"Epoch [4/5], Step [2550/3236], Loss: 1.8050, Perplexity: 6.0800\\n\",\n      \"Epoch [4/5], Step [2560/3236], Loss: 1.8995, Perplexity: 6.6826\\n\",\n      \"Epoch [4/5], Step [2570/3236], Loss: 1.7035, Perplexity: 5.4933\\n\",\n      \"Epoch [4/5], Step [2580/3236], Loss: 1.9858, Perplexity: 7.2845\\n\",\n      \"Epoch [4/5], Step [2590/3236], Loss: 1.9361, Perplexity: 6.9318\\n\",\n      \"Epoch [4/5], Step [2600/3236], Loss: 1.9168, Perplexity: 6.7989\\n\",\n      \"Epoch [4/5], Step [2610/3236], Loss: 1.8765, Perplexity: 6.5308\\n\",\n      \"Epoch [4/5], Step [2620/3236], Loss: 1.8626, Perplexity: 6.4403\\n\",\n      \"Epoch [4/5], Step [2630/3236], Loss: 1.8169, Perplexity: 6.1530\\n\",\n      \"Epoch [4/5], Step [2640/3236], Loss: 1.7875, Perplexity: 5.9743\\n\",\n      \"Epoch [4/5], Step [2650/3236], Loss: 1.8466, Perplexity: 6.3381\\n\",\n      \"Epoch [4/5], Step [2660/3236], Loss: 1.9186, Perplexity: 6.8115\\n\",\n      \"Epoch [4/5], Step [2670/3236], Loss: 1.9001, Perplexity: 6.6863\\n\",\n      \"Epoch [4/5], Step [2680/3236], Loss: 1.8137, Perplexity: 6.1332\\n\",\n      \"Epoch [4/5], Step [2690/3236], Loss: 1.9623, Perplexity: 7.1155\\n\",\n      \"Epoch [4/5], Step [2700/3236], Loss: 1.8592, Perplexity: 6.4184\\n\",\n      \"Epoch [4/5], Step [2710/3236], Loss: 1.9161, Perplexity: 6.7946\\n\",\n      \"Epoch [4/5], Step [2720/3236], Loss: 1.9172, Perplexity: 6.8017\\n\",\n      \"Epoch [4/5], Step [2730/3236], Loss: 1.8532, Perplexity: 6.3805\\n\",\n      \"Epoch [4/5], Step [2740/3236], Loss: 1.9483, Perplexity: 7.0165\\n\",\n      \"Epoch [4/5], Step [2750/3236], Loss: 1.8250, Perplexity: 6.2030\\n\",\n      \"Epoch [4/5], Step [2760/3236], Loss: 1.8299, Perplexity: 6.2334\\n\",\n      \"Epoch [4/5], Step [2770/3236], Loss: 1.9568, Perplexity: 7.0765\\n\",\n      \"Epoch [4/5], Step [2780/3236], Loss: 1.7527, Perplexity: 5.7700\\n\",\n      \"Epoch [4/5], Step [2790/3236], Loss: 1.9974, Perplexity: 7.3700\\n\",\n      \"Epoch [4/5], Step [2800/3236], Loss: 1.8066, Perplexity: 6.0898\\n\",\n      \"Epoch [4/5], Step [2810/3236], Loss: 1.8226, Perplexity: 6.1880\\n\",\n      \"Epoch [4/5], Step [2820/3236], Loss: 1.7978, Perplexity: 6.0363\\n\",\n      \"Epoch [4/5], Step [2830/3236], Loss: 2.0545, Perplexity: 7.8033\\n\",\n      \"Epoch [4/5], Step [2840/3236], Loss: 1.9234, Perplexity: 6.8443\\n\",\n      \"Epoch [4/5], Step [2850/3236], Loss: 1.9955, Perplexity: 7.3556\\n\",\n      \"Epoch [4/5], Step [2860/3236], Loss: 2.0304, Perplexity: 7.6168\\n\",\n      \"Epoch [4/5], Step [2870/3236], Loss: 1.9783, Perplexity: 7.2307\\n\",\n      \"Epoch [4/5], Step [2880/3236], Loss: 1.7487, Perplexity: 5.7471\\n\",\n      \"Epoch [4/5], Step [2890/3236], Loss: 1.9729, Perplexity: 7.1912\\n\",\n      \"Epoch [4/5], Step [2900/3236], Loss: 1.8837, Perplexity: 6.5776\\n\",\n      \"Epoch [4/5], Step [2910/3236], Loss: 1.8173, Perplexity: 6.1551\\n\",\n      \"Epoch [4/5], Step [2920/3236], Loss: 1.8728, Perplexity: 6.5067\\n\",\n      \"Epoch [4/5], Step [2930/3236], Loss: 1.9913, Perplexity: 7.3250\\n\",\n      \"Epoch [4/5], Step [2940/3236], Loss: 1.8018, Perplexity: 6.0608\\n\",\n      \"Epoch [4/5], Step [2950/3236], Loss: 1.8565, Perplexity: 6.4013\\n\",\n      \"Epoch [4/5], Step [2960/3236], Loss: 1.8570, Perplexity: 6.4048\\n\",\n      \"Epoch [4/5], Step [2970/3236], Loss: 1.8426, Perplexity: 6.3127\\n\",\n      \"Epoch [4/5], Step [2980/3236], Loss: 1.9053, Perplexity: 6.7212\\n\",\n      \"Epoch [4/5], Step [2990/3236], Loss: 1.9151, Perplexity: 6.7876\\n\",\n      \"Epoch [4/5], Step [3000/3236], Loss: 1.8564, Perplexity: 6.4007\\n\",\n      \"Epoch [4/5], Step [3010/3236], Loss: 1.8844, Perplexity: 6.5826\\n\",\n      \"Epoch [4/5], Step [3020/3236], Loss: 1.7858, Perplexity: 5.9646\\n\",\n      \"Epoch [4/5], Step [3030/3236], Loss: 1.9163, Perplexity: 6.7957\\n\",\n      \"Epoch [4/5], Step [3040/3236], Loss: 1.8533, Perplexity: 6.3811\\n\",\n      \"Epoch [4/5], Step [3050/3236], Loss: 1.9647, Perplexity: 7.1330\\n\",\n      \"Epoch [4/5], Step [3060/3236], Loss: 1.9032, Perplexity: 6.7075\\n\",\n      \"Epoch [4/5], Step [3070/3236], Loss: 1.8783, Perplexity: 6.5423\\n\",\n      \"Epoch [4/5], Step [3080/3236], Loss: 1.8459, Perplexity: 6.3337\\n\",\n      \"Epoch [4/5], Step [3090/3236], Loss: 1.9710, Perplexity: 7.1775\\n\",\n      \"Epoch [4/5], Step [3100/3236], Loss: 1.8833, Perplexity: 6.5749\\n\",\n      \"Epoch [4/5], Step [3110/3236], Loss: 1.8998, Perplexity: 6.6845\\n\",\n      \"Epoch [4/5], Step [3120/3236], Loss: 1.8052, Perplexity: 6.0811\\n\",\n      \"Epoch [4/5], Step [3130/3236], Loss: 1.9462, Perplexity: 7.0017\\n\",\n      \"Epoch [4/5], Step [3140/3236], Loss: 1.8889, Perplexity: 6.6121\\n\",\n      \"Epoch [4/5], Step [3150/3236], Loss: 1.8103, Perplexity: 6.1123\\n\",\n      \"Epoch [4/5], Step [3160/3236], Loss: 1.8787, Perplexity: 6.5448\\n\",\n      \"Epoch [4/5], Step [3170/3236], Loss: 1.8961, Perplexity: 6.6601\\n\",\n      \"Epoch [4/5], Step [3180/3236], Loss: 1.9219, Perplexity: 6.8340\\n\",\n      \"Epoch [4/5], Step [3190/3236], Loss: 1.8796, Perplexity: 6.5506\\n\",\n      \"Epoch [4/5], Step [3200/3236], Loss: 1.8666, Perplexity: 6.4662\\n\",\n      \"Epoch [4/5], Step [3210/3236], Loss: 1.9286, Perplexity: 6.8797\\n\",\n      \"Epoch [4/5], Step [3220/3236], Loss: 1.7139, Perplexity: 5.5504\\n\",\n      \"Epoch [4/5], Step [3230/3236], Loss: 1.8257, Perplexity: 6.2071\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Device configuration\\n\",\n    \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Create model directory\\n\",\n    \"if not os.path.exists('models_dir/'):\\n\",\n    \"    os.makedirs('models_dir/')\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Image preprocessing, normalization for the pretrained resnet\\n\",\n    \"transform = transforms.Compose([ \\n\",\n    \"    transforms.RandomCrop(224),\\n\",\n    \"    transforms.RandomHorizontalFlip(), \\n\",\n    \"    transforms.ToTensor(), \\n\",\n    \"    transforms.Normalize((0.485, 0.456, 0.406), \\n\",\n    \"                         (0.229, 0.224, 0.225))])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Load vocabulary wrapper\\n\",\n    \"with open('data_dir/vocabulary.pkl', 'rb') as f:\\n\",\n    \"    vocabulary = pickle.load(f)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Build data loader\\n\",\n    \"custom_data_loader = get_loader('data_dir/resized_images', 'data_dir/annotations/captions_train2014.json', vocabulary, \\n\",\n    \"                         transform, 128,\\n\",\n    \"                         shuffle=True) \\n\",\n    \"\\n\",\n    \"# Build the models\\n\",\n    \"encoder_model = CNNModel(256).to(device)\\n\",\n    \"decoder_model = LSTMModel(256, 512, len(vocabulary), 1).to(device)\\n\",\n    \" \\n\",\n    \"    \\n\",\n    \"# Loss and optimizer\\n\",\n    \"loss_criterion = nn.CrossEntropyLoss()\\n\",\n    \"parameters = list(decoder_model.parameters()) + list(encoder_model.linear_layer.parameters()) + list(encoder_model.batch_norm.parameters())\\n\",\n    \"optimizer = torch.optim.Adam(parameters, lr=0.001)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Train the models\\n\",\n    \"total_num_steps = len(custom_data_loader)\\n\",\n    \"for epoch in range(5):\\n\",\n    \"    for i, (imgs, caps, lens) in enumerate(custom_data_loader):\\n\",\n    \" \\n\",\n    \"        # Set mini-batch dataset\\n\",\n    \"        imgs = imgs.to(device)\\n\",\n    \"        caps = caps.to(device)\\n\",\n    \"        tgts = pack_padded_sequence(caps, lens, batch_first=True)[0]\\n\",\n    \" \\n\",\n    \"        # Forward, backward and optimize\\n\",\n    \"        feats = encoder_model(imgs)\\n\",\n    \"        outputs = decoder_model(feats, caps, lens)\\n\",\n    \"        loss = loss_criterion(outputs, tgts)\\n\",\n    \"        decoder_model.zero_grad()\\n\",\n    \"        encoder_model.zero_grad()\\n\",\n    \"        loss.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \" \\n\",\n    \"        # Print log info\\n\",\n    \"        if i % 10 == 0:\\n\",\n    \"            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'\\n\",\n    \"                  .format(epoch, 5, i, total_num_steps, loss.item(), np.exp(loss.item()))) \\n\",\n    \" \\n\",\n    \"        # Save the model checkpoints\\n\",\n    \"        if (i+1) % 1000 == 0:\\n\",\n    \"            torch.save(decoder_model.state_dict(), os.path.join(\\n\",\n    \"                'models_dir/', 'decoder-{}-{}.ckpt'.format(epoch+1, i+1)))\\n\",\n    \"            torch.save(encoder_model.state_dict(), os.path.join(\\n\",\n    \"                'models_dir/', 'encoder-{}-{}.ckpt'.format(epoch+1, i+1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## predict caption\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<start> a dog is looking out of a window at a dog . <end>\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fac65700b80>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"image_file_path = 'sample.jpg'\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"# Device configuration\\n\",\n    \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"def load_image(image_file_path, transform=None):\\n\",\n    \"    img = Image.open(image_file_path).convert('RGB')\\n\",\n    \"    img = img.resize([224, 224], Image.LANCZOS)\\n\",\n    \"    \\n\",\n    \"    if transform is not None:\\n\",\n    \"        img = transform(img).unsqueeze(0)\\n\",\n    \"    \\n\",\n    \"    return img\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"# Image preprocessing\\n\",\n    \"transform = transforms.Compose([\\n\",\n    \"    transforms.ToTensor(), \\n\",\n    \"    transforms.Normalize((0.485, 0.456, 0.406), \\n\",\n    \"                         (0.229, 0.224, 0.225))])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Load vocabulary wrapper\\n\",\n    \"with open('data_dir/vocabulary.pkl', 'rb') as f:\\n\",\n    \"    vocabulary = pickle.load(f)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Build models\\n\",\n    \"encoder_model = CNNModel(256).eval()  # eval mode (batchnorm uses moving mean/variance)\\n\",\n    \"decoder_model = LSTMModel(256, 512, len(vocabulary), 1)\\n\",\n    \"encoder_model = encoder_model.to(device)\\n\",\n    \"decoder_model = decoder_model.to(device)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Load the trained model parameters\\n\",\n    \"encoder_model.load_state_dict(torch.load('models_dir/encoder-2-3000.ckpt'))\\n\",\n    \"decoder_model.load_state_dict(torch.load('models_dir/decoder-2-3000.ckpt'))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Prepare an image\\n\",\n    \"img = load_image(image_file_path, transform)\\n\",\n    \"img_tensor = img.to(device)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Generate an caption from the image\\n\",\n    \"feat = encoder_model(img_tensor)\\n\",\n    \"sampled_indices = decoder_model.sample(feat)\\n\",\n    \"sampled_indices = sampled_indices[0].cpu().numpy()          # (1, max_seq_length) -> (max_seq_length)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Convert word_ids to words\\n\",\n    \"predicted_caption = []\\n\",\n    \"for token_index in sampled_indices:\\n\",\n    \"    word = vocabulary.i2w[token_index]\\n\",\n    \"    predicted_caption.append(word)\\n\",\n    \"    if word == '<end>':\\n\",\n    \"        break\\n\",\n    \"predicted_sentence = ' '.join(predicted_caption)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Print out the image and the generated caption\\n\",\n    \"print (predicted_sentence)\\n\",\n    \"img = Image.open(image_file_path)\\n\",\n    \"plt.imshow(np.asarray(img))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter04/lstm.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Collecting torch==1.9\\n\",\n      \"  Using cached torch-1.9.0-cp39-none-macosx_11_0_arm64.whl (41.5 MB)\\n\",\n      \"Requirement already satisfied: typing-extensions in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==1.9) (4.9.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mInstalling collected packages: torch\\n\",\n      \"  Attempting uninstall: torch\\n\",\n      \"    Found existing installation: torch 1.12.1\\n\",\n      \"    Uninstalling torch-1.12.1:\\n\",\n      \"      Successfully uninstalled torch-1.12.1\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"torchdata 0.4.1 requires torch==1.12.1, but you have torch 1.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed torch-1.9.0\\n\",\n      \"Requirement already satisfied: torchtext==0.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.10.0)\\n\",\n      \"Requirement already satisfied: tqdm in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.10) (4.66.2)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.10) (2.31.0)\\n\",\n      \"Requirement already satisfied: torch in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.10) (1.9.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.10) (1.26.4)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.10) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.10) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.10) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.10) (2024.2.2)\\n\",\n      \"Requirement already satisfied: typing-extensions in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch->torchtext==0.10) (4.9.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mCollecting matplotlib==3.8.3\\n\",\n      \"  Using cached matplotlib-3.8.3-cp39-cp39-macosx_11_0_arm64.whl.metadata (5.8 kB)\\n\",\n      \"Requirement already satisfied: contourpy>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (1.2.0)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (4.49.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.3.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy<2,>=1.21 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (1.26.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (23.2)\\n\",\n      \"Requirement already satisfied: pillow>=8 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.3.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (3.1.1)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (2.8.2)\\n\",\n      \"Requirement already satisfied: importlib-resources>=3.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.8.3) (6.1.1)\\n\",\n      \"Requirement already satisfied: zipp>=3.1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib==3.8.3) (3.17.0)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib==3.8.3) (1.16.0)\\n\",\n      \"Using cached matplotlib-3.8.3-cp39-cp39-macosx_11_0_arm64.whl (7.5 MB)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mInstalling collected packages: matplotlib\\n\",\n      \"  Attempting uninstall: matplotlib\\n\",\n      \"    Found existing installation: matplotlib 3.5.2\\n\",\n      \"    Uninstalling matplotlib-3.5.2:\\n\",\n      \"      Successfully uninstalled matplotlib-3.5.2\\n\",\n      \"Successfully installed matplotlib-3.8.3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# requires python3.9\\n\",\n    \"!pip install torch==1.9\\n\",\n    \"!pip install torchtext==0.10\\n\",\n    \"!pip install matplotlib==3.8.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"from string import punctuation\\n\",\n    \"from collections import Counter\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader, TensorDataset\\n\",\n    \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"from torchtext.legacy import datasets\\n\",\n    \"from torchtext.legacy import data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"TEXT_FIELD = data.Field(tokenize = data.get_tokenizer(\\\"basic_english\\\"), include_lengths = True)\\n\",\n    \"LABEL_FIELD = data.LabelField(dtype = torch.float)\\n\",\n    \"\\n\",\n    \"train_dataset, test_dataset = datasets.IMDB.splits(TEXT_FIELD, LABEL_FIELD)\\n\",\n    \"train_dataset, valid_dataset = train_dataset.split(random_state = random.seed(123))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"MAX_VOCABULARY_SIZE = 25000\\n\",\n    \"\\n\",\n    \"TEXT_FIELD.build_vocab(train_dataset, \\n\",\n    \"                 max_size = MAX_VOCABULARY_SIZE)\\n\",\n    \"\\n\",\n    \"LABEL_FIELD.build_vocab(train_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"B_SIZE = 64\\n\",\n    \"\\n\",\n    \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\",\n    \"\\n\",\n    \"train_data_iterator, valid_data_iterator, test_data_iterator = data.BucketIterator.splits(\\n\",\n    \"    (train_dataset, valid_dataset, test_dataset), \\n\",\n    \"    batch_size = B_SIZE,\\n\",\n    \"    sort_within_batch = True,\\n\",\n    \"    device = device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## If you are training using GPUs, we need to use the following function for the pack_padded_sequence method to work \\n\",\n    \"## (reference : https://discuss.pytorch.org/t/error-with-lengths-in-pack-padded-sequence/35517/3)\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.set_default_tensor_type(torch.cuda.FloatTensor)\\n\",\n    \"from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence\\n\",\n    \"\\n\",\n    \"def cuda_pack_padded_sequence(input, lengths, batch_first=False, enforce_sorted=True):\\n\",\n    \"    lengths = torch.as_tensor(lengths, dtype=torch.int64)\\n\",\n    \"    lengths = lengths.cpu()\\n\",\n    \"    if enforce_sorted:\\n\",\n    \"        sorted_indices = None\\n\",\n    \"    else:\\n\",\n    \"        lengths, sorted_indices = torch.sort(lengths, descending=True)\\n\",\n    \"        sorted_indices = sorted_indices.to(input.device)\\n\",\n    \"    batch_dim = 0 if batch_first else 1\\n\",\n    \"    input = input.index_select(batch_dim, sorted_indices)\\n\",\n    \"\\n\",\n    \"    data, batch_sizes = \\\\\\n\",\n    \"    torch._C._VariableFunctions._pack_padded_sequence(input, lengths, batch_first)\\n\",\n    \"    return PackedSequence(data, batch_sizes, sorted_indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/nn/modules/rnn.py:62: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\\n\",\n      \"  warnings.warn(\\\"dropout option adds dropout after all but last \\\"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class LSTM(nn.Module):\\n\",\n    \"    def __init__(self, vocabulary_size, embedding_dimension, hidden_dimension, output_dimension, dropout, pad_index):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.embedding_layer = nn.Embedding(vocabulary_size, embedding_dimension, padding_idx = pad_index)\\n\",\n    \"        self.lstm_layer = nn.LSTM(embedding_dimension, \\n\",\n    \"                           hidden_dimension, \\n\",\n    \"                           num_layers=1, \\n\",\n    \"                           bidirectional=True, \\n\",\n    \"                           dropout=dropout)\\n\",\n    \"        self.fc_layer = nn.Linear(hidden_dimension * 2, output_dimension)\\n\",\n    \"        self.dropout_layer = nn.Dropout(dropout)\\n\",\n    \"        \\n\",\n    \"    def forward(self, sequence, sequence_lengths=None):\\n\",\n    \"        if sequence_lengths is None:\\n\",\n    \"            sequence_lengths = torch.LongTensor([len(sequence)])\\n\",\n    \"        \\n\",\n    \"        # sequence := (sequence_length, batch_size)\\n\",\n    \"        embedded_output = self.dropout_layer(self.embedding_layer(sequence))\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        # embedded_output := (sequence_length, batch_size, embedding_dimension)\\n\",\n    \"        if torch.cuda.is_available():\\n\",\n    \"            packed_embedded_output = cuda_pack_padded_sequence(embedded_output, sequence_lengths)\\n\",\n    \"        else:\\n\",\n    \"            packed_embedded_output = nn.utils.rnn.pack_padded_sequence(embedded_output, sequence_lengths)\\n\",\n    \"        \\n\",\n    \"        packed_output, (hidden_state, cell_state) = self.lstm_layer(packed_embedded_output)\\n\",\n    \"        # hidden_state := (num_layers * num_directions, batch_size, hidden_dimension)\\n\",\n    \"        # cell_state := (num_layers * num_directions, batch_size, hidden_dimension)\\n\",\n    \"        \\n\",\n    \"        op, op_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)\\n\",\n    \"        # op := (sequence_length, batch_size, hidden_dimension * num_directions)\\n\",\n    \"        \\n\",\n    \"        hidden_output = torch.cat((hidden_state[-2,:,:], hidden_state[-1,:,:]), dim = 1)        \\n\",\n    \"        # hidden_output := (batch_size, hidden_dimension * num_directions)\\n\",\n    \"        \\n\",\n    \"        return self.fc_layer(hidden_output)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"INPUT_DIMENSION = len(TEXT_FIELD.vocab)\\n\",\n    \"EMBEDDING_DIMENSION = 100\\n\",\n    \"HIDDEN_DIMENSION = 32\\n\",\n    \"OUTPUT_DIMENSION = 1\\n\",\n    \"DROPOUT = 0.5\\n\",\n    \"PAD_INDEX = TEXT_FIELD.vocab.stoi[TEXT_FIELD.pad_token]\\n\",\n    \"\\n\",\n    \"lstm_model = LSTM(INPUT_DIMENSION, \\n\",\n    \"            EMBEDDING_DIMENSION, \\n\",\n    \"            HIDDEN_DIMENSION, \\n\",\n    \"            OUTPUT_DIMENSION, \\n\",\n    \"            DROPOUT, \\n\",\n    \"            PAD_INDEX)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"UNK_INDEX = TEXT_FIELD.vocab.stoi[TEXT_FIELD.unk_token]\\n\",\n    \"\\n\",\n    \"lstm_model.embedding_layer.weight.data[UNK_INDEX] = torch.zeros(EMBEDDING_DIMENSION)\\n\",\n    \"lstm_model.embedding_layer.weight.data[PAD_INDEX] = torch.zeros(EMBEDDING_DIMENSION)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optim = torch.optim.Adam(lstm_model.parameters())\\n\",\n    \"loss_func = nn.BCEWithLogitsLoss()\\n\",\n    \"\\n\",\n    \"lstm_model = lstm_model.to(device)\\n\",\n    \"loss_func = loss_func.to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def accuracy_metric(predictions, ground_truth):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns 0-1 accuracy for the given set of predictions and ground truth\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # round predictions to either 0 or 1\\n\",\n    \"    rounded_predictions = torch.round(torch.sigmoid(predictions))\\n\",\n    \"    success = (rounded_predictions == ground_truth).float() #convert into float for division \\n\",\n    \"    accuracy = success.sum() / len(success)\\n\",\n    \"    return accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, data_iterator, optim, loss_func):\\n\",\n    \"    loss = 0\\n\",\n    \"    accuracy = 0\\n\",\n    \"    model.train()\\n\",\n    \"    \\n\",\n    \"    for curr_batch in data_iterator:\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        sequence, sequence_lengths = curr_batch.text\\n\",\n    \"        preds = lstm_model(sequence, sequence_lengths).squeeze(1)\\n\",\n    \"        \\n\",\n    \"        loss_curr = loss_func(preds, curr_batch.label)\\n\",\n    \"        accuracy_curr = accuracy_metric(preds, curr_batch.label)\\n\",\n    \"        \\n\",\n    \"        loss_curr.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        \\n\",\n    \"        loss += loss_curr.item()\\n\",\n    \"        accuracy += accuracy_curr.item()\\n\",\n    \"        \\n\",\n    \"    return loss/len(data_iterator), accuracy/len(data_iterator)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def validate(model, data_iterator, loss_func):\\n\",\n    \"    loss = 0\\n\",\n    \"    accuracy = 0\\n\",\n    \"    model.eval()\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for curr_batch in data_iterator:\\n\",\n    \"            sequence, sequence_lengths = curr_batch.text\\n\",\n    \"            preds = model(sequence, sequence_lengths).squeeze(1)\\n\",\n    \"            \\n\",\n    \"            loss_curr = loss_func(preds, curr_batch.label)\\n\",\n    \"            accuracy_curr = accuracy_metric(preds, curr_batch.label)\\n\",\n    \"\\n\",\n    \"            loss += loss_curr.item()\\n\",\n    \"            accuracy += accuracy_curr.item()\\n\",\n    \"        \\n\",\n    \"    return loss/len(data_iterator), accuracy/len(data_iterator)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number: 1 | time elapsed: 187.57019424438477s\\n\",\n      \"training loss: 0.685 | training accuracy: 54.87%\\n\",\n      \"validation loss: 0.665 |  validation accuracy: 59.75%\\n\",\n      \"\\n\",\n      \"epoch number: 2 | time elapsed: 192.59944200515747s\\n\",\n      \"training loss: 0.647 | training accuracy: 61.99%\\n\",\n      \"validation loss: 0.618 |  validation accuracy: 68.85%\\n\",\n      \"\\n\",\n      \"epoch number: 3 | time elapsed: 216.21610808372498s\\n\",\n      \"training loss: 0.570 | training accuracy: 70.57%\\n\",\n      \"validation loss: 0.592 |  validation accuracy: 69.94%\\n\",\n      \"\\n\",\n      \"epoch number: 4 | time elapsed: 668.1886627674103s\\n\",\n      \"training loss: 0.510 | training accuracy: 75.07%\\n\",\n      \"validation loss: 0.556 |  validation accuracy: 75.45%\\n\",\n      \"\\n\",\n      \"epoch number: 5 | time elapsed: 216.72939014434814s\\n\",\n      \"training loss: 0.473 | training accuracy: 77.64%\\n\",\n      \"validation loss: 0.592 |  validation accuracy: 74.74%\\n\",\n      \"\\n\",\n      \"epoch number: 6 | time elapsed: 843.4122788906097s\\n\",\n      \"training loss: 0.442 | training accuracy: 79.81%\\n\",\n      \"validation loss: 0.561 |  validation accuracy: 76.83%\\n\",\n      \"\\n\",\n      \"epoch number: 7 | time elapsed: 439.7972311973572s\\n\",\n      \"training loss: 0.399 | training accuracy: 82.28%\\n\",\n      \"validation loss: 0.662 |  validation accuracy: 74.97%\\n\",\n      \"\\n\",\n      \"epoch number: 8 | time elapsed: 200.3659451007843s\\n\",\n      \"training loss: 0.382 | training accuracy: 83.27%\\n\",\n      \"validation loss: 0.584 |  validation accuracy: 77.92%\\n\",\n      \"\\n\",\n      \"epoch number: 9 | time elapsed: 196.59090566635132s\\n\",\n      \"training loss: 0.361 | training accuracy: 84.36%\\n\",\n      \"validation loss: 0.533 |  validation accuracy: 79.22%\\n\",\n      \"\\n\",\n      \"epoch number: 10 | time elapsed: 287.94251704216003s\\n\",\n      \"training loss: 0.340 | training accuracy: 85.32%\\n\",\n      \"validation loss: 0.577 |  validation accuracy: 80.12%\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_epochs = 10\\n\",\n    \"best_validation_loss = float('inf')\\n\",\n    \"\\n\",\n    \"for ep in range(num_epochs):\\n\",\n    \"\\n\",\n    \"    time_start = time.time()\\n\",\n    \"    \\n\",\n    \"    training_loss, train_accuracy = train(lstm_model, train_data_iterator, optim, loss_func)\\n\",\n    \"    validation_loss, validation_accuracy = validate(lstm_model, valid_data_iterator, loss_func)\\n\",\n    \"    \\n\",\n    \"    time_end = time.time()\\n\",\n    \"    time_delta = time_end - time_start \\n\",\n    \"    \\n\",\n    \"    if validation_loss < best_validation_loss:\\n\",\n    \"        best_validation_loss = validation_loss\\n\",\n    \"        torch.save(lstm_model.state_dict(), 'lstm_model.pt')\\n\",\n    \"    \\n\",\n    \"    print(f'epoch number: {ep+1} | time elapsed: {time_delta}s')\\n\",\n    \"    print(f'training loss: {training_loss:.3f} | training accuracy: {train_accuracy*100:.2f}%')\\n\",\n    \"    print(f'validation loss: {validation_loss:.3f} |  validation accuracy: {validation_accuracy*100:.2f}%')\\n\",\n    \"    print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"test loss: 0.557 | test accuracy: 78.59%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"lstm_model.load_state_dict(torch.load('./lstm_model.pt'))\\n\",\n    \"\\n\",\n    \"test_loss, test_accuracy = validate(lstm_model, test_data_iterator, loss_func)\\n\",\n    \"\\n\",\n    \"print(f'test loss: {test_loss:.3f} | test accuracy: {test_accuracy*100:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def sentiment_inference(model, sentence):\\n\",\n    \"    model.eval()\\n\",\n    \"    \\n\",\n    \"    # text transformations\\n\",\n    \"    tokenized = data.get_tokenizer(\\\"basic_english\\\")(sentence)\\n\",\n    \"    tokenized = [TEXT_FIELD.vocab.stoi[t] for t in tokenized]\\n\",\n    \"    \\n\",\n    \"    # model inference\\n\",\n    \"    model_input = torch.LongTensor(tokenized).to(device)\\n\",\n    \"    model_input = model_input.unsqueeze(1)\\n\",\n    \"    \\n\",\n    \"    pred = torch.sigmoid(model(model_input))\\n\",\n    \"    \\n\",\n    \"    return pred.item()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.015025223605334759\\n\",\n      \"0.006463728845119476\\n\",\n      \"0.49227291345596313\\n\",\n      \"0.7560229897499084\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(sentiment_inference(lstm_model, \\\"This film is horrible\\\"))\\n\",\n    \"print(sentiment_inference(lstm_model, \\\"Director tried too hard but this film is bad\\\"))\\n\",\n    \"print(sentiment_inference(lstm_model, \\\"This film will be houseful for weeks\\\"))\\n\",\n    \"print(sentiment_inference(lstm_model, \\\"I just really loved the movie\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter04/rnn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"from string import punctuation\\n\",\n    \"from collections import Counter\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader, TensorDataset\\n\",\n    \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset Instructions:\\n\",\n    \"\\n\",\n    \"- You need to download the dataset from here: https://ai.stanford.edu/~amaas/data/sentiment/\\n\",\n    \"- Then, you need to unzip the downloaded zipped file in the present working directory\\n\",\n    \"- That should result in a new folder named aclImdb under the present working directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12500/12500 [00:00<00:00, 46053.84it/s]\\n\",\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12500/12500 [00:00<00:00, 46676.23it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of reviews : 25000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# read sentiments and reviews data from the text files\\n\",\n    \"review_list = []\\n\",\n    \"label_list = []\\n\",\n    \"for label in ['pos', 'neg']:\\n\",\n    \"    for fname in tqdm(os.listdir(f'./aclImdb/train/{label}/')):\\n\",\n    \"        if 'txt' not in fname:\\n\",\n    \"            continue\\n\",\n    \"        with open(os.path.join(f'./aclImdb/train/{label}/', fname), encoding=\\\"utf8\\\") as f:\\n\",\n    \"            review_list += [f.read()]\\n\",\n    \"            label_list += [label]\\n\",\n    \"print ('Number of reviews :', len(review_list))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25000/25000 [00:01<00:00, 13191.97it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[('the', 334691), ('and', 162228), ('a', 161940), ('of', 145326), ('to', 135042), ('is', 106855), ('in', 93028), ('it', 77099), ('i', 75719), ('this', 75190)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# pre-processing review text\\n\",\n    \"review_list = [review.lower() for review in review_list]\\n\",\n    \"review_list = [''.join([letter for letter in review if letter not in punctuation]) for review in tqdm(review_list)]\\n\",\n    \"\\n\",\n    \"# accumulate all review texts together\\n\",\n    \"reviews_blob = ' '.join(review_list)\\n\",\n    \"\\n\",\n    \"# generate list of all words of all reviews\\n\",\n    \"review_words = reviews_blob.split()\\n\",\n    \"\\n\",\n    \"# get the word counts\\n\",\n    \"count_words = Counter(review_words)\\n\",\n    \"\\n\",\n    \"# sort words as per counts (decreasing order)\\n\",\n    \"total_review_words = len(review_words)\\n\",\n    \"sorted_review_words = count_words.most_common(total_review_words)\\n\",\n    \"\\n\",\n    \"print(sorted_review_words[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[('the', 1), ('and', 2), ('a', 3), ('of', 4), ('to', 5), ('is', 6), ('in', 7), ('it', 8), ('i', 9), ('this', 10)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# create word to integer (token) dictionary in order to encode text as numbers\\n\",\n    \"vocab_to_token = {word:idx+1 for idx, (word, count) in enumerate(sorted_review_words)}\\n\",\n    \"print(list(vocab_to_token.items())[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"one of disneys best films that i can enjoy watching often you may easily guess the outcome but who cares its just plain fun escape for 1 hour fortytwo minutes and after all wasnt movies meant to get away from reality for just a short time anyway the cast sparkles with delight magictrain\\n\",\n      \"\\n\",\n      \"[28, 4, 4534, 114, 94, 11, 9, 68, 343, 146, 388, 22, 193, 683, 477, 1, 3709, 18, 36, 2264, 29, 40, 1017, 249, 1072, 15, 467, 558, 36644, 232, 2, 100, 31, 267, 92, 945, 5, 75, 241, 35, 638, 15, 40, 3, 347, 61, 577, 1, 176, 17217, 16, 2991, 43770]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"reviews_tokenized = []\\n\",\n    \"for review in review_list:\\n\",\n    \"    word_to_token = [vocab_to_token[word] for word in review.split()]\\n\",\n    \"    reviews_tokenized.append(word_to_token)\\n\",\n    \"print(review_list[0])\\n\",\n    \"print()\\n\",\n    \"print (reviews_tokenized[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# encode sentiments as 0 or 1\\n\",\n    \"encoded_label_list = [1 if label =='pos' else 0 for label in label_list]\\n\",\n    \"\\n\",\n    \"reviews_len = [len(review) for review in reviews_tokenized]\\n\",\n    \"\\n\",\n    \"reviews_tokenized = [reviews_tokenized[i] for i, l in enumerate(reviews_len) if l>0 ]\\n\",\n    \"encoded_label_list = np.array([encoded_label_list[i] for i, l in enumerate(reviews_len) if l> 0 ], dtype='float32')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def pad_sequence(reviews_tokenized, sequence_length):\\n\",\n    \"    ''' returns the tokenized review sequences padded with 0's or truncated to the sequence_length.\\n\",\n    \"    '''\\n\",\n    \"    padded_reviews = np.zeros((len(reviews_tokenized), sequence_length), dtype = int)\\n\",\n    \"    \\n\",\n    \"    for idx, review in enumerate(reviews_tokenized):\\n\",\n    \"        review_len = len(review)\\n\",\n    \"        \\n\",\n    \"        if review_len <= sequence_length:\\n\",\n    \"            zeroes = list(np.zeros(sequence_length-review_len))\\n\",\n    \"            new_sequence = zeroes+review\\n\",\n    \"        elif review_len > sequence_length:\\n\",\n    \"            new_sequence = review[0:sequence_length]\\n\",\n    \"        \\n\",\n    \"        padded_reviews[idx,:] = np.array(new_sequence)\\n\",\n    \"    \\n\",\n    \"    return padded_reviews\\n\",\n    \"\\n\",\n    \"sequence_length = 512\\n\",\n    \"padded_reviews = pad_sequence(reviews_tokenized=reviews_tokenized, sequence_length=sequence_length)\\n\",\n    \"\\n\",\n    \"plt.hist(reviews_len);\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_val_split = 0.75\\n\",\n    \"train_X = padded_reviews[:int(train_val_split*len(padded_reviews))]\\n\",\n    \"train_y = encoded_label_list[:int(train_val_split*len(padded_reviews))]\\n\",\n    \"validation_X = padded_reviews[int(train_val_split*len(padded_reviews)):]\\n\",\n    \"validation_y = encoded_label_list[int(train_val_split*len(padded_reviews)):]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## If while training, you get a runtime error that says: \\\"RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long\\\".\\n\",\n    \"## simply uncomment run the following lines of code additionally\\n\",\n    \"# train_X = train_X.astype('int64')\\n\",\n    \"# train_y = train_y.astype('int64')\\n\",\n    \"# validation_X = validation_X.astype('int64')\\n\",\n    \"# validation_y = validation_y.astype('int64')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# generate torch datasets\\n\",\n    \"train_dataset = TensorDataset(torch.from_numpy(train_X).to(device), torch.from_numpy(train_y).to(device))\\n\",\n    \"validation_dataset = TensorDataset(torch.from_numpy(validation_X).to(device), torch.from_numpy(validation_y).to(device))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"batch_size = 32\\n\",\n    \"# torch dataloaders (shuffle data)\\n\",\n    \"train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\\n\",\n    \"validation_dataloader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Example Input size:  torch.Size([32, 512])\\n\",\n      \"Example Input:\\n\",\n      \" tensor([[    0,     0,     0,  ...,    12, 32364,  1943],\\n\",\n      \"        [    0,     0,     0,  ...,    55,  1469,  2213],\\n\",\n      \"        [    0,     0,     0,  ...,    55,   275,   142],\\n\",\n      \"        ...,\\n\",\n      \"        [    0,     0,     0,  ...,   285,   611,   142],\\n\",\n      \"        [    0,     0,     0,  ...,   224,    14,    73],\\n\",\n      \"        [    0,     0,     0,  ...,   691,     8,   142]])\\n\",\n      \"\\n\",\n      \"Example Output size:  torch.Size([32])\\n\",\n      \"Example Output:\\n\",\n      \" tensor([1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\\n\",\n      \"        1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 0., 1., 1.])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# get a batch of train data\\n\",\n    \"train_data_iter = iter(train_dataloader)\\n\",\n    \"X_example, y_example = next(train_data_iter)\\n\",\n    \"print('Example Input size: ', X_example.size()) # batch_size, seq_length\\n\",\n    \"print('Example Input:\\\\n', X_example)\\n\",\n    \"print()\\n\",\n    \"print('Example Output size: ', y_example.size()) # batch_size\\n\",\n    \"print('Example Output:\\\\n', y_example)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class RNN(nn.Module):\\n\",\n    \"    def __init__(self, input_dimension, embedding_dimension, hidden_dimension, output_dimension):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.embedding_layer = nn.Embedding(input_dimension, embedding_dimension)  \\n\",\n    \"        self.rnn_layer = nn.RNN(embedding_dimension, hidden_dimension, num_layers=1)\\n\",\n    \"        self.fc_layer = nn.Linear(hidden_dimension, output_dimension)\\n\",\n    \"        \\n\",\n    \"    def forward(self, sequence):\\n\",\n    \"        # sequence shape = (sequence_length, batch_size)\\n\",\n    \"        embedding = self.embedding_layer(sequence)  \\n\",\n    \"        # embedding shape = [sequence_length, batch_size, embedding_dimension]\\n\",\n    \"        output, hidden_state = self.rnn_layer(embedding)\\n\",\n    \"        # output shape = [sequence_length, batch_size, hidden_dimension]\\n\",\n    \"        # hidden_state shape = [1, batch_size, hidden_dimension]\\n\",\n    \"        final_output = self.fc_layer(hidden_state[-1,:,:].squeeze(0))      \\n\",\n    \"        return final_output\\n\",\n    \"    \\n\",\n    \"input_dimension = len(vocab_to_token)+1 # +1 to account for padding\\n\",\n    \"embedding_dimension = 100\\n\",\n    \"hidden_dimension = 32\\n\",\n    \"output_dimension = 1\\n\",\n    \"\\n\",\n    \"rnn_model = RNN(input_dimension, embedding_dimension, hidden_dimension, output_dimension)\\n\",\n    \"\\n\",\n    \"optim = torch.optim.Adam(rnn_model.parameters())\\n\",\n    \"loss_func = nn.BCEWithLogitsLoss()\\n\",\n    \"\\n\",\n    \"rnn_model = rnn_model.to(device)\\n\",\n    \"loss_func = loss_func.to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def accuracy_metric(predictions, ground_truth):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns 0-1 accuracy for the given set of predictions and ground truth\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # round predictions to either 0 or 1\\n\",\n    \"    rounded_predictions = torch.round(torch.sigmoid(predictions))\\n\",\n    \"    success = (rounded_predictions == ground_truth).float() #convert into float for division \\n\",\n    \"    accuracy = success.sum() / len(success)\\n\",\n    \"    return accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, dataloader, optim, loss_func):\\n\",\n    \"    loss = 0\\n\",\n    \"    accuracy = 0\\n\",\n    \"    model.train()\\n\",\n    \"    \\n\",\n    \"    for sequence, sentiment in dataloader:\\n\",\n    \"        optim.zero_grad()     \\n\",\n    \"        preds = model(sequence.T).squeeze()\\n\",\n    \"        \\n\",\n    \"        loss_curr = loss_func(preds, sentiment)\\n\",\n    \"        accuracy_curr = accuracy_metric(preds, sentiment)\\n\",\n    \"        \\n\",\n    \"        loss_curr.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        \\n\",\n    \"        loss += loss_curr.item()\\n\",\n    \"        accuracy += accuracy_curr.item()\\n\",\n    \"        \\n\",\n    \"    return loss/len(dataloader), accuracy/len(dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def validate(model, dataloader, loss_func):\\n\",\n    \"    loss = 0\\n\",\n    \"    accuracy = 0\\n\",\n    \"    model.eval()\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for sequence, sentiment in dataloader:\\n\",\n    \"            \\n\",\n    \"            preds = model(sequence.T).squeeze()\\n\",\n    \"            \\n\",\n    \"            loss_curr = loss_func(preds, sentiment)   \\n\",\n    \"            accuracy_curr = accuracy_metric(preds, sentiment)\\n\",\n    \"\\n\",\n    \"            loss += loss_curr.item()\\n\",\n    \"            accuracy += accuracy_curr.item()\\n\",\n    \"        \\n\",\n    \"    return loss/len(dataloader), accuracy/len(dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number: 1 | time elapsed: 76.7141444683075s\\n\",\n      \"training loss: 0.620 | training accuracy: 66.97%\\n\",\n      \"validation loss: 0.935 |  validation accuracy: 31.85%\\n\",\n      \"\\n\",\n      \"epoch number: 2 | time elapsed: 87.61576223373413s\\n\",\n      \"training loss: 0.533 | training accuracy: 74.32%\\n\",\n      \"validation loss: 0.859 |  validation accuracy: 45.87%\\n\",\n      \"\\n\",\n      \"epoch number: 3 | time elapsed: 84.97845983505249s\\n\",\n      \"training loss: 0.499 | training accuracy: 76.05%\\n\",\n      \"validation loss: 1.104 |  validation accuracy: 2.30%\\n\",\n      \"\\n\",\n      \"epoch number: 4 | time elapsed: 70.93470454216003s\\n\",\n      \"training loss: 0.596 | training accuracy: 68.49%\\n\",\n      \"validation loss: 1.082 |  validation accuracy: 15.71%\\n\",\n      \"\\n\",\n      \"epoch number: 5 | time elapsed: 73.70327711105347s\\n\",\n      \"training loss: 0.558 | training accuracy: 71.45%\\n\",\n      \"validation loss: 1.042 |  validation accuracy: 25.72%\\n\",\n      \"\\n\",\n      \"epoch number: 6 | time elapsed: 82.68725538253784s\\n\",\n      \"training loss: 0.520 | training accuracy: 74.38%\\n\",\n      \"validation loss: 1.013 |  validation accuracy: 35.74%\\n\",\n      \"\\n\",\n      \"epoch number: 7 | time elapsed: 91.2055242061615s\\n\",\n      \"training loss: 0.465 | training accuracy: 77.90%\\n\",\n      \"validation loss: 0.934 |  validation accuracy: 48.88%\\n\",\n      \"\\n\",\n      \"epoch number: 8 | time elapsed: 101.68787813186646s\\n\",\n      \"training loss: 0.385 | training accuracy: 83.21%\\n\",\n      \"validation loss: 0.736 |  validation accuracy: 66.11%\\n\",\n      \"\\n\",\n      \"epoch number: 9 | time elapsed: 94.43510675430298s\\n\",\n      \"training loss: 0.323 | training accuracy: 86.82%\\n\",\n      \"validation loss: 0.596 |  validation accuracy: 74.29%\\n\",\n      \"\\n\",\n      \"epoch number: 10 | time elapsed: 81.06615042686462s\\n\",\n      \"training loss: 0.299 | training accuracy: 88.32%\\n\",\n      \"validation loss: 1.023 |  validation accuracy: 57.65%\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_epochs = 10\\n\",\n    \"best_validation_loss = float('inf')\\n\",\n    \"\\n\",\n    \"for ep in range(num_epochs):\\n\",\n    \"\\n\",\n    \"    time_start = time.time()\\n\",\n    \"    \\n\",\n    \"    training_loss, train_accuracy = train(rnn_model, train_dataloader, optim, loss_func)\\n\",\n    \"    validation_loss, validation_accuracy = validate(rnn_model, validation_dataloader, loss_func)\\n\",\n    \"    \\n\",\n    \"    time_end = time.time()\\n\",\n    \"    time_delta = time_end - time_start  \\n\",\n    \"    \\n\",\n    \"    if validation_loss < best_validation_loss:\\n\",\n    \"        best_validation_loss = validation_loss\\n\",\n    \"        torch.save(rnn_model.state_dict(), 'rnn_model.pt')\\n\",\n    \"    \\n\",\n    \"    print(f'epoch number: {ep+1} | time elapsed: {time_delta}s')\\n\",\n    \"    print(f'training loss: {training_loss:.3f} | training accuracy: {train_accuracy*100:.2f}%')\\n\",\n    \"    print(f'validation loss: {validation_loss:.3f} |  validation accuracy: {validation_accuracy*100:.2f}%')\\n\",\n    \"    print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def sentiment_inference(model, sentence):\\n\",\n    \"    model.eval()\\n\",\n    \"    \\n\",\n    \"    # text transformations\\n\",\n    \"    sentence = sentence.lower()\\n\",\n    \"    sentence = ''.join([c for c in sentence if c not in punctuation])\\n\",\n    \"    tokenized = [vocab_to_token.get(token, 0) for token in sentence.split()]\\n\",\n    \"    tokenized = np.pad(tokenized, (512-len(tokenized), 0), 'constant')\\n\",\n    \"    \\n\",\n    \"    # model inference\\n\",\n    \"    model_input = torch.LongTensor(tokenized).to(device)\\n\",\n    \"    model_input = model_input.unsqueeze(1)\\n\",\n    \"    pred = torch.sigmoid(model(model_input))\\n\",\n    \"    \\n\",\n    \"    return pred.item()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.10942309349775314\\n\",\n      \"0.33907952904701233\\n\",\n      \"0.9813206791877747\\n\",\n      \"0.8531726002693176\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(sentiment_inference(rnn_model, \\\"This film is horrible\\\"))\\n\",\n    \"print(sentiment_inference(rnn_model, \\\"Director tried too hard but this film is bad\\\"))\\n\",\n    \"print(sentiment_inference(rnn_model, \\\"This film will be houseful for weeks\\\"))\\n\",\n    \"print(sentiment_inference(rnn_model, \\\"I just really loved the movie\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter05/out_of_the_box_transformers.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: transformers==4.36.2 in /opt/conda/lib/python3.10/site-packages (4.36.2)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (2022.6.15)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip3 install torch==2.2\\n\",\n    \"!pip3 install transformers==4.36.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e0668e16e6584a04a559e7afec18abe7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"52040374968e4745975b78fcab44a8db\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias']\\n\",\n      \"- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"efec38a0ca654248ad04fb2911f59cd0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c35ac2d0a6ac4d03879b193ceed54b39\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4b79b270d21642c8af368031f885b809\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from transformers import BertForMaskedLM, BertTokenizer\\n\",\n    \" \\n\",\n    \"bert_model = BertForMaskedLM.from_pretrained('bert-base-uncased')\\n\",\n    \"token_gen = BertTokenizer.from_pretrained('bert-base-uncased')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"ip_sequence = token_gen(\\\"I love PyTorch !\\\", return_tensors=\\\"pt\\\")[\\\"input_ids\\\"]\\n\",\n    \" \\n\",\n    \"op = bert_model(ip_sequence, labels=ip_sequence)\\n\",\n    \"total_loss, raw_preds = op[:2]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter05/rand_wire_nn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: networkx==2.8.5 in /opt/conda/lib/python3.10/site-packages (2.8.5)\\n\",\n      \"Requirement already satisfied: torchviz==0.0.2 in /opt/conda/lib/python3.10/site-packages (0.0.2)\\n\",\n      \"Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from torchviz==0.0.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: graphviz in /opt/conda/lib/python3.10/site-packages (from torchviz==0.0.2) (0.20.1)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch->torchviz==0.0.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->torchviz==0.0.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->torchviz==0.0.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->torchviz==0.0.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"\\u001b[31mERROR: Could not find a version that satisfies the requirement yaml (from versions: none)\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0m\\u001b[31mERROR: No matching distribution found for yaml\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0m/bin/bash: line 1: brew: command not found\\n\",\n      \"Reading package lists... Done\\n\",\n      \"Building dependency tree... Done\\n\",\n      \"Reading state information... Done\\n\",\n      \"The following additional packages will be installed:\\n\",\n      \"  fonts-liberation libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin libgvc6\\n\",\n      \"  libgvpr2 liblab-gamut1 libpathplan4\\n\",\n      \"Suggested packages:\\n\",\n      \"  gsfonts graphviz-doc\\n\",\n      \"The following NEW packages will be installed:\\n\",\n      \"  fonts-liberation graphviz libann0 libcdt5 libcgraph6 libgts-0.7-5 libgts-bin\\n\",\n      \"  libgvc6 libgvpr2 liblab-gamut1 libpathplan4\\n\",\n      \"0 upgraded, 11 newly installed, 0 to remove and 17 not upgraded.\\n\",\n      \"Need to get 3032 kB of archives.\\n\",\n      \"After this operation, 11.5 MB of additional disk space will be used.\\n\",\n      \"Get:1 https://deb.debian.org/debian bullseye/main amd64 fonts-liberation all 1:1.07.4-11 [828 kB]\\n\",\n      \"Get:2 https://deb.debian.org/debian bullseye/main amd64 libann0 amd64 1.1.2+doc-7 [25.3 kB]\\n\",\n      \"Get:3 https://deb.debian.org/debian bullseye/main amd64 libcdt5 amd64 2.42.2-5 [62.2 kB]\\n\",\n      \"Get:4 https://deb.debian.org/debian bullseye/main amd64 libcgraph6 amd64 2.42.2-5 [85.5 kB]\\n\",\n      \"Get:5 https://deb.debian.org/debian bullseye/main amd64 libgts-0.7-5 amd64 0.7.6+darcs121130-4+b1 [158 kB]\\n\",\n      \"Get:6 https://deb.debian.org/debian bullseye/main amd64 libpathplan4 amd64 2.42.2-5 [64.3 kB]\\n\",\n      \"Get:7 https://deb.debian.org/debian bullseye/main amd64 libgvc6 amd64 2.42.2-5 [695 kB]\\n\",\n      \"Get:8 https://deb.debian.org/debian bullseye/main amd64 libgvpr2 amd64 2.42.2-5 [212 kB]\\n\",\n      \"Get:9 https://deb.debian.org/debian bullseye/main amd64 liblab-gamut1 amd64 2.42.2-5 [221 kB]\\n\",\n      \"Get:10 https://deb.debian.org/debian bullseye/main amd64 graphviz amd64 2.42.2-5 [632 kB]\\n\",\n      \"Get:11 https://deb.debian.org/debian bullseye/main amd64 libgts-bin amd64 0.7.6+darcs121130-4+b1 [50.3 kB]\\n\",\n      \"Fetched 3032 kB in 0s (32.9 MB/s)      \\n\",\n      \"Selecting previously unselected package fonts-liberation.\\n\",\n      \"(Reading database ... 141773 files and directories currently installed.)\\n\",\n      \"Preparing to unpack .../00-fonts-liberation_1%3a1.07.4-11_all.deb ...\\n\",\n      \"Unpacking fonts-liberation (1:1.07.4-11) ...\\n\",\n      \"Selecting previously unselected package libann0.\\n\",\n      \"Preparing to unpack .../01-libann0_1.1.2+doc-7_amd64.deb ...\\n\",\n      \"Unpacking libann0 (1.1.2+doc-7) ...\\n\",\n      \"Selecting previously unselected package libcdt5:amd64.\\n\",\n      \"Preparing to unpack .../02-libcdt5_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking libcdt5:amd64 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package libcgraph6:amd64.\\n\",\n      \"Preparing to unpack .../03-libcgraph6_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking libcgraph6:amd64 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package libgts-0.7-5:amd64.\\n\",\n      \"Preparing to unpack .../04-libgts-0.7-5_0.7.6+darcs121130-4+b1_amd64.deb ...\\n\",\n      \"Unpacking libgts-0.7-5:amd64 (0.7.6+darcs121130-4+b1) ...\\n\",\n      \"Selecting previously unselected package libpathplan4:amd64.\\n\",\n      \"Preparing to unpack .../05-libpathplan4_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking libpathplan4:amd64 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package libgvc6.\\n\",\n      \"Preparing to unpack .../06-libgvc6_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking libgvc6 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package libgvpr2:amd64.\\n\",\n      \"Preparing to unpack .../07-libgvpr2_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking libgvpr2:amd64 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package liblab-gamut1:amd64.\\n\",\n      \"Preparing to unpack .../08-liblab-gamut1_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking liblab-gamut1:amd64 (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package graphviz.\\n\",\n      \"Preparing to unpack .../09-graphviz_2.42.2-5_amd64.deb ...\\n\",\n      \"Unpacking graphviz (2.42.2-5) ...\\n\",\n      \"Selecting previously unselected package libgts-bin.\\n\",\n      \"Preparing to unpack .../10-libgts-bin_0.7.6+darcs121130-4+b1_amd64.deb ...\\n\",\n      \"Unpacking libgts-bin (0.7.6+darcs121130-4+b1) ...\\n\",\n      \"Setting up liblab-gamut1:amd64 (2.42.2-5) ...\\n\",\n      \"Setting up libgts-0.7-5:amd64 (0.7.6+darcs121130-4+b1) ...\\n\",\n      \"Setting up libpathplan4:amd64 (2.42.2-5) ...\\n\",\n      \"Setting up libann0 (1.1.2+doc-7) ...\\n\",\n      \"Setting up fonts-liberation (1:1.07.4-11) ...\\n\",\n      \"Setting up libcdt5:amd64 (2.42.2-5) ...\\n\",\n      \"Setting up libcgraph6:amd64 (2.42.2-5) ...\\n\",\n      \"Setting up libgts-bin (0.7.6+darcs121130-4+b1) ...\\n\",\n      \"Setting up libgvc6 (2.42.2-5) ...\\n\",\n      \"Setting up libgvpr2:amd64 (2.42.2-5) ...\\n\",\n      \"Setting up graphviz (2.42.2-5) ...\\n\",\n      \"Processing triggers for libc-bin (2.31-13+deb11u8) ...\\n\",\n      \"ldconfig: /lib/libnvinfer_lean.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvparsers.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvinfer_dispatch.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvonnxparser.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvinfer_plugin.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvinfer.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"ldconfig: /lib/libnvinfer_vc_plugin.so.8 is not a symbolic link\\n\",\n      \"\\n\",\n      \"Processing triggers for man-db (2.9.4-2) ...\\n\",\n      \"Processing triggers for fontconfig (2.13.1-4.2) ...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\\n\",\n    \"!pip install networkx==2.8.5\\n\",\n    \"!pip install torchviz==0.0.2\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install yaml\\n\",\n    \"# mac\\n\",\n    \"!brew install graphviz\\n\",\n    \"# ubuntu\\n\",\n    \"!sudo apt-get install -y graphviz\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"from torchviz import make_dot\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import yaml\\n\",\n    \"import random\\n\",\n    \"import networkx as nx\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"use_cuda = torch.cuda.is_available()\\n\",\n    \"device = torch.device(\\\"cuda\\\" if use_cuda else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## helper functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_results(list_of_epochs, list_of_train_losses, list_of_train_accuracies, list_of_val_accuracies):\\n\",\n    \"    plt.figure(figsize=(20, 9))\\n\",\n    \"    plt.subplot(1, 2, 1)\\n\",\n    \"    plt.plot(list_of_epochs, list_of_train_losses, label='training loss')\\n\",\n    \"    plt.legend()\\n\",\n    \"\\n\",\n    \"    plt.subplot(1, 2, 2)\\n\",\n    \"    plt.plot(list_of_epochs, list_of_train_accuracies, label='training accuracy')\\n\",\n    \"    plt.plot(list_of_epochs, list_of_val_accuracies, label='validation accuracy')\\n\",\n    \"    plt.legend()\\n\",\n    \"    if not os.path.isdir('./result_plots'):\\n\",\n    \"        os.makedirs('./result_plots')\\n\",\n    \"    plt.savefig('./result_plots/accuracy_plot_per_epoch.jpg')\\n\",\n    \"    plt.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## training routine\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_lr(optim, epoch_num, lrate):\\n\",\n    \"    \\\"\\\"\\\"adjusts lr to starting lr thereafter reduced by 10% at every 20 epochs\\\"\\\"\\\"\\n\",\n    \"    lrate = lrate * (0.1 ** (epoch_num // 20))\\n\",\n    \"    for params in optim.param_groups:\\n\",\n    \"        params['lr'] = lrate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, train_dataloader, optim, loss_func, epoch_num, lrate):\\n\",\n    \"    model.train()\\n\",\n    \"    loop_iter = 0\\n\",\n    \"    training_loss = 0\\n\",\n    \"    training_accuracy = 0\\n\",\n    \"    for training_data, training_label in train_dataloader:\\n\",\n    \"        set_lr(optim, epoch_num, lrate)\\n\",\n    \"        training_data, training_label = training_data.to(device), training_label.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_raw = model(training_data)\\n\",\n    \"        curr_loss = loss_func(pred_raw, training_label)\\n\",\n    \"        curr_loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        training_loss += curr_loss.data\\n\",\n    \"        pred = pred_raw.data.max(1)[1]\\n\",\n    \"\\n\",\n    \"        curr_accuracy = float(pred.eq(training_label.data).sum()) * 100. / len(training_data) \\n\",\n    \"        training_accuracy += curr_accuracy\\n\",\n    \"        loop_iter += 1\\n\",\n    \"        if loop_iter % 100 == 0:\\n\",\n    \"            print(f\\\"epoch {epoch_num}, loss: {curr_loss.data}, accuracy: {curr_accuracy}\\\")\\n\",\n    \"\\n\",\n    \"    data_size = len(train_dataloader.dataset) // batch_size\\n\",\n    \"    return training_loss / data_size, training_accuracy / data_size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## accuracy metric\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def accuracy(model, test_data_loader):\\n\",\n    \"    model.eval()\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for test_data, test_label in test_data_loader:\\n\",\n    \"            test_data, test_label = test_data.to(device), test_label.to(device)\\n\",\n    \"            pred_raw = model(test_data)\\n\",\n    \"            pred = pred_raw.data.max(1)[1]\\n\",\n    \"            success += pred.eq(test_label.data).sum()\\n\",\n    \"\\n\",\n    \"    return float(success) * 100. / len(test_data_loader.dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## hyperparams initialization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"num_epochs = 5\\n\",\n    \"graph_probability = 0.7\\n\",\n    \"node_channel_count = 64\\n\",\n    \"num_nodes = 16\\n\",\n    \"lrate = 0.1\\n\",\n    \"batch_size = 64\\n\",\n    \"train_mode = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## data loader and load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def load_dataset(batch_size):\\n\",\n    \"    transform_train_dataset = transforms.Compose([\\n\",\n    \"        transforms.RandomCrop(32, padding=4),\\n\",\n    \"        transforms.RandomHorizontalFlip(),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize((0.4983, 0.4795, 0.4382), (0.2712, 0.2602, 0.2801)),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"    transform_test_dataset = transforms.Compose([\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize((0.4983, 0.4795, 0.4382), (0.2712, 0.2602, 0.2801)),\\n\",\n    \"    ])\\n\",\n    \"    train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"        datasets.CIFAR10('dataset', transform=transform_train_dataset, train=True, download=True),\\n\",\n    \"        batch_size=batch_size,\\n\",\n    \"        shuffle=True\\n\",\n    \"    )\\n\",\n    \"    test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"        datasets.CIFAR10('dataset', transform=transform_test_dataset, train=False),\\n\",\n    \"        batch_size=batch_size,\\n\",\n    \"        shuffle=False\\n\",\n    \"    )\\n\",\n    \"    return train_dataloader, test_dataloader\\n\",\n    \"train_dataloader, test_dataloader = load_dataset(batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## graph class def\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RndGraph(object):\\n\",\n    \"    def __init__(self, num_nodes, graph_probability, nearest_neighbour_k=4, num_edges_attach=5):\\n\",\n    \"        self.num_nodes = num_nodes\\n\",\n    \"        self.graph_probability = graph_probability\\n\",\n    \"        self.nearest_neighbour_k = nearest_neighbour_k\\n\",\n    \"        self.num_edges_attach = num_edges_attach\\n\",\n    \"\\n\",\n    \"    def make_graph_obj(self):\\n\",\n    \"        graph_obj = nx.random_graphs.connected_watts_strogatz_graph(self.num_nodes, self.nearest_neighbour_k, \\n\",\n    \"                                                                self.graph_probability)\\n\",\n    \"        return graph_obj\\n\",\n    \"\\n\",\n    \"    def get_graph_config(self, graph_obj):\\n\",\n    \"        incoming_edges = {}\\n\",\n    \"        incoming_edges[0] = []\\n\",\n    \"        node_list = [0]\\n\",\n    \"        last = []\\n\",\n    \"        for n in graph_obj.nodes():\\n\",\n    \"            neighbor_list = list(graph_obj.neighbors(n))\\n\",\n    \"            neighbor_list.sort()\\n\",\n    \"\\n\",\n    \"            edge_list = []\\n\",\n    \"            passed_list = []\\n\",\n    \"            for nbr in neighbor_list:\\n\",\n    \"                if n > nbr:\\n\",\n    \"                    edge_list.append(nbr + 1)\\n\",\n    \"                    passed_list.append(nbr)\\n\",\n    \"            if not edge_list:\\n\",\n    \"                edge_list.append(0)\\n\",\n    \"            incoming_edges[n + 1] = edge_list\\n\",\n    \"            if passed_list == neighbor_list:\\n\",\n    \"                last.append(n + 1)\\n\",\n    \"            node_list.append(n + 1)\\n\",\n    \"        incoming_edges[self.num_nodes + 1] = last\\n\",\n    \"        node_list.append(self.num_nodes + 1)\\n\",\n    \"        return node_list, incoming_edges\\n\",\n    \"\\n\",\n    \"    def save_graph(self, graph_obj, path_to_write):\\n\",\n    \"        if not os.path.isdir(\\\"cached_graph_obj\\\"):\\n\",\n    \"            os.mkdir(\\\"cached_graph_obj\\\")\\n\",\n    \"        with open(f\\\"./cached_graph_obj/{path_to_write}\\\", \\\"w\\\") as fh:\\n\",\n    \"            yaml.dump(graph_obj, fh)\\n\",\n    \"\\n\",\n    \"    def load_graph(self, path_to_read):\\n\",\n    \"        with open(f\\\"./cached_graph_obj/{path_to_read}\\\", \\\"r\\\") as fh:\\n\",\n    \"            return yaml.load(fh, Loader=yaml.Loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## randwire def\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def initialize_weights(layer):\\n\",\n    \"    if isinstance(layer, nn.Conv2d):\\n\",\n    \"        torch.nn.init.xavier_uniform_(layer.weight)\\n\",\n    \"        if layer.bias is not None:\\n\",\n    \"            torch.nn.init.zeros_(layer.bias)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SepConv2d(nn.Module):\\n\",\n    \"    def __init__(self, input_ch, output_ch, kernel_length=3, dilation_size=1, padding_size=1, stride_length=1, bias_flag=True):\\n\",\n    \"        super(SepConv2d, self).__init__()\\n\",\n    \"        self.conv_layer = nn.Conv2d(input_ch, input_ch, kernel_length, stride_length, padding_size, dilation_size, \\n\",\n    \"                              bias=bias_flag, groups=input_ch)\\n\",\n    \"        self.pointwise_layer = nn.Conv2d(input_ch, output_ch, kernel_size=1, stride=1, padding=0, dilation=1, \\n\",\n    \"                                         groups=1, bias=bias_flag)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.pointwise_layer(self.conv_layer(x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class UnitLayer(nn.Module):\\n\",\n    \"    def __init__(self, input_ch, output_ch, stride_length=1):\\n\",\n    \"        super(UnitLayer, self).__init__()\\n\",\n    \"\\n\",\n    \"        self.dropout = 0.3\\n\",\n    \"\\n\",\n    \"        self.unit_layer = nn.Sequential(\\n\",\n    \"            nn.ReLU(),\\n\",\n    \"            SepConv2d(input_ch, output_ch, stride_length=stride_length),\\n\",\n    \"            nn.BatchNorm2d(output_ch),\\n\",\n    \"            nn.Dropout(self.dropout)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.unit_layer(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class GraphNode(nn.Module):\\n\",\n    \"    def __init__(self, input_degree, input_ch, output_ch, stride_length=1):\\n\",\n    \"        super(GraphNode, self).__init__()\\n\",\n    \"        self.input_degree = input_degree\\n\",\n    \"        if len(self.input_degree) > 1:\\n\",\n    \"            self.params = nn.Parameter(torch.ones(len(self.input_degree), requires_grad=True))\\n\",\n    \"        self.unit_layer = UnitLayer(input_ch, output_ch, stride_length=stride_length)\\n\",\n    \"\\n\",\n    \"    def forward(self, *ip):\\n\",\n    \"        if len(self.input_degree) > 1:\\n\",\n    \"            op = (ip[0] * torch.sigmoid(self.params[0]))\\n\",\n    \"            for idx in range(1, len(ip)):\\n\",\n    \"                op += (ip[idx] * torch.sigmoid(self.params[idx]))\\n\",\n    \"            return self.unit_layer(op)\\n\",\n    \"        else:\\n\",\n    \"            return self.unit_layer(ip[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RandWireGraph(nn.Module):\\n\",\n    \"    def __init__(self, num_nodes, graph_prob, input_ch, output_ch, train_mode, graph_name):\\n\",\n    \"        super(RandWireGraph, self).__init__()\\n\",\n    \"        self.num_nodes = num_nodes\\n\",\n    \"        self.graph_prob = graph_prob\\n\",\n    \"        self.input_ch = input_ch\\n\",\n    \"        self.output_ch = output_ch\\n\",\n    \"        self.train_mode = train_mode\\n\",\n    \"        self.graph_name = graph_name\\n\",\n    \"\\n\",\n    \"        # get graph nodes and in edges\\n\",\n    \"        rnd_graph_node = RndGraph(self.num_nodes, self.graph_prob)\\n\",\n    \"        if self.train_mode is True:\\n\",\n    \"            print(\\\"train_mode: ON\\\")\\n\",\n    \"            rnd_graph = rnd_graph_node.make_graph_obj()\\n\",\n    \"            self.node_list, self.incoming_edge_list = rnd_graph_node.get_graph_config(rnd_graph)\\n\",\n    \"            rnd_graph_node.save_graph(rnd_graph, graph_name)\\n\",\n    \"        else:\\n\",\n    \"            rnd_graph = rnd_graph_node.load_graph(graph_name)\\n\",\n    \"            self.node_list, self.incoming_edge_list = rnd_graph_node.get_graph_config(rnd_graph)\\n\",\n    \"\\n\",\n    \"        # define input Node\\n\",\n    \"        self.list_of_modules = nn.ModuleList([GraphNode(self.incoming_edge_list[0], self.input_ch, self.output_ch, \\n\",\n    \"                                                        stride_length=2)])\\n\",\n    \"        # define the rest Node\\n\",\n    \"        self.list_of_modules.extend([GraphNode(self.incoming_edge_list[n], self.output_ch, self.output_ch) \\n\",\n    \"                                     for n in self.node_list if n > 0])\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        mem_dict = {}\\n\",\n    \"        # start vertex\\n\",\n    \"        op = self.list_of_modules[0].forward(x)\\n\",\n    \"        mem_dict[0] = op\\n\",\n    \"\\n\",\n    \"        # the rest vertex\\n\",\n    \"        for n in range(1, len(self.node_list) - 1):\\n\",\n    \"            # print(node, self.in_edges[node][0], self.in_edges[node])\\n\",\n    \"            if len(self.incoming_edge_list[n]) > 1:\\n\",\n    \"                op = self.list_of_modules[n].forward(*[mem_dict[incoming_vtx] \\n\",\n    \"                                                       for incoming_vtx in self.incoming_edge_list[n]])\\n\",\n    \"            else:\\n\",\n    \"                op = self.list_of_modules[n].forward(mem_dict[self.incoming_edge_list[n][0]])\\n\",\n    \"            mem_dict[n] = op\\n\",\n    \"            \\n\",\n    \"        op = mem_dict[self.incoming_edge_list[self.num_nodes + 1][0]]\\n\",\n    \"        for incoming_vtx in range(1, len(self.incoming_edge_list[self.num_nodes + 1])):\\n\",\n    \"            op += mem_dict[self.incoming_edge_list[self.num_nodes + 1][incoming_vtx]]\\n\",\n    \"        return op / len(self.incoming_edge_list[self.num_nodes + 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## randwire NN model def\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RandWireNNModel(nn.Module):\\n\",\n    \"    def __init__(self, num_nodes, graph_prob, input_ch, output_ch, train_mode):\\n\",\n    \"        super(RandWireNNModel, self).__init__()\\n\",\n    \"        self.num_nodes = num_nodes\\n\",\n    \"        self.graph_prob = graph_prob\\n\",\n    \"        self.input_ch = input_ch\\n\",\n    \"        self.output_ch = output_ch\\n\",\n    \"        self.train_mode = train_mode\\n\",\n    \"        self.dropout = 0.3\\n\",\n    \"        self.class_num = 10\\n\",\n    \"            \\n\",\n    \"        self.conv_layer_1 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(in_channels=3, out_channels=self.output_ch, kernel_size=3, padding=1),\\n\",\n    \"            nn.BatchNorm2d(self.output_ch),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.conv_layer_2 = nn.Sequential(\\n\",\n    \"            RandWireGraph(self.num_nodes, self.graph_prob, self.input_ch, self.output_ch*2, self.train_mode, \\n\",\n    \"                          graph_name=\\\"conv_layer_2\\\")\\n\",\n    \"        )\\n\",\n    \"        self.conv_layer_3 = nn.Sequential(\\n\",\n    \"            RandWireGraph(self.num_nodes, self.graph_prob, self.input_ch*2, self.output_ch*4, self.train_mode, \\n\",\n    \"                          graph_name=\\\"conv_layer_3\\\")\\n\",\n    \"        )\\n\",\n    \"        self.conv_layer_4 = nn.Sequential(\\n\",\n    \"            RandWireGraph(self.num_nodes, self.graph_prob, self.input_ch*4, self.output_ch*8, self.train_mode, \\n\",\n    \"                          graph_name=\\\"conv_layer_4\\\")\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier_layer = nn.Sequential(\\n\",\n    \"            nn.Conv2d(in_channels=self.input_ch*8, out_channels=1280, kernel_size=1),\\n\",\n    \"            nn.BatchNorm2d(1280)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.output_layer = nn.Sequential(\\n\",\n    \"            nn.Dropout(self.dropout),\\n\",\n    \"            nn.Linear(1280, self.class_num)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.conv_layer_1(x)\\n\",\n    \"        x = self.conv_layer_2(x)\\n\",\n    \"        x = self.conv_layer_3(x)\\n\",\n    \"        x = self.conv_layer_4(x)\\n\",\n    \"        x = self.classifier_layer(x)\\n\",\n    \"\\n\",\n    \"        # global average pooling\\n\",\n    \"        _, _, h, w = x.size()\\n\",\n    \"        x = F.avg_pool2d(x, kernel_size=[h, w])\\n\",\n    \"        x = torch.squeeze(x)\\n\",\n    \"        x = self.output_layer(x)\\n\",\n    \"\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"train_mode: ON\\n\",\n      \"train_mode: ON\\n\",\n      \"train_mode: ON\\n\",\n      \"epoch 1, loss: 2.035217761993408, accuracy: 25.0\\n\",\n      \"epoch 1, loss: 1.9825671911239624, accuracy: 32.8125\\n\",\n      \"epoch 1, loss: 1.671643853187561, accuracy: 35.9375\\n\",\n      \"epoch 1, loss: 1.6595923900604248, accuracy: 28.125\\n\",\n      \"epoch 1, loss: 1.384980320930481, accuracy: 54.6875\\n\",\n      \"epoch 1, loss: 1.5679996013641357, accuracy: 45.3125\\n\",\n      \"epoch 1, loss: 1.4984831809997559, accuracy: 45.3125\\n\",\n      \"test acc: 40.60%, best test acc: 0.00%\\n\",\n      \"model train time:  18390.26490855217\\n\",\n      \"epoch 2, loss: 1.5289409160614014, accuracy: 39.0625\\n\",\n      \"epoch 2, loss: 1.3056232929229736, accuracy: 50.0\\n\",\n      \"epoch 2, loss: 1.6011831760406494, accuracy: 46.875\\n\",\n      \"epoch 2, loss: 1.33833646774292, accuracy: 46.875\\n\",\n      \"epoch 2, loss: 1.3880982398986816, accuracy: 53.125\\n\",\n      \"epoch 2, loss: 1.5733935832977295, accuracy: 40.625\\n\",\n      \"epoch 2, loss: 1.1148715019226074, accuracy: 59.375\\n\",\n      \"test acc: 49.83%, best test acc: 40.60%\\n\",\n      \"model train time:  31489.650629520416\\n\",\n      \"epoch 3, loss: 1.1639909744262695, accuracy: 56.25\\n\",\n      \"epoch 3, loss: 1.426891803741455, accuracy: 53.125\\n\",\n      \"epoch 3, loss: 1.1435348987579346, accuracy: 57.8125\\n\",\n      \"epoch 3, loss: 1.0688117742538452, accuracy: 62.5\\n\",\n      \"epoch 3, loss: 1.2964831590652466, accuracy: 59.375\\n\",\n      \"epoch 3, loss: 1.0372081995010376, accuracy: 64.0625\\n\",\n      \"epoch 3, loss: 1.1616079807281494, accuracy: 57.8125\\n\",\n      \"test acc: 62.27%, best test acc: 49.83%\\n\",\n      \"model train time:  44312.12883114815\\n\",\n      \"epoch 4, loss: 1.0059452056884766, accuracy: 54.6875\\n\",\n      \"epoch 4, loss: 1.0927919149398804, accuracy: 62.5\\n\",\n      \"epoch 4, loss: 1.1355857849121094, accuracy: 60.9375\\n\",\n      \"epoch 4, loss: 1.013630986213684, accuracy: 71.875\\n\",\n      \"epoch 4, loss: 1.0893126726150513, accuracy: 65.625\\n\",\n      \"epoch 4, loss: 1.1147949695587158, accuracy: 57.8125\\n\",\n      \"epoch 4, loss: 0.9936147928237915, accuracy: 67.1875\\n\",\n      \"test acc: 67.32%, best test acc: 62.27%\\n\",\n      \"model train time:  56758.9196870327\\n\",\n      \"epoch 5, loss: 0.8844163417816162, accuracy: 71.875\\n\",\n      \"epoch 5, loss: 0.8939657211303711, accuracy: 70.3125\\n\",\n      \"epoch 5, loss: 1.0952390432357788, accuracy: 62.5\\n\",\n      \"epoch 5, loss: 0.7492791414260864, accuracy: 75.0\\n\",\n      \"epoch 5, loss: 0.888742983341217, accuracy: 68.75\\n\",\n      \"epoch 5, loss: 0.8301115036010742, accuracy: 73.4375\\n\",\n      \"epoch 5, loss: 0.8155155777931213, accuracy: 75.0\\n\",\n      \"test acc: 70.45%, best test acc: 67.32%\\n\",\n      \"model train time:  57755.688624858856\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rand_wire_model = RandWireNNModel(num_nodes, graph_probability, node_channel_count, node_channel_count, train_mode).to(device)\\n\",\n    \"\\n\",\n    \"optim_module = optim.SGD(rand_wire_model.parameters(), lr=lrate, weight_decay=1e-4, momentum=0.8)\\n\",\n    \"loss_func = nn.CrossEntropyLoss().to(device)\\n\",\n    \"\\n\",\n    \"epochs = []\\n\",\n    \"test_accuracies = []\\n\",\n    \"training_accuracies = []\\n\",\n    \"training_losses = []\\n\",\n    \"best_test_accuracy = 0\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"for ep in range(1, num_epochs + 1):\\n\",\n    \"    epochs.append(ep)\\n\",\n    \"    training_loss, training_accuracy = train(rand_wire_model, train_dataloader, optim_module, loss_func, ep, lrate)\\n\",\n    \"    test_accuracy = accuracy(rand_wire_model, test_dataloader)\\n\",\n    \"    test_accuracies.append(test_accuracy)\\n\",\n    \"    training_losses.append(training_loss)\\n\",\n    \"    training_accuracies.append(training_accuracy)\\n\",\n    \"    print('test acc: {0:.2f}%, best test acc: {1:.2f}%'.format(test_accuracy, best_test_accuracy))\\n\",\n    \"\\n\",\n    \"    if best_test_accuracy < test_accuracy:\\n\",\n    \"        model_state = {\\n\",\n    \"            'model': rand_wire_model.state_dict(),\\n\",\n    \"            'accuracy': test_accuracy,\\n\",\n    \"            'ep': ep,\\n\",\n    \"        }\\n\",\n    \"        if not os.path.isdir('model_checkpoint'):\\n\",\n    \"            os.mkdir('model_checkpoint')\\n\",\n    \"        model_filename = \\\"ch_count_\\\" + str(node_channel_count) + \\\"_prob_\\\" + str(graph_probability)\\n\",\n    \"        torch.save(model_state, './model_checkpoint/' + model_filename + 'ckpt.t7')\\n\",\n    \"        best_test_accuracy = test_accuracy\\n\",\n    \"        plot_results(epochs, training_losses, training_accuracies, test_accuracies)\\n\",\n    \"    print(\\\"model train time: \\\", time.time() - start_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## test model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"total model params:  6900326\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def num_model_params(model_obj):\\n\",\n    \"    num_params = 0\\n\",\n    \"    for l in list(model_obj.parameters()):\\n\",\n    \"        l_p = 1\\n\",\n    \"        for p in list(l.size()):\\n\",\n    \"            l_p *= p\\n\",\n    \"        num_params += l_p\\n\",\n    \"    return num_params\\n\",\n    \"print(\\\"total model params: \\\", num_model_params(rand_wire_model))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"best model accuracy: 70.45%, last epoch: 5\\n\",\n      \"test accuracy: 70.45 %\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if os.path.exists(\\\"./model_checkpoint\\\"):\\n\",\n    \"    rand_wire_nn_model = RandWireNNModel(num_nodes, graph_probability, node_channel_count, node_channel_count, \\n\",\n    \"                                         train_mode=False).to(device)\\n\",\n    \"    model_filename = \\\"ch_count_\\\" + str(node_channel_count) + \\\"_prob_\\\" + str(graph_probability)\\n\",\n    \"    model_checkpoint = torch.load('./model_checkpoint/' + model_filename + 'ckpt.t7')\\n\",\n    \"    rand_wire_nn_model.load_state_dict(model_checkpoint['model'])\\n\",\n    \"    last_ep = model_checkpoint['ep']\\n\",\n    \"    best_model_accuracy = model_checkpoint['accuracy']\\n\",\n    \"    print(f\\\"best model accuracy: {best_model_accuracy}%, last epoch: {last_ep}\\\")\\n\",\n    \"\\n\",\n    \"    rand_wire_nn_model.eval()\\n\",\n    \"    success = 0\\n\",\n    \"    for test_data, test_label in test_dataloader:\\n\",\n    \"        test_data, test_label = test_data.to(device), test_label.to(device)\\n\",\n    \"        pred_raw = rand_wire_nn_model(test_data)\\n\",\n    \"        pred = pred_raw.data.max(1)[1]\\n\",\n    \"        success += pred.eq(test_label.data).sum()\\n\",\n    \"    print(f\\\"test accuracy: {float(success) * 100. / len(test_dataloader.dataset)} %\\\")\\n\",\n    \"\\n\",\n    \"else:\\n\",\n    \"    assert False, \\\"File not found. Please check again.\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## visualize model graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'image2.svg'\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x = torch.randn(2, 3, 32, 32)\\n\",\n    \"y = rand_wire_nn_model(x)\\n\",\n    \"g = make_dot(y.mean(), params=dict(rand_wire_nn_model.named_parameters()))\\n\",\n    \"g.format='svg'\\n\",\n    \"g.filename = 'image2'\\n\",\n    \"g.render(view=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/svg+xml\": [\n       \"<?xml 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class=\\\"edge\\\">\\n\",\n       \"<title>140041525034144&#45;&gt;140041525032704</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M4233.77,-5821.27C4133.89,-5810.76 3948.47,-5791.25 3837.4,-5779.56\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3837.53,-5776.06 3827.22,-5778.49 3836.8,-5783.02 3837.53,-5776.06\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034288 -->\\n\",\n       \"<g id=\\\"node45\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525034288</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4433,-5899.5 4296,-5899.5 4296,-5880.5 4433,-5880.5 4433,-5899.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4364.5\\\" y=\\\"-5887.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034288&#45;&gt;140041525034144 -->\\n\",\n       \"<g id=\\\"edge43\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525034288&#45;&gt;140041525034144</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M4355.35,-5880.37C4345.83,-5871.38 4330.7,-5857.11 4319.07,-5846.14\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"4321.36,-5843.48 4311.69,-5839.17 4316.56,-5848.58 4321.36,-5843.48\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034432 -->\\n\",\n       \"<g id=\\\"node46\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525034432</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4537,-5965.5 4442,-5965.5 4442,-5946.5 4537,-5946.5 4537,-5965.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4489.5\\\" y=\\\"-5953.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034432&#45;&gt;140041525034288 -->\\n\",\n       \"<g id=\\\"edge44\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525034432&#45;&gt;140041525034288</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M4472.64,-5946.37C4451.58,-5935.59 4415.32,-5917.02 4390.64,-5904.38\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"4391.99,-5901.14 4381.49,-5899.7 4388.8,-5907.37 4391.99,-5901.14\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034960 -->\\n\",\n       \"<g id=\\\"node47\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525034960</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5179,-6031.5 5018,-6031.5 5018,-6012.5 5179,-6012.5 5179,-6031.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5098.5\\\" y=\\\"-6019.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">NativeBatchNormBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034960&#45;&gt;140041525034432 -->\\n\",\n       \"<g id=\\\"edge45\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525034960&#45;&gt;140041525034432</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5017.87,-6017.62C4909.74,-6012.21 4712.07,-5999.18 4545.5,-5971 4540.44,-5970.14 4535.17,-5969.07 4529.97,-5967.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"4530.67,-5964.47 4520.13,-5965.57 4529.05,-5971.28 4530.67,-5964.47\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274912 -->\\n\",\n       \"<g id=\\\"node493\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274912</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5146,-5778.5 5051,-5778.5 5051,-5759.5 5146,-5759.5 5146,-5778.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5098.5\\\" y=\\\"-5766.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525034960&#45;&gt;140041523274912 -->\\n\",\n  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class=\\\"edge\\\">\\n\",\n       \"<title>140041525034960&#45;&gt;140041523673504</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5141.88,-6012.44C5299.18,-5981.42 5839.33,-5874.9 6011.4,-5840.97\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6012.1,-5844.4 6021.24,-5839.03 6010.75,-5837.53 6012.1,-5844.4\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024160 -->\\n\",\n       \"<g id=\\\"node48\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525024160</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4841,-6092 4704,-6092 4704,-6073 4841,-6073 4841,-6092\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4772.5\\\" y=\\\"-6080\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024160&#45;&gt;140041525034960 -->\\n\",\n       \"<g id=\\\"edge46\\\" class=\\\"edge\\\">\\n\",\n   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\"<title>140041525026656&#45;&gt;140041525033568</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5499.23,-6496.49C5560.78,-6485.97 5665.51,-6468.05 5731.47,-6456.77\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5732.44,-6460.15 5741.71,-6455.02 5731.26,-6453.25 5732.44,-6460.15\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025456 -->\\n\",\n       \"<g id=\\\"node56\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025456</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5555,-6581.5 5460,-6581.5 5460,-6562.5 5555,-6562.5 5555,-6581.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5507.5\\\" y=\\\"-6569.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025456&#45;&gt;140041525026656 -->\\n\",\n       \"<g id=\\\"edge54\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025456&#45;&gt;140041525026656</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5499.68,-6562.37C5490.65,-6552.4 5475.59,-6535.79 5464.33,-6523.36\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5466.88,-6520.96 5457.57,-6515.91 5461.69,-6525.67 5466.88,-6520.96\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026512 -->\\n\",\n       \"<g id=\\\"node57\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525026512</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6084,-6647.5 5995,-6647.5 5995,-6628.5 6084,-6628.5 6084,-6647.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6039.5\\\" y=\\\"-6635.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026512&#45;&gt;140041525025456 -->\\n\",\n       \"<g id=\\\"edge55\\\" class=\\\"edge\\\">\\n\",\n       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class=\\\"edge\\\">\\n\",\n       \"<title>140041525026560&#45;&gt;140041525026512</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6689.44,-7354.23C6684.71,-7333.36 6673.5,-7279.03 6673.5,-7233 6673.5,-7233 6673.5,-7233 6673.5,-6752.5 6673.5,-6693.88 6248.16,-6655.14 6094.25,-6643.05\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6094.32,-6639.54 6084.08,-6642.25 6093.78,-6646.52 6094.32,-6639.54\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025360 -->\\n\",\n       \"<g id=\\\"node59\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025360</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6897,-7434 6808,-7434 6808,-7415 6897,-7415 6897,-7434\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6852.5\\\" y=\\\"-7422\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 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140041525024736&#45;&gt;140041525025360 -->\\n\",\n       \"<g id=\\\"edge58\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525024736&#45;&gt;140041525025360</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6980.08,-7469.98C6954.78,-7461.16 6915.05,-7447.31 6886.64,-7437.41\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6887.71,-7434.07 6877.11,-7434.08 6885.4,-7440.68 6887.71,-7434.07\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025072 -->\\n\",\n       \"<g id=\\\"node61\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025072</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"7066,-9095 6905,-9095 6905,-9076 7066,-9076 7066,-9095\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6985.5\\\" y=\\\"-9083\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">NativeBatchNormBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025072&#45;&gt;140041525024736 -->\\n\",\n       \"<g id=\\\"edge59\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025072&#45;&gt;140041525024736</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M7066.18,-9081.66C7201.48,-9074.69 7459.5,-9050.91 7459.5,-8971 7459.5,-8971 7459.5,-8971 7459.5,-7594 7459.5,-7512.42 7180.65,-7489.01 7059.55,-7482.69\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"7059.54,-7479.19 7049.37,-7482.18 7059.18,-7486.18 7059.54,-7479.19\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523269920 -->\\n\",\n       \"<g id=\\\"node208\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523269920</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6914,-9040 6825,-9040 6825,-9021 6914,-9021 6914,-9040\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6869.5\\\" y=\\\"-9028\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025072&#45;&gt;140041523269920 -->\\n\",\n       \"<g id=\\\"edge214\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025072&#45;&gt;140041523269920</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6966.86,-9075.98C6948.2,-9067.46 6919.25,-9054.23 6897.76,-9044.41\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6899.03,-9041.14 6888.48,-9040.17 6896.12,-9047.51 6899.03,-9041.14\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025840 -->\\n\",\n       \"<g id=\\\"node62\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025840</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6720,-9150 6583,-9150 6583,-9131 6720,-9131 6720,-9150\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6651.5\\\" y=\\\"-9138\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025840&#45;&gt;140041525025072 -->\\n\",\n       \"<g id=\\\"edge60\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025840&#45;&gt;140041525025072</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6705.16,-9130.98C6764.12,-9121.63 6858.75,-9106.61 6921.52,-9096.65\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6922.37,-9100.06 6931.69,-9095.04 6921.27,-9093.15 6922.37,-9100.06\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024928 -->\\n\",\n       \"<g id=\\\"node63\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525024928</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6651,-9210.5 6514,-9210.5 6514,-9191.5 6651,-9191.5 6651,-9210.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6582.5\\\" y=\\\"-9198.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024928&#45;&gt;140041525025840 -->\\n\",\n       \"<g id=\\\"edge61\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525024928&#45;&gt;140041525025840</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6592.68,-9191.37C6603.38,-9182.3 6620.44,-9167.84 6633.43,-9156.82\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6635.91,-9159.3 6641.28,-9150.17 6631.39,-9153.96 6635.91,-9159.3\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025792 -->\\n\",\n       \"<g id=\\\"node64\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025792</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6514,-9276.5 6419,-9276.5 6419,-9257.5 6514,-9257.5 6514,-9276.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6466.5\\\" y=\\\"-9264.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025792&#45;&gt;140041525024928 -->\\n\",\n       \"<g id=\\\"edge62\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025792&#45;&gt;140041525024928</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6482.14,-9257.37C6501.6,-9246.63 6535.05,-9228.18 6557.94,-9215.55\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6559.67,-9218.59 6566.73,-9210.7 6556.29,-9212.47 6559.67,-9218.59\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026224 -->\\n\",\n       \"<g id=\\\"node65\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525026224</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6191,-9342.5 6102,-9342.5 6102,-9323.5 6191,-9323.5 6191,-9342.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6146.5\\\" y=\\\"-9330.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026224&#45;&gt;140041525025792 -->\\n\",\n       \"<g id=\\\"edge63\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525026224&#45;&gt;140041525025792</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6191.03,-9324.38C6243.64,-9315.18 6333.73,-9298.85 6410.5,-9282 6414.86,-9281.04 6419.39,-9280 6423.91,-9278.92\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6424.82,-9282.3 6433.71,-9276.54 6423.16,-9275.5 6424.82,-9282.3\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025744 -->\\n\",\n       \"<g id=\\\"node66\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025744</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6191,-9403 6102,-9403 6102,-9384 6191,-9384 6191,-9403\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6146.5\\\" y=\\\"-9391\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025744&#45;&gt;140041525026224 -->\\n\",\n       \"<g id=\\\"edge64\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025744&#45;&gt;140041525026224</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6146.5,-9383.87C6146.5,-9375.75 6146.5,-9363.31 6146.5,-9352.89\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6150,-9352.67 6146.5,-9342.67 6143,-9352.67 6150,-9352.67\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025696 -->\\n\",\n       \"<g id=\\\"node67\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025696</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6271,-9458 6182,-9458 6182,-9439 6271,-9439 6271,-9458\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6226.5\\\" y=\\\"-9446\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025696&#45;&gt;140041525025744 -->\\n\",\n       \"<g id=\\\"edge65\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025696&#45;&gt;140041525025744</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6213.65,-9438.98C6201.36,-9430.84 6182.59,-9418.41 6168.02,-9408.76\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6169.86,-9405.78 6159.59,-9403.17 6165.99,-9411.61 6169.86,-9405.78\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025984 -->\\n\",\n       \"<g id=\\\"node68\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025984</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3297,-9832 3136,-9832 3136,-9813 3297,-9813 3297,-9832\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3216.5\\\" y=\\\"-9820\\\" font-family=\\\"monospace\\\" 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\"<title>140042247610240&#45;&gt;140041523266224</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6101.5,-9680.8C6101.5,-9671.7 6101.5,-9659.79 6101.5,-9649.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6105,-9649.84 6101.5,-9639.84 6098,-9649.84 6105,-9649.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026272 -->\\n\",\n       \"<g id=\\\"node114\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525026272</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6142,-9458 6053,-9458 6053,-9439 6142,-9439 6142,-9458\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6097.5\\\" y=\\\"-9446\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026272&#45;&gt;140041525025744 -->\\n\",\n       \"<g id=\\\"edge112\\\" class=\\\"edge\\\">\\n\",\n       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\"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5558,-9573.5 5457,-9573.5 5457,-9554.5 5558,-9554.5 5558,-9573.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5507.5\\\" y=\\\"-9561.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523266560&#45;&gt;140041525026176 -->\\n\",\n       \"<g id=\\\"edge126\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523266560&#45;&gt;140041525026176</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5469.84,-9554.49C5423.9,-9544.12 5346.19,-9526.57 5296.14,-9515.26\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5296.73,-9511.81 5286.21,-9513.02 5295.19,-9518.63 5296.73,-9511.81\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041542994096 -->\\n\",\n       \"<g id=\\\"node128\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041542994096</title>\\n\",\n       \"<polygon 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x=\\\"5978.5\\\" y=\\\"-9391\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025936&#45;&gt;140041525026224 -->\\n\",\n       \"<g id=\\\"edge133\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025936&#45;&gt;140041525026224</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6002.93,-9383.99C6031.79,-9373.94 6080,-9357.16 6112.5,-9345.84\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6113.75,-9349.11 6122.04,-9342.52 6111.45,-9342.5 6113.75,-9349.11\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026080 -->\\n\",\n       \"<g id=\\\"node134\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525026080</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4487,-9458 4326,-9458 4326,-9439 4487,-9439 4487,-9458\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4406.5\\\" y=\\\"-9446\\\" 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d=\\\"M6985.5,-9130.75C6985.5,-9123.8 6985.5,-9113.85 6985.5,-9105.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6989,-9105.09 6985.5,-9095.09 6982,-9105.09 6989,-9105.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042247611520 -->\\n\",\n       \"<g id=\\\"node186\\\" class=\\\"node\\\">\\n\",\n       \"<title>140042247611520</title>\\n\",\n       \"<polygon fill=\\\"lightblue\\\" stroke=\\\"black\\\" points=\\\"7183,-9216 6788,-9216 6788,-9186 7183,-9186 7183,-9216\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6985.5\\\" y=\\\"-9204\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">conv_layer_2.0.list_of_modules.5.unit_layer.unit_layer.2.weight</text>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6985.5\\\" y=\\\"-9193\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\"> (128)</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042247611520&#45;&gt;140041525025408 -->\\n\",\n       \"<g id=\\\"edge190\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042247611520&#45;&gt;140041525025408</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6985.5,-9185.84C6985.5,-9178.21 6985.5,-9168.7 6985.5,-9160.45\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6989,-9160.27 6985.5,-9150.27 6982,-9160.27 6989,-9160.27\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025216 -->\\n\",\n       \"<g id=\\\"node187\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025216</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"7299,-9150 7198,-9150 7198,-9131 7299,-9131 7299,-9150\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7248.5\\\" y=\\\"-9138\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025216&#45;&gt;140041525025072 -->\\n\",\n       \"<g id=\\\"edge191\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025216&#45;&gt;140041525025072</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M7206.24,-9130.98C7160.46,-9121.76 7087.36,-9107.03 7037.95,-9097.07\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"7038.58,-9093.63 7028.09,-9095.08 7037.2,-9100.49 7038.58,-9093.63\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042247615040 -->\\n\",\n       \"<g id=\\\"node188\\\" class=\\\"node\\\">\\n\",\n       \"<title>140042247615040</title>\\n\",\n       \"<polygon fill=\\\"lightblue\\\" stroke=\\\"black\\\" points=\\\"7584,-9216 7201,-9216 7201,-9186 7584,-9186 7584,-9216\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7392.5\\\" y=\\\"-9204\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">conv_layer_2.0.list_of_modules.5.unit_layer.unit_layer.2.bias</text>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7392.5\\\" y=\\\"-9193\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\"> (128)</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042247615040&#45;&gt;140041525025216 -->\\n\",\n       \"<g id=\\\"edge192\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042247615040&#45;&gt;140041525025216</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M7358.01,-9185.99C7333.98,-9176.23 7302.23,-9163.33 7279.1,-9153.93\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"7280.35,-9150.66 7269.77,-9150.14 7277.71,-9157.15 7280.35,-9150.66\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025600 -->\\n\",\n       \"<g id=\\\"node189\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025600</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"7061,-7544 6948,-7544 6948,-7525 7061,-7525 7061,-7544\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7004.5\\\" y=\\\"-7532\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025600&#45;&gt;140041525024736 -->\\n\",\n       \"<g id=\\\"edge193\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525025600&#45;&gt;140041525024736</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M7004.5,-7524.75C7004.5,-7517.8 7004.5,-7507.85 7004.5,-7499.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"7008,-7499.09 7004.5,-7489.09 7001,-7499.09 7008,-7499.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025504 -->\\n\",\n       \"<g id=\\\"node190\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525025504</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"7058,-7604.5 6951,-7604.5 6951,-7585.5 7058,-7585.5 7058,-7604.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7004.5\\\" y=\\\"-7592.5\\\" font-family=\\\"monospace\\\" 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font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525026752&#45;&gt;140041523268960 -->\\n\",\n       \"<g id=\\\"edge244\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525026752&#45;&gt;140041523268960</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6882.1,-7651.37C6881.66,-7642.07 6880.95,-7626.98 6880.39,-7614.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6883.88,-7614.73 6879.92,-7604.91 6876.89,-7615.06 6883.88,-7614.73\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523270064 -->\\n\",\n       \"<g id=\\\"node325\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523270064</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6808,-7604.5 6701,-7604.5 6701,-7585.5 6808,-7585.5 6808,-7604.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6754.5\\\" y=\\\"-7592.5\\\" font-family=\\\"monospace\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140042245597568&#45;&gt;140041523267184</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6113.33,-7711.97C6212.13,-7704.28 6327.17,-7692.78 6430.5,-7676 6435.96,-7675.11 6441.65,-7674 6447.27,-7672.8\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6448.23,-7676.17 6457.21,-7670.55 6446.68,-7669.34 6448.23,-7676.17\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523267856 -->\\n\",\n       \"<g id=\\\"node229\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523267856</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6659,-7670.5 6558,-7670.5 6558,-7651.5 6659,-7651.5 6659,-7670.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6608.5\\\" y=\\\"-7658.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523267856&#45;&gt;140041525025120 -->\\n\",\n    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points=\\\"6421,-7676 6038,-7676 6038,-7646 6421,-7646 6421,-7676\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6229.5\\\" y=\\\"-7664\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">conv_layer_2.0.list_of_modules.9.unit_layer.unit_layer.2.bias</text>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6229.5\\\" y=\\\"-7653\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\"> (128)</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245589248&#45;&gt;140041523267568 -->\\n\",\n       \"<g id=\\\"edge241\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042245589248&#45;&gt;140041523267568</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6229.5,-7645.8C6229.5,-7636.7 6229.5,-7624.79 6229.5,-7614.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6233,-7614.84 6229.5,-7604.84 6226,-7614.84 6233,-7614.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525025024 -->\\n\",\n       \"<g 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x=\\\"3441.5\\\" y=\\\"-8522\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523272896&#45;&gt;140041523272800 -->\\n\",\n       \"<g id=\\\"edge276\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523272896&#45;&gt;140041523272800</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3441.5,-8514.75C3441.5,-8507.8 3441.5,-8497.85 3441.5,-8489.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3445,-8489.09 3441.5,-8479.09 3438,-8489.09 3445,-8489.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523272992 -->\\n\",\n       \"<g id=\\\"node265\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523272992</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3492,-8594.5 3391,-8594.5 3391,-8575.5 3492,-8575.5 3492,-8594.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3441.5\\\" y=\\\"-8582.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523272992&#45;&gt;140041523272896 -->\\n\",\n       \"<g id=\\\"edge277\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523272992&#45;&gt;140041523272896</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3441.5,-8575.37C3441.5,-8567.25 3441.5,-8554.81 3441.5,-8544.39\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3445,-8544.17 3441.5,-8534.17 3438,-8544.17 3445,-8544.17\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273520 -->\\n\",\n       \"<g id=\\\"node285\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523273520</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3725,-8534 3618,-8534 3618,-8515 3725,-8515 3725,-8534\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3671.5\\\" y=\\\"-8522\\\" 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d=\\\"M3954.41,-8641.37C3958.71,-8631.88 3965.74,-8616.36 3971.27,-8604.16\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3974.53,-8605.46 3975.46,-8594.91 3968.15,-8602.57 3974.53,-8605.46\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273424 -->\\n\",\n       \"<g id=\\\"node272\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523273424</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3741,-8660.5 3640,-8660.5 3640,-8641.5 3741,-8641.5 3741,-8660.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3690.5\\\" y=\\\"-8648.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273424&#45;&gt;140041523273280 -->\\n\",\n       \"<g id=\\\"edge285\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523273424&#45;&gt;140041523273280</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" 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\"<title>140042245588128&#45;&gt;140041523269344</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3739.72,-7579.99C3759.62,-7570.45 3785.78,-7557.9 3805.25,-7548.57\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3806.98,-7551.62 3814.48,-7544.14 3803.95,-7545.31 3806.98,-7551.62\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024784 -->\\n\",\n       \"<g id=\\\"node324\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041525024784</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6763,-7489 6650,-7489 6650,-7470 6763,-7470 6763,-7489\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6706.5\\\" y=\\\"-7477\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024784&#45;&gt;140041525034768 -->\\n\",\n       \"<g id=\\\"edge339\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041525024784&#45;&gt;140041525034768</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6704.02,-7469.75C6702.04,-7462.72 6699.18,-7452.62 6696.69,-7443.84\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6700.02,-7442.76 6693.93,-7434.09 6693.29,-7444.66 6700.02,-7442.76\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523270064&#45;&gt;140041525024784 -->\\n\",\n       \"<g id=\\\"edge340\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523270064&#45;&gt;140041525024784</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6750.89,-7585.47C6743.11,-7567.06 6724.6,-7523.29 6714.1,-7498.48\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6717.27,-7496.98 6710.15,-7489.13 6710.82,-7499.7 6717.27,-7496.98\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041525024640 -->\\n\",\n       \"<g id=\\\"node326\\\" class=\\\"node\\\">\\n\",\n       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points=\\\"5783,-6878.5 5646,-6878.5 5646,-6859.5 5783,-6859.5 5783,-6878.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5714.5\\\" y=\\\"-6866.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523271168&#45;&gt;140041523269536 -->\\n\",\n       \"<g id=\\\"edge345\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523271168&#45;&gt;140041523269536</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5682.22,-6859.49C5643.25,-6849.23 5577.62,-6831.93 5534.68,-6820.62\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5535.38,-6817.18 5524.82,-6818.02 5533.6,-6823.95 5535.38,-6817.18\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523272512 -->\\n\",\n       \"<g id=\\\"node330\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523272512</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5762,-6944.5 5667,-6944.5 5667,-6925.5 5762,-6925.5 5762,-6944.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5714.5\\\" y=\\\"-6932.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523272512&#45;&gt;140041523271168 -->\\n\",\n       \"<g id=\\\"edge346\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523272512&#45;&gt;140041523271168</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5714.5,-6925.37C5714.5,-6916.16 5714.5,-6901.29 5714.5,-6889.27\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5718,-6888.91 5714.5,-6878.91 5711,-6888.91 5718,-6888.91\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273616 -->\\n\",\n       \"<g id=\\\"node331\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523273616</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5585,-7010.5 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\"<title>140041523273904&#45;&gt;140041523273760</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5463.81,-7524.98C5492.35,-7516.09 5537.33,-7502.07 5569.14,-7492.15\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5570.47,-7495.4 5578.97,-7489.08 5568.38,-7488.72 5570.47,-7495.4\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274048 -->\\n\",\n       \"<g id=\\\"node335\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274048</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5408,-7604.5 5295,-7604.5 5295,-7585.5 5408,-7585.5 5408,-7604.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5351.5\\\" y=\\\"-7592.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274048&#45;&gt;140041523273904 -->\\n\",\n       \"<g id=\\\"edge352\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523274048&#45;&gt;140041523273904</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5364.04,-7585.37C5377.6,-7576.04 5399.44,-7561.01 5415.6,-7549.88\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5417.65,-7552.72 5423.91,-7544.17 5413.68,-7546.95 5417.65,-7552.72\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274144 -->\\n\",\n       \"<g id=\\\"node336\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274144</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5405,-7670.5 5298,-7670.5 5298,-7651.5 5405,-7651.5 5405,-7670.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5351.5\\\" y=\\\"-7658.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274144&#45;&gt;140041523274048 -->\\n\",\n       \"<g id=\\\"edge353\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523274144&#45;&gt;140041523274048</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5351.5,-7651.37C5351.5,-7642.16 5351.5,-7627.29 5351.5,-7615.27\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5355,-7614.91 5351.5,-7604.91 5348,-7614.91 5355,-7614.91\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274240 -->\\n\",\n       \"<g id=\\\"node337\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274240</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5340,-7736.5 5239,-7736.5 5239,-7717.5 5340,-7717.5 5340,-7736.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5289.5\\\" y=\\\"-7724.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274240&#45;&gt;140041523274144 -->\\n\",\n       \"<g id=\\\"edge354\\\" class=\\\"edge\\\">\\n\",\n       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140041523273952&#45;&gt;140041523274336 -->\\n\",\n       \"<g id=\\\"edge364\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523273952&#45;&gt;140041523274336</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5232.62,-7651.44C5247.16,-7631.1 5284.03,-7579.53 5303.32,-7552.54\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5306.37,-7554.29 5309.34,-7544.12 5300.68,-7550.22 5306.37,-7554.29\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273664 -->\\n\",\n       \"<g id=\\\"node345\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523273664</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5578,-7071 5489,-7071 5489,-7052 5578,-7052 5578,-7071\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5533.5\\\" y=\\\"-7059\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273664&#45;&gt;140041523273616 -->\\n\",\n       \"<g id=\\\"edge366\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523273664&#45;&gt;140041523273616</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5534.53,-7051.87C5535.5,-7043.75 5536.99,-7031.31 5538.24,-7020.89\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5541.75,-7021.01 5539.46,-7010.67 5534.8,-7020.18 5541.75,-7021.01\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274384 -->\\n\",\n       \"<g id=\\\"node346\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274384</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5180,-7126 5019,-7126 5019,-7107 5180,-7107 5180,-7126\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5099.5\\\" y=\\\"-7114\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">NativeBatchNormBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274384&#45;&gt;140041523273664 -->\\n\",\n       \"<g id=\\\"edge367\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523274384&#45;&gt;140041523273664</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5169.23,-7106.98C5255.34,-7096.47 5400.03,-7078.8 5478.64,-7069.2\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5479.32,-7072.64 5488.82,-7067.96 5478.47,-7065.69 5479.32,-7072.64\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273808 -->\\n\",\n       \"<g id=\\\"node347\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523273808</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4998,-7181 4861,-7181 4861,-7162 4998,-7162 4998,-7181\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4929.5\\\" y=\\\"-7169\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523273808&#45;&gt;140041523274384 -->\\n\",\n       \"<g id=\\\"edge368\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523273808&#45;&gt;140041523274384</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M4956.81,-7161.98C4985.35,-7153.09 5030.33,-7139.07 5062.14,-7129.15\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5063.47,-7132.4 5071.97,-7126.08 5061.38,-7125.72 5063.47,-7132.4\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523274624 -->\\n\",\n       \"<g id=\\\"node348\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523274624</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"4929,-7241.5 4792,-7241.5 4792,-7222.5 4929,-7222.5 4929,-7241.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"4860.5\\\" y=\\\"-7229.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 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-->\\n\",\n       \"<g id=\\\"edge437\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523275152&#45;&gt;140041523275728</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2824.21,-7052.43C2882.83,-7041.84 2985.63,-7023.28 3048.82,-7011.87\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3049.77,-7015.26 3058.99,-7010.04 3048.53,-7008.37 3049.77,-7015.26\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275920 -->\\n\",\n       \"<g id=\\\"node410\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523275920</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2836,-7126 2723,-7126 2723,-7107 2836,-7107 2836,-7126\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2779.5\\\" y=\\\"-7114\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275920&#45;&gt;140041523275152 -->\\n\",\n       \"<g id=\\\"edge439\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523275920&#45;&gt;140041523275152</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2779.5,-7106.75C2779.5,-7099.8 2779.5,-7089.85 2779.5,-7081.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2783,-7081.09 2779.5,-7071.09 2776,-7081.09 2783,-7081.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276016 -->\\n\",\n       \"<g id=\\\"node411\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523276016</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2833,-7181 2726,-7181 2726,-7162 2833,-7162 2833,-7181\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2779.5\\\" y=\\\"-7169\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276016&#45;&gt;140041523275920 -->\\n\",\n       \"<g id=\\\"edge440\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523276016&#45;&gt;140041523275920</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2779.5,-7161.75C2779.5,-7154.8 2779.5,-7144.85 2779.5,-7136.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2783,-7136.09 2779.5,-7126.09 2776,-7136.09 2783,-7136.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276112 -->\\n\",\n       \"<g id=\\\"node412\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523276112</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2830,-7241.5 2729,-7241.5 2729,-7222.5 2830,-7222.5 2830,-7241.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2779.5\\\" y=\\\"-7229.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276112&#45;&gt;140041523276016 -->\\n\",\n       \"<g id=\\\"edge441\\\" class=\\\"edge\\\">\\n\",\n    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\"<title>140041523276112&#45;&gt;140041523276640</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2799.28,-7222.49C2822.14,-7212.66 2859.99,-7196.38 2886.26,-7185.08\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2887.9,-7188.18 2895.7,-7181.02 2885.13,-7181.75 2887.9,-7188.18\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276256 -->\\n\",\n       \"<g id=\\\"node448\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523276256</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3177,-7181 3070,-7181 3070,-7162 3177,-7162 3177,-7181\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3123.5\\\" y=\\\"-7169\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276112&#45;&gt;140041523276256 -->\\n\",\n       \"<g id=\\\"edge485\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523276112&#45;&gt;140041523276256</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2813.41,-7222.48C2821.6,-7220.55 2830.34,-7218.6 2838.5,-7217 2914.18,-7202.14 3001.94,-7189.05 3059.87,-7180.98\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3060.54,-7184.42 3069.97,-7179.59 3059.58,-7177.49 3060.54,-7184.42\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245602048 -->\\n\",\n       \"<g id=\\\"node413\\\" class=\\\"node\\\">\\n\",\n       \"<title>140042245602048</title>\\n\",\n       \"<polygon fill=\\\"lightblue\\\" stroke=\\\"black\\\" points=\\\"2767,-7313 2510,-7313 2510,-7283 2767,-7283 2767,-7313\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2638.5\\\" y=\\\"-7301\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">conv_layer_2.0.list_of_modules.16.params</text>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2638.5\\\" y=\\\"-7290\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\"> (3)</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245602048&#45;&gt;140041523276112 -->\\n\",\n       \"<g id=\\\"edge442\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042245602048&#45;&gt;140041523276112</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2669.44,-7282.95C2693.95,-7271.83 2727.89,-7256.42 2751.58,-7245.67\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2753.08,-7248.84 2760.74,-7241.52 2750.18,-7242.46 2753.08,-7248.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275344 -->\\n\",\n       \"<g id=\\\"node414\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523275344</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3148,-7071 3059,-7071 3059,-7052 3148,-7052 3148,-7071\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3103.5\\\" y=\\\"-7059\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275344&#45;&gt;140041523275728 -->\\n\",\n       \"<g id=\\\"edge443\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523275344&#45;&gt;140041523275728</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3103.5,-7051.87C3103.5,-7043.75 3103.5,-7031.31 3103.5,-7020.89\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3107,-7020.67 3103.5,-7010.67 3100,-7020.67 3107,-7020.67\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276064 -->\\n\",\n       \"<g id=\\\"node415\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523276064</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3412,-7126 3251,-7126 3251,-7107 3412,-7107 3412,-7126\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3331.5\\\" y=\\\"-7114\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">NativeBatchNormBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276064&#45;&gt;140041523275344 -->\\n\",\n       \"<g id=\\\"edge444\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523276064&#45;&gt;140041523275344</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3294.87,-7106.98C3255.59,-7097.86 3193.13,-7083.33 3150.31,-7073.38\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3150.95,-7069.94 3140.42,-7071.08 3149.37,-7076.76 3150.95,-7069.94\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275824 -->\\n\",\n       \"<g id=\\\"node416\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523275824</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3513,-7181 3376,-7181 3376,-7162 3513,-7162 3513,-7181\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3444.5\\\" y=\\\"-7169\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523275824&#45;&gt;140041523276064 -->\\n\",\n       \"<g id=\\\"edge445\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523275824&#45;&gt;140041523276064</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3426.34,-7161.98C3408.25,-7153.5 3380.22,-7140.35 3359.31,-7130.54\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3360.53,-7127.25 3349.99,-7126.17 3357.55,-7133.59 3360.53,-7127.25\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523276400 -->\\n\",\n       \"<g id=\\\"node417\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523276400</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3523,-7241.5 3386,-7241.5 3386,-7222.5 3523,-7222.5 3523,-7241.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3454.5\\\" y=\\\"-7229.5\\\" font-family=\\\"monospace\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140041523672592&#45;&gt;140041523672400</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6330.88,-4556.5C6177.55,-4551.15 5672.87,-4532.13 5512.5,-4508 5507.02,-4507.18 5501.3,-4506.07 5495.68,-4504.84\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5496.3,-4501.39 5485.76,-4502.52 5494.71,-4508.21 5496.3,-4501.39\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523672688&#45;&gt;140041523672592 -->\\n\",\n       \"<g id=\\\"edge553\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523672688&#45;&gt;140041523672592</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6440.39,-4615.37C6428.49,-4605.21 6408.5,-4588.16 6393.85,-4575.65\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6395.81,-4572.73 6385.94,-4568.91 6391.27,-4578.06 6395.81,-4572.73\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523672832 -->\\n\",\n       \"<g id=\\\"node515\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523672832</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6844,-4695 6731,-4695 6731,-4676 6844,-4676 6844,-4695\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6787.5\\\" y=\\\"-4683\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523672832&#45;&gt;140041523672688 -->\\n\",\n       \"<g id=\\\"edge555\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523672832&#45;&gt;140041523672688</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6730.83,-4677.25C6673.48,-4669.52 6582.41,-4656.19 6504.5,-4640 6500.11,-4639.09 6495.55,-4638.05 6491.02,-4636.96\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"6491.77,-4633.54 6481.22,-4634.52 6490.08,-4640.33 6491.77,-4633.54\\\"/>\\n\",\n       \"</g>\\n\",\n       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-->\\n\",\n       \"<g id=\\\"node537\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523673552</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"7165,-4755.5 7058,-4755.5 7058,-4736.5 7165,-4736.5 7165,-4755.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"7111.5\\\" y=\\\"-4743.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523673024&#45;&gt;140041523673552 -->\\n\",\n       \"<g id=\\\"edge579\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523673024&#45;&gt;140041523673552</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M7126.21,-4802.37C7123.74,-4793.07 7119.73,-4777.98 7116.52,-4765.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"7119.82,-4764.67 7113.87,-4755.91 7113.05,-4766.47 7119.82,-4764.67\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245203472 -->\\n\",\n     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points=\\\"7128.44,-4832.32 7126.84,-4821.84 7121.56,-4831.03 7128.44,-4832.32\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523672640 -->\\n\",\n       \"<g id=\\\"node519\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523672640</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6602,-4634.5 6513,-4634.5 6513,-4615.5 6602,-4615.5 6602,-4634.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6557.5\\\" y=\\\"-4622.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523672640&#45;&gt;140041523672592 -->\\n\",\n       \"<g id=\\\"edge559\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523672640&#45;&gt;140041523672592</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M6532.95,-4615.37C6501.12,-4604.17 6445.41,-4584.58 6409.51,-4571.96\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" 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140041523673984&#45;&gt;140041523673792 -->\\n\",\n       \"<g id=\\\"edge600\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523673984&#45;&gt;140041523673792</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M1845.18,-4615.45C1853.01,-4613.39 1861.53,-4611.38 1869.5,-4610 2055.85,-4577.73 2106.88,-4604.65 2293.5,-4574 2298.71,-4573.14 2304.14,-4572.06 2309.5,-4570.88\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2310.68,-4574.2 2319.62,-4568.52 2309.09,-4567.38 2310.68,-4574.2\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523674080 -->\\n\",\n       \"<g id=\\\"node558\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523674080</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"1787,-4695 1698,-4695 1698,-4676 1787,-4676 1787,-4695\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"1742.5\\\" y=\\\"-4683\\\" font-family=\\\"monospace\\\" 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d=\\\"M1490.5,-5687.8C1490.5,-5678.7 1490.5,-5666.79 1490.5,-5656.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"1494,-5656.84 1490.5,-5646.84 1487,-5656.84 1494,-5656.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523675248 -->\\n\",\n       \"<g id=\\\"node570\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523675248</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"1541,-5459.5 1452,-5459.5 1452,-5440.5 1541,-5440.5 1541,-5459.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"1496.5\\\" y=\\\"-5447.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523675248&#45;&gt;140041523675040 -->\\n\",\n       \"<g id=\\\"edge614\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523675248&#45;&gt;140041523675040</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" 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d=\\\"M2588.52,-5506.48C2574.97,-5504.37 2560.19,-5502.33 2546.5,-5501 2134.78,-5461.14 2028.9,-5496.99 1616.5,-5465 1595.12,-5463.34 1571.68,-5460.78 1551.24,-5458.29\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"1551.51,-5454.8 1541.15,-5457.04 1550.65,-5461.74 1551.51,-5454.8\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523676592 -->\\n\",\n       \"<g id=\\\"node593\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523676592</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2456,-5459.5 2361,-5459.5 2361,-5440.5 2456,-5440.5 2456,-5459.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2408.5\\\" y=\\\"-5447.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523675536&#45;&gt;140041523676592 -->\\n\",\n       \"<g id=\\\"edge640\\\" class=\\\"edge\\\">\\n\",\n       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\"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2263.99,-5621.99C2288.02,-5612.23 2319.77,-5599.33 2342.9,-5589.93\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2344.29,-5593.15 2352.23,-5586.14 2341.65,-5586.66 2344.29,-5593.15\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523675440 -->\\n\",\n       \"<g id=\\\"node587\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523675440</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"1553,-5525.5 1440,-5525.5 1440,-5506.5 1553,-5506.5 1553,-5525.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"1496.5\\\" y=\\\"-5513.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523675440&#45;&gt;140041523675248 -->\\n\",\n       \"<g id=\\\"edge632\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523675440&#45;&gt;140041523675248</title>\\n\",\n       \"<path 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font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677264&#45;&gt;140041523677168 -->\\n\",\n       \"<g id=\\\"edge712\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523677264&#45;&gt;140041523677168</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3680.01,-4054.44C3620.9,-4042.8 3515.29,-4022 3451.59,-4009.45\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3452.22,-4006.01 3441.74,-4007.51 3450.87,-4012.88 3452.22,-4006.01\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677456 -->\\n\",\n       \"<g id=\\\"node662\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523677456</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3858,-4139.5 3769,-4139.5 3769,-4120.5 3858,-4120.5 3858,-4139.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3813.5\\\" y=\\\"-4127.5\\\" font-family=\\\"monospace\\\" 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id=\\\"edge720\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042244902560&#45;&gt;140041523678032</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3954.4,-4862.8C3949.7,-4853.41 3943.51,-4841.02 3938.47,-4830.95\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3941.52,-4829.22 3933.92,-4821.84 3935.26,-4832.35 3941.52,-4829.22\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677648&#45;&gt;140041523677552 -->\\n\",\n       \"<g id=\\\"edge721\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523677648&#45;&gt;140041523677552</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3896.5,-4615.37C3896.5,-4606.16 3896.5,-4591.29 3896.5,-4579.27\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3900,-4578.91 3896.5,-4568.91 3893,-4578.91 3900,-4578.91\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677984 -->\\n\",\n       \"<g id=\\\"node670\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140041523678080&#45;&gt;140041523677984</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3898.2,-4736.37C3897.93,-4728.25 3897.5,-4715.81 3897.15,-4705.39\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3900.64,-4705.04 3896.8,-4695.17 3893.64,-4705.28 3900.64,-4705.04\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677504 -->\\n\",\n       \"<g id=\\\"node672\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523677504</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3539,-4200 3450,-4200 3450,-4181 3539,-4181 3539,-4200\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3494.5\\\" y=\\\"-4188\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">MulBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677504&#45;&gt;140041523677456 -->\\n\",\n       \"<g id=\\\"edge726\\\" class=\\\"edge\\\">\\n\",\n      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class=\\\"edge\\\">\\n\",\n       \"<title>140041523678128&#45;&gt;140041523677504</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3494.5,-4235.75C3494.5,-4228.8 3494.5,-4218.85 3494.5,-4210.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3498,-4210.09 3494.5,-4200.09 3491,-4210.09 3498,-4210.09\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677792 -->\\n\",\n       \"<g id=\\\"node674\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523677792</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3563,-4310 3426,-4310 3426,-4291 3563,-4291 3563,-4310\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3494.5\\\" y=\\\"-4298\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677792&#45;&gt;140041523678128 -->\\n\",\n       \"<g id=\\\"edge728\\\" class=\\\"edge\\\">\\n\",\n       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\"<title>140041523678368&#45;&gt;140041523677792</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3366.31,-4351.49C3391.75,-4341.57 3434.01,-4325.09 3463.01,-4313.78\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3464.62,-4316.91 3472.66,-4310.02 3462.07,-4310.39 3464.62,-4316.91\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678560 -->\\n\",\n       \"<g id=\\\"node676\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523678560</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3250,-4436.5 3155,-4436.5 3155,-4417.5 3250,-4417.5 3250,-4436.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3202.5\\\" y=\\\"-4424.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678560&#45;&gt;140041523678368 -->\\n\",\n       \"<g id=\\\"edge730\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523678560&#45;&gt;140041523678368</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3221.65,-4417.37C3245.89,-4406.44 3287.87,-4387.52 3315.91,-4374.88\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3317.52,-4378 3325.2,-4370.7 3314.64,-4371.62 3317.52,-4378\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678752 -->\\n\",\n       \"<g id=\\\"node677\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523678752</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3911,-4502.5 3822,-4502.5 3822,-4483.5 3911,-4483.5 3911,-4502.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3866.5\\\" y=\\\"-4490.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678752&#45;&gt;140041523678560 -->\\n\",\n       \"<g id=\\\"edge731\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523678752&#45;&gt;140041523678560</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3837.85,-4483.5C3829.72,-4481.38 3820.82,-4479.34 3812.5,-4478 3568.88,-4438.86 3502.29,-4480.08 3258.5,-4442 3253.31,-4441.19 3247.9,-4440.12 3242.57,-4438.94\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3243.06,-4435.46 3232.52,-4436.56 3241.45,-4442.27 3243.06,-4435.46\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678848 -->\\n\",\n       \"<g id=\\\"node678\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523678848</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3834,-4568.5 3745,-4568.5 3745,-4549.5 3834,-4549.5 3834,-4568.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3789.5\\\" y=\\\"-4556.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678848&#45;&gt;140041523678752 -->\\n\",\n       \"<g id=\\\"edge732\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523678848&#45;&gt;140041523678752</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3799.88,-4549.37C3812.22,-4539.12 3833.01,-4521.84 3848.09,-4509.31\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3850.33,-4511.99 3855.79,-4502.91 3845.86,-4506.61 3850.33,-4511.99\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678992&#45;&gt;140041523678848 -->\\n\",\n       \"<g id=\\\"edge733\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523678992&#45;&gt;140041523678848</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3696.93,-4615.37C3714.72,-4604.73 3745.18,-4586.51 3766.28,-4573.89\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3768.17,-4576.84 3774.96,-4568.7 3764.58,-4570.83 3768.17,-4576.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679136 -->\\n\",\n 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id=\\\"node681\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679232</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3613,-4755.5 3506,-4755.5 3506,-4736.5 3613,-4736.5 3613,-4755.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3559.5\\\" y=\\\"-4743.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679232&#45;&gt;140041523679136 -->\\n\",\n       \"<g id=\\\"edge736\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679232&#45;&gt;140041523679136</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3558.17,-4736.37C3556.91,-4728.16 3554.97,-4715.54 3553.35,-4705.05\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3556.81,-4704.52 3551.83,-4695.17 3549.89,-4705.58 3556.81,-4704.52\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679328 -->\\n\",\n       \"<g id=\\\"node682\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679328</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3703,-4821.5 3602,-4821.5 3602,-4802.5 3703,-4802.5 3703,-4821.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3652.5\\\" y=\\\"-4809.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679328&#45;&gt;140041523679232 -->\\n\",\n       \"<g id=\\\"edge737\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679328&#45;&gt;140041523679232</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3639.96,-4802.37C3624.7,-4791.87 3598.73,-4774 3580.43,-4761.4\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3582.36,-4758.48 3572.14,-4755.7 3578.39,-4764.25 3582.36,-4758.48\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679376 -->\\n\",\n       \"<g id=\\\"node686\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679376</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3738,-4755.5 3631,-4755.5 3631,-4736.5 3738,-4736.5 3738,-4755.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3684.5\\\" y=\\\"-4743.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679328&#45;&gt;140041523679376 -->\\n\",\n       \"<g id=\\\"edge743\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679328&#45;&gt;140041523679376</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3656.82,-4802.37C3661.56,-4792.88 3669.32,-4777.36 3675.42,-4765.16\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3678.71,-4766.41 3680.05,-4755.91 3672.44,-4763.28 3678.71,-4766.41\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679904 -->\\n\",\n       \"<g id=\\\"node714\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679904</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3488,-4755.5 3381,-4755.5 3381,-4736.5 3488,-4736.5 3488,-4755.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3434.5\\\" y=\\\"-4743.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679328&#45;&gt;140041523679904 -->\\n\",\n       \"<g id=\\\"edge775\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679328&#45;&gt;140041523679904</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3623.1,-4802.37C3584.47,-4791.03 3516.52,-4771.08 3473.6,-4758.48\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3474.36,-4755.06 3463.78,-4755.6 3472.39,-4761.77 3474.36,-4755.06\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245208592 -->\\n\",\n       \"<g id=\\\"node683\\\" class=\\\"node\\\">\\n\",\n       \"<title>140042245208592</title>\\n\",\n       \"<polygon fill=\\\"lightblue\\\" stroke=\\\"black\\\" points=\\\"3738,-4893 3487,-4893 3487,-4863 3738,-4863 3738,-4893\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3612.5\\\" y=\\\"-4881\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">conv_layer_3.0.list_of_modules.8.params</text>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3612.5\\\" y=\\\"-4870\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\"> (3)</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140042245208592&#45;&gt;140041523679328 -->\\n\",\n       \"<g id=\\\"edge738\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140042245208592&#45;&gt;140041523679328</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3621.38,-4862.8C3627.31,-4853.31 3635.15,-4840.76 3641.48,-4830.63\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3644.64,-4832.18 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font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679280&#45;&gt;140041523678944 -->\\n\",\n       \"<g id=\\\"edge741\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679280&#45;&gt;140041523678944</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3697.44,-4675.87C3715.14,-4666.28 3743.96,-4650.67 3764.64,-4639.47\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3766.37,-4642.51 3773.5,-4634.67 3763.04,-4636.35 3766.37,-4642.51\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679376&#45;&gt;140041523679280 -->\\n\",\n       \"<g id=\\\"edge742\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679376&#45;&gt;140041523679280</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3684.06,-4736.37C3683.64,-4728.25 3683,-4715.81 3682.47,-4705.39\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3685.95,-4704.97 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points=\\\"3827.51,-4508.3 3836.37,-4502.5 3825.83,-4501.51 3827.51,-4508.3\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679424 -->\\n\",\n       \"<g id=\\\"node688\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679424</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3195,-4634.5 3034,-4634.5 3034,-4615.5 3195,-4615.5 3195,-4634.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3114.5\\\" y=\\\"-4622.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">NativeBatchNormBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679424&#45;&gt;140041523678800 -->\\n\",\n       \"<g id=\\\"edge745\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679424&#45;&gt;140041523678800</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3157.32,-4615.44C3215.4,-4603.82 3319.07,-4583.09 3381.84,-4570.53\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3382.81,-4573.91 3391.93,-4568.51 3381.44,-4567.04 3382.81,-4573.91\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679088 -->\\n\",\n       \"<g id=\\\"node689\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679088</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3251,-4695 3114,-4695 3114,-4676 3251,-4676 3251,-4695\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3182.5\\\" y=\\\"-4683\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679088&#45;&gt;140041523679424 -->\\n\",\n       \"<g id=\\\"edge746\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523679088&#45;&gt;140041523679424</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3172.47,-4675.87C3161.92,-4666.8 3145.11,-4652.34 3132.31,-4641.32\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140041543171808&#45;&gt;140048507139120</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2906.43,-3146.98C2890.64,-3138.61 2866.28,-3125.7 2847.87,-3115.95\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2849.33,-3112.76 2838.86,-3111.17 2846.06,-3118.95 2849.33,-3112.76\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523671872 -->\\n\",\n       \"<g id=\\\"node757\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523671872</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3060,-3226.5 2923,-3226.5 2923,-3207.5 3060,-3207.5 3060,-3226.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2991.5\\\" y=\\\"-3214.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ConvolutionBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523671872&#45;&gt;140041543171808 -->\\n\",\n       \"<g id=\\\"edge820\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523671872&#45;&gt;140041543171808</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2981.32,-3207.37C2970.62,-3198.3 2953.56,-3183.84 2940.57,-3172.82\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2942.61,-3169.96 2932.72,-3166.17 2938.09,-3175.3 2942.61,-3169.96\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677600 -->\\n\",\n       \"<g id=\\\"node758\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523677600</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3097,-3292.5 3002,-3292.5 3002,-3273.5 3097,-3273.5 3097,-3292.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3049.5\\\" y=\\\"-3280.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">ReluBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523677600&#45;&gt;140041523671872 -->\\n\",\n       \"<g id=\\\"edge821\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523677600&#45;&gt;140041523671872</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3041.68,-3273.37C3032.65,-3263.4 3017.59,-3246.79 3006.33,-3234.36\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3008.88,-3231.96 2999.57,-3226.91 3003.69,-3236.67 3008.88,-3231.96\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678224 -->\\n\",\n       \"<g id=\\\"node759\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523678224</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3181,-3358.5 3092,-3358.5 3092,-3339.5 3181,-3339.5 3181,-3358.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3136.5\\\" y=\\\"-3346.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523678224&#45;&gt;140041523677600 -->\\n\",\n       \"<g id=\\\"edge822\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523678224&#45;&gt;140041523677600</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3124.77,-3339.37C3110.63,-3328.97 3086.64,-3311.32 3069.55,-3298.75\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3071.45,-3295.81 3061.33,-3292.7 3067.31,-3301.44 3071.45,-3295.81\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679712 -->\\n\",\n       \"<g id=\\\"node760\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523679712</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2457,-3419 2368,-3419 2368,-3400 2457,-3400 2457,-3419\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2412.5\\\" y=\\\"-3407\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AddBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523679712&#45;&gt;140041523678224 -->\\n\",\n       \"<g id=\\\"edge823\\\" class=\\\"edge\\\">\\n\",\n    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class=\\\"node\\\">\\n\",\n       \"<title>140041523680624</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2561,-3529 2448,-3529 2448,-3510 2561,-3510 2561,-3529\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2504.5\\\" y=\\\"-3517\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SigmoidBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523680624&#45;&gt;140041523679520 -->\\n\",\n       \"<g id=\\\"edge826\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523680624&#45;&gt;140041523679520</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2489.72,-3509.98C2475.32,-3501.69 2453.19,-3488.94 2436.31,-3479.22\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2437.96,-3476.13 2427.55,-3474.17 2434.47,-3482.19 2437.96,-3476.13\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523680240 -->\\n\",\n       \"<g id=\\\"node763\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523680240</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"2561,-3584 2454,-3584 2454,-3565 2561,-3565 2561,-3584\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"2507.5\\\" y=\\\"-3572\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523680240&#45;&gt;140041523680624 -->\\n\",\n       \"<g id=\\\"edge827\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523680240&#45;&gt;140041523680624</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M2507,-3564.75C2506.61,-3557.8 2506.05,-3547.85 2505.55,-3539.13\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"2509.05,-3538.88 2504.99,-3529.09 2502.06,-3539.27 2509.05,-3538.88\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523680720 -->\\n\",\n       \"<g id=\\\"node764\\\" class=\\\"node\\\">\\n\",\n    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class=\\\"edge\\\">\\n\",\n       \"<title>140041523685520&#45;&gt;140041523685424</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5263.65,-2272.37C5271.82,-2262.5 5285.39,-2246.11 5295.66,-2233.72\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5298.44,-2235.84 5302.13,-2225.91 5293.05,-2231.37 5298.44,-2235.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523685616 -->\\n\",\n       \"<g id=\\\"node957\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523685616</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5435,-2357.5 5334,-2357.5 5334,-2338.5 5435,-2338.5 5435,-2357.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5384.5\\\" y=\\\"-2345.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523685616&#45;&gt;140041523685520 -->\\n\",\n       \"<g id=\\\"edge1040\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523685616&#45;&gt;140041523685520</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5367.24,-2338.37C5345.58,-2327.54 5308.21,-2308.85 5282.93,-2296.22\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5284.41,-2293.04 5273.9,-2291.7 5281.28,-2299.3 5284.41,-2293.04\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523685664 -->\\n\",\n       \"<g id=\\\"node961\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523685664</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5435,-2291.5 5328,-2291.5 5328,-2272.5 5435,-2272.5 5435,-2291.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5381.5\\\" y=\\\"-2279.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523685616&#45;&gt;140041523685664 -->\\n\",\n       \"<g id=\\\"edge1046\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140041523685616&#45;&gt;140041523686192</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M5350.34,-2338.47C5341.93,-2336.53 5332.91,-2334.57 5324.5,-2333 5268.03,-2322.46 5111.43,-2335.18 5068.5,-2297 5051.26,-2281.67 5047.39,-2254.07 5046.9,-2235.64\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"5050.4,-2235.56 5046.94,-2225.55 5043.4,-2235.53 5050.4,-2235.56\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523686336 -->\\n\",\n       \"<g id=\\\"node983\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523686336</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"5185,-2291.5 5078,-2291.5 5078,-2272.5 5185,-2272.5 5185,-2291.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"5131.5\\\" y=\\\"-2279.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 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font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523687872&#45;&gt;140041523687776 -->\\n\",\n       \"<g id=\\\"edge1134\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523687872&#45;&gt;140041523687776</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3303.85,-1876.37C3320.16,-1865.82 3347.99,-1847.84 3367.49,-1835.23\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3369.55,-1838.07 3376.04,-1829.7 3365.75,-1832.19 3369.55,-1838.07\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523688400 -->\\n\",\n       \"<g id=\\\"node1070\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523688400</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3316,-1829.5 3209,-1829.5 3209,-1810.5 3316,-1810.5 3316,-1829.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3262.5\\\" y=\\\"-1817.5\\\" font-family=\\\"monospace\\\" 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font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523687872&#45;&gt;140041523688016 -->\\n\",\n       \"<g id=\\\"edge1177\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523687872&#45;&gt;140041523688016</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3326.23,-1876.44C3374.19,-1864.94 3459.41,-1844.51 3511.93,-1831.92\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3513.07,-1835.25 3521.98,-1829.51 3511.44,-1828.44 3513.07,-1835.25\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523689408 -->\\n\",\n       \"<g id=\\\"node1169\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523689408</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"6492,-1829.5 6385,-1829.5 6385,-1810.5 6492,-1810.5 6492,-1829.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"6438.5\\\" y=\\\"-1817.5\\\" font-family=\\\"monospace\\\" 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d=\\\"M3869.36,-1546.37C3890.42,-1535.59 3926.68,-1517.02 3951.36,-1504.38\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3953.2,-1507.37 3960.51,-1499.7 3950.01,-1501.14 3953.2,-1507.37\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523690176 -->\\n\",\n       \"<g id=\\\"node1087\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523690176</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3813,-1631.5 3706,-1631.5 3706,-1612.5 3813,-1612.5 3813,-1631.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3759.5\\\" y=\\\"-1619.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">SelectBackward0</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523690176&#45;&gt;140041523690080 -->\\n\",\n       \"<g id=\\\"edge1189\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523690176&#45;&gt;140041523690080</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3772.04,-1612.37C3787.3,-1601.87 3813.27,-1584 3831.57,-1571.4\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3833.61,-1574.25 3839.86,-1565.7 3829.64,-1568.48 3833.61,-1574.25\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523690272 -->\\n\",\n       \"<g id=\\\"node1088\\\" class=\\\"node\\\">\\n\",\n       \"<title>140041523690272</title>\\n\",\n       \"<polygon fill=\\\"lightgrey\\\" stroke=\\\"black\\\" points=\\\"3721,-1697.5 3620,-1697.5 3620,-1678.5 3721,-1678.5 3721,-1697.5\\\"/>\\n\",\n       \"<text text-anchor=\\\"middle\\\" x=\\\"3670.5\\\" y=\\\"-1685.5\\\" font-family=\\\"monospace\\\" font-size=\\\"10.00\\\">AccumulateGrad</text>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523690272&#45;&gt;140041523690176 -->\\n\",\n       \"<g id=\\\"edge1190\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523690272&#45;&gt;140041523690176</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" 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class=\\\"edge\\\">\\n\",\n       \"<title>140041542095776&#45;&gt;140041523690272</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3670.5,-1738.8C3670.5,-1729.7 3670.5,-1717.79 3670.5,-1707.9\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3674,-1707.84 3670.5,-1697.84 3667,-1707.84 3674,-1707.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523689888&#45;&gt;140041523689792 -->\\n\",\n       \"<g id=\\\"edge1192\\\" class=\\\"edge\\\">\\n\",\n       \"<title>140041523689888&#45;&gt;140041523689792</title>\\n\",\n       \"<path fill=\\\"none\\\" stroke=\\\"black\\\" d=\\\"M3877.65,-1480.37C3885.82,-1470.5 3899.39,-1454.11 3909.66,-1441.72\\\"/>\\n\",\n       \"<polygon fill=\\\"black\\\" stroke=\\\"black\\\" points=\\\"3912.44,-1443.84 3916.13,-1433.91 3907.05,-1439.37 3912.44,-1443.84\\\"/>\\n\",\n       \"</g>\\n\",\n       \"<!-- 140041523690224 -->\\n\",\n       \"<g id=\\\"node1091\\\" 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    "path": "Chapter05/transformer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Collecting torch==2.2\\n\",\n      \"  Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl.metadata (25 kB)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl (59.7 MB)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mInstalling collected packages: torch\\n\",\n      \"  Attempting uninstall: torch\\n\",\n      \"    Found existing installation: torch 1.12.1\\n\",\n      \"    Uninstalling torch-1.12.1:\\n\",\n      \"      Successfully uninstalled torch-1.12.1\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"torchdata 0.4.1 requires torch==1.12.1, but you have torch 2.2.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed torch-2.2.0\\n\",\n      \"Collecting torchtext==0.17\\n\",\n      \"  Using cached torchtext-0.17.0-cp39-cp39-macosx_11_0_arm64.whl.metadata (7.6 kB)\\n\",\n      \"Requirement already satisfied: tqdm in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (4.66.2)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (2.31.0)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (1.26.4)\\n\",\n      \"Collecting torchdata==0.7.1 (from torchtext==0.17)\\n\",\n      \"  Using cached torchdata-0.7.1-cp39-cp39-macosx_11_0_arm64.whl.metadata (13 kB)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (2024.2.0)\\n\",\n      \"Requirement already satisfied: urllib3>=1.25 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchdata==0.7.1->torchtext==0.17) (2.2.1)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (3.6)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2.0->torchtext==0.17) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2.0->torchtext==0.17) (1.3.0)\\n\",\n      \"Using cached torchtext-0.17.0-cp39-cp39-macosx_11_0_arm64.whl (2.1 MB)\\n\",\n      \"Using cached torchdata-0.7.1-cp39-cp39-macosx_11_0_arm64.whl (4.8 MB)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mInstalling collected packages: torchdata, torchtext\\n\",\n      \"  Attempting uninstall: torchdata\\n\",\n      \"    Found existing installation: torchdata 0.4.1\\n\",\n      \"    Uninstalling torchdata-0.4.1:\\n\",\n      \"      Successfully uninstalled torchdata-0.4.1\\n\",\n      \"  Attempting uninstall: torchtext\\n\",\n      \"    Found existing installation: torchtext 0.13.1\\n\",\n      \"    Uninstalling torchtext-0.13.1:\\n\",\n      \"      Successfully uninstalled torchtext-0.13.1\\n\",\n      \"Successfully installed torchdata-0.7.1 torchtext-0.17.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchtext==0.17\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"from torch import nn, Tensor\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.nn import TransformerEncoder, TransformerEncoderLayer\\n\",\n    \"from torch.utils.data import dataset\\n\",\n    \"\\n\",\n    \"from torchtext.datasets import PennTreebank\\n\",\n    \"from torchtext.data.utils import get_tokenizer\\n\",\n    \"from torchtext.vocab import build_vocab_from_iterator\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Transformer(nn.Module):\\n\",\n    \"    def __init__(self, num_token, num_inputs, num_heads, num_hidden, num_layers, dropout=0.3):\\n\",\n    \"        super(Transformer, self).__init__()\\n\",\n    \"        self.model_name = 'transformer'\\n\",\n    \"        self.position_enc = PosEnc(num_inputs, dropout)\\n\",\n    \"        layers_enc = TransformerEncoderLayer(num_inputs, num_heads, num_hidden, dropout)\\n\",\n    \"        self.enc_transformer = TransformerEncoder(layers_enc, num_layers)\\n\",\n    \"        self.enc = nn.Embedding(num_token, num_inputs)\\n\",\n    \"        self.num_inputs = num_inputs\\n\",\n    \"        self.dec = nn.Linear(num_inputs, num_token)\\n\",\n    \"        self.init_params()\\n\",\n    \"\\n\",\n    \"    def init_params(self):\\n\",\n    \"        initial_rng = 0.12\\n\",\n    \"        self.enc.weight.data.uniform_(-initial_rng, initial_rng)\\n\",\n    \"        self.dec.bias.data.zero_()\\n\",\n    \"        self.dec.weight.data.uniform_(-initial_rng, initial_rng)\\n\",\n    \"\\n\",\n    \"    def forward(self, source, mask_source):\\n\",\n    \"        source = self.enc(source) * math.sqrt(self.num_inputs)\\n\",\n    \"        source = self.position_enc(source)\\n\",\n    \"        op = self.enc_transformer(source, mask_source)\\n\",\n    \"        op = self.dec(op)\\n\",\n    \"        return op\\n\",\n    \"\\n\",\n    \"def gen_sqr_nxt_mask(size):\\n\",\n    \"    msk = torch.triu(torch.ones(size, size) * float('-inf'), diagonal=1)\\n\",\n    \"    return msk\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class PosEnc(nn.Module):\\n\",\n    \"    def __init__(self, d_m, dropout=0.2, size_limit=5000):\\n\",\n    \"        super(PosEnc, self).__init__()\\n\",\n    \"        self.dropout = nn.Dropout(dropout)\\n\",\n    \"        p_enc = torch.zeros(size_limit, 1, d_m)\\n\",\n    \"        pos = torch.arange(size_limit, dtype=torch.float).unsqueeze(1)\\n\",\n    \"        divider = torch.exp(torch.arange(0, d_m, 2).float() * (-math.log(10000.0) / d_m))\\n\",\n    \"        p_enc[:, 0, 0::2] = torch.sin(pos * divider)\\n\",\n    \"        p_enc[:, 0, 1::2] = torch.cos(pos * divider)\\n\",\n    \"        self.register_buffer('p_enc', p_enc)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.dropout(x + self.p_enc[:x.size(0)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tr_iter = PennTreebank(split='train')\\n\",\n    \"tkzer = get_tokenizer('basic_english')\\n\",\n    \"vocabulary = build_vocab_from_iterator(map(tkzer, tr_iter), specials=['<unk>'])\\n\",\n    \"vocabulary.set_default_index(vocabulary['<unk>'])\\n\",\n    \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"def process_data(raw_text):\\n\",\n    \"    numericalised_text = [torch.tensor(vocabulary(tkzer(text)), dtype=torch.long) for text in raw_text]\\n\",\n    \"    return torch.cat(tuple(filter(lambda t: t.numel() > 0, numericalised_text)))\\n\",\n    \"\\n\",\n    \"tr_iter, val_iter, te_iter = PennTreebank()\\n\",\n    \"training_text = process_data(tr_iter)\\n\",\n    \"validation_text = process_data(val_iter)\\n\",\n    \"testing_text = process_data(te_iter)\\n\",\n    \"\\n\",\n    \"def gen_batches(text_dataset, batch_size):\\n\",\n    \"    num_batches = text_dataset.size(0) // batch_size\\n\",\n    \"    text_dataset = text_dataset[:num_batches * batch_size]\\n\",\n    \"    text_dataset = text_dataset.view(batch_size, num_batches).t().contiguous()\\n\",\n    \"    return text_dataset.to(device)\\n\",\n    \"\\n\",\n    \"training_batch_size = 32\\n\",\n    \"evaluation_batch_size = 16\\n\",\n    \"\\n\",\n    \"training_data = gen_batches(training_text, training_batch_size)\\n\",\n    \"validation_data = gen_batches(validation_text, evaluation_batch_size)\\n\",\n    \"testing_data = gen_batches(testing_text, evaluation_batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_seq_len = 64\\n\",\n    \"def return_batch(src, k):\\n\",\n    \"    sequence_length = min(max_seq_len, len(src) - 1 - k)\\n\",\n    \"    sequence_data = src[k:k+sequence_length]\\n\",\n    \"    sequence_label = src[k+1:k+1+sequence_length].reshape(-1)\\n\",\n    \"    return sequence_data, sequence_label\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\\n\",\n      \"  warnings.warn(f\\\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_tokens = len(vocabulary) # vocabulary size\\n\",\n    \"embedding_size = 256 # dimension of embedding layer\\n\",\n    \"num_hidden_params = 256 # transformer encoder's hidden (feed forward) layer dimension\\n\",\n    \"num_layers = 2 # num of transformer encoder layers within transformer encoder\\n\",\n    \"num_heads = 2 # num of heads in (multi head) attention models\\n\",\n    \"dropout = 0.25 # value (fraction) of dropout\\n\",\n    \"loss_func = nn.CrossEntropyLoss()\\n\",\n    \"lrate = 4.0 # learning rate\\n\",\n    \"transformer_model = Transformer(num_tokens, embedding_size, num_heads, num_hidden_params, num_layers, \\n\",\n    \"                                     dropout).to(device)\\n\",\n    \"optim_module = torch.optim.SGD(transformer_model.parameters(), lr=lrate)\\n\",\n    \"sched_module = torch.optim.lr_scheduler.StepLR(optim_module, 1.0, gamma=0.88)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train_model():\\n\",\n    \"    transformer_model.train()\\n\",\n    \"    loss_total = 0.\\n\",\n    \"    time_start = time.time()\\n\",\n    \"    mask_source = gen_sqr_nxt_mask(max_seq_len).to(device)\\n\",\n    \"    num_batches = len(training_data) // max_seq_len\\n\",\n    \"    for b, i in enumerate(range(0, training_data.size(0) - 1, max_seq_len)):\\n\",\n    \"        train_data_batch, train_label_batch = return_batch(training_data, i)\\n\",\n    \"        sequence_length = train_data_batch.size(0)\\n\",\n    \"        if sequence_length != max_seq_len:  # only on last batch\\n\",\n    \"            mask_source = mask_source[:sequence_length, :sequence_length]\\n\",\n    \"        op = transformer_model(train_data_batch, mask_source)\\n\",\n    \"        loss_curr = loss_func(op.view(-1, num_tokens), train_label_batch)\\n\",\n    \"        optim_module.zero_grad()\\n\",\n    \"        loss_curr.backward()\\n\",\n    \"        torch.nn.utils.clip_grad_norm_(transformer_model.parameters(), 0.6)\\n\",\n    \"        optim_module.step()\\n\",\n    \"\\n\",\n    \"        loss_total += loss_curr.item()\\n\",\n    \"        interval = 100\\n\",\n    \"        if b % interval == 0 and b > 0:\\n\",\n    \"            loss_interval = loss_total / interval\\n\",\n    \"            time_delta = time.time() - time_start\\n\",\n    \"            print(f\\\"epoch {ep}, {b}/{len(training_data)//max_seq_len} batches, training loss {loss_interval:.2f}, training perplexity {math.exp(loss_interval):.2f}\\\")\\n\",\n    \"            loss_total = 0\\n\",\n    \"            time_start = time.time()\\n\",\n    \"\\n\",\n    \"def eval_model(eval_model_obj, eval_data_source):\\n\",\n    \"    eval_model_obj.eval() \\n\",\n    \"    loss_total = 0.\\n\",\n    \"    mask_source = gen_sqr_nxt_mask(max_seq_len).to(device)\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for j in range(0, eval_data_source.size(0) - 1, max_seq_len):\\n\",\n    \"            eval_data, eval_label = return_batch(eval_data_source, j)\\n\",\n    \"            sequence_length = eval_data.size(0)\\n\",\n    \"            if sequence_length != max_seq_len:\\n\",\n    \"                mask_source = mask_source[:sequence_length, :sequence_length]\\n\",\n    \"            op = eval_model_obj(eval_data, mask_source)\\n\",\n    \"            op_flat = op.view(-1, num_tokens)\\n\",\n    \"            loss_total += sequence_length * loss_func(op_flat, eval_label).item()\\n\",\n    \"    return loss_total / (len(eval_data_source) - 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch 1, 100/451 batches, training loss 7.86, training perplexity 2588.27\\n\",\n      \"epoch 1, 200/451 batches, training loss 6.44, training perplexity 628.79\\n\",\n      \"epoch 1, 300/451 batches, training loss 6.03, training perplexity 416.68\\n\",\n      \"epoch 1, 400/451 batches, training loss 5.83, training perplexity 340.21\\n\",\n      \"\\n\",\n      \"epoch 1, validation loss 5.65, validation perplexity 282.91\\n\",\n      \"\\n\",\n      \"epoch 2, 100/451 batches, training loss 5.68, training perplexity 291.70\\n\",\n      \"epoch 2, 200/451 batches, training loss 5.58, training perplexity 265.24\\n\",\n      \"epoch 2, 300/451 batches, training loss 5.45, training perplexity 232.82\\n\",\n      \"epoch 2, 400/451 batches, training loss 5.37, training perplexity 215.16\\n\",\n      \"\\n\",\n      \"epoch 2, validation loss 5.40, validation perplexity 220.48\\n\",\n      \"\\n\",\n      \"epoch 3, 100/451 batches, training loss 5.37, training perplexity 213.94\\n\",\n      \"epoch 3, 200/451 batches, training loss 5.31, training perplexity 203.13\\n\",\n      \"epoch 3, 300/451 batches, training loss 5.20, training perplexity 181.45\\n\",\n      \"epoch 3, 400/451 batches, training loss 5.14, training perplexity 170.07\\n\",\n      \"\\n\",\n      \"epoch 3, validation loss 5.27, validation perplexity 193.97\\n\",\n      \"\\n\",\n      \"epoch 4, 100/451 batches, training loss 5.18, training perplexity 177.30\\n\",\n      \"epoch 4, 200/451 batches, training loss 5.14, training perplexity 170.42\\n\",\n      \"epoch 4, 300/451 batches, training loss 5.03, training perplexity 153.42\\n\",\n      \"epoch 4, 400/451 batches, training loss 4.97, training perplexity 144.43\\n\",\n      \"\\n\",\n      \"epoch 4, validation loss 5.16, validation perplexity 174.62\\n\",\n      \"\\n\",\n      \"epoch 5, 100/451 batches, training loss 5.04, training perplexity 154.86\\n\",\n      \"epoch 5, 200/451 batches, training loss 5.01, training perplexity 150.22\\n\",\n      \"epoch 5, 300/451 batches, training loss 4.91, training perplexity 135.80\\n\",\n      \"epoch 5, 400/451 batches, training loss 4.85, training perplexity 127.64\\n\",\n      \"\\n\",\n      \"epoch 5, validation loss 5.10, validation perplexity 164.12\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"min_validation_loss = float(\\\"inf\\\")\\n\",\n    \"eps = 5\\n\",\n    \"best_model_so_far = None\\n\",\n    \"\\n\",\n    \"for ep in range(1, eps + 1):\\n\",\n    \"    ep_time_start = time.time()\\n\",\n    \"    train_model()\\n\",\n    \"    validation_loss = eval_model(transformer_model, validation_data)\\n\",\n    \"    print()\\n\",\n    \"    print(f\\\"epoch {ep:}, validation loss {validation_loss:.2f}, validation perplexity {math.exp(validation_loss):.2f}\\\")\\n\",\n    \"    print()\\n\",\n    \"\\n\",\n    \"    if validation_loss < min_validation_loss:\\n\",\n    \"        min_validation_loss = validation_loss\\n\",\n    \"        best_model_so_far = transformer_model\\n\",\n    \"\\n\",\n    \"    sched_module.step()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"testing loss 5.03, testing perplexity 152.75\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"testing_loss = eval_model(best_model_so_far, testing_data)\\n\",\n    \"print(f\\\"testing loss {testing_loss:.2f}, testing perplexity {math.exp(testing_loss):.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter06/GNN.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"b61d29b6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torch_geometric==2.4.0 in /opt/conda/lib/python3.10/site-packages (2.4.0)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (1.11.4)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: pyparsing in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (1.3.2)\\n\",\n      \"Requirement already satisfied: psutil>=5.8.0 in /opt/conda/lib/python3.10/site-packages (from torch_geometric==2.4.0) (5.9.3)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch_geometric==2.4.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric==2.4.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric==2.4.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric==2.4.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torch_geometric==2.4.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->torch_geometric==2.4.0) (1.3.2)\\n\",\n      \"Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->torch_geometric==2.4.0) (3.2.0)\\n\",\n      \"Requirement already satisfied: seaborn==0.12.2 in /opt/conda/lib/python3.10/site-packages (0.12.2)\\n\",\n      \"Requirement already satisfied: numpy!=1.24.0,>=1.17 in /opt/conda/lib/python3.10/site-packages (from seaborn==0.12.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: pandas>=0.25 in /opt/conda/lib/python3.10/site-packages (from seaborn==0.12.2) (1.4.3)\\n\",\n      \"Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /opt/conda/lib/python3.10/site-packages (from seaborn==0.12.2) (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=0.25->seaborn==0.12.2) (2022.1)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn==0.12.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: networkx==2.8.5 in /opt/conda/lib/python3.10/site-packages (2.8.5)\\n\",\n      \"Requirement already satisfied: scikit-learn==1.3.2 in /opt/conda/lib/python3.10/site-packages (1.3.2)\\n\",\n      \"Requirement already satisfied: numpy<2.0,>=1.17.3 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.5.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.11.4)\\n\",\n      \"Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.3.2)\\n\",\n      \"Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (3.2.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: pandas==1.4.3 in /opt/conda/lib/python3.10/site-packages (1.4.3)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (2022.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.21.0 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas==1.4.3) (1.16.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torch_geometric==2.4.0\\n\",\n    \"!pip install seaborn==0.12.2\\n\",\n    \"!pip install networkx==2.8.5\\n\",\n    \"!pip install scikit-learn==1.3.2\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install pandas==1.4.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0ffc92c7\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Full screen\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"17058114\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>.container { width:100% !important; }</style>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# setting up the notebook width to 100% of the screen\\n\",\n    \"from IPython.display import display, HTML\\n\",\n    \"display(HTML(\\\"<style>.container { width:100% !important; }</style>\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3bc9ade9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Install and import libs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"577c5743\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# !pip install decorator==5.0.9\\n\",\n    \"# !pip install torch-geometric==2.3.1\\n\",\n    \"\\n\",\n    \"%matplotlib inline\\n\",\n    \"import os\\n\",\n    \"import torch\\n\",\n    \"import pandas as pd\\n\",\n    \"import seaborn as sns\\n\",\n    \"import networkx as nx\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.manifold import TSNE\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"from torch.nn import Linear\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch_geometric.nn import GCNConv\\n\",\n    \"from torch_geometric.nn import GATConv\\n\",\n    \"from torch_geometric.utils import to_networkx\\n\",\n    \"from torch_geometric.datasets import Planetoid\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18a0b0e4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Define helper functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"88a8a802\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def visualize(data, labels):\\n\",\n    \"    tsne = TSNE(n_components=2, init='pca', random_state=7)\\n\",\n    \"    tsne_res = tsne.fit_transform(data)\\n\",\n    \"    v = pd.DataFrame(data,columns=[str(i) for i in range(data.shape[1])])\\n\",\n    \"    v['color'] = labels\\n\",\n    \"    v['label'] = v['color'].apply(lambda i: str(i))\\n\",\n    \"    v[\\\"dim1\\\"] = tsne_res[:,0]\\n\",\n    \"    v[\\\"dim2\\\"] = tsne_res[:,1]\\n\",\n    \"    \\n\",\n    \"    plt.figure(figsize=(12,12))\\n\",\n    \"\\n\",\n    \"    sns.scatterplot(\\n\",\n    \"        x=\\\"dim1\\\", y=\\\"dim2\\\",\\n\",\n    \"        hue=\\\"color\\\",\\n\",\n    \"        palette=sns.color_palette([\\\"#52D1DC\\\", \\\"#8D0004\\\", \\\"#845218\\\",\\\"#563EAA\\\", \\\"#E44658\\\", \\\"#63C100\\\", \\\"#FF7800\\\"]),\\n\",\n    \"        legend=False,\\n\",\n    \"        data=v,\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"906042c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def visualize_graph(G, color):\\n\",\n    \"    plt.figure(figsize=(75,75))\\n\",\n    \"    plt.xticks([])\\n\",\n    \"    plt.yticks([])\\n\",\n    \"    nx.draw_networkx(G, pos=nx.spring_layout(G), with_labels=False,\\n\",\n    \"                     node_color=color, cmap=\\\"Set2\\\")\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"74887d70\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load graph dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"1e094b27\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.x\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.tx\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.allx\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.y\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ty\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ally\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.graph\\n\",\n      \"Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.test.index\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset: CiteSeer():\\n\",\n      \"======================\\n\",\n      \"Number of graphs: 1\\n\",\n      \"Number of features: 3703\\n\",\n      \"Number of classes: 6\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Processing...\\n\",\n      \"Done!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset = Planetoid(root='data/Planetoid', name='CiteSeer')\\n\",\n    \"print(f'Dataset: {dataset}:')\\n\",\n    \"print('======================')\\n\",\n    \"print(f'Number of graphs: {len(dataset)}')\\n\",\n    \"print(f'Number of features: {dataset.num_features}')\\n\",\n    \"print(f'Number of classes: {dataset.num_classes}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"7ea8c87d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data = dataset[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4704d571\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Describe selected graph data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"7feec64a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Data(x=[3327, 3703], edge_index=[2, 9104], y=[3327], train_mask=[3327], val_mask=[3327], test_mask=[3327])\\n\",\n      \"==============================================================\\n\",\n      \"Number of nodes: 3327\\n\",\n      \"Number of edges: 9104\\n\",\n      \"Average node degree: 2.74\\n\",\n      \"Number of training nodes: 120\\n\",\n      \"Training node label rate: 0.04\\n\",\n      \"Has isolated nodes: True\\n\",\n      \"Has self-loops: False\\n\",\n      \"Is undirected: True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data = dataset[0]  # Get the first graph object.\\n\",\n    \"\\n\",\n    \"print(data)\\n\",\n    \"print('==============================================================')\\n\",\n    \"\\n\",\n    \"# Gather some statistics about the graph.\\n\",\n    \"print(f'Number of nodes: {data.num_nodes}')\\n\",\n    \"print(f'Number of edges: {data.num_edges}')\\n\",\n    \"print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\\n\",\n    \"print(f'Number of training nodes: {data.train_mask.sum()}')\\n\",\n    \"print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\\n\",\n    \"print(f'Has isolated nodes: {data.has_isolated_nodes()}')\\n\",\n    \"print(f'Has self-loops: {data.has_self_loops()}')\\n\",\n    \"print(f'Is undirected: {data.is_undirected()}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e793dbda\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualize graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"89cb2687\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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AAAAAAAAAAAAAAAG8gkA0AAAAAAAAAAAAAAAAAAAAAAAAAAABASiAbAAAAAAAAAAAAAAAAAAAAAAAAAAAAgJRANgAAAAAAAAAAAAAAAAAAAAAAAAAAAAApgWwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAUgLZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKQEsgEAAAAAAAAAAAAAAAAAAAAAAAAAAABIfQNQIcZ3QCe++wAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 7500x7500 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"G = to_networkx(data)\\n\",\n    \"visualize_graph(G, color=data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"49814ae4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Graph model 1: Classic MLP\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dc9d5cd7\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Define and instantiate MLP model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"25b849f1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MLP(\\n\",\n      \"  (lin1): Linear(in_features=3703, out_features=16, bias=True)\\n\",\n      \"  (lin2): Linear(in_features=16, out_features=6, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class MLP(torch.nn.Module):\\n\",\n    \"    def __init__(self, hidden_channels):\\n\",\n    \"        super().__init__()\\n\",\n    \"        torch.manual_seed(12345)\\n\",\n    \"        self.lin1 = Linear(dataset.num_features, hidden_channels)\\n\",\n    \"        self.lin2 = Linear(hidden_channels, dataset.num_classes)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.lin1(x)\\n\",\n    \"        x = x.relu()\\n\",\n    \"        x = F.dropout(x, p=0.5, training=self.training)\\n\",\n    \"        x = self.lin2(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"model = MLP(hidden_channels=16)\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"055058bf\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize initial MLP embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"7a5b2791\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4HB3gc3Nm5gAuzbPzy3DX06DSM2McTm1wz8sPvROcubPEY4nV1LK/2kdgyev/LglCEwGal7YQQQgghxO5PZuCFEKINtLOYeLRbEdWrAxSvDTS0q0mNdx6ehd1ppPfAbM67chAZDiuBaj99RxwHP89Ke83gh19gHtgXxb31Jel1FZUEQhGae06bTBPcpxNNhAgkKkhqCayKE5cpd9suIIQQQgghdojMwAshRBtQdDryzEbUcNOScQChQJzpvxZTWx0FwJmRy16jTkfzBdNeU/UHIJH6ek2uX1xMey39DHuuyYBNadmvAC2pUle+gcoNc5m+6g0enD2Ch+cexMKaSUQS6ccrhBBCCCFalwTwQgjRhjIy02deVww67M5N2eDd7Tph3ntg2uPNg/qic9hadF/FbKbsk08YaW66x10HnJ/jJcO49UVYyaoaAh9+Ru3ld8PFTzP4NbjR+TH5hm48Pm8M60KzWzQeIYQQQgix42QJvRBCtCGXx8xBR3Sk05AcMju7iOs0zHGNGRPXYkhqeLybAnyD0Yxt/2GEPvwS1edvfCGTEceJR6IFw2hGIzp9889fbTk5rHjpFUZ060bXQYOZoBqoSSTobDZyokGlIwm0eAItHkdnNqFTmgb6yRof1Q89Q2z2goa2+JzFcN1STrr/36w2z+b9FVdxVb+JOI1ZO/ZGCSGEEEKIrdJpmraNuyD/3vx+P263G5/Ph8vl2tXDEUL8DawJRXh4XTklsThQv/RptNvBidlesmxNS7nF15fge/kdolNngaph6tsT55knEnjvc5LFpdiPPRTbQSNQMr1p76lpGmUzZvLRqEPwdO1K16uuxJKTQ2DBAgYcdyw2xUhw/Dck1hVj6tUV26EHYcjLQbdZVvroomVUXnV7yusbu3Vm/mUe3i2/ngf3XkOmpf2OvUlCCCGEEP9gLY1DJYDfggTwQojWVBmLc/3yDfgSTfejn5GXwbHZnpS13dVwGDUQRIvEqPv1D0JfTkKt8TX0m/r1JOOWK1G8nrT3VpNJAmvXsfb77ymb+Sd5QwfTccwhKKs2UH3vk7D5x7/RSNZDt2Lu3b2hyf/OJwTe/Cjt9SNPn8WTFadz5+B5eM3ttvJOiOYkYlHi0TBGiw2D0byrhyOEEEKInaylcagsoRdCiDa0JhJLGbwDfF5Ry0iPkyxT049ivc0GGlQ/9RTRGXOa9MfmLSaxvqTZAF6vKLg7daTfBefT74L6tkRZBeWPPNc4eAeIx6l56BmyH/1Pw8y+3mFP/8KMBlSdysEFV+Iy5qU/TjQrFglTW7aGqV+8QMXaxeR06MXeR12IN68jRrN1Vw9PCCGEELsZCeCFEKINrYvE0vYFkyoxTU3br4bqiM6cm7a/bvI0zP16bdN4kuVVaJFo6r6SclR/oCGANw/uDzpd02AfMIzci1X6hRyQfyGKXn6VbA81mWD1vMl8/NC5aGr9z0HJijnM/elDxt7wOl0GjUKfIjeBEEIIIf65JAu9EEK0oQ6WpnvcN3Iqekwpls830IHOZEzbrbe3LCN9UksSiFUQitdAMn1pOajfO7+RkuHFc9X/NTlGKcjFdeZJDGt3Nh5zQYvGIJoK1JTxxdNXNgTvG2mqyhdPX0WgpnQXjUwIIYQQuyuZNhFCiDZUZDHhMSgk/gqMg8lNwdrxOV68zZRy02xmrKP3J/zV9yn7rfvvs9X7V0ZW8VvpG/xZ9QlmxcFlWW+C0QDxpvXk9RkeFJdz09c2C9aRwzD16kbdz1NIVFZjHTYYY7dOGLIzkQXeOybsqyQSrE3ZVxeoJuyrwp1VuHMHJYQQQojdmgTwQgjRhtyJOA+aVdYvXERCVbF2684Pigm308mBHkfKBHYAyXiMZXN+wDu8J8qfC0gWN56NdZ41FiU7s9l7V9St4L7Z+xKIVzS0faC7lePPPYnIi/9rfLBOh+eq/0O/RWZ7vc2KvkM7jGeN3YZX3XJqMkmgpox4NAyaiqapmCwOHN4cFEP61Qt/C1tNISs5ZoUQQgjRmATwQgjRSuJqhEgiiElvxWywE/H5WPrBR/xw2RWo8b9KyBkMjHjoAbJPOw1dM8vnAzVlfPn0VRjNNo4/72HsNQn0c5aj2S3oRgzA2LlLs0nmYsk6FpV9x3mO5yGsgk3PDG08f/jeIbdXF0Y9eAPh974kWVKGsUtHnGecgKFdfrNjam3B2nLmfP8+U8c/T12wBld2O/Y5+iLidSFc2YV0H3ooZptz6xfaQzm9eZx308fYFRsoCpW+9Ux47y5qy9Zgsbuxu7N29RCFEEIIsZuRMnJbkDJyQohtodZFSNbUEq+pJqrUUWpax8zkt4zIPQ/LYpX39hmR8rzR33/Hhv6DOCTDhUHfNGheu/AP3rrt+IavM/I7k9+5P7FomNXzfuXs+78kt0PvtOOq3bCaqQ88wOKX3iYZjaKYTHQ/91TyrjqU5yvGMbrgSk7MuxMtGkNnNaO37twF8ZGQj29fvY15PzUtU3fAqdezfskM9j/5agq7D96p49pZ1GCI8ORp+F98Gy0UBkApyMN1zYV8/undDBx9Oj32Pgy9Is/ZhRBCiH8CKSMnhBBtLFnrI/jRlwQ/ndCQHM6bm80hN53BlOLPUB5ZnvbcZY8+SvjJp6lx2chOkahuy8Rm1SUrqS5ZualBTf/sNRYI8Metd7Pi08+xeL2Ey8tJxmIseuFN4r4Ah9x0OXVqsD4JXgsT4bW2kK8yZfAO8Mf45zjikkeY/NETHHf1c5itjp08urYXXbwc3+MvNmpLFpdSe+tDnPDUQ+iyvVsN3kNlZfhXrWbdTz9jzcqk6MADsRfkY7Ttmu+pEEIIIdqeBPBCCLEdNFWl7pc/CH78ZaP2ZFkFutteY9hD/+L7VZemPT+4ei3OeJxQUiU7Rb87px2K0Uwy3rTkm8Obi9WVkfbaddU+2p10Nq7jLqUunCAvz0T5pC+Z/eA9LP/gM4648VOMuenP3xlqSlen7YuGA+jQUblhGfFI+G8XwCf9AQJvfJiyT6uLEJ48HfcpxzR7jWBxMV+dNo4Nv/za0KbT6zni3bfofNSRGO3pt1cIIYQQYs8lZeSEEGI7JKtrCbz7aco+LRDCUFNH1rABac/PHDqEDQYTphTL5wHs7hwOOefOJu06vZ4jL3kUZ0ZeyvPisSSrS+CRp6t48rEVvPj8Gu76zzLmKvtz4Ovvgaah90GOpWsLXmXbMdua36KkGIxkF3XHaPn7zSardRESazak71+xmpjPl7Y/mUgw59kXGgXvUP9Q6avTxhHcUNxqYxVCCCHE7kUCeCGE2B7xBGpNM0HW3CV0v/A0FLO5SZ/eYKDbVVcSMBhwK0rK841mC332P46z7vmczgMPwpvfiV7Dj+H8h76lQ9/haZPNVVfW8dCtU6kLNy4T9/vP5awKF1Kw//54vEU4Tanm/Xced1YhNlfqLPqF3QdTtnoBI076999u9h0ARUHJz0nfn5tJc+lpwqVlzH76mdSdmsaK8V/s4ACFEEIIsbuSAF4IIbaDzmRoUnJtc1qHTL7nRU78YSLe7t0b2t2dO3PwxK/5wenlgsJs7IbUATyAxe6mqNfenHDNi5x1z+ccdenj5Hbqg9GcPuHc7KmlJOJqyr6JE0rpc/XNOHJTz97vTM6MPE6+6c0mM+zOjHxGnnod2e17ktWu2y4aHaiJBOpfeQ1amzErA/vJR6fpNGAePhSLt5mfLTVJtJkZ+uCG9LP7QgghhNizyR54IYTYDvoML65xJ1L75MtN+9xOgh1MdLcdSE7vIYz9+XtClVXENY2oy4WWlcMpJgMZxpZ9BJttDsy2ls1EF68LpO3zVUfJHrQftpxdu/8d6rcC5HcZwIWP/8T6JTOoWr+MvC79yCzoisnmxO7OQtkFGdiDxcWUzZjJgtfeQLFY6H/xRWT27okte9tWLPhroyQTEbRkBKfH1eShi7F3d6wnHUndpxMbEiDqnQ6sF49Dl+lBp0//fN1os5E7ZAhlM2ak7O8wZsw2jVUIIYQQew4J4IUQYjvodDosI4birK4h8P54+KvOu6GoAOMN40jmKgy1DMekWDDl5eHI2zmz3r36ZzPxf6mz37fr6MLq2n32lOsVBU9OEZ6col09FACCG4oZf/yJlE7fFBgvef8Dep5+Kgc+/ii2nGaWvf8lFIxRvqGKWHA9c79/iZqSZWQX9WTYcZeRWdAJ018rDsz5uahHj8E8chix9cXojAZ0HjdKVgb2/Pxm72HNyuLAxx/hg5EHwRZL7b09epDdv992vHohhBBC7AmkDvwWpA68EGJbqNEYak0tqi8AJgOaywIeF2Zl59ZV36iyPMytF39PTVWkSd+ND+7HoGHNB4f/VJqmMevJ//Lzv69J2T/2h+8oOujAZq+hJjXmzSwmUjWFSa9c0Ti41uk46bpX6TbkEPRb5D2I+v1oqorF42nxeGOhEGXTZ/Dj5VdROX8+islEz9NPY987b8fVvn2LryOEEEKI3UNL41AJ4LcgAbwQYk8WrClnw9pa3n15HYvnVgPg9po5+dwuDB5egDc7/d7qf7JQWRkf7H8QtcuWpezvesJxHPHu2xhSJCXcqKo8zNplK/j++ROJhptuZbA6Mzj/kW9xZxW22rjD5eXE/AH0RgPWnByM1l3z4EgIIYQQO6alcagsoRdCiL8JTdNY/MdX/Prho4w85CJOPG04ySSo8WrmTbqZDu0ux5s9eofuEfJVEgn50On0WB0erM6/xwMBLZkkGWm6amGjRCiMtpWkdsFADIMukDJ4B6gLVBP2VbVqAG/LyWnR0n4hhBBC/D1IAC+EEH8TYX8Vs755g7C/ij/+d1+T/hlfv0rHviOazWKfTiIWpXTVPL5+/joq1i4GoKD7YI68+GGyi3qg0+sJJ3xEkgH06HGZ8tDr9pxCJ5bMTLqdeAKznngyZX/vc87CaGs+f4DBsPXXm678nxBCCCFES+w5f10JIcQ/RG11hIrSENUVdahqy3c5aZpGMhFP259MRFHV7SuNVlO2hrduP6EheAcoXjqTN245htqaDawLzubFRadw87Su3P3nEL5Z/wi10ZLtuteuYDCbGXTFpVizspr0ZfTuTeF++231Gk6PGZcnl/2Pu5qeex+J3mBs1G9zZWJzZbbamIUQQgjxzyMz8EIIsZsIBWIsnlfJO8/PZcOaAC6PmWNP78F+h3TAk2HZ6vk2ZwZ99j+eXz98NGX/gFGnYba2rBzd5uLROn7/5CnUFA8HzFYH5ckVPPbnISS1BAC+WAn/W3UDC2q+4cKe7+Iy5W7zPXcFd6dOnD71d2Y+/iRLP/wIxWym3/+dT59zz8bZrvGydzUaI1lRReTXP4gXl2Ee2Bdj5yKW/fcxSqdOJ3vwAE697DV+++ZZ1iz6HZ1ez5GXPobDu2e8F0IIIYTYPUkSuy1IEjshxK6gqhqTJ63lmXunNekbOaY9Z18xCIfTtNXr1Jav581bjiFQ3Xj2O6uoO6fd9h6uzIJtHluwpqx+pr1sbZO+EWf9m9/bf8rKwB8pz71p4BS6uIZt8z13pUQ0SqSyCvS6+v3liSRqTS3xlWvQYnGMPbqQWLWW6rufAFVtOE+f4cH4rzP58LjjiVRXo5hMHPn5B6ypmE3f/U8gI7/Tdm1fEE3FYxECVcUsmTaRmtLVdOq3P3ndBqA5dagkcRgysRicu3qYQgghRItJEjshhNiD1FTW8dYzc1L2/fLtWo47s1eLAnhPTjvOuvdzZk96h/m//A+9YmDQIePos9/x2xW8AyhGM3ZPTsoAPrNrD1ZWpQ7eAeZWfklmRTaRmhps2dnYCwpQjMa0x+8ODGYzjsL690qtixCZMoOaR5+HRP32g4z/XEP1ff9tFLwDqNW1qOMnMejii5hy3/0kYzF+vepGTvz+m/rXLfvfW0UiFmX1vMl89MA5aH9tCfnz27dwZuRz6M0P8WzJqXRz7cdJnR7EEXKjVwzYMptujRBCCCH2RBLACyHEbiAUjKOqGkec2ZP2vTMI+6NM/mQlK5fUALB+lZ/C9i1bFeTJKWL/U65l8OHnoQPs7ix0+u1PeWJ1eBhx4pV8eN+ZTfq0ZBKDzkRCi6U81xBWqF6zhM+OOpauJ53A0DtvRDPqMJgt2FwZODytm0Fd0zSSldUkS8pJ1vowFhWgz/CguFO/d+F4Lf54GUt9vwDQ3T0SlzEPm9ENQLKsgpqHnm2o6a7P8JCsqoF46lwD8bmL6HT1BUy5734AapYsoaSyih9UI4dnuckxGbBtUQdebJtgTRmfPPJ/DcH7RoHqEqa/+TwjTz6PCRUPs9w/mSu8HzPzsvsYeOWl5A4ejKuw3S4atRBCCNE6JIAXQojdgNlp4OKnR/J1PMy3kSieDIVRNw5gzIYIL94xFbtj22atFcWA09t6wXFht0EMO+YS/hj/XEObTq/HojoZlnMGk8teS3leN20fQqVlDH/oHpKFRt6+dyyxSAiAzMKuHH/18+R06L3N2dkT8SjxaB1Gsw2DsX5lgqaqxFespuqWB1B9m0q5mfbqR8a1F6NkZjS6RjBexbfrH+PrdY0z9h/Z/hYOKfw3dsVL6OsfGoJ3AJ3JhBZOX24OaDIzH0mqTKoJMKkmwMWF2ezvsWORIH67la9dRCKW+nuwZu5vHHPGWUzgYYKJKmbHJ6DpNL449iR6nXUGI+6/F1dB65XxE0IIIXY2CeCFEGI3UGnR8VBJNRtDv9JYgndjfobmWTj58v7kttv25HOtyebKZPhJVzJw9OlsWDYLvWKgoNteONzZeBKdWOr7hfLIikbnnJhxNyuf/RiHMxfPwQP44oV/N+qv2rCct247gQsenYQnp6jJPdVgmGR1DdHZ80HVMA/sQyLTiU8toya4FjWZxBQ3k2nriDOzAF1tgMqb7kMLhBpdJzZrHv73PsN94Tj0pk3bENaH5jYJ3gG+WnsvvTyj6W4fQaKkrFFfsqIKY4f0AaCSm0WgfNM53u7dKbc54K8FCi9uqKCfw0reHhDAB2vKqVi3hAW/foLRbKPfgWPx5LTH5srY+sltKBLyNduvbfYAZXHyF4YNH8H6739i0ZvvMPDSf0kAL4QQYo8mAbwQQuxitfEELxVXoabom14X4cRR7fDat56Fvq1Z7W6sdjeZhV0btdtLHIyre5CqjDIW6X/FqWYyUH8Ya1/+kgXPvcWx345n0pcPpLxmNOxnzfzf8Iw6tVF7MhAk+OkEgu980tBmOv9YVu2n8s7qywklqgHwmAo4Le9x8jd0x1tJk+B9o/DEn3COPRp9bjYAkUSACeseTPtaJ657iI69P8Q8qC/RaX9uNrAk0UXLsOy3N5HJTRMOGk86kj9uvxUAvdHI0Bee4wWdCahfcq8CK+oi5Jl37zwAgepSPn38EtYt3JTfYMaEVxly+Hnsf/LVu7QcXn7n/mn7XNntCOirGr526DOJ1wQbvl726afk7713m45PCCGEaEtSB14IIXaxOlVlXTT1HnKAZfE4BsPu+3Ftyc6i9vt5LD3uSfJuS2C6ZgXf7nUKC554BQBnx/ZUrl+a9vz1S6Y3aUus2dAoeNc57fhHtePF5Wc0BO8AtbFiXlw3jrDJT6K0PO09DDmZxNQYkZAfgLgaxRdLX6feFyshoUaxDh+CzmFv1Bf8YDyWfQbhvvgslOxM0Osx9uyK974bWbt8MVoySY8LzufwGdP4JL8DxdHG++Xj6u5d/EXTNJb88XWj4H2jGRNepap45S4Y1SZ2by69Rhybsm/fMy/ne/8zDV8PV05j5QfjG76WwjtCCCH2dDIDL4QQu5geHTogXWhh0+/e2csVg4H+F13I6m+/Y/mnnzXqG3b7rQTWrMWT2z5lFnuA3I59G32tRqMEP/mq8T3G7M1XJalnzJNanD9873Ni32ub9OnMJkyXn0FpvJxvn7scNZlkwKhT6DTwQAZkHM360NyU1+zlORiL4kLJNZD92B3UPP4S8UXL6seSl42SnYnxgO5YRw5DU1V0ZhOKy0mfHl3oeMbpjK+L81YgSjyaaHLtbrZdv5qiOSFfJdO/fiVt/8wJr1HYdRB6w675E8Lm9DLmvLso7LYXf3z2DMHacvI692fIaRfyp/Er1tbUr5g4yH0R0e9XU1dR0XBu9xNO2CVjFkIIIVqLBPBCCLGLOQ16BjttzAiEm/TpgZ723b92uKOwgGM+/R81S5aw4vPxWDIy6HTkESx66x1+v/Y2htx6PpPeu6vJeQaTlS57jWrcGK+vu745tX0GxXXz096/OL6AWK4BQ8ciEqvXNbQbLz2dCT89y9rFUxva1i+eRla77px06+tM0j9BVG287N6st3NgwcUY9PXL3I0disi66zqS/iAkk+idDpQMz18Hmxuda3I4MDkcDA3W8aW/uMk4j8x04THs3vvfNTXZkGgwlUjYh6ol0e/CPyEcnhz2PvICeo84Bk1V0QwaIaOP7KrFnOC8j87hgRR/9CNT//OfhnN6jjsNR5HsfxdCCLFnkwBeCCF2MZuicE5BJitWRKlJNC6NdUm7bDy78fL5zTny83Dk51F04AFAfTIxW042vcadjuKxE47U8Mf451CT9bPSdncWJ1z7Cg5PfqPr6KwWzEMGElu4rKFNX+Ijp6AblZHVKe+da+yG3mAh867rqH32daJT/0TJzaZSq2kUvG9UuX4py6Z8w+0Hz+K1Zeey3P87AF1dwzmj67NkWTo1Ol7vcqJ3OVv8XnS1mXmkWzs+LKthcThChlHhxBwvvewWjHod1fEEesBtULY5A39bszo8dB08mtmT3knZ33fkiRiM5pR9O5NOr8eZkdfwtZtCCuy9UVWV2hUrKI/8QvaAAVi8HgZeeRm5QwbjzJcAXgghxJ5Np8mGsEb8fj9utxufz4fL1bKay0II0RoqYnHmB+uY4Q+TbTIwKsNFttGAVdkzAvitCdT4qK0sp6pkPYrBQjBkY+L4Sk44qw+9B2RjNG2amU6UlFN+2c1owfqZYL3bhf/R43h41RFNrqtDzy19/6C9exB6xYAaCpP0+UmS5NNXrmXl7J9SjiezsBtn3vUJml1P+K999TaDF4ex9RK01SVV6lQVg06HQ9FTFkvwRUUt0/whLHo9R2S5Gea2k2Hcec/Ta6sjhENxFIMOl9uM1dY4oZ6qafhqK5n00k0smz6xUb11b34nxt35Ma7Mgp023u0Vj0Wpq6xAMZmxZ2Xv6uEIIYQQzWppHCoB/BYkgBdC7GqqpqHfzWZlW8PsqaXcf/2vKAYdalJrKK+uKDoeeX0MBe03feZqmkZiXTG+594gOmseAKbLxrJwrwo+WPVvYmodAFbFzXndXqOH60BsFm+j+8UiIT5+8DxWzf0l5XgyCrpw5t2f4vDsnOCuOBrjhmUbCG9RJ76nzcy1HfLwtnEQH6lLsGxhFa88PouSdUF0ehgyopAz/9Wf3AIHcVWlIpbgh5oAq+qidLQYGabEmfn6rVSvXcywYy+m+96H77T3SwghhPgnaWkcKkvohRBiN/N3DN4Dvigfvlq/hz2ZaPzcOJnU+HniGk79v74Ny8l1Oh3G9oVk3HIlaiAEaOgcdobbDPTJPJTayAZ06HGb8vBYijDom/46M1nsDBx9etoAvv+BY7E5d05N86iq8lFZDYVmA4cY47j0UJLUMTGqZ3E4ytpIrM0D+LUrarn3ml8aHpxoKkz/dQOrltZw59MHUWrTcfeqYjZ+e2YH6/hSBzdf/CQZZUtZ/Nt45nz/Lj32OQJnRh5mW8u3FAghhBCidUgAL4QQos3FYyrlJekTo61ZUUsioWI0Nk7wpnfY0W9Wxk0BspSOZFk6tui+7brsRX7n/pSsbJxt3pPbnr4jjkevtDyhnJrUqCgLMWdaKUvnV9Gpu5ch+xWQmWPbapm/YCLJQF2Egct+YM5nTxOoKianQ28uOP0WZns781NNgAFOW4vHsq0CvihvPTuHVGvuKsvCLFtUzecFsMWzFRIaPLWhin9FAkz78kUAfnr3AQ77vwfoO/IECeKFEEKInUwCeCGEEG3OZFFo19HFojmVKfu798lsErzvKE3T0E1bzDFH3ciaisXMnvoZajJOn8FH0L3rcMyVYcjb+nWSiTjBmjJC/gAlG+LMm1HL9Mll/PrdWt57aR63PX4A3Xpnom+m3J8uEqTmy6dZMOnthrbyNQuZcP8ZjLrsv5T1HpX2XIBITQ2h0jLKZs7CZLeTPbA/9vx8DJaWlaSLRZMsW1idtn/utFLcJ6VO8FabSKLPateobeJLN9Kh774SwAshhBA72d8jM5IQQojdmsNp4tQL+qbsM5kVhh/cvtXvqdb6CH8+geh9L1L041qO7ncWx+71f3SdFSR6+38JffI1aize7DWCteVM/uhxXrzqQF67fhQ/vHACfTr9wuU390Snr19Z8Ohtv1NbVdf8WALVjYL3zf3x5n/YV9e0hOBG4fJyfr3xZt7o3Y+JZ57N+BNO4rUefVjx+XhiofSrGjan0+twedNnjs/KtRFKqmn7Va3pw4mFv3/ZonsLIYQQovVIAC+EEKLNxdUoRV1tXHffcOzOTVnPs/Ns3Pb4AWTltsHycQ20aKz+/stXE3/nS+JvjSc+f0l9dzRWvxE8jVhdkF8/fJTJHz/eUBc9Fgkx8+tnqFj0OmOOqZ+x9lVHqa2ONjuUqg3L0vaF/VWoYX/a/lUTv2Heiy83alPjcb46bRyBNWubve9GngwLR5/SI2WfTg97H9CO1ZHUr8Gi12EM1zYdd21Fi+4thBBCiNYjAbwQQog244+VsbBmEi8uOo2XVpyE2m0yD7y/Fw+9cggPvXoIdz8ziu59Mre6h3x76J12rPvvk7bfdshI9Ob0s9JBXyV/fpe6FvrCye+z93BHw9fxWDLlcRuZrY5m+41GU8r2UFkZ0+57MPVJmsbCN99q9rob6fU69jukPYOH5zdqVxQdV94+jIxsK+cUZKU89zSnwvxPn2rS3n3vQ1t0byGEEEK0HtkDL4QQok34Y2W8s/wyZlZ+3NA2v2Yi7ez9ubLvV3jN7Zo5e8fpjEbsxxxK+PtfUX2BRn2Gju0w9e3Z7PmRQE2jGuib01SVZLx+1lwx6PBmNb8X3ZvXEZPVQawu2KSvoPte2Fyps+Gr8QSh4uK0161dthw1mWxRMj5vppWLbxhKdUUdS+ZXYnca6dY7E2+mFZNZYV+znnyTkffLqimOxikwKZyU6aT46xdYNfPbRtfK7tCL7PbNv39CCCGEaH0SwAshhGgT60PzGgXvm9rnMrX8Xca0uxa9rm0Xghnyc8h+8m6Cn06k7pcp6IxGbIePwnbISAxZzZeQM5qtzfYrhvqg/cSzeuH2Nh/AO7x5nHTdK7x/3zjUxKZ993Z3Fkdf+kTacnYmh53coUNY98OPKfs7HDpmmzLpuzxmXB4zHbt5mvTZFYU+Dis3WPIIhoMsm/wpK5dMo8+IY6jotQ/rFk3FaLExaPSZ7HPMhTgzWpABUAghhBCtSqdpqYrK/HP5/X7cbjc+nw+Xy7WrhyOEEHukhBrjxUWnMavqk5T92ZbO3DjwN9ymnRMEqrE4WiAAOh16txudsvUHByFfJe/eeTLlaxY16csu6kHPQ5/CaPHSd68cnO70S/E3SsZj+KqKWTb9GyrXL6VDn+EU9RqGOzt19veNSqdN591hI9iyBpw1K4szpv+Bq2OHrd57ewRrygjWlBMNB3Bm5KPT61CMJuyuLJQ0S/53B5qmodOlrwgghBBC7I5aGofKDLwQQohWp6ER1yJp+xNaDK2ZBHKtTW8yQmbzM+5bsruzOOHal3j3rlPxV6xvaHdlFXL8NS9jdrTDtZWZ980pRhMZeR3Z5+iLtmkcGX16c8LEr/j+ksvwrVwJQLuR+3Pw88+2WfAO4PDm4vDmttn101GTScL+KjTA5spAUVr2p0qotJTKefNZ8MZbGMxm+p5/Lp5uXbFlZ7ftgIUQQoidSGbgtyAz8EII0TqmlX/Ei4tPTtk3pvAaTuh0Hwb97juTu5G/qoSa0tVUb1hBRkFnvPmdcGXmb/3EVhYsKSFaW4veYMSSmYE1Y9seSOwJfJUbmPPDB8z76QM0VaXfgScz8ODTcGc3ny8hWFzC16ePY/3PvzRq733WmYx8+AFsOTltOWwhhBBih7U0DpUAfgsSwAsh/q5CiSS1iSTBZBKrouBW9LiNbbMQK5xMsiy4lk+Xn8j60J+N+lzGHK4bMIV8W+c2ubfYM/kqN/DOHSdRU7q6Ubs7ux1n3v1Zs1sN5r74MpMuuiRl30mTvqH9waNac6hCCCFEq5Ml9EIIIRpUxxO8vKGSqf5QQ1snq4nr2ueRazY2c+b2CSVVHlsHl3b+iPX+T5lR9gIJLUrfzLEMzL6IOeFM8tug9PueIBCrREPFYcxEr2t5Aro9RXU8wZq6KNP8ITwGA8M9DjKNCrZmku1pmsay6d82Cd4BfBXrWTTlC/Y56kJ0+qa5C8IVFcz+7zNpr/3nf5+hYMRwDJaWb3cQQgghdlcSwAshxN9cJKnyTklVo+AdYFVdjPtXl/CfzgV42mAmPqSqPLgOetlOYZ8Ox2IA5octfLEmxsm5zddNb2sJVcOXSJJEw6TTtcnr31JNdAPzqr/m55LniatRhmSNZVjuGeRYu7b5vXeWylice1eVsjYaa2j7sLyGCwuy2N/rxJYmeWAk5GPez00rFmw0/+eP6X/gydhcGYTKyojW1KJTFCyZGWiaRjwUSntuLBBATSS2/0UJIYQQuxEJ4IUQ4m+uNpHkl9qm9ccB1kXjVMeTrR7A2hU9gxxWZgXrWBSOsSi8MXCrD+yGuOyter9tUR1PMLHKx4RKP2FVpchs4pyCTLrbzM3OEu+ImugGnl94MisCvze0jV+7gMllr3BNv+/JtXVrk/vuTHFV5fOK2kbB+0YvFlfSx2HFpqTOeaDT6ZvNbK8YTZBUWf/rZCZdeAnVixcDkD98Xw558Tn6XXwhk2+4KeW5vc44DZPDsR2vSAghhNj9tG0BXiGEELtcRFVpLt97Vbz1ZydtisLZBVns47ByhNXAMLsZ41+lvQ72OsnaCTPeqfjiCZ5aV8b/ymsJq/XvyrpojLtXlbAgWNdm910VmNooeN+oOrqOH0ueJRyvabN77yy+hMr31YG0/dP86WfJLXYXQw4/N23/3kddSGhtMR8dNLoheAco+X0KH+x3IF2POwZLZmaT89ydO9PhkENa+AqEEEKI3Z8E8EII8Tdn0esxNFMWO7MNgulkIoF93Rr2feU5XBeeT7+H7+fuhJ8HCryckZ+B07Br9n5XxhPMC6Yub/daSRUVsdYP4uNqlMmlr6btn1X5Cf54Ravfd2fT0Ig2kxc3kEiSTCbwVxZTW76OkK+qUX9Rz33o2H//JucV9dqH9t33Ycpdd6Mlm269iNbWsvzTzzhj1lT6XnAeJpcLa1YWQ667hrE/foezqPkM9kIIIcSeRJbQCyHE35zXqHCQ18l3KWZHO1lMZBhbP5gunzmLjw48mETkr2D5p59Z+OJLHPXBuziOPabV79dSS8PRtH1lsQTBRIxsk7XV76sj/Xus1ylo2q7NCdAaLHo9PW0WFodTPyDZy2FhyqdP88fnzxINB8jt1JdDzrmTvC79MVsdODNyOfaK/1K6aj6zvnkDTdPYa8yZ5HUegC6SpGTK1LT3Xvf9jwRyogy982b2veN2dDod1pxsFGPrJ2gUQgghdiWZgRdCiL85s17PKbkZHOhxNPrQ72WzcH3HvFbf/x4qLWXCuLM3Be8baRoTzzmfYElJq95vWzibeVahB+oSFSTV1t1SYNSb2T//grT9g7NOwmrwtOo9dwWnQeHcgsyUjyq6Wc2wZh4/v/cg0XAAo8VG+97DiIT9VK5fiq9iA2oyicObS9e9DuaEa1/ixGtfptuQMTgzclFMJux5eWnvbW9XQE35at6791R0dgOOwgIJ3oUQQvwtSQAvhBD/AF6jgfMLs3iqRxEPdi3kye5F3NAxjxxT6wc5dZVV1C5fnrIvEQ7jX7W61e/ZUp2tprTbCYa6jKzzTyKpxVv9vkX2AfTyjG7Snmvtxj45p+E05bT6PXeF9hYTD3RrRz+7BR31yQxPyvFyVZ6LiQ+cCYDV4eXEa16ipnQ1/3v4fF6/8UhevnY0s759k3CgGgCD0YzBZG64rjUri31uSZ2kDqDLaSeQSEbpe9TJhNRatGaW8gshhBB7MllCL4QQ/xA2pfla3K1FU5tfDq7GWz9ATjmOpEqyqppkRTVaJIIhP4cMp43LC208tT7M5qPMNxk5zFNLdViHSWn9JfSZlvac3f0llvt+Y3LZqyTUKAMyj6Gv91AyzO1RtqgHHwn5CPkqidUFMdtc2N3ZmG27fyZ1k15PZ6uZazvkEVFVdDodHoPC9PEvEIvUJ7E7aNwtfP/mXVSsW9JwXiRYyzcv34xiNDFw1Gkp670XjtyPQVdcxp9PPd3QplMU9nvkPsydczC0y+fb4AtMWvUKB0QuYq+s4/GaZf+7EEKIvxcJ4IUQQrQqS2Ym9vx8QimWyuuNRjxdurT5GLREgtiiZVTf+Rhq4K8Senod9uMOY9CJh3BL+zqWR6zUJhS6WOIY1UV8vOxqruz3ddprJqtqUIMhUBT0LieKa9sC6ixLR7zmIrq7D0TVYpgVB05TdpPj/JXFTHjhBpbPmgSATq+n9/BjOfjs23FmpF9GvjtxGBQcmy2mt7m8AJhtTkxWe6PgfXM/v/cgXQYdhCuzoEmfLTubfe+8g34XXsDK779BMZvx9OqKodDN05UnUR1d23Dseyuu4LfSV7m875d4zYWNrhOoLiNWF0QxGLC5MjFZd/8HI0IIIcRGEsALIXZriUiEUHEJpdOnU1dVTf6wfXAWtcOW3TTwEU0lYlEC1aWsW/QH/qoSinruTUZBF5wZuW12T3t+Pgc/9zTjjz8JtljKPOLeu7Hltd29N0pWVFF50/2w+Wy/qhH6ZALuDkXk7J/HjNI7qY6uZVHdMjo59+bSvp+RaenQ5FpqJEJswRJqn3qVZGk5AKY+PfBc9X8Y2xc2Ob45ik4hw5L+nHCgmvH/vZI18yc3tGmqyoLJn4JOx+EXPrhHzMRvqajXMPSKAVdWIVXrl6U9LlRbQTwSTtkX9MeIxkyQ3QHroM789M69ZJZ3xnhiUaPgfaO1odks8/3K3jmnAhANB1izYArfvnorvvJ16PQKPYcdyagzb8WTU9Q6L1QIIYRoYxLACyF2W4lIhDXffseXY08lGYs1tHc87FDGvPoSjvz8XTi63V8yHmPNwil8dP/ZJBOb3r/sDr049ea3cGVtW/DZUoF161j+2ecc+9n/mPvCS1TOm4+rYwf6/d8FOAoLMFhbf4n6liLT/mwcvG8m+O4n5Ox9N6d0eZxwwoceHXZjJlaDK+XxiTXrqbrlwUYPI2ILllB5zZ1kP30vhtzWeZiUTCQIVpU1Ct43t/C3zxh5yrU7FMAnq2tRQ2F0ioLO5UBx2Lf7WtvC4c3l+Gte4NtXbsPRzCoCg8mCYjQ3aotGE6xf5eft5+ewaE4lTpeZw0/szjFXf0DJhu/5IvRw2uv9Wvoy/TOPwqI42LBsFh89cHZDn6YmWfT7eEpXzWPcnf/DlSmfJ0IIIXZ/ksROCLHbCqxfz/gTxjYK3gFWT/yGeS++TDLRutnC/24C1aV89MA5jYJ3gIo1i/j5vYeIR1u/5jlAye9/sPD1N5l49nm4OnZkr6uuoGDfYUy+8WYmjDubUHHbZ6GPr1qfti9ZVomWSOAwZpJj7UyWtVPa4D0ZDOF79f0mKwkAVH+A6My5rTLecGUlc597gcpli9Ieo6kq0bB/m6+diCepKAmy5M9Sli6oomJNDZVPv0713U8QX1e8I8NuMaPZQpeBBzHuzo/J69QXsy31+z1g1Kk4PI0T+q1eWsstl3zPwj8r0VTw10b54JUFvPn8Ojr3OR69Lv1chF5nQI+ekK+SSa/dkfKYmpJVVK5dvP0vTgghhNiJJIAXQuy2Vn89ES2ZOiHarCeeIlxatpNHtGcpXv4nNmcGHfqOIKdD70Z98yd/SshX2Sb3LZ89G4BobS1znn2On6+5jukPPUKwuJhQSUmTBzJtwdy3e9o+Q4d26FqYfV+rixBfujJtf2TmXDRV3ebxbU5NJln64cf8eMVVmMy2Zo/d1v3akboE038r5rrzvuP2q37lP7fO4sa7l7Bo4JEk8gqovPYuEmUVOzL8FjOarWTkdyKvc39Ov+MDrA5vo/6OA0Yy4sQrG2Wf99dGeO3JP9FSvMXzZ5bjq9RxUP5lae85quBSTIqNeLQu7b57gNULft/2FySEEELsArKEXgix2/KvWZO2L1pbi5ZsOgMfC4VQ43HMbjc6XZp6YXu4RDyKXm9Av5WM8h5nIQeMvJzKGX9ibzeU7OOu549JL7F6wW+oiXiTmfnWkjt4L6A++Vq7A0Zib9+ewIoVbJj8G47CQhSzqU3uuzlTv17oXU5Uf6BJn+v801E87hZdR2cwoGR6SYRTr1YwFOSlzJi+LULFJUy5404AamYvJL/LAEpWzGlyXNe9RmN3Z23TtUs3BHnyP380WkAQjSR5/plF3HX3fjgn/0Z01jwMh4/a6rUiqkpNLMG8YB0hVaWfw0q2yYDbsG1/SugVhfzO/Tn/0e+oKVlFyFdJVlF3nJ5cbO7MRsfWhROsWlab9lrzZpYz+pST+KXkedaH5zXq6+05hI7OIfX31CuYbU6i4aY/DwDubMlWL4QQYs8gAbwQYrfV/uBRzHzsiZR9Wf37Y7Btmq0Ml5dTPms2M594kpg/QPexJ9LthONxdWi/k0bb9nwVG1g192eWTJ2A3Z3NXoeehTevI1aHp+mxq1bx9bGn4V+9uqFNMZk45J1XSCYTBGpKMLdS9m01qVFTVUcoEMNgUsgefhA9zj2HTtdczUyTlZVGM+1jUQ6PhFDWrMa+E3IXKDlZZD1yG9UPPE1iZX2CM53Djvv/zsDUO/3sfJPreN04Tj2O2oefbdqp02E7ZOQOjzUeClJXWb8aYvp/HuCwz97hpy8fbRTEd+i7H4df9CAWe8sePADEYkm++nBpqtX/AIz/uoSzRu5HZMYcbIce2OyDiHC4jqnhKM8WV7H5ZPgAh4XLinLJMG7bnxM6vR53ViHureRh0Cs6FIOOZCL1i3A4jbhMOVzZbwJLan/k19JXUPRGRhVcRifnUNym+p81uyebIUecz28fP5HiHgY6Dzhgm8YvhBBC7Co6TUv3q/2fye/343a78fl8uFyp9+gJIXYO/+rVfHTwofhWNl3CfNL339J+1EEAhCsq+OXaG1j45luNjnEUFHDK5J9wd+pEbbSEYKISTdNwGDPxmAr2qBn6mrK1vHXb8QSqGu9ZPuC0Gxhy+HlY7Js+r6J+P1+fNo5VX09och2DzcZhX74DDiPdhx66w+MKBWJM+3UD7zw/l4Cvfkb/oKM6su+FvblvbSmbx10mnY7/dMyjh7P5ZeKtKVnrQ/UF0BIJ9E4HSqYX3VZWLqS6hv/Njwl/NWlTo9GA9/p/Ydl7L/QWc/qTW8C3ejWv9+xLMhoFwJKRwZDbrydjUB+ikSAZHbvhzivC5srYpusGAzHuu/YXViyuSdlf2MHJtWMS2CI+PBeckfY6ariO9XVR/r2hKmX/GXkZHJvtQWmDf0/RSIKXH5vFL980XY2j08Hjbx9GfjtnQ1skEQCdHovSNDlfoLqML56+klVzfm5oUwwmTrr+FTr22w+DydLq4xdCCCFaqqVxqMzACyF2S4nySqJPv8Fxr7/G748/zvLxX6Alk7g7d+agJx8jd8jghmN9q1Y1Cd4BgsXFLPn0EzLOH8YrS86iIlL/IMBjKuDs7i/TWTeEWKWP2hUrMNntOPLysLk9GLxudMaW7ZHeGeLRMD+/92CT4B3q62b3HHZEowC+rryCVRMmprxWIhyG2hjt926dGceFsyt4/sEZjdp6H1jI4+vL2XLSNKZpPL6+gvu6Fm7zjO32UjzuFi+X1zSNRCyC3mBEUTaNT/G4cZ17Ko7jDye+bCU6qwVjx/boMz3oTTu+HcCem0ufc85m7gsvAhCprmbyVTcCYMvJ4fTpU7Y5eAewWAx06u5NG8C37+BAKV+C/cTmH+SooTC/BSJp+7+s9HGg17nD39NkMkGotgI1mUCn16PXK6hqkrHn9mbJ/ErKNoQaHX/xDUPwZjauaGAxOEnHmZHLsVc+Q6CqmA1LZ2JzZZLXZQBOb26jffdCCCHE7kwCeCHEbkeNxQi8/xmxeYvRLVvFsANHs+8l/0JNJtEH63AP3Qed1Uxt+TpikRAJfZxe55zJkrffQ90sM71OUfAc1p9H5h5MQos2tNfGivGVruX3p79k3vMvNWQYt2RkcPQbr+FWDdj334eIPk7YV0U8Gsbq9GLzZGO27JyyW5sL+6tZ9Pv4tP3Lpn9HVrtNy8KTsVjKrOkbxf1BLPb0gU5L1VZHeOf5xlnYdTowZ1sJ+FPvGa+IJ/AnkjstgG8JTVXxVaxjwW+fs2beb3jyOjD40HPw5LZv2GagOO0oTjvGooJWv7/BamXY7bdQu2IFayd939Buzcri+Alf4my3ffuzDUY9h5/YjR++WoWabPzzoNPD0Ufm44wqKNnN76tPrN1AVUb6YwKJJOoOLuYL1JTx53dvM/3Ll4iEfGS1686w4/5F6cp5GExmbn3kctasDDHr92Iysm0MH1VERrYVi3Xbfo7s7kzs7kzyOvfbofG2BlXV0Os3rVqoSwTwxUpYWPMdMTVML+/BZJiKcJpap0yhEEKIv4fd5y8oIYT4i1rrJ/ztLwBokSixiT/BxJ8wdGhHYtyBlJjWEKkMEli7nlkfvErVhuX0HXE8hx33FhPHnoX6V/3vjkceyozEp42Cd4CuzuHovq1k3nMvNmqPVFfz6cmncsY3E6kpW81nL11D2eoFAOj0CnuNOYv9xl7VpMxVW9M0DTVFwr6N4rHGwbLZ7cZRUECwOHWJsJy99mqVccVjSUrWBxu16fQ6kjQfzCV3s51b5esW89atx21KcDYX/vz2LY6+/El67Xs0RnPb1613FBRw5HtvEywupnrxEmw5OXi6dMbRrt0ObfXIybdz00P78+x906ipqp9Fd3nMXPTvAeR3dGLxtkNvbn72OVnrY3BGBj+m6e9pt2DZgUR+YX81E164nmXTv21oq1y/lC+fvorDLnyAquJVLPz1XYYcdhpD9xu63ffZXVRXhFmxpIbJ363F5jBy8FGdycwzMjP8Gu+vuHLTgatgcNaJnN7ladzmvF03YCGEELsVCeCFELsfVYW/gvCNlL37UH1ef14vv5SqefX7Yb3mdpx8+UOs+PBr5vz8AZEhfgZdeyUz738EAFfvzixVZzS5/EjDucx76K6Ut07U1bFu7hwW1fzaELwDaGqSmRNfw+r0st9JV6EYdt4Se7PNSYe+w1kzP3Wpq66DRzf62l6QzwGPP8JXp5ze5NjOxxyNI791ggG9osPpNjXsfYf6hHamOhWjTkc8RaBu0+txGbZtD3prUMN1aJEoOrMZvX1TQB72V/HVM1enzE7+1XPX0r7XMDy525YIMRCvpDKyij/K3iapxdg753TyrN1xmXIbHReMVxFLhtDpFFymPKxZWVizssju33/7XmQKJrNCv8E53PfCaPy1UUDD6TbjzbI2mv1t9hpdOtKhppZcq5OyWOMHSXrgrNwMHDvwPQ3WlDYK3jf328dPcsjF7/DB66UsXbuMw0/sQU6BHbNlz/zzpaoizIM3TGbNCl9D2w9fruKIsV1wHhRqcvzMyv/R13sY++dfsDOHKYQQYjcmdeCFELsdncWCsWvHTQ1GA/HzDuCJ9SdRFd2UzKomup6X1p1J/1PGYTBZWTJjAu0OPbChP14dItfao8n17ToPoZKShq8Lhg/nuPfe5fSvv+a0CV9jz8xEr6QO0Kd9+SLBmp1bf97q8HDIuXehGJvOlHYbMqZJCaxgTRlRj8qYd18lo2dPAMweD/v+53YOef5ZrFnbVoosHVPcx6FHNs0o/8vHyznOkXqJ/jkFmXi3sezYjlDDdcSWLKf6gaep+PcdVN/7BNEFS1CDYQDqAjUpS7YBqIk4ZWsWbtP9/LFyPll1E/f+uTffFz/FTyXP89Cckby25Hx8sVIAoskQK/y/89/5R3H9tA78Z2Y/vl57L7XR1CsmdpROpyMj20rHbh46dvOSmWNrcfAOoM/0Ypsxh1uNMNJuZmOo3tFi4q4OubS37tj+8ZIVc9P2BapLiIRDLJpbyfdfruG6879l7vQyEokUheF3c8mkyvdfrGwUvG/09UcraJc4HLO+6RadiesfxhfbuZ85Qgghdl975iNsIcQerzqyjg3heWwILaCdvR8F9j5kmIsAUDwu3JecTeW1d4GmYRwxiG9Cr5PU4k2uo5JkcvB1eu53BPN/+B8J4mT27UuXo4+k34UX4M/y81vZq2ibLeuu1tbj6daN2mXLGH7LzXTrN4jYpxNJ1vhAr8e772AOOvYyNiyeTl2wcQKwWF2QeDT1/u62lNWuOxc8/C2TP36CVXN/wer0ss/RF9FtyJhGtcHrAjV8/fx1LJ85iczCrgy661+4vQXE4xE0C5izvK0ynmQ8zpynn6F7/73Zd/8cpvxa3tA3749Sjj+nB9065vFBWQ0l0ThFFiOn5WbQ2WbGsA3B447QkkkiM+dQc8+Tm8ZdWk505lw8V1+EbdQIVLX5QDAZjzbbv6UN4fn8Wvpyk/Z5NV+xqGYSw3LHsTowg0fmHtTwMxlK1PD5mjtYUvsTF/Z6H5dp527R2BrF6cA59ihMC5ZyxvoSTurfG+w27C4HbpsZnbJjcwE2ZzMJ+nQ6dPpND9M0FZ65fxoPvzqG7Lydn49iR/hqokwa37SixkYzv6uj52GjmFP9RaP2YLwSVUu/hUYIIcQ/iwTwQoidrji0kEfmjsIf3zSr5DblcW3/H8m31c8YG7t2IuvxO/G98BZqx0xWx1LPkgKsj8yjW9FZAHjad2Ls999g9npRjEbMiSAX9nyP15aeR0ytn3X9JfY6J913G7MffIpuAwYTefm9TRdTVaK/TUdZX8yR4+7m4+cva3Qvo8W2U/ZEb0kxGMkq6s4RlzxCNORHpyg4PE2TW4V8FSyfWV/urGrDcia9v2mrgMFkpX2fffHkbNuS8FQiVVUs/fBjfA89wsAbb+OQ/xxJWVkcu8OALVnDigfvZOgjD3JFUQ7+RJKEppFtNLAzC/clq2qofbJpMA3ge/Z1zAP7YHW48eZ1pKZ0ddODdDryOrU82VksWcek9U+m7f92w2N0c4/kneWXNnqgtNFi34/URNejxDxE6hIYFD3ujN2jtJnicWMbMRSzPwCJJDqbtaF8XjwWIVRbQSIexWS24czIa7am/JayO/TCYLKSiDV9MNZ54Gjm/dm4vS6UoKYq0ioBvKZpqFU1JGv9oGno3c7tKjXYonupGtFIMm1/LKxh0jVdzdDdfQBWRcraCiGEqCcBvBBip/LFSnlm4fGNgveN7c8tPJFr+/+Ay5SL3mLG3KsbmXddR1yNkFs8gTXBmSmvmWnqQLiykqyi7ti9Odg2C2wtBgcDM4/j7iHDqIysIqklyLZ0wdrRTkHnXkQfSx3gJddsIMPoxeLwEAnWNrQPPeL8NktiF/ZX46/cwOI/vkRVVXoOOwJPdnts7syGY0wWGyZL+jrqteXr0vYlYnVEQ/5WGatOr0cxm9GSSf689z/o7r8La1YWibo6YoEAR/w+mRdLa/jD33hf71l5mYzOdGJvgwBpS6ovgBZouq8YQN8ujxpLNSWJFYy58F4+vOcsNLVxcLXvcZc2+lnamqSWoC5Zm7Y/kvATU8MUhxeQa+2GojNSVresYWXJQNdYwuuzeOq1qSxbUIUn08JxZ/RkwN55uL27SSDvarw1IlBVwq8fPsrcnz4imYhhd2dxwGk30GOfI1pc+s6RkcvYG1/jg/vORE1sWmXjyW3PwMNv4MHbljY5R1N3PBFiIhYlUVyC7+EXSSxfBYDe6cBz5QWYh/RHb23dB3V2p4nBI/L5bVLqf6MDDrTxdXBqozZFZ+TYjnc2Wx5PCCHEP4sE8EKIncofK6Osrukf5ADF4YUE4hWNkn3p9Hri303j4EHnMY0PUp430nkeMxc+z9jrX0s5K21UzGQqHci0dNjUaIV4XZJwRVX6wa4pwZNdRGmwFp1ez4CDTmXoERegGHe89veWQr4qfn7vQf6c9DY99j2C7mOOpCK5mnCtn1ylF05Hyx4abC1oMrTS6gFrdjYDLr2EHy69HKgvxRYur19GnzNoEGs7deGP8qZ7fd8sraKfw0pn205IZJcme7uhMI/QtQfz5vKTOdX9CEunTODkG19n1rdvUrpyHs6MPIafcAVFvfbGYmt54GRRHAzJOpmlvl9S9g/MPA5zwso17b6havkSEtEoOT36sCw5hRnBjxkcvZU7/z0V7a9V/aFgnGfum86oozpxxkX9cbha/+duR4R8VXz2xKWsXThls7ZKvn7+OlQ1yaDR49C34EGNwWCiQ+99ufjJX1k152dqytbQrtcwAuF8nrhnJXWhxsvHTWaFjKzt/zn2xcooDs3np5IX0LQEw688kfyy/Ug+/CFqIEj1vU+S/dQ9mLp33u57pGKxGjjp7D7M/K2ESF3j19Spu4eePYpYVLk3tdFiVJJ0cu7N6V2fJtfSPc0VhRBC/BNJAC+E2KliavP7x2PJcKOvk1U1+F98G/sx+3H2IU/xbvn1xNX6clgGnZmTOzxMvrEXp9z8Fs6MTdnV1USCwNp1rPluEhVz55G/7z4U7r8frvbtG8py6YxGdGYTWjRGKsaCDuzX/xEUXR0ZuTnY3VmYtyGg2xblqxfw56S3Oezqh1juncnzVeOIqWEMOhPDI2dzdMf/4DVvvQa5MyMfV1Yh/soNTfo6DjgAg6Nls6Jbo9Pp6HrsMSx8621K/6ifNTQ5nfS74RI6X3wJD1eG0577TZWPC63ZKDtQHq0lFI8LfYYHtbq2Ubt2zsG8UP5/jM16kG/uu5ZgbTkLf/ucvvufQMf+I6kLVFNbvq5Jdv+t0el0DMg8monrH6Q6uo4erpEMt5yMETNL1KkcnHU5q//4mYkv3YS22d77niOP5qzjPuKZW9Y2BO+b++HLVRw5tvtuF8AHqksaBe+b+/ndB+k6eDRWq4e68nISkSgmhwNHYUHK5fWK0YQ3rwPevPqtMGpSY8ZvG/DVRJoce/ZlA7Z7a4EvVspbyy5mdtXnDW2z+JTujv04+9bbSdz2KmgagXc/wXvDpa0+C59X6OD+l0bzyZsLmfl7CWaLwphju3DA4R3JzLZxbtbrjI1XompJbAYPDmPm1i8qhBDiH0UCeCHETuU0ZqNHQaXpXlBFZ8BhbJwhPTKlftl8cvxkulX05/axE6g2V6GhkpnMw+PqiXWLWXdNVSmdPoOPRx9KIlwfSM559jksGRmc/MsPZPXpA4De68Z2+ChCn01sMhad2UyVLZcHb13AAy8fQkZ+2yXMikVC/PHF8/Q9+CQWeX7jp4rnG/oSWoxfyl4iEK/k7E7P4bDnNnMlsHhyOOrGNxl/z2kEazcllssu6sHAc++jTLHgbqVxOwoLOOZ/H1I2YyaL3/uAPnddwid1d5GMT8OfGJT2vJpEElXT2jyA12d68V5/KVW3PADJTT9vdYUWouUh9FXJhvcoGg4w85s3Go4xWR30HHZEkwz/W5Npac8NA34hUVkFkxehTZyOFo3TbcS+2I7RM+2LlxsF7wCLf/mC/ofcTMm6YNrrLltQRbuOu9c+6Iq1i9P2qWoC1V/HpCtvYumHH6EmElizsxlxz110O/F4rJnNB6Z6RUf/oXnc98LBfPzGQtat9JNbaOeks/tQ1NmFybx9KzhW+6Y1Ct43WhqczNLcxfTo3IH4yjXEV69Dq4tCKwfwekVHQZGT/7tmMKddGEOn0+HxWtAr9f8WLIoDi+Jo1XsKIYT4e5EAXgixU7mMORxU8C++L/5vk76DC67AZWpco1yt2zRjn5wyF6bMxftX8iw1EsX45lNNrhMsLmb8cSc2BO8bRaqr+erk0xn743fYcnLQm4w4xx5NfNVaYnM2lQvTWS0oV1/Jj1NqufPpg8jOS7/nvDWoyQSRoI/+J53BUxXHpzzmz+pPOT7vduzW7GYThPkTKs8lnBx/+yeYqtcTrFiPp7AbVY5sHvbDUAJ0t7deUOIoKMBxTAFZY4bw34XHsDo4gyxrH3rZhjItkHplw94uG8ZtSHK2vXQ6HeY+Pch54SFCX00ivmwVhk5FRDPsuGvy8BWvT3turC5IItZ09rclPCEXVQ++RHzppozj4S++I/LTFE685WFeeeBkkonG70081HS7webM1t3v17XDm35bx0En3sjXp4yjbMamvBV1FRVMuugSdHo9fc87Z6uJ7ixWA517ZHD5rfsQqUtgtijY7Nu/CiESD/B9SdPPnY1+DL1Gt4OuhpVrMBTmo7O03YoHs8Wwx9ayF0IIsWvJbw8hxE5lMTg5sv0tuE35fLP+YUKJGuyGDA4vupHhuWdjVhoHy5Zhgwm+33jGTIvUl/YyDeiNzt40uA6VlDTsx95S1cKFhCsqsOXUBx9KVgYZN19BsrKG+Mo16NwudAUFBBUbpww04XTtWI3rljBbnfTY53DihmjKUnkb1fhWkqMVYchOPXvpiyeIaRq9bVaerUmgM7XH2b4jEVWlr87Gfh49GYa22XvuS5SxOjgDgKnlL3Fh338xK6gjsUWusQyDwgBn2z4Q2ZzOZMRYVID7gjPQolF0JhOJxDqC8So8hR3SnmexuzGYtu9BR2zpqkbB+0ZqIAiTptN72FHMm/xJo77ylVPoM6gnC/6saHKeoujo0rN1yv+1poz8Lk2SPEJ9pQaPo6BR8L653265jY6HjcHZrmWrG6w2I1abcesHbkUiHGx2C088WYf2V1DtPOME9LaW/ZyqWhK9bifkdBBCCCGQAF4IsYO0WJxkVTXRBUtQK6sx9euFIT8XJcOT9hyXKZdDi65nWM444moEo2LBbSpASfFHsCEvB9Pg/sRmzm3cYTTivuhMFEfTpe3xUPr91wDJaOPa3orHjeJxY+rasaFtZxaK0+n19B5xLCWhhc0eZ9M50cJNA5DaeILZgTo+q6jBl0jS3Wbh3+1z+KrSR57ZSD+HlSm+EGsjMZx2K2XRONkmA/pWXMJeGVnV8P/RZJBv11zKde2f5ZMKE0vqYuiBfdx2xuVlkm3a8WBsW+kMCjpDfUDm1ucxqvBS4vEkrux2+CuazsQPO/ZfODKa366QipZMEv7uZwx7DyJx8rEEHXYUNGzFZShvfEBi+ly6nTeqSQDvK13A+f8+jTsu/4mAr/Hs/EU3DMGzm5ST25wrM5/Tbn2Xd+86lWh4U3WDTv0PILhibdrzwuXlxIOpqwO0BrUuglrrQ4vG0FmtKBkedEYDlpCefSzHsdz/W8rzhnpPwvSbD8s1F2HssPWHC9WRdSz1/cKsqk/wmArZP+98Ms0dsRlba5OKEEII0ZQE8EKI7abF4kTnLKDqP49AYtMeY2O3TmTecQ1KmpliAEWnkGEp2uo9FK+bjGsvpu7XaQQ//Ro1EMI8qC+uM0/CUJCX8hxnUTt0en2TvcYARodjq/tvdwVPThHJ2gTdfSNZ6m+axTzX2g3r2gi6Ho2DX38iwRslVfxSu2n/9IxAmD8DYe7onM+iUISH12wq2bcgFOHLSh/3dCmgg7X1Vhd4TIWNvl5S+y2l4f3Zv+B6jsvYl3b2PniNNqxK2y+d3xqTYuWA/ItY5pvM4Tc8wk/P3EPZqvkA6A1Ghh5xPgNHn46ibMevSJ0O9YB9mdOhA28EI0QC9Q+LMjOyuOq2q8l+4wPUZONVFnrFwLBjLya3g5v7XxzN7KmlzJ5WSk6+nVFHdiIr17ZbLrfW6fXkde7PBY9OonzNYnyVG8jr2JeM/I6U/TYt7Xl6oxHF3DYrWxKV1fhfe5+6H3+HZH29esfJR2M/fBQAveJDybF0oTyyotF5blM++2aOw316JorXjc7Y/EOmyrpVPDT3QKqjmx5U/FD8X07t8gQjcs/DKmXfhBBCtBGdpmk7Xkz1b8Tv9+N2u/H5fLhcu1fCICF2N4mScsouuLpR8L6R7ciDcV98FnpT6+wj1TQNtcaHpqro7dZms0PHAgGm3HkPMx99rEnfQU8+Tv9LLkLZyh/om6uJrqcisoqayAbybN3xmtvhMrVNLfjy4HKeXnwsxeFNs/EZ5vZckfMO9i8W477gDPS2Ta99TV2Uq5el3sv9n0753LWqhBSJzelpM3Njx3ycrbSkvjZazENzRjYJjACG5YxjXLfnmk3OFS4vJ1Jdg6apWLxe7HmpH860Nn+0nESojngwRDIWx+rwYDCZiUfrUAxG7J4cDNtYNnBujZ871zVdCm/S6Xgk38PSic/z+2dPg6aR06EXh553L3kd+2JybAr6kgkVvaJrqJiwvbSkihaLoTOZ0LXBw5PqyjomfLyMqT+vx2DUY7EaOOWCvnTIivLOoMFEfU339vc552xGPfMUxhYuUW+ppM9P9T1PEpvbdCWL8+yx2I89FN/zbxE7oi9TtM/53f8eKkn2dp7IAZZxZLm7Y8jaepWGaCLEm8suZGrFuw1tOnRo1P85dc+QxeTZerTeCxNCCPGP0NI4VAL4LUgAL0TLhSb9Qu3Dz6XuNBrJfeUxDLlZqfvbWLiighWff8Efd91DYN06PN26sd/991B00IFYM1peSm1DaD6PzRuDL1bS0NbJsTcX9viQbHv6PdTbS0smqalZSbVvOWWxFWQai/BUmFG+mIX3qgsx5DR+P7+urOWV4qa17HOMBo7IcvN6Sfo690/3KCLf3HqJusrCS/nvgmMorVvS0NbXezhnd38Jr7kQf6ychBpF0Rtwm/KB+nJ/5bPn8O25F1A5v34W3NOtG2NefoG8vYdisOy8peN1wVpWzf2FH968G1/FeoxmKwNHj2Pf4/7VqERhcwKJJPeuLmFZOJqy/7RsD6P/nEnMY0HTVPQl1SS/+gX3+adj2WfQVmd+W0qLxUmUVRCe+CPx5aswdOmI/ahDULIy0LfSFgZfdYTH75jCormVTfrueGJ/vLG1fHLoEY2C+Pzh+3LUB++2eP/7toivWkv5xTek7NNZzOS8+DCgo+qOh9DleEmO7Al6PcqM1XiOORJjt84teshRFVnDzdO7YtLbOT7zFnrq9kEfTqDaDcxXf8Xo8nJQwb9a+dUJIYT4u2tpHLr7rckTQuwx1Kra9J3xeKPSXalEk2H8sVLWBGcSVyN0dA7FZczFbtzxhF227Gz6XXAenY48HDWeQDGbsOdu257mmugGnph3WKPgHWBVcBofrLyaMzq+hNfZOnXVN9IpCl5vZ1xJD+3W5hMvKcPUuQOGa/dByWj6vph0qQMOvQ4SW3k+29qPb3Nt3bm2/4/442UE45V4zYU4jTnodArzqifwv1U3sCE0nwxze47ucDsDMo4msdbHhyMPIrFZtYHaZcv4+OAxjJs9o6HkX1vTNI2Vf/7EZ09c0tAWj9Yx/auXKF+7kOP//Tx299YfRsVUjeJo+kSEy6MxDli4gvh3v6Azm+q/R7E4NQ89S85LD2PIzU57bkvFgkHq1m0gNmcB2oIlaB472qDOLJoxgYQR2vXeB0dGHjbnjv07qygLpwzeAV54+E/uevpAzpwzi+pFiwkWF5PVvy/OoqJt/nfYUoni0rR9WiSKFq7D2Kk9WffeRKKkjPiyVSi52RjPHo6SldniFQqqpmLS27m53TcoT35NfMFzDUUxB+zVG+NlB+74ixFCCCHSkABeCLHdTH3TLxM1FOahs6Tf51qX8DOr8hPeXPZ/JLVEQ/uogss4qv3tuEw7HsgAOPLzt/vcqsgaamIbUvbNqfmMYwrvxUvrBvBQH8QbcrMx5GaTav5Z0zR8FevZsHQmnXoMQwdsGYuXxxJ0s6WfvW5vMWFvgyXVHnM+HvOm9zypJfmj7C1eW3puQ1tVdA2vLz2f0YX/pv+iYY2C943URILpDz7C6BeexdjKtbg3Vx1dT2XdSqoj63AUeRh16R389tpjRMOBhmPWzPuNQFVJiwJ4k15HnsnIirrUM/AdNRUlOxvuuJ3KkA6dXkeGOYH+qy9IFJfuUACvJhLUrljB1HvvZ/WEbzC5XOz/4D1EMzS+fPgU1OSmf2d99jue0ef+B4dn+7eCLJmfOngHKN0QJBxOkN+hPa4O7bf7HttC7/E006mDv1abKJlelEwv5r49t+s+VoOLG9v/hPLYp8QXLm3Ul5i1EOUFM8nrO6RMsCmEEELsKAnghRDbzZCfi7F755QlszxXX0RdpI7oolIUowFLVhaWzf7AroqsbhTUbfRD8dN0d49kSPbYHR6fP5GkLBbnx5oASVVjpNdJodmIx9iyj77aaEnaPg2VWr+ffGcSo3HnlpAqX7OQt247gWjYT6/R4xh3xGW85Us0OsZpUMgyKhyT5WZ8ZeN9yAYdXFyYhdto+OthwDpW/Pkja+b/Tnb7nvQZcSyu7AIMxh1fvu6LFvPhyqtT9n2/4UmG9jgm7bklU/4g5ve3WQCfantER/cQTrrtacb/52Li0U0PFkpXzSevc7+tXtNpUDg118u9q5vOBht0MMJuZZq+G2/fsZB4rD47gdmi8H8XH81AszXlA5uWqlm6jHeGDiMRrq/CEK2tRcl1M/7Jc5ost1gw+VPa9x7GXoeetd33c3vTP6BTDDoUw85NWGjIzUKf6UWtqmnSZxk2GMXTOtvijEk3dr+P0BbB+0bRqX+i1volgBdCCNEmdn06YCHEHkvJ8JB5+9XYjjoE/tpXa2iXj+fR29iwegXvD9+fN3r349VuvfjihLHULK3/gzeZiKGvCnNb9kRuLpjAARnnY9Bt2ov91dp7CcTTz+61hC+e4K2SKm5cvoFvqvxMqglw+8pi/ruunJp4YusXALItXdP2mfV2dHErqrpz04gEqkv56IFzGsp2LZr0NoYfXuc2D4x2WRjstHFhYRYPdi0kz2zi+BwPN3XMo7vNTLbRwP5uB490K6LTXxnoK9Yu5pVrxzDxxRtZ9Pt4Zn//Lt+8cgslK+eTTLTsfWpOKFFFKNE0oIL6hyA1+hJMafZ5OYvatdke+Oroeh6fd2iT7RGrQzOYFHmGfoed0qi9JbPvG3W3WTi3IBPTZgno3AaF2zrkESup47WXlzYE7wDRSJJnnlxIleImWVVN0h9MddlGEpEI6mZbVKJ+P5NvvqUheAdod8BIViz6Je1eiSmfP0OwprzFr2tL3XpnohhSJ9kbPqoIt2dTgK+G61DrItt9r5ZQsjLIuu8m9FtsNTH26Iz7krNbXNd9a3zVEeoq/M0eo4WbL2UphBBCbC+ZgRdC7BAlOxP3ReNwnnIsqEl0ZjOlC+bz2dHHNTpu3Y8/8cHIUZwzczrxP2bCu59hCIYwGA0cetBQ9j1hPI9tOJ6YWocvVkJCTb0EuaXWRGL8UBNo0j47WMfsQJiDMrY+G+c159PNeQDLAj836RuZ+W8M0UzM5m37GE3Ek9RURQj4ohgMepweM97Mls8wh3yV+LaoWz7ny+dZ8O3rdBp4EKecfw8ZmZvqULsMBoa4DPSwWUhoGjZFj1mvb7jW509eSiTkw5lZwIGX3oLfVsnq2J/M41uMESdZtk4Y9Nuf6E7RNX+uw51LMpr6e733zTdidrdNTe2qyBpqY8Up++bUfsmB+/wfsz5/HQCTxU5Oh14tvrbDoDAmw8XeLjs18SQGHXgMBqwJlSfe2pSlf9DQbI47Kh+nPo6m6InW+PBN/ZPEzNm4zjwJY/cuKK5Nmfs1TcO/Zg3LP/mMNd9+h6tjRwb862JcnToS8/lY+eXXjcZh9noJBppmw98oWFOBqjafp6I5GVlWrr1nBI/c8hvJ5KaHBIUdnJx6QT/MFgOJympicxYS+uZHdIqC/YzjiWZaiUbDGM1WbK5MLPbWSxhr7FhE9lN3Ey8uJVleiVKYC14nCYe51f7gqamO4LA08zBAp0Nnb90M+0IIIcRGEsALIXZIPFpHqLaCyvXL0LQkGfldWPX9dymPLdhnHyI//kborf9tdoEE8W+n4Civ4eRz7+Ht8mvo7ByORdn+P+qjqspXlU3LV230ZaWPwS47rq2UUHOZczi325t8svpGZlV/iEoSs+LggIyrKaw5idxu25YELBSIMeXHdbz93FzqwvWz27mFdq76z7507OJBr2y9ZFgiljrYTcQiLJs2gf1OvAKyCpr0pyoXF/ZXU75mERaHh0NvepBXys+j2reuoX98+Z1c2fdrurlHYtBvX+ZyhzGLIvsA1oXmNOmzGTxk2Tpx4JOP8+NlV6D+NeOv0+vZ9847yNlr0HbdsyX8sWYSnqGS1CVwZ7dj2Ohz6TX0cMzKti2HNun15Jj05GyW8b02GKGsOATACSd14MCcapJPPogWrkMH2HOzsF55IcFZc6i65QFc55+G/ZhD0f+VS6J60WI+2O8AIjWbVjTMfeFFxrz8Ih3GjMZgsRAPhRr6qhcuosfYC1nE+JRjLOy+Fybz9geaRpNCv8E5PP72YcydUUZVeR19BmVT2MFFRpaVZGU1Vbc+SGJVfa1084UnM3P2F0yZ8CKxSAh0OroMGsVh/3c/npyi7R7HlnyxWj5/51pCvgpCvkqSiRj9DjiJUeNuxeHd8fKPRpPCnMUhBvfrTWJe05J15uFDUDxt8+BJCCGEkABeCLHdIiE/i37/gokv34SaqM+8rdMrDD/qUobceh0z7nm40fF7nX8e4df+l+pSJGcvZqhyOz0zhuDSvBgrwqgeI3rrti+hVjWoU1NVP69Xp6qoLUzBnuNoz0mFTzPKewuBUABd3Eqi2kPX7jlkZG/b3uylC6p46dFZjdrKNoS484qfePi1Q8jJT18nfSO7Owu9YmiUkGwjxWjG5mp5Uj01Wf89G3DU6Xzhu4/q6LpG/QktxtMLjuWuIQvItGxfyTyHks3ZXd7k8YUHEUpUN7QbdCYu7vE/vLYi3ONOp8MhB1M5bz5aIkn2gP7YcnMwOZ3NXHnH5Fib2R6hOMjO6Mw55zxL+K1PqX3jDgz5uTjPPAlz/97gsuGLFhNTwxj1Vtym/BY94LDYDHTq7iGRUDmgl47Ew2806k+WVVJz56Nk3HYVVbc+iP+ND7GOHIY+L4e66mq+u/DiRsH7Rt9ddAnnLl5An3PPYfbTzzS0Vy1cSIarPQ5vLsGassYn6XQcNO4WLI4dCzSNJoXcAgeHHNP4Z1fTNMK/TG0I3o179WF5fA0/f/r45gexYtb3fHj/WZx++/s4vDuenT5QVcK7d5+Kf4tVKvN++gi7K4sDTr8BgzH93v3mqJpKNBkiM9fIT79W0OOC03EaPiD+57z6A3Q6jHvvhfPCs9DLDLwQQog2IgG8EGK71ZSu4uvnr23UpqlJfhv/FMdf/DS2nBzC5Zv22BpNZhLhphnHN9LXRvHgQq0LEQ+uQ2cpx9ilI4pr2wI5q6JnhMfBglDqPbf7uOw4lJYnnsvyZuB1evD7ouj0Oty9zeh0W58t35y/Nsr7L89P2RepSzBrSgmHndCtUXssWUcwXgU6DbuSgdlgx+7OYp+jL2LKZ880uc7wEy7fpqziVocXmyuTvP6DWFx2e8pjomqIkvDi7Q7gqyvr+OTpAP86byql6nSWRb4jU9eTzoYxTH03RvuzkzhcdjydO+Pp3Hm77rE9PKYCursPYKmv6faIw9tdj3u9Su0djzW0Jdasp+aeJ7A+cx3T/N8zYd39hBI1mBUHowuuZFThZbhNzdeKt1gMHDeuFwt+XYV+/P9ItXhdi0aJzl+MqU8PYguWkCitwJCXQ6SqmuLffk95XS2ZpOzPPxl643WsnjCR2hWblun/csnVjP3yfX755DFWzPkRNI3Mwq4cesF9ZBelryKxo1Sfn/DEHxq+1h0yjN/fuCLlsRVrF1Nbvq5VAviasjVNgveNZkx8ncGHn7vNs/2aplEZWc20ivdYWPMtHlM7/u/hy5j2ZQXWLoex13EnocSiqGYLSn4G5rzMHX4dQgghRDoSwAshWiwZCKLW+IivWY+uIIs/xj+f9tjZv39A93GnMPux/za0aYoedLqUSbXcl55LdMYcQuO/hb9mz5XcLLw3XIauVzf0+m3LubmX00a20UDFFgnrHIqewzLdGPTbFoArBv027VXfUjyeZMOa9ImvlsyvahTAV9St5Kt19zK1/F00TWVw1kkc0/FOcixd2OeYi3HnFDH5o8cJ1pThzMhn5KnX0W3oGAymlq9YcHhzGXP+vSRpPlnd5jPn20LTNJLRcvbdp5gV331PZrtejCy4ngmf1/D+D8sAOPS4Xjhc2zcjuiOcpmz+r+fbfLzqRqaXv49KEovi5LCiG9jfexa+f/+nyTnGMcP4If42E4s3zSJHk0G+WncvNbENnNblCayG5me089s5cO+fS9341PvvARJrN6DkZMIC0P1VMUFLNr9XPRGuw1lYyNifJrFh8m8sfvd9rFmZDLj4ItwFnTnumueo89egJuOY7a4dKh/XIpoGm405aVaoC6ZOZghQsWYx7XoM2eHbVhWvSNuXiNWRiKZ/gJhOSXgh988eQV1y07acqRXvMPbAR8mpPI4J31fTtVcG/Qbn4s2VmXchhBBtSwJ4IUSLJKtr8b34NnU//gaA6cQx1JatTXu8r7KYTu33avhaMZtRMjwYhu1FdMrMRsea+vZEDQYJfTax8T3LKqm6/WGy/3sP+oLmZze3lG0ycleXAr6oqK0vI6fBcLedk3K95Jh2/kefwaAnt8DB+tWpg/jO3Tftp6+KrOGBOSPwbbZPe2rFu8yvmcitg6aT7e7MXmPOovvQQ0nGYyhGEw5v7javCtArCl0GHUR1ZC2u6lz88bKUxxXZB2zTdTeqWLuYt28/gUiwtqHNYndz+OVvs2KxnbLiEPHY9idR21FeczvO7PYCx3W4i5gaxqI4cZsKUFesJxhqmkU8efhAJlVen/JaU8re4Mj2N281gDdbDCheK/GCXOLLV6c8xlCQR2zpCnQ2K0p2/WyuyW7H26MHNUuWpDwnf5+9AXC2a0fPU0+h24knoNPr0W+20sRia71kcVujd7mwjtqPwJsfAaBoehSjmWQ8dQ4Hd067Rl8nYlGCteXEIiGMZhsOTzZGc/MP0EK+KqwOT9p+o8WGYSvXaHLNeDVvL/tXo+B9o49XX8s9Q4/mor12/MGDEEII0VJSRk4IsVVaUiU86deG4B2A4kradeyf9pzCbnvRa+zJHPbmaxzz2f84Z9E8vH164/nXORi7dGx0rO3wUfUz76nuHQwRX5J+Vq05OSYjZ+Vn8mT3Ip7u0Z4L22WTbzZtc6DbGtxeC2PP7Z2yz2jSM3T/QgBiyQg10Q2c3PkxDsy/GLOyaW9xKFHN5NJXSKpxdDodzow8PLntcWbkpXxNSU2jKp6gMpYgkEgdKFvsLvIyenNK58dS9g/NOhnXVpaGpxKsKePjh89vFLwDREI+fnztMo47LQ+704jdsf0Z7luDRbGTbe1Mob0vmZYOGPRGdGmSG4aMIRJaLGWfhoYvWpKyb0vJikrsR49J3Wk0YB7Yh9iCpXhvvAwlwwOASYVRD9yHLsXWjwEXnI/F2Tg4V4zGRsH7zqZT9NgOGYmS+1f5vd/m0H+/E1Iea3VmkNVu0+oTX9DP0hXz+H3Ca7x5yzE8f8V+fPfa7QSq0yceBAhUlxAN+fHkpt7uMfTw83Fu4zL9UKKapf5fUvZpaKzwp97WIIQQQrQVmYEXQmxVsqaW4MdfNGqLTZvDgHsuY9bP75OINd5rrlcM7Hv8pXiKOuPpuMW+ZouFzHtvIFlWSXztepTsTBSvB7Umfdb4+Op1afu2xqjXk2na9meVwUSS2kSSxaEIJr2O7jYLHqOCZRuX8m+uz6AcTj6vN/97cxHJRP02ApfHzNV37UtWro2qyFq+Xf8Yv5W9SkKN0jfjcC7p9TGfrL6JtcE/Afiz6jMOLrwSl6n5JdDV8QTfVfn5uspHMKnS02bh7PxMOlhMmJXGr0Gv09Mv4wiu6PMlH626jpLwIhyGTA4tuo7huWfjMLY8Md5GIV8VNSWrUvbVlq2hYye4/p7+eDJ3bQCfit7twtAun8T6xgG5iea3J1gMW8/VkKz143vyFSzD9sJ5+vEEPvoS4vGG+3quuoB4SRk5zz+IkpuNzlD/azq2YAmW6Qs47ZsJTH3iKUqmTcNeUMCQf11CtisDYzNJG3cVQ04WWY/cQd33kwn/9DvD/u9U/DWlrJj9Y8MxDk8Op972Ls7MAkLJJOvronxUHqBUl0O7/c7lsIPPZMXn/+XP796mLujjiIsfxpom8V5t2Vp+eu9BjrrsCX56535KV84F6hNr9j9wLAMOPg3FuG0/b9pWkl0m1NQPdIQQQoi2IgG8EGLrkklUX6BJm/bORE678mW+fvdOqjbU72n25HbgqEsfx5uXPumZ4vWgeD2YetZnAk8Ul6LP9KJWpd4ja+rWtsnNEpEI/rVrWfbR/6hespSiUQdhGzGCx5JGSmJ/BVfA/xVmsZ/Hga0FM5vxeJLaqgh1wSgmvYZ5xWJ069Zy2OiD2O/gMdTUxDAa9XgyLHizrNTG1vPo3FGURzatNphd9TmLar/nX73/x3/nH0NCi2JRXCi65jOe18YTPLG2rFESv8XhCLes2MBdXQroZW+6jNhm9NA/80g6OocQU+tQdEbcpjz0uu2bxU3EUycQbOivq2DyhzdQs//x9B5+LO7swu26T1tQMjx4b7qcyuvuRtss6aJpSRUdC4awOjijyTnZls64jFtfqaBFIsRXriG+cg2W/fch45Yr0OKJ+pl1Q/1/jqMOQbfFgyIlw0Nixlx0C5cxfPThcObZEKpD/W0G0Q2l6Ebuu02vMZxMktA07IqC0oYrUgw5WThOPgbbYQeBDo658r+EaiuoLl2NzZWBO7sdzox8EprGH74Qz67fVLe+JAYzQnDZMVdSsGE5i6d8wYGn35A2gHdm1GfbH//kZexzzEXsf/LVJBNxDEYTS2d8t10rb2wGD0X2gawLzU7Z38293zZfUwghhNgREsALIbZKZzJiKCogsa5x4q3E/CWYqms57ZaXiJl1aJqKzend5mzS+rwcnKefgO+/rzTtczsxdm+7AD4Zi7H2+x/4/NgTGhKFLXrrbazZ2Vz+/Xc8oNjxJ5OowAsbKulms9DJ2nxQ66uJ8M2ny/nyw6VE65LoFR37jszn5DH9CV92I7YjD6bbacc1yq6/1Pdzo+B9o2gyyO9lbzA0+2SmlL/FmHbXoMQUKkqXsPiPr4iE/HQfeiiZhV0aEpOVxRIpM/CrwCvFldzWKR+3IfXHv8u045nAAWyuLBSDiWSi6Qyl3mBEp9dRtmo+ZavmM2PCa5x196e4s9uluNKuYezcgZznHiAy9U9i8xdj7NoRy+B9+T/3QB6bdwhV0TUNx7qMOVzWZzwec36T6ySrakgUlxFftRZDfg5KQR6Grh1JLF9N5NepRH6dCnpdfe1D6pM5agP64F+7ltJp0/GtXEXe3kPxdu6EkptNsqyC2Nc/NrqH5YB90bew7rgvkWB5OMr4Ch/BZJLBLhsHe13kmAxttrVEp+hRvPXjswE2VybZ7Xs2OqYmnuDlDZVNztWAN/wql4+9luJ7TiFUW0FmQZeU93FlFeLOKcJXvo4f376vUV+f/U/A7s7a5rE7Tdmc2e0FHpqzf5PtEwcXXI7b2PR7LoQQQrQlCeCFEFuleD24Ljid6jseadKnxeLY3Vm4s7e/dJJer8e6/96otT4C73/esKTYUFRAxm3/xpCz7X94t1SwuIQvx57aJMt3XUUFsy75F0e9+jrvbtY1sdLHhe2y085axmNJJny8jE/fXtzQpiY1fvuxmNrqKP932imEXn8T6/77oPSuD+DjapRpFe+nHeOimu85qsNtRJMhupr25c/v3uaHt+5u6J/2xQsU9dyb4695AWdGHvOC6TNtr6qLUZfUcLfxp7/Dk82+x13K5I8fb9K315izWDTly4av/RXr+XPSO+x/8jUoyu7xa0mn12PIy8Fx7KFw7KEN7bnkcOPA3yirW8qG0Hxyrd0psPUmw9K0NFmitJyqWx5otBRf73KScdu/qX3ypU3tfwXv6HWY9h5I6fQZ/O+Qw4iHQg3nebp04YSvxhO+/VG02k2JEA0d2+G+4HT01q1XH/AnErxTUs33NZtW06yOxJhY5eeejgUUmo0ohl2TGqcmniCWZrm6P6mCt351g8WePhGfMyOP0259l/fvHUdt2aYHLB377ceoM2/FZHWkPbc57R0DuX2vP5mw7kGW+X/BZczliKKb6OIajs3o2a5rCiGEENtr9/hLSQix2zP37Ynnun/hf+kd1Nr6/eqmAb3wXnkhhh0I3jdS3C4cJx+NbfT+qP4AOpMJvduJ4vVs03XUpIpeaXkQUrVoIYm61AFvyW+/MzoSAjYtOa+MJ0hoWtoAvqYqwlcfLUvZt2BOFZHTBmErKsQfLYWwgl5vxGnIwmFI/5DCojjp7j6AIVljqSuubBS8b7Ru8TTm/fQRw479F85mXr9BB0orTbRqmoZaXYuWSKAzGFAyN2XSN5qtDDnifJxZBUz+8FEC1aU4vLkMOeJ8jGYr3716W6Nrzfv5Y4Ycdk6r1AJva15zIV5zIT09B6U9JhkIUfP4i0320av+ANUP/BfPpedQfddmDzf0erw3X0FdJMynRxzdKHgHqF2xgh//fQ2HvfQC2tJVJMurMPXqhqEwr9H73pyqeLJR8L5RKKnyTmkVhwcNdOnoxune+WX9dDT/Q6kDsoq6Y3M1/zAvs7ArZ93zGcHqMkL+StxZhdg9Odhc257HYSOD3kSBvTfjuj1HJOnHoDNh3468EEIIIURrkABeCNEieocd26gRmAf0RguGwWRA73KhOO2tdw+TCX1eDuRtW41qTdOoKAsz6/di5s8qp7C9i5GHdiArz4bZnP5jri5QQ6i8+czWWjwBm2057+cwURlehNlgx23Mw6g0nvkMh+LEoulLo0UtSYK3DOPDqospmbEIk97Kfnnnc0T7m5hV9QmRZNMAa3ThlRTa+qDT6Zj64zNprz3961fod+BY+jsz0FG//HhLIz1OXGmyrG+LZK2fyB8zCbz9McmKapScLJxnjcWy90AUd/0sqd2dyaDRZ9Bt8GgSsQgV65bw+6dPs2FJ0z3kO78uQNtSfX5isxek7quqQe/1kPX4nUT/nI/e68YysA/6TC+Vk38jWlub8rxVE78hUhfGe9CI7RrTNF8obd+sUB0H4WD21FL2H5M+f0UqyVo/ak0tyYoq9F4PSqa3IXt+S3mNCla9jjq16U9thkFB5ytl7PWv4fBu/bPBmZGHM2PbKydsjVmxYVakzrsQAJFwgLCvkmjYj9nqxObJ2qmlKoX4J5MAXgjRYjq9vn62vRVm3FvT+lV+7rj8R0LB+qX30ynm8/cWc81dwxm4Tx5GU+qAdd3i6dg6FKS9rqOwEL/dAX9tfXUoejoa13PHrEEY9RZO7Hg/w3LPapSl3WxR0Okg1WrggvZOovkr+O/SsQ1tMbWOH4qfZpV/Gpf0+pjH5x/a6Jzu7gPYK+vEhv3JodoK0omEfGiqitdo4LJ22Ty9vqJREF9gNnJyrhfzDmTSB1AjUYKfTSD43mcNbcnySmofeQ7n2WNxnHQUelN9tu+N5e4AqopXpgzeAfofdMpWZ1fbQjwaJlhTQawugNHiwO7JwrydS603p0VT1ztv6A+GsOw9CHPv7o3awxVN94FvOkkjGWk+OeAO0en44v0l9Nkrh4ysltVLT5RXUnP/f4ktXNrQZigqIPOu6zAUtDyI9hoMXF6Uw8Nryhr9zCrApQVeumLH4clu8fWEaEuRujiaBlZb8wlF/64C1aV899odLJryRf0vO52OHnsfzpjz78GVKXkhhGhrEsALIfZo/tooz9w/rSF430hT4am7p/Lom4eSk2evX+6tJVH0BkKlpdSsW82UT5+mXee96HHW6Sx5890m1x76xGO8pzcDMXrbTJz4/+ydZZQURxeGn+ked1k33N0lWJBAIArE3T3E3YgTd+eLuxJDEiAQCBAI7r6wruMu348Nu0xmBl0s6eecnJOt6q6uHkb6rbr3vWk+vtp8LgChqJ/Ptt5MhrY1na2j688xWVT0OC6HJfNKEsY7+dJMvtp5btL72Ob+E4Wo4cHuy5lf/h7BiIfeGeeSrWmLSdUghNr1P5k1875NOkazzoNQaQ2oBIG+Jj2ttGoWOtxUhyP0MGhpqlZhUx7813601oH7yx+S9rk++Q7t0AF1kRT/IKtZB5p1Gcy2FXPi2s2ZTegy9JzDXrfcXl3E/K9eZOWsz4mGQ8gEgbZ9T2L4xQ8d9EOooNchU6lSCnkxRZRJWscOKcdU22yoTPtmVpeM3iYdX1Qkr/TQU6+hGREmjNWhqSohItr2mr4SdXuwv/K/OPEOEN5ZQvWDz5D21H37nAIjF2R0MWh5tlUeP1c72OkP0lyjYpTNRKZSjuIgF50kJA6USHUtoW078M5dQHjwULY7Vcz4cTuRcIzjRzejY/d0rOn/ncgMv8fJ9LfvYcOfUxsaYzE2LPqZSCTEKTe8hEZvPmLzk5D4LyAJeAkJiWMalzPAto32pH3BQARPoIZC13rmlL6FK1RBD9s40iuycSzdgNtewcJpb3HihY+T1qUjq15+C3dREendu9P7wbtI63scN2n1uMOVrKz6kPfWPYs7FL9D+t32+2hm6I1BUbd7rNUpuWRCN2qrfWxZ1yCWbOkaWndW8cma9aRis2Meowvu5mx9ovHbLrJbdsWa04KaknjHelGuZMi5d6HS1hnjqUWBXFHJOHXj5+pG7Q4Ip0gTCIWIOpxJ0yD0lkxOueFFdq5fzOKfJxMJBeg0aDyteo/ElHZ4y8g53KXM+ewpVs/6sr4tFo2y7o/vCXidnHbTa2gMdbnlrmAlNYGdrK6dilymppNtNGZF9h4NzESrGf05p+J674uEPvVxvRAsyYW4LiuT5ieNYeuPPyX0DXj8UXQ5qSNG9oZNITLCauCXmvg0Db0ocK5aJHLd3eD1UQPIm+Rhm3g78uzUIetRu5PAomVJ+8I7ionUOPbLw0ItCDTRqLg8J41gLIZKJiAX/m3JFRLHEpGqGqoffo7Qhi0IN93AOx8UsXp5dX3/2uWVFDQ3ctdTA7EdAhEfqXUQi0QQNGoE3dGxSOC2V8SL993YvOQXvI5qScBLSBxiJAEvISFxTBONJHeuBhh1TjYrI+/yw7IH69uWVX9HmroZ1w/7khzXCuzlhUx9/26yW3Sh67N3oNVbqS7fSk2klBYmEyYB3thyA0urE3e9BUSCER/haHx5qbQMLXc+MYCaSh9lxW4sNjVpNiWoqlEIakLR5GHQJuXeQ46NtmzOfeAzFk55neUzPyUc9NG080CGX/ggttzk5bUaHeWew0ZlitT9eksm7fqdRLMug4hFo6h1pkNWviwV0VgUR20xa377Jmn/1uW/4XFUoTFYcATL+WzzjSyuahDiX267jdOaPMzxOdelNDOTKRToThyGoFbj+uTbOmNGlRLt6GEYzjgZ0ZA8TF+TlsbwN19n2cuvsPyV1wi53Rjy8xnwxGM0HXXCQUUpGOVyzsm00kuv5YcqB+5YlK5qNUNlEYQHnyayW837cGERNY88j+3xuxDNJiK1DiIVVYS27UBMsyIvyCW6l3D+qMO5x/5UKASBIxWYHIz48IRrkCFgVGYgyA5vVIjE0UMsGsU7ax6hDVsQszIoDJlYvbw44bgdW50smlPEieNaNdp3WcTuILB0Fa7PphCprkXZtiXGi89Enp+LoD78JpO743Mlj+Kp73fbD89EJCT+w0gCXkJC4phGp1diTddQUxnvJC/KZXQbruSVogcTzqnyb2Om9zWGDr+cDYunEQkHKd2ygtItKwBQaQ1c9swv9WKpmbFPnIAXZXJOT7ufzsIQ5DvsqFdVEM5XINosyP6ur26yqDFZ1DRr3eAQHoyq6J9xEXPK3kyYkyiT09o0eJ/u2ZSex7CLHqDfadcRI4ZSrUejP/DQ6v1FNJsQM9KIVCTma4s5WftUk/xImh05g2W4XGXEoqnNBt32CtLyWrG29pc48b6L7wofoL1lJM0VvVOOIZqN6E4ZiXpAL2L+IDKlAsFiRtjLAog+J5v+Dz9El2uuIhIMotBq0R/EzvvumBRyuhl0ZPph4bxiBnaA8M13Ekli2hDasp2ow0UsFKbmkecIbdha3yfT60h/9kEQRYgkfx331R3/aCAai1Lp38rUnU+yvOo7FKKGwVlXcVzWxVhUeUd6ehJHgEitA88PvwAgdO/KzLnVKY/99futHDesAJNl7+Uc93pdlwfnB1/h/enX+rbAkhVULl2J7fF7UHfreNDXOBiEvaSzKNVHR6SAhMS/GSmpTEJC4pjGkqbmspu7J7S3bGdla+znlOctcHxMMOTm9GteJbNpQ95xXtveXPjoFMzpDXW9e6SNRyk0mHpdn/MJ3b5WE7vxdULPfErNvZOouOoOAqvW17nWp0ApqBlTcC8Fum5x7aJMzjXtv8as3HeRJleoMKblYErLTRDv4WiQan8hJZ51VPsLEyIEDhbRZsH64K3ItPFGZzK9Dtv9N++3A/nhJhqLIEthbLgLrcGCM1jJ9KKnUx4zq+RlwtFQyn4AmSggT09DkZ+DPDN9r+J9F6JCgbGgAEvLlo0m3nchiDJyMnQcPygffcSf3HHxb2LRKI7Jn8SJd6gz4XP/PBPtyCFJz1N267hPCzlHC5X+rTy6tCfzyibjDldTGyjiu8L7eWn1ydQGEnddJf79yGIxYr66KJOYIBDZQ7RXNBLb08dov4jW2uPEe0NHDPtLk/EXlRAONu53+v7gtlfSpGP/pH15bXujVDdeZRoJCYnkHLM78E8++SR33303EyZM4IUXXgDA7/dz66238tlnnxEIBBg5ciSvvfYamZlHf11hCQmJA0Mmk9GhWzqPvHo8n76zim0b7NgytJx5aQfWCvF5eqJMQU/zWPprzkAhU6ELWPjliqvpfMk5mEbeiMZqxdaiDTpLvNu1VdWEWzvP5I11Z5KtbkvGQh/h3+Nzf2M+P9X3TyLz7WeQZ6f+zjEqs7iu/bf4I26KvCsIRHy0NQ/BosxNKEl3IDiCZcwsfomZxS8RiHpQCTqG5d7IsNwb9ylEf19RNC8g4/VJBNesJ7ilEGWrZig7tCFgteAIhpAhwygXjkrzMZWop0rcQdMuA9m+4veE/vSCtmhN6YRiITyh1LtuzlA5kVgI+REL+D5wBEGGLUNLOGQAQQZJyrcBxIjhn7MwaZ/3h1/IeONJZKKIZ+rMOl8EQYZ6QG/MV12IaDIcyltoNIIRH1N3PoEv4kjo2+lZTqHrLyyqw+vRIHHkkRl0qPp2x/fLXFixkiGjurN6aUXSYweNbILR1Dih7cGNW1L2RUrKcKxdh3upl4IRw1Fo9q1aRGOiVOvoe+q1REIhijYsrm/PadWdIefcgbIRqnhISEjsmWNSwC9evJg333yTzp07x7XffPPN/PTTT3z55ZeYTCauv/56xo4dy/z584/QTCUkJA4HGq2C1h3TuPWR4wj4w8jlAiaLGpXjNGYUPwOAXpHGzVlfov5pLZEZPxALBhG7d2TMyy/x6z33YN+6jbPm/ZYg3gHkgpwWxn7c23URitoQzkcfSz6RUBj/0lXoxyQX8LXVhbjLS6lYuRxBqSS7Qzv02TlYNftXdzsV/rCL77c/FBeiH4h6+HnnE7hD1ZzZ/BnU8sYRVTJBQJ6VjjwrHe2wgYRjMUoDIT7YUc5ytw+5TMZQi4HTMsyk7+Ou8+FCp7DQNK036Rc1JfCGi9KNy+v70vJaM/aOt9Gb0wlEvLQ1D2NBxQdJx+lqPfWI1AV3hSM4whHs4QgGUcAsFzEpDuznXLCY0AwdiO/XuQl9it5d8DtrIRpNfnI0SnDjVoxXnIt+3BiiXi8ytRrRYkTQHjthtJ5wDcurpqTs/6P8PTpZRyMKx+Qjk8QBIqhUGM4+Df+8PwnvLKGlNUizVia2bYpf6EnP0jJoZFMEsXHy3wXVnhcCYsD3p4/nwtXLsbVr1yjX3B/S8lrx1VOX0rrXKI4bdyN+jxON0Uo44EdrSENrbHzjVAkJiXiOuV8jt9vNeeedx9tvv82jjz5a3+5wOJg8eTKffPIJQ4cOBeDdd9+lXbt2LFy4kL59+x6pKUtISCTB5w1RW+Vj8bwSHLUBuvfLJreJAYvtwHcU9AYleoOy/u8MTUvam4ez1v4rV6W/jfrR7wkXldb3B/9aBavWM+b114mY9Bjz85MNW49ZlUNYVknU6Up5TLi4LGl7bfl2lr7wEismvVIfsiwqlRz35lNwyklYrQcv4n0RF6tqkqcN/F72DqPyb280Af9PygMh7thURPDvewvGYkyrcbLc7WVi8xzSjjIRn6VtS3EsTNsrzqJv6Aa8NVWYrQVY05pitTUFQCVqGV1wN0uqvkgwHjQps+hiO+mwz7sqGObVnRWs9DR4PjRVK7mjSRaZqv1/jQWtBtOlZyNTyPH+MufvXXQB5cCe+I7vhMdejNpoSPmeVzTNR1CpEPbgVn+0I0OGQkgd/aIWjchkR18kicShR56dSforj+P66CsCb7/DTddfx/KducycXkwkEmXgiCYMPKEJaZmNt2ClaNkM5GLSSh/yDm0oXPAHsWiUVe+8y+Cnn0T2jygnn6sWe8UONi+dTX5BV2SeCP7qWqxt26LLzkKblnZQ8zNYsxh729vM+/J5/vzxbUae+xDOJRvY8vm3yASBLtdeTVbvXuizpXrwEhKHClks1lhZO4eHiy66CKvVyvPPP8+QIUPo2rUrL7zwArNmzWLYsGHU1tZiNpvrj2/SpAk33XQTN998c9LxAoEAgd3q9DqdTvLz83E4HBiNR85kSULi34zPG2LB7J28+dRfce3N25i585Ge4K4hYLej0OnRZKSjsSau6MdiMVw1ZXgd1URjYazaTCirwr9oGaLZhLp/T8Q0K07RztraGbRcayM06eOk81EP6IPl1qsQtHtfPIjU2qm6/RHCOxPrvANYH7oVTb+eCXNd//2XTD3tvKTnjF86m7yu/REOQCREgkFcO4vY8euv1G7ZgrF/a6KtNHziug1HsDTu2Hu6LqS5sc9+X2Nv+CNRXi+qYJ7Dk7T/5oIMBpiPznBqV6gKf9iJKFOgU9gSdtTD0TAl3tV8vuVmNjh+Q0CkW9rpjGv2BBmalod1rt5IhFd2VrDI6U3oa6JW8kCzbMwHuBMf9QeI1tqJeDw4XVUs//M7/pr1MQVte3NCu7MIvPt1wjmK9q2xPXALYoqSeMcK0ViEn3c8wXeF9yftv7vrH7Qw9jvMs5I4moj6/MTcHmIyGYLZiNsdIRaLYTCqGm3nvf5agQD++Yupfeq1OH8KwWhAecNFfDH+DHyVlTQdeQKnfPc1cnXD4pPXVcsf37zE1mWzGXbynfxy7uV4KxrC/pucMIKR/3sHfe7B+2qEgn48JaX8NP5syv9aGtdXMGwooz58TxLxEhL7idPpxGQy7VWHHlM78J999hlLly5l8eLFCX1lZWUolco48Q6QmZlJWVnyHTGAJ554gokTJzb2VCUkJPZATaUvQbzLBDj55AwW3X8f6979H9FwnRlc3pDBjHx3MqamDTvUkXCIks3L+ObpK/A4q7no9o+xP/sZkS2F9cc43/0M84TLMQ7pT9+MC6id/DKp7MYCf60g6vHuk4AXLWaMV5xPzQNPJfalW+t2T/6Bq6qUZY+/mHLMtW++j+2FDujU++faHQ4GKZ4zl2/HnEI01HB3xqZNuerHd3nZfia+SEMpL5V4aMyF3OEwy92+lP3z7W76GvVHZU1vgyINgyL1jpRckFOg78q17b/BF7YjkwnoFDbUYuPmeXpCNdQGilhdOx0ZAp2sJ2JSZqNTNLwnHOEIRYEQl6sggyguZEwX1Gz0BSj0B7GHIwcs4AW1CiE7k4Cjiq9fuojq4s0AbFs9j60dBtH80nGEvp5B1OECuYj2+OMwXnTmMS/eAQSZyHFZF/NX1Vfs9KyI6xucfRUZmlZHaGYSjYkzWEGVfxsrqr9HJerpajsVszIHrcK813MFjRo0DULZaD50j8+CSoW6X0/SX3sS1w8zoMZOrFke4bxMvr/yKnyVlQDkDDgO8R/h9vay7Sz6/g3GXvM608+8GH9NTVx/4YxfWPjIYwx54dk44X8gKJRqtv88PUG8A+yYOYvShX/S6vRTD+oaEhISyTlmBPzOnTuZMGECv/zyC+qD/NLZnbvvvptbbrml/u9dO/ASEhIHTywWI1JVQ9TlRiYIyMxGXBo3PrmLS+9txszPaijcUpdPOGBINu5p77Hm7bfixij6bQ5TThvHuOk/ofvbkNJRVcwnE88iHPTTrvdolIs2ENxNvO/C/uI7KDu1Q5Gfs0dDLZlOmxCGuCdUHVpjuet6HG9+SLS2bv7Kbh2x3HgZ8nRb4usQCOEuKko5nnvLTghGYD+/2jwlpUw5dWyceAdwbt/OqjtfZtDjlzLd/gIAebpOGBSHJsw5EgqgFQTckeS50gZR5CjU7vuFTmGJE9ONiTNYwZTCB5hT2uBd8OW22xiVdyej8m9Hr6h7T4lOJ5evW87Se+5j3bp16HNyOOnuOwmMPJHXfFEcSUJu9xe1zkSL7sPqBTzAL58/TtMO/el7zcWkZTRFZ81EsJiOeD3qxsSiymNCx5/Y5lrCgvL3UYkGhuRcTYam1R4XeCSODRyBUt7bdHlcitE32+/htCaPcHzOdXGfbY+jmkg4iEKpRmM4MqUQBY0aZfMCOGEAi5+YRPXPX1O+ZEl9v1yrpe05Z8fVnY9Foyyd8SFao41wpTNBvO9izXvv0+uu2zE1bbpPc7GHwlSFwhT5Q9iUItlKBWlKBb6qKla++XbK81a8+hpNhg9FaTg6o68kJI5ljhkB/9dff1FRUUH37g3loiKRCHPnzuWVV15h+vTpBINB7HZ73C58eXk5WVmpnZdVKhWqvRiGSEhI7D9Rf4DgyrXUPv82Ubsdxd0XsCy4jGnVL+AKVZJubs7Iux+iclFLvn27iAF9dcw5NfnDQNWKFbiLiusF/Po/fiAcrMtJ7tLndEIvJIb37sI//08UZ5+GdtTx9TV9/4n+1JEI+7GTKOh1aAb3Q9WpLVG3FxQKBJMBUZ98h1tjspLRqzvuKcnD7rMH9UOr33/jn+rVqwn7ku98F/48gxMeu4zpvIBN1YSrW35G9L0Z1Lg96IYPQp6fjWgx7/c1kyHUljFUreCzFCEOw0waBNkxruAPIdtcf8aJ911MK5pEZ+uJtDYPxl1WRul3U5h5zfX1/e6SEhbdMIG216xjzIRbsBzg7vvuiHIFPUddzIqZnxLwNkRvbF/zB5VFG7n4yZ+RZxy7+e57wqzKpZsql87WMchkwgGltEgcnSyrnpLUH+S7wvvpaD0RnaIHXlctRRsWM/fTp6gt205aXiuGnHs32S06o9abD/+kAUNeLi1OO5Xt02fUt5lbtWL0x+9jbFIQd2wsFsXvcaAxWHDvTF36MBIIEPEHUvbvTlUwxKTCMrb6GsrWmeUiDzTLJj0aTVg83p1oKMQxlqUrIXHMcMz8Og0bNoxVq1axfPny+v969uzJeeedV///CoWCmTNn1p+zYcMGduzYQb9+Uu6ahMThJlxUQvUDTxOtqUU8YxhTLV/wZdm9uEJ14X+V/q18Wnoh+l6L6NInDVnYRySQ+qHCsW0b3lAtNf4iSjYvr29XKJT1tXqTEbHXiRAxMwPDhWck9Cs7tEE7bOB+7cBDnQu7mGZD0TQfRW5WSvEOoDQY6PvAfUmvodDraXfuOXu8fqTGTmj7ToKbthIuryT290OTrzp1iTNiMUyyTG7vOJtbzZ/Bda/i+fonfNN/o+r2h6l99k0iNbX7fsN7IBTw0NpVTHt1ooA8WS9iCrob5Tr/RrwhO9N2TkrZP73oORyVRZQuWMj8e5LnaK9/400GRQKY5Huubb+vmDMKuOTJn2jT+0RkgoAgyukw4HQuevwHzBn//gg1UZBL4v1fhCNYxi/Fz6fsn1P6OqGQnxWzPuXLJy6ifPsagn4PJZuX88nDZ7Fu4U9EwqmF6qFEaTDQ8rRTOO+vRZy/bAkXrlrOmXNnkdWrF4I8/vtWEOV0GHg6rupSLO1Sp32obTbkukTTPU+oBnugBF+47jfTG4kwuaQqTrwDhGMx5tvdOI1mjvvfO5iaN096nQ6XXoJK8pKSkDgkHDM78AaDgY4dO8a16XQ6bDZbfftll13GLbfcgtVqxWg0csMNN9CvXz/JgV5C4jAT9flwffJtvQFPZFAr5pVckfTY6VUPcd5Zs6EaBLm8Pvf9n+jycvhg0zU4giX0bHYCLKprLy5cTYv2rQit3ZT0PHXfuqgd0aBDd/IINP174p27kJjHi2ZAb+R5OYhW88Hd8D5ga9uO06f/yK9XXItz+3YA0rt25YR338TctEXSc2KxGOFtO6h57MV693yZRo3x4rPQDBtAWrfOSc8D0OfmYrE1IX17DdV3P57QH1i8nMCKtWiPP+6g701nSuOLxy9kzCWPMKagI8ujStTE6C4GqVjyE9pBpx30Nf6thGNBPKEaZAjESExBcIXK2TZrBlrRhL82xYJLLAaF2zG1b9soc5IJArbclpx840v43XaQydDoLSjVR7Y0nNtegb18BzvWLkRvTie/XV8M1kzkysZLq5P49xGNRfCEk4eTAziD5fg8duZ8muhrolBrCUf0bFpbw/pVNZitatp1Scdi06BUNc6C2d6QCQKGvDwMeXl7PTa3dQ+M6bn4Yh6sHTpQs2ZNwjH9HrgPfU6DiZ07VM1212K+L5xIdaCQJvpunNJkIhpFKxb/wzCzr0nHEIuBaVUOZta6sNlyGDNjOvIZ01l0bUN0kK1DBwqGHX8Qdy0hIbEnjhkBvy88//zzCILAuHHjCAQCjBw5ktdee+1IT0tC4j9HzBcgtHk7UJdfXhktIkbyUDpfxIGg87B9tZ62F5zP2nffSzjG3KIF8hwjXVUnoRS1ZKW1ZMk3kwkH/fw5831aX/EaoYlbEupVK1o2RVGQS8ThIlxciueHGcT8AXSnnICiWRNE8+HbHVBotTQdPoKz58/FX1ODTC6isdnQpifWnd9FpLyKytseJuZpeIiK+fw4Xn8fId2KOj+N5qNPZOvPUxPOHfTcU2hsNmpfTV7DHMD97VRUPTojGg8uR1FvzWLk5Y/xzXNXodFbyG7RmUg4yM8bl3L6za+ht2Qe1Pj/ViLBINESLxf6nyUQcxO2wa+B11jnmV1/TCfziWx+7yu6XnXVHsdSH4I8U5VGj0rTuGZ9B4qzupSvn7mCko0N5peCXMH42yfTtPNAFJKIl0iBRjTT3jycPys/S9rfI2083toqIqH4CDBBlDPmho/55D0nm9fNrW8XRRm3PNKfzj0zD5uI31eMtmzOue9Tls/6jCFvP8/SR59j+9TpEIuhNBrpe989tDn3bASxbt7+sJs5pW/y7fZ768dYWVPKqpqpXNN+Cs3VXdnir9uBb6JW0suoY9L2svpfc3s4wkvAqaNPpvujtWz/6GM6XXk5rc8Yt08LDhISEgfGMVdG7lCzr/b9EhISqYl6vFRPfI7gijWgUOB+8QyeLj4l5fEPdV2DKdYCmaeamdfewJYp39f3Wdu1Y/hXb/OS80xqgjsA6GI+iZMUd/DDcxNw1ZTSuvsIhg27mvAXMwit2YBMo0Y9egjqU4cS0McQv1uC64Mv464pz83C9uS9yDOOXoMqz/TfsD+XmBsNIM/PQT/+JAJEWf/bLJa9+RYBux1ru3YMfPxhNH2bk6VvSfX9TxJMEZ0gz88h7ZkHEM0H7yQe9HmoKd3Kgu9epapoI+kF7eh76rVYs5sd8Z3bo5GA08mW777n12uuI+ytW6BR6PX0f+NxlrefzwLXx+jkFu7puICPzZ0Y8NgjbPzqGyqWJjo+q8xmLlixFGPB/oW3x2Ix3LXlRMIhRLkCgzW1X0xjEwlHcbu8EPVDLIBaZ0q5WBAOBZj14aMs/umdhD5BlHP1S79jyWpad2w0hCNYgidcg1ymwqBIx6BMvUgm8d+gxLuOh//qRjgWL9Ktqnzu6jKPQHEt/7tjVFxfm76nYhcu5JfvE/PJRVHGcx+OIiv36Fjg+ieRcAiPoxqCYcIuH9FAAKXRgC4nB1GhqD+uwreF+xa3IUqiAaZZmcMprX7jxeK6dJKrc9P5uqKWylBilJwAvNgyB5PLiTY9fb9T0iQkJOr4V5aRk5CQODYQdFoM551O9Yo1EAphqlWjV6ThDlUlHNtU3xOTJgODQgWmHEa++w7eigo8ZeWoLRY8RjfPl50eV9N8hf1H7LpSLn3kPey1O9AJVkRbEzT3XUOtaxv2UAlzvR+zdMNlmFTZXN//A1Szs4nsbBgjXFyGZ8o0jBefhWy3B5qDweeqxeuqIRoJo9aZMFizCEQ8OIPlFHlWEYtFydN3wqjIQi3f+4NfcN3mlH3hnSUIOi2+x16kVbeOtP7gQ1ApodpOdO4yYj2bINNpUA/un1LAqwf2QTA0zgOoUqMjq3knTrrueYJ+L0q1FoVq72X5DgZ3bTmhgA9BVKC3ZCDKG+ff8XBQu34D0y66JK4t5HYz54IJjFnwDQGbl9ObPY7Ba8Pati3LXn6VEya/xbQLL8FX1fA5ElUqTp3yzX7XdfY4qtmydCbeygrSM1oR8fpxZ+eiz8rBkL3/NaJjsRiR6lpiLjeIIoJRn3RhKByOUlnqYcZ3m9m4poa0DBVDRpoJ2ufQslsfrNnN45y1ATz2Spb98nHS60YjYQpX/4ElqynuUDULyj/ku8L7CUTqfBea6LtzeduPydY2TnqBxLFJhrol93ZbxOdbbmG9YxaiTEGf9HM5pcmDWNUFOM1ytEYbXmeDr0iLXmfx/BPJyxBHIjFWLy0/agW8KFdgtP29ILeHUuwVvs1JxTuAPVhChtwD1EX36EUhqXgHiAI7QxFyMqVoKwmJw4Ek4CUkJA4JiuZNMF51Ac7Jn8DkX7n2jg94oXg8wWhDOLhRkcnlbT+KK9OktlhQWyxY27TBHijlteVj4sT7Lgo9f7E8+AOLvV9wWZv30WrS+K7wPqYXPRN3XG2giOfLzuTO6z6Du+J38DzTfkN3+mjkafvvAL87sViMqqJN/PjaLfUhvqb0PE67+002K/7k0y03EInVPfjIEDit6SMMyb4anWLP11W0aJKyT8zKIFJjh1iM4NJVsHRVXL8pokcmk6Hp3xPPVz8QqYzPARVMRnQjhyATGzcEVKHSHHLh7nfb2b76D2Z+8DD28kKUah09TryEXqMvO6y7yAdK0OPhzycT820BiMXY8sqXXPLGZDRaC2hh6Csv8tXwkcyecAsj3nodZ+EOqlatxtyqJa3Gno6xWdP6kNh9un7Ay9IZ75Od3ZH1b77NH7N+q+/LGzKYUe//D2NBQeoB/kHU6yewcg32lyYTra7L05c3L8B6+7XIm+bH7cZt21jLxAm/EQrWpbtsXgcL55RywdVtKPnyNY4/ZwLmjPhrRyNhwsHk1RYAXDV1Imt1zTQ+33pzXF+heylPrxjCfd3+xKre93s6lESDQaI19rrymioVgslYX+YyEovhCEeIxmKoBAFDIxkT/hvxlJfj3F5I5fIV6PNySevYEX1ebtLPglxQkK/vwjXtv8YXsSNDQK9IQyXWRQfpzZmMve1tPn34bCLhupBxmVxFwJ+6NGNNZer35LGCXNjzoqdOVHBBlpWvK+zsrZCIUqo0IiFx2JAEvISERKMQjcSw1/qJRWOo1CJ6ox7d6GFo+vckXFqOOaxhYtcVbHT/Tol3Hc0NfWlm6LnHh+poLERNYGfK/ir/NlqbBmFS5uAMlTG7JLnnhTtURUVaBRlmE1G7o749FgzWG+0dDI7KIj687zR87gaTMZ/bTk20iI82XxN3bIwo326/l+aGvrSzDN3juOqenXGqVMSSuPMbzj4Vz4/Jy+LJVCoUyroHU3lGGmnPPoT765/w/vo7RKNohvTDcOapyLOOvXJgsViMLctm890L19a3Bf0e1s6fgkKlodeJl6PWH93pT2Gvl9oNG1P2167fCIEY/F3YIKtPb86eN4c5t93B9+POJLt3b7rfMoH844fs0UMhFR57JTF/mGUTn6VoN/EOUPTbHKZfegUnffEpGuu+LWyFdxRR89CzcZ+l8NYdVN46kYzXnkSeXfc+s9f4ef3JxfXifXc+eWcb9z5xHit/+4oB4ybEibCYQiAtrzVVRclfs4IO/bAHSvlm+z1J+52hcgrdS48KAR+xO3BPmY77yx/h72oSijYtsN51Pc70NGbWOPm5yoErEqWtVs1FOTYKVEpUohSSvDuunTv5fuwZlC9p8ERQmUyMnf4zmT17pFzQ0inM6BTmhHZBFMlr04MrX/iNtfO/p2zrSjRqJfnNjOzc5kwcCOjY7dj7/vwnaermKAVt3ML6LrI1bTEq0zhZZ+Y4s55IIEpTtZLt/mDCsQqZjBy18nBMWUJCgmOojJyEhMTRS221j+8/W8+dl/3CdWf+xNP3zmfT2mpCMRF5Vgbqbp1Qt2pJur4lx2VdwhnNn6JH+ti9PlCLgpJMTepyOHm6LpyYfxdGZQahqD/pQ8guKkPbEUzxRl/q/j0R9lD+bW/4vS5qSrfiqNzJSdc9R9fh5yGIdeuirfuNZIHno5Tn/rTjUXxhR8p+ADEjjbRJ98bXqBdF9GefirJbR8KlFUnP04w+HmVaw8OlPDMd4+XnkfHW02S+8wymay5GnnNshjq6asr49f2J9X9rjTZG3/Ax7U98gxUb+vHNx9vYuc2Bz3tkyj7tCwqdjrQunVL2Z3TrgkLX8L5U6nTk9O/HaT98xxWFWzj5my9oNW7sAYl3AK+jmpz8zuz8dVbS/p0zZ+GtSP7e+idRtwfHe58lXQiLeX345v1Z/7fbGaC40JV0nEg4RnWVjB1rFuDfrf58JBpiie9r+px/XdLzMpp2wJrTnFDUT01gR8p5bnP9mbLvUOP3OHFUFuGoLMK7fgPurxrEO0BowxaqNm/nxR3lfFZeizMSJQas8/q5Z3MxW337VrP7v0LQ42HunXfHiXeAgMPBN6PG4C5OXQN9T4hyJdbsZgwYP4Hh4+9l8zPPc8YZyePPm7QwkV3Q+MaRh4NQwI+9YgeFaxYQKndxaev3kBG/e64UtFza9gNMykxEmQyVN8LXL6/gXJUejRB/rAy4Pj8dqxQtIiFx2JB24CUkJA4KR62fNyYtZvmi8vq29Suruf/aWTzwwhDadz1wAymTMouxTR/n9XWJ9dt1ciudrKOxqHKBugcOndyaslxQjrItkeqf6v+WaTUYzx+PoNmze3V1pZeibU62bKglO09Py3ZWbOla3PYypr9zLxsXT4NYDEGU037AaZxy40tMefEGNFYbNeFEw7Fd1AaLCIY8aOSpDeRkooiiTQsyXn6MSK2DWDCIaLMgmE3IVErSnryH6nsnEXU2iCJVn24YzzglIa9fUCoQDjJV4Ggg4HXhrq17r4kKFaOu+5A3niujrHhL/TFTPtnI5bd0Z8DwAjS6oy8vXqHV0uvO29n4+ZfE/lE5QSaKdJtwI3KVKuE8jc0GNttBX1+uUBH27Dn8N2jf8+LSLmL+AOHNhanHWbWW2GmjkCnkew12iURiaAyWOC8DZ6icHwofoodxHKPveJ6FH71CTckWRIWKtgPH0GvsFRgsmYQDRRgU6bhClUnHztF2SNru9zjxOqsI+jyotEb0loxGSwGJhMNUF2/i1/ceYtvKuQhyBR36nkz/idcRevYDolV131UyrYbagjxWO/0JY0SBySVV3N8sG5NCemQD8JZXsPGLr5L2Bex2atatj0sB8Tqr8TpriISCqPVm9NZMRHHPr6WoVLJj1mzQmrjp9qv54vMSSna4UCgF+g3K5IxLOmGxHdpUoUOB3+Ng1ZyvmPnBI/Wu+x2Gj+fesxYxv+YDyr3raWUaSJ+M87CpG1K4air9zJ1eyI5tDm67swerhTDbCJOGSF+5igKZAoVkXCchcdiQfg0kJCQOiuoKb5x430UsBu++uIz7nhuEyXLgJZ7amodyTouX+Gb7PfXGVDna9lzV7nPS1c3rjzOpchhTcC9fbL01YYwMdQsyVC0IG/XE5HLUfbujP/Nk5Fl73oUuK3LzyC1zqCpv2NnX6OQ8+UZfZv7vRravmlffHo2EWT3nK4jF6DrsXGq2b6Vlz/5scS5IOnYzdQ+Ufjns5RlQJgiI6TbE9EThpmjZjPTXniBSVkHU4UKel41gMdfn0/4bkStUyASBWDRK237jmDnNQ1mxO+G4d55bSofuGUelgAewtGzJqd9/y4xLr6jf7dZlZTHqg3cxt2i+l7MPDq3JhmcvpQNVFvO+DaaQo+zcnuCqdXELSbsQ83Lg7505vVFJZo6O8hJPwnGCKCMjU0ZB/rlxbvTRWARfxMm82nfZolnA0BuuxSrkERViLPV+x3zvR5xJV0zKHE7Mvyvp518l6mlp7J/Q7qwqYdrbd7Ppr1/qFuHkCrqNOJ8B425Cbzn48Gh7+XbevWtMff5+NBxi1bxvKFy/iHOue57AxLqUH3l+DqtTp1qzzR/EF41x8LUi/h1E/H5ikdQvmLu0wTOlqmgTU168nrKtKwFQ60wcf8F9tOt3EkJUwFteQcjtQmk0os3MRKmve+/pc3IY+b+3mXLqWCwLfues629De2ZzZNEwBhNY04/NsoVl21YzY/J9cW1rfv2KDXN/5MoX56BtloZCUCPI4sX45vV1i03bN9p54rKZtO2SRnYzI84qP88sKuOpySPAdGy+JhISxyKSgJeQkDgo1ixLvuMFsGOrA583jMly4OPrFFYGZ19NV9spuENVyAU1BkUaRmW8+BZlIv0yLiAQcTF15ySC0bqH5tbGgVzS5j1s6mZEn30IYlFkej2Cas/5em5nkNee/DNOvAP4PGFqK8rjxPvurJn3HeNvf4evnr6ci6+ayuzy1+rn0jBXOSfor0Yoc8BBCAWZICBPtyFPIu7/rWiNNlr1HMnGP6fStNupfPlw4uLRLpYtLCUn/+hczFDodDQ7cRTn/bUIX2UlyGRo0tLR5+YkuLA3NgZrFsF8F01GnUDhtBkJ/U1HjUSbsff3pbukBPuWrVRbVRjHj8BgtBD+4iciJX87dwsyfN16Eyr3kp6lw2LTcPWdPXn0lrlEIvHb8WPPK8Bbu5GmfYfHtatEHc0MvdjmWkypbz0f+26s7+tuGssY9SUsn/kpfredroNOpjxrA3PL3ib2d6VqoyKDGzr8kJCu43FW8+3z11C0viG0PhoO8dfUd5EhY+gF9x3UTnwo4GXeVy8kNd9zVhWzs3wdOc0LCG/dQSwYQk/q8AS5DATJH6wepcGAJi0trhrD7qR3qktPcVQW8eH9p8c5y/s9Dqa+cTtavYVYoZOA303I5WLTx19jbdeWAY89ij4nG5kgkH/8EM5b+ieLn3yK9a8+i6VNa/rcfSeWtm2SRsgc7fg9Dn7/8rmkfeGgn4Xfvc4Jlz6cIN4BjKb438v1K6pYv6Lu9ZcJIFdIu+8SEocTScBLSEgcFDpjaiEsijIE8eCfPOWCApu6SVxIXzIMynRG5d1Fv8yL8IZrUQpaDIr0erd30Wre52s67H42rKpOaFepRbyO1PnBsWiESCRMLBpBXyVya+53fFB9K8Xe1QBkalpxQdozKN+dh+yCc/Y4h1gshr3aTygURa4QsNjUBPxhnLVBwuEoGq0cS9qxF8Z5MKi0ekZcMpGqoo3IBDnhUKIh2i48rkSzpaMJmSBgyMvDkJd3SK8T9vvxlJZSsWw5gVo7mb17oc/OYtjrrzD7+gls+2lq/bHNThzF8LdeR23Z86qbfctWvhk5GvuWhtQFXVYWp33yMeK7XxG1OxGvuowvfiinWYcYo89ojSDIaNnexqT/jeD7T9azeV0t6ZkaRo8vIDtXicHcCq0h/rp6RRpnNX+eSSsG1otygD7ms+lSNYL/PXpCfRrCrI8eZdiVDzG8383UhorQiEZMqhzMypwEUeKprYgT77uz7JeP6H3ylVgy9/x9syf8HifbVsxJ2b9h7RzyWx5HeOsOwtt30lGsy0JOJuMHmPWYpPzienQ52Rz32MP8etW1CX05x/VHn1/3eSre+FeceN+d3z59ksFn38nstx9DrTfTecIZmOUZzL/vAY5/8TmUBgMKnY6MLl04YfLbhNxu5Fpt/Q790UgsGiUSDCKqVEkXAcNBP/by1D4R1cUbCYcCSctxNm1lQaEUkhpQ9hqQi9GcfEEjEo5SW+Mn6A8jygWUahGTWY0grUhJSBwUkoCXkJA4KNp3SUcmQCyJjup7fB5G0+HdqVCIKtLEpkDTgxonFEguDIOBCCrdHsSNTIY5o4CrX5qHWmHA+OQXXDP8QYLNzcSIoSr2IntzDlG3ByFJnexdOO0B/vqjhC//t4bqSh+WNDXjLmyPyaLi+YcWEo3ESM/ScuH1XejYLROt/ugMFT8UmDPyOX/iVzjtflq287J5XW3S47r22UMB5H8pEZebqMMFwSAyvY6YXkvhjF/56axziAQbFjRajRvL0Fde4sSPPsBbUUHQ7kBpMqHNSN+rePdVVzP1govixDuAp6yMHy+7nFO//Q67S8EX3xSx4q8qKqpCHD+mGTq9EqVSJL+piStu7YHPE0ahEtHuJc0hX9+V2zvP5pMtN1LkWYlCpmGE8UY+fnhcnHleLBrl1zce4HjXPfQ99VrCBHGEyij1rkUl6jEps1AIdWG+zuqS1K9hOEjQm9xsb1+RCSJqvRmPI/kusVZnAU9dDrKYkYZZq+Z6o5FXiirjRHy2UsHZmVZUUn5xPYIo0mrcWGSCyPx778dbUYGoVNLu/PPoN/FBdH9Hj+xcvzjlGNXFm0G2Kz++mt++mkSzToNo3aM/3vIKlIaGyB2FVotCqz3k9xWIePGGawAZBkU6cmHfXN3Dfj/OwkJWT36PqpUryerdi3YXnI+xaRPE3bxQFGodGQVtcVYWJR0np2U3FMrkC8IWm5pbH+3P03fPj4ueyczVccG1XdBoEz/DPm+ILWsrUHirKJs1DU/hFtJ69afJ8QOxtmiKQiktSklIHCiSgJeQkDgoLFY1193dm1cf/zPOpCojW8fZl3dCrTm6v2bCoQhOe4AYYDAqUarq5qszKtBo5fi84bjjYzEoLRFJL2hL5Y71CeO17D4MS1ZT1Lq6Umamq86n6vZHEf8uBRcBUChIe+JuRFtyoRQMhJn+7Wa+em9tfVttlZ93nlvKqLEtGTC8gLnTC6ks8/LsfQu4a9IAuvU98mLV7QpSVe7l9xmFeFxB+g7Jo6CFGeshiBIwWLMwWOGSCUbuv24W0X+EZLfvlk5G9oFXGDgWCZdWUPviWwSXrQFAptdhOH8cjnXr4sQ7wKavvyGnfz+633QjarM5YSxPWVndf+UVGHJy0GZl1rve+yorKV2wMOkcHNu2UVlmZ+ITxfXfB3qTArk8XoAqVfL6z9reUIk6WpsHc0unXwhEXCgFHSu//wRiMQzWbAS5HGdVcf1O/OKfJ9Nm5ClMLXmSeWX/IxwLohDUHJ99LSPzb8ekzEJnSm2uKRMEFOqDe+/ozen0OflKfn7jjqT9PU68BENQh/Gs0xAtZkSbhT6RKK20av5wuKkJhelu0NFcq8ImmdcloLHZ6HjpxTQddQIhlxtRrUKbmRkntNPzWqc8X2/JxO+2x7VtWzWX7teeS8CZvGzcoSIai1Lh28wPhQ+zrPpb5IKSAZmXMix3Ara9VGqJhsMUzf2d78acQjRc91u1ffoMFj/1DONnziD3uAbvB5VGz8Azb2Xz0pkJVSNEuZKOQ85m+Z8V2Gt8NG1pwZahqfevUShFOnTL4LkPR7FySRmVZV46dE2noLkZa3ry7/eqEifRrcuZev4ZRHdVXXjvXZZmZDB21q9kdGh3oC+ZhMR/Hlks1ghFkP9FOJ1OTCYTDocDo/HoriUsIXG04PeFqan0smD2TqoqfHTvm03zNhZsGYd+1+JgqCzzMO2bzcyZtp1wOErfwXmcdn47MnN0RMJRpn+3hQ9eWZFw3ojTmzP2LBtfTbqYyp0b6tvz2/fltAmvYEzLrW+LRSJEKqrw/bGE4NqNKFs3RzOwD2JGOrIUYbEVpW5uvmB60vBwUS7j1kf689Td8+vbcgoMPPjiEMzWBhOhcDSEPVhMiWcN7nA1BfpumJTZGBRpB/JS7RW3K8j0bzfzxeQ1ce3NWpu54/HjsKYfmvdCKBihaLuTj99cxdrlFeiNSk4c14ohJzY9Jl2iD5RIVQ2Vt00kkqS0oPqSM5n+/DOU/RkfMq7LyuK8vxahz8mJa7dv2cqUU8dSvabh3zLnuP6M+fQjDPn5lC9dysc9+qScy9DPv+eZt7314bb3Pz+Ijt0br2yht7IST1kJOo2e8PYiQh4nkVwba1fOZP7Pr9P7jKvY0mktS6q+SDh3cNZVnNHiGcJODx8+MI6aki0Jx7TtO4aTrnsBlfbgwqXdtRX8+NotbFk6M66918kT6D7yUtKyj/064kczteWFvHXTEMLBRHf/wWffweZlsyjesCSuvdcJl9H3xKswFuQfrmlS4dvMI0t74ovEV37IULfgti6/YVWlTrFx7tjJR1174K9NjEIyFBRwzoLf4z7fQZ+HbSvn8vMbt9enF5jS8xhz3ct88ZGPpQsbIkbadU7jxgf6phToe8LnDbFz+UamjxxAyJ1oMprdry+nTvkWbfqh+T2SkDhW2VcdKi3rSkhIHDRqjZycAiPjLkpequlopKrcy8M3zaGitMERe/bP21kyv4TH3hhGZo6eQSc0QadX8PnkNdRU+tDpFYw5szXDTmqO2abm3Ie+xF1bjtdZjd6Sid6cjtYYbygnE0Xk2ZkYxo0hdvqJyPYhFNZpD6bM7Y6EY4SC8Q7MJTtcBPwNkQLhaJBNjt95ec0pBKMNJnxdbadwfss3MKsaf7e+utybIN4Btm208+v3Wxl3cXtEsfHDgBVKkWatLdw8sS9+bxiZIMNsUTeK90Jj46opw+eqBRlo9BYM1qxGGzu0szipeAcI/vArva67lh/+IeA95eXEIvHvM295Od+fPj5OvAOUzP+DX666hjGffITaYkFUqYgEktcnV6VnEgrWCeMRpzanoLn5AO8qnlgsRvXqNWz+8mtate9M7Qdfw25u5O1HDsRy4WNouzXj820PJR3j97J3GJl/OxmWFpx1z4d88cSFdeHUf9Ok43GMuOThgxbvAAqtlbZD76fd4KspWTcLUaEht/0w/lrk47E7lvLAC4Ox2DTEYrFDblz4X8Roy+XcBz7nyycvxuf+W+DKZHQaPB6t0ZYg3gGUGh2qPaQ27SIcjlJT6WP7plpqqny0aGslPUsXt4i6L4SifmYUPRcn3lWinram4xEFBVudC7Gmj095vresLKl4B3Dt2IG3sjJOwCs1Olr3Gkl2iy54XTV1RqhKE08/uJZtG+xx569bWcU3H6zlwuu7olTtX7h7wB/BX7Q9qXgHKF2wEG9VtSTgJSQOEEnAS0hI/CdZsbgsTrzvwuUI8suULZx9eUcMJhWDRzWlc68sgoFIvZHcLiGqN6ejN+97nft9Ee8ACuWej5Mr4h+mNFp5XIhybaCIF1ePJhyLD5teXv09+bpunFRwH6LQuF//c6ZvT9k3Y8oWhp/SAmu6Bre9kmgkhFKlRa03N9r1dXolAX+Emkof2zbW1j9MpzJXOpyEggGKNyzmh1duwllVDNTtep18w0vktuqOXHnwcwxt3JayL1pVgy4t8UE5q2dP5Jp4weEpL6dq1aqk42yfOh1vRSWG/Dy63zyBxU8+lXBMm7PPwhvVc/r5bekzJI+0DC2GRvLBcBYW8vXI0Yz/5BP8T7ya0B+a/js5rc+nZg/BHlEieMI1QAus2c04f+LXuGvL8TiqMNpy0JnTEhbhDhR7tZ+n71+NQinQvPUgwqEomz/cSiRcF/hYWeplzbJKFs8rplU7Kz0H5mAyq5PmE0skxxeJEiWGTkwUmKJcjrFZEy58agqemipCPi+WrKbsXLuIn964Lel47Qedimov0ZfhcJSNq6t48s55BPwNC0gt21m55eF++xV55vJXsqL6ewBkyBjT5FmyDWP4y6MnFBNAUFJud2AmhipJqsuusPlUJCu3JxMEjGk5GNPqhP2fc4vZvslORnZd5Fl1ZUPlhN+mbefU89qSnrV/KSVKlUjEl/j7Gje30NFtMiohcTQjCXgJCYn/HD5viPm/pnbj/XNuMSed2QazTUQmkx2SHO49YTKrycrVJ61vnp6lxV4dX5pqxGktMFkaRNI6+8wE8b6LmSUv0kN7GtSEMGc1Bb2VKDH0oohwELuALmfqhzGfN0w4FGTl7B+Y/81LuGpKyWnZlePPu5f0/NYoNcl3OyPRMNFYGIW4912tou1OnrzzdyrLGiIOOnbP4Lp7eh2y8P19pbZsG588fDaxaMPDtKOyiE8ePosrnp1JWl6rg76GPCd1iLpMqyHkTwwjHvTMJDT/EPb+6po9XifociHXaOhx8wSURiNLJj1NwOFArtXS5Zqr6XHrzeizs+gx7MDuY0/s+HUm2b17EVuyMuUxsZ/noR101h7HUYsN7ze9JaNRar4nI+ALEw5FCYeiScttbttYy87iCoaeq2Fn5Bf+qHXTUTGMTFkTTBopvH5P1IbCbPT6mVrtJByNcbzVQFe9Fpuy7rHWH3ax2TmfjzdfR6V/KxrByBl5k8ijNW2b9cN264f8MfNdNi9rSG8YdNbtGNP3XhGittKXIN4BNq+r4av31nLxhK6o9sHfIej34qwqQfX3+/Gkpi+yk5P4sCgK1H3Hz7V7aKWUc5nfQYbZjqlp07gxdNnZyNVqwkk+3yqzGU3a3heYNXI/D97fnNpVyxFVKvRtOjLjl1oWzq8iFIwSShENFovFqCr3sm5FFZvWVlPQwkTnnpmkZWjR6hTkdO+U8prazEzU1oOoLysh8R9HEvASEhKNSrV/B0XulZS615Klbku2pg1GIQON4ej5sRZFGWpt6q8/lVp+REOwzTY1tzzSj4kTfsPjCtW3a/UKLr2pG++9vLy+rUP3dEaPaxW3K1/lT70b6w3bsVftJOg3sMrhZU5lgHAsxmCLgX4mHWnKA9v96398PnOnFSbt69o7k9KNv/PjKxPq2wpX/8F795zE2fd+RItuQ+OOd4eqKfOuZ3bJq3jDdnqln0Vb81Cs6uR5qTWVPl57/E9OPD2TgiYC0WiIQFDNtCnVfPLWKi6/pccRM1MMBf0snPJanHjfRTQcYvHP7zDikkeQK/bNcToVitbNkWk1xLyJdce1J49g/cqVIJNBLIapWTOOf/lFMrp1TTw2K3VYv0wU68OLtRkZ9Lz9Vtqddw5hjxdRq0GXlXVI62Pv/G1unZFebWqTsUh1LQaZjRxtB0q8iSkdLQz9MCj2PWrmYFBrFcgVQsp0mMw8HbGOK3ml7Nr6Enk/V0M321jOb/UqJmXjpVj8m6gNhXl5ZwUr3A3v9XVeP7kqBQ80yyFNKWeHZzkvrD4RAKWg4abcLzF8tY7q2XdDOIKo0zLyjLH0Ov581q74hR4jL8Sc0aTefHRPbN1UmyDedzF3RiFjL2xHetbev2/c9gpmv/4I/S++gGmhZ0jXj+SDosT3yqZgmOUaPdr7HmTw00+iz25IgdJmZTLwqSeZfeNNCecNfeVFdDl7TpfyVlRQ/slLrHz1tfo2QS6n37MvoT2hDcuXOVGrk9/Lzq1OHpowO+43SqUWuf/5wbRoa0WbmUGHyy5jzeTJCecOef7ZBO8NCQmJfUcS8BISEo1GsWcNz64cijPUkItrUeZyVcFnZIVbH7Kdrv0hHAqgUCgYPa4Vf80vTXrMieNbHvHQ64LmJia9M4JNa2vYtrGWpi3NtOpgRRQFzruqMw57gJbtrNjSG5yCd9HKNAB2Jh83S9MGnSWfT1wWNjjCQF0I5rbSaqZWO7g/z4Yx4kdn2r/cxCYtzTRpaaJws4N2nW2MHG5GowwTExTktM3n4weHJJ4UizH1zTu56PEf6vPB3aFqftzxKL8Wv1B/2KraqaSpm3F759nY1Im1uWtrfFxwpY35n97O8i/rRJtGb2HY2LvZXtQSR60fdYpd/kNNyO+hbEvqHePSzSsI+j0HLeDFNBtpT9xD9f1PEXU2lEBTD+iN4bRRdBMF2l5wLtFQGKXJGCcCdkebkU7B8GHs+HVmQl+Hiy9Cm9mw0y/K5RgL9uyS3ZikderIlik/QJ8BsCTRXBJA0bYFRjGN6zp8ywurRlHp31rfl61tx+VtP0Z/iIwc/4nZqmLYyc2Y/k2iUV5uEwPmrBivbL8moW9Z9Te0twxnSPbVUm58Erb6AnHifRfFgRBz7S5GWgS+3HprffuZ6Y+hf2MhoeXr6ttiHi+e974k7eIzOfGyxxH3Y+GppjLx2rsIh1LvWP+TonWLKNn4Fz28lzI0ewKLXDogcScdYHZM5IIRI3Bs3Rb32VVoNLQ7/1xs7dvxx4MTsW/ajK1De/o/MpG0Th0R5Xt+zC+aMzdOvENdWP4fE65l1Izfad+nE5a0xAgoe42fFyYuiBPvUJf7/vS983nireHYMmwc9+hEMnt0Y/ETk3AVFZHepQuDnnqCzN699jmlTEJCIhFJwEtISDQK9kApr645LU68A9QGi/mo9FrOUk2ile545Mr9M/lpDGKxGI7KnWxcPJ1ty+dgyiyg67DzufyW9rzz3Nq4Yzt0Sz8q6ofLZDLSs3SkZ+noPzR+53lvOZZ5us6kqZsl3Yk/Ke0eyqM2NvgTQ97Lg2F+rawic+6H9Dv5iv0yWbOmabjzyQGUrC/CtegXll/xGL7KSuRqNe0uuoAxFz/Bd2/eGOcILYhy3LUV+D3O+mtV+wvjxPsuqvzbmFH0LOObPY1CjH/Y1ijsTHn5vAajKsDnruW3D+7gxOv+V59zfCiJRiJ4SkuJBAKIKjX6nOy/DaLUWLKaxlUr2B1LVlMUjfCZkIkCitbNSX/tCSJlFURdbuR5OQhmE6JRjwioTHs359LYbIx89x1+u/lWNn/zHbFoFEEup8OlF9PvoQdR6o/MQghAq3Gn88f9DxJ97FFkBh0x1z9ybAUB44VnImg1ZNKKO7v8TnWgkGp/IWnq5thU+ZgOgYFjKpQqOaef3w6/L8zc6YXE/tZ1LdtZueDaziz2vZjy3OlFT9Pddtphne+xQDAaZXp16giMWTUujjOZ2O6qM6gTZXLaRHsQXv46ipZNUffriUwhJ7huM/5FS3F//j3aoQMgc9+jMlq0tabss6Vr9jnaJ+CtW2ib+tRtjJ34AesUqQWtPxpD1GqpWr06rjQcgNpioWDYUDK6dSXs8yPXaZOWhvwn3qoqFj0+KWV/8Tcfc9zTzyRdRHLaAxQXupKcBY6aAPZqP7Z0LfqsTLpcfSUtTzuFWDiMqFbXl6M8WJzBCir9W1hc8TmCTKR3xjmkqZsetgU6CYkjiSTgJSQkGgVXqIIK/+akfUWeVUSt4LZXYs44fOV5dlFdtIn37zs1ru7vX1Pf5aTrXuDpyccz9ZtCQsEoQ05sSl5TI+bdyo9FwqE6p3lXDXKFCq3Rtt+704cbkzyXm9r/whfbbmJV7U/EiGFW5nBy2n0oy9RMzZIDyXPWFwRknJPXmnlfvcCIix/arwUXs0VJ4bIZLLz95vq2sN/PqjffpnbjRgZeeTOzv3yCvPa96XbmxUR0MaKEiZhjBCM+lKKGRRUfpxx/Xvm7jMq/A4sYn6davvXPOPG+O3/9+Axjb+8GGPb5PvYXT0UF6z/6hD+fmISvqgptZiZ977+X1meOR5ueTr+xN7Bx8fSk5/Y95RoUqsbxWJAJAvJ0G/L0gzNhM+TlccLktxnwxGOEXC6UJhO6zEwUuoOrjX6wGAsKOP3nH/j17rsZ+dTTRL6ZRmjdJgDkedmYb7wceV7DopNZlYNZlUMLY78jMt+Az00sWMn487M59ZyWeN1RNDolao2cWT9voarPupTnukKVREkepv1fJgbsaX87SgxiAgZFBs5QOTq5DcrtWO68jkhlNd7f/iDmD6Du3gnbI3fgeP19Yh7vHkZMJCNbR/O2Zrautyf0nX9N5332TMlv3xeAcNDHzFfupsetH5Hoi19HN4VAxfTpdDwztSO92pp6YQHAUesnHIoiVwiYLGqigSCekpKUx7u2b0MpT774mSotZBe7pxjIZLKUET8HiiNYxsebrmVp9bf1bTOKn2NA5mWMbfYERuXhSZORkDhSSAJeQkKiUQhE9uw4G4r6iYSSl506lPhcdn5684448b6Ln9+4jatfmsdVd/RKeq7XVcvquV8x55NJBP1195fRpD2n3fw66fmtD+W0D5hoJMaWDTW88OA6+pxwD1f1n4g1E+xbN7D8nQ9Ib94FsgamPF8GEIuxYtZn9Dvtur0uuMSiUZzVJVTu3IBWbmLBgxOTHlc0ew7d77mFNgNGk3PqAN4uvRjf37nM8mIVZzR/iuMyL40re/dPwtFAfa7w7hRv+DPJ0XVU7FiHXH7ohFDQ5WLRY0+w/KVX6tu85eXMuv5GvOXl9L77TtLyWnHiVU8x43/3138G5Eo1o658EmtO80M2t4NBZTSiMhqJxGLUhMKUR2MogyHMchHlEQp9FZVK8oYM5qQvPsVdXILmsjMx6gwIgoBoMiBazIdtLrFItC5VQQaCyZiwS+mqLuPX9x9i3YIfiEWjyASRjoPGcvx592CwZtGjXy6bZCeylG+Sjt/KOAC1eOgWnY5VVILAcKuBZa7k3xODzQasSi0j827jy2234484MRa0w/PqJwRXr68/zlNSjm/uIix3XQ/76dtgtqq57ZHj+OLdNcz7ZQfhUBRbuobzrulMl977HrVktGXTuvcoNv45jerizfR1FJGryqc4EB+WrhUERoT9zJ02nT43XEfNhg1oMzJQW/bNV8blDLBueSWfT15DaZGLnAIDZ1/WiTZtNGT17cPW739Iel6TE05Ark6+gGsw1S1E+X2JLviCKMN2ALXj94eNjrlx4n0X88on0zvjbNorhx/S60tIHGkkAS8hIdEoGJUZyBCIJdkfEWUKVDEtCtXhdwP3uWspWrcoaV80EqZs60osWYl51QDbVs7ll/89ENdWUbiWjx4Yy6VPTcO0D47Fh5vqSi+P3ToXnzfMTx8V8dNH0H9oFq1zNlK6aTleexX9T7iSpSnOP04to3D+t0RCASJ7KfMTi0Yp3bqSTyaeRcDrZNwVrxGw25Meq8vOJhaI0Ofc63l0Qx8iNDykhmMBPt0ygXxdN3qln8VvpW8kHaOr7VS08sSH1vSCNinnaErP22se6MHgrahgxauvJ+1b/NQzdLjkIkzNmtFp8Bk07zqY2rJCZIKAOaMAnTm9UcLnDxWOcJjZNS6+rrDjjUZRyGQcb9FzRqYVq+LIPD7syrs/nLn3/yRcUYVv5u94Z84DmQzticPQDOyDPL1uB9TntjP1rTvZtGRG/TmxaIRVv31JJBxi2OUPkdfMhtY3lJm1udQGi+PGFxAZ2+xxNPK9pzz8F2mtVdNao2KjL35BOF0hZ6jVgEIQ6ZtxPlucC7GJuQjV7jjxvouo04V39nxMV52/33OwZWi59KZujLuoPeFQFLVaxJKm2S/PAq3RxolXTqJJx+NYOOV15rx0HTc++DULDEZm1roJxWL0UMkZHfWz6oYJDH/zNX4653xq1q2j3fnnMuiZp9Blpq4+ARAMRJj14zY+ebOhNOTOrU6evnc+l93Sjf4PPcC2n35OKDentlhoefopKcc129ScfXnHOEPVXZx0Vuu4qiiNjSdUw4yiZ1P2/1L0PC2M/VCJRzZiSELiUCI5SEhISDQKBkUmg7KuTNo32HYlcq98v3KqG4toEvfv3QkFkxsSuWvL+e3jJ5L2eZ3VFG9MJYGPLNs32fF543dFFswuQ5N9Gj3HXI/XWY2+bCMdNImmadlKBYONegKuGvTmDBTqhgWX6mCIVW4vP1XZWeb0UBUM4XZU8tkj5xLw1u2kC0plndP5bqgtFk779BPGvf4maVsr0f66ngezZjLQdFHC9b8rvI90dQvamo5P6FOJek5r+khcCbBdtOo1ElGe3ARu4Pib0Vv2/JCbikiNHf+27VRvXc/KXz/lj29epmjDEtz2Bp8HT3lF0lrLAJFAAF9VNQAKlRpzRgHNOg+kacfjMGfkH9XiPfR3rvGHZTV4o3WLcqFYjBk1Ll4vqsAVbrhnpz1AdYUXe01yA64jSdDvxeeqJRLZc73sfSVcUUXV7Y/gfO8LwjtLCO8oxvnmB1TfP4lwVV0JPq+jKk68787a+VOoqt6EX1ZFtrk5t3X6je62scj+fhzL13Xlji5zyda0a5T5/huxKuTc1jSLa3LTaKZRkq9ScF6WlUda5JD+dxUNkyqLy7JeZ/SO0/DNnJdyLP/8xcT+seO9r6hUcjKydOTkG7Cmaw/IcFBvyaDX6Mu45MmfuWTSz+RZbJyTlcbTTTN5NtfGmZ5axDVraX/eucy55XZq1tWlXaz76BNWvfk2kb3UgbfX+Pny3cRqDAAfvbYSMasJ43+djqV1Q0RZznH9OWvebxibNCxsx6JRYtGGxXmFQmTACQXc8nA/svPrvpPTMrVcfWdPTj6rDWrtgVUz2RfCsRD+cGofBF/ETiTWOJ93CYmjFWkHXkJColHQyA2c0vQhzMpsZhQ/hy/iQCe3MtR2LV3UJ5FhaXlEXGfVWiPWnBbUlCQ6QQPktOqetD0cDmIvT14WDaB401LaH5d6h+JIUVGWmMoQi8Ebz2ykz6CBXPLUechkYa5WqFkTDTJHVBOKxegXDdKqppRpYy5kyHsv4w7V1C+4lAVCTNxaQkWo4aHIKArcl29DrTfV559v37yQZmNOZNuPPwMgKBSM/fIL+PxHAoW77TJ+ACfecAaxgijz7B/WN1f6tiDI5Fze9mOWVX/LzOKX8EWcdLaexKj820lXJw83N6XlcM79n/LVU5fi9ziAunzwXqMvo2XPEQf0OoZLK7B/9g3VndP55q2biYYbHvJzWnVn3O3vYLRlo9DuOaokVQjq0U5tOMJ3FfakfUtdPuzhCII/wpZ1NXzy5ip2bHWQlqVl3IXt6dYnG+Mh3IHbF7yuWip3rGfh96/jqa2gWZfBdB12Dqb0fARR3PsASYhFo/hm/0GkrCKhL7xtB8Flq5GPGIQvSbpOwyAx3O4qdlatYXDOVWTqWnJpm/dwh6qIEkUjGjFI+bt7xaaQM9xmordJRwwZelFA3E1Ax0Ih/N/PJLyjGJk2dTi37BBG5+wrMpkMgzV+kTFdpwUdbJ5byNTzLkha5/2v51+k/SUXYcxPnebkqPUTCibPVw/4I9Q6YrQcMpgz58wkYLcjE0TUaTY0f+fTu2srqNixjpWzPkMmyOk6/FzScluhM6dhMKroMziPNp3SCIeiiHIZFtuhDZ0H0MmtdLadTGlRYlQFQPe0cVL6icS/niP/zSUhIfGvwaTMZHTBPfTLuIBA2IMYk6ONWdAZbIjikfm60VsyGH3VJD6eeGbcDgJAj1EXozcnf1gWRQV6Sybu2vKk/RkFbQEIRnzYgyVsdS7EF7HT0ngcZlUehiPkhNu8TfK8yFgMNqx2IKoy0cr9TDltHN7yCo4/60xkCgWlP/zI9D/rcsmrFiyny3V1Jayc4Qgv7CiPE+8AzkiUp4truezCh5jx1MUArJjzKWPvfB3HtkJq1qyhzfhxiH+tIVhY/M/pEHrlK054+Rrm2z+uT7vI1XVCJerQyI0cn3MtPdLGEY1F0MotKMXUD4aiXEl+u95c/uxMnFVFBP1eLFlN0ZrSUGv3/0Eu4nBR8+TLyC4YzTdPnUn0H7u3JZuWsnDKawy94D60GRkYmzbFuX17wji29u3RZBybYswTiRKIpXbvd4fCbFtQyauPNfgPlBd7eO2JxYw5sxVnXNwBje7Q7cLtCb/XyeKf3mHel8/Vt5VuWcGSqf/jose/J6PgwHa3o04X3pm/p+z3TJuNun9PVHt5z8nVaqYXPU3XtFMxKbNQyw2o5ZLgOBCMKQR4pMaO59tpoJBjvuFSfL8m/3fTnjAY0bT32u9HiqrVawj7/YhKJdqsLIJOZ32aUsBuJxrcc5qTXL7nRfNd/bqsLHRZ8RFyrtpyvn/xBravanjtVs/9irb9xjDy8ifqfzvN1sO7SCkXFAzJvpp5Ze/gCcebl5qUWfRIG4cgkwKMJf7dSAJeQkKiUREFOWnaZkd6GnHktOrOpZOmMeezpyjZtAy9JYP+4ybQtONxqHXJ80z1lkyOG3cT09+5O6FPodbSpEN//GE3K2q+Z/L6C+Mco7vbTue8Vq9hUh7+lIGsXD15TY0UbU8MMTz7yrqavu6SGqpWrCTgcFD78CMJx5XNX0ivW24BwBmOsMmX3HywMhRGnt0KmSAQi0YJ+j1MeXsCxz9xFxr0ZBe0xnP/08knGoshLCukScvubHfXeS+f0uQhNPKGh2mjct9D3wVRjik9F1N67j6fk4qo3UHM66No+4oE8b6LZb98TJ+Tr8KUk8epU77hy+OH46+pqe/XZmRw0lef7TVH9WhFJew5HFjvjvLsK8uT9v381SZOOK3lIRXwztoAHk8QQZBhMCnR6hpSKDy1FXHifRdBn5vpb9/D+DvfRaM37/9FZTIQ9yAMRLEuJ96URk6rbpRsWpZwSEHHfmwJL6pzmZfCfA8ZMZ+fWCAAgQCR8krUA3rjnxdvdilmZaA7dSSy/fRziNTaiQVDyEQRwWJGtqf3xEGS3asXA154EkuXdtRUbEdrsKEIiSy673ECNTUgE3CXlqJJT0/q9WGyqLGkqamtStzBT8vUYjQnRsrEYjG8FRVsWTUzTrzvYv2Cn+gy9Bxadh/WODd5AKSpm3FP14VMKXyQv6q+RoZAn4xzOangPmzq5J42EhL/JiQBLyEh8a/H73GyffUfZBS0o2WPEcgVSjLy26BUp65pLZPJaNf/JGpKt7Bk6v/qd+915nTOuPM9jGm5lAc28c768xOc0ZdWf0tr8xCG5dxwQHmRB4PFpuGuSQN47+Xl/PVHCbFonWPw2Zd3pEe/bGQyGaJSiaFJEwIrVyYdw9ahPcLfD4PB6J5rqAdFBaJcRfhvLwGfu5af37uT0Vc/jS4zA3cg9Q6RzOlDKWrRK9I4v+Xr5Gg7HOBdNy4xfwBBp8Xlqkx5TDjoqxf3aZ06cv7SxVQsXUrl6jVkdO1CRtcuGPYQ2nq0YxJFuuo1LHcnekRYFSIhTwiXI/m/bSwKZUUusnIbt2a8x16F1+2mslLJey+vpnCLA5kMuvbJ4qLru5KdX7eLXbhmQcoxdqxdiN9tPyABL5qM6MYMx/Hy/5L26085AUGrQYeG0295k6+eupTybavr+7NbdqXnpVfzWtEZtDINQi0evTu/xzoylRLkIoQjON/7AtPl56Lp2wPf7wuJ+gNoBvVF3af7fpVcjLo9BNdsxPH2R4R3liAY9OjGj0F3whBEq/mQ3Iepc2vmzHmVnS8/U9+m0Vs4+ZVniJS6mXvbHVQsXUbb886l85WXxeWtA1jS1Nz8UB8evXUewUDDIrNKI3LzxH4JJe8iwSAlfyygdOliVpf9mnJef/74NgXt+6JUHxmjOJlMRqa2NRe2eofxzZ5CJpOhk9v2GKklIfFvQhLwEhIS/2o89kqmvHQdhavmx7XLBJELH51CXpseKc/VmdIYfM6d9Bx9Gc7KIpRqHXpLJgZrFjJB4M+KT5OWNQOYXvQ0PdPOwKxq3Pq3+0J6lo7r7umNyx4gGIyg1Smw2DQIYt1igjY9nX4P3scP485MOFcmirS/8IL6v3VyAaVMRjBFOHWuJY1Og8ay8rcviYSDaI02Bp9zJ236jEaGAkWbloQ2bE56rqF3H85t3get3IJZlYMgO7Dc5MZGMOgIl5RTsAcXZltuq3qTP5lMhrFJAcYmBbQ8/bRDNi+vq4aQ34tMENGZ0w9pWopOLnJVXjpPbCtjx26LMCa5yH1NsxHL91wSUqVuvLm5HVX43XamvXknXcY8wuN3LyEaqXs/xmKwbGEZWzfO5rHXh5Gepatr3AOpPrP7gqZvD7zTZhPatC2uXdmlPcp2rer/NmfkM/bud6ioWo/HXoXGbKVYto7Xi88iGPVyetNH4qJNJBoXwWpGO3wQ3mmzIRrF8dZHCFYz6t7dkFtMaI7rjWjZd5f/WDSKf8kKap94ub4t6nLjevdzwlsKMd1wKaKxcdMgQkE/f0x5lZ3rF8a1+9y1THn7Zs657UM2f/sdAH8+/gRrP/iQs+fNwdikoUKDo3Inq2c8yd2PXcm61UGKdoYpaKKg9+DmZOUl3r9j23a+PuFE+jx6P6FAcoNXgJDfSzSFeefhRC3XoZZLbvMS/z0kAS8hIfGvxl6xM0G8Q53o+vW9Bznz7g/QGq0pz1dp9Kg0eqxZTRP6Kv1bU57nDJYTjR25BxytToF2DyHMeYMG0ue+e/jziUn1LuoKvZ4xn36EsWnDLo5FLnJqupkvK2oTxhhk1mNRKhlx2aP0HzeBSCiAQq3FYMmqNyw0XXMBVbc8BP/YyVe0bYkqr4C8AwllPsQIFhPq/j1Q+EQymrSnonBtwjEjLp2I3pxxWOYT9HupKFzLL+89RMnGv1BpjfQafRndR154SCs7ZCgVPNA8m8pgmJ2BIOkKOTkqBWlKBQ5zjCYtTBRucSSc17ythUxrFE95OSqzGfl+1tneHVd1KZv++pU1v3+LraAbP39TWS/ed8dRE2DlknKGndScgg79Uo6X26Ynap35gOcjplmxPXQbgdXr8U6dBYKAdswwxJZNiOrV7L4EZbU1IawNMTf6PkvKvyBKhAJ9N85r+SpZmrYHPAeJvSOoVBgvPIOIw0lgwV8ARGvsBFauw/bw7fsl3ned63jzw6R9vrkLMZw/rtEFvMdeycrZnyftC3idVO5YjzYzE295nU+Lu6iItR9+RO+770QQRfweBz+/cQfbVsxh/R/fktOqG7m2HGrXFPHFb0Vc9vR0jGkNKUfRSIQtU76n61VXotYaadFxMFVFG5Nev+OgsXv1epCQkDh0SAJeQkLiX83WFXPi/m7aeSA9Rl5MNBquc5V216LUaJEr9t+Ip7N1DAsrPkra18JwdNeh1aSl0evO2+lw8YXUbtqMXK3G1KwpupwcREWD8FcKAqOsBrTE+KbaiSsSRS3IGKlXcaJFh04uAiLmjOTh4m4tqB+4jsiXMwit2YhMp0UxvB/yoX0JqWQcHXvu8QgaDcYLz8Q9ZRpjz32MBfM/ZfWCKURCAaw5LTjh0ofJa93zsM2nbOtKPnxgbP3OcsDrZN5Xz1O4biFjb3kzpRFjY2BRyLEo5LTWxX8+TGY1N9zXhwdvnI3HVefQL4oyrrq+BWneDUwdexoBu51mY06k2w3XYWrWbL+rUIRDARZ89yrNugxix9oFtD7uEjZMSVxI2sXSP0oZPKopeksGfU+9loVTXgPq/BF05gyIRTnxyifRGpIbPe4rYpoV7ZD+0LElpVtX8ON3j1FTvJlWvU/guNNvxJLVtP5eMzQtuaDVm4xt+pjkMn+YEW0WLLdeTdTuJFJVjWDQI1rMiLb9//ePuj1Ea+wp+0NbC1E0yTuI2SYSCQWIhFJHurjtFWhstnoBD7D+40/pdMVl6DIz8TqrKduykoGn3kR2XicigQAypciqJVMo3bICe2VRnICPVNXQslU7IjUBKHNRcMk5rF74HR57fCqRKT2Plj1HJE0PC/jDeD0hFAoRvTF5aU8JCYmDRxLwEhISxyS1gSK2OhextOobzKoc+mVeiE1VgEYev7Oi2c2kruOgceS27sH3L99AyO8FQK7UMPLyx2jbdwxq3f6FtLY09seiyqM2UBTXLkPG+OZPo1McnFA41Cj1epR6PeYWLfZ4XKxwO5Hb7mTCddciWCxoZDKqpkznt1mzGfnuZIwFycW711HNdy9fj7u2nN7DLiJ33CUEgz6W/fE1m+5/kCuf/y2lieCRRrSaMZw3lmiNneHj72TA+JuIymIoNXr0lrqd90gkjLumnHDQj1ylRm/OQJQ3rnGbx1HF9HfuTRoWvnPNAuzlOw5YwHvCEfyxGEqZDIN8/5dS8poZmfTOCFYvKWftikqGHm9h8/MP8NdXX9cfs+zFl1nz7vucu2g+1rb7t+vscVSx7NdPyG/XB4CgtxazNaN+weCf2NLUyLwe1EYT/U67luZdB2Ov2IkpLRd7xU6MabmotEYi4XBSw6/9wVVTxudPXhCX475y1udsWPgzl06ahjWnoeShRm5AI7nMH3YCDgfeigqchTtQGY3o1Fb0B5qrrtjz51owNK7fA4BCpUVrtOF1Viftt2W1YPnOnXFtokpVXyYxGolw+pUvs+iuR1gy7z4A5FotXW++jvyz78fnbDDdjFTVUDvxOUJbtjeMtbOU829/n8XzP2fdgh8QBIFOg8+g5+hLMaXFm4WGghHKit18+9E6Nq6uxmLTcPoF7WjZzprUKE9CQuLgkAS8hITEMUe1v5BnVw6jwt9Q23160TOc0+Il+mdeFJdb2qbvaDYsnkbp5uW0638yX066JE4MhYM+fnrtFtLz25DbOnlN+FRY1QXc3vk3vth6KyuqfyBGlGxNW85r9Rq5uqPDkO1gCfv9LHn6WbZ9/wP+WgddH5zE2m1hymWDaXrJcByuGKmWPfxeB2XbVgEw6+tEN/qd6xaRltcqof1oQVAqEbIykAP/jM/w2CtZ+stHLPr+DQJeJyqtgT4nX033Ey5A14g74kGfO2kI/y62r/59jz4OyfBFouzwB/i0rIbt/iAZSgVnZlhoo1Pvl5CXyWRYtTF6RjfTWViP39WOTbuJ9/p7cDqZd/e9jPrgPZSG1EI26vMR9fqRKeSIRgORcIhw0IdcqUYmCGyY/wHDx0zi/ddcSc8f0tdIzeMvYr39OrQ2G+n5bVg640PWL/ix/hilRs9Zd39AbpueB7XYUrplRZx430XA62L+Ny8x6oonUKgkQ60jhaesnPn33sfqd9+v/77XZWdz2o9TyOjaZb+jQQSTAVX3TgSWrkrok6lVyPMPvvrFPzFYsxh8zp1MffOOhL6MgrYEi6sJuho+C6bmzRn41BNo0upKmIohkVkXXotja0OqV9jrZcljT9Pv8YdIHzgaqHOd983/M068A0TKKojc+Sx9772egrSupHfuhK1Z66Sfm60bapk44Tcif6e3VJZ5mXTXPE49tw2nndcWrV7ajZeQaEykQokSEhLHFMGIjx93PBYn3nfx6ZYbcQRLAQhXVuP7fRGhd75mdO4pXHb/t6ydNyWlwdX8r18k6Pfs93wyNC24rM0HPN5rE4/13MDtXX6jrfn4ozp8fn/w19Sw7edppHfvTuv7XuSxJwv59MMdzJpezP/e3s7Eu5exc5s9+cl7qcUrNPJu9eEi4Pcw/+sXmfvZUwS8deX6Al4Xcz9/mt+/fJ6gz91o1xJEOaI89cOvVp/avyEZ0ViMFW4v92wpYZXHjysSZYsvwBOFZUyvduCPRPdrvHBhEc5X3yVa62TLz1NTHrfl+x9xl5fhSbKbGA0ECW0tpPbp16m84V6q730S3x9LkCNiyW7GtpVzadt3DOXb15BuKabPoPgFEpkAl13ZGs2ieQSXrSG0pZBIJMyyXz+JE+9QtyDy6aPn4qou2a/7jJtvNMqqOV+l7N+4eDo+t/2Ax5c4OKKRCKvffY/V/3sv7vveU1rKV0NH4PrHrvW+IOp1mG+4DDEjLb5DIcc28XZEm/ngJp0EmSDQtu8YRl7+OOq/vUJkgkCbPqMZdf5jzLu5rsSpNjOT4bNn0nzGDKa07czHpdUU+4O4tu+IE++7s/y5VxD8da9N1OHE89PM5JMIR4j8MAuLLQdTRl5S8e6o8fPm00vqxfvuTPlkA47aANFYlJpAETvcy9nhXk5toJjYXswmJSQkUiPtwEtISBxTuEKVLCj/IGX/qpqppLmsVN3xKJGyivp2JRFqy7alPK+mdBuhgO+AyuJo5MZ/raO0IJejMpvpcu+jvPjyFkLBeIHncgR5+dE/ue+ZwRgt8aGSGp2Z/PZ92Ll2UeLAMhn5bXsfyqkfMrz2SpZMey9p39IZH9Dn5CtRahonpFZrtNFx0DhWzPo0oU8mCDTtPHC/xqsJRXiruCpp3xfltQw0G1DvY13raCCA++uf6v6IRRH2UE9bJoqUb1/D/DdeZcy1z5LZpD3C3y76oQ2bqbrzMfi7VGO0upaaic+ivWAcg8fdxvdv3MSpE15BlCuZ+b8b6X3qHQwfM5Btm8MYjDpaZ8kQf/2F6Jy6mtWen2eiaJbOnz+8mXQu4aCfwjULMGceWL1omUy2x+8JhVJ92MtHSjTgKSnlr6efTdoXcDgoW/JXQrm1fUGek0nacw8R2rKd4Kr1iDmZqLt3QkyzEfX5idqdxPx+BL0OwWxC0Oy/r8o/0RqtdD/hQlr1OoGg14VcqUZrSiPs9DD6k49YPfl/tJ/0JM+GBMo9YaBu8fAvl5ezViZGC+zCV1VFNPh3KkqM+s8egLJ9a9T9eiKTiwTWbCDq8ZLRoxvyFNEzbleQ4sLkUTEAldW1VOvm8+7GS3AEywAwKbO5rM37tDIORCEe/OskIfFfQ9qBl5CQOKaIESUcS23sky7Lx/neF3HiHYCyajLzUufgZjbrcMRq2h7NaDMy6HHbzYS0Gbidyet+F2524HQk/ptoDGZGXfFkUrfioeff26ih5ocTr6uGWDR5hYFYNILXVZO070BQqDQMPPMWbLkt4ztkMk654WX0lsz9Gs8dieAIJ597BCgLJs8vT0YsGCZqr3OhD67fTIsTTkh5bIuxp7Jl7RzKt63mw/tOw15RtwsaqbFjf+HtOAGxC++HX5OV1pJRFzzGbx/XhaSffssbtO7ai2yFm4HiVjr8/A6y+x+oF+8AyAUikTB+T6JD/i5qy7bv833+E5lMRrcRF6Ts73bChehMx+Z7+99AyOfFX5va7LBm7boDHluebkPTtwfq809FPqQ38uxMIjW11DzyPBWX30rl9fdSftmt2N/8AM/OQhyVRUTCyb839xVBFDGl5ZJe0BZLVlNUGj26zEyanTiSYR9/yE8KLeXBcNw5jnAEedNmKcdU6HTIVXXCWTAa0Bx/HDKtBut9N6Hq1hHvtFm4v/kZwaDHdOX5KcU7gCCkXqxSqUX0+TW8uHpMvXgHcARLeWHViVT+HUnnj7jxhZ379HpISEhIO/ASEhLHGGrRSEvjcWx2JpaGA2gl6459zv0J7cGFy+g+8WpW/P4V0Uj8w45MEOh/+vVSzmoKmo0axeplyXdtdxEOJReF6XmtueyZX1g152u2r5yL3pJJn5OvxJbbElUj7VIfbpQq7R77FXvp319M6Xmc99CXVBSuZfPSWRit2bTuMwqDNRulev+uJexlZ1i+HzvHglaNqmdngus2QTCEfHsJXS67jBWTJ8cdp83MpOP1l/PFq1cAEAr4WD7zEwafcydRt4dwcVmy4QGIrt7Etg+/oP8Zl6DOSocqP1qrGXHNctwff5P0HN2Y4YRVGizZzagtTR51k9e21z7fZzKs2c3oMeoS/pr2blx7RtP2dBl6dr2RmMThJRTw4fXUoM/JwV2SPE0is8f+eZ3sjqu6jK0r57J0+vsQi3LyxU8SeelTwlsLGw6KRPBNnU1EFmOJfBPNew6jedchh+T3xSOTMd+RmLJjD0eItWmN2mbDX52YttLlumvRZdeVoJSJAtphA1H37YH9hbcIbWwIu/dOnYV//mJszz9E1KJPavSqNypp0dbClvXxiybtOqdx1tWtmVlxFzESF+iiRPil6HkGZl/Bd4X3E4r66ZdxIR0tI7GqkxujSkhI1CEJeAkJiWMKvcLK+S1fZfm2bymgI1FZlOXRqSxyfkZzQ1/kKCGSREyGwsi+nctZt7/Lj2/dgaumLldeZ07npGufx5rdPPEcCQB0WZnkt1chE9YSS5IirdMr0BuTOw3LBAFLZhMGjJtA75OuQC5XIlce267EWmOdQVrlzg0JfWl5rdEabY1+TYM1C4M1ixbdhh7cOKJArkpBcaBup10rCAwxy2ihDhKMCeQo9/2xQCaKaIcNwj1lOvL2LQh3y6LL4KtpccZprHjtLXzVNeSNHELWkH78/Mk9hIO++nN3rFlA0OdGvjczMbmCqhUr2T51Wn3TyHcn02b0aHyz5hEpjY+0UfXpjqJpPmqLmWEX3M9XT12aMKQpPY+MJu33+T6ToTVaGXTWbXQaPI6lMz4k4HPTafB4clp2xWDNOqixJQ4ct72C2V9PouvtNzLv5rsS+vW5uaR16nRAY7uqy/jyqUso3bwcAFGuJFbriBfvuxH8ZT6d7r2YD565gCuem0V6fpsDuu6eiMYgnCKV/CPU3DxjGtNPPjVuMaP1mePpftONiMo6b43QjmIckz9B06d7nHivv4bThfPLKazP9pLTtgfZLbqg2a0Uo8Gk4qo7evLg9bPxeesWx4eOaUbTVmY2biymKH95yvkXepair/6OtbW/ALDJ8TsZ6pbc1nkmVnXB/r4cEhL/GSQBLyEhcUwRCQaRrw/hvmk2v85/DEGhoMXZp3P//fPRFeQg92tRtGxGaHPizlt48UryLjuPi5/8CZ+rllgshtZoxWDJ3G9X4v8a5jQdJ45txc9fbUroO+/qzljS9pzHKIgi6t1C6QORKPZwhE1eP55olDZaNVaFHOM+uKCHQ0FEuWKf8owj1bWEyysJl5Qjz8lEnpl+QHWgd0dnTmfc7e/w8UNn1i8EQZ3IHnfH5ENal/1gsSjkTMjP5IGtxbTUCIy11fJb0UN85ZiNVm5BlnsTvdLPxqzKjjvPHgpTGgjxu92NXCZjkEVPhlKBISsdzeR7+a30DWZVnYGv3ElBWnfOfesFhLIIv3/+HHNeSMxFN6XlIVeqEQwiijYtCW3YnDhZUSRqNSbspNo6tkeekUba0w/g/3MZvpnzkCkV6E4dhbJNC0SLGYCCDv045YaXmPnBw3gcdREkzboM5sQrJ2G0Zf/zavuN1mhFa7SS07IbMWIIgrTrfqRxVBaxY91CWpxxPL0evJvlz7xEyFNnTprZsycj338HQ37yeu2xUIhIjZ1oKEwwFAS5iDYnuz6aonDNH/XiHUCjNxOrsqeeTCiEPCojFo2yacmvKDNMxIiilVtQio2zG68RBZprlGz1JYbp7wyGiLZtyzl//oGnuARfbS2mpk3QZmSgttR9B4bLK6m6bSKqHl3wL1mR+lYWLMd2yfF8+sg5DDzzVvqcfFVcalR+MxOTJo9g3q872Lahll4Dc5h013yGnp5DWouWFHsTKzYApKtb1BvP7qLCv5l5Ze8ypsl9iDLpMyUhkQxJwEtISBxT1GzYwGf9BxEN1e0gRkMhNn34BeVzF3HW778h5hsxXXcxVbdOTMirVXbvhGgxYTSbGuUB/r+EVqfgtPPbktfUyNcfrKW6wkduEwPnXtWZtp3SEPfR+AzAH43yl8vDizsq2D1Wop9Rx2W5aViSmKFFIxEclUWs++N7dq7/k/T8NnQ+/ixMGQUoUuzoh0vKqLr3SSIl5fVt8twsbI/ehTxn/3LH/4kttyUXP/kj1cVbqCraSFpuK2y5LTGm5RzUuIeDpholz7fKp8a/gudX9icSq/ss+SMuPt96C6tqpnJ52w8xKuteo9pQmFd3VrLM7a0f46dqByOsBs5Mk/F50W38VdVQPm6HZylPbhjE+c1fR1QmrzTQ55SrUCjVoFRjufkKKm95iJjXF3eM6vzTWfja6/V/6/Py6P3Q3ahy03HVlqNPy0A/Zjjawf1BkCFo40WRRm+m46BxNOl4HH6vE7lChdZoSxoGfDDIBAHJsu7oQPzbGHH2l0/QuudITvj+A2L+CKJKSUX5RmKG5O/HiMOJZ+Y8wiY92xb+wepPPyMaCtHmnLPoeMlFqDJsLPvlo7hz/F4nMqtpT5MhKhcYfedzVNvKeXbVMDzhWjpaRnFi/p2ka1ogyg7uMdwoF7k8J437tpQkBKl30auxKOUYcnMx5CYvcxdctY6owwWRMDL5HkwolXIikbpFgt+/fI6Og8bFCXhBkJFuFjhlRBq+YVk8/3TdYsDCXyq5/uSb2OyeSyQWwh+JN7vrl3kBb68/L+F688onMyj7yoSFRAkJiTokAS8hIXHMEHA6mX/fg/XifXechYUUz59P27PPQtGiKekvPYpj8icEV61DMBrRjz0R7bABiOY9PHBJ7BGTRc3Qk5rRvX82kXAMhULAZN1/B+HqYJjnd1Twz8jPBU4P7fRqRttMCbvr5dtW8+GDYwn560Tk5r9+ZeH3b3DmXe/TrMughPJGEbuDmkdeiBPvAOHiMmoeexHbY3chmg9OyBltORhtOTTbTyf4I40ok6ETXLy77eZ68b47a+2/UOnfVi/gl7u8ceJ9FzNrXIwy++LE++58t+M+Jlz+A4U3jasv5yUTRIZd/AAym4oK3xaMykxUTfLIeOVxvL8vIrhiDWJWBtrRwyjbvJHCWbMAaH/5RbS4ZDzzvn+JqRMeQW/Nov/pN9C2z4l7NEOUCQLGtByMHP0LKxIHj9GWi1KtI+j3sHHJdDYumV7fZ85sQudR5yacE4tE8f22gJBawdTbbqVi+fL6voUTH2H15Hc5a+4s+Md3Ujjop9pbjjkni0hJoo+DclAvXJowc6Lvsbp4Rn37/PJ3WVz5Ofd2+5NcXYeDvuemahWTWuXxYUk1a70+DKLISWkmBlsMmPcgygH8K9YCEFi2BtO1F+GbsyDpceKgXqz66+8ykbEYZdtWYc1uMMkLl5bjeOsj/Av/QnbtNezcWmdIN+T4LJr4M5kYm4Jf7sWTGWOK8ym2eBdxZvNnWVr1DYFIYg5/NBaBhF8ICQmJXUgCXkJC4pgh6HRRNPu3lP2bvvqG1meegaBSomzVDOt9NxHz+ZEJMgSLWQqTbwRkMhkyGfi8IbwxiERjWNP2Lxx0nt2V8tFsSqWd/iZ93C68u7aC7168rl687yIWjfDdC9dwxXOzMKXHh8VG7U5CKXJTQ5u3EXU4D1rAH8v4Ik42Oeel7F9V8xMtjH1xhiP8WJXczd0kF9nmTh4aC+AOVyM3abnm5fmUbV1JTBZDmWNhjvMt3l59FQIiA7Ov4CTrbYSefBeZSom8ZVOU7Voht5rJP2E4F6xYSjQUorxqA188dUn92M7KIqa9dSfFG/9ixCUT0fxdJ1viv43emsHJN7zE189cHlcDXlSoOOXGlzFYEyNvIjW1eGfNo6pdQZx434W7qIg1733AgHET+GTNH3F9M758gnNueB3ZG18TLiyqb1f06oz/+E449Q5WF83455AEo16+3nYHV7T99KBLkKpEgeYaFbc1ycQXjSHIwCwX92pYCaAoyMNHXZ57pKoG9YDe+Of9GXeMWJBDsGszts6YU98mCA3fz+HK6rqyrRV1aSqymmoysvWcfU4zOlavIHDbSwSidf8WGpWKK+58lGiXJpRHtvLh5quTzqtPxrnoFUdvKpKExJFGEvASEhLHDKJWS5MRw9n0zbdJ+/W5OQi7iXRRrwO9VBqusQiHo2zbWMurj/9J6c66XRNbhoar7+xFm042VKq9/6REIxHKAqlLlTnCESL/UPc+Vw01JVuSHh/wunBWFScI+JjPv8d57K3/344MAblMSTiWvMSVWqyLVInGYviSlHgDCERj6ORpe7yOUtRizW6GaNXy+trxbN7RsGgQJcKc0jfQhNQMys4jOGcJwVXr8H47FWX3TljvvA5jQT6umjJmPP1A0vFX/fYF/U+/XhLwEkCdsVzzrkO44rlZ/DXtfaqLN5Lbqgddhp2NKT25s3nMH0Boksu6b5JXNQBY//GntL/0QrJbdo3Lg3fVlDHt20mc8fBrCJ4AodpafEKQjet+Z/uMt5GNTy1CV9VMxRu2H7SA34VOLrK/v3aa43rifO8zCEdw/u8zjJedg6ZfT3x/LCYWCCLr2xmXWcY3r15TvyAiiHIymzVEDgTXb64X7wCymTM59/KbSLMXE3nv57jrxQIBnI++TMZbT5OR0YJWxgEJC4kmZTbH51yHXEie7iAhISEJeAkJiaOccChCZbmXP2btZPtGO81PuY8Tr7uTZffcRNmihXHHdrz0khSjSDQGlWUeJk74jVCwQdBVV/h4/Pa5THp7BE1amvc6hreigvYBH3P//jtHpaBrnRkyy4NgFkVU/6grHE1WVWA3wqHEGvSC0VAX8hpLstcvyBCMx2YJu8bCoEind/q5/FHxXtL+LraTANCJAj2NOn5KsgvvjUZJU7dAKzfjDdsT+tubR2D4exet0r+Zza7kO/6zat6i/5gpMGdJfVtw6SoiFdWIZhMBjxNnZVHScwHKtq4kLa9Vyn6J/xZKtZaMgraccOnDhEMBFErNHsv6yZQKZIKIsKcccLkchVrLGXe8y9aVc1g6/QNi0Qhdhp1Dqx4jUKfV5WorogVEastpYTmRdsPGM9f3bsoxRZkC2RF2TxDTbNgevYuah58j5vXhfPtjBIsJw6XnIO/alo+evoDyravizhl52WPoTA0LE/7Fy+P6I+VVNFV78Ez9KflFozG8v8zFfOk5XNXuc1bV/MysklcJR/30Sj+b47IuxqZu0ti3KiHxr0IS8BISEkct0UiMjWuqeey23wmH6kTjn7+DSi1yy2OvE7j2LGo3bgRg8LPPYGwq/egfKiKRKLN+2hYn3ncRi8K3H63j6jt7odbs+WfFXVREViBEdlou52iiyLYvYdvsTwE45/hzaN6uD4Z/ONFrDBa0RhteZ2I9Y0GUY85I/HcXzEa0wwfi/WVuQp/2hCEI/3EvBKWo4ZSmD7LR+RtV/u1xfWc2ewaTsi5nXCEIjLaZ+K3WhScS/2+fppBjUmZxY4efeG7VCILRhhSHNHUzLmj1BlqFGYByX2L1gl0Eo15CymjCA0lg9XqUrZvvUVgBqDSGPfZL/DcR5YoEb4xkCBYz8nQrnc45h227lSvcnc5XXYE2PR2ZINDl+LNo3WskxGJx5dTgb88FW3a9SWoPz3h+2Plw0jHrwsT3HMFyqJEpFag6tSXjzaeIlFYQ9flQ5OcgmI2gVjH21rdYOetTCtcsxJSRT5+TrsCS3QylWls/hjwzMcpAVl1FpCLx+3oX4R3FxMJhzKocBmZfThfzOHx2GS57BPt2GTGLF4tNjSiX0t4kJJIhCXgJCYnDgrNwB2V//knpn4tJ69CevMGDMRTk73FnpLbax3MPLKgX77sI+CNMfnsHN03+mNpFs2k+5kR0OTmojP/dnOZDTcAfoWU7Kzfc34dNa6uZO70Qr7shFH7L+lr83vBeBXwsGmPhuedz//xf+fm1GynZ+Fd93/ZV89jQugfjb38nrpa23pLJyCse59tnr0oY77ixExDlyoR2QafFeOnZyAx6PD/+AsEQMpUS7UkjMIw/KcGx/GjG73XhsVdQsmkZMpmMnFbd0ZnTUWkOLoogTd2UO7rMZYvjD5ZUfYVZmc2ArMuwqZqg3S2sN1Mp58mWuXxRVssCZ10ZucFmA6dlmLEpFZgVvXm45xq2OBdQ4d1Ec2NfcnQdsKjqnK/9djvNhJ5kalolFfJKQYMiKCb4IojGOmGuMVhp0rE/hav/SDhXrlST3qTtQb0Oxyo+TwiPO4RMBnqjEpVaeqTbF7zOajyOakIBLxq9GZ05Hf2ooURXraXJiOEU/vJr3PFpnTrR+ozxcR4q+5qyYVHmMTr/bn7e+URcu1WVz0kFDzRaObmDQSaXI89IQ56RuJhgzWrKoLPuIOj3IFeqkCsSK35oBvXF9eFXcdFO4ZJyFM0LCK5cl/Says7t613vfd4QK+c7efvZpfh9dXXkdXoF197Ti049MqX3tYREEmSxWLL4wv8uTqcTk8mEw+HAKIkBCYlGoXrtOr4YPBRfVUOenEKnY/zMGWT16pnSXG7L+hruuWpmynGf+t8ImrQwN/Z0jxq8FRW4du6kdNFitJkZZHbvhi43F7kyUbAeSqorvfwyZQu/fr8VnzdMpx4ZjBzbkm8/WMeG1XW7LB26pXPbo8eh1e95x8tZWMiPZ55Li9vO49fPHkl6zCk3vkynwePj2gJeF+Xb1zLn00lU7liPObOAbiMuwF1bxroFP3L2vR8nLeEWDQaJ1jiI+f3INGoEixkhRWmzoxGvq5YlP0/m9y+fa3hAlskYev69dB1+XqPlfkdjUQTZnne7/JEonkiUGDFEbwRXTQBBAL1RhTlJNQJXURHbfp7Gmvc+QFTIaXHlGYS7aXi/5lqC0YaSccNt1zH0xxaEZu1mniWKZL7zbH25v+qSLXx4/+l47JX1h8gEkfF3TKZ5lyHIU5QS/LcQjgapDRaz2TGfUCREQWAMX7y1kaULSxFFgf7D8hl/UXsyc/7bqSF7o7a8kO+eu5qSv/PYZYJI1+HnMeisW9EIWrxVVVSsWM6KN98mEgrR8dJLyBs8CENe8jJs+4I7VE2FbxOzSl7BFaqkZ9oZtLecgE1d0Eh3dWSJ+gP4Fy2ldtKr8He6k2AxYb75KmoeeCrheJlWQ8brTyLPygBg87oa7r068XdeJsBTk0+goHnjRUsFXS4CDgfIZGgzMhAVx85vgcR/g33VoZKA/weSgJeQaFy8lZV8NXwU7p076XL5ZTQZOJBYLMbmadPYMm06Z82dhSEvL+m5G1ZV8cD1s1OO/cRbw2jexnqopn5EcZeU8PN5F1L0W4Pzr1yt5tQfviNv0EDEwyTia6p8TLrrd7Zvis+BVigFbn20Py9OXIjPE+aB5wfToXvGXseLBIOULl3Mr1OeoHi33ffdyW3dg7Pu/ShBnJZtW83aed9hzmyCx17Byt++xF5e5zQ/YPzNdB11PeEQqLVydPrDu8hxqNi+ah4fP3RG0r4LH/ue/La9Dut8Ar4wa1dU8tYzf1FTWSfCc5sYuO6e3jRtaa4PeXXtLOKrEaOo3bAh7vycIQNo8sp5TK6+Ahky+qafz8nBywnd9SbsMssTZFjvmYCqdzcEVcO/o6OyiJ3rFrF1xVysOc1p1+8kjLZcFKr9L2V4LBCLxXDVlOL11FCp3sGrG04H4LrsxTxz/Zb63cpdWNLUPPraUNIy925lFguFiQUDyJQqZIr/xg6nq6acjx4cl9QQs9/p1zPorNvqd5hDXi+xaBSlvvEWRCLRMJFY6KjYdW9sooEA0Ro7wXWbibo9KDu0QbCYCK7dhOP/7J1lgBNX24avmYl7su7A4u4OBYoV2qIVqFDXt+4u1N291IWWGi1tcYfi7i7Lusf1+7FlISRZFlis31y/4JyZMyebZDL3Oc9zP+9OIFhSBoCiQSbW+25BWS8DQRRxOX288+xSli84EHXcvufX49o72qNUxY7Uqw0Bv5+yLVtZ+Ohj7Przb5QGA61vvJ42t9wU8/lDRuZ0UFsd+v/jri0jI3PacBUWIikUXPrbbwT+nI333a8RRJGWXdrR6ssvcBeXYExPx1VcjD0nh91TpyOpVGQNHIDZmopCKUaE0ANo9QpMlv/mg3vA62XFa2+EiXcAv9vNr0Mv5MqNa7BmNzwlc9mzoyxCvAP4vEGm/bKDPoPrEZeoq5WBHYCkUmFu1JBgwB/zmGDAH9V8buW0L1k17auo56ya/hWSbRDvvbiNFu0SGHtTK9KyzKhO8MHvdOJxVrLw57dj9i+Z/AHJ9VugVOtiHlPX5Oyr5MUHF4S9PTl7Knnqjjm8PGEgSWkGQsEgm775NkK8AxyYs4AOO+7gsT4r0CiMmFSJKMsC+J68B8+KtUgJ8Wi6dUCKt4WJdwBzQjrmhHRa9h51sl/maScUDJK/ewPfP3sZA+9/gXf3jMQf8tLJegXzfqmIEO8ApUVuVi7OZeDw2PeGoNdLIK8Ax+Tp+HbuQdkgC/35/UGrIVTpQNSoESymqgoe/zHKC/fHrGax/M9PaT/wSiyJVU71Sl3df6ckUYF0nI/ddl8Jlb58ity70SvisKrTqlNUzgREtRoxJQlFyqEyfXt3ljNpqpe+V96GTR9CUEi4VVp0ccmo/o2687gD5OypiDnu/p0VeD2BExbwZVu38k2nrvhdVYuOgZISlj7/Ijv/+JORf/2BIS0yektG5kxGFvAyMjInlYDHy+A338D9ykeHSncFAngXLUfctA3Lcw/iKChgwYMPs+GzL8LOHfTdRC6+ujnffhRZa/rKW9tgjftvCnhHXh5rP/goal/A62XH339i6tyUlOzWYbniJ4N/Zsd2/167PJ9XPh+INU4blvvuKS/HmV9Aweo1KPU64po3R5+SjEJT9X4ZbIm07nsJuTvWRB23Tb9L0Ryx+x4KhfA67THn4vO4EEJBAoEQa5cXsOHGWTzxbg/SG6rRK8/OKA2/z4O9NC9mf0VRDn6v55QJeJfTx6TPN0Q19ve4A8yduofR45rjKipk/YTPY46z7v1PuGDAjyi1/+5ExoMi3oa2S/uTM/GzkIriA3zz1MWotQYKhJ34Q1WVFrIUvfljeeSC2kH+mbOf3oOy0GgjQ4NDgSDetZsofuyl6mgH74YtOKbMwHrPTdgnT8W3ZQfqDq2x3Hl91Jzos5nSvF0x+3weuBohLQABAABJREFUFz63M2b/6aTMc4Cvt93M6pLJ1W02dQZ3tPyTNH3L0ziz2BTmOXj6zjlUlntZMi98d/2G+zrQb2h9BEFAo1WQ2cBcXZb0SOo1tJxwDry3spKFjz1RLd4Pp2jdOgpWrZYFvMxZh2zvKCMjc1IxJCURXLQyat3tYGk5vpXrKd6wMUK8A0wdcwldOuq477ke1GtkQaNVkN3UyiOv9qZL7/T/rENt0OfD53DE7Lfvy2HRz2/x08vXU1kSW+DVBQZT7FB0rU6BWq0IE+/OwkIWPf4UnzVpzpRLxvDr+cP4vFlLdv4xBZ+z6gFZEAQadxqELTU7YkxbajaNOg1EEMLLKwmCQMt/8+LTmnbmvMd+4LyXZzP0ldmce9eHtO53ORvXH3pACwRCfPPeRrblr8Hljy14zmTUWiOZzbvG7M9q2QPVCRrZHQtup5/dW8ti9m9ZV4TX+2/0RIza8VC1uxx1FUCmmoLdG3Hby1DrTFQEDuX9e0J2dPrYebtGsxpJin5fDJSUUvrSu5HvTTBI+UdfYRg+uOoaK9ZS8uSrBErLTvh1nElYkmLnnCtUGpSaUxfJEguP209xgZPiAicetx9v0M2Uvc+FiXeAEs8+Xl3bnxLPvtM0UygrcbN5bSHffLiWX7/ZxP49FTgdVcamOzaXUlnujXrepM83Ulpc9Tyg0SoYflkzhCjV9ERJYNDIhiiUJ/Y77ykvZ/dfU2P2b/7u+xMaX0bmdCDvwMvIyETgdTlwlBdRmr8bhVKNOSEdgzWpViV5DuJx2XFVlqBGQWDj1tjH/bOS4uTYZaCWP/4gg7/6gkYteuH3BlGpJYzm/7ZhldpioenYMWz76WcCnsga5wkd27JqyhScFcXs27SE5j2GnbS59BqYxR8To79//Yc1wGwLfy/2zpzFqrfCw74DHg9/XDyGcRvWENesGQCm+FQue+IHNi6azJqZVWXk2pw7hubdL8QUF303JKlec7pd/ijBDufzdmWQCnuQJJWCGzI60iqpFc6CSgb1asu8RSX8MXkvW9aW4HUlUOEtQKs4diMkj7Oy6nuQtxuV1oA5PhWDNbnGygl1iUKlpvPQ61kzayKBI2rdK9Va2g24/Ji+kyeKUiURn6SjuDByJwsgJd2AUimhSkig+bgrWPT4k1GPa33j9SclRPl04/SXUektwBkoRyeZMaqS0B3H5w6g5F9vh7L8PXRS/6+6faXzU84ZOYEvX46+KHXeqIYxw42DZeUEyyuj95VXIhzmq+HbsZtAYQmS1XJc8z8TsSRmYk7MoLwgUvS2G3A5BsvRPTxOJvkH7Pz05UYWzayaX7d+GYy8NY75eZ9EPb7Cl0++cys2dcapnCYApUUu3npmCRtXHVpc+u6j9VxxS2vOvaABu7aVxjy3pNCF3xeo/n9qhpG7nurGhy8vx1FZtQBgsqi59eFOdWPKKIiojMaoO/AA2ri4E7+GjMwpRhbwMjIyYTgrSlj+92cs+PF1QsGqH1mV1sCIuz4gq1VPlLVwey7N283ML8ezddnf9B1xD9lGA4HcgqjHiiYD9oLofQDOvHyCHg9my3+/1nOgrJxAXiHef1bS87rr6PLIvZRs3cH0a2/CXVICgLVZMwK6UHVN9JXTviS7/bknXFIsFvFJOi66ujk/frYxrD2rkZmBFzYM2+1zFhSw5JnnjxyiilCI9RO+4JyXX6huMsWn0uX8G6od53VGW8yKBABGWzIp/a/i0V1VUQdpaiUPCX6Ex1+jLK/qMySJAgP69qbpfefw5hsbCeJlR+ViknSNjul1O8qLWPDj6yz/+7Pq3WKtwcroBz8jrVEHpKPUJq8rLElZXPnMr/z5/r3k794AQEp2G4bc9HJ1vu6pwmBSMWpcc567b35EnyDAwBENQQCnI0Cz625gx2+TyV+xMuy4lG5dSena5VRN+ZRR6tnP19tuZc2/O6UCAm2sF3Bp4vNYQvFIifE1fraPJLleCwC8bgfuvUXUN3Zml2MpOc71GJvuo233OFYvCq+zPfTiRqRl1WC+e9Soh/B+/4E8VI0b1HrOZzpGWzJjH/ueH1+6mqJ9/y5KCgIte42g24j/1Xklg4DfT+Xevez+ayqFa9eS0rUrmX37YMzKjIgwKsxz8NgtsygvPbRQN+/vPXQZQXX6RDSK3LtrnEMwEKKk2EV5iZtQMITZpsFi05xQTnkwGGL+9D1h4v0gX723lnZdU2r0RLHYNCgUh66v0Sno2COV7KYDqSjzIAhVAt4ar0UUD/2dvG4nXrcDpUqDWlf75wF9chKtb76Rf56KXvWkxdXjaj2WjMyZgizgZWRkwti/eRnzJ74S1uZ12fnhhXHc8MYc4tNqNk8rL9zPV4+NqA7tXvPPLzTsfx9s3Rn1eP3wwQQ+/zTmePXPH4pS/98zVDqSQEkZZW9/it/nJTSiN0vnfUveng2YEzMZPPV79kz8g8o9+2lx+3X89vEd1eeFQqEjn7vrFINRxeBRjejYM415U3fjqPTRtW86WdkWbPHhbsoBnw/7/tg582XbthH0+xEPE7+CKKI31y7X1uEPMLGgrPr/N6sluP9ZAuWHmSAFQ/hnziXDFsdVt7dEoVhDRqA1QZcLUVs79+dQKMSOVbPYtXYeCqUGv7dq58ZlL+W7py/lhjfmYE3KqtVYJ4qkUJLasC1jHp+I21GGgIDGYEFnOj15/Q2aWrn0+pb8MGEDwUDVB0+llrh7fDeUKpGJn6xn/coCLDYNQz78FXX5LhbfdRuSUkmbW2+i3sCBGFL/W/mmDl8pX269gXWlf1W3hQixunQyoWCAi7eMwdq+B6qG9Wo9pi21AdbkepTm7Wbexy8w4pG3Wab5mSWl3/JN7qVces3nXHhZb1bNL0apkuh6TjpxiboaU15EixlBpyXkjNyJFHRaCIbfSKSE/97OpC21AZc9OQlneRFetwOd0YbOEo9GV7dVh0LBIPlLlzGp/6Dqnd91H32C2mLh4rmzSGjdqvrYYCDE/Gl7wsT7QbyVCrSSGVcgesRFqr55zDl4PQE2ri7k7WeWYK+oCmdXayWuvr0dXXqnoTtKpY4S915221ewo2IxKbpmNLH0warKwFHhJ6GZlbs+6EPIF2TpH3tYMmMvgX/vBzN+38GQ0Y3RG5Q47L6IcUde2QxrfLh/jaQQiU/SEZ8UGZnj87goyd3Fol/eJnfHGiyJmfQYdQdJ9Zqj0R89wkWUJFpdfy27/pgSsaDY9bFHMdU7NfdyGZm6RBbwMjIy1Tgripn/w6tR+0LBAOtm/0Dfyx+ucYzd6xeG5WUX52ynSOMkrlt7fIvDfzwNo4aizMqg9Y03svaDjyNC3DRxcTS+aNQx7VydrXhWrcOfk4fj8j5MfPHSapf2wn1b2L5iOgOuegqj0IJJ795YLSihKvRTrTu5edAGowqDUUW9hm1rPE6p15PUsQN7Z86K2p/Z/9ww8X6seEIh9rirHgitCgnz/hyC5dEdjAN/TaXny50of3cLwYKFlDasj3HsSBTpKYia2DttIb8fX34BqS4TI7v9DyErhZzCbUz9/ln8Xhd+r5tdq+diHXTlcb+O40FvjkNvPv2CymhSc96oRnTvm8GBfZUoFCIpGUacdi8PXjcjzB19xaJcLrwii5aTxuOnmPTM/hjUKadx9ieHSl9BmHg/nDXlUxjR/n5Kn3+L+JcfR7JZajWm0ZbMmMe/Z8p797Bn/UJ+e/JGmp8znPtHzEFjMqNRmrCoU2jRsvYlsCSbFcvt11L6wjsRfaZxF+P481AtbikhDkVSQq3HPpswWBIwWE7ua7MfOMDkEaMjftM8ZWVMuWQsF82ZgT6pyrHdYfeydH5O1HFK9ksMaHc3k/c+EdGXomtGvKZezDkU5Dl48aEF1QttAB5XgA9eXE5qhpEmraIvnAZKyylUHODlDf0o9x76LVeJWu5qNYOcQHM+Uzqx+4KoBIFel2Zy+3lZvHP/AnzeIGUlbuIStTzxZh9efmQhhXlV3ieSJDDkokZ07ZMeEYEQi1AoxL7NS/n+mbFV3hlAae4udq2Zy8BrxtO2/2Uo1UdfmDWmpTFs8q8UrVvHlu9/QG2z0vyKyzBlZaGxWms1FxmZMwlZwMvIyFQT8HkpK4xtilO4bwsBvxdJEX3lPhjws3Xp3xHtv3xyL4MueZisQbei3LIfQalC270jYpwNyWTArNcyZvECZt9xF/vnzkMQReoPHULvV17EXK9eXb2804bPW5WKcGTYYrknjwL3doRKD6ZJ0xFH9OXPb5+IWmJt1tfPMuq+j8PEe3J2azKbdzvh+YV8PgIl5YR8XgSNBo/ZhDsUQhQErMdQI1pjsdDzuWf4tmuPiHBdjdVKg/OHnNA8lYJAokpBhSuAVSnB/tyYx4bsDoL78/Ct2wxAIL8I9+LlxD11P+pObaI+QIZ8frwbt1D82MuEPB6UDevjCxpJSmzJuMd+5Yvxw/B73RTuiyyP9v8JjVaBJs1AUlrVwpG9wsNb41dGLW02+as9tOzdkPfzLiClvCl3t5qB5T8m4p3+shr7XYIdYX8uwfLKWgt4AGtSFqPu+wRnRTF+rxuN3nzMXiSHIygkNF3ak/DmeCq+/Rn/3hwUmWkYzh+Aa+EyPKuqqn1IyYnEjb8fKf7srN5wJuDIzcUZIzWsZPNmXIVF1QJeUghodZHvqSBARssgRUoLA9LuYm7uB3iDVff/ppa+XJj5JAohungNBIJM/21HmHg/nB8/38DdT3dDpz/0Wx50OPGs20Rl+X4+TXwuTLwDeIMu3tlwARc3WYw9UPVb5g2FmGl3UGzQMOz6lkx6dy1dz8lAFEWyGloY/24/ykvdeL0BzFYNZqsmzPT0aNhL8vnj3burxfvhzPjyaRp1HFijOeHhGFJTMKSmUG/QwFpfX0bmTEUW8DIyMtUo1ToSs5qxd8PiqP0ZzTrHFO8AoqSIakAWCgb4+7vxJGQ15/KnJqEzhq94SwoFCW1ac+Evk3CXliIIIpo4G2pT3YY1nmpKilxs31TM7Cm7kRQCA4Zlk5VtwWLTUOrJ4b0No9hlX8LViW9jcDgImrWUF0YPQQ/4vQT8fjKadcHvddN+4JU0aNcXU9yJiaFASSmVk6bg/GMGQue22K++lO/35rPZ5cGskBieYKGzSY+llkLe1rwZI6ZMZubN/6NiT5URV3KXzgz89GNMWScWqmhUSFySZOXZ3XkU+wKQFXv3UTQbCXmOcEEOhih982MS3hyPIoo4CRSXUPToiwiCgPTAvazJVTB1eh5uVxEduyZy0WOzmPr+ONKbdjqh1/Ffw17pY8u64pj9uzZ4SE5vzAHnRvJcm/9zAl4rHbpP2dQZDLLdQ1Iwm6DfT0gLZmUKPiAUZWHuqGMbLGiPKKl4rARDAUShSnCJOi2qpg2xPXgbIbcHQaOGgB8pORFtry6IZiOSzYoUJ+9Kngj+KGkKhxPwHgqX1+lVDL24ERtXh+eUJ6cb2O//h1923EGH+NFc2+QrQoRQiCp2VCzm7Q0X8lj75eiVlojxvZ4Ae7aXxbx+7j47HncA3WHZaZ51myh54hWCb13PrvylUc9z+EuQQgdQCZl4D1ukXe1yM6RjAinpBho2O3RvtcZrscbXLnUpGi57KZXFB6L2Bf0+SvJ21VrAy8j8l5AFvIyMTDUag5k+Yx7ky0cjXc1VGj1Nu51/1DHaDbiM5X9NiNrX6byrIsR72PWt1v9MOFtJoZNXHl3Ejs2H3HiXzT9Ahx4pXHdPO2aWvcEu+xIANvsW0ah9d2IX3qpCpdUz+v4JeJwVBIMBCIUIBYO1TjFwOypwVhTh87jR6E3oNCYq3v0c94KlSKlJFF15CU8VO6rn4fb6+TCniPV2F9enJWBUHN34SGUwUP+8wVy6aB7u0jJEhQJtnA1tfHi4ps/pxJmfjyMvH0mlQpeUiCEt7aihlY10Gq5ItvFdfgnFKcnEx1kJFkc6HusvGIhzxryI9mBRCaFKO0QR8N6tO1A1rEfwwmF88HMZG9eWVPf99etu5s3M4YHxH5Kc/t83VDwmjuLBEArCwTpR28sX0NTS9+TP6RRiVCXSxNyHCm8+l5nfYO67z/LPvyZpKo2e3qPvpsFVIxBNp+5zEwwFKfHsYU3x72wum02Krjndk67Eps5EJWkRdVrQHRJWotGIMjPtlM3vv44hPR1BkggFAhF9KqMRbVz4/bBhszh6nJvBwpmHIuBEUUBUVN2NVxRNYkXRpIixgjHKNarUEvUbW9m0pihqf3o9U1h99UBJGeUffAmAn+jl3w7iDlSgFAW8R+zu2zUCj77Wm7jEuqswcbTfA1E8NRVBZGTONGQBLyMjE0ZiVjNG3vsxf3/0YLXTeVxaI4bf+S7m+KPnW5oSMhh03fNM/fThsDDqpl2H0qjToFrPw1lRgquyhIDfh0ZvxmhLPmty4UOhEMsWHAgT7wdZsTCXcy+ox2rF79Vty8p+YsgF/0O918PAy5/CaEhCEAX27VzBmgU/4LaXISnVmBMyWPjzW6ya9iU+jwudKY7el95Hs27nozPVnB9dVrCPvz58gJ2rZwNVlQV6jLyDRs2yYMFSgpcM5xOXP+oiwsJyB6MSrbUS8AcxpKbGNCtzFRez9qNP+OfJpwl4qx4W9cnJnD9pIsldOtfo8G5USAyON9PNYqDU58f8wiPYn38b386q3X6UCgzDz0M0GfGs3hB9EDHyodCRn0+Zz02OVY0CMxvXRpouOip9zJpayVXXp+EvKkE06BA1mojjDlLmySUY8iGJKsyq5JjHne3ojUoaNbexbWNJ1P76LTX8lV8laK2noeTVycagjOOaJl9QXLCdPx77Hy77oe+91+1gxtfjGXHbu8TbTt3i5AHHel5c07va/GxV8a/8ve9Fbm3xKy2sA1GINRuYyZwYuqREOt53D8teeCmir+fzz6JPDY9Csdg0jLu9LeeNbsSCGXshBD0HZKKyJvFTjEyhLEMHVF41RPF4lSSRARc2YOrP26vN5Q5n9FXN0ekPhe2HXO7qSjEaR2zjPAEBizobZyDylyLJpCHecPy77dHQGq3VZo5HolBpsNSBmaizsBB3SQlBfwCNzYoh5b8VISTz30QW8DIyMmGodUaadhlCWqP2uCpLESUFWpOt1qY/Gp2RVn1G06BNb3avX4jXZade616Y4lKOKjIPUrR/G7+/cwcHtq0CQGeKY8A1T9OwQ/86dws+GVSUeZj+246Y/dN/2UXjcf3Jd1XlUvtDXqa7PqO/9hq23v8Z+cuXgyCQOeBcRj76Dn//8ATdht3CxoW/sfT3D6vHcVYU8/dHD+JzO+k09LqYubGVpflMfPZyivYfqufuddmZ/c2zSJc8TP22LXBnpbPvyJDzw9jgcJGlrZsyS/vmzGXhw4+GtTny8vip/yCuXL8GS3bNpas0oohGJZKkUoI+DfXzDxEsryTk8SKaDCBKFFx3d9RzFWnJETuh9gMH+HPsFeyfO4+GI4azh9g7xEvm5jCsswLeeRdNl/YYLxuBIiUZ4bByepXeQlaXTOb3PU9R4tlHkrYRI+o9R1NLXwzK029EV9cYzWquvbs9T/xvNh53+I7jwItT2Oj7niABFIKKxubetR631OcnGAKVKBzT4tHpIE6TyYHdS8PE++HMmfQKmW16YrCe/Frjld5CPt1yZYQACxLgo02X8nTHDcRpZOftk4nKYKDD3Xdia9qEf556hvJdu7A1a0bP58aT3rsXkjLyXm22aDBbNDRqfugesX7yNHpnXsO8ivCoNoWgYrjuMVQuFcRYF0pI1vPwy714+5mllJW4AdAZlFx7V3vS6x3xO6qQQKkEnw/ppyWMGPcY3+bdGzFml8RrWO3QEjoi7MaqkEhQ1b2kMFiTuPD2t/n6idEEfOEu/UNufgWD9djMCL3BIGX+APleH8EQxAcDbH3zbVY9W1X+1NygAQM+/oCUbl1R1rJqiYzM6UAW8DIyMhEIoogpPhVT/PGVe1JrDai1Bmypx15D+GAZuoO7/1AlVH9741bGPP49Ddqcc1xzOpWEQuDzxg6I93oDxKsO/W1Uoo6u3tH8eE7/6h1pQiH2TptB4crVXLxwNoJRxfv/6xF1vPk/vkaz7hdgTogeIVGWvydMvB/Owj8/oMGVbyKGQggIMaOhtXUU/eAoKGDRY09G7fO73eyY/Dsd7rojan8sJIsZyXKonFDI68Ny941VbtuHm+mplFjuvwXJaqluCgYCrPtkAvvnVoXbhwIBFFLssE1JIYLHQ8jhxDVrAe7Fy0l4+1mUGVXfFbffzt/7X2bq/perz8l3beODTRdxaYM36JN6839y9zOzvpkXPx3A3z9tZ8OqAkxWNeeMNlERt4DfCl5EKWr4X4vJWNWRn9Gg202wwg6AqNdRqVKzstLBpIJSinx+sjRqrkyxUV+jRn8GC/kD21bH7CvN3UXAX3Nocl1h9xexz7GGJG0jOiZcjFYysd+xluWFk/AEHRS4tssCvpYEvF4cuXk4CwoQJBFdYiKG1NRaRYPpEhJoMe5KsgYOIOj3o1Cp0P1rXFdbzOZ0Gi9rTna3D5nn/5wKbz4N1F3orRpHwfcLUd17bsxzlSqJ5u0See6jc6ko8xAKhjBZNFjjNUhS+PxFixld/544/5qNb/l6mrcczA2dPuGXkucodO/EqEzgvPQHaJcwlmf3+IBD5eGMksij9VOwHYPhaTRCoRD2/fsp274D+4ED2Jo2xZieRkp2a65/bSarpn3N/q3LsaXUp/P5N2BNrodCGTsC6khcgSDLKxy8t7+wOn9fIcBFV15NW5Wa1U88SfnOnfw08DwuX7mUhNatT+j1yMicTGQBLyMjc0axb9OSMPF+OLO+HE/y4y3RnQHltGrCaFbR/dwMfv5yU9T+PkPqk5Vs4tdcgRAhuprGsPmFCYfE+2G4iorY/ec04gd0jnk9r8uOx1EBMTYjCvZEnwdQlaagllAsWUH7Xj1Z4YysRSwCzfR1sxsR9Hgp2749Zn/+8uUnfA1Bpaxy237vBRyT/8afk4eqeWP0A/sgJYXnnjrz8ln11qGyWvvnzqPrTY8zZ1r0sc/pk4xiycLqx9eQy03F15MIjL2cgKREtOYzff9rUc/9Zc+jtIsf/p8UT5JCJCXdyOW3tMbl8BMQXdjFPWyvUHBz3CQyDe2xqtMiFi/8B/Kp+PIHXPOWQDCIumMbpGvG8LcP8rxVpm87XB6e2JnLPZlJdDXrEWtZgupUk5jVLGafMS4VUTo1j1yBkJ8x2W8hChKL8j+n0ldIQ1MPbms5mcl7nsQbdJ6SeZzteB0Odkz+gxk33ITPXrXApE1IYMg3X5LWqyeKGtJnDudEQrJtTRqx5JlnKXtuB92uuxhVgoWypRuY+dtVXDp/NipDzSVERVEgLkFHXELNeemiWoXxslH4tu/Ct203gc//JmtuJndc/BpSm0YodCbMmlREQeSJBn7yvT72ur0kqpSkq5XEKRW1Lg0XjVAoRNHadfw08Lww9/7kzp24YNJE4jKy6Xv5w/g8LhQqNQrlsUeD5Xt9vLEvvDKAPwTfuYPcf+ml6N57H2d+PqFAgH/GP8egzz456t9XRuZ0IQt4GRmZM4o9GxbF7MvfsxG/L1JgnmlIkki/ofWZPWUXpcXusL60LCMt2iVg0MVzX+u5fLfjdjJpxda50UUfwK4pf5I5rObSNwp17IdJS2LsvGOFSoMkKvBNnsaVfXqyUyFR6g8Pg74lPQGLom524CW1ClvTphStWxe1P6XbiZfFAxC1GlQNMlHeeg0hnw9BrUKQIndvQ8EA7pJDudue8nJc6xbQ85xWLJgb/rCXlKpnQE8LvidXhLV7/llJQfsBvPzyBu6eYCZIpHEVgCdgp9JX9J8U8AdRKiWUFglQY8VChqFNzGP9+YUU3vMkwZKy6jbPstUI6zZx+xvjudtb9YB9kE8PFNFEpyZOdXxl1E429Vr1RKnW4vNEOpD3uvhuDNZDu69Bj4dgaQUhvw9Bo0GKs56QADoctaBnR8VilhZ+V91W5N7NyqKfubHZD6TpWtTJdf6LBCrtBAqKcM1dTNDlJiE7g34vvsCMu+4m4PXiKizklyEXcOW61diaNjnp89EnJzP4y8/Y8dvvrHzjLbwVFTQ4fwhjFs3D3ODYI9xqQpEQR9zTD+DPzce7YStSnAVVsyZIRivCYd85q1KBVamgaR0t6gLY9+dEiHeAvKXLmHvvAwz89CPcJSUUrFpN4Zq1JLRuRWK7dhgzM2r83rj9lfiCHpSigb+KKmIe95egoucN17N2/DNV112yFG9lpSzgZc5YZAEvIyNzRhGX1jhmnykuFeEscZ1NSNbz9Hv9mP7bDhbO2Fsl6s+vT++BWdW7IY0tvbi71TR8JZXsS/wa+4Ho5XIMaamo9Sb05ngc5ZGuwvVb90ZrjB2VEJ/eBK3BGjU/t+25YzE1bkbo4dsJbN/Jc53bscEfZLndRaJSQV+biQSlAo1UNwJel5hIz+fG8+sFwyP6lHo9DYacVyfXOYigVCDUENqp1OtJ79WT/fPmV7cte+geur76Np3u7cT8f5y4nX569EykWaIfXn8djnB+FtRqfL4gAX+IkLfmnSGlWDc+AgCeigr8ThdOvxJBqUarV6I3nB3h+aFQCPfiFWHivbrP7UGaPJWeF5zHHPuhBbAyfwB7IMiZGn9jjk/jsid/5McXr8ZRVlUSTBAlulxwA407DaoWGoHCYiq+moRz5nzwBxDjbZivG4u6Y1skYxRHsmMgGAhQ6SoIE+8H8QZd/L3vJW5qNvGErvFfJVBRSeV3v+D4+a+w9vg2zbngi8/5dcxYAIJ+P2s/+oTeL7+AGGVRsK4xpKTQ5qYbaDhiGKFAAI3ViuIk5WdLNguSzYK6ReTihMflp6zUTWmRG6VKxGLTYI3XIkYxBT1WynbujBDvB9n20890feJRfuzTH1fhoVJ7mrg4Lp4zg/iWLSPOsfuK2e9Yx9/7XqTMm0Mj0zn0TbqFfW4dW1yR5RwLAkG0mYfK0RnS02sdYSEjczqQBbyMjMwZReNOA5n9zbME/b6Ivh6jbj8lJlB1RWKynkuuacGQUY1AqDIpEo/IrzaqEiA5gU4P3MefYy6LOk72hRfw5+jLGPHm+0x6/TrcjkPmVLaUBgy56WW0BnPUcwFM8amMffIHvn9mbLWwAGjYoT/dR96G2hYP/5Y10gOJwDk200kLVU7t3o1+77zF/AcewudwVM2xXj3O/+E7jFknt6Zveamb4gIX2zeVYLapyW5io9fLL/J9t56E/hXmoWCQxXfdSkq3btz06y8ojWakgjwKbrgv6phCn97MnFdEKASuQgMmZRIVvvyI41J0zTAqT/zzG/D7Kdu6lcI9hewq1DN1aiFlJW4aNrMy5obWpGeZUGtP3s976N8IDeEE8tFDbjeuRctiH7BiLS3OH8ScI5oVZ2j4PIAoKUht2I5rXpqKvTQfn9uJKT4VnTketbZqJy9QWkbx06/h23qoykGwqITSF97B+tBtaM/pdkI78Y6KYtYVTonZv61iPt5gzTXK/7/i35sTId4B/Gs2YmrdlIS2bSlcvRqAwtWr8btcp3SHVn+M+fNQtSgR8lZFIEnG459rZbmHv37azq9fb6p2tTdb1dwzvjvZzWwoTjBCy5Ebw2qfKl+S8u07wsQ7gLu4mMnDR3Px/NlhaQoufwWzD7zLb3ueqG7b71jHgvxPuL75HD7MT6bIFy7i60kilYdFhXV88B7WLvwRrdFKRtMuGONSYprEysicDmQBLyMjc0Zhjk/j0ke+YdJL1+B12avb2w+8kiZdhtRZmOmpQqGUsMYffbcko+85tLh6HBs+++JQoyDQ5ZGH2D9vAQfmL2DOuNsZ/uYbBHRQXrSfxHotsKXUx2iruUSZIAgk1WvBNS/+TXnRfpwVJdiS66O3JqAzRtZDB05qnrHGZqPV9dfSYOgQnEWFSEoV2oQEDKknt3xPSZGLd55ZwoZVhx4ElSqRp17vzsXz5jD79jspWLkSSaWi6eWX0e3xRzEmVu33BqwWDKOGYv8pXBxJWRnY23Rl9Q8rAfjtowIue/x7Ps0ZGpZrrFdYubHZREyqExfwZdu2sf6HP1gb6sqCObuq29etKGT9zTN55JXetOpQu4f9gM+Hq6CQUCiExmZFqYudKxsoKcO3ay+OP2cCoD+vH8oGWUg2y7G/CIWixrroolGP84jPYLZWfca70QuiiCkuBVNc9M+yP68wTLwfTsUn36Bq2RRFfPTvZG3wuR2Iodh/IwEBOLvuoaeCkM+P/bepMfsD85bSauwYZv0r4OPbtsHrcyH5VEjKMy/qJVBpx7d1JxVf/kjgQD6KzDRMV16EMjsL0XDsUR5rl+Xz0xcbw9rKSz2Mv3sur34xiKTUE1vIsDWJnY6gMpnwOaP7NpTt2IGroDBMwFd485m858mIY31BN3/svpnBqT/y9WGb/SJwXsjL3M8+RxBF2t59GwX2Hcz5qcqIVKHSMuaxb0lr3LHGEqcyMqcS+ZMoIyNzRiEpVWQ278ZNby3AWVlMKBhEqdZhsCZW72L9F9EnJdH75Rdpf9ed7Js1i4DHS3zLFmz98Sc2fF4l6ovXreO3fhcwavpUOg297pjGFwThhCoL1DWSSoWpXhameqcmH9znC/Dnj1vDxDtUVQt4/M5FvPHNYEZOnYKvshJBktAlJISFqUomA4ZLh6E9pxuOv2YSrHTgadeJfFU8bz63rtrsft/OSn55WeKxF9awzTGbfY41ZBu70tDcizj1iUcXeO12/hn/HPWuu4cFT22L6A8F4dPXVvLkW32xxNVQo97np8TtoaKiEnVFJfm//oZn+zY6P/gAluwGES7bgZJSSl/9AM/ytdVt7gVLUbVrie3+W5COsca5qFRiGD4Y9/wlUfsDwwYz3XcoVcEgidyWkYjpDBfwAH6fB3tpAY6yQkRRQm+Jx2BNQpQU+LbELi8ZKCwh5HbH7K8NgiCQre4as7+l9bz/ZCnDEyUU8BOyO2L3O51kndsPTVwc3vJysoYP4oM7etKm7xi6XHhjzAWb00HQ68U1cwHl7x9aDPau30zR/eOx3HMTun49jyl6pqzEzY+fbYja5/MGWbkol/NGNzqhORvSUknt0Z0DCyM9cDo/eD+bvvom5rl+V3hEyc7KJRFl7g6y176KC9QuoOreblNK3JwSh3n7VgZO+BhtZjJr//mZRT8dqiLi97qY+NwV3PD67JiVXmRkTjWygJeRkTnjcDvK2b5yBvMnvkplSS6m+DR6X3wPjToNQmc6/t2pMx1tXBzauDh0iQn8NPA8itatDy+D9i+bv/uOrP79TsMMz17KSzxM+y26ePL7gmxYWUCf8+pDfHzUYwAkkxHJZETZuAEuh5fPX1zO0nmRZnwGowaTkEnvlOvrbP5QlfPuKiyk/nmDKXPF/vnO3W/HYffGFPAHPF5e2J1HjudgmoqCbqMuZdC+HXzXtQdjlizC2jA7/NobtoaJ94N4V63Hs3YTuj7dj/n1KDLTMIw+H/ukP8LaNT27oGnfinOCAnvdXloZtLQyaElQKnD5K3D6y/EGHWglMxb1mSOcANyOCjYt/p3pEx6rNrPTGqwMu/NdMlt0R6ppd12pQIhSH7y2lJW4qHSYkRzNuTdzLT+X3MRO+yFBZFDEcVG9l9AqTDWM8v8TUaNB26sLnpXRzTWllk1Z/933DP32a5z2YpbO+RyPo4Klf3zIvs1LuPjBL447vcuem4urqKjKZDMYpGDlapR6PYnt2qJLST7meuTBkjLKP/02al/5+1+gbtsCRWLs+9yRBPxB8g7YY/bv2hbprXKs6BITGfr9N8x/8GG2TvyRoN+P2mym8yMP0WjUCBY98VTU80SFAm3isdWCT1Ipeb1xBqFQCIMkYVNKCB07UJqRwHv/6xbhcQJVlV6Kc7bLAl7mjEEW8DIyMmcUXo+LZVM+ZuFPb1a3VRTl8Md7d9PrknvpNvxWlKr/trlMyB/AnnMgqniH48uF/P9OIBDE44ruDg9QlF/70lqCIKAzqLny1raUFbvZuuGQi32rjolcf08H9Ma6C6sNeL2UbtnKgkcfZ9+s2agtFlrdcgv3PTyYN17Zis8b+cApxchJLfb5eXpnLoVH5IAudvswZmaTeeEFrHztdc557ZVqE6egw4nj139zgxUSiqQEQl4vgcKq1+349e+q8m/HGJormYwYLh2Grn8vXIuWE/L50HbviJSUgGQ2MfKwY50VxeTs3c3m5X+CQiCtTSeWB/8hO7EH2aZuqKUTM3+rKwr2bOLP9+8Na3PZS6t38CyN6iOo1YQ8kdU0dOf2QrQcu7gO+IPs3lHG2+OXkLuvSmiZLGrG3f4lnbJmsMrxLU3UveiSegVJ+hPbKf0vo+7YBikhjkBheBlTQatB7NmRlec+iLuiDEX3DHasmVXdn7t9NSV5u45ZwIeCQQrXrOWPiy6l/Z23k7t0GZu++rq6X1QqGfT5BOpfMBSNMXa6yZEES8rAG+khAxByugiWV8IxCHiFUiQ9y8S+XdFd3Bu3qJuIDmN6Ov0/fJ/uTz+J3+VGZTSgT00l6PXS4Z67WfbCixHntLvzdnSJ4X/3BqYuCAhRd+GzDB0wquIwRkl78HucUcX7QZyVJTH7ZGRONbKAl5GROaNwlBaw+Lf3ovYt+vltWp1zEdakk2t0drrRJiXS9tab+efpZ6L2Nx176Sme0dmPSi2Rkm4gd3/0naSmrY9tFweqKg3c93xPKkrd2Ct9GE0qTFY1RlPdOc0DFG/YyHddexDwegHw2e0sfvhhUntP5aqbX+Tj98Nzqlu0S8Boir6AUODxRYj3g8xx+3n4+utYNPpiujz6MIbUqnSLUDBIyB9AdcWF+BqlkbNvA2qtkcSEBgR/m02o1F7jg29NSEYDktGAsn7s77S9rIC/P3mYLYsP8x/4DjqOup4t7WahVZipb+x0XNevS9yOCub/8ErUvlAwwKrpX9Pv8keIe/YBih95MUzEK5tkY7p8FKL62D87RQVOnrpjTtgCVUWZh7efXskTb15Ay/geGPQJGAwJEakRModQJMYT/8rjlH/9E+65i8EfQNWxNdKg3vx1+x0EvF42ffkN/Ud/EXHurjXzyGzW5ZiuV7F3Lz/0ORdTZiYBny9MvAMEfT7+uvxKRqxeQbBBNnF5BQR/+RNFciLaPt2Q4uMQtVEWs48SHi/UoqJIKBSitMhNRbkHQYBbH+nMO88sZf/ucBGv1Sto3almD5ZjQaXXozqiPJ6kUNDh7jswpKWyZPyzOAsK0CYk0OXhB2k69tIII0GzMpnhWeP5Zc+j4WOLWsbW/wCFzwRRAl3UutiVXgASs5qf2IuTkalDZAEvIyNzRuGsKI7qQA8Q8HlwVRT/5wW8pFDQ+obr2T11GnlLlob1nfPaKxgzYtd1l4mONU7LFbe24aWHFkb0pWUZScus/Q7X4ZjMakzmuhXsh+MuLWXO3fdWi/fDOTBvLs3vKkNvUOKwV31nTBY1190dOwIg3xtdvAN4QyECag1KvT5M6IkGPapbxjDz55fZ/Mvf1e2SQsWF175Ehib9uIyxDqfSW0SxZzdrin9HIahoG38hFlU6eqWVnavnhov3f1n+08dc2Op9pux5lmuafo5OYTmhOZwoPo+L4gPRDeoA8ndvIBDwoWrWiMSPX8a3fTeB4lJUjRsgJcUjWS3Hdd1Fs/bFjC6Z9MVW7hnf/awpL3iqCIVCBItKCBSVELQ7kJITES0mFMmJFKfZUF89ClGhYNOsWawdej7eiirh6ne7EYVIAaw3135H+yB7Z87CW1FBs8vHsu7jT2NNlG2ffcGy2++hnaSknSDg+vonKr/5Gev9t6Dp3glRE37/Ea0WRJORYEVlxHBSQhyiueZ7ncflZ+OaQj58aTmlxVWeDAnJOq6/twN/TdrGqn/yAEhJN3Dnk92IT4ptfFlX6BISaHvLTTQcPoyAx42kVmNITY26IKVRGDkn9WYaGHoyLecVyv05NDL2pmf8deSsFjngy6VTrzQ0R1TqMNqS6XflY/z+9h0RYzbuNAiD5eypgCPz30cW8DIyMmcUCmXNYkg6Sv9/BUNaKsN+/YmSzVvY8dtkNHFxNBo5AkN6GmqTnMN6PDRrHc/d47vx5TtrKMp3IkoCXc9JY+yNrWtVKeB04K2oYP+cuTH7SxfN4JJrrmL9qmLadkmidadkEpJji+kUdewca40oYLSZ6PXmS+gOT9MIhdi6dT6bl/8ddnzA7+XXj+/mhldnYzyBnd0Kbz7fbb+dZUU/VLf9sudRBqXfx0DrHSyZ/H7Mc7fN/AvDoDg8AXudCfigy03I4QKlhGSu/XdNqdERn9GYyuIDUftTslujUGoQpKo0BEXSsUd9HInPF2DLuug7hgB7d5TjdvllAX8YoWAQ3869FD/2EsGSQ/nb2j7dMd90JfqMdCb26hP13JRu3SjMDTePFESRBm2jH18TBxdnNXFxNZZRc+3ciYEgn1W6aTV8COL0eRAKUfry+yR92ggxNTylSrJZsT1yB0WPPA/+wxZ2VEqsD92GFFezj0zu/kpefGgBocOCagrznLz00EJe+Lg/F1/TAoVSwmRW12iUWdcIoogxPa1WxwYcOlZM1HPhgGfJ2TGP3Jkr+HrZedhSGtDj0uexV1jRaMO/24Io0qjDQEbe+zGzvhpPWf5e1DojnYZcS/vBV/2n/Xdkzj5kAS8jI3NGoTPHYU7MoLxgX0SfNbnece10nK3ok5PRJyeT0eec0z2V/wQ6g4ouvdNp1DwOt9NX9RBqUUfsxJxRiCKSWk0gSs40gNZq4ZxRTRgwIoQoHr08WKJKQapayQFPZJTLQKuaFZ538TWqwOhqTLymPgpRhb2sgH8mfxB1vFAwyJZlfxOf2fjYXtdhbCqbFSbeDzJ1/8v01I/DVVkW81x3RTkWRSNE4cRrNIe8Pvw5uVR88zPe9ZsRrWaMFw9D3a4FksV81PM1OiO9L76HXavnRPSJCiVtz70MUapbF32FQiS9nrl6V/RIElP0qNRnvnP/qSRQVELRg88Qqgx3nXfNWYSUnIhlcG/SevYgZ0F4tI4gSXR5+mH+mPjQYY0Cw+5496ilPKOR2L498CklmzaR1KE9e2fOinpc3ID+LPSHCAI7RZEmSQkE8gshGMSzcQuKIwS8IImoWjQm6aOXccyYj3/HbpSNs9H164F0lNx3t8vPr99sDhPvB/H7gsyasovLb2od02PjTCAUDFJRVEmHzgq+f2QowcChqKOi/VuZ8tZlXPHsVCBycU5rtNCs2/lkNO2Ez+tGkpToLQlyDXiZM44z9xsoIyPz/xKjLZnR909ArQv/cdXozYy679PjdvqVkTmILV5LaqaJxBT9mS3eAV18PM2vvDxmf/bwYQC1Eu8AVqWCR+ul0Eh3KJJFAvpblTRQrGT6/hfYVDaDbeUL2WtfyY6Kf/D6ndjLCmKOWZq/p3YvJgqV3iKm7Y+eNw6w0TWTeq17xexPa9eJ+raumFUnbuzo3bGbglsfxj1/CcHScvw791L6wtuUT/iewp0FbF6fT27xPort0cUyQEJGE4bf+V7Y/ctgTWLMY99hSaz71BdBEOg7tB6iFP39H31V8zr3ZDjb8e3YHSHeD+KYPBW1pGToxG/p9tQTaOLiEESRjHP7MXbpYhLatuXC29+m24j/MeDqp7n57QU06jgQpfrYI3jqDRqAUq9nwxdf0fa2W6OHg8fFYe7fnx2uqgU8fwg4LIc9ZI9uvikolSjSUjBdeRHWx+7CeNlIFKnJCEepY+52+dm1tSxm/45NJbjdsdNwzgQq9uwhUFnMpvkTwsT7QfxeN2tnf00gRqoeVH1nrUlZmOJTZfEuc0ZyZj+5yMjI/L8kqV4Lrnt1Bvu3LCN/13qSG7QmrXEHuYTL/zOCDieBsnJClY4qJ2iL6ZhCmv8LKLRaujzyEHtnzqZ8Z3h+da+XXsCQeuxl1JLUSh6ul0KFP4Aj4MXh3c6awk/4ctd7ZBracWHWk3yz/VZKPFVRMCOSxpPSsA0Htq6KOl6DNr2P/YX9S8DtwOGPXYZqbcUfXDHiHTYtmozfG14j3WBJJLNdd+KM2THOPoZ5lFVQ/tanEIjMJfeuXAdX9WW1+0vW7fwRpajmnKSbaZtwIVZ1eEivWmekafcLSG/WGWd5EYIooTPFYbQlIwi1W2Q5VkwWNXc92ZX3nl+Gy1klWCRJYPRVzWlURw7h/yX8Ofkx+0JOF3h9GNJT6fLIQ7S69hqCwQAqoxGNxQKAMT6Zeq16nvA8jBkZjJ41nd9HjGbTV98w+MvPWfzk05Rt3w5Aau9etH37Ld4TNECV2GwkQiDv0GKaqlXTGq8hCMIxlSZUqSUSU3Xk5UQ3+0zNNKJWn9nSYdtPvxDXoysFu1bHPCZ36zK8bgdag+WUzUtGpi45s7+FMjIy/y8RBAFLYgaWxAxa9hp59BNk/nMEikso++Ar3POXVJfTUzZrhO3B/6FIPnujMCpL8ijN203xgZ3YUupjTa6HKa5mEW7KyuLiuTPJ/WcJW36YhCE5mZbXXoUxKwu1+eih3VHHVEiYFBLrS2bz3vrB1e0XZj3Jx5svwx04ZIA1p+xDrrzkLSaPvyViHKMthbQmHY9rDgCqYh/NdX2Z594Vtb+VdQiW5Ppc9fwfzPjsSXavX4AgSjTpeh69x9yHPiERndJy3Nc/SMjhxLczSiSBKMDjY3h1az9KvTnVzd/svIVFBV9wa4ufsahTw06RJAXm+DTM8bXL1z0RAv4gq/7JY9bvO7npgY4EAiF83iAmixqlSkR7hkeYnA6UDbNi9okWM6ir/AJEScKQlhrz2BNFVChI7tSRMUsX4cwvQBBFRv71B263m4IQbFHreNEnUPFvusv5BjXK2QsIBqvuh5puHZHi6zYvW6dXMvLK5qxdFhlxIwhw3qhGKJRnbvCu3+2uMgf0uDDFp1Kcsy3qcZbELBT/lqMNhoJRjQllZM5k5Du7jIyMjMwZRdDlpuKzH3DP+yes3bdpG8VPvUr8cw8dt2P36aQkdxffjb+Usvy91W3mhHTGPv49ttSad5GN6ekYR6fTaNTIOt7JPVQrOVXXggPOjWHiHaDUs5+l2p8Zev8bLPzitar5CwLZbfsy8NpnTkio+pds4Nwu17CkbCKeYHhYs0mZRFvLBSgUSpLqtWDUfR/jdlYiCAJaoxWVpg5rv8dIQVB2bs08349h4v0gu+xL2FW5lHbq4XU3j2OkpNjFhNdX4nL62bS2CKVKRJQEPK4AWp2CVz4fSHxSHf6d/gMo01ORkhPDdrIPYrxyNFKc9ZTNRRAEjGlpGNOqvkNBpxtp/WaMyYns94VQB/w0UqsZoZJorFXh+n0aYpwVw+jz0fXpflIikjLrm7nmznZ89d4adHolA4ZlU7+xBbNFg96kIhQKnbRokhNFVCqxZGez9q336PvjR+xaE90AtMtFt5Pn3MWKnO844NpAE3Mf2sSdj02dJYt5mbMCIRQKhY5+2P8fKioqMJvNlJeXY5KdnmVkZGSOGb/Pg720ALejHKVai94Uh+YYQhX9ufnkX3N3zNriCe89jyq7Xt1M9hThKC/iu/FjyN+1PqIvIbMplz3xA3rLibuSHyuFrp08urwpgZCPFtZBWNVpLMibEPXYNF1Lbq83GTwBREmB1mRDozux30nn7IVU/PwH3rsH83P5c2yomI4oSLQ3D+cCw10kmpqiqONdxmgE7A5Kn30Tz8p1Ye3i3RfxuuUuSjx7o57XyjaEW5r/hFKs2s3zOZ347HYUOl1EfeqTwfbNJTxy48yY/c99eC7ZTWX37CPxH8in9NUP8K7fDICgUWMYOxL94D6nNU3Htz+XguvuQVCrkAacQ6BJNkqtHn9IhWDQY0iPQyuFEC2mqDnzdYXH48de7qWy3MOnb6xi6/piAGwJWsbc1obM1nFYDSrMyjNvH7Bo/Xq+bN2ejo/cS6iRiYW/v0MoWJUaIyqUDL75bfwNRT7ZNZJA6FCOvFYyc3+buWQY2pyuqcvI1FqHnnnfPBkZGRmZsxZHeRErp33F4l/exudxAVCvdS+G3vxqrU28gk5XTPEOECwqhcMEvC/oJhDyo5FOvmA6XpzlRVHFO0Dh3s04KoqPScB7XXbcjgoIBNCqTSj0eoTjeJg2qZK5pMFrfLvjNko8e2lmOTfmsZ6gA4VJi0aRiDMQxP/vLly5N49KXxGBoBeDMh6LOg1JqJ3zuapZY4J7DqC872vGXHgpwbYPQBDE+VvQGHKQxnY+5td0PEgGPeZbrqLonicJlh8WgRAKIQmx/66SoAQESlz7sbvycZeWoagUUBzwYspugDYl+aSWfZSOYl4o1NLc8P8bitQkbE/cQ7CigpDHi2jQI9ksx5QvfjLwrFwLoRAht4fgomXQuQc/TS9iwZzd+P1B2nVJ4bKbWpFiCvOyq3PUagUVQQ/P3DOPynJvdXtJoYt3H/+HO17rxd+JIpcm27CcYSLeVK8e5331BdOuuY5GYy7ikv9NoLwsB21iMn4xHTFe4NM93cPEO4ArUM4nmy/nntYzManO3jQtmf8fnFnfOhkZGRmZs5ZgIMC6uZOY9/1LYe27187n+2fGctmTkzDakvDa7Tjz86nYtQeFTosxIx19Sgrivw7Jol6HYdRQVM0aEQqF8Cxfg2vOIkKefx8krRbyD9gJCQE8mv1MyX0Sp7+EromX0dwyAJumdgsFAa8XR0EB/mAIh06PXaMlTqXAqpBQ1vHultcT3S36ID53dFfsIwn4/ZTk7mTudy+yc80c1BoD7fuMoUXfMeQcULFlYwlJqQaatIrHlqBFcZRyT2pJR5eky8k0tOPPvS+QoW+NVjLjCpRHHDui3jO4QnF8sb+QzU43LXQqBlpy+GzzGArcVcZbOoWFS7PfpK1tGDrl0fPzpXgrcc8+RPHjL+H/Zhp8U9Wu7NQWw12XHteixPGizEgl4e1ncS1ajmf5GqQEG8HM9nRWXMWUvMejntMn5Wb2Va7ioy1jKfo3jz9Rk82VLd9CP2MJvpYtkBpno6hFGbrjwWRRY7KoqSiLLDNosqgxW2QH+lhIJgOS6cxa9As6DrtP3Hwzz726jcK8Q20rF+eyYXUBL3w8gNQM40mZgzMQwB4IUimF6HVhfeb+shOHvSoPPy5By4XDk4ivzGFAAJw+B8aMdKTTvPBxOCqDgYYjh5PavStF6zbgzy+nUc/B/P5HEX9N2sYt7+oi0oQOkuNcj91XJAt4mTMeOYT+COQQehkZGZnjo7woh0/u6Y/bXha1/8pnfyMuvgErX3+TZS++TOhfx2+N1cr5kyaS1rMHoqTAvy+H8o+/wbNiLUgi2u6d0A3qQ9lbnyDq9ewbfjUvPbcWpUqk24BEel6kYkLueTj8pSRoGnBv69nEaTJrnGvlvn2sfPNt1n38KT6Hg8zBg2kx/im+01nom2Cls1mPrg5rdpfm7eb923pWh3IejiCK3PTWAmwp9XFWFFNemMOOVbNQqDQ0bH8uBmsSGn3V71Hh3i1MeGBwhCN7WpMudBr1Bg6HgmAQ3G4/WdkmMutbal2z2R2w4wt4KPXu5f2Noyl0V7neK0UNF2Q+Tpv4a7h7hx1/qKoG7UtZSorzZuILuVnk/olNFYdCue9tPYumlr61um4oECBQVIp/Xw7B8kqUDTIRbVYk88kRKLUh6PEiKCQKC9wUufbyXfFIcl2bw45pbTufSxq8xuMrmkfs5kmCksfSZyLc9hGma8eiP68foqbuxXQwEGL9qgJeuH8+gcChxzlJEnjgxZ60ap8Us8SczJmHd8t2Cm9/DEWDTNb3voyPPtgS9bi+Q+px9Z3t6tQR3lfpIE8U+TK/lFX2quipNjot5yu0fP3UUkL+EDdcZWPpnTdStL4qmkhtNtPzpedpctFoNNZT5x1wrLjdfl55eCHbN5dwzevwZX5sc9wn2q+Ww+hlThu11aGygD8CWcDLyMjIHB/FOdv54PbYNbtH3PMh4l4Xf46JrGsuKpWM27AWg05P4S0PEXKH7yiKNivW+2+mwKfjqfHrsVccCuvMaGBk+MMePs8dDsCg9HsZUe85FGL0XaHKnBx+GnAeJZs2hbUrNBoGLV7I0woDTzVIpaFOU9uXflS8LjuzvnqGFVO/iOhre+5Y+l/1JD6vi2mfPsqmRb+H9Z8z9kE6DBqHKEr89ub/2LZ8Wli/WmdiyO3fMGuqn0Wz9xPwh7DGa7hwTBM69kglMeXYdxnLPLlU+grxB90YlQkopUSe2l3CHreXJJWCx3RKlH/+jW/+CgRJggHtcfZM483cS3H4S8g2duW2ln9gUJ79Zcwcdi/2YC5bK2fzT9HnGJWJDLY9B04bpRXFiDo7W/2TmVn8HP7Qoc/lObZrGPR7C3wLV5H0yasnrXqC1xOgMM/BjMk72L29nKxsMwOGZZOQrEelrrtFKJmTT6CsnJIX3yGUkMSneU1YvSQv6nHWOA3PfdQfW/yx15+Pet2iEvK9fh4oceA8In1JJ4rcozUj7t7HgosH4i4piTh/2G+/kH3h+XUyl5NBMBDih8/X88uXm3n440a8k9eBEJFpWkZlAo+1X4lNLZeslTk9yDnwMjIyMjKnFEmpRqHShO0Ox6U1pN2AyzHFpZKY1ITJ10Tf+Qj6fGz7+Rea1m8SId4BgiWluHfu54uF2jDxDrBvZyWBggaYlElU+PJZmPcZA9LuxKKO7o6ev2x5hHiHqhJEG595hn7PvcSvhWXclpGI+t9Q+tIiF5X/XtdoVmGNO7YHZ5XWQM+L7kZjsLB0ysf43E6Uai0dz7uGzuffgFpnZMvSvyPEO8Dcb1+gQZtzMFiT2LFqVkR/r7HP88WHxezeVlHdVlrk5ou316DTq0hI1h+za7RFnYJFfai83QG3lz1uLyLwgFYB9z+Np/SwMPuvp6Kdl8rND07glZzh7Heuxxd0HdM1z1T0BhV6skgyXUXHpNGUF4R449F/2LVtVfUxHXv34dqre/FJzmACoapw452elQTq94Y5fvw5ecck4EOhEBX+AEHAIIk1pnSo1BJpWSYuv6UNXk8AlVpCOpkJ0jInDclixnbfLbg278QwwxvzOJ1BeVT/g9oSdLmpnPcPM9u0jhDvAM5gkB3KIJlbVkQV7wALHnqE5C6d0Ccl1cmc6hpREugzqD5/fL+VJX/Z6dP/HmYXvxxx3Njst7GoTl7pQBmZukIW8DIyMjIydYLBkki7AZezbMonALTtfxkZTTuz6Jd3KM7Zxoib3qViV/R63wCFa9bQSBvbMdu/dAVZ6QNZvyKyb/sqD6m9m1NRlk8g5CNWaFkoFGLz9z/EvMb+v6fR/pln+dkj4QkGEf0htm8q4b3nl1GQW5WnnpCs4+YHO9GoRRwqVe13OA3WRHpddDftBlyOz+NEodJisCaiUKpxlBfxz2/vxTx3+V8T6HfF4yjVOjzOQ0JdpTUgarPZvW1r1PMmfrqeVh0TiUvQ1XqeAF63E1dFMcFgELXOiKSu2sXvpFejnjqHQGlkjnxw7wEs24OkxDVDEpQohP9e/rWnXMFLD84hZ094Du3yeQVotCl0H3YD84vfBSBBVR+p0FG1z3cMYqvE52dJuYO/isvxBkN0Mes5L85MkkpR40KMJIlodbJwPxECfj+OAwdw5OUTCgTQpySjT05Goam7aJyjIdms6Lu2Y5CxiAUz90c9ZsjoRphtdTOnYFk5zmCQ1YHYAbllihCG1cti9hdv3EjQG3vB4UwgPlnH42/04Z1nljCg3kWMadaKuRUvUOzeQ4ahDSPqPUeGoZ1cRk7mrEAW8DIyMjIydYJCpabb8P9RVrCP8oJ9pDVqx+/v3FHdX1Kwi7iWLSlYuTLq+em9ehIstcccP2Qw4nBFd6c32kRy/VWiqkPCaPSK6AsBgiCgS4iPeQ2VyYQnBNlaNRpRpCDHzvi75xLwH3q4Lcxz8uw983hpwkDS6x1bqpWkVGFOiAzPDAb8uCpLY57nKCtEqdbStv9Ylkz+oLrdnJDO3l2xd7pLCl143JF59zVRlr+XOd+9yKZFkwkG/KRkt2Hgdc9xoSWJTH8QFi6Nea5iziZaXN6Xxkn9MapOfVm8k01JkStCvB9k8Yw87rloLPOpEvAD9Nfjnz0JVEoUKbXbmSz1+XllTx5bnIeiUP4oKmdOaSUvNkwnWX3mmIX91/C5XOybOYu/Lh+Hp7xqgUqh1dL3zddpfPFo1OaTY0QYDUEUSck0c+HYJkz+NjwPvk3nJDr0qLtd4pDLjVhRiamGRaZAAOJatIzZb8rKqjYhPVNRKEQat4jjqXf6UlnuRZKa0LrRuUjqIGpJh14pl1uUOXuQl5lkZGRkZOoMoy2JC259nZH3fsSCn94M61u9YCLtHrwj6nlqs5l65w1G27l9zLH1FwzA5w9y5AaJIEKTjir22FegV9gYkvEQKil2iHuLq6+K2dfo5puYr9BwQYIFwR/iz0nbwsT7QQKBEFN+2IrXe2ziOBYavYl6rXrG7G/caTBqrZ7OQ68nPqNxdbvX7SS7aTwjr2jGoBHZmK3hu95KlYhSWfuf+oqiA3z95Gg2zP+ZYKDKmC13xxq+enQY/f0lKASxZmd4lZLmtoE0Nveu9TXPJkoKY1cTCARCBNwKFIKKy5JewThjLyGHE8vt1yHaLLUaf5fLEybeD2IPBPm1sAxvMEhlhYcDeyvYuaWE/AN23O5wA71AeSWB0nJC/rr5bP5/oXznLn4bNrJavAP4XS6m33ATReuil4CsDV67HVdxMQG//+gHH4bRrGbYmKa8NGEAI65oypDRjXj6nb7c8lDnY07hqQlBpyU09x+G1hBM1NmiJ/uC82JGInR9/BH0KSlR+840rHFaMhuYScsykWBOxaZJl8W7zFnHmb1cJiMjIyNz1qE1WnFWllBesC+svbxgH/uL19P77VdY+vizuEurdpxtzZox5LuvMGVlETRZ0PTojPuIXV79+f3xb9zMmGQ35z7RieeeWYvfVyXmr3uoCWt9nzIg7S76pt5KvKZ+jfMz169H1yce45+nxoe1J3XtQsLYMYxIjCNJpcBd6WP7pug5nwA7NpfgdvqPKYw+Fkq1jh6j7mDzP1MiHOYNlkQadqiqz26KT2XMY9+Tu301OdvWUa/DJcyfUcKG1YVYrBquuKUNeTl2Jn2+EYDeA7MiRH1N7N+yLOJ9g6oIgfnfvsDgO98nOLQ//g++jHq+7sL+NElsglo6tpD9s4W4xNivS1IIJJkzeNK2GMW0dYillRjeeQ5FWjKiSnXUsYOhELNLo+/uAywutzPMYuSjxxezcVVR1TUlgX4X1Gf0uOYY8eBZsRb7r38RcrnR9OyMfsi5J808779EwOdjzXvvE4qSAw6w+KnxNLt8LAqNltTuXTFmHCpV6fd58XmcKNU6FEoVHref8lIPJQUOBAJQvI8dE97DXD+Llldfhal+vVp7UhhMKgwmFVnZlrp4mVERrRa0XduTtmkrAxs3YpozPBR+sM1IA60KfWYmo6b/zW/DR+EuLgaqIgXa33kHDc4fetLmJyMjE4ks4GVkZGRk6hxJoUSUFNW7uAdZPv0zspp3Z9T86QjeEKJKiTY+vtr8SLJZsNx+LYFLLsA1fykEg6haN8O7bhOVX/wIQGKnfTz1/Bh2HfDRqmMSGrMXVA9iUMShEI8ulDRWK+3vuI1GI0ew+dvv8ZSXkT16FIYmTRATk7ApJQRBIKSWSErVs3tbWdRxElPr1uXbmlyPq577g2mfPcbeDYsRRImmXYfS57KHwsLuTXEpmOJSUMd14/FbZ1WHyO+hnDXL8hkyuhEDhjVg9/ZyRl3VHFUtS02FQiE2L/krZv+eDYsRPXYMvbrgn7UA39adYf2anl1QN6iP9B8V7wDWeC1Z2Wb27Ij0AOg3pD7JiQmopASCo5sgqJW1Eu4HEQBVjDBmi0JigE5PxQEnqekmdm0pw+X0EwiEmP7rTlRKkaFx+3B//l31OfaJk3H+PZuEN8dHhPD7PAGChOq0DNnZTMDtpnj9hpj9Zdu24yosYt59D2BIS2P0rGk4sUMoiM/jYu2cH1AoVHS68F4Wzyvjhwkb8fuqFgOMZhU33nIPu165n9Vvv8ulixcQ16zpqXppR0VUqzBeMozQtz9zod1B/87tWBUEQZLoGG/BplZhVFTd51K6deXyVcuw5xzA73BgqpeFLjERlfH0lXyUkfn/iHznlpGRkakDQqEQ9gMH8FZUIKlUaOLj0ZzCnMlQMIirqIiQz4fGYELQqBDVp89ETGeKo2m389m44NeIvv3bVqCOt2JJil6rXbKYCAX8+A/kEywvxz55KorUZFQ3XwLxVii3k5WioGGnmmu914TGakVjtZLQulXMY9QaBReOacqSuTlR+4eNaYpGW3c/o5JCSVL9Foy+/zM8znIEQURrtKLS6COOrSz38MmrK6Lmt/85aRsvTRiA2arBcgxGV4IgYIqLnVurM9kQRAmF1Ubck/fg3bwD59Q5oFKgP38AyqwMJOup+8yfDiw2Dfc+24N3nl3ClnUHdyGh14AsRl3VHLWm6vMg1ZRmEANBEOhvMzHnCB+IYXoDDUqCzPpoAwvznGRmm7ntsS7M+Ws3S+dVfTan/baTcx9rEjFmsLySyomTMd8yDlGlorTYxa6tpUz7dQehEPQbWp9GLeLqrBzZ2Yqk1ZLUsQP7582P2m9r1pSKPXsBsOfkMPvOuzENb8/SqR+jM8Ux8Npn2L5yJpvWFvDth+GGkpXlXt54ZSuPPvocf/Xrwty772Xo998cd069127HnpPD9l9+w55zgPqDBxHfphXG9KOXPnOXleEqKMBVWorCqKNUn89y369kG7vRYtx5JNhFgg4XWSY9gt6ApA//XIiShCkjA9NhEQgyMjKnHlnAy8jIyJwgnvJy9s6cxezb78KekwOCQL1BA+n37ltYGjQ46dev3L+frT9MYv2nnxEKBml28WgaXzgMQ2IiiqQEBNWpN75SafT0u/wR8ndtoDhnW3W7qFBy0QMTMFhrNvUKOVzVYfTKMUMpTJaY98fLlOTuxBiXSk/hNpoYz0dvjm1IVxekZhi54b4OTHhjVfWOmkIpcvXtbUmrd3J2nbQGM1pDzQ/3jkov2zbGDu/fvqmEc89vQMDvw1leRCgUQqUzoNHVbLrXuu/FLJn8ftS+zoOuQeOVCDpdSHE2tD1sqDu3RRAEhCgGVl5vgLJiN26XH7VGwmLVoK7DBY/TRWKKnvue7UFFmQeX04/BpMJsVaPVnfj3LE2tpI/FwJyyKhHfXatFs7SEtz84lINdkOtgxaID3PJgJwpyHezeVobPG8QTkoi2ZOeauxjjZSMpF3W89/wy1i7Lr+5bvSSP7KZW7n22x/9rES8pFLS64XpWvf0uQZ8vor/1Ddcx7dobqv+/5+9pDL3nRpbyMc6KYn5/+w6ueOY33noxP+JcAL8vyOr1HtJ79WTf7DmEKh149hzAMX0ugiCiG3QOirRkJEvN33uvw8H2n3/h76uuhVCVN8fqd97F1rw5Iyb/jCkjM+b9vjInh5m33MbOyYdKVab16UX79+7lve1jUIoa7mszj5SUyIUgGRmZM4uz/5dURkZG5jST+88Sfh918aGGUIjdf0/lx74DuHTh3FrtjBwvlfv38/OgoRRv3FjdtvDpZ9jwzXcM++QTDOUVqFuennBNc0I6lz35A0X7t7F3w2JM8WnUa9UToy0Jharm6ABBqQBBQNmxFbv1ZUz/+NnqvsriA/z16UMUF+ym9yX3otYaTtpr0BmU9BqQRasOSeTl2AmFQqSkGzHb1Gd0+LEggL20gKVTPmbl1C/xuh3Ub92bflc8Snx6QyRF9NBuc3wag294gb8/fqhaIAA0aNWbhrZmFIy7HfPNV6I7tzeiQYeojC4Wykrc/DFxC3//vB2fN4ikEOg9KIuLr2l5VgrFoNtDsLScYHkFglqFzmzCmGUBwBtw4fDn4/KAURmPUjz+8l4mhYIrU+LoazPyV1EFQwQN4z9eFHFcKAjffbyei65uwYcvLUdSCKik6GXABEkCSWLbhuIw8X6QHZtLWbEoh269zSiUajT6Y6us8F/BXL8eo6b9xV+XXYn9wAEANDYbPZ55mu2/Tq727AAgFCIUOBT9EvB72b5iOhZbVyAyvQLgQH6QpqlpDP3kI+wffoVn6erqPufU2WjP6Yb55nE1RrE4DhwIE+8HKdm4kaXPPE/PRx9Fm10v4jxPeTlz7rgrTLwD5MyZT/B6H+e//yA/lj7M51uu5raWv2NQxsWcg4yMzOnnzH36kJGRkTkLcBYUMO/eB6L2Ve7dS/6KlSdVwO+eOi1MvB+kbMcOds6bSwOVCUVqMlItXbDrGqMtGaMtmfqtex3TeaLJiLpLO0IDOjH3g+ujHrNsysd0HHzVSRXwACq1RGKKnsSUyFD204XeqKJRc1vMXfjGLWxMfO4S8naurW7buXo2e9Yv5OoX/yKpXvOo56l1Rlr2HkX9Vr3YsXwG7tIi6jXqjCa3EvcrEyAUovy9L1C1bIrKUC/qGB63n5+/2sTUn7dXtwX8IWZP2Y293MtND3bCYKx9bvjpJlBWjv3nP7H/NAX+dXZXpKdge+o+SuOcTNn7LMsKJyIIIt0SL2dw+v3Ea2s2UqwJs1KBWamgiU7D+qX5BGLU5y4pdKE3VC2g9BqQiWrpYiL3jkE3oDc+lZapP6+Jec0Zk3ehcu9m/6bp9L/qSeKs6QiChGjQ1dpw7WxHUqlIP6c3Y5cuxllYiN/tpmLXLta89wE5CxaGHWtp2BCHozisrXDvZho0HsTqJXlRx89MFWE7xOktOJf+HtHvmrsY7bk90XaJXYlj99TpEeL9IBu/+572V45DZbNGLAI4CwrZ9vOvUc/LXfgP7bwPArCjcjF2X5Es4GVkznDkMnIyMjIyJ4Df5aJofewSQ/vnzK2T67gDAQ54vPxcUMpH+wtZVu6gtLiEDRO+iHnOpp9+JphgJWh31MkcToSSQhdrlubxw4T1zJ+2h6ICJ35/dMdnAFGvw3LzODyKEF5X9NrwoWCQ8sLw/HRvZSU+V+y66P8VjGY1193TAbUm0kRv1JXNCHnzwsT7QQJ+L7O/eQ63syLm2GqtAbM2jgarKmmywon00jd4P/oBfIcMCR1TZsZ07C4rcTPjtx1R+5YtOEB5qTtq35lIKBjENXcx9omTq8U7gP9AHsXePTy3uisL8z/DG3TiCdiZk/sBL6zpSbF7zwlf2xeoIBCI/R0B0OgUdOqZysVXNUcRiCw/JyXFox8+mJAgEKjh++bzBkCQ6Nj9IsR1Oykd/wYlj72I448Z+AuLTvi1nC0IgoA62YYrO8Be6yY2/TQxQrwjCHR59jGWzw2vxBCf3ogWbaIv8qk1Eq2aqkhs3gzfnMUxr+/45S+Cztj3L1dR7Pci4PEQ9HoJVkbeLz3l5TGFP4C3uByFULWoFghFWwaSkZE5k5B34GVkZGROAEGhQBMXV11W50jMdZAD7wkEWVHp5PW9BRx8BJtaUsFleiWiIrYLuqhQQDBUFU99GinItTP+rnlUlnsYe10WZsMBNi2cT1JmBomZDTHHp0bd5VMkJ6Jy1SweVJoqx/OKvfvY9edfbJ34AyqzmXa33Up861boEhJOymuqazzOSuxlBexeOx+/10O91r0wxaWgNVpjnpNRz8RLEwYy/bcdrF9ZgMWm5sIxTclsYGbet4/EPG/n6jl4nfaa8+EDAbzrNkcVAwDBohIIBkGM3AdwVHpj7hoDlBW7ScusOUzb6SurMvFTnN5w7kBJGZXf/hrRruzUigXuSdh9kZ/PMu8BVhb9TP+0O49799rhK2FD/ly0mu6o1BJeT6RZYUq6gcRkPTc90AmDSUXgusvQ9euJ/ZeqMnLavt3RdG6HIjEeBXDO4HpsWhv9+9Sph4XMDBW6XxfhXXXIjd27aRv2n5OIf/FRFIkn12+iLgh6vQSLSnGvXEugoAh1u1aQkYQHL2qtAY3+6MZxex2reHF1LwRB4Nrxn5LQrR0bX5+AIy+P5C6d6fDw3WzY+DdF+w6Z1SnVWjKadmbrqh+47u6RfPPhRlyOqgWvxBQ919/cAMfqGbQcdwUVz7wVe/4ud8yFMb/bTePRo4hv2QJnQSFrP/yYorWHFukS27dHyC+C5pGfObXZXPU7EEPEq+Mt+O1ezKpktArLUf9GMjIypxdZwMvIyMicAPrkZDrccxcLH340ok9UKqk/ZHCtxgkGAjgqqhYB9KY4REnC7/MSCgYoRcEbh4n3g8z2w9U3Xh/TObnlmDGIhSWIxtMX+u20+5jw+ipKi13cP74Zy3+5n2XbV1T3a402xj4xkaR6LaKKHZ3BSkJmUwr3bo7o05vjMdiSKN+9hx/O6Ufl3r3VfTt+m0zLa66m5wvPnvEi3u0oZ/WMb5n55dNh7S17j6b/uMfRW6LPX1KIJKcZGHN9S1xOPwqlWG2kFpfWMOb11DrjURd1BJ0WVetmuBcui9qv6do+qnEdgCEuxOX3ZlJWEGThlCJKi4+oa19D+HyJex/rSv9icf4XSIKSvqn/o6GpBxZ1So3zPWl4fQTLInOaA20zWe38OuZpywon0iPpanRKy3FdtsSzj3Xlf2Ba25DLbmrF52+tDtNeSpXI5be0wWCuqhMOIFnNSB1ao2rRBAIBRH14Ob/WnZJIyzKSsye81nxcopZ2nbQYisrDxHv1az2Qj3PaHIxjRlTl05+hhLw+vKs3UPzkqxAIoOjbBbs3haWfvUlhzjbi0hrSddgtWFPqo4uxMFbpLeSb7bcSJAAh+KhwHA379aDnwAfQiRayrJ3I27GW7WtmVp9jTsxg0DXPsHvDItr2uQBrcjrtumZQUeZGUogY9BI6lQ9V9xsRRRFfz85U7tkf9frac7pFvG9Q5XWy5Jnn2PD5lwQ8Hsz169PpwfspXLOWNe+9jyCKnPPkE7B0PeKw8yLO1yUm0GjkcLb99EtEX3L3LuxWVqVXXNrgTSyq2JUoZGRkzgxkAS8jIyNzAoiSRMurxpG/bDnbf/m1ul2h1XLhz5Mw1CL/vbxwP2tmfc/aOT8A0KrPxbToMYx/Jn8IBFGNfIhoezIHPD7cHTuT1rsXOUeI+KT27cls0wZDVtZRnY1PJhVlHlYvzaP/BWlsnf82uYeJdwBXZQnfPX0p17w8FXN8WnV7oLwCz7pNuP+azYVXPse3b16Hq/JQvrdSrWX0g5+j1duY/8QDYeL9IOsnfEarG687YwV8KBTCWVCAq6KEPesXIYhi2O7b+nmTyG7Xl5a9R9Y4jkIpYTAKVOzdy+6FiyhYtZr4Nq256LZPmPPryxTu2xJ2fIdB447q3i/qtJiuGI17ycqw0HEA0WZB3bF1xDnegIt81zamFDzLnqTlWDMyubL3Pez9J4NfPtkHQFqWEXOM0nYlnn28uq4/+a5DO5tbyufS1NKP65t8jfl0iHilAtFsIlgennIguP2opdjeCxrJhCge/yPW8sIfWVcxmYua/49VM0q477keLJ69j8I8J5kNzHTskYrX68doijSDFDXRDSLjEnU88kpvFszYy6w/dhEMhujcy0aHzir2r/6J+L2xox2cU+eiH3Iuki12RMjpJlBSSvHTr0EggLJlE4raJfPLy2Ord50L921h85I/GX7HuzTuPARllDKbrkAFe+2rwtq2Vy5kO1Vh9KOMzzOw2z2kN+2Ms6IYUVKiMZhRqnVktepRXfJRo4f4pEghDqAb1AfHlJkRC0NSgg1tz84RC5mO/Hx+H3UxeUsPLaaV79rFjBtvpv+H79PsyitodcklqFZsxDTuYiRz5PuoNpvp8+brBHz+MCO71HN60ub9+5nsfZ57Ws0gy9ARUZCza2VkznRkAS8jI/OfJRQMUlmah6OskGDAj96SiN6SiPJfB/SD/R6XHUmpRm+0Ve1OHiP6lGQGfPwB3Z9+gvwVK9HYbMS3aokhNRVJVbNZV3lhDl8/MYqy/EMCdMEPr7J+7iQGXPUk8394DYOzMub573gE3v7mK0qWLGXN+x8QCgZpOXYsKW3bVl0/qW7CXv3BEJWBAAJgUkiItQwN9vkChELQsaueP9+ING4CcFYUU5q7q1rABx0O7BMnV5mGAVJ+AVfe9CEHCrZxIG8rSfVbUL91L0zxaThy89n45Vcxr7/xi69I6dz52F7sKaAyJ4ftP/3Cmg8+IuDxUH/YEDrd+QV/fv0w5QX7qo9b/Ou71G9zDnpzzaZShWvW8mPf/lW5rv+isVoZ9NNX/P3j49Wfr6QGregwaByS4uglzxRpKSS89hRl70zAt3UniAKabh0xXzcWRWLkosj2ioW8sW5w1e4lUOjeydaKOQzu8DQ9D/Rm6yoHd4/vRqiiAld+DoHcfJRJcVWlDuPM/JP/dZh4P8jmslnstq+gjfr8o865rpHirBjHDKf8g/B8Z2HWWs7tdB2fVC6Net6A9LvQ1CDwj4Y36MTuK6IsYSGCug1vPPkPHbqnUK+RhfwcO1N+3MLNDx775zouUccFlzThnMH18LjszP78bn55cQZtel+MQA3pCqFgzBRqT8CJ018CCJiUiUjiqS9bCeDdurPap0EYdg5/fXJzZMh4KMTfHz9ESsO22FIijQZFQUISFARC/og+oCq83O3FIOowJtui7pYfDUVSAglvPEXlD7/jmrMIBAFd/14YRg2NmqZQvmt3mHg/nKXPv8ion35E2rYH3XVj8ZttlBS5EEUByxELZca0NAZ//inOwkI8pWWoTCakOD0hk8At4k8YVWfmQqeMjEwksoCXkZH5TxLwedm/dTk/v3IDzn9D0xUqDf0uf5SW54wCYN+WNfj8euwOFXqTiNnmIRDwodGqMFlV6PS1d8rWxsWhjYsjvmXLiD53aSnukhJCwRBqqwVdfNVDWigUYvM/U8LE+0HK8veQt2s9aoOZhlIQiC6YE1UKfPF6Ei/oxoWDBhDy+1EoVYgadZ2FuxZ4fUwtLmdhmQOFIDDAZqSn1UBcjBJih6PTKzFb1RCqSgeIRWXJIefmQGlFtXgHCOzLJfDEuySlp5DerBHmzheG7TIF/dEftgECHu9R53iqqczJYfKwkeSvWFndtvr1d9j2/U8M+XEC378xrnon3llRTDBQs6mU/UAuk0deFCbeoepzN/eGOxn6zQdsXPkHTbsOJSGzGUZbUq3mKaiUqJpkE/fMg4QcThAFRJMRURdZBq7Mc4DPt15bLd4PZ1rRUzx+40Zy1+sJFZXg+fQjnDt2V/dLCXFYn72f9aVTY85lzoH3aG7pj1I6/hJtx4Mgimj7didQVIL9l7/g39JhoUCQJgn9aeO5gDUl4QtTnRPGkmXocELX7RA/muk5r/Nr4e0MHfIStw/oz+o5LtweF+deFE+D+ulY446vHJ8oVYk7nydEi17D2LFqBrs2LqTL8Kdh8Yqo52jP7RWxsxsMBSl0bef3veNZVfwrSlFDr+Tr6JtyCzZNxnHN7UQIlh2KknArArgd0cu5uR3lOMoLowp4gzKBy1PfwW4vpFDYzdLKH3AHqhZQBQSaantS/MgLBPIKUTbIxDTuYhRZaYjaY3svFClJmG++EuPYESD8+72KUb89d9E/Mcep2L2bkF6HbvhQ8nId/PTyctatKMBgUjH04sZ07JEa9jnRWK1orGduFIWMjEztkAW8jIzMf5LSgr189/QYAv5DAs7vdTNtwqPEpWWj0NjYs9fEVx/sYtjYpgiClz9+WICj0ocgQPtuKVx9RzsSko8/fzwUClGyaRMzb7292o0+qUMHzv3gXRJat8LrcbB+3k8xz9++Ygb1W/fGtW05LTO7sd4dLlQF4OKEIG+v64w34GRE/edobRuKWnnsu0KxKPD6eHh7DqWHhVF/mVfC3DI7j9RLIU5V88+INV7L5Te3weMtQ6M3x3yojk9vXP1v32Hi7nD8+3Px78/FeNEF1WJCY7PSaNRINn39TdRzml1xWY3zOx3k/rMkTLwfxJGby64fJtOofX+2Lp8GQFaL7qhrMpujqpRhxe7dUfvKtm9Hr7Ex5KaXj3u+ktkI5pojUxz+Eko8kQtRAEEC7CldT/HW+mRtnxrx/gYKiyl97BXOf+IOXquIXrUhSJBQhAvEqUGymDFePgr9+f0JllXVgRctZiSLhSt1H1Pg2sri/K8QBYluSeNI1GQfdTezrNhNSbGL0mIXcQk6rHEazNZDixOJ2oa0sg5hXemf/FF0H2pRT6N+vVCLejLqPUuc/tgjhY5EqdbSpNMgbnh9DmvnTMQbp0fZsgm+9eEpF1KCDf2QcxGOMMwsdO/gmVWdcAWqhLMnYOevfS+wqugX7mk9A6v65JXPjIaq2SHfh6OZB0br91RUULppM8XP/k3p1m1YWzXhtnu+ZZ72a5bYJ3JZ5luEPp2Gb3NVeUTPynUUrlqP7Ym70XTtcMyGhaJKhZhw9HJt+tTkmH2SSoVCqyFnn51Hbp6Jz1u18FdZ7uWTV1eyYtEBbn6gU9hnS0ZG5uxHTnSRkZE5q3AEAuxxefgmt5iP9heyttJJqS9c2HrdTtbN+SFMvB/Owp/exO2z8skb26nXyIJSJTLx0w04Kqt2OkMhWLEol+fvn09p8fGXJKvYvZvvu/cOKyWXv2IFE3ueQ/mOnSAINYYySwoVgYCPBZ88yCh3DiONCoxS1W27kVbJfRkia/KfIt+1jVJvDhO2jGNZ4UQCodg73ceCPxhienFFmHg/yB63l02Oo/9tRFGgQ/cUjHFJdBhyW9Rj0hp3xBR/KL9ZiLETVY106KdLqdPR9fFHo+4qZQ0cgK1pk6PO8VTid7vZMOHzmP07f/mDrIbdgKr3v8foOxGC4HM6Y54TcNdcls1ntxMM1M1nIhZCjAiRgwR8Im2bG/CtjCxtBxDILyTNnYUkRH/vz0m5EZV0fDvOdYGo1aBISULVrBHKBllINgsAZlUSjcy9uLLxR1ze6H2yTV2PKt7zD9h5+q45PHT9DF56cCEPXDudFx6YT2HeoXKPJlUi4xp/wrhGH5Oqa45WYcaqTmdk/edI1MU2KDxW3JKTNcLfbOuwjqmGCVTe1gPd3eNQNslGUT8T0zVjiH/tKRRJ4a/JG3Dx976Xq8X74eS5trCtfGFE+8lGSohH1bYFAGqPgNYQfadZa7CiM4WHqge8Xrb/8hvfde3Bzt//oHTLFnZOmsyU7iPpsWc4z7bdTNM/IDj9iFD2UIiytz4lWFx6Ul5TZUk+xsb1kKLk6wM0u+JyMMbxxTurq8X74axanEdhXux7h4yMzNmJLOBlZGTOGhz+AH8WlXP3tv38XFjG1JIKntqVy4u78yg5TMQ7K4qjupYfxJLShOmTq+qH9xtanz9+iMy7BcjZU0l+zvHVUA8Fg2z+dmJEWDNU1etd+tLLKBQaOg65JuYYzXpcyPYVMwgFA0x5bgz11vzBKw1TebtxAt3UvzBpSy9WFH4eds6vux+lzJMTfcBjpDIQYGF59DJiALNKK3EfpU41gN6oonmbZNqcexHnjnsKjcECgCgpaNFrJCPv/RC9+ZBAUNbPBGX0nX1l04aIpvDdR0vDbC5bvoT2d9+JuUED4lu3ZuCETxj8+QT0SbULFz9lCAJiDb4IolJJMBQgtXF7rhj/C64dOfw+8iJ+H3Ux23+bjD03L+IcXWIiYox0Bkmtxl1Syp5p0/G5jn8x6mjolfGkaJtG7VMIagy+euDx1FiLWmkPkKprFtHewNiVBsYudTXVWlOZk8PuadNZ/PQzbPrmO8p3764xXaM2lJe6efXRRRFO8Du3lPH+i8uwVx5adLSoU+iVch33tp7No+2WMSb7LZJ0jRGFukmN8QZcTM95ne933sHyoh9ZVPgFz+8dyBtx91Fwb0esz9+PYfT5UfOynf5S1hT/FnPsJQVf4w8eei0V/gDFXh8VURYD6wrJYsJ2/60YLh1GaOoihlzxFMKRZQ4FgcE3voApPtxp3Z6by8xb/hc5aCjE3BvvRpMDgd+jL0oES8oIVMT2KTlenBXF/PnBvcz6+UUGfP1JhIhP6tCebk8+hscDG1YVxhxn+cIDdT43GRmZ04scQi8jI3PWUODz831+5E7HNpeHmSUVjEy0IgkCe9YvxJaaDUyPOk5cRguWrq/aldBolZSXeGJec9e2Upq2PnYjOK/dzu6psXN6c+bOx1tWRlaL7mS26M7eDYvC+jOad8WSmMmQSx5HF1RBCIz1s1EKAqsqpvP77jujjuvwl+IKRA9TP1YEQFFDWKhSEBCPIWrUmpBI56HX0qzbULxuBwqVBr05rtq5+SCi1YL17hspfem9MMEnGPRY77oB6QgBLwgC5gb16fX8s3S67x4ESTpjnecVajVtbr6RHb9GFz+trr+WJv1G0Kz3MGbfeHuYY/Tuv6eS0bcP533zJYaUQxEL2qREOj1wH0ueeS5ivHa33cqmb75l64+TuGLNSuJbNK/z1wRVO9HXNPmCl9aegy8YHhFwefYHTH+3hJGD09GplOCNntOvTErmtuTfWVb4A4sLvvq3jNyttLQOwqKuu9JWzooSQsEgGqMFSYr+GFS2cyeTzh0Ulpqg0OkYPf1vkrt0RjxOf4nyUg97dkT/fm5YWUhFqSeizJ5JlXhc1zrqXLy5TN//WkR7jnM9b+4exbOdtpAoRQ/xFgSxyoXfVxC1X6MwIyBS6Q+w1enm+/wSDnh8pKlVjE220VCrxqCo+5J0UpwV0xUXESgtwxTwcs0Lf7Hkj48o2r+NuNRsulx4I5akLBTKcDHsOJCLP0aUi6uwEHdFzffUk1Fez15WyPYVM6rGR2Do1EmUb9qOMy+fxI7tSGrdHmN6Op58B5JCIOCPvjim0Z65pf9kZGSOD1nAy8jInDXMKYm9y/F3cQX9bCbilAr2b1lO0y5DWPH35/i9keHFWc3aUX+Hi60bigmFQihVYtTwQ4DEY8iB97qduB1lVQ+3WjOGtLSYx+qSkpBUKvQ2G8Pveo+8HWtZOa3K6brdgMuxxGeg2J6L+71v8Dirdk49KiXmW6/G0KXmvEmlWDf5jmaFxECbic9yi6P2D443ozpyh+soiJKEOSH23wVAVKvQdOtA4gcv4PhzFv4D+ajbtUTboxNSUmxhLqlU6JNj54ueKSS0aU328GERIj6uZUuaXT4WY3wq6yZ8HibeD7Jv9hxyFiykyUWjq9tUej3tbv8fpsxMFj81HntODsaMDNrd/j9CwSDLX6kSaavffpe+b71+1MoIx0uGoT1PdljHgrxP2F6+kERtQ85NvR2zVI9/hE1Mm13E6MED8E/+M+JcdYfWiFYLNo2RAel30y1pHKIgYlAePUe4tlSW5LNr7VyWTfkUn8dFs27n0+bcMVgSww3X3GVlzLjxlghfAb/TyS9DL+SKNSsxZR6fSZujsmZTRZerZsPCWARdbkJ+P6JOW2sxafcX4w9Fn0+IIBXeAhK10cP1jcpE+qX+j4k7747a3zflZgKIzC2tCLt/7HB5GL8rlxtS4+lnM6I8xvtHbRAUEoqEOBRAMikMvuF5PE47ap0RtTZ6ZYCj5swrFAgaNSF35GKvIiMV0XT8FQdiUbT/UGTYnk2L2LNpEZakLDR6M0sm/sCVrX4FwGhW0+PcDOZNje5B0bFHzfdbGRmZsw9ZwMvIyJw1VNSQx+sMBDnocdW40yAW/vI2w+54h2kTHqeyuCqEUKU10PuSezHbEhg4XM3MP/aydH4OvQZkMWvKrogxtXoFmdlHr6EeDAQozdvN/B9eZduK6SjVWjoOuZa2t9/Klu8nRj2n84P3obHZADBakzB2HED9Nr0BUCjV+Hbto+DVZ8JDjr0+yl//iLjvnkSvsOHwl0SM29wyAKOibnafBUGgm8XA3NJKdrrDH/Q7m3TU15wcIQggarWI9TIx33gloUAgpkNzbaksr3rwNphUx2w2Vdfok5Lo//47tL7xela/8x4Bt5sWV19F+jm9MaanUbwnn7Xvfxjz/NVvv0u9QQNRmw6Z2+kSEsgeORxJo0GhUeMqKmb9hM/JX768+pjCNWvxOZ0nTcArRAVJ2oYMz3oGT9CBStSiEKuudc2d7fjy3dXsyuxA/RESgb+mVYkhSULXrwfGqy6pMssDREHEdJwlrSpL8nA7KpAUSnRGa3W6hr20gN/e+h971i2oPnbBpNdZNeNrrnruDyxJmdXtrsJC9s6YGXV8T1kZ5Tt2HLeAr8lMTJQE9IZje28C5ZX4d++j8qc/CJaWo+rYBrFHD6bOL8Vk0dC2SzJxiToUikihrBJr9hRQS7EXL0VBpGPCJSwv/JEdlYvD+vqm3EqSrgml/gDf5EXeowC+zCumnUlHourkZ3KqtUbU2ppN//QpKaiMRryVkYvE+pQUtImJKB74HyXjX4PgYVFBGjXW+29FslrqetroTJGLV2X5e/69sIBCVfVZUmsUXHR1CzasKqS4IDxN5pJrW2CNlw3sTgelnv0UunZS6t1PkrYxVnUGZtUZltIlc9YiC3gZGZmzhu5mA/PKoudktzVq0UpVwiypfkuc5UXM+/5lel9yD1qDleC/Jczi0xtjsCWhNgR49NVevP/ici6/qTUFuQ7WrzwUDmowqXjopV7EJR7d0b00fzcT7h+E112VL+912Zn77QuIlzxErxefY/6Dj4QJ8bb/u5WU7t0ixjkY1hny+7FPnhozX1j8eh53jJvCq+sH4Akc+nskaBpwRaMP0CktR51zbYlTKnioXgpbnG5mllaiFGBQnJl6GhWWGHnqdYkgiQjS8T/kFxc6WbEol1m/7wSgz5B6dOqZVqv39WSiT06m/uBkMvqcQzAQQKWvEkuOSi/rV+QR9MXeiQ36/ISiLGYp1Gq2Tvop6s49QEKbVih1OjyBqhrjEEKrMKNTWI55/kG/n2BhMcGyiqoFFosZKc6KqNUgiQp0YvjCly1eyw33dqS81A1t0rAO7Yfo8yJp1YhWM6LmxESGx2Vnz/pFTP3kYSqKqjwgslr24LwbXyQuNRtXaREZWW0I+v3s23SoLJejrJAlv3/IueMer/7++Y9iCugqjh6RUhtMVjXtu6ewclFuRF/vQVlVJRdrSaDSgf3Hydh//KO6zbd1J8JvU+n54AM8On4DX7+/lkdf602j5nGIR+S7GJWJpOlakuNcHzF2gqYBRmXNoftWdSo3N/+JHMc6Fud/iVphoGfSNcRrGmBUxpPndOONcQ9TCSLeYJC9Lg++UAi9KKJXSBhPQlh9bdCnpjDo80/5ffQlYfddUaFg8JefYUhPI5SQQOKHL+H4azb+vTmoWjZB16c7UhSPgLrAmlwPrcGKyx6ZNpbdrh868yGBn5hi4Ol3+rF+ZQFL5u3HbFEzcHg2SamGY14UkjlxcirX8/qGQZR5D/kPZBnac2vzX7BpMms4U0amdgihUA2OMv8PqaiowGw2U15ejslUc+keGRmZU0ux18/4XQfY5wkXN0pB4KVG6WQetiNcXrifhT+9xdo5PxDweUht3IGB1zxNYlZzlKpDYqG02EVluQdJIeL1BMjdZ8dsVZOUasCWoI146AUI+HwEvV4UOh1+r4sp79/Hhvk/hx0jiCLZbfvSd+zDqBR68hYsxlNRQXrvXuiTk6O6pgdCIYp9fpyVDvTPvYH3iHJOB5GSE4l/8ynKNGXsrlxGgWsH9YwdSdE1w6o+eeGSnmAQEarDXgMlZfj27sc5ayGiVoOuf2+k5AQkY92Hkx4PpXml7Fy9l/JyLwsX21m/qgiAtCwjj7zS+7SL+Gjk7q/kwxeX0ce2lJVPPxb1mHM/eI82N14ftS9v2XK+7Ry5OCRIEuM2riWQoWTynidYWvg9wZCf5pYBXJT9Cim6ZkhC7RZkgm4P3k1bKX3xXYKlVbnBglqFcdzFaPt0RxF36utM7934D189NiK8URAYds1LZCe0wP7rVHA6oU1j/C3q8fNn91CWXxVyrDXauO7VGZjiqnwFKvbt4+u2HXGXRN89HrdhLXHNI832aktJkYtv3l/Dotn7CQZCSAqBfkPqM+qq5sdU2923Zz8FN9wXtU/ZrhVzMwcyaeIuzFY1z3/UP+rnPde5mVfXnhsmNIzKBO5tPYs0fctjf3GHsdPp4b7t+yPazQqJuzIScebno92/j5Jly9BmZGBr3474zEysmtovYtQlXoeD8h07WPnGWxRv2ERi+3a0u+1WzA3qozhsgSkUDBLy+RFUypMazRMKBjmwYw3fPnUxXtehhVpbajZjHvsWS2J0Iej3BRFFAVE6vZFG/x8JlFdQ5t7Pi7sHU+LZF9HfxnY+1zX9Gq3i6JF9Mv8/qa0OlQX8EcgCXkbmzKbI6+PPonKml1TiDgZpa9ByeUocaWoViiPEtt/rxlFRTCgYRKXVozPaTujanooKynfsZPW771OxZw9ZAwaQPfJCJn9yFznbVlQfl5jVjH5XPs725dPZt3kpOlMcXYfdTHL9lmgkLUGnC0EA0WxC+Nc9PBgKscPl4amdB2ilVXPVtBn4f/076jw0PTphve9mRO3pK6sVKC6h5Nm38G4IX2QwjBqK4dJhEUZzdXZdnw9XURGCKKJLTIz6AB3w+ynbtp2Fjz/F3qlTURmNNLrqeqx9h/P6K9twu/xce1c7Bg6vu3JcdcW+XeXce9U0Hn2iKUuvG0X5rvDUDmvjxoyeMRVjRvQa297KSnZO+ZMZN96Ct6KqxJcmLo7zvvwMY59mvLi2J6Xe8CoFKlHLY+1XkaKrXck93579FNz6EPgiHdltT9+Htkv7Wo1TVzgrS5j4zGUc2L46rH3AxQ9Rb1cI3+x/wtpFmwXpvnF89vIYJIWShu37M/DaZ9CZqu4PwUCA9RM+Z8YNN0Vcq9GoEfT/6AO0thO7l7icPspLPbhdfrQ6BRabBrXm2CJaKn/+k4oPv4rap6ifiXT7rSxcWMiyVWVcfXs7sptGn3OJZx8HHBvY71hLiq45GfrWdbJLWOz1cf/2HMqOcJ6/ITWeeqVFLLnoEorWrDk0Z52OwVN+J71bF3QxyqadCvweD36nE6VeX51uEqh0ECwrJ1hpR9TrEC0mJPPJf0YMBgJUFB8gf9c6SvP3ktKgNbbUBhhtZ77Xx/83gg4nlRN/I/8cEy/lnB/1GAGBZzptIUnb6BTPTuZsobY6VA6hl5GROauIVykZk2xjaLyZEKCTRHQxTJsUKg3m+LrZkfY6HGz5biIzbrqlum3vjJksff4Fhvz2Hb99fheO8iK0Biv9Ln+UX169AY/zUD7lrjVz6XTeNXRM6Ynrna8QNBr05/VDP+I8FAlxlPj8PLsrF1cwxAqHm8sH9oEpMyJFkiBgHDPitIr3UDCIc+7iCPEOYP9pCtpeXU5IwHs8fjzuAGq3HdHtAklENBlxlJWx+t332DrxRyS1mtY33UDji0djPMIssGzrVr7p2BX/v2XTvJWVrHruKZKm/8V1D77NO2/uYNaUXXTrm4HRfPqEQjT0BiXWOA3vvL2b2z79iYJpP7N70vcgCNS/+DJaX315TPEOVC1WjBpJavduOPMLEEQBXWIi+tRUFhR8GiHeAbxBF3/tfYHLGr2LWjp6VIJz1oKo4h2g8pufUWbXQxFfe4Hr87ioKMph48LJlObvIbtdX9Kbdqr1d9fvcZO7c11Ym1Kjo35GW7wT3o84PlhShmLaUs7/3+e4/FZWr3Az8bM99OgfIjnVgMmqptGoEaiMBhY8+AgVe/agNptpd8dttLnpxhMW7wBanRKt7sR8HQhFGm8KGjWWO64jUFKG68OP6eYP0KVrVwza2OZ5NnUGNnUGLW2DT2w+R2BVKrg3K4mndubi+3evSAKyCbLhiSfDxDtUmQROvWAYY9auQle/Xp3O5VhQqNUoDltA8BeVUPbWJ3iWrKpuUzZtiO3h21HUYKpZF4iShCUxI8JoUebMI1BWjnPqHOw9usc8JkQIbyB6tQMZmWNBFvAyMjJnHUpRJO4UmB8djjMvL2qdYE9ZGUseHk+7Gy5jwW9v0rrfJSz5/cMw8X6QZX9NoNWDg0EUCTldOGctwGNLROzWFYdCJFmloNLlJQB8FhC47un74bUPCRRW5dyKZhOWu65HkZ4SMfapJFhWgeO3aTH77VNmYG2cfcy56z5vgPwDdjYtO0DbeAfOz78ikFflS6Bs2hDlxeeza8pfVOypMnKae/e9bPo/9s4yTqqyjcPXme7cLrq7WxoDBAW7FVsxsAO7xe5uxVYUFFBaurtj2c7pjvfDyC7jzCy7sCD4zvX78YHznNrdmXOe/3Pf9//+/AvOmv4TmuxImzGf3c5fD0ypEe+HUrpyBe29RRhMikhpxAmYYWpMUXL5LV15+eFlPPHoNjp2O4XuD54GYahEjSz18JE3sVSKLi8PXV5tFNUXdLO64oeEx2y2zMIdsBxWwIe8XgL7YtOiDxIoLCH8jzZxwVAAi68IZ6ASsSBDK01B97eZk9/nYdeaP/jxxesJhyKCdOP8b9CaMrn08R8wZjQ93I+LSCxGY0yvMasEyG7dA1bHL0EBCLTvwpzZEpbOr93n9x920W9oDlfe2g29yUTbC84n55RTCLhdiGVyVJkZiCUnxrTJWVICzWOj5IZJV+H46Xf823fXbty1F+f8eSiefRBJauO5+h8OkSDQSqng5da5/GW1s9vlo71agdpSyd5vv4t7jN/hoGrjRlL+RQF/KCGXC+s7n0eJdwD/tl1UPf4S5ifuQWxIpkMngcCefEI2B2ZJ4sUWmUiVTJ9P0igc3xlwkiRJkpyklKxYVSMw/knRX0vIbdYDgJw2Pdm7cVHC8+zdsRxp8yaIBw3AetOdvLlAwq2Xz+G5SQvotd7FzXojImCtx8+zGgP5j99L6JXHMb7xFKlvPIWiT3dEyn/ZVTgUIhxHIB8kbHfGjQ4ejr07qnn89gV0yg7he/bFGvEOkQmz+6X3OO21V6OOKVuzluJly2v+77VY2TPjt4TXKJn9M83bGhl+ZnO0uhMr+g4R5//OPdO5f+ogcprp2Limgh9/KCWoMDDizJZHfM8iQYJWmjhaqJYY8QYdbLPMJ9++Fos31mQNQCSXI2maeIIqyclEOKRjgNNfzV+lH/HI6k48tqY7D6/uyPMbhnLAsYFwOIyzupSfXr4p5rtlrypm1gcP4omzEBZz74Y0+p89KWqbIIgSmkCK01PIF1JYOr8oZmzpvAJ2bK41qdNkZWJo0QJtbs4JI949ViuL7rmPzd9/j3RobbRPkp1ByOWOFu9/EygswT3vr4TPsGOFRCSQIZcyIc3EnU3SGZNqQPD5CAXiZ3AAuEpKgUjk27d7H77tuwmUlhOuw9jxWBGqtuFZvCzumH/n3hoPiCRJBJkUQiEUm8pppx0ad59ROXegl/67C/BJ/hskBXySJEn+84TDYWwWLzaLl1CoYbYfwWoL/qKSuBHdQ9GZsjj//s9QqA+zuh4OI05PpbTvKB6ZspatGyoJ+ENUlLr45rUNrPpwO+doIunn+R4fz9q93B8Q48zLQZJqRjgGfZMbiqBVo+jdLeG4cugAhAaKHWuVh3enrmb4iExEv0yPK77CdieiPQfI7NMnavumDz+qcQ4XRAJSVeIoslijw5yqpFufE7eGVK2R0aVXBlNeHMzr35zBk++dQt9xcuzybVR58gmGEoufREhEUoZlxWaQHGRQxjW8vfU8pm4YymNru/Pc+kEUO7fG3Vc1bABI46d/ay8eH5U+v9u2hE93Xos7aKvZVuzayvMbBlPtLaBo93pCgfjCbPfaubht8Y3kDkUQBNr1G03HwefUbCvauQahe9v4+/fvx+9/lCc834yvd+ByHn+xWF/cpWVs+ewLljz1NCVKkN94GbJO7VAMHYBn6eqEx7lmLyBkPfyCyLFC9LdfhVyrRZ2ZWMRkdOuKb/tuKm6bQvmN91F+y4OUXXMXzt/mErTH70LyT8LhMFXlLkoK7FSUuvD7E7cgrYuQ2x3VNu6fBJMCPsnfSJrkglRC8IOZXCp5lH7Gi2qMQeViDePyHmVY1s1IxSfewnGSk48TYzk5SZIkSY4RlWUuVi4qZO6MvYTCYYac3pR+Q3IP60Ae8nrxb9uN5dX3CRSVknbXtQn3NbdvjzIlhZT0DridVpp1PoW96xfE3bdZ6z54tWE++mh33ADhqvkFDLuoNRIBAn+PX5hhxHQcWrbVF5FcjuaCs3AvXkHYHd1yS5KTiaxj/czQDsXp8HFgr43zJuQQnLcn8bX3F2Fu14bi5bVRd7FCDn8vbCjT0uh07dWsev6FuMe3u+gierZsjyn13/MQqC96o4Ji11be23Yx+Y5ICq9KYmBCs2fpmXIOamnDarHTlK0Yk/cgswqm0kd3AV3FpyOERdjkVcikWg44a2uSyzy7eXHjSO7vtgyjPLrm3qnWY3r8bizPv0moMtLiSlDI0V5xPlZdGgFfAK1Mgs1Xxg/77ot7L66AhT225YiddbRsC4cJBesnpNWGVEZd+TgDxk+ieM9GFEot6tyWCAN741m8ImrfkEaL25X4vC6Xn2Cg8SPVwWoLwbIKfFt2IDIakLVtidhsrDGyrC+OoqKaBa4/bpuMJieHDhdeQJ5JzYn/qQZDTjYDn3uGWZdeHjOWNWggmtQ0Km6ZQthbW7cf9nqxvvExksw0xL0SLx4C2G1e1i4r4at3N1JV7kauFDNqXAtGn9e6QS7/ACKVMvJsSZC5IDYZGnS+JP9dRCY9xjuup/rZN/Df9y5jTh3AmNMmEZBl4/EoCLlEeCoVKExBZPJ/p11ikv8OJ86MMEmSJEkOwWu3E/L5kBsMiBKY1B2OynIXT9+ziAN7aqN/n72xgT9+3sODLw0mpQ4RHzhQRMW9T9REX4Qtu+hy9dWsf//9qP1EEgkj3nkTdXqkplep1jPyysf4+L7RUa1/AHoMvRjxxr24W/WkuGBLwmvv31JFbgcFnWVyBkgUKNwC9moPBtOJMz2XZKaT+tqT2D79Fs/S1QgyKerTh6Ied1qDDMwOctBNvtrip0mKiVCCSFvYqMO5piJqW9cbb0Dyt1u0WCql26Sb2DPjN6q2RP+Oe99/H6ltm6Mwnji/x7qo9OTz3PrB2P210WJXwMJnO69DJ02jW8pZDTqfRmpiVM6dDApewrLHnmT+V1cQ8vvJHTmcLk/dSnNVX/a4atOFq32FlLl3xwj4LVutLJ1l4aqH7kUR9EAwSEitZdb8CmbevYrH3huONl1DIOSl0Lk54f1stszi9NaTE44bM5shV9Xf6VupNaDUGkjJaV2zTXbzlXiH9Mf5/QxCTheyvl1RjOpNr1AFe3dY4p6nz+AcVJqjNJj7B8GKKqqefg3fpm21G6VSTA9PRtGlQ1TZweGQ/cOZ2FFQwPLnp7L9x58Y+/SzsGZj3ONUpw5FpD823SEagiAItBh9Oqd/+TmL77kP+4EDSBQKOlx5Bb0fuJfw8g1R4v1QbB9/g7R1i4QO8KFgmBULCnl3am0mgtcd5JdpOyjYb+PG+3qja4BxpcigRzm0P+4/F8eMSdu1QmRIditKEkEkk6Ho24O0t5/FOfNPgghU2fN46ak1VJZHMvikMhHnXtmBYWOanZAlXElOHpICPkmSJFG4gyGsgSCFXh9iQSBLLsUgESM7TqnbrrIySlauYvULL+G1WGlx1ljaXXIRuiZNGizkN6wojRLvBykucLByUSGnjW8Ztw1ZyOnG9vE3UamTvl/+oMuV55LTtw+r3nobZ0kJWf370WfK/RhbRbeEScluydVT57D2jy/Ys3YeSp2Zvmdeh6E8gOfFjxBP6YUgJCzRJU0n526Dia/e2ciUeQUEg2EyczVceWs32nQ0I/W5CdmdCCIBQatFrFU36PfSGAhiEdLcLIyTryXkcIEgINZrGxxNPIhGJ6NFWyOz5pTQZcLp8OY7cS4qIHTrwP57bq/Z1GrCeFI6dojaTZuby4TZMylduYqtX05DaTbR6ZqJ6Jo2RWE8/j3Kj5TdtqVR4v1Qvtt7D811/dD/bQZXXwJFVn4Yejr2A7U9ig/M+ZPixUs4d+n3vCQahy9UWy5S7tlNGwZHnaNwr40Vi4pZsSh+nXzQF4lWigUJqYpmlHlia7IBslQd0OozaNf/TLYu+SV6UBA49eqn0BjTGvTz/ROx0YCoX3s8rcLssSxlk/db1m+7gkkDV6D/QY612hu1v84gZ9DIJogbaMBYF2G/H/t3v0aLdwC/n6pHppL6znPIcrLqfT51Rjq6Jk1qzBwPYtm1C49Kjrx9a3xbdkSNSXIyUQ7pd0KU4AAojEbaXXg+uYMH4Xc4EcllqNPTEctkVG3+JuFxgQPF4IufPRHweakoc/LVe/EXMNYuLcFS6WmYgFcp0U+8kHAwiGfBspqHtqxLe4x33pA0sEsShUipQNQ0F/31l1FW7OLxq2bj9dSWb/h9Ib58ZyMZ2Rr6DE7cTSRJksORFPBJkiSpwR4IMqfKxlclVRxMGJQKAjfmpNJLp0bZiJPaeLgrKlhw1z1s/fTzmm1la9ey7rU3OOuXn5AZDOibNkGiOLyJm9PuY97MvQnH5/+2jwHD89AZYidzYbcb/7ZdMdu9H32LPiuD0x99HFnX9sj0emQaTcx+gkiEMaMpgy+4m75jr0cskSNXaQiUlqN+Kh2n1UGXXmmsW1EWc6xYLNC8lZGnbl9IcUFtFLr4gIMZX26l5VXZ2N76CP/ufQDIurTDcPNVSHKz4y5GHGtESmWjtLTT6uVcd1dPHrp5Htt92bQ5dSTBWXNqd5BKMNx+LVYx5A4dglihoNvNN5LSuROq9FgRq83ORpudTYuxZ54woqWh7Lb9lXCs1L2DQKiO9PME7P/jzyjxfpCA2822qR/S584LWGT9qGZ7pqoDnoAdZyCSKq+WmmneKvEiiN4oRyaLLLTp5Zmc2eRhPth+Wcx+EkFOF/OZKJVGRk18gtx2fVj605s4LWVkturGiMseIq1Juwb/fPEodG3i6R3RrZ2+rDiXSVO/Z9F3Xpb8GXHV7z88lwmXticts3EXxILVVlwz58YdExn0uIoL8GmkaAyxJoPBUIAwISQiWc02TVYWZ/3yE98OH4W7vHaBR9ekCeo2rdD16YV3/Racv8whHAyiGjkYRd/ux9WBvr5osmIXLmRtWsSUPRxEkp0BcbIVnNZKVv/+MdrsIditiVvmFey1kte8YaJbbDZhvOVqQpedR8jhQFCpEOl1iHWxz/4kSSAyB1i3oiRKvB/K1x9som2nFPSmf9mQNslJS1LAJ0mSpIY9bi9flESbRvnDYV45UMYLrXJoqjy2KV/WvfuixPtB3BUVrHrhJRRGI63Gn03eiGGIDmOSJggCgiixoBWJBBLqXYkYkckQN407WFSCaH8R6jNGHFYwiyVSVDoz4UAA3849WF75gGBFJbJO7bjsinPZv9tGdWWtCBMEuPnB3tht3ijxDiBXiLnywmws9z4GgdpJgW/9VsonP0La608hyTi6aOW/TW4zPc99OJIFv+/Hl92bblOHIi8tRKxSIGuWh8hsQC2TMfbH7xDE4qhezYk4WcU7QJa6Q8IxvSwTsdCwbIeA18uu739MOF44eyHt77qPRUQEfCfjGSjFWj7cfgVrK39CQKBH6jmM7fQYrTqa2Lkp1mBu3CXtSE2tLU3paDqN03PvZdaB5wkR+dxqJGZubP8jJnmkDZrGkEbP06+ibd8xhMNBpDIlSm3jZEq4AzZ+2f94zPZyzx7eLOnHRRe/y3lXjUdAQKOXIZcfg2lRMEjYGx3pRyJGdt35VKv8LJz9GoGZXjoPOY8mHQegM2di85VR5NrC/KI38YfcDMi4kmbaPhjl2QCkdOrIJauXU7F5C9U7dpLaqSOG1q3QZkfGVcMGIu/VDQgj1p5cQlM5sDe2z76LG2nXXn5eTPp8OBRiy+KfWPTNVMbeObjO7CZtnAXb+iBSqxCp6/ZNSZLkUHZtrUw4VlzgIHAMfDaS/P+QFPBJkiQBwBkI8l1ZdcLxmRVWrs1ORVKHKD5adiToDQyw++fpnP75J8y8+FIuWbcKXW7iVlYAKo2UUeNasG1DRdzxEWObo02QSik26NFecBbVz74ed1x9+tAGRbsDxWWUT36kZkLqWbgM8fZdPHzH9exyqFi3soz0LDUDhudhTlPx9QebYs4xeFgWolm/EwrEruiH7U7ci5ajmTD6pBasIrFAepaGCZe3w+sJIJWKkXZqFrNfXS7z/yXaG0YiFSnwx4m0j8l9EL2sYe2IRGIxytSUhOMKkwkPTgBa6U7h/BYv8tS6vrgCFgDCwMryr9lq+ZN7nl7G1EkBCvZFSlQkUhGjz2vNgOG5iA55RmilqYzOfYBBGVdT5c1HJlJhkGVhkGchEmpLYgRBQGtqWDlAfXB7LZS64/eD94c8LKl8lx4dx6I6hr2ZBbkcSU4mgYLakgPZNecyf+O37FhTm2Wyb8MiUnJbc87jn/BD4QMsL/uyZmx91a/kqbsyqeMvNZ4E2txctLm5NDvt1LjX/TdKaxoDcVoKKc88QNUTrxCq+vudJJOiu+J8ZB1ax+xvryph8XcvAVC07Q869+rL+hWxpSdqjZTMnJNrMSPJyUvLdmYWzsqPO5aZo0EsOXnf1Un+fZICPkmSJAD4wmHKfYnbUxX7/PjDIST8S+6pf4dUPFVVuIpL6hTwoWCQgNtNuy4ptOpgZufm6JXwZq0Nh20jJu/eEdXoEbhm/FG7USzGcPu1iDMS99OOuRefD8ePM2OiScHSCnjsCbreeDkD7xuJcEh9f2p6rEBt1UxFePqOmO0H8a5aj3rMSIR/u0d8IyAWi1CpZYff8T+OUZ7H5E5zeH3zOJyBSLRbQGBI1g30SD2nwSUTIomErjfewJZPPos73m3yJNJaDKKTMA6tNJ3fDzxbI94PxeGvYK3lGx557TaqKnz4vEG0ehlGkwK5InZaoZBoUEg0pClbNOh+jxZHdRl71s8nw9iGck90Z4NURXNaaPrRxjAUuejYLgiJTQa011xE9cORzggiox6rjijxfhCXtZJCx6Yo8X6QfOc6VpRNY2TOZETCf3fyL0gkyNq3JvXVxyNt7wIBRAY9IpMekSz2uRDweXDZIs/4DX9+wNm3nU5ZkTsqi0muFHPvc4MwpRzd3zpotUXuKRhC0KojXQT+hdKl/yKBympCFive9VsQpFLkndshMugSGhae6HTrk4FcKcbrjl10P39iRwzJ9PkkR0FSwCdJkgQApUhEC6WMcn98Ed9OrTjmRnatzz0nYfuvFuPGkj/nTwBCgfj3GPB4sO3bx4Z33qd83Toy+vThlntuZ9cuF3/8sodQKMzwMc3p0C0V0yFpvh5PAGuVB4fdh0IhQWuQozPo0V15AZqzTsO3fReCXI6sVTNERgMiRf3TMMNON74N8ftpA3jmLUE9fBCCpjZa1q1fJp++uZ7wIRl2NkcAkVFPsDx+Wp4oxYxwArWaO1aEAwGCldX4d+0jWG1F1qY54lTzf85MKhwKIQ5CC10/Hu6+lirvATxBO6nKFmglaaikR/bz6lu2oO9DD7LssSeitjcbfQYtzxyLWh1Z2LL7ytlQ9Uu8UwCwtvJHBmdeT5PmJ6YpYDDgZ/XsT1g14wNGPvYcG62/AZE2fJemv0G4yEv+vEVUK9dRNKoz5qzmqHR114i7XX4EARTKhhs1Stu3RnPv9bg//gFJx1asWzU97n7Nug9mceVHcccA5he/Rd/0S9DL6l6APNkRBCFSs1+Pun2xTI5cpcXrsuNzO/j9jYu59Krn8YVbkb/HQ3azVFp3zMCcqkQkPjKxHQ6HCRSWECgsAb+fwIFCXEtWobt4PPIu7RvFB+T/mUBZBbbPvsM9+5D2q4KA7qoLUI08BbHR8K/d25GSkq7moZeG8NLDS6kodQERF/rzrupA+271DwIkSRKP//5sL0mSJPVCIRZxXroJhVhMN60SEQLbXB7mV9vxh8IMNWoRCwLBYABHVQlepw2xTI5KZ0apMTTKPeiaNaXdJRez9fMvou/NbKbTNROZPv5cJAoF6qzY1OFwKEThosX8eMaZNQL/wPwFrH7hRc5dMJc7n+gPgoBKHT35tlR5+PHzrcz5aTfBYCTK36q9iUlT+pCepUGsVSPNyz7in0mQSREZ9HCgKO64yGyEfwhvU6qSyY/24+VHltXc05w/Sulz6RmwI35av2bcqQiH8QU4Emy2YvxBD0qJHpW24e3hGpOwP4Bv83YqH3ouqs2UrHM7TPfcjPgI2tedCITCIaq8+Wy3zGeffSVNVN1oFe6O7Jf1yJrkoevZBVPagEa5ltJkovvtt9L6vHPY9eN0Ai4nLcaNRd+sKaq0Wg8FsSBFJUksztUSE2JR47Zaa0wcljJW/voeXpedPb/P4bKRb/ND6f1MzP6EJS9PpSK/Nq1+44Lv6D7qMgZfeHdcEV9V7mLL+grm/roHkUhg1FktaNXejDGl/qJNqtMh6tIWz3Wj0XToRejN+CZtIomUAN64YxBJ+w+H/39rZ4PVVsI+H4jFiI0GBLEIjSGN3mOuY9E3UwFw2Sr5/c2rUGpNpDftQOdzXkVnOvJygrDfj2/HHiyvfkBgX8QAUtq6OfpLJmD7/HtEOi3y9rGp/Scb4XAYv9eFSCxFIj2+GVDeTduixXvkhrB98BWyjm2Pi4C3VRZTVbSH8vytmLKak5LbBn3Kkb/7RWKBFm2NPPRaX6qqHXh8blQ60JlkSFQ+INlGLsmRI4TDiaw+/j+x2Wzo9XqsVis63cmZtpMkyZFS4vXzbWkVi60OQmHoolUxNkWPXiIiRyHHa69my5LpLPjyGTxOKwBNOg5g9A0vYMxo0ij34Coro3j5ipo2crlDh9D01JHMu3Uy1Tt2MPS1V+h09VUxTvT2ggI+79GHtK5daDVhPBKlktKVq9jy6WeI5XIuXr0cbU502xa/P8j3n2zlx89iI+SZuRoefnlInZP0gM+Hs6CQ/LnzsOzeTfbAAaR26RxzHc/KdVQ++Gzcc6S8+CjyOHWdPm+Q6ko3OzZVYrN6adc5hSyTgPvrH3H9ckjqrSCgv+5SVCNPQaRpvJpXi6OA3bZlzCl7GYe/grbKIQzLugmTNA+F9t+JdgdKyii9+k7wR8oRBLkM1enDkHftiCCXIclMQ2QyIKqHud2JRL5jLc+vH4I7WNvyUCHWckebWRhWOgnMXYrxtmsiDtzHkbUVP/HGlrPjjt3eaTYdjCOP6/00hKqSfbx1U7+a/+e278OAyydzYPVS/vrm5bjHXP7Ur+S06RF9nnIXz963mH07rVHbO3RLZdKDfRok4gFK3G5eKahkXOla/nzp2phxY2Yzut59LR/uuzLu8cOzbuWcZs8iFZ9cn/GjJeRw4tuyA+v7XxLYX4BIp0VzzmhUIwcjNhlwWMqY98XTbJj3dU25lS4lm/Pv/4zUvLZHlebuzy+k7MZ74R/ZaYJCjmnK7di/mY5pyu0nnVngoVjKDrBj5Sx2rpyFSp9KrzMmYs5u0WiL83URKK+k6pEX8O+K3zVGOXwghtuuiVs+0VhUlezjy0fPw1pW26FDpTNz8SPfHlU3jCLnFp5ZPyCmFOnyVu/TO+1C5OL/Dz+XJPWnvjo0GYFPkiQJAOU+Pw/tKaTSX1uvtdbuYqvTzfOtchABu9f+yaz37os6bv+mv/ji0XO57Imf0ZkbZqoVD1VaGi3OHENGn964SkrZ+vmXzLrqGvQtmjP8zddI694tbhs5d0UFI995k8JFi1ky5WF8Nhu5w4YyetoXLH/yaVxl5THC2lLpYeZ38evKiw84KC9xJZygBwMBiv9awg+njyH4t8P0SkCbl8e5c+dgaNG8Zl9p6xZozhmD47tfa08gCOiuOA9JXvz+zzK5mPQsDelZ0ZNC6RXno54wBku1Dz9ipBolMrMckbrxUjjtnjJ+yn+YxRUf1mwrcW9nqfUL7mr1B7nyboiP4WQqEb7N22vFu0KO8b5JuH6fT9VjL0AoDFIJ6jNGoL1gHGKT4bjf35Fg8Rbz5pYJUeIdwBO089buC7hUeBpF5xaIl63EcPpwRMfRwK+Frh990y5mWVl0RszgzOvIU3c9bvdxJEhlCrTmLOyVkcyXA1uWU71zFxv+mJbwmLVzPiO7dfcasRcOh1m5uChGvANsXlvOji2V9Dml/r2c/aEQM6sc7HB7CeR1JKNlV0p2rYvex+2klXEQueVdOOBcHzWmkaYwIvuW/zvxHg6H8axaT/XTr9VsC9ns2D6chn/XPvSTJqIxpDHyikcZMH4S9soS5CoNakMaWtPRLXqFvD4c3/4SI94Bwh4vnhVrETRqwh4vnKQCvqp4L58+MBantdbwdcviHxl0/p30Hn0NgiAi4PcgV2mRSBv/sxf2BwhaYr9jBwlVWSJZF8foneOyVfHzyzdGiffI9kq+fvoyrnjqlyP6HDn8lXy685q4PiKf77qeNoYhx90XJMl/h6SAT5IkCQDr7K4o8X4QTyjM9HILF+mkzPvimbjHWssOUFGwo1EE/EHUaWmo09LQ3n8P3W+bhESpRGGMTel1lpXhLCpCJJaw/ImnKV29umZs78zfyP9zLmN/+DbuNTyeQFyDmYMUF9hp3TF+DaazsJCfxp5dI94PYs/P588bb2bM118iNxgAEOu1aC88C9VpQ/Ft2gZiEbL2bRCb9A0WZK6ghJVrHEx7fxPWKi9yhZiR41ow5vzWGM2NI+KrPQVR4v0g3qCD7wrvY6L4I4zmxsm4aAiBstr6f+2FZ+H4bga+jYdkT/gDOH/+HcIhdFdfdFJE4u3+Mio88SNPVd58hBwVv5x+LmM+/hCdy3NcBbxOls75LV5mRPbtrKn4HgERPVLPwSTPRSM98XqKH4rGmM7Qi+5l+mu31GwTS2T4fbGO/gfxuh2Ew+EaAW+3+vhj+p6E+8/+cTede6ajVNWvlMAWCLHQEjFWe9sW5tab36T5uj/Z8cdnBPxeWvUfR+9Rl2DQ5nJLx19ZWvo5C0vewR/y0jPlXEZk30qqsvlhrvLfI1hRhfWd+MaL7oXL0F4yAbFOg0KtQ6HWYcpsvN9R2OWOLBwmwL9zD/I+3Y9J+dLxwOt2MPfzJ6PE+0EWfT2V1j1HMfeLp7BXFtGkfX96jbkaQ1oTxI3484q0amTtW+NZuCzuuKxze4Rj+Nxz2Sop2rk27pitvACnpfyIBLzTX8Uu25K4Y8FwgHzHmqSAT3LEnJxPnCRJkjQq3lCIJVZnwvHVdhdn6Y010ax4FO1aR/Mugxv93hQGA/wthP+JbX8+v5xzHvYDBYx87+0o8X6QoNfL2ldfZ8S7b8eMyeUSpDIRfl/8mtK0rMQp6VXbtuN3xPaJB9g/ew6uiooaAQ8g0qgRadRIc+NH3OtDMBhi8Zx8Pn5tXc02ryfIr1/voKTQwfX39ESrO3rRuqn694Rj2+zz8BD/5z7WyNu3xg4gCEib5WH76Ou4+zl/m4tm/GhEmWlxx08kAqHE9c4AASGScbDwkcfIPGUQ2uNc56+VpqCVptBU2+PwO59ACIJAyx4jGDXxSRZ89Sxel40D21bQovswNi/8Ie4xXYaeh+gQo85wOEwolLjKMBgMJew3Hnf/QK1vgzsU5pnqMK3ankrfDsOQEMat0qP/27TNKM/htNy76Z9+BRBCLTH/30XeDxJ2uQlVWRKO+/fsR9qk/pkQDUGQSRGlmKCwJO64yGRE3rEtYuPJaaLpsVvYseK3hOPbls3EY6+m4sAOKg7sYP28aVz2xM9ktujcaPcg1mrQXjAOz5KV8I82qSKtBuUpfaO+l42N3+euc9zjstU5nogQiYMDAL5Q3ddNkqQu/rt9SJIkSVJvxIBWnPhxoBGLEQNylTbhPsbUPPxFpYRs9sa/wTh4qquZc811lK5aTXqP7uz+ObFj9v4//iTSxToag1nBiLHxozUp6SrSMxOnRHqqquq8v6CnbmF2JFRXePjmo81xx1YtLsJSlTi62BAO7c/9TwSEf61tkiQnE0nTHASlgmB14pRL/AFCzsQLUicSWmkqEiG+MJMIMhSuSFaFbd8+/J7G+fv+v6DUGul+6mVc89Jcrn7hTwZMuI1B50xGroqtK8xo3omM5tGiRKuTM2hU4kyTYWOax5hiJsJSdoB1371AP2X0c3an28dnjjAfOaClRh313RIJIgzyDAzyrP9b8Q7AYaK9jen9EXNutQrt+eMSjqtPH4Yk58gXZf9twoQIhxKbIgYDXgRR7fsg4PMw8+27atr2NRaSrHRSnn0QaYumNdtkXdpjfu5BxFnpjXqtf6JUG5DIErR0EwS0xnQCxaX4C4oJ1rGQ9E9UYgMZyjYJx5tr+zTwTpMkqSUp4JMkSYJEJOKMlMQRhDNT9JiUKnqdcXXccZlCTZo0hbIrb6Py4an4CxJH6hsLV1k5++dEerQHPB4kdbTxkarVUX3WDyKTiRl3UVv6D8/lUE2alaflgamnYEpNfM6Uzp0SjqnS05Efg7ZmTocPl8OfcLxgTxX2ypI6J2T1oaPxtMRjulNRkHghp6EEQwEs3iKqvUX4/C4CpeW4l67C8ctsfNt2RQl1sdmI+fF7kPfphkhXR72pICBSnhw9dnXyTM5sMiXu2Aj9zex6M1KzLZJKEZ8kP9OJhFgsQZ+STXrT9pizmmPKbMZVz/1O1xEXo9KZ0aXmMPTi+zn33k9i0mRFYoGBI/JIy4wViE1a6OlQz1ZQLnsVP786idW/vsspgguzNPZZ1EurIlv+77r6V3kL2GaZx6LiD9hpXYzFe+yf44cjaLEhyKTIu8d/3goKOZI4XUJC4SA2Xyk2Xymho3Ttl7Zqhubi8US9JEQCumsvQdq8CWLDyWt4LFfpyOvQL+F4TptelO2PNnkt2bMBt6OOBdQjQKRUIu/YFtOjd5D2znOkvTcV472TkDVvcsyi7yGfj0BJGQqvQJ8zr4+7T8eBZyOs30XplbdTNnEyFXc9hmfNRkL1WEzVyzO4rNU7iIj9vg9LvQGNv/Heo0n+/0i60P+DpAt9kv9XbIEAP5VZ+Lki+sXcU6vi+pxUjFIJjuoyZn3wINuW1ka7lVoT517/GrIv/iCwI1LLKzIZSH3lcSRpKcfsfktXr+GLnpEVbLFMxphvp/HzuPFx9+1552QGPPUEYmn8CbLT7sNm8WKp9qBSS9EbFBjMdYsld2Ulf1x3Azu//zFm7LTPPqHdRRcgNPLEo2C/jTsum5VwfPLDrVn5zdWMv+NdMpp3RhRn0aI+OH1VzNj3JLNLXozarpYYuavtXLKNXRolCl/lyWdhyXssLvmQYNhPT/O5DJNcgvD4NwSLIimr0rYtMT94G+JD+kGH3B6C1VaqHnqOQJz2fIp+PTDedSMi9cnh8OvwV7CpahY/7Z9ChWcvKYqmjFLfgnJxkKXXR0wj2112KSPeeh3pcayBP5Fx2y24bBX4PE4UagNqQyoyRf1/N36fB4+9GkQi1LqUOr8rFaVOFv+Rz8JZ+xGJBIaf2Zzep2RjTq3f9SoKdvLOracAEWf04Xd9xCaxllUBCXKRwBlGNe20GgzSf6+qsdi5lRc2jsDiq/0+pSlacnunWf9K3X2w2op3zUbs30wn7PNjvPN6qp9+jWD5IZFfiRjzE/cg79Quqga9ynOAZWWfs6T0EwAGZkykd9qFmORHnmYfcroIWaz4tu8GsRhZq2aIDAZEqhN/US1os0MwhEirQZDEfs5L923h4/tGE/iHP0Sb3qeT0bwTC6Y9F3PMDa//1aheA8eboNWOc+af2L/8AXx+5DdeyC7PXpb89i5uexUypYZep19F5yYDcD/5Dhy6KC4IpLz0KPJ2rQ57HV/QTYltM9PzH2OPawV6WSbnpD6C2dERHAGUTTLRpye1RpJa6qtDkwL+HyQFfJL/ZxyBIJX+AEusTnyhEH31GtLlEgyHTI7cdgtOazmVBTuRB8Wo/VJCX88isCc/6lzGB25FdUrfY3avlj17+LhNh5qe790m3YxYIWfV8y9E7Wfu2JHxM39Bm9v4NZLOkhI2vvcha156GU91NYZWrRg89VmyTxkUqd1vRA7stbLqryI2ry1j46qymHGtXsZNk3XMfO0iZAo117w0D0Na7hFfz+4po8CxgT+KX8EeKKe9ajj9Mi/DrGiGpBHcgKu8B3hhw3BK3Tujtmulqdyd+hOhSa9HnOUB1WlD0d94eYwpXaCwmMqHnidQUFyzTdaxLcZ7b0aSemxM1pzWcuyVpVhK96M1Z6BLyT5qp+uDWNxFuG2VFM5bzLbnPqJsbcRYKaVTJ8769Wd0eUf+9/wvYSk7wK9v3Mb+TRGDKJFYQrcRFzPw3MlojMfG9yAUDGO3RcpidAZ5gxawinat46N7To/altu+L5ldhhHyuenc57RGrSluKFZvMc+uP4Uyz66YsZa6/tzcYfpxNS0M2uxY3/kc9x8La7aJU03orrkURAK+rTuRZKaj6NEZcaoJ4ZCF2Ur3fl7YOJwyz+6oc2Yo2zC50xxMiiP7DoV8vr/d0P0IchkirQaRqvE6fxwLglXVeNdswvHjb4Q9HhQDe0dS/jOivyPBQABL6T6W/vQGezcsQqk10ufM6/B73fz+3n2EQ9G13Fmtu3P+/Z+h0h5fP47GxDlnIZapb0Vtk3brgHD6AIRm2UiVKqRFVqrvfbLmPXQoso5tMT1yx2FbBwYtViofnkq4Qy6hlmmI0jqwYaOHn37Mp7LMRdOWBi6+vjPN2xpRa45/Z5ckJx5JAX+EJAV8kiT1I1BRRdn1dxO2x681Vo8dheGm+L2MGwO/08nCO+9h/dvv1Gzrdust5A4dzP7Zc/DZ7LQ+/1zSu3VFkx2bYtlYhIJBnMXFhPwBJCol6vTGr9erKHVx/3V/4PeHuPWhvnz86lqKC2qN5JRqCbdPacuyr2+i4kAk3fGs29+iw8Czjvra3oATn9+FSm5CLDqyiH48/ir5mI92xP98nGq+hcHfZeNf8rczsFRC+gcvIkmPTVkOVlYTrLIQqrIgTjMjMhkQ64/Ns9taXsD3U6+meFdtey9Deh4XPPgl5qzGcRMO+nw4CgopXLwYe0EhWQMGYGzdEk1m43V4OJlxWMr46rELYtJ6AfqMvZ4hF96LRHZi1YtXlezl7UkDE5a2XP/qYszZ/54bdb5jLb/ueYZuiqsQh5XY2M8i+3MUujYB8HiPLWSqj7wXdkPx7ztA1S/zCDRrgeByIVkwF/+ufQDIe3fFeM/NiOPUvQf8Pv4seoVv998d97yXtnqbwZnXNfh+gtUW7N/PjHS48PlBJKAY1BfDtZcgPs6mkvUlWGWhaupb+FZviNou0utIfeUxJJmx7ym/143XZUMkliKRylk58wPmf/l01D5ShYrLn5xOetMOx/T+jyXBymrKb50Snc1xCMb7b0E1uB9VU9/CPWdh3H0Qi0n/5JXDLhQHikopvfI2AERnnckv1U35c1ZhzH63PNSHfkNzEYn+HX+ZJCcOyT7wSZIkOSa4SktxFBVj27cP5cTzkLt9+L+dSaiqOmo/Se6xE80QqWvv+/CDiGQyNrzzLkGvl3Wvv4HXZmXwc8+gTImfvu8OWPGF3CjEOuTio09HFonFMf3lG5uCfVas1ZHo35tPr+Ci6zphMMnYufEAKWkyzGYPy7+7tUa8A1QWxkbTjgS5RI1c0rgmUZ6gg6Vl8dtCAaxx/srAXk/DQQHvDxD2xa/9F5uNiM2x7QUbG4/Txsx37okS7wCW0ny+fupSLnr0GxQGAwrx0fWCFstk6Js3Q9+82VGd57+KvbI4rngHWP37x/Q8/UoMaXnH+a7qRq1PpfOQ81g/N7YHfdu+Y1Dpj12pUX2QOLKQL7iVt2aU4HXbyMhO4axrP6Mw5UvmVz2PN+Q6bvfi9wUpcsr4tqgpW2dXoNXLGH3GBXQa6yb4yut4V6wjbLVDHAFvcRWyrOLLhOf+q+QjOhhOw6jIRizUb/ob8vqwf/srzu9nHLIxjGfBUqqsdkz3TzpmC4ZHitNWibC3IEa8A4SsNuzf/IL+hssQ/SOTSipXIpXXZhV0H3UZeR36svSnN3FUldC000C6jrwEQ+qxywQKOV0ELTYC+YUIchmS7AzEJiNCI5aXhP3+hOIdIq0BGdwPSUZijwuRUV+/EjmxCKQSCATxd+vFn/esi7vbJ6+to22nFMxpyRKpJPUjKeCTJElSb2z79zN9/LmUrantmWps3ZozP3gf/xuf1rb6kUpR9OpyzO9HnZHBoGeepPttk/DZ7Eg1alTp6cg0sSLK6a8i37GOGflPUOXNp6m2N2fk3Ue6ohVSsQJLlYfyEif7d1tJSVOS01SHKVV1QqyIFx2odfa3Vnt565lV3HxfawIlr7Fx+VbsVcUxx2S17Hrc7s8XdGPzleAIVCITqdBKU9HKEk9+xEiQixJPVKQiJfhq0zZFKSaEf9nAzWWtYM/auXHHqov3Ulq+ld+KXmRkzmTy1N3Qyv5dUfZfxVKan3As4PPgc5943QfkSg1DLroXiVzJujlfEAz4EEmkdBp8DoMvuBul5t9rQWat9vD+M1vZvLa8ZltJoYO3H97JtVMuobl5EWrJ8Ysy799t5aFbFhEMRJJDHTYf77+zgz7907jokosIfvo5JHgm28qLkIgSGwGKBSmLSt6jnXE4LXUDkIgOn7IcqrbgnD477phv3SZC1dYTRsCHQyHK8reycf53dCtJ/DdzL1iK9uLxkfZ4daDUGsht25uM2zsS8PuQKTSN2v/9nwStNhzf/ILj+xkc7M8oyOUY75+EvFsnRPLGSTEXpBJEJmNM0OEg0uaRzhPKIQPwllYgtGpCOBCEVRvxrdkI4TCac8YgitM60FZRSOGONexZPx9DWhPa9jkD9cTzcU/7lQMliTvTWKu9OB2+pIBPUm+SAj5JkiT1wlNdzayrrokS7wDVO3Yw4/obGD3lYbwffoOgUmJ+5E7EKY1TM2k/cIDSNWspWroMU5vW5A4ZjCY3t2YiIVEq0TerO1rpCThYWPIe3++9t2ZbmWc3q8q/4Y7Of2L29WLqg3+xd7ulZlytkfLAC6fQrLXxXxfxuU1jJwq//VjOmNGj44pKrSmTtKbtj8etYfeV8Ufha8wqeJ5AODJBydN047q200hXtY57jFSsYFj2Layv+jXu+BDNFYj/3MTBhGP9xAsPG2UPh0LY8w9QtGQp/nAYXbu2mLIy0aSnN4rhns9bdxTSYS1jV+AvNm38naGZN3FW08dQS2MnyF6XHae1gvID25HKlZgzm6MxpiOWJusfD+IsLsZVVo7HZkWZZiZgBDRitNJ09KmJM3tEEinSBhjZHU80xnSGXzolUlvsdiJVqNEYU5HK/937rSxzRYn3Q/n+nXwmvfA2elnjeDwcDpvVy4cvr6kR74eyfEkZY0d3xdCvByJdfPfuqp076dXiPPbaV8Yd75F6DvOL3mJWwfM81nMzacqWh72nkNMF/sSdP4KlFUibnhjeFJbyA3z64Fl0H3kpxDGrO0hDzVW9IjfVFLCh6FdAoItpDAZ5Dpo4z7ejwbt2E47vot8JYa+XqkdfJO3d5xHlNk67PpHJiPbis7G+9mHMmKBSIuvQhlAwiN1uZeWqxex+5D6kKhUdL72E9vfdCMs3oBrcL+b3WF2yj88emoC9stYIcsG0Zxl/+zukDu2DUlV3CZpEkmwMlqT+JAV8kiRJ6oWrrIwDc+fFHavcvJlQ8zxSnpuCODMNsckQ5QocD3fAjidoRypSJJwIVO/cxbdDhuMoqn0hSpRKJsz5ncy+fQh6PDiLS9j/x5+4y8vJGzEcffNmMXXoNn8pP+59AIhEYYLhyIQsRJBNpQsomCaLEu8AToefJ+9cyHMfjCIl/d+dZGc10WJOVVJZ7q7ZtntbNSX9WjL0kkdZ8sNUvK5IlD67bS/G3vwyOvOx700cCgdZXvYlMw48EbU937GWqRuHc1/XpQmdn3PVnemdegEryqPTilto+tHB2R3/jvcQmY3oJ16EvFfXw4rw8vUb2PDjT6RcfDHzJEoqwwKt7V6Gq5xkaNWIj1LEK1Q6xBIZwYAv7rjKZMZbHPElmFf8BoMzr0UZUBGyWAkUloBEQiDTwNKZ77Jixns1ESapXMlZt79Ns86nIJWf+I7WjY3T4cNpi3wfNXopnvw9/DRuPJadf5sbCgKtLp5A5gNjWBb4lguavUxKTmsqCnbEnKvT4HPQGOrX2u3fQCpXYkxP3Ff+32Dn5oqEY5VlbjSBHGTi42PW5nb42b0tflQUYNNWG2OuvzxhdwlzVnOkTi3N1L3Y64wW8S10/VBJjJS4twOwsvwbRufdf9h7EikUkfZxCeyi4kVhjwe+oBtP0I5MpEQh0RIOh9mzbgGjH3yFIvE2ws7OMGdR3GOVI09BrK9fCzO7r4wf901hYcm7Ndt+3PcAQzNvZmyThxst0yhYbcX+xQ/xB0MhXH8uRn/FeY1yLUEQUA7sTbCkDMf3M2sc5sWpJkyP3Ik41Uz1rl180bsffkfkme53OFjxwovsmT2Hs3/5CbHJEHVOj8vO7I8eihLvEFlY/vGVG7n+pQVkeLQolBI87kDMPbVqb0KrP7G8O5Kc2CQFfJIkSerFwRdZIrwuJ/JePQ57Hm/QRal7Oz/ve4T9jlUY5dmMzn2Q5tLuqDxyRDotIqUCd2Uls66cWCPeW5x5Jt0nXoVMLge3F09pKfkLF/HbxZfVmEMteegRsk8ZxOivPkeTVStgS1xbGZkyhdaSsTgtoFCLqJZu4veqe2giGs53C2JNZQCcdj/FB+z/uoA3p6qY8vJgXn5kGft2WgCQSEW4vQo6DbuEDgPPwOOwIJYpUOvMKLXHviYcwOIr5tcDT0Ztk4s1BEIeqr0FFDu3JBTwOlk6F7R4hSGZN7Cg+G0CYR8DMyaSq+yExiYn/P4LCEoFYrPxsOLdUVTEus++wHXDjbzlDoE3kgmwFZiVX8bjTTNorjm6v6HakEqP065gxa/vxow16TyAPaFVhKmd5K+rnI5xxyAsL74LoRCSJjmUju0Yc7zf6+a7567iupfnY84+fETwZMHnduKwllGwfRVBv5/ctr3QGNJQ/J0uHgqFKS92MHfmPlYuKqRwv52HHmvPXxedEbVgRzjMzs+/Q5GRivgSCV/tvZWLHvmY7x65IkrEt+59GoMvuPtfj2ifbOi0iaOCIrGA+DhmHwkiAUEEidq2K01aJOmJBWNKbmv+fOpxzjh/Mt6WAZZUfAKCQHfz2UhFCj7feUPNvqWunbj8VjwhOwICelkGIiH2dyEy6JD36Y532eqYMXFW+nHx3zgUf9BDmWc3sw48z177ckyKJozOu59MeTsMnVrwZv45WH0lyNJepvXg7gQXrIm+5/QUNGedFuXcXxd77SujxPtB5hW/Tkf9GNrpRyCTH725aTgQqLMuPZBfQDgUarTWrGKDHu3FE1CfMYJgtSXSWcCgR2w2EnC7Wf7Yk3HnPBUbN1K2fj3aJtE+G25bFbtW/xH3WqGAn+J9G2ndewx3PNGfZ+5ZFJVlojPIueHeXkkBn6RBJAV8kiRJ6oXcYEAQiwkHg3HH6+u+vtu2hJc2nkr47wRpi6+I17eM4wzzZPovaoW00ov+ygtw260U/RVpE3Xqm6+TLlbi+/gHAi43iES4B/bG0K8LEpUq6kVbuHAR6996h74PT6lJszcHurNvmpbvFtQau2XlmbnyoZn4Az5CwcTNOKor3QnHjieZOVoeeP4UbFYvXm8AjVaGwaxALpcAKvSptULZ7bDgcVoBUGoMKNTHJkrkD7px+CsQC1JG5jxKS+NZVAfEqMUCHt8OqrwH6jxeJ0tDJ0ujpX4gEK6dQDcw4OcqKyPn8st42B078/eEwrxVXMmUZnJ0daSVHg6pXEm/s29CJJaw6rcPCfg8CCIxbfqfQauzz+TtgvOj9g8HA7hmzq2J7ggj+7J0zqtxzx0OBdmw4Fu6DrsQj9OGSm+OpNWLT85XtMdpY+P8b5jz8cNRzuu9Rk9kwITbUej0lDv3s8o+E0//LYwa1J2McF9CW/KjxfshbH3zEwZd9i5vVF3Auc2f5+JHv8VpqcDjtKIxpKLSp6DUGI7TT/jfISdHgUwuxueNfa737JuGPHz8nn8KpYSeA7JYuSj2MyAI0Ll33Z0YtKYMzr37Q/787Amyu/WmdYshVPsOMLvghai2cm0Nwzgl82re3XYB2yxzUUmMDM+axICMKzHIozOXRGoVhpuuoMpmw7+ltuWlOCMN82N3H3cBv8e+nBc3jiAYjkRxi93b2Fw9i/OaTSUQCmD1lQDwedkdXHnOGzQdfBmSmevA40UxuB/Kvj2QpNUvau4O2JhV8HzC8T9LXiQt1J3MOsze6ougUCBt0RTfpm1xx+VdOzSaeD+ISKlApFQgyYqeu3iqqtkzY2bC4yr2bEPn7ojVV4xEJEUnzUAQwgmzNAC8DhsSiYh2nVN48ZNTWbm4iML9Ntp3S6Nd5xRSMxrXKDbJf5+Tc3aQJEmSeuGyV+F12hBEYpRaI3LlkTtkq9LT6TjxSja++37MWPMxo1GlH77/ssVbxKc7r6kR74fyW+VL9Bm+gOCkt/Bt3Irk7ki7nxZnnkm6WIn3+99qdw6F8CxchqS0nKFPP8XsSbdEnWvda2/Q+bpr0eZk4/MG+O3LYlYtiO6dXpRv590HD3DHc13R6Apx2OKnRuc2+/cMpv6JzihHZ0y8Sh8KBqgo2Mms9x8gf8tSAJp2HsSoq54gJbtlo0+AJCI5arGJi9p8y3x7C77Kr00NTJF25K68gfU6j0g4uvsKh8MUyRWEEpSq7nH7cASDRyXgATSGNAZfcDc9Trscj8uGnUqWO7/izQPn1tT/H6STZAi+XbU1loJBg60ifqYHQGXhbuZ/9SxbFv+EQq1n1MQnaNVzFAr1v2OQFQwHCYZ8SEWKBnsIVBfvZfaHU2K2r5zxAU06DkTRLp1n1w/Ed4i7eTNtb04vuyLhOQMuFxJf5O/nDljIMLRGYzg2Pd//n1DJfNx0a0tee2knAX/tczm7iZaxZ+iRK45fXa7D7mXk2Bbs2lJFdaUnauzCazuhMxw+QqlPy2XMjS/gdlhQizbz0+YHa8aaaXox3nA/CmM6z2wYXFNKZfOX8uP+B9lU/TvXt/82puZfkpaC+eE7CFZaCJaWITYZEaeaEJsPXwMeDoep8gexB4MIgFYixnSEjuoWbzEf77iqRrwfyvf77uWm9j/V/D9EkA9Krscoy6bHRePoYRhPy6xhDfouB0I+nIGqhOPOQCWbNhSh0+iPuoe5WKtGd9UFVEx+JGZM0KhR9O52VOdvCIJIQKpW47VYYsZ6PH0vlWf4+GhVu5pnvkaawnVtptFx6Dlsmvdd3HNmt4lkJ0plYjJytJx5QZtjdv9J/j9IOiYkSfIfJOj3UbxrPV8/dSlv3tSPN2/qxy+v30Z1yb4jPqdMo6H/Iw/T9eabEP/dfkYQi2l3ycWMePtNFMbDRyJcgWoqPPHvIUyYotAOREYDoWorUpEYhdFI94lX4fttftxjAtt3k9Gufc39HMRrtRIORSJKliov82bEv2Z5iYuwX855E+P3tG3TOQVT6vGp/2wMLGUH+Pj+MTXiHWDfhkV8cv8Yqkv3N/r1DLIsLmr1LuvdLVhhj55UVvgDPLmvmooE7d8aE6XZTIi6J6Z1BEcahEQmx5CWR0bTjmjS0lhu+SpGvA9MuxLluopIz+iDFJST3rRjwvOmN21PdfFeADxOK9NfnURZfvx2accST8BBoXMj03bfyhtbzub3gucod+8hlCiv+R8EAz5W/vZBwvElP7zKluJZUeIdoMS1HXnLxMaXMp0Ovyzy+1RKTpxFtcPh8FdS6tpJiWsbNl/pMbmGvbqU6tL92CqLCAZjxV1dqE0G2DiHB+7N44qJTRg3Ppvb72zJpaf5kDhL0GTWHfVuTJbPL+S9F1Yz8fbuXHhtJ3r0z2TIGU2588n+WKo8eDz1+9lkSg361Bya6/twZeuPUIr1DNJfzjX2RzCv9/HtvntrxPuh7LQtptS9M84ZIynXshZNUPbvhaxty3qJd28wxHqHm3t3F3DHzgIm7yzgwd2FbHW6CYQa/kByBqoo9+yJOxYMB6j2FaCRRH+Hqn2F/FH5JruCqxu8EKeS6OlgPC3heEv5qaydb8XjathnLhHSZnmYHpqMyFQ7l5C2bErK1IcQpx8/bwulyUTnaybGbNc1bYr8zGb8UPJg1DPf4a/g5c2n0+OSayKpIv+gbd/RaIz1y1BMkqS+JCPwSU44XLYqQj4fCrkGsUbdKA7S/29UFu/h4wfOJBSITFLCoSDbl82gcPsqrnj616h063/i93lwVpdRun8Lfq+bzOadUBtSUaj1qDMzGPTc0/SYfOth27bFQ4hTY3goEkEOf09AwyvWM+iZp5DJ5ZG0+QSEyypRpqXhKCio2ZbZtw9SdSQlzesJREWW/knBbgfdemm5/Ka2/PjFXmwWLxKpiEEj8zjvqg7ojSeHsVjA72XljA/we2Ld0r0uO+vnTmPwBXcjEh99veJBxCIJebrTeXlnSdxxWzBIgddPiqx+9ZZHijIlhZYSG0KFk3jT4my5FLW48der05VtmNJ9DQuL32Nj1QzUUhOn5txFM2V3fF98xqHT2sDsvxg86UY+37Y85jwKjYGMZp1YOC06XXXhtOc45+4Pj1kJxD/xBz2sr/qF97ddXFPPv7l6FjPyn+SeLovI1Ry+NWTQ76sz08BhKUcajo2kuoNWHJku9M2aYd27N2a8w+1X81foSzqbzkAnPfEj76FwiGLXFj7dcS277ZEFtSxVey5t9Q5NND2RiY/+ueJ2WNi3YRFzP38CS2k+cpWOXmOupseoy+otGOR6PV0nXkbpuvW4v34Nsd1GkUZD7/vuIa3rsW8FeiiWag/lJS6mPriEJi31NGlhwGHz8cqjy/D7Qow+N35Xi0SoJAb6pl1Ce8MI5HtsVD/1OKLnrmGHdWHCY9ZXTqe1flC9rxEKBhFEorjzlGKfnyf3Fkflm5X6Ajyyp4iXWueS1cC2aMJhFimlgiLKh+NQOphGNehaAM5ANT1SxrOw+F3cQWvUmFpipIP0fBbtzG+0Li0ilRJF/56ktmlB2O4AiRiRVovYcHyzkEJVFlqfMoRdXadTtm5dzfa2t17GHO+bcY8Jhv2stvzA1S/8ycy376Jo5xo0hjT6jr2BDoPORqU7fq0Yk/x/kBTwSU4YbJXF7Fo1h7VzPicUCtCh1xja9jodlT4NzEYUjZz+e7ISDodxOvyIRKBSx04AfB4ni755sUa8H4qjupS96xfSdcRFcc/t8zjZufoPfnn1lii37R6nX8mgcyej1qcgrUfbtkRoJGZy1V044FwfMyYRZGQE8whZI27q4WorLSdeSKiiEotIVFNL/E8Egxaf9ZDJhSAw+MWpKM2RSIRCKUGuEOP1xK/dT02X8vHd/cls3pXrb70ekTQDsSiIMVWJwXTymMp4XXb2bYrvOgywb8NCep16FZqUxosEeKxWJKWl3Ou2EdJomS9Vstgd/bkr8fqhfobHR4xUpcIcCHB2AH6wRPcBFwM35KRiOMK01boQCSLSlC04u+njjMq5A6lIVhMd9l1yDuUr19eE/kNVFpR/bWf89a8w55unsVdFFj0ymndm8IV3M/fTx2POX3FgB36v67gJeIu/mI+2XxkjAjxBOx/tuJLbO85CK6s7EiaVq2jWZTD7Ny2JO57TtieFgU1xx751PMCdM2bx52XXULpqFQAiqZT2N1yO/LyWBILruLzVB6ikx7fu+Eio9OznmXUDo4RPkWsLUzcM5aHua8lWx2ZjBMNBXP4qRII4bgvCQwkFg2xf/hsz3pxcs83rsrH4mxcp27+V0ddPrVM0hEMhgqXluOYtwbtuM7r0FEa/+AIhrRrksprn5/Gke79Mfv8+4lOyf5eV/busyORiVBop2XlaFMqGLz6KRRL0QRNVn30KgBAIIRUp8Ic8cfdXSer32XIUFVG6ajVbPv0cqUZD5+uuwdiqJcqUSH25NxTixzJLnGIxCIRhVqWNSzPMSBogftUSExnKNjVO+ociEWQ01fbCG4w1XuudegHGBGaiiQiHw6wu/44FJW9zXbtpzCl8mS3VswGBjqbTODv3GV65pZAzzmmF3tR4i9yCICBJMcFhetQ3Fnarl7JiJ/N/34fPE+S08S1J2boez8dfc9qjj2OxVrF9xgxkGg0tJpzJNwdeTHiuA861jG73ABfc/xl+rxtBJEJjSGv00rUkSeAkEvBPP/00P/zwA9u2bUOpVNK/f3+effZZ2rSprSPxeDzccccdTJs2Da/Xy6mnnsqbb75Jej3NtZL8e9grS/j2mSso2bOhZlvZ/q2sXfQNF1z9KiF/E3xpKUddw3oy4SwtxbJrNzu//wGJQknbi84nZMpj9dJSFs/JRyoTcdqEVrTpaMZork3z9jrt7N/0V8Lz7lj5Ox0Hn4MkTt9pa3khP710Q0y+8erfPiKnTS86Djobj8WCtzrS6kduMNQrdf4gWlkqV7b5mGfXD4qZaFya8TLCl5H7lrZoivbiCUhTzITUKjyD+uBZsDTmfCKjnpBWTfBv1/HULl0Y+trLpHbuVLOPwazg9Amt+OmLWHOc7CZanOXr8Htc5G9ZQv6WWtGh1Jq4eupsdCmJ+06fSIhEElTaxJNuldYcZSh2tNjy8/nzhpvZ+9vvEA4jUSjodNstdLnqat44xEwuT3F8+pvrdTrGqtS006n5ocJGVSBAG5WC8WlG0mWN/6oLVloIBwIIEjFisxHdP4StJC8b0yN3YHnlA0JVke9LePs+Wpx7FtnPzMTjsCASS6ku3cfMt+6sEfSHYshshkR2/Eo4ip1bY8oBDpLvWIsjUHlYAS+IRHQYcBZLf3wDr8sWNSaSSOk//mYez+8X91h30IZDK2PMTz8RsFbiczqQGrX4jSFQiukinXBYYXsiEAqHWFk+LSZqCZFU51/2P8Flrd4nICiQCiKUYhEVnn0sLf2UVeXfIhOrGJ51K20NQzHI46ew26tLmPf5k3HHdiz/jSEX3FOngA/sL6B88iOE/85u8gGu2QvQ3XwFoe5tCFlDqPXHtyVfbjM9ec115O+xkZap5sILM9BiwV1aSk63XEQeK+gann0Rdnvw746UEIn+3MiAMy5mflX8Mo/uKWcf9nz2wkJ+Hns2ZWvW1mzb8smndL7+OgY8/gjKlBTcwRC73fG/SwA7XB68oRASUf3nNHp5Ble2+Zjn1w+J+Z5e1PJ1jLJcpnRfw6/5j7PdMg+NNJXTc++mvWEkKrecUMCDSFk/sW31FfPrgSew+op5d9uFDEi/gkEZVwOw07qQbSUradu5MwNG5DVaBP5YEs/B3mbx8s2Hm5jzc6QsYdiEljh0EtQVVYRdbrxvfIIm1UzfDj0hECSwfAuZLdqy274s7jWaa/siEUmRaI3HrRNMkv9fThoBv2DBAm666SZ69epFIBDg/vvvZ9SoUWzZsgX136myt99+OzNmzODbb79Fr9dz8803M378eP76K7GYSXJikL91WZR4P4ildD9bN8+jrWUAe9RquuqP3ITtZMJZXMKsiVez77dZQGRSnDL8TF5/fBHlJbUp0lvXV9C1TwY33NMLgznyYhbEYlQ6E257fPMZjTEdUYJJw/q5XyUsFt7y189kGFsxf/Jd5M+JtEvJHTqEoa+8hKl9u3qnZueoO/Fw93WsLJvGdut8UqTNOEV+AYrpm+BABYp7b6Rg0wZW3HEH5nbtaHPxBegnXkiwvBL/ltq2USKjnpSn7oesNK7csYVQIIhMp0WVGj3plErFnH5OKzzuAHOm765p39Kmk5nr7+rC148MjnufbnsVLlvVSSPglVoDPYZfFlX/fiid+5+DWGicR76ztJTpZ58TNYENeDxseOY5uspk9LnkKpa7fWTKpGTIj236/KFoJWK6G7S00ajwhcOoxCLkjRz9CFrteFatw/7JtwRLyxGnpaC99BwUfboh1temeoqUChR9upP6WrNIOqhIQKTTIjYakAE6c0SYCYKA0xq/F/fg8+48rs7qiaKSBwmF42ex/BN9Wi6XPzmd39+7l/wtkcluetMOnH7ds+jSchkavJHfDjwbc9y43MfJMOagUasg++RdePeFXGyxzEk4vtv+FyXOSt4oE2GWiLk8w80L6wdg89fWyL+//WI6GE7lyjYfY5BnxJzD67LjsiVuu1V+YDupefGNsoI2O9Uvv1cj3g/F9uaniB+7gZ+/eICzJ79Dam6b41bGZkpRcu+zg5g7Yy9dmnlYcNk52A/UdrLIGtCf0V99gTa3YdFkZFLE6SmErDb8C1YzYuwNbFUuotS9I2q385u/iF5W9/M+FAyy5ZPPop59B9nw9ju0v+wSlCkpyEQC6TIJxQk8QLJkUmRHIHybaHrwSI8NzC9+i922JaQqmjEq507Sla1QSrRkSztweesP8AQsiAQpKqsE59d/UrliHSKDFs05Y5A2a3LYPvCBsA+rrxgAV8DCnMKXmVP4cs34iMzbufDaS9Boj88C7ZFiLS9k36a/2LlqNoa0XDoPPR99ag5ypYbCfFuNeG/V0UzTcXm8WFnJE53bwzfTAQiWV0ba20mlyNwexva5j5fs42KuIxMp6Zkav0+912LBVVaO12pFbjCgSktFrj9+Ph4WXzElrm2sq5yORmKme8p4DPJsVCeRl0iSWE4aAf/7779H/f/jjz8mLS2N1atXc8opp2C1Wvnggw/48ssvGTZsGAAfffQR7dq1Y9myZfTt2/ffuO0k9cDrdrB2zucJxzetnEFbY0esVVaqVQqMxyAV9kRjz4yZNeIdoMkZZ7B8XSBKvB9k3fIS8vdaawS8xpBK33E3MuPNychVWiRSOU5bZY0w7z7q8rhiOxQIIFdqGXTeHfi9brYtm4HloPGZIND7lMv5euAQvIekqx+YN5+v+g3kknWrMLasXw9rkSAmTdmCM/LuZ2TodgRPkHBxJd48D54BA/j6tDPwVNUuPix74knO+nU6OQ/cQrjKSqCgCFGKCUlmOpLUSMRZ2qRJndc0mBRcdG0nzji3FU67D7lSgs4gx1Gxo85JcDjBYobf6cReVITfbkeiUaNISUFtOv4pp/8ko3knupxyAesXTova3nPEFej0WTGLG3XhcvixWb0EAiFUaimmlNpIsKOgIO4EFmDziy8z/IILsavU3JybdsSOy0eCxVtMKBxAEpKgF5kQNfK1Qz4fzpl/Yv/465ptwbIKLC+8jfbi8WjOG4tIUVt2UZ90UH1aNuc/8Dk/vXgDbkckUi+RKRh2yQOk57Rt1N7HhyNH0wkBIW4dbYqiKep6phcLgkBqXhvOuecj3A4L4VAo4qGhj3xHRmXfQbqyNdP3P0qVN590ZSvGN32KNoZhaKQnfx93iSDHJEv8TDLIsgh7xRR4vWTLZcwpeDFKvB9ks2UWBfZNcQW8RPr350wQaNV9BB17jkMUFhMWhdi8ZkadEcCQ3YF/264EgyFERZX43E4+nzKeiVNn13imhIJBHNUl+H0eJFI5Gm0KYacbQRAQGfWNIvTNaSpOHaHn6wFnRol3gKK/lrBg8p2M+vA9ZNr61+WIdVq0l0yg6qHnIRAkPOUTJt39OoUZRaz1z0avyKJfxmWYZHmoJHXXW3uqq7Ht209a9+6UrVkTM77+7XfI7NMblVjMhHQj6xzx/VvGpBqQHsH3WiKSkqFqzTnNnsMbdCATKZCKo7N0FGI1CrEaf34BZbfdR9hZO2/wrlyPZsJoNBeehVibOCAiFclJUTSjwhPrRwHQQt/3hBfvVcV7+WzK2Tiqa79by6e/zZibXqJNnzOZ/WPtd2DkZW34xGnHFgxSnJpCTrM8gnvzQSRCfPUYPF3T2OpdQa5ay2Wt3uXbPXfiDkYyjEzyPK5rNw2TPPY7by8o4M+bbmHP9F8iGwSBVhPOZujLL6LJPvbBgWpvIW9unsBeR633yk/7p3BB81fon3F5UsSfxJy0Ssj6t4gwmSITo9WrV+P3+xkxYkTNPm3btiUvL4+lS5cmBfwJjCAIdU5Qhb9XqV2hEMHGspL+FykvcbJ1QwVb1paR01RHz4HZmFOVSGURUe0qK2PNy9H9orNHT+DjPxK3c/lj+m46dE1FLIn8Hlt2G84l93yNfV8+focTXYtm7N62AH1WHvr03Jjjw+Ew1spCvC47e9bNQ67S0Xfs9QDM+uBBmnUaxP6ff48S7wfxO52sf+NtBj37VIwbfF0IgoBMrAI10FJLwKBh9mljosQ7QDgY5JcJ53LJ3D9xP/U6Yp0GWYe2aC8e3yBxI1dKSP9HG72Q14hcpcXrssfsr9AYUOljRbmjsIi/HnqYrZ9+TigQQCyT0f6qy+hx9x0Y8poi+hd7d2tTMuk24ALadTuNgr1rEAQR2c264SupxphXf9+CkkIHH760hvUrIxOflHQVV93ajfbdUlGqpFTviO/UDOB3OMj2e7m7VQu0EjGeUAhrIIjFH0QmEtBLxBgl4kaN6tl8Zayt/IkZ+U9S7T1AtroT41Mfpmm4PWp9JmJD40xSQlUW7F/8EHfM/s10VKMGI8poWIqvRKqgaccBXP3CHByWcoI+L2q5jvCfK3E8+CLB4QNRntK33r2bjwadNJ3Tcu/htwPPRG0XEHFpy3diemQfDqXGEDeDQCtLZWDGVXQ0nk4o7EcikqOTnbwR938iEUkZkn4zS8o+jjveXz2Z/O1hdKliuqg8TC+cFnc/gCVlH9FMPgi1NtqPQ6Uz0azLENr3GI118SbmnnMNPrsdqVpNh2uuxGSIfc7XcDgH9EDEmM3tqKZg+yr0qTk4rRVsWvg9f33/KkqNgXMmvoRlySz8S9ciyGWoRo9ENbhPvdzZD4ejoCBGvB9k5w8/MvCZJxsk4AFk7VqhvfQc7F/8QMhmhwc/JCcvm453PIo0Nw/RYd5dgUCIilIXqxZVsDfnMvL6XEGPJiJW3X0T5WtrFzP9NnvNeylPLmNilplPiiv5O/ELmSBwQ04qGUdp7CkRSZGI6likcbqwvvN5lHg/iOP7GahOHVKngNfLMhnf9Cne3XZhzJhGmkJzXZ8ju/HjhNdlZ87HD0eJ94PMeOsOctv1xeWstRmVGeU4PB7666QEdE6Cz05EursUIRzme8nLrCr8MbJjBXQwnsqdnecSIoREkKGVpsZ9Nrqrqphz9XXsmzW7dmM4zM7vfiAcCjHqw/dRHMNIfDAUYH7R21Hi/SDT9txKW8MQVJrOx+z6SY4tJ6WAD4VC3HbbbQwYMICOHSNGMCUlJchkMgwGQ9S+6enplJTEd0gG8Hq9eL21tUQ2my3hvkmODTKFmh6nXs6+DfENuLr0GQdqA9skUvqd5I70hfttPDxpHnZrrUHcl+9u5L5nB9GuayoSiYhQIBDTf1QQhITRYIgE1w+OBjweKlas45dzzsPvrDX06nzDdXQYeybKOIZYVcV7+Pi+MXgctdct2L6SNr1PZ/AFd+O1WCh4+7eY4w6yf/Ycut82Cd1hIuF14amooHT16rhjAZcLy969KMRigmWVuMv+wrNiLWmvP4Uk68gn/hpjOmfcMJUfX7guekAQGH3DVLT/cHL2WCzMveVWdv3wU822oM/Hxrffx+/20P+pRzBkHfnv4GiRqlSkd+yCs7gEuaAi4PagS2+KunOfeqfsVZa5ePTW+VSV10aOKkpdPHf/Xzz08mA6dEtDk5tYHIgkEpQaDVqJGFsgyO8VVr4vr66ZwJqlYu7KTSPdZ0cilaLSHt2E3xWwMn3/o8wvrnUHLnBu4FXnBK7KfIsOszLQnDb8sCmj9SFktYM/QVs8f4BQtRUaKOABRGIJupRs1IKKqqdexb2p1q/Bv2c/zumzSZn60FGL+IDPi62yiG1Lf6UsfxtNOvSjWZfB6FNzEAQBpUTHqOw7aK7ty6/5j1PtLaCpthfjmjxKhqrx+xYnqu/+L6AJ5XFW+itML51MiNrSg0GmSTi2NceQKcEd9CMWQBDqWoQU4XEHYgS8Qq1n9DXPseKJZ9n45js125ucPoqcU4dSWbQbHx7UhlRUuuiFSJFGjSQ3i8CBoviXbJJR00mgcMca2vY5g1UzP2Dxdy8jkki59NYPCTz9fo3hKIDtnU9xz/8L08OTkRyliHcWJ56zhUMh/HFE6eEQ67RoJoxGNfIUAsVlCIKAOCMVscmIcJhMnVAozO6tVTw+eQF+X62/h1Il4Y4XPmTlNedg2b0bgHaXXYpIEjmfRiJmuFFHD52aUq8fkSCQJpNglIgTRt+DldWRBYZwGEGnjWTwHAEhuwPv6tiyxIN4Vq1H2qTuUoT2xpGc3/wlfto/pcazJkfdiWvbTsOs+Pfec/XBZa9i1+o/4o6FQyEKtq9kxJn9Wbc88lmTikTcmwfry57n0y0f4Au5ydN0Y3yzpwmXRGcsbq6exZbq2TzaYxNZ6vYJ78FdVh4t3g9h148/M+jpp46pgLf5S5lf/EbC8RXl08hJCviTlpPSGvGmm25i06ZNTJuWeNW6vjz99NPo9fqaf7l1TEyTHDuyW/ckr32ssVFKTmtatR5IZYtm9DNojomT9PHCZvHyxlMrosQ7QDAQ5oUpS6iujAgmhclEszOie68W/vYTffomfpH3PFVJhT8ygbAfKOCnM8dFiXeADW+9E5WWfxCf28H8L5+NEu8H2b7iN1JyW9N+8HhU6YmFiTI1ha1ffMXu6b/i+dvgrqEEEwmjv/E7nQiS2r9/2OnC8ctswoc5ri7EEiktug3jqudm0bbvGFJyW9Ou/1gmPjeL5l2HxETTXaWlUeL9ULZ//hXO4mKCfl/c8WOJz+OkumQ/e9YvoHD3GtBIyOzXh6ajRmJq3apB9XY7t1RGifdD+fytDdgsXvTNmiZM/2t7yUU1n5V1dhdfl9WKd4BKf5BH9pVwoKqcr5+6lIJtK/HFaX1XX+y+UhYUvxV37NuKh3A1VxEsLT/i8x/K4Sb6HGVUzbdtF75NsWaLwdJyXLMXEA7WrwY9HsFAgPyty3jntsHM++IpNi/6gZlv38UHd42i/ECtq7VWlkK3lHHc1vF3Huq+mmvafkETbXfkYvURX/v/Ea3SQGjjYG5KX8V5aR8xIfUdJmWuIvzX+Uz/oBhS5LhDYTa7VPQ2X5LwPJ2kl+Bxxf+7h91+Nr9ba8Y26JVnybh4BKtXfsUv703m3duH8vWTl1BVFN07XGzUY7jtGohTSiUdPYRNa36vMbxMb9oep62SpT9HvmMdeo+GuauixPtB/Nt3498ZP+W6IRhaNE84JlGpkOkavhjntduxFRayd/FiCg/sxaNTEtZpDv+dBqor3LwwZUmUeAdwuwK8914+ne55CICUzp3J7N0zah+5WES6TEpnrYqOGiVpMmlc8R72+fFu3Er57Q9Rdv09lN1wLxW3TcGzZiMh7xG+U+rKWKxHNqNGamZo1o081mMTU7qt5rEem5jc6Y86ReuJQjgYrPNn9LkdtGpvIj078lzTyqv4Zfe5/FXyOr5Q5P2X71jLK5tOp1fqeZjkedHnJ8yS0k9r/l9R6mLFwkK+encDi2bvp6zYid+b2MSQcBiv1XLkP2A9CIWDuAKxWZMHsfiK8QaP/N2b5N/lpBPwN998M7/++ivz5s0jJ6d29TAjIwOfz4flH5HL0tJSMjJi68cOct9992G1Wmv+HUiQtpXk2KI1pXPW7W9x9uR3yOvQj5y2vTnj8ieZcPPbVKblsk+lpoP6+LkxHwvsNi+7t8UXt25XgLKiiOCWKBT0vOsOpIf0Vt/3y3T691JgTov9HbTrZiSUtoMvdtyA01/Nzu9/IBQIRO2jMJnoc9edaA0m3IXRPZrdTivbl89MeN8F21aS0bIjPe+cnHCf9pdewvq33uHncWeze/qvhI5AaCiMJtSZCSJygoCpZctIZOIQvMvXEHI44x9TT+RKDZktOnPmpFe45NHvOfPml8ho3gmZIlawuMrKEp4nHAzirqzE646d2B5L3PZqVs78kLdvGchXj13A5w9N4J3bBrNrzVz83vhCvC42rk78M+7ZXo3PF0Sbnc2E2b/FZFw0GTWSAU88hlStptofYFpp/LIPTyjMLrGagNfDJw+Oo3hX/Hr6+lDi3p6w97HdX45HF8Y1P35Ls4Yi0usQZ8ZfyBKnpRxVqn7I68P529yE464/FsYVTfXFUV3CD1OviWkv6XFYmP7KzTFGelpZCgZ5NsrD1AQDhAMBQgnMuv5fUaqk9B/Uki+fL2P6w6n89lgOT1+7h0W/VnD90/343huJaM63eBicMQmTPDZ40El3Fs78NJSa+AtDnsqqmmd919tvIeu0ofgCLkyZzRhy0b2MveU1LGUH+PyRc7FVRkfbpa2bk/bm0yiG9EOcakbatiWy2y6jIAeWznovso9CRZOO/XFaygn6I2KkVfvBBFZsTPhzu2bNP6qFJgB1ViZtL74Qc4cOiKTRP3uP229N/J5IgLuyktXPv8iHrdvxyznn8fPYs/moTQe2T/sGr/3w3ylLtQdrdXwxVlroRNmiI/0fe4SzZ/x8xHXNgZIyKu59kmBp7fcwWF5F5QPPECyOTQM/HIJGjaxbbKvCgyh61C/yKhHJMCua0ETbnUxZa1QWMYGScoKWEztbVa7SkZqbOGsot10fDGYlD700hAmXt6Pav5MDztj3UJgwvx14hsGZ18WMVXr2Eg6HKcy3cf/1f/DClCX89MV2Xn9yBXdeOQurKB1zx04xxx1Epot9X4TCjdcpRiqS0cYwJOF4G/2QuG0Hk5wcnDThzHA4zKRJk/jxxx+ZP38+zf7Rh7pHjx5IpVL+/PNPJkyYAMD27dvJz8+nX7/4LWsA5HI5cvnJ0+v5v4zWlE77AWNp0W0oPo+XsESOVSrDIBYxrI6Us5OFgL/uB7PHXSu69c2bc9GKJfx1/4Psnv4rIqkU+6oF3PPSuSyZW8DaBXZkMjH9z9Sha13EFyVX4A95cAaqqNyyNeq8rcaNY8DttxH4bQGBz37G+st8vOeMQtm3B3JzGgJ/p+gnuC/R37/3lE4d6XXPXax89vmo8Q5XXI6nqgpHQQEAC++8m7xhQ9A2MJtFk53FsNdf5ZcJ58aMdb3mali3NWZFXdCoQdI4jzGZQoVMUbeBllxvqHNcqlEjlh7f50nRrrXM/+KpqG1+j4vvn5/INS/OIzW3dYPOl56VONJqMClqWgaZ27fj/L8W4CgsxF1ega5pE9Tp6TV9kIPhMKW+QMJzFSDFbM6gbP8WZr3/ABc/8i1qQ8PbVinEdUfjJEghcHSC4iBisxHTlNupuOvxqNpSQaXE9NBkxOajbB1UV4nQUZYPWcoOxPV6ACjdtxm3vQq1vmEp+kGLjUB+YSQTxu1BNXwgsk5tkaT8+4aOJwIp6Wrue+4UykucHNhrJSVdhTlbw8u2ag54IwseIWBupYZrc+aw2fYLmz3fIxer6SG/Dv+B5qToMqIMJA/icvgRKyLbc4YPIfuc4Xz64FhCwdrvnDGzGWNveY3vp15Nef52dObaOl2RTIaoaS7G26/Fb7FQtH8jMz99CEvJPgA0hjTOvfdjdOZsAr5IdwKZUoPKnIaiX3c8y9ZESkb+gSCXHdVnNeRyI/UGOOXCywhcFMbbPROrvwyP1YJOnk6quTWSBs7ZSlasZNnjT0Rfx+9n9lVXk9KxPerMTNSZmQk7qQR8db+7FRmZtL//3np3YvknYb8fx8+z4j+nQiHs3/6K4ZaJiOT195gRa9QYrr+M8tseIuyO7i6hOnMUogam5gcrqrB/9yuumXMJe71IWzZFf/3lSFs1RaSIbU0XrLYQrLIStjsQmQ2I9HrEuuPXRUhtSOG0a5/l84fHx7RPbTdgLFpzJLCXkq5i/GXt+Dn/i4Tn2u9Yw+m598Vsb2ccid3q5bXHlmOtil7g8bqDTJ2ynNuef43fTx8Sc2yTUSNRpUXed/6glypvPivKv+KAcz2tdIPoah6HWZGHSDjy1slqiZkz8x5ip3UhwXD0uzhL1QG9LB2ZKJlZdbJy0gj4m266iS+//JKff/4ZrVZbU9eu1+tRKpXo9XomTpzI5MmTMZlM6HQ6Jk2aRL9+/ZIGdicZcpUWuSoyKT987OfkQaOVoTPIsVliV/IFAbLyaoWISCzG3K4dp37yEV6rNVKfmprKosoPOND5O/r1PI1A2MtS91cUFx0q2MPkDh3C1s8irv6Gli0ZcONNuJ95C/5+iQVdbpyvfop/wza011+CQmOkXb8z2bz4x7j33a7/WACUZjO97r2b9pddyp4ZM/FZrWT27UPBwkUsvKf25eauqMBTbWmwgBcEgbwRwzhv4TwW3XMf5WvXoc3Lo8+995Cu1uP5ILZkRjNhdJ1GPI2NKiOdlE6dqNgYG4HKGjgARUoKcuXxux+3vZpF37wYdywcCrF2zueMuPzhBk0sew3K5qv3NhEKxi7pnHlBawym2smaNjsbTWYmdksZAY8LX9iDzO9DLJUhEQQyZdKEbZTy8FNcEYkKlh/YjtftOCIBn6JohlKsq3EEPpSmmp5It5ShHNq/znOEvL7aVm8GfZ3GiNLmTUh76xl8m7bh27EHacumyDu1R5x2dKJVJJehHj0c74r42QjqUYMR6Y/8iej31p0qGQw0LIIetNiwffgVrlnza7Z5V65DkpuF+en7azpE/L9jMCkwmBS0ah/5fQTDYSab5PxeaWWzw41BIqZbqhajX0er8ssQ7x+G3RJgazGMOrsZ2U2i/+ZOu499uyx8/8kWRo0yk9a9O93vvZ3vXr0xSrwDVBfvZc3sT+k8+FxK9m6iRbehMfcnUiiQZ2SQZdBy0ZSvsFcWI1WoUBvS0JoyEAQBlc7MqFueQpprYIF3GoFTPfQaPZrUQjWhF78lfEiKt3r0yCPumhByOHHOmo/tvS8Q92pP1VUd+WDfqJp2ZiqJgUv0b9EpMBqlpH5p9J7qapY9/lTC8bWvvI5YoSCtW1fanH9uzQLkoRjMCsQSoaYF6aEolBL0ZtURi3eAkMeLf/vuhOP+nXsiIrwBAh5AkptN2pvP4Px1Dp7VGxDptWjPPRNpq+YNem8GqyxUPjI1qjzCv2sfFXc9RsrzU5B3ahe1f6CwhMpHphLIr832k/fqgvG2axEfYU3/kZDZojNXPfs7c794isJtK1HpU+h31k207n1qlPeKRCJGL0ucqSsTqQiGo5+PBlkWzbS9sVX42LvTEvc4a7UXSXYrsgYNpGjR4prtOUOHMOr9d1AYjQTDAXbaFvPKptNrrrGm4gd+3v8Qd3WZTxNN9yP++cUiKUZ5Dje0/4HZBVPZYV2ITKSiT9pFdDOfhUGWjUKSFPAnKyeNgH/rrUj91ZAhQ6K2f/TRR1xxxRUAvPTSS4hEIiZMmIDX6+XUU0/lzTffJEmSEwGjWcllN3fh9SdWxIyNGNscqVREKBSuiXACyHU65LraCVxr/WC+2HUTO5gXc46mmp6oJEbyhg1BYTbjqayk96234P9uZo14PxTf/BWEzz+LSpWePuffyd4NC2NaqnUZfhG61NqUQIXBgMJgwO908sd1N7LyuamE4tSgi48wq0Wu05EzaCBn/fozAZcLkVSKQqXG9vE3MfsqBvZB3rldnLMcO9RpaYz57mt+PvMsqnfU9hBO6dSJwW+8iDbz+PaMD/i9WMsSl/1UFu4kGPAhEte//MScquLOx/vz0iNLo2o++w3NYeDIJohEAi6nn+pKN2uXFeO0u2ndRoqzfA3Lf3qKbiMupteYazAY07kow8gL+bEp+SqRiGYBBxvyI/XeIon0iN379bJsbmz/Ay8fMgGCiFPy5cYXEM3ZjK2jFp03gFwefY1wKESwuAz7t7/gWbIKQSFDPWYkqmEDE040BUFAkp6KJD0V1fBBR3TPiZC1boGsawd86zZHbRdnZaAcPghBfORZSKbM5ggiUUw0CkClM6PUNCx7IFBYEiXeARAEAiVlOH+bi+7i8QhHIWr+q4gFgUy5lEszzLhCQaSCCOXff1ezUUFuMz1+XwiFSoxKHS3YvJ4AC2ft5+PX1jFweBoqbRlD3n2ZssLtNSnu/2TnqjmMv+Pdw3Z9kCvUyDPUGDOaxowFlWG2ZS5nQUGtWd4SPqOltj9XPvQYgQfeB0A5bACS3CM3JgwUlWB793MQiwlcOZhXCoZHfaddAQvvbruQ+7suq7cLesDrxfGPsrFDcRQWYurQnv2zZtOkXz+kQRDUSsSaWmGjMHp49IvWWOzVSEJaNsz3MOubIoKBMBff0AmjKTYC3RAEmQxxdgb+nXvijosz0yOZDQ09r1iEJCsd3ZXnozlvLIJUjEjdcMEWKCiO720QDmN9+1PMT92H+O/FxWBlNZVTniVQGG1E6F25HusHX2K45WpEyqP7fQE4/dU4/OX4Qm6UEj16aSZScfS8QypXktG8E+Mnv4PP40QkFqMxxC+B6mQ6na/33B63HGtg+kT22JYCICDQwXgqI7NvRy0xYvXXnd3l8YYZ98O3uMrKcVdWokwxo0pLQ2mOLOhZvUW8vfWcmAUCT9DOe1sv4q7O89HHaSNZX0zyXJz+SjoZz2BY1iSCYR9bq+ciCGJSlS2O+LxJ/n1OGgFflwP3QRQKBW+88QZvvJHYdTFJkn8LkVige79M7p86iC/e3kD+bivmNBUjx7VApZHy4A1zOf/qjvQckIVWH18AG2RZDMm8gfn/MO2SCHIuafUWGqkZmpg5f9E8Zl0+EXPz5gR+WZjwnhwbt/OsX4w/LOaGR3+mePkvFKyahVxjoO+Z15HerFNcl3BVehrWPXviivfM/v1Qph6dW7bSZAJT7XV1l5+L+oxhuBcug2AQ5aA+iDPSaiYNxxNT61ZM+ON3HIWFWPbuQZuXhyYrE2V6GgrV0TudNwSpXEVq0/Y41sWvW89p26u2X3Q9kcnFdO6Vzoufnsb+3RacDh8t2pgwmBRo9XKcDh8LftvHJ6+vjzquU48sRl/5KjNfu4zq0nzOuP45Omm0XJZhZlppFRJBYJRKRjcxZIrDLPn545pjOwwYV9MjvKFIRBJa6QfxeM8trCv/mSLnFlpJetJC6Ipol8C+DkN45YbFPPPeSHKbRdccBorLKJ/0QG06vBVsH3yFe9FyzA/fcVyjRQBikwHTPTfh3bgN5/TZhAMBVCNOQdG3+1FHtFX6FHqPuYbl09+JGRt11eNojOmEgkF8djtiqRRpHRP9cDiM87c/a+87Iw0mjsSdI8cdciCI0pA4y1Hrjnzi+V9HIhLQiWKnYIme/QDWKg+fv70BnUHO0JFSfn7hQpq070frXqclPCYcCiIIAhnNE9fiHo5i11YWlMR+bnY5l7Apay09zhuDskdXJE1zjtgHIhwI4PgpYrIq692ZP91fx4iag0zf/yjXtZtWL38GkVxKRu+e2PPz446ndu9G+/POQ1i2DvcTr+FyuZF1bo/+2ouR5GVTHSrhwx1XsN0aWTQXEOjW/zzuHvgQYZuBVh1TkEiPbqFKJJehPWcMngReHdoLxh2V6BWkUsSGIzfY9KxN7Hng37Uvkh1wUMBXVMWI94O45y9Fd+m5Ry3gKzz7+GzH9Wy2/P15Eak4LeduhmTdgE4WK9AVah0Kdd2fFYM8m6vbfsH72y4hTO0iZxNND7qljKPQuYnr232LSBCxw7oQb9CBVppKUBtEoZRElUAeRBAgLUONMkUTN7MDIn3aXQFL3LES93YcgYqjEvASkYxmut4Y5bk4AhWEwyFa6wdjkGU1aivXunDYfAgiUGsavgiVJDEnjYBPkuS/gFojo0uvDLLzdOTvtVJW5GThrH015nZvP7uK6+7uyZDTm0ZF4muOlxo5I/Ne2mqHMKv4Bez+MlrrBnNazt2kqVrW7Gdu146zZ05HsDupFISEbqx+hYJAGEp8AR72QcduE2jb4yxGpZkxaxKLUU1WFmN//oEfTj2D4CFOq+qMDE776P2IAG8Alb4Ae9xe1jtcpMkk9NSpMUskyP+OTIl1WsQ6LbIWTRt03mOFLjcXXW4uWf9yeY5CrWPIBXezd/2CmL+xVK6k46DxR5TOKpWJSctUk5YZK+IqSl0x4h1g4+oqOnRpSlarHmxb+guDL7iLlBwDZ6ToGKRVoCgpw/XJNHzrNuHXqOg7oh+dJo9i9nfPMPjCe5DK6/YfqAuJSIY23ISyX4ejU58KRjHLS13MW1BG8d/GpLN+2MXlt3RF+vdkO+T14pj2U9w+yf4de/Dt3ofyOAt4ALHJiGpwPxQ9uxAOhRqtRESh0jJw7CR69TuP6rJ8Nq2eSUXJXoZceA+Zrbpizz/Atq+msWf6ryhSzPS4/VZSu3SOP/EMh2vSpiU5mXinjOPNsiuoKNgHREROP9/lTFA8XWdqapKGUVbiIuAPMez0NNb99gKEwxTuXEOv0VcnPEaflos5uyW6lCM0Vwv5mFf0esLxedXv0/PiecgVR9cSMOwPEKyImF6GsgwcCCQ2Vi1ybcYbdNYI+JDHS6iiCvfSVQTLKpD37IK0VXNs3mrmfvYk3W64il0//hxjridVq2l/6cWE3v+W4CHRb9/6zZTfMgX1Z4/xdv6F7LXXZs2FCbOm+msUaUou6v0aCnHjiBJJdgaGO2/A8ur7cLDsSCpFf+PlSPOOb2bXPxEbDQnH/ul5EKysowtNKETY40k8Xg+s3mJe3TSGIldtlpIv5GJ6/iNIRXJG5dyJOM7C2OGQi9V0NY/liV7b2Fw1C6uvmA7GUaQpWxMM+yh2bWNZ2eekKVtySsa1mBVNkIoVGM1Bzp/YIe47cfiZzdEZ615A94fr/n0kWsRqKAZ55nFv21lZ5mL10mIW/LYPsVhg5Fkt6NAtLa6nR5KGkxTwSf7v8HkiruXxXMaPF9ZqD8/eszju2LT3NtKldzrm1FhBY6ss5sdnrsbjtNF3+DjkOh2Vq7ZR1HwFplNykahqJxPKlBS8chGyHh3xrYqzgi4S4W/dgmJ7bf3iJpePTYBeF+KMOnSDSCIhq38/Lt+ygf2z/6Bq2zayBw0ko1cvdHkNq30v9fp5dG9RlOHZZ8VV3NUkg65aJbKT3LywsfG7XLhKSwm43Ug1GsxZrTjn7g/57Z27cVoi7dJMWS0Yd8tr6FPr7vN7JMydkbhN1J8zy7jo4ksp2rma6tL9pOS0QioSoSkqofzWKeCP/I2Dbg/Br2ag6tyOy+75CkXq0Ys8ryfAysXFlBTGd9Xdv9uK3xuqFfA2B+4lqxKezzVnIYpeXY+4nvdoEamPfEEjHv6CYlxf/4xn8QpkwIAh/VDfdC+ynCyqd+zkq/6D8FTWltDsnTGTrjffRP9HH0LxjwU5QSRCPfIUPAuXEb5pNC8Vn4vdX9uqL0yYJWUfY5TnMLbJQ4hFR9de72hw+Cuw+koocG5EK00hXdkagzwbsXDyTX8OLurmNJGz/K9Ij++Az0NV8R6adBzA/k1/xRwz4vJHMGe3POJoWzgcwh1I7DjuCdoJC4fPkDwcgkKOolcXfOs3Iyq1kSVpw3YWxN03Xdm6pq1hyOvFs2IN1U+/BqHIfTinz0b+xK188c71OKpLCXq9nPbtpyy966Gafu2pXbsy4InHkNhcuOOlrodD2FyFUeL9UJaVfcaYJg+iaCTPE5FKiXJwP+Sd2hEsLSccDiHJSENkNDTIvO5I8XsjQlIqj42Oy3t0BpFQ8/s9FOWIUzg061ycXkf2nVSCoDo68Vbh3R8l3g9l5oFn6J12EWZFXtzxwyEXq0lXtiI9u1XM2LDsmxiceV3M4oBEKmbQqCZoDXK+fn8T5SUudAY54y5uw6CRTVCp6372meVNEAuSGJM5ALXEhEZydNmM/xaVZS6euGMhRfm1xqnbN1XSqoOZyY/1S4r4RuDke4MlSXKE2KtKKdyxmjWzPyUcDtFtxMXktuuD1nT8I0T7dlkSjlmrvXhcsQ/zYMDPqt8+pGRPRIwv+/K1mrFNf35Pkw79Y1K45VoT2hsuofqupwlVRV9TMmki3yUwCT/gqaN/6d+IpVIMzZtjuP7aw+6bCHcwxCfFFTFu5SHghfwSXm2TR7osKeAP4igs5K8pj7D18y8I+f3IdDpGfP4exiGdGPvC+0gFGYqQBpVIj1rfcEO4wxEKhhL2iAdw2H1IFZHPoFJjACBod2B957Ma8X4o/g1bESqtUEerz/oiV0jIbqpNKOCbtNQjldd+lgSRCEEmI0z8NoSCUnHUzu8nCoHCEirufDTKNdw1cy6eZWsxTJ3C4vsfjBLvB1n3+ht0umZijIAHkLZoirxXVwo0pdgd5THjAH8WvUK/1IlkaJo22s/SECzeYj7fdT3rKqfXbFOKddzS/leahNoiCokQGw0IkpOjVj81Q4VcIcZqCaA1Z+B1RYT1gmnPM+6W10hr0o4N877G67KTktuaEVc8QnarHkeVKisVK+iTdhGbqn+PO97VfBZq6dEbFgqCgHJQH+xf/4xv+XpOueQaFlR+QIjYGuOxTR6uib77rC6qUeO/7wGkPg/SP2cTrqikoGgzjupI+7Vd6/7AUpFPz+fvQqNOQabUEHb62Prpl/TpOSD+/SgUWNwFCe83RBBPHOPMo0EkkyLKSEWS0fjP7kTYq0sp2bWe1bM/gTB0HXEx2a27R82LBIUc/U1XYn3joygRL2meh7JPN8rvfpzU56cgSU9FbDIibdU8bj2/+owRiOqI5teHYteWhGPuoPWYtkVLFNnX6uUMGtmEjt3S8PtDSCQCBrMybhZlzLHSNEbnPcj0/Y/EjF3Q4hX08qzYg+pJOBgiHArGtGI8FlRXuqksc1NW7CSnqZb1y0ujxPtBdm6uZMemCvoOaVigJ0ksSQGf5P8Ce1UpP750PQe2LKvZtm/DIjJbduWcuz9EZz6+qUVGc+LVR7FYQCqLnVA6rRWsmfVJwuO2LplO6vl3xmxX5ORhfGkKnrUbCK7cAmkG1KcO51dBxkJbfGfq9uqjWx0NuT2E7JEXqUitShhNtAWCrExwD4Ew7HJ5SJf9e9G7Ewl3RQW/X3k1+XP+qNnW983HWdd0PvPXXUggHMmkaKrpyTXtviJQrY20mpIIaPVylKqj/z2KxCL6nJLNioXxTaHadTJRvn8hakMqupTIxCPs9uBbn3jS5VmyCnmHxP1664tSJWX8pe1Z/VdxnPsWOG18y5roO4DIoEN9xjDsn38f93yaM4YftxrBY0nI78f5x8K4Lb9CVdV4SkrZ9dPPCY/f9fN0UjvH1k+LzUaMd17P2qr3Eh7rCdqxWKykKkOIj8KA70gIhPzMLXotSrwDuIM2Xtp8KlPMvxO+/2PUY0ehGTsKsfn4l0s0FINJwQ339eL7T7YwfvwNzPv4dgCCfi8/vnQ9LboO5dSrnyKzRWcUGiOaI+jqEI82hiGkKVpS5tkVtV0p1jMq53akosZpnSlOTyX1xUexvvUJgc+XctPFX/BhyY04A5HUerlIzYUtXyVbFfk8VlW4+enz3cz9bT9+Xwi1VspZ409j0DgJe/+KTvuvKNjB758/CIBEpmDs+c9hzz9AeFD8euywx4NOkp7wXgVEh21heaJjryrl51dvZv/G2mzA3Wvnkt2mFxPufLdGxAuhEP5de0l99Uk8y1YTsjmQtW1J2Oej6pnXCbvcOH78Df3ECxEb9Zgeuh3LS+/iXfN35p9YjOr0YZFa/qPMJjDLmyIWJEhEihixLhHkyBpg2NrYGI8gqqyQaBiWdRM56k78vO9hKjx7yVF34uxmT5Gn7o74CNrIhRxOAsVlOGfMIVhpQdm/J/IenZGkHZtoflmxg2fuWUzh/ohgP39iB/76M7G57h/T99C1TwYKZXJudzQkBXySEx53ZSUBtxuJUlnj3NlQ8rcuixLvBynetY496+bTdfiFR3ubDSK3mQ6lWoLbGRuVHDAiF32cuqlwOITXnXh1+Z8O8oeiyMhCcXoWoVEjaty+m9ucEEc868Ri2qqP3GQmUFSC9aOv8SxeAeEw8p5d0F9zMZKcrBgX7UCUXUws9kDd/Xf/n3CWlESJ97zTR1LYpYg/ql6L2m+fYxUvbhjBBNl0XrhtO4IIeg7I5tIbO5OedfTpnm27pJKSrqKiNPqzI5YIjJ6QxrwPfuaCB75Aa/p7UUwQQCatrev8B0IjpopnN9Fy68N9eO+FNbgcketp9TJufqA3aZnRP7sgFqM+bSjuxSsI7IuebKhGj0CclXjyfjIRstjwLluTeNzpjOtMf5B/1g0fitigJzvcDRIEKtUSE1XFISqlbtLSj2/JktVXzNwEtdu+kJt9os00M+hwTPsZ38ZtmKbcVmet74mAVCamW59MsnO1WCrK6DLiCtb/8TEQ6aiwd+NiOg87H11KDjJF432vTPJc7uj8J3OLXmNxyYcEQl66pZzNmU2mkKJo3mjXEQQBaV42xgduJWx3YBILPNRlNfZQOaFwEJ0sE700A5HLj7Wsig9e38KqJbULdk67ny8+2YXhzi7oTYnLh9SGVDJ69UQx5X5UOXn4/jbPiyIURuNRkaPuTIFzQ8xwz5Rz0UpP7mdEwbYVUeL9IIXbV7Jn3QK6DDs/skEuQ9DrcHz3K4GCIgSlEveCJYSstRFW99zFKMePQp6WgSQtBeP9txCy2gi7vZFFfJM+br94AE/Ajs1fij/kQwD8IQ8qiRG9LCNGkJt83bhGtwanw4chQ8zOwC/MqXycYNjPgIwr0EkTZ3MFKqrw796Hd+U6xKkpKAb0RGw2NYor/tGgkabQPWU8LXUDCYR9yETKiCHxERByunD+Nhfb+1/WbPOuWIvIbCR16sNIGvm9Zrd6ee3x5TXi/SB1GY+HwwltmZI0gKSAT3LC4qmupnj5CpY8+DDVO3diatOGAU8+RnqvnigMhvqfx2Vn9e8fJxxf/fvHMX1BjzXmVBUPTD2FJ+9YiPuQdPnmbQ1ccHUn5IrYr6ZcoaFpxwHsi/PCBWjdO7ET8UEObdXVRqVgUk4qHxdXYg9GJvAtlHJuyU0j9Qij3oHScspvf4SQxYrIZERk0OLbtpPyW6eQ9ubTSLKiX64qkYgMmYQSX/xc/jZ1LCQE/EEsVR7sVh8SqQidUY7e8O++iI8llt2R2nO5Xk+TU0+l7QNX8Zrt3Lj7Vnr3EzAdQKOT4bD5WLmokL07qnns9aGY045uYp+SpuLhV4bwzYebWDL3AMFAmDYdzVx0bWuUkmIue+IndOZah1uRXotqxCm4Zv4Z93zKAb2O6n6izqWS0mdwDq07pGCzeAiHwmi1YowmBVJ5bCRDnGrG/OS9+LftwvXHQgSVEvXokUhyMo5Lh4Ogz4ervBzCoEwxI0kwwT0qxOJIOUAChNJKmo8+gz2/zog73nLc2DpPn6ZsjUmeR5U31uV7iOlO5r1vpdntx38hLhj24wnGpnAepCJwgBY6LcGiUnybtxMoKT/hBTxE+o7ntTCQ1URHszZ302/s1ZTu34xUpiQ1tw0aYxoSWeN/jsyKPM5u+hQjsm8DwqgkJuTixvVpOIhYo4a/27iZATNNa8aCVRas736BfcipUeL9UD56ezPPvDqBpTPeiqsU+p91M4asphizmxFyuQjfdAXWNz6O2kfSLA9DWmtuVv/E21vPY5+j1i+js2kM57V4od696E9EvC47q377KOH46t8/onWvUSi1RsQ6LaqhA3B89RP+XfsSHlPo2oTZB3pZBmKtpl4GnFZfCb/se4w2xiFsrJrB8rKvCIb9SAQZAzOuYkzeQzXma/l7rDxzz19UlkXKuKQyEeOvHMudw0ezzvEVI3NuTxiBD5SWU3HvUwSLah3ybR9Nw3jvzSj69kCkaJwskqMhnoN+QwlWWaLE+0FCldVYP5qGcfJ1MQsWvpAHu6+MUDiAXKxp0H3YLF52bK6K2rZhVSm9BmVTuH9b3GOGn9m8UTIC/99JCvgkJyQBr5etn33BvFtvr9lWsnIl3486nWGvv0ru0CFINWrUWVmIJYf5GIdDhIIJir0hMnaclwNFYoEWbUw8/9Eo8vdYqSp307SVgdQMNYYEPWUVGj3DL3+Ij+45I+bnSW/WkdTctvW+fjAQIFBdSmeXm5dNarwqFT6pDL1Mhu4I60HDoRDuxSsQjDpE959HsbKQisB+cqR9MJUrcc5agPbi8YgOWRwwSiVcnZXKE/tiJ2I9tCpM0vh/W7vVy+I5+Ux7f1NN+5YmLfXcMqUvOU2Pf2u544EqLZWejz6NpvcI/lrhIk+jwGlJ7Phb6tuKMaUTDtv/2DvL6KiuLgw/c8ddMvEEd3d3K16klCp1L6XuRp16oaXupS1QgeIUK4Xi7g4JcZvJuH8/piRMZxIsSL/mWatrlXPu3LmTZO4979l7vzucWl+Y5+TA7uJzFvAACclqbnuoLWNvbUYoCCq1FI1OBkRHvQSZDO1VI/Bs3UkgOy9iTnfz1Yjjzqz/+KkQiwWU2CjcsoKNr7+Fq7CQGv370f7Rh9DXrRt1v5CYTUi6dUDRqQ0IwgUzrSs9dozN705h9zffEgoEaDD2Sjo89gj6OrWr9H0kJgPqof3w7toXc16RmkyPN17j+Mo/8ZZG1vQ2ueF6tOmVGyGaFenc22ARXx+5jmP2TeH3FMnpETcB1bEBZB3NQS6/8EsNmaAkTl6TIs+xmPO1ZC0J5Cwr+7dn607kjaPNqy5VJBIBrcEIBiNxqRemn7NEkGKUXzxH9JA/gGPeEtwbt5DftFeFxzntfnwOOSPuncJvU++PeF427TaCBh0Hlm8wqlSo+nZH3rIprpVrCZRYUXZph7RWOmKzCTMmJjSbT6kvH5ffikZqRi0x4Q06KXAdQSHRopX++4zGQqEggUA4S8mYXBuxWEpx7hGC/vBYwO8jeFJmjiQ+DlXfbrgqaHUn9GrN76Uf01Q+iG7JN5/WNfiDXpZmTUEqVrChYDqbC38pnwt5WZHzEZ6Ak2vqTcFZLOHFB/6g1BL25+l1eSKdLpex1f0h8/MzaGzshz/kJRQKRZU+Bd0eSr+eESHe//4hUDLpfRI/fxsh5cJ5Ibn+Dpgoz0NZkXtjtBt+2dyq9QRvvSZCwBe7M5mf+Qqr877CF3STpm7OVXUnU0vTDoXk1Bswsdrn7dlWyNCxDUlO05BzPDJrtF4TE41a/Pu+L5ci1QK+motCkTuDQ6Vr2GtZRqKyPq3NIzDK0pCKwzcWR04Ofz7+ZMzX/vnYEwz8+gsW33I7fae+T+1hQ5BrK94JV6j1tOw9lqx9sR2nW/S+EqWmakXE6SCIRcQnqYlPOv3UUnNqA256bT5Lv3mBoztXI1OoaTNgHO2H3ILWVHFqlMdlD6ezq7QE/D5smUfwH8siuOgvQoUlCPVqoB3UA3FaCpjObhc45HLjPXQE5+MDeDd7NE6rpWwuUVmf8b2/Q+N0giyyT3AjtYIX6qTwVU4hh11etGKBYfF6+hh16CvYTNixKZ+vpmyNGDt20Mrz9y3ntU/7Y048P5Ghi4m8Zn0OKoMsevUAAG0H10Uu1lRo2hMnrYe1ONKMcOu6HDr1qhpnerlCQnyMTJFYSBLNmF9/JhzpXrkWsdGA6rKeSBLjETRVm1rtKizkjwcfYe/3P5SN7friS/b98CPXbFyLoVF9QgSj6nZFp9oIrEJKMzKZ3qNPRG/qHZ98ysFfZ3HN+r/Q16pVpe8na9oQecfWeNZtiRhXdG6LrG5tlPEmrtu8gW0ffsSR+QtRGI20e/QhUjp3qrB/8cko/em0z/2YIfUC+HEjeLX8NcvO7KiBrK8AAQAASURBVEWHufvJ9hfFcdggT+GK2pP4eO9VUXPxijrEFxsJWMs3LIQLkHFRzbkRKLFg/3UBIa8PrbbyCJ5Ipade7f7cOWUV2Qe24HXaSW3UDo0hAZUuMtvuhE+L9PorYp5LK4tHKwv7CRS5j/Hz0cdZm/ct/pCXWpp2XF1vCunqlhe1/vpMUaj1dB1xH4I7RPG2XfhdbhIub03msU38NX8qzXpegUpbvi4S1Cok9Woj79Aaz/rI+4hgNuEb1IzNx+8j07ODFnFD0MlOnapt9eawNOs97mg8nSm7hsU8Zm3+dwyt+TS5x7Rl4r1DHzO1h+7mg6y7yo7bUbKAeRkv8VjLVaSomwAQsJQS8vnA68P1x5rYFxEM4dm+Jyo78HxQ7POz2+Hi96JwZtBlcToaqRUVBirOCk8lBsTBYIQJocWTzeRdQyNKRI47dvDW9j483GI5DQ09T/l2aq0MQSwiGIgMgn38+kZuf7gtBXkOVi46hiCIGDCiHi3aJ1Y70FcR1QK+mgtOnnM/r2/vhdVbHnX95egTjG86h0aGPkgEGc68fPyu2G7XPoeDUCiEx2pl/rXXM/bPFSS2a1tp+mndNn0wpzWg8Pj+iHFDYk0adx560VpFncBZWoS1MIuDG39HLJFRv11/tKZkFJpIsSuRyUmq05zRD38WbocnEqHWmxFLYi9mbMV5HN+/kc0LvyIYCNCq3zXUbtQZ/4r1eH48KWU2IwvvnxvQPn8/fq0eifQs0skkEvzD2/B+zkicfkvEVJ7rAN9bnuT2+O/5556u3O+jod3GS1IIanX4jHrUMiniCgzESopc/PhpjLZ4gM3q5cDuov9LAV/qEFg0t9w8bvVsO92uvoelhZOijtVJE5DZalFqORAxHquvOxAzalHVSOLjkMTHoeja/rx+32yZxyPEO4Tr3Tt+8AKZ0p3M3PscvqCLrok3UUfX6YJHFEOhEId+mxMh3k/gKihg5+df0um5Z06dWXQGSBLjMdxzM/6RubiWrQJEKPt2Q5KSWGZsZKhbh26vvET7Rx9GkEpRGE9/UzM+Xk2LFg34aNIG9m0vAvJQqiXcML4VbTpfWIPQk2liHMCNDT7n5yOPY/MVIEJEU/0AxqqfRfL5CiQDeoLHg3fTdhStml6066zmNPH5CTnC3hs6rwWjWUFJYXQf7ebtEjAk6pBrZci1OoyJNavk7Us8Wby9YwB5rvJ1xFH7RiZt7caTrddSS9uuSt7nQuApLcW55QjL7h4f4YHR4JqxDL3lLWq27R51n5aYDBjuvxXL9g2E5q4h5PYS6tYId6d0Psi/lkDIj91XGLMlWiy8ASeeoANv0EmI2FmQIYI4fMXkZZU/n/qONfJh3vioYx3+Er47cBf31f4Z9mVS+uV0fBnHMT3zAPgr9vI4Ybh7Piny+XnjaC4HXOUCe6fDRQOlnEdqJmGSVc39Xt62JXw5PeacrEkDUCmwHDzEoXnzEYYmx/R3CBHix0P382DzxWUbVwD27ByK9+7l6KLFqBMTqT10MFpzEr0G1WLZ3Mj2sqUWD38ty+S2h9vQvX9NRCLR31l6EHS4CPn9CGrVv6YDyKVItYCv5oLi8JXwzYE7IsQ7QCDkZ+ru0bzQbhdmRS2EU+xICuLyL/26l16h66svkdCyZYXCQBeXwtXP/MDuv35j29IfCIWCtOh1JU17jEJnvngpgQAOSwGLv3yW3atmlY0tn/YKXUaOp+Pld0Xsgp9AodFHift/YivOY9a7d5OxqzzlLWP3Gu58di6eGfOjX+Dz4/r4B2QvPoQk6cwX3YJcRqnZR2l+fsz53aVLcYgsaCiP8AeKiin99iecv68EfwCRSonmymEwqA8YYn8+vy9IXnbs1l8AB3YX07n3/1+LklW/Rwq+TX/m0WnQ1bQ35rCx5NuyBZBZUZur46fz5dM5SGUCPm94cSYSEdW6xZ9XgGfrTjwbtyNOTUbVpyviBPN5rQc835tlRxZEG1J1+fQ1NrVayeacX8vGthfPo4a6FeObzcEor5qshNPBW1rKvh9nVDh/4KdfaHXv3agTq9ZsSJJoRpJoRtG6WYXHiGUyVAlnl4GTkqblkZe7Umrx4PMG0WilGOOUiCUXb3NULTXSJfEGmhj74/KXIglKEK8+gKAMccwk5/Di31DFxdHyvtvxScTVC6JLHJFMimA2ESwsRvzjDzz6yL288spObFZv2TGpNbXc/nA71Nqq751+zL4pQryfIEiAmYcf5e4mP6OWXvhsvrPBnnmcpXfeEzW+//vp1L7sMkqsakrtVvRGBTpD+fNAEmfiSFMr2YkeZCjY6f2CfZl/lM031PdCKal8bXICmViFXKxBKlTu2aAQa0mvHT5nXIKSArZXuEmgEKnxrd5C6XtflI35j+cgqZUeZVZ6AnmLxqd1vefCNpszQryfYL/Lww6Hi56yqvFTEMfHoejaAffq9ZETUgn6CbeSt2MHP/W7jOSOHQj0alLheTIdW/EE7WgJC3jb8ePMGjaCgq3lKfp/PPwoA7/5irE3DEaplLB49iF83iASqUDvwbUYfUMTVOry72HAUorvwGFsM+cQtNlRtG+FelAfxInxFz2I9m+k+nlVzQXF7itkn3VFzDlv0EmOcw9mRS1UCQmoEhNx5uVFHadJTUVZqxYynQ5vaSnFe/eRvXoNqvh4tGkVL8R15hQ6Dr2dZj1GIwIEiQSfx4WjtAi17tz72J4prkAQeyBA9o7VEeIdQGtKIufwdmxF2TEFfEXYrB78viAqrZSs/ZsixPuJ8waOZUWkUZ2MPyMLnKfuAV8R9lDFNdkQNks5QaDURsnbH+PZWL4DHHK6sH01A3x+tFePQBSjf6lYImAwKbAUR0deANLr/H+mwnrckQuWUAimPn2AQVffwT09J6Aw21DJdCiJJ2O7wPX3pOK0+1BrZRw7ZCE5XUtcfHnqmi8zm8KHJhI8KY3YPn0WpicnIO/YGkFW9Qvg84XP48JhKcDncZE2tA91N4/g0C+zIRRClZCA0D6OzcW/Rr0uw7GV9fk/0j/tQQTRhVlAiCQSpJWUDUjVaoSTou/+oBdf0INcrEI4i5ZCFxKtTo5Wd/HNoE5GEIkxydPh78uy1BGY0bsf9uzssmP2/PAjnZ55mjYPTjgjg9RqLixCnBHdjVdiefMj/Fm5qD6dygsPXEeeW05+kZeazVNITNNW2qb1ZOz+AK5gEAEROomA9BQiYlvRnArn9lv/wBOwn7OAt/sK8QbdCIjRy5LKMqOCfn/EfeFM8QdDFPn87HK4yPP6qK0z0HfxQtaOuxFHbmRt+MbX3yLuoQZ8+1UG9Robuf/5zhGlfnV0nfjx8P1RgRixSMrwWs+jEJ9etxO9LJl+KfeR49xDTU0bjtmju2XU1nZAK01AkaomKVVDKBTCFyp/9osQaK4fRrqkK25RCZ1UfbE9Py3iHI55S9HdMIaS196P8juStW6GOLFq2i1WhN0fYHFRaYXzi4qstNOqUFdBNFps0GG49ybcHVph/2luuOVfyyborhuNW4A5V4wl4PHgtdkxiiouj5ILaoS/JaLf42HD629FiHcAQiEWjruR63dsZviVZoZcWQ+HPYBcKcZgUkR4nwRK7ZR+MwPnvHIzW/vhDBxzlxD/7gtIa6T+49QhrN4cggSQCsp/pc/E+aZawFdzQfGHKheHLn+4V7EmJYWhM37g5wGDCJxU0yNRKOgy7Vt+0MczZPFCFnbtjqF+PayHDuMsKKhUwEM4+ieWhMXtiu9foyjrEHEpdeh5zWOkNWiH8gzEckXYrB683gASiYDeGHtn2eYPsKjICk4r1t8+LBtX6eLoP/ZZgoVOclet5dC3vyC9UoI2LQ2puuJFf2mJhz3bC5g1bS/WYjdX3FSXnA3RDrOnkyp9LjuhieqK+3krxFpUJ+3MB0tteLbtiXms/ae5qAb0QpIU/WA1xikYOa4RX767NWpOrhDTtNW5O7leinTpnc7vsw9HjAUDIeZ9l0VhZip3Pd4FpUpKfo6DmV/+xZH9lrLjmrSKp1u/GsiV4Vt+wGbHMvmzCPEePmGI4knvk/jZWwhJ/46fo604jz9nvsX2ZdMJ+L1IFSpaX3YtfYcNZOlNd1FjcD82hKLF+wn+yPmITonXoZddGBMjmVpNmwnjObZoccz5NvePRxkXh9tvI991kLX500gXWpAsboBGZkZrSEKuOPd2gP9FfA4Hfz3/QoR4P8HaF1+iwdgrqgX8JYxIJELRsQ36O8dR+s1MAsez4bXXSe/Yhub33ogk4fSEmDcYJMPt5ZucInY53CgEEQNMOoaaDcRVkspskKVUOKeWmsAXgLNsAOD22zhm38zMww+TYd+CQZ7C4PQnaCbvz+43vqBkz17S+/Sm7vBh6GrVPKOSJ38oxF6nm5eO5OA7ScAmptfjvt8XsqRrjwgDS2deHrU0YTF5cE8J772wlkdf6VYWiY9T1OCxln/y8+HH2FI0iyAB6mo7cXW9KSQqG5z2dUkEGX1Sx7Mo801G1XqFHw8/QI6zfE2QomrC7Y1+RCszgxmefLM7H03aQKq0DQCpypaMNn7FxsUO1mx0oNHJaHBFbVKvuYrA51+VifVAdi6uVesxPT0B209z8e05iKDVoB4xMBwBriDbr6oIAYEKSgQARCHA66U0uxBCIWQ67RmVMP0TscmAemBvFB3bQCCASK1CUCoo3rylLCiWv3Ur7XkBEULMZr49k+8o8zFw5eez8/Mvoo4Jf7gQ+3/9maO+nXQZfR/pjTogkUVv4gYLiyLEOwASMZLkBOzzlqAfdyWCOrzxVurNY0PBDBZmTsLizaaGpjVj6rxJTU1blJL/zwDN2VAt4Ku5oKgkRgyyFCze6AUUQLqmJT6PG0t+BpnFO7lq60Z2TZ+JbctWdG3bkjhqFD9IlOy2uzEZ46l1+XCaXnUlS++5j8bXXXPK9/f7POxc+QuLP3+qbCzv6C5mvDKO/je9QOsB45DGuPmcDg67lyP7Svj+kx0cP1pKfJKaK25sQtM2Cej0kec87PLwQ14JN2sh1x6OWktkCobf8g4rb7mfkv3laXrrnn+JQd99TVL79gQDfhQmE6r4k+qSbF5mfLkzQtxlHLQgCUXflO0leQSTTSAIYUOTfyCpXQPJOTzMdNJEWseNYEvRrKi5YTWexSBLIVBsIVBQhD8rB9Pj9xIosWL79qcIMRnyeAk6onvUQ3gR17lXOjkZdhbNOsiJj6k3ynnkla4RUeb/J1Jq6mjSyszurYUR43KlmDE3N0WhlGC1uHnnuTUR4h1g99YCPn97M+Of6YhaKyNYase7PfbmCT4/vsMZSP4FAt5ls7Dw08fZv35h2ZjP7WT9wk9p23ccjcZdiyAT4xNVvHHoC7oJxfiunE8S27ah4dgr2Tc9MpW+Zv9+pPftgzfgYmPhT2zJ/YXe4ttY980HLDu8HUEsoVGXYfS+5nEMCTUu6DX/P+AqKmL/zJ8qnD80ey7mptW18JcyYp0W9bABKLq0J+RwgEyKoNedVssyCIv3oy4vTx/K4kRVtDsY4rdCK9vtLp6qnVyhqVj7+LHMyXgh5lxf/W2EvliM/5rRSOLPLKMvFAqxx7KUD3aPLBsr9mTy3cG76agdSz11LQ79NodDv81hzfMvMPbP5ZibVVwK80+KfX5ePRop3gHyvH5+UuroNOE+tr/4Utl4YqfOHMkoL0s4sKuYUos7IpU+QVmXmxp+yRj/m4RCQZQS/Vn1LtfLkhhe63ls3kLuavwTTr8Fizcbs6IWRlkaenn5xmpiioYHX+yC01dC/9BDNA7ewNv3HcTlKM9O27WlgL6XpTLsytEEp5d/191/rsO75wDmV55ApFQgEgQEox6R+PxnNWnEAr2NOg67CqPmtGKBe8R+1jz8KLu/+hq/y0Var570eOtN4ps1QXwOmXBiY+RaLujzlf8jFGLPK59yw3Mf8E3BvQQp9wiorelI/7QHkQh/16wHAvidsddjAJ6iEoKaAD++fC23v7OcuJTorhiudZHZFcLoEXiat2Xz7vC6r1WOC1OiGBR2Zhx+mLX535Ude8y+mTe39+GeJr/S2jzitD///zvVAr6aC4pBlsLVdSfz4Z5ot9fOCePQShLI2PUX018ZR1rDdhxqOYj9191M3DgoDMIeh5vQ3y1hlnmDvPT6JA6++RYhvx/laey+20vyWf7dSzHnlk97lQYdBmJIOPP66UAgyKbVOXzwSnnd0fGjpbz7/FpG39CE4Vc3RPF39NPhD/Bzfli0HwzJqNmiB9Yl02jSaTh7pn4VId4h3J5twfU3cvnsX5g19HLiW7ViwOcfE9+iBYJEgqXIFRWZ3bG5lFGjR3NsZ3TLl/Urf6D7zWNwfBZpdCKSyzDcf+s57UZrpCaurTeVeEVdVuR8iDfoRCuNZ3jN52lvHksor4SiZybhzyzfwJHUTMP46N2UvPZ+uZmMSIRIIiZgs8dcmOmNCsbe2oxBV9SnINeBUiXBGKfEaFYiCOfXjO1iYTApuO+ZTqxbeZwFPx3E5fTRslMSva6sz084aVQEjUtDHN4Xu4xhy7pcrBZPuDbUX7nJUMgVuzzhbAmUWPFn5eBcthqRIKDq1x1xcsI591p3lBZGiPeT2frHj1zz8DR2vf85HbVXsqM4hu8D0C7+SjQXOD1PlZBA78nv0Gr8Pez8/EuCfj/NbroBU+NGqJOSKHAd5tejT3GL7nNmPXcboWB4cRUM+Nn9569k7dvIuBdnoTNXHBGsJgahEMFK/vYD7tjGqf91AiVWAiUWgpZSxCYDgkGP2HDxImEiiRhJohk48+9toc/PtNwiYlmaHXV7yXB7KxTwRnk6N9T/lG8O3B5hutZA042O3gG4F3yCLD0dzciBZxQht3iz+f7gvTHn1tmm033kPLZOfJfEtm1pddONuI8dx5OQiPw0Mw6y3F7cFZTNbXV5GXH58DIBLxKLaTLhUV57OzI93mHzRb1WIdGikJx77bZCrEGhPL0NGK1OjpYkBkie5sOXt0WI9xMsXZRFv1dbIZfOAt9J8z4/IpXyjDdYzhWRSEQHnYoFRVKyPZE/xwmyIIsGX07JvvI2n8dX/MGPnbpwzfq1JLRqUWXXoUpMQKJQ4HeHn++Hp/9GKBjkoafmcFS0BY/WS2NTfxIUdSM2TmQaDYlt25K3aVPM8yZ168TWOb8T9PvYufJnel71aIyjyr8Pwo3jWHTcyILHy7sZfPfNIYaOrU+f62UR4v1kvj80ntra9hguYivLS4lqAV/NBUUkEtHY2J+Hmi9h5pFHwqlishQGpT9O+/grCZZ6mP3eeELBAFK5EgtidjliiwlXMITH4WT7Rx8zdMYPaJIrNl4LBQIEikqQFzm5cfxXFJXm8Me89ynKOlh2jN/rwmEtOCsBX1Lo5qspW2LO/frdHnoOrFn2gPKFQhT+/VD5y+FlwKDb2L/qV+o37c3CR2JnEYQCAQq378DUuDEFW7cyvVsvrt+2GWP9euzZHr2re/xoKQpDCxJrNSPv6M6IuYyDG5Bd9xTKZk2w/zSfQEEhsqYNUQ/th6QKasEM8mRG1nqZvqnj8QU9yMQqDLIUQlY7RS+9FSHeAfzHjlP6zUw0owdT+lU4Iilv0xzn4j/wHjiM4a4bkNRIjdopV6mlqNRSklIvbjpxwOvFWVAIhFCazUjkVVsDHCi24M/Kwb1lJ1K9jr7d2tC6ew/2OTxsC3h5yVGCNxRirc3J/d7KW7K5nOHFg6BRIU6MJ5BXEPM4af2q60UeKLZQ8t6neNaW78A75ixG2a8H+tuuOacNI1tRToVzAb8XqUlH3w+m4JBaSS9sSaYjsoZPK42nT8r4skjDhUSVkIAqIYHUrl2iSluynbvpor+ezdM/KxPvJ2PNzyTrwOZqAX+GyPV6avTrS8aSpTHn6wwbeoGv6NLHn5tP8cS38R0+VjYma9IA4xPjyzoY/FtwB4IUev0VrikA1lkdtNLG7mKilGhpZxxN3drN2WVfiiNkoYm8B/ojfvyTv4RQCOeCpaj6dDmj+5rTb6HEm1XhfG7gACNnTEdjc+Fftobg/NXYVm1CdPPVSGqkIpzimWMLVJxhFAL8f4src7NmtHnxDWYtdEQJ4xMO4pcKPqeU7etjG+YCbN1RSrc6NfHtOxQekEoxPfMAYtPFMRk0y6Q8VzuFv6x2lhWXAiKGmvWoVi6PEO8nCPp8rH76WQZP+xq5vmpS/FWJiXR9+UX+eOiRsrEjM+dyZOZcBv/4PfVHjUAcw3dIaTbT6723md69V5SHQHzr1njlPly2YgDyjuwi4PdFdUZSdmyN7avpCHFGco21WPBR5LoUYO2KLGpfXlzh9Zd4juP0W6oF/N9UC/hqLhgnjKbslnwMknjGp/+KSCsDEWVmLTnW7WU3gvyju2kr8rKkgvM1Ucpwbt3KNRvWYmrUsEKDl6DTiXvDNqxTviiL8BoT4xlzx6vMm/cmmfvWlR0rFs4uncpW6om5Qw3hOuWCXCeJKWGhqRQL1FcqyPPa8YfgS4+Cm16YjSwnLzLF6R94LBYS27RGptVSuHMn2z/5lG6vvIRUWkHNugh6XvMYeYd3sGfNXELBAPXbX0Zao/a4/C7iGjdA+mhNQl4fgkKB6Bx6kQZs9nB7H5EIQatBqlISJ45s3eOzWPEdDLcaEQx6JB2bEZKKCW07iG/fIXTXjgLCafyakYMofmUyIaeLggnPED/1NaRpF68dVUVYjx1jy3tT2PPd9xAK0fDqq2g5fjwBbSJqjRS15twWPf7CYopfeLt8EQJIFi1n13OP8EGpJfLYEMh1FfdGFgmUXY84zoRh/M0UPfM6hEKIUxIRicX4s/NQ9e+BYKy6mkDP9t0R4v0EriUrw673bc8+wqDUGCqdlynVyDQaZGi4r9lc1uZ/zx85H+EPemhnHkPf1PuIV1bdZsXZ8s9oXSDoo6asNUv2/Fjha/avX0Tjzv9dwRmwlhIoKMa7ax+CVo2scX0EkxFBXvF3Tm4w0PPtN/ihY9eoNqV1R16OoV506ud/mYDFSvFL70WIdwDv7v1Y3vkY45MTTjt1/VLAGQxiCwRQCgLOGCVkAPpTGInJQ0pkU5fQstSOSK7Cd/RX/CeXewUCVFLuHBOJqPKe9g10nZFsWY5n/dayMc/mnRRsfRrz688gbx7toh7y+wn5/IjkMmoqKv5OGCRiDOm1GbFjO0XZQT758lhUl5cW7RLRGSveJPB6/FiKPThsXuQKMTqjAs156AIQQaDitnAAYrUKzZhhuP/aiLReLZSd2yJOMCMSXzy3c7NMwlCznp5GDSBC6gmy/KefKzw+Y8kSPNbSKhPwUqWSJjeOw9igPqufeR7LwYOYGjak68svkNShfUzxfoKE1q24atUfLJ/wIHkbNyJVq2l0wzXUvmo4v35c3tIvtUHbmG2NBbMJ1bD+eAUZ8xZHm1MDeD0BRKcIQohFl9ZG0sWkWsBXc0FwlhazefE3rJr5DgF/uLZKY0xk9COfkVKvVdkC9uRaVLslH0X+IWrpG3LUE7kbLAZuSI6jRvJlyDSVLyB8R49T8srkiLFAXgGB1z5h4POP8emroyEUQmNMRO4V8B3PQVDIEEzG0zZ0E5/ioSCVlc/LBYHRiQbWWO0EgEMeP5P8ah5Ork1c06YU7doV8xx1hgxGfjSbUFcXoga1KD5+HK/NRsPmZkSiyI3RxFQ1vtJ9zHj3XlLqtaJhx0EIgpgj2//kr18m06TbCIbc9SYyhRrOIWIc8vvxHT2OdepXeHftA0GEolNb9LdeiyQ10hQs5HKDICC+bxRF9eEP9zQ8ISedBoygjn0wyJSYnr6fgKWU4knvE3KGF9ghjxf7T3PQ33VjpYvzC01pRgYzuvfGllnemmbrlPc5+PMvdJ02l09+K+L62xuhcOehTkhAk3JmGxAhnw/HL/MjxDtAqGNblthiR5AOigM0bZfAro3RkYnOvdMjahj9jRugn/wi7qIicjZsIOD1knrnNchTU6tsUR6w2bH/Ejt1HcD+y3xkTRogKM/O+UltSMCYVIuS3KNRczWbdUV1UncJozyNy9IepkviOEKhIBqp+aJE3k+HNE1z9lmWodKZsBXnxjxGZ770NrQuFIHiEkre/QzPyXWVgoDx8XtRdGxTaRtEU+PGXLdlI+tfm0TG4iXIjUZ6THqV1BYt8K5cR8mR48ibNUDWvHGVZCT9mwn+3fYpFp7NO8Mp9f8iAS8ViTjgcNPTqGFBBa7gXQ2VCwhBqUA1oFfY0TwGyr7dEXRnllaukZppoOvB/tKVUXMqsQG9W4vlJPFeRjCE5f0vMU96qiziH3Q68ecU4PhtEYHcfGStmmIY3I/2WhUbbNF1zKMSDHxSZONwSM4DjYzUaGglP9dBKBhuPdqmczK3PNimwu4SlmI382bsZ8HPB8paljZtHc+dj7UnIbnyn+W5IC/Jp1X7BLZUEIVv3SUFVZ3GqLp3PG/XcDYIIhH6v4NNNocfmanie4zCaAzvvFchSpOJOkOHkNSpIwG3G4lCgdJ86kwaqUpFSpfOjFowB3thHvkZe9izeT4zJt9aliUmkSlp0nU4cCJgl4/P40amVKMxJqK7/gpK82yUboy9xi21eND5GyETlHiD0eVMDfW90Uj/2/fkk6kW8NVcEI7u+JM/fpgUMWYvyWPa81dy+7vLMSaGo7VqvRm5SofHGX64rpgynpufmc4arYnlDj/uYIimagU3JJtJV0iRnUJgB+0OSr+uoOey14do0z5qNe3K8f0bGXHzG3gmf4fyxqsofOdjdLdeg6Jjm9NaoOgMclJrask6ZouaU2ukxMVHpuQlyaQ8VyeFqcfzyfX6sQWCzBZUXDP5HX7td1lUmlJaj+7Ic4rwTJtVNmbo2BpJMITBpGDcva34esrWsrm6jYxk7f4FgOyDW8k+uDXifIc2L8PtKA0L+HPAn51Hwf3Pgs+HoNchkstwr92Md9d+4qe8FLEAFmk1iMeP5LeU6azPnlk2votFpCgbMyH9NzxPf0TIHS1OPVt2EnI64RIR8KFgkAMzf44Q7yewZ2eTv+Q3gsEuvPTYeh64N4F5V/Rm5MK5GOvVO+33CFhKccyPleoboqK/+tlOO48/0ArF1B1s/iuHUCj8/O/atwbX3tkClVpKKBQi0+Plr9wCavy+mHX33U/opGhU89tvpeuLE8+6H3jkhwiUbcTEIuR0QSXpnadCa0rkyie/5fuJY7EVlZdmmNMbMPSed6Ii9IJIuGBu8+eCTpqISC2l2cArWfP95JjHNOsx+gJf1aVBKBjEuWQVeDwYHrwDQaXEn5OHY/5SSl6dQsJnbyKkVVxaIJZIMDVsQN8PpuCxWBDLZAg5BRTc9QT8nQHlXLAUQafF/OazSGtW3tnk/5mg3VHpfGXf7UsRrURMU40KRLDX6eaIyxsxf2NyHHGn0apN1rwx0rq18B06GjEuTjCj6t/jjKO8aqmJ6+pO5c2dfSj1lQtSATH311iBb/2RCl/rP5pJyOECg56g24Nr5Tos73xSNu/ZugvRL/O57fN3qKWUM6/QijMYJFkm5dokE3ucbvb93Tr2HWsJA8bV5ZHrG+F3+knVKzCZlKg0sSOzfl+A3387xG8/RKaA79pSwOtPrOKpt3qcdku/MyFgteH9ahrXjL2OfbtLcNojMxcvG1YDo+nSWCtUhkIlod5V17Dj/Qru8XfdiSbl/DyvVKch2mOhNJsRqWTk5u7m6K7VZeJdn5DOyAc+RB+fhq04lz9+fJ0df/xE0O9DptTQecQ9tO53Lbo6ybRoX8ixQ9aY58/dJ+XOzjP5YPcIAqHywJ1elsy4+h+jlhrO6rr/H6kW8NWcd+yWAv748Y2Yc36vi4ObltJ+8M0AaExJDLrzdWa9fScAHmcps58aSr0Og3j1xhdQ6OJQicVoTrNfZsjjwX/0eIXzomO5dBx0IwOGPUjwh4X4D2fgz80HsRjLGx9ifPr+09rBPWEw9vx9y3E5y286YomIB17ojDEuMsIoEwSaapS8VDcVeyCIiLAbqdKsYewfy/jjoUfJ3bABucFAq7vvonGffrjf+zziHL51W/Bu2o76st70HFiThs3jWPjzQYoLXHTqlUbhroprveQq7Tm1iwMIejzYpv+GuHldQtf3IleSgStYSrq0KZL1GbjXbkY9fEBZdoXYqKekuZL1R2ZGnSvbtYe/ir6lY5O6+DZH7s6qh4cdhz3bdiOSy5DWroFgNFzUaLzbYmHvjxVsDAHH5/5K0zv7sGtLIQczBGR6Pb+NuIIrli5CnZh4em8SDMY0kxM2badfz67E8pB3B0PkKeHeJztitbhxOXwo1VIMJgVKVXgRVujz8/LhHO6zF7Lo3vuizrHjk8+o0bcPDa8cc3rXWQmCRoOiSzvs03+LOa/o3gmR+uwXeKFgELlKy3XPz6S0OAdbUQ6mlDrozWlojJe+i35FKCRaWsUPx9E9n5xdWzi67c/ySZGIwXe8gc7836wDDJZYESfFI21Yj9LPvydotSGpUwPd9WPw7NiD+891SK8eecrzSFUqpCoV/vxC8ie+VSbey96n1Ebxa1Mwv/rURTVsu5gI+koiyYIIQXP+IqzniwYqOXMKrQyM0yMRidjjcKESBDrrNSTIJKhOY20hMZuIe+ERXH9twDFvKQQCqPp2Q9m3+1n7AoiL4Bb95+RKDnLEvwmTKI2OxtvYvcaHwl/JUl0Qwd8bBsESC5bJn0cdEiq1E3j6NUa/8gT9TOkEQiFkgggxMKug3PTUFwoxz25nHnB1opG28VoklZjClhS5mfvj/phzmUdKKcxznhcBT8BPoLAY2Wcf8fLzd7BybQlbt1vQ6WQMHpBAisiC+tLX70ilYrTpabSb+DIbn3sqYi6pcxea3nADwgVwyT9TFCodTbqPpEazLjhLixDEElTaOLSmRJylxcz54AGObF0BgFSuJD69IfvXLyQQ8NN11Hj6Da/Lkt8OR6yVAZRqCS1ap2Ey1OKFdrvZUvgrea4DNDb0pa6uC3GK6s4rJ1Mt4Ks57wT9PkpyK95Bzj20vez/xWIJ9dr05aZJC1j983sUZO7DnNaAbiPvIU6tQX6mok0mQ5KcgNcSe7dPkpqEeVcxjl9Ocr0MBsvEbemn05A3aYA47tTGJzXq6nn9i/5sXpPDnm2F1KxnoHPvNMwJKsSS2GLZKJVg/HtzOxQI4M3IRvLVL/S/azyilxIRJyUg7DuM/Y2PoqLyAPaZc5G2b02BUslyTYC4G+rQSiqlrl5J3Tp3IFdpyDuyk6M7VkW8rt3Am9Dozy0VKWh34hf7yb2tAZ9kD4pIeerYZCwjxROQOT34ggJyhRhBJefPjNjuogArC7+gXd/JcJKA1912Lf6sXIoef7n8QKkE44N3oOjc7qxTr88VQSJBqqp4YSJRqcr0wN6DXpo1a8beb77BmZ9/SgHvdvkpLnDhzHOhq1+nLIVV2q4FQt+ulObnUV8iop5SxsF/RJBqKWS01qlRSSUVRk2OuDzUU0g59s5nFV7D+lcmkd6n91nv0p9AJBGjHtQX54LlBEsjs1MEswll13Zn5NZ8Ms7SIvaunc/K6W/gsBSgMSXRcejt1G7RHY0xAZetBLfdit1agFKtR2Uwo9KazunzXEg00jg0CXEMHf8utsIcju1YjUKtp3aLHmiMCeecPfNvJeT34/x9JZ715aah/sMZlLz+AYb7byNQHLsLQ0UECooI2WJHmv2HM/CXWP+7At6gR96hVUTt9QmUvboiGP99Pxe9VMLIeAMWf4CjLg/d9BpS5DLUYgHFGUTOxWYT6mEDUPboBCEQdNpzqq8WBAmznrmNuNT6xKXWxeHYT/FgB5+9u5c3Xm+NWBBBDCd5ecc2CLq/DXIPHauwNty37xBYrJj/kZ3ycM0kvsguZEOpkxAgF4kYkWCgv0lfqXiH8LPK7aq4q0N2po36Tare8V2k0SDv0Arnb4vhmWfp07YlPTvVQ3A5CX41HdXVlyNSKrCXenG4nNiDeQQFFxqlDr00GZXy0mg1GwoEUWVn0WjUFdTo158jc+fgt1mpNWgwcfXroku8dE0ixWIJenMq+n9sJDssBRzZugJBLKHrlRPRJrVjzw4HUqmAubYaa5GVhOR4Xpzah6/f38qOv8v9mrdL5IZ7WxKfrEYQRCQq6zMwPZabfTUnqBbw1Zx3xBIpcan1KMyMvVOb2qBNxL/lSg0p9Vpx+X1T8HpcyORKZKfZYiTqvbUatNdfQdGTr0ZPCgKK9q0oeu7N8jGRCElqEoGCIiBcKx/yVNxDOvJ0IhKSNQwcVZ/LRtY7Y2ESKCqh6OlJ4bTEH8MRS1X/HvhLbTHFO4SjRBaPl4ePF5X55lynFVBmZXJgwWe4bMWkN+5Ap+F38ftXz1GUdZC0Ru1p1nP0OUfgRRIx/tHtmXqsW0QPUYB11unUqtkZ6086tq7LpV5jE0OvrocvWLH7byDoRVIrjROSVJKWjEgux/nPNHKfn5LXp5Lw0SSEWmfeMaAqkOt0tL7/Po6v/DPmfJ1xt/HDKgsARr2AZ2f478lTwUbSCTxuPxtXZ/P+S+uIS1DxzD1j4NXXkfbuQr5GwrIhQ/C73Ug1Gq759WcKGzdlmctPiBD9TDpaalTExTAjDPh92EvyCPh9HEOLNhjEHSP9/wTOvDyCXm+F82eCOCme+PdeoHTar7hWrkEkCCj7dkN75fCzrjEO+LxsXfYjy78tbwlpL85l6TcvkH1wG92HT2Dz6+/gKSwmpX9P1A1rsHfHIvpd/wzauH9X7bjemILemEJa/bYX+1IuCYJ2R4R4Pxnb979ievbBMzrfqVomuu0eqravxL8HsVaDYcJtWD/+Bveq9WEBKQio+nZHd9NYBFVst/ZLBX/QS4gQUiHyN6iWiFFLxKRWYu52OohEonPqonEyKn0cibWaknd0F0VZB6jdogeb1oU3ln6bn8OYW27E/+mXEa8R4owYbr+u7PcQOkV70FgbAPEyKePTE7D6g3iCQVRiAaNEjPQ01gdyhQSJVMDvi10GlZB0fjYZBZkU7YhBuJb8ScjpwrdxK2zcSpDwz0TRsQ2lVg9ZBRls8X3K6uKpeINO5IKaXonj6Zs6HpPq4nfwCBSVYH3zQ4IuF8ruXWk++HIAQsuW4frgK3SfvYWQfHGCFGeLJT8DgP63f8iyJWo2rYksrxh7C1w20kB6bT0PTOyMo9QHIlBrz930979GtYCv5ryjNsTT6+on+On1m6LmZAo1dVr1ivk6mVJz1sL9ZKQN6qC7/TpKv/yxrCeoSK3CcNcNOOb8HrFjrRk5CNfqDWWCWaRUwGnUxP2Ts4kq+jOyomoKfYczUPbshHtd7AWrrGVT5rl8ZeJ9pEZAsvxLFsz7tOyYrP2b2Lb0R8Y88TXBgJ+4lLpVkl4s1uvYVrw8SryfYFHO6wxM78qBL4o5sKuYLWtzuf6tG9hSNCvm8e3ix6BPaYjokzdwr9+KpHY6ti8qcOEOhXDMX4r+jnEXzVU2pXMn6gwbyuE5cyPG0/r2x5vYjJzMcIvCzh01LH0+3EtBlZhA0O0hkF+Ic9lqAtm5yNu1RN6qKZIEMyVFbj54ZT2hEBTmOZm51MaVTz1GwFvM4n4Dyt7DZ7eztP9lxDVtyr2zf0WVmoZGKo5ppuiwFrLl9+9Y8+v7yFU6mj/1E5tDMhr368fRRYtjf7auXZDpqia6JhKJkKQkYbjvZnQ3XolIBCKdFkF29g9re0keq2e+E3Nuz1+zadl+JLs+/wpCIbL/WkNSxw60fvoBln//GgNvfblK7isnsHiysfkKCYR8aKVm9LLkS9Yc7/+Bf9Ydn0wgvxDOMOVUnJIYTkOOIW5EGjX51hChYjcG079rIV1VSMwmjA/cQfDGqwi6XAgqFYJRf9Gyn06HUm8exx07WJH9If6Qh25JN1Nb2xHjJdx+Sq03M+qhT5j2wlhKC44jkSmwOMJ/k3+uyEUuT2HwxOeRbliLYCnB17gpxi4tkaSUP8ul9WoT5Wj7N5K0ZARtbEGtEotRnUWqtt4kp/fgWvw+O9ro0BSvJCHl/GUJiZMTiH/vRUq/mo57zUYQi1H16oL22lFIEuPJP3ic1e5XWVfyVdlrPEEHi3Jewx0sYUy9N1GIL64BY9BmK+uM5F++Ev/ychNDcVICQUe06eCljkofR2LtZuQVJrFpTXTm7fTP99CyYwp1G5pQa2TVov0cqBbw1Zw3PAEn3oADuVhDjSad6H/zC6yY9io+T1ikGhJrcvmEj/AFjTgdPlTqylupnC1irQb10H4ou7YnUFCESCJBMBkI+QN4tu8OP9jijKgH9MSfnY/zJNds9bD+iI2G83Jd/yRQbIka8x06iva6UQhxRoJF/0gNlUpQXjOSRY5whoBEBK2CdmafJN5P4LKXsHb2VIaPn4xcdWYOuZWR743tUAxg9eag0ZffYnKP2/FlNqCWpj1H7RsijtVIzfRPfRCZUgc1dUhrphEoLCZQWHFPUH92HqFA4KIJeHVSEv0//YjiPXvZ8ennhEIhUi+/mqJQIh99cBhBLGLczbXJmPYJSR070OGlp/BKPBRm7kO09QD+xSsIFpXg+mMNglGP+c3n2L/bQzBQvvjatKEQQ7ySuPXfxLyGol27WH3n3ehum0RWToD+l9chPkmNXBH+uQf8Prb8/l2ZgaTX7cBoLyBPSMQ0dAiKV1/DXRz5MxYkEjo99/QpuzucKYJcjhBfNbFMt6MUr7tigy2bJY+4pk3o+fxzaKQKQtn5SIu8dOk5DpfdUiUCPhDyk2HbzEd7rqTIE26zpRBrubLOm7Qzj0Elrbp+wxZPDsWeTPKc+5GKFaSpW2CQpaCQ/HscwKsCV3ExKCoRjiIRwhlGVQW9Dtmg/njnRW9miceO4Zc5Odza8NIVfhcCQaVEqKRk6FLC6s3j+4P3sKmwvD3X9uJ51NS05d6msy9pEW9KqcMNL/9Gcc5hSouy8YpT+GNR2MNnyaJsli/NoVnr2qg0DdBkyLhmYOQ9RmzUoxkzFPuMOZEnFgTUd9+MX6mhKiuq5XIJo8Y1wVLkZsOqchPRxBQ1j73WLcq8tyoRCQLSGqkYH74rbLYoEiHoNAhyOTarB6+4iPUlsZ+bf+Z9xmXpj6BQXfj7p9/rwW7Jx1aUTcjvR/n83YTmr8K3PlxKKk6MR3r9SAqOHmHv+5MxNW5Mjf590aanV9rm7WITDAURRAI6UzJNe93GTzOLYh4Xn6QiL8uOwahArpSc/3aD/8dUC/hqqhy3306++yALMl4j27mLVHVzBqY/SvN+Y2nQfiC2kgIcthDZ2SLemJhDXs4BuvevyTV3ND8/hif8LR6SEpAkRUae9ffeTMjuIGC1UfzSuwSycsrm5O1aohkx6Jz6o58J0ro1Y45bP/oG4yN34Zi/FPeqDRAMImvSAP3dN5ARZ8JtC/fUrK2Qk71lbsxzABzYuBiXw1KlAr6xsS9/5sWupa6haUPO4cjyg4+fPcxrP85kp20Wy3M+wBt00c48hj4p46P6cYuUCqQN61aYLitv1RRBdnEfaOrERNSJiaT16B6uXS9yU7Alj1vuakhKnJd9H76LrkYCSY+O49fP78NlC4vlxNrNGPrgc4g/+oVAZg7BEiuWKV+QOPp6RAIMviaVlr2UWIPHMGocSNuPpmDbFop2RLdfKT12DIXdyeLZxykscDLy2kaYE1UYTEpsxbms+TWy3dFfHz3AQ499xyy/hlHLlrDjoYc5vnQZAObmzen30QcY69c//z+8c0Aiqzz6p9QaGfLhh/g/mobn73IYLyBoNWheeQyqoBNNsTuDN7b3xhssj5K4Aza+OXAHcYraNDX2P/c3ASzuHLJduzhYuppM+1YM8lTkYhVF7qPU1XX5z4h4W+ZxFlx/A92ffBJkUvD6oo6Rt2uJoD+zzBGxWoW//2WIE1Ng3lwCeYVIaqYRGjWKlYckeDwOFIrqpdK/heOObRHi/QTH7JvYXPgzfVLGn7XvxoVAF5eM7u8yn+JCFzXr6sscuwP+ENs25CGRCrz6cd8onxNBrUIyZCDilFoICxcQLCpBqFeHwMDBfDwzj8tVpdRrXLU+ICazkjsfa8fVt3soKnCh0ckwmhQYzRdmwyfW5pLfH8QdKiFE7NT+IAHsvkISqHshLrEMj8vOgQ2Lmf/Rw2WBLIlMyYCrniQ1vieBVVsQ33IlP111NY7c8vahEoWCUYvmk9KlM8JZZISeT4rcx9hjWcqO4vnEK+rSPelWarXohe3ztVHHjhrXmIRkNXOn7+fzd7aQXkvHVbc1o0YdQ4WePdVUjCgUqqC49j9KaWkper0eq9WKropSSP9L+IN+thbN4uM9VxKi/E9LhIg7GvyEprArPjf8tTyTZXMj02sGjKjLdXe3QC6/8DeokN9PoKgEf0YWAWspsjo1EeKMiM9wMXguBCylFL86Ge/WaJFmeORuFJ3bEbLbIRQKpzHqNOR5fNy3PwN/KOyu23vTDDbMiO34j0jEPVPXYUiourrxIncGr2zthNWbEzV3a9p8vn08SGFeZBrYpM/7U7OuHpsvn2AoUGk/bu/BIxSMfyoqvVWkVpEw9dWoDZlLBb/HgyM7h4DXS7Etg+mvXh91jFJj5Pq7PsH73AdlY4YP32RDZgEHTJNZX/JV2XdIJ03gJuPHbLvhNfLXb4o4T90rrsDd72EatU5lw6ostm/MQ6GQMGh0fZq01PLVI+3xeyPrfLVxKbS7+gmSWvdD4fMjs1oRBQMoDYaqaR93nnHZSpj5+k1k7l4XNacxJHDFPZ8g/fI3fPujM0TE8SbM776IxHxuC9n1+T+yMvcTjtk24QpE9pSupWnH/c0XopGem4GTL+jhqG0D7+8ajsNfnoEjQuC6+lNprO9HgurCLkIvBm6rlYXX3cDhufOo0bcPfe5/CNfUbyLKn8TxcZjfeAZJ8ml2eTiJglwHn7y5id5dTRiNMnJyXPw2N5vcLDsvTe1DvfNgxFVN1eMLuvlo95VsK54Tcz5Z2YjHWq2q8HsZdDgJWErB60WkViGOMyK6yC7gRflOFv5ykN9/O4Tb5adFu0SuvaMFqTW1SKTR17bwlwPMnb6fAQOSMeokHD7mYtmSbNwuP83bJfLAxE7/92nLHpefQ0Vbeftw+wqPmdh2B6nqZhfwqiDn8A6+eGRAzLnrH/4W9aESFn8wmZy10eJXrtdz/fbN6GpcOk7sec79TNrWA7EgpZtuAsnSVjgCRdRPaMXPU0r4a2m5z07n3mkkp2n55dvo/jn3Pt2BLn3SY5YA/hc5XR16aW3lVPOvJhQKUeI+ztf7b40Q7wAhQnx7+DbGSpcw+dGD9B9el+vuasF3H5Y70C+bd4ShVzUgMbnqI0oBixUCQUQaFYI8Oo1XJJEgSYw/a1OtqkBs0GF69B5sM37DuWAZIY8XcbwJ3c1XI2/fCrFaCf9ouaWXirk2KY6vc4o46vKS3LoPVCDg67TshUJdNYY7J4hT1ODRFn/w7YE72WsNR3GN8jSGxr3Jtt/UFOblRRxvNCvQ6WWIRCJ0slMvtCVpKcS98gSW9z4jkBN2K5U2rIvxgdsRX8Tf1amQyOXoa9fCaS1ixSd3xzzGZS/heM5uUmqm4T8WTpOUKCRYkxeyLjfSrKjUl89HRddz7+RpLOhU3v9bJBbT6O4H8WjTmPzCWmzWcuO5j17fSNPW8Vw27i2WfHZPxPlsRdksf388416aTUrjDhB/6brdxkKpNTLsnnf5/oWxWPIyysYVaj1DbnwdhVuEO4Z4BwgUFBMsscBZCnh7qZeiAicHltfF5HmJbp2VWFQb+LXgXgKhcFQ4x7kHj89+zgLe6slh+qEHIsQ7QIggPx66n0dbroQLHEW6GLgLCynetx+pRkPG0mWslMvp8ugjiA5lILKUomzfGnnj+kjiz+7nHZ+k5qb7WvPp2xvZvaUQCKcBP/F6d9JqV+09s5rzRygUxBeKbUrYXjOGPtLb2P/hNFwFRdTs3x9jg3qok8J9tv15BVimfIFn4zYIhRCplGivvwJV3+6IK2und56JS1Ax9pamDBpdj1AIlCpphdHKgD/Ijk35FOQ6mfbNoaj5w3uL8bgCqP/Pk3bkSgkmdRKpqmZkOXdGzaerW6KVXtiNap/XzdrZUyucX7dyGn2HPxxTvAN4rFasR45WqYB3+Iqx+QpwB+yoJQa00iQUktPzLXD6LEw7eC+NVUNp7ruPOZPzOLS3BINJRb8rrFx5S1M2/5WN2xXeZO0+oCZvP7sm5rm+fG8LjVvGY064tE0xLzWqBXw1VUJhnpPNa7JJbJOLKxDbadvhL0aWYCMUhMWzDnHjfa1Ir6Mj83A4euX3BXFZ3fhxIpj0MYX2mRIotuBetxn7L/MJ2h3I27ZAO/ZyJMkJiC6xVCQAcZwR3S3XoBk1BPw+RHI54n8IjZIiF3lZdg7vtxCfpKJDXQONa6fwXX4xmRIjTXqNZfeK6RGvkSnU9LvxORTqqs8oSFTV564mP2H3FRIIeZELeg6sC7Hs18gHkUiAOx9tf0apdYJCjqJ1c+Lfej5c5yYWI2g1VbagchUV4cwvwGOxIDcYUCUmoDRVXYqh3+8hPyNWx/YwxzN2UCOlJv5jxxEnJ+DQuVh+9M2Yx3oCdnINRzHUr4/lwAG0NWrQ8Y3JHMhVcWTV0QjxfoJdWwoYNKoFar0Zh7UwYk5jTERfhdkYFxpjUi2uf3EWRVkHyTuyE1NyHeLTGrBh4uu0HnVFpa/9p1nk6VJa4ua37/cyZ8aBsrFFM6Bl53pcd+t0vs4eBUCCsh7O4iIMshTEkrNPDXQHbRy1b4w55wu6KXAdpra24ijT/wP+giJkJXaGPf0cooQ4SgryWfrwI3zXuw+JbduiiDPRrVdH/BI1/nwngiDCEKc44zTplBpaHnqxK3arl0AgiEojPW8lXdWcH2RiFV0Tb2R3ye8R4x00V9J6W1fm3DKkzOBt3UuvkNShPcN+mYlKoaLo6Un4M7LKXhNyuij9+FtEUinqIX3PuWvLuSCRijGdRj25WCKQlFqxOjealYgll275QFUSp07mtvozmbz3Moo95Zu8ZkUt7mw8E53swgp4v8dFcXbFnkElOUcIniL79FRdbM6EQvdRvtx3M/usywEQiyR0T7qVYTWfQy9LOuXr7f4iSn25tHW+zjtPlzvNlxS6mfnRMTL3+Xjl4358+tYm9u8qwu3yV9itwGHzYbN6qgX8GXLpKZhq/nUU5DqYOGEFBblOHvm88qiqiPKHx+JZh+gzpHZZFF4sESEpzCPvoVdRDeyD7tpRiE2Gs76uQImVknc/wXOSg7vr95W4V64jfvKLSC9SC7JTIcikCBX0/yzIc/DqI3+Sday8p7ZSJeHpt3vwaL1EvID4+qdo2mkQa2ZNxWUroW7r3rQdeCOGhPOXeqWWGlGfZNqlaOdl4pTe/PrdHvKy7NRqYGDktY1JStOcVf2hOM6IOK7qTMEASjMzWXTTrWT+Xf8NkN6nNwO/+gJtelqVvIcgSNDHp0VEiU8mzlyTwGEriERoH78Li9eG3R/b/AXAosxj1MJ5eEptHM4I8N2sAgaPUbHpr9gtGgHW/2mhZZ+r+evXKWVjGkMCVz/zQ1mt5b+VE/WitVt0Lxvr8vyzhApL8MllhDwxWuGJRAhnEX0PutxkHSqOEO8n2LamiOZdapKW2pzjjh30M9zHoncfY8xjX6KPP/u/pVAo9oKnfD52B4iqxFNaiiMnh6MLF+NzOKl1WT+0NWqgij//GTC+jCwKH385wsBTl5bM6JkzmDlyFHmbNtFswkNkexP44eGVZByyYopXMvL6xnTonoreeGZO6Rqt7JIxVfK6HTgsBThtxUhlSlS6uCrpHvL/TgN9j4jIqwiBnuIbmXfL5VHu7LnrN7DlvSl0vOPOCPF+MrZvf0LetgXSlDMvzbgY9B5ci3kz9sfsPDvq+sZn/J34tyKViUmTNeKRZqsodB8i332QJFUDElR1L4qRoVShIrlOc3IPb485n1i7GXKVFoXJFGUsewJT44ZVci1Wbx5Td40kw7G1bCwQ8rMi5yOkgpKRtV9BJlT+dxIM+emheYKf3siOOb92eTajxzXj4Ze74rT7KCmsfNO8On3+zKkW8NWcEz5fgPk/HaAgN1zn7LdqUUkMOP2WqGM1kjg8JeXpOQW5DvQntebp1TcV6drV+P0BnHN/B58P/V3jEJRnFwXx5+ZHiPcThDwerJ9Ow/TkfQjqf8+On9Ph46vJWyPEO4DL6eeVR/7k9c8HYE5UgSEeXfvLSG/ciaDfh1ytRSK9sJ2M1RoZjVqYmfBcJ7yeAEqVpMwZ/VLAVVzM4ltujxDvAJnLlrPwplsYOuOHSiPxwUCQYBAk0sofOhpjAt2ueIC5HzwQNSeWyKjXsCuirb9jfv9lcj255O7ei0leIyJicDK1dR0xmGsTDIY4Zssm4/BBgoEQgrjiTRGJVKDzyHtp3HU4RVmH0JoSMSTWQBd38fvgng80yckETCaCY4Zh/y7azErVvydiw6kzUSzFbuylXkKhEGqtDJXXxqI50W1xTrDyVws9H72NVpo8vDsKyDuyE3tJ3jkJeK00HpM8nWJPZtScCBHpmtZnfe7TwW2xsOuLr/njoYfLxlY/9TT1Ro6g79QpZenH54NAUQlFz74e1X3DfzwH6a+L6fDA/RxctBhx92t57fHy1MyifBefvbWZYwctXH17cxQqAYe/GEEkPueShguFw1rI6l8ms2nBlwQD4dancan1uOLRzzGnNbjIV3fp4SktxedwIFUqMRrSuL/5AtbkfcfK3E9IUTWlaMHmmK3VALZ9+DEtrrmmwnMHraUELVZCyQkVbj67XX5KLR4C/iAKpeSCGbjFwpyoZsJznfjglfX4vOUbgANH1aNZ2//eBlC8Op14dTqN6XVRr0MildNh6O1sWz697Dt9ApEgpvOIe1DHJ9LjjUksvuW2qNc3venGKvOnsXiyIsT7ySzPnhphKuz3efA4bYilMhSq8uemUqzHQAPycyrOKji4p5g+dWqj0coQBBFqjRSHPdp8NDFFjVoaJGh3IGjOX+vB/zcunRV1Nf9KbBYvKxcdK/v3wm+KGDX+Q6ZlX/MPEzuBUQkfsuCd8p3FGnX05GfbEYmgc49khndV4p9U3gfT+ftKtFeNiBLwDpsXny+IWiNFKqvYYMb9V+zUUwDPpu0EHc4KBXzA5iDk8SCSSqu8/s3nceJx2ZHIFBE3xFNRavGw6a/Yu50Om4/cLHtYwP+NUnPxazdVaul5aw94LrgKCsn4fUnMucyly3Dm58cU8Darh9wsO4tnHcJu89K1bw0atzATV0nqV722/egw9DbWz/usbBEpV+m44pHPMKQ0QPxsCzxBN0tevANEIgbe8TDfZ98XdR69LJkafws2QRDRol0iL3/Ul61rc+jcO50lv8V+kPYeXAeFWkdS7WYk1b6wpj0XC7FcjmbYAMR6LbZpvxK0WBGpVWhGDUY9uC+CuuJFgt8f5OgBC1NfXV+2WTZ0dB2G1LNjt1f8ng6bl+aKIWyY9Qp/rfocgGCw8gj6qTDIU7i23lTe3zU8ylekT8p4DLLzuwlTevQYBbt3MHjOdERiAcvuA+yY8jEHf51F7UEDaX7bLeftvQMl1jLfi3/i272fGo/egbZLX958L/amypLfDjPwijpsKnyPjYUzkAkq+qaMp4lxAAb5pZt5EgwE2LZ8OhvmRrYDLco6yHfPXcHNkxagM1+6rdAuJF67naJdu1k78UUKduzEULcOnZ57hoRWLRmY/ghdk25EjIRNX1dg7Ar47HaopNOMSC4jaCklWGxBHGckGAjhcfuRSAWkMjGFeQ6+/2QHa5cfJxAIkZii5obxrWjcMr7s2ed2+RCLhUrXKxXhLCjAVVhE0OdDYTKiSUmpNJ1foZTQtksKb38zkMzDVtxuP7XrG9Gb5P/35nWXOvrEGlz19PfMmTIBW3HY+FdjTGToPe9gTKqFIBZTb+TlKM1xrHz0CUr27UOVkED7xx+l0TVXoTAYquQ6Ct0Vb0T7Qx48ARvBgJ+SvGNsmPc5R3esQq0303nkvaTUa4lKF4denoRZBSLR4Yr2xlCf5NVgjFNw//OdeO2xVQROapUrk4u5++4G+J9/EXvf7uHWzdr/c5OGKqJawFdzToQIEQiUL1L3bC3BOCOZe65azVrH++R5d5OsaEYH5T0sn+Zm/86CsmPH3toMjUqgY6OWSLdtJvD63AhXYYJBgnY7EE5dK7W4Obi7mFnf76XU4qF520QGX1GfhGQ1Ykn0A02kqCTqLBED0bvp7hI7wWMZ2L+ZiT8jE0liAtrrr0DepAGCrvKbirO0iNLCbIpzj6LWmdGYEtEYE5Erw2LB53FTknuE1b9MJufgVnTmVLpdcT+JtZuh1BgqPTeAzxugsoxaa3Fs857/OqFQCHfAhkQkRSoObwZ5rJZKX+O1lkaN2Uu9zP5+L3N+LE9X3/xXDompap59p1fE5snJqPVmuo99hLYDb6I45wgyhQp9fBpaUxKCOHwL9uYXk3tkBwB1d/djRKOJLCx4E3cgLCBradpxa6NpmBTlZR8yn4saWg+J/RJwiBRsW59blglzgh4DapCYeunsaPvdbmwZmeybPoPCXbup0ac3NQf0Q1ezZpW3dhIbdKiH9kfRuV245ZhUijjOcEpX6YJcB8/ftzwieqWWh2DnLtq16sKOjbFFZYv28Wya/R77Vs0Dwu2BtKZzj1A31PfiiVZ/8cuRpzhm34hRnsaQ9KdobOyPSnr+NulCwSA+wYerkYRfv72foN9HUp0WdPvyHfZO/pKNb75NneFDUSeen9TikN1R6bwuOQWn1Eyp5VjM+VAI9h08zHLlB9h9Yf+HL/bfSCN9b25r9D16+fnLHjgX7CV5rP31g5hzDksB+Rl7L7iAdzq8WEs85B63o1RLMSeoMMYpYj53LxTBQICjCxczd8zYsjH78eP81Kc/vae8R7NbbkKvDP+O6wwZzKY33455nqT27ZHqdIg06ph/c8reXXH9sQZJg3oUZNlZufAou7YWYE5UMer6xrz59F9kZ5RnxeVlO3j9idU8+UZ30uvo2Lgqm3V/ZKHRyRg0uh6pNXVo9afOiAuFQhTt3MWCcTdRsHUrAKrERPq8/x41B/RH/g936qDDGfaJASRaNQnJ4f+quXSQyhTUat6Nm16bj9NWDIRQak1oTUllzz+F0Ujd4cNI6tiBgNuNIJGiTk6qUg8Go7zirDCxSIJcrKEgcx9fPTEMvzec+l6UdYCM3WvoMPR2uo95EIVGj0Gvo0WHRLaty4s6j0QqULtBedmjWCLQuGU8b351GX/MO0TGMTv1ainp1EaP+Ptv8R/Pwfb1DBRtWyBuWC3gT4dqAV/NOaHVyejSO52lJ7WE+2tRPptWiunY+y5uuLYmSpma957ZzKG94VRIrV7GDeNbUb+xCWlBLvkPPkdFlZyiv6Pv9lIvM7/czeJZ5c6qOZl2/lh4lBc+6EOteoao1yq7tMP2zcyY51X17oqgj7xJOGxuvGs34Xz3o7Ixn+0Ixc+9gfamsWhGDEKoYFPAXpLP6l8mU79tf45sXcHhbX8glStp1fdqGncZjj4+jaz9m/j+hbGEguFPW5J7lGM7V9Pn+mdoO/AGZIrKH7ZKtRSNToa9NEZdL5BW69/T9tDp8FFa4iHjiAWpVExqTR3GOMVZRSgqo8h9jC2Fs9hc9AtqiYl+qfeTom6KXF+58JEboucL8hwR4v0EeVkO5kzfx3V3tUAao60PgEKlRaHSYkquHXNeEMTIFGq8bgerv36buu36csfQbwgogkiQoRQZSFKVp876MrOxvP0x3t3h65HUTOPZlx9hxx4bq5dmolCG28jVqKu/ZGoeAz4fmctXMHv4SIL+cArh/ukzUBiNXLlyOeZmTav8PUWCcEbO5IFAkBXzj0aId4DsXC8+hUDLOjJM8UqKCyLr+eRKMQOGJTFj4qyysQE3v4jGeO514gqJhjq6Ttzd5Gc8QTtikeyCGDDZinKY/cl9lOSU39tzD2/nl6l3MuaBT1l152Nlv8fzgWCuxPNCKkFiMiD1Vr6EkasEfIHIjc291uUcd+64ZAS8q6gIv8uFIJWiTkzE73PjspdUeHxB5j7qtel7wa7PWuLm5692s3j2obJIm1or5ZGXu1K/aRySiyTiHdnZLLkzdoePlQ8/So3LBnIoR05BroNa9RoyYNZC/rz1OlyFJ5l5ikT0evctVKkpSCY+QvELbxM8afNW3qY5ivYtcSxZRVZxkGfvW4znb1dtvUlOi3aJEeL9ZL75YBuXjazH5++Ul/GtXXGcwWPqM3pcEzS62NFwu82L0+5D4shjeo/eeCyWsjlnXh5zx1zFlSuXk9a9GxDeaPMfz8H6yXdhB30RyDu0Rn/rtUjSki/pnvf/RUQiEdq4JLRxld9/ztfGKIBJnk6Coh757oNRc50SrkcR1PHzJxPKxPvJrJ/7Ca37X4tCo0enU3HzhDZMnLAi4pkoEmDCsx0xxEWuPaQyMYm6IJd5NhKI88PBfLyz9hI4KYRv/20xxgduRyS5uO0b/w1UC/hqzgmJRMQVN9anuNDFlrW5ZeMeVwBLfhCDKh6DScFjr3XDZvXg94fQ6mQY45QIYhEBnx5pnZr4DkdHUWQtmyD8XataUuSKEO9l7+MO8OV7m3nk5a5odJHiWjDHobl6JPYffo0YF8fHob12VJTLfaCgBNfn38b8nLZvf0LVqwtCBX3HD21dTv22/fn1nTtx2y1l40u/eZG96xZy+YT3mTv1gTLxfjLLp71Co05DkCVVLuCNcQrG3tqUz9+Orutv3i4Bo/nSEGqnotTiYd6Mfcz+fl/ZglAqE7j7yQ606ZSMQlk1t6V81yEmbeuG1Vv+d7mlaBZ9U+5jSNxj1B40kCMLFka9rtZlA2LWmq36PXZdOsDy+UcYfnVD4k7DKTgWakM8bQfeyJpZ4cjboY1LObRxKRAWoXdOXlV2rD+/kMJHXiBYUu5I6z92HO6ZQLe3n6dL364IYhHyUzjaXmgc2dnMvfLqKNHnLilh4bibGLVo3gUxRquIoNeHu8TBvh2FUXNrV+Uw+uU+iD74gGceG8+shfms/iOHgD9I6w4JXHNbM/av/xSdOQVjUm26jXmA+BoNkUir7jupkhpQYaiy852K3KM7I8T7CULBIGsWf0L7px495UbYuSAY9Ch7dMK1MrqtkvrygQhGPTo31G1kLNscPhmlSoLcbMWTHVn3UEvTDltBNq5QEUrTxauJ99hsFGzZxp9PPEnB1m1o09Pp9MyTpPTohlJjjBLxSbWb0b7PzcTF1SZ/y1aU8eZTplOfK6FQiHV/HGfRP569DpuPlx9eyVtfX0ZiysWJlrkKC3EXxTb8DHg8ZO86zLtTLAT84YdMak0tE5asZsGA7jjz80lo04be771NfMsWiAQBIcmM/t6bIBAgaLMjTjDjO3SM4tc+QDXlDT5+dXOZeAdISdNyeH/FGy3Hj5aiM0Rv+M+feYCeA2tFCfiAP0jWsVK+mLwFjyvAkDq7IsT7yfz5+JOMmDMLpclEILeAggnPlHfWCIFn7WYKdu0jYcorSJL/e3Xv1VSOQZ7ChOYL+GDX5WQ7d5eNtzJdzshaL+EvsSMIYpr1vBJ7SQ7Hdq4mdFI52LGdq8u8OJJSNbz4QR/27ypi+4ZcElM1dOyZRlyCEtlJAZlSbz42Xz5qlxTP+s1R3iYnCJZYCAX81QL+NLi0VnjV/GvwuhxYCjLZvPgbLHnH6NG5K1ff3I/pX+VSXOBh0Jj6tGiXiOFvkzq9UREzEig26jE9+wBFz74R4QIrrV8H40N3ldXC7NwUO20VYO/2Ihw2X5SAF2vVaEYNQtmpDfbfFhG02lB274i8TXMkCZEu7067D3eBhZCtgrRNfwB/XiGSGALeYS2kMPMAR7atjBDvJ8jat4GirAME/dHmHQChYIDCrAMYk2pW+Bkh7NLZuXc6UqmYHz/diaXYjUwups+Q2lx+bSN0hn+HgN+7o5BZ0/ZFjPm8QSZPXMsbXw4gvQr6LnsCTuYcmxgh3k+wNHsy3ZNuod8nH7Lkjrs5Mn9B2VztQQPp98mHKIzR0T+Pu+Joo98bhArqwE4HsURK+8G3krlnHcf3lXs3iASBEQ98GJGK7d1zIEK8n4xl8ueYX3sKsTH8M7SWuCm1ePD7g2h1coxmxUVze7UcOhyuN41B/pYtuIqKLpqADxSXYPtpHj6LjcSElvyz8Z/PG+SHX3O54abr8U5+j9EtmjPi6Q4gkaAxa9Gkq0lMu5eOQ25GKlMhV/37UwD3b1hc4dzxfRsYcvsbyDTn73OKNeqwianJgGP+UvD6EKmUaK4YGvYxkMvRyuHuJzsw8b4VlFo85a+ViLj92fr8br2rbEwtMXKz4RPsyw5w+JuPOczHNL/9VmoNugxt6oVNSQ+FQmT8voQ5o68sGyvZt48F191AmwfuZ9id7zDjzRvL5uq17EvzJkP589ZHceSEa2dVCQkM+PwT0vv0Rqo6P2asliI3v3wbuw2mzxtk2/pcBoyod17e+1SIhMoX+CKxJKI2N+uYjenTs7ltyzYEjx2pVoPKXL4OkJrjoK4X68ff4jtwhKDVhthsxPTM/ZQICg7vixQcDrsPfQyBfgKlSoLfFzu3cO3yzKiswbwcB0/dtQyvJ0CX3qmUbPyrbE6VkECje8ehb9kAd24Rhz79Cb/TSUirwzF/Scy2mCGbA+eSlWivGXnKsqFq/nskKuvxUPOl2Hz5OPzF6GXJaKUJKER68rxW5DWfYP8RP0kpYkY8+gxbF77Oka1h3yCRKPLvyZyowpyookuf2J2dCt1H+Wj3GI7aN9LTdAuXtWxKcNn6mMcqOrerkhbS/wWqBXw1Z4zP42b/hkXMnnxvmSnXoc3LkKveY9yLs1Cb6qDVnf4XUJKciHnS0wSKSggUFSOONyM2GcpECIBIqDwNTCRA0Osl5PEiKOSIpGHzDLFOi1inxVivNqFAAEEeO23N6w0QCJ0i1awCo5ug309CzYZsXfJdhS/dseIn0hq1Z89fc2LOC6cZRdHq5PQaVIsW7RPxuANIpQIGU9Wnnp8vbFYPv3yzO+ZcKARL5x5m3D2tEE7x+z4VDl8R6wt+rHB+Q8EMRtZ+iUHffY0zvwCv1YpMr0eVEB9TvAN07p3O77NjG8W16ZKCSnNuZn3auCRGP/oFlrxjZaYxNZt3RWtMQiovN3L07qi4r7z/aCYhn49QKETmkVImv7CWzCPhlFCVRsp1d7WgU8801JW0yjrRvsrjsiNXalDrzciU5y7U/C4XjW64joQOrfFabez/djrFe8o/S9AbuzTkfFJq9RD0+hAtWYnj53mI5DIGPN6fFUuiW0qt+yuP4df1pvY7zxOw2IAQgl6H2GREJBaQAQGXF/uRDPb8sRKxXEZa9+6okhKj6lX/DWgraTGo1BiRqs5/fa3YZER3y9VoRg4Om4oqFeGf90nRmbSaOl75uC97dxSye0s+abX0tO2SzN7QdA4fKc9cudn4KVuufIHineX3n9wNG4hv1YoRc2edkYgP+H3YS/JwWAsRBDEqfRxa4+nXqdqzsll2T7RRJcDmd9+jxR230mHonWxc8BkiQUz7nuOY3Xc4oZM8Ypz5+cy+fBTXbl5PQsuW+H0eXLYSRCIRKp0ZoQpEWyAQoqSwYm+V40eivUIuFMr4eLTp6dgyozs0yPV6PBIDwUBBxPjmv3Lw3NOKpNqx27RKU5MxPnoPQasNAgEEtQpxnJHiY9GfM+OwlWvuaI5YLIow5TpBj8tqsmb58Zjv4/UGKC504XX7UWtlyBUS5s3Yj9cT/v1aLV5UtcMRzgY3jaXGo6NZ5HqPDOdUDCmp9P3mbvy6IEGHE/eGbRX+jNzrtqAeMbDaFKyamOjlSVGlRIf2FjNxwgo87vJ7zYJfRIx/8jE8DgvZBzZSs1mX034Pm7eAj/eM5ag9HJhYY/meAaOWwqotYW+akxBMBhQdWp39B/qPUS3gqzljHNb8cFusf1hPepylLPjwQcY+NQ04sx00sckQ7vleP3aNcPN2FaeB3XBbI/T2AizfzMOflYu0fh00l1+GJCkBkSwsqkQScaUpOUq1hIKQDG1KIoHsaEMOkUaNOC52WzG5WotcpUcsqVjASeUKJPLYURKxVE5c6ulHMUQi0Vmnap+KQImVQHEJgfzCv3uvm6q0/7rPG6QoPxwt0OhkmMxKrCVurCXh6FlOpp2AL4ggD/+ugsEQhXlOtm3IZe/2QmrVM9C+eyrmBCWSCurNIWyuGKykR3YgFH5wKIzGCgX7P0mtoaNJKzO7t0amWMuVYq6+rRlK1ZkL+KDHQ7DYQtDuRKSQo9Tr0DRsR1rDdhW+RpJesfO4YDIiEospzHPy/H3LcdjKH5BOu49P3tiEMU5Bm86xz2ErzmXF96+x44+fCAUDiAQxTbuNoM/1T5+TIVsw4EfbrA7evUrWbP8WpcZAixfvRm6TsOzWe1GazSgqadtX1YRLfnJYPOsQfl+QLp1T6TLxOZgyBdXq5dw1vjOffbyvrBZeEIu46pamJNXQIdbKEJujU6+dBQX89exEtn/0cfmgSESP11+j2a03V5mD8IWiadfLWTXjrZhz7YfchsZ4YfpiCzIZQlLlmRnxSWrik9R071+exSR1D8CcU4tC91Fqa9tj/31vhHg/QcHWrWQsWUrTG8ad1vW4nTYObFjEos+exOMM1z+r9WYuv38q6Y3bn1bZhLukGEdudHYQAKEQJfv20/PqR2g36EYCXg9bXn4nQryXHRoMsuvrb5E8omP93E/Zt3YeYqmM1gOup3nPMegq2YQ5HSRSgdSa2qjWpSdo2CK2EL4QaFKSGfz9t/zU7zICnvLsC5Eg0GXKx8yeG10KEwqFzWArQ6xRI/5HKyu1Vhbz57Dg5wPc/kg7Pn1rE35feYpxk9bxdOyZxksPrSQWjVvG89ANi3DafdRvYuLOR9uzY2P5umP3tgLGPDuKnOWLiX+kH1PyR5d1oXA5S/nOOZ4c6X6GpzyDUIk4F/RaRJLqZX41p0dJkYv3Jq6NEO8Q3sj79N2D3Pfow9TN2YDaePplGaW+fI7YyqPt3qCLr22PccOkVxB9sQTfjn0gCCi6tkd/81VIEi9eGd2/jepvdjVnTP6xPQT8saNl2Qe34rIVo9JV7WLcYFIw+obG/Px1ZPSxS48kuidZKLz3lbIx375DOBcuw/zKk8hbNjmt88vlErTp8XDrrYgmvUnopAUBYjGGx8cjqcBUSaZQE5/ekMZdhrFxwZcxj2nV71rEEhnbln6P3xsZ0Rh0xyTU+gtXpxYKBrGV5OIsLQZEqHUmNKYkAgVFFL/4Dr795VFmSVoycS8+iiSlagyfFCoxLdon0qJfGiQqyAkEiBfEKK0+Zr69laat45HKy4V5xmErEyeswPl379BVv2fw42c7eerN7jRqHh/R/9yenY3f7UYsk6FM0NPCNJRtxb/FvI425tFnfO2GOAX3PduJtSuOs+DnA7gcflp3TmLEtY1JOos60ECJFdv02TjmLAZ/+IEpa9YI4yN3xSzVOIGifWus0mngi07p1151OYLJwM4FRyPE+8l8/8kO6jYyRZW0uJ02ln79ArtWlXtGhIIBdq78GZ/XzdC730KhPrvyhsLj+/ny8aFlpjilBcf5/chOGrS5jE6vPk9crXpoUi5MX/riQhdvP7uGA7vK62dnHC1l6VIlzzwygeBzz9OwrZXXJw4h1wpoNaTWMaI3Kir1Z8hauSpSvAOEQqx85DHSenYnqX378/SJzg/auGSG3vMOc6c+GLFZW7N5V1r2uapKIrznkzhFDR5tsZLtxXMRWUMc+frrCo/d/vFn1L18+GltshRm7uW3yeMjxhzWQn586Rpuf2f5aW3GCtLKN/skajUyhQpZUk1cxcUUbIkdZRWkUupedTlfPTEUl628ReuKaa+ye/VsRj3+GVpTEjLx2fUlN5gUjH+yOWtXZLJto40jByxlczqDnIbNLp6AB0jq0J5x27ew88uvyV2/HnPTpjS59Va++yGfw4dKqFFHj8vpK+vOodXLUJ5mW9MTWRZetxO5UsPtD7flhQf+KKupB9i2Po+OPdN46+vLOLyvBGuJm4bN4ohLUGG3eZHJxLhdkffplu0Tycuylz3TDuwuZs2KDDQ6GXnZ4RK+UBDmLrQwdsaHvF8wMqqFJMDS7Cn0TrkX/ZhhFFeQlaUZPQRB+e8oravm4mOzevH7g9z+YF0SE4ME/C7EMgPrV9lYPCcLhb4BDVu3OKP2x05/dK37fvufTPKMpv8t99BD9wpyiQZBp4lqGV1N5VQL+GrOGL/PU+n8ufY+joVaI2PQFfVp0T6JedP3YSn20KpTEkP6xFF896MxLjJAyZsfEv/uC6cdQY5LUJHjSkby/LOIt25GeuwowZQUNP17IEuOr7SOTGdOpt3gWzi89Q+KcyLTrFv0uQpTUm3kaj23vb2ULb9P4/jeDRiTa9FhyG0Yk2ohlV+Yh6zX7eTojlXMm/ogztKwgNGakrn+yR8IfPJzhHgH8B/Poej5t8J11SbDOb+/Si1jxP0tefV4Hnk2S9m4TiZw3yudqCErX1xZit1MfmFt2UKn7Jp8Qd56Zg2TPu+POUGFq6iII/MXsPqpZ7FlZqIwmej4zFNceeub2H0FZDq24g2W1wi2iRtFvCJ2psepMMYpGTiqHp17pREMhfucyhVnfhsN+XzYZy3E8euCiHHvzr0UPfM65klPV/jzFsebML/0OEUT34qofVRd1gtlj06IRKKYRmwnOH6kNCJaFAyGsBa78bvy2bV6VszX7Fs7j97XPnFWAt5tt/D7l8/FdLTdv3kRnV+8h/i0hufVjOtkDu0pjhDvJyjKd7FirZW+bVrh27AJNmwiSa0ifvJLSP+xQRMKhcJptsEggl6L22Jh/WuvV/ieWyZ/wIDPP0Esiy5dcDttBHweFOrKs3guNHKlhsZdhpHeuANHtq3EZbdQu0UPDAnpqA3/jiiJSZFOr5S7sEmzOS76psLjTtep2+20sXJ67KyEYMDP5t+/o+/1z5xycyOkS+ay5Zvwuz34so+w662XKdodzg6QqtUY69UtO1aqUmFs1JDcDRuizlNv9Ah2rJ8VId5PkH90Nwf3LEdopKWZ6TKUkjMr47AV53Js519sWvgVoVCQIYNGoRrXgalvHCI+ScX4pztW2DrzQiGWyTA2qE/Xlybid7kQKxQ47AH6D5Bz9WAzHD4MOh2epIb8MCOTtt1TUMrdQOXX7bAWsnnRN6ydPRWv24FYIqPntc/w2qejmf39fg7sKsJoVjLy+sbUbWhEq5eTlBp5j9Dq5Ez6rB9zZx5gy5oc1BopfYfVQa4Q88mbmyKOXfV7JiOvb8SHr5X7n2xcW8iA29UUeyo2T810bCW+0SBUg3rjXLA8Yk41fADSupX76vzXcRUWYs/JoXD7TlQJ8RgbNECTmoLwH81aEAkw/rE0Vnx9L2uzD/09JtC0x7Xc8+h1BJGg1J5ZRqZGGtsotNSXzy8FE2lT62rUyn/H8+RS47/5V1rNOZFYqymIRFEp9ACGxJooNefHmVirk9OouZzaDQwEfCEUSgm+3XsJeWJnAwTyC3HmloBUdVo1+VKZmBr147AWq/GmJCISBVGp5Sg1FdcLn0AskRGXUpern/mBjD1r2fXnr8gUGtoNuglzekNU+vBNzJRch17XPoHP7UQikyORXlizjuKcQ8ycdCOEQqj18fS7YSLm5Lr4fQHEl/dHXjMF7/wVERkI/mPHCVhKq0TAl/oDfJhbSJ43MipRGgjysd3Ky3XL61BtVk9UyqIgFjHs8hp062hEmZOB167EUZDPqieexp6VhTY9nR4ff44hLZHcH1fR334lpi4vclyzjz89X9MvdQKNDH3Rys7+gSESiTDEndtOcaDYEiXeT+DPyAqXMFTw8xZJpciaNSTho0n4c/IJOZ1I0lMRDHrE2nDqZ826sV8L4XTjE5kLJUUu1izLZO70/dz5gCnmd/oErpM2XM4Et9PG0R2rKpw/smsVaU0uTHTa5wmwdG5sHwOA1avz6TmmLWwMd3qQpCUj/P0zDQWCBAqL8O4+gO9YJpLUJASVCl9WDoou7RFkFYtve1YWAa83QsA7rEXkHNrG2tkf4LKVUKdVb9oMuB5DQo0LtplxKmQKNabkOpiS61zsSzkntPEpNLv1ZvI2bYo53/z2W08r+u73OCk8fqDC+bwjO/B7XRV6Rvi8AQ7tLWHqa+vJywpHWxOSNYx781uOTXmGzMWLGDL9e9TJ5anvEoWCtg/ez57vpkV9P1P6dOfPtR9REcdWr8BhFjAralJb1+GUn+8EtuJcfn7jNrL2lwvKrP2bMKc3YNLH3yFTJcR0WL9YCGJxmaGi2l9K8rxpeHfsLZuXSaXc+/gEDrv3MG3iXYx98lv05tieB36vm/VzP+WvXyaXjQX8XpZ9/QwNO6/jpvFv4ffLkMqESr1EBLGIpDQt4+5pyZgbm+DzBtmxKY8lcw5HRPEBcrPsCIKIXoNqsmJBeUcen6fyjSW5oEGs16K7+Wo0wy/DtW4zIpEIRcc2CGZTde17Jdizc/j99js4Mq/8OSzX6xk5fw5JHdpfMiLeVViIx2pFJIhRmuOQabXn7b1Uchu/fHgDdku5aXQoGGTnim9przOT3D5228bK0EoTaGocwK6SaFPU9vFj0UkvTCnW/yOXxl9oNf8q1IZ4Og27g7W/RS4cRILA4DsmnVNtZLkRnQJRBaZxcrmkvMS+EsEBYClysWX7EQaOqo9Mfnopn3rT2UfDDYk1MCTWoHHnYYgEMRJp9ANeLJYgVl94Uyuv28HqnyZDKESjTkPpMPQ2Vv30DjmHtqMxJtC621XoUuKJf+g23G9/SshdLuJDttg1kGeKzR9gnzN2BkeBz4/V7ydOFv69+/2RmRwiAR59vDkpm5fhn/gnlmD4dy+pkcroH37g9ycep/PkTyncuI75wwZF1IzWHjqY8R//hC4htkvqhSbk8kSWafwDf1YuskYVp+KKJBIkifEx68V8HictO8QjlQlR/cwBrrixCcY4JVaLm49f31jW/lEsqXxT4myd1UUiESJBHLOFIhDzO3LeEFGpQaIgiBDJpYjijIjkMkyPj0es1xEKBvEdPELh4y9HZD2I4+MwPHA7JS+8w6D33+fLdu0j2u2coNbAgRFO4S5bCSunv8HmReVp3fnH9rDl92+54ZW5xKc3qKIPXM0J6gwdwrapH1G4Y0fEeHzrVmia1KQk9yjGpFqVnkMiV2JOrYetKDtiXGNIoFW/a6ndqiduZylimQKxOPr5lZ/j4IUHVkQIuPwcB++8sZ8X33mfPu+60dVIj8rUMNSvx9Dp37P4ltvx/n0vlqhUJLZtg3hzxUJaLJPjCzlYdPwNbmr4NXLx6UXMM3avjRDvJyjM3M+RLYtpN/jm0zrPhSbk82H7ZX6EeAfA58P26jskPX8HBcf2sPzblxl81xvIFNFGjPaSfNbN+ThqHGDfmrn0uupRzGn1T/uaHDYve7YVsujXg/i8AVp3Subyaxrx6VubsBaXPwNWLTnGXY93YPCYBmxbl4tYIpCepKW2p0NEDfEJpIKCFHW4RPCEWa+0zn8n4u6wFuJ1OxAEMWq9GYns9NdtAa+Xze9OjhDvAB6rlZ8HDGLczq3oa9Wq4is+MwJeLwXbtrPsnvvI3bABkSBQd8Rwekx6DcNJGTpViSXvYIR4P5ntSz6nw6BrgDNbB2ikcdzY4HOmHbyXbUW/ESKECIEO8Vcxps4bZ5wZVE051QK+mjNGodLRecS9pDZsz+qf3sVWnENyvVb0GPvIGT3YTibocuPPycPxy3x8GVlI69VCc/lAxMkJCDHSTk8gTogHmTTKzRJAiDOS5xCY8cUuOvVKJyH5/Lsmn+Bk1/ALjdtvo8SbxcaCGZR4smkZN5QamtbIPHLyj+0mLrU+zXqM5ttnR5aJDZetmMXfP0+L7mNwKBpSY8RleH4srx8XTFVjZOcJVr7h4giUix+tToZKIy1Loe/cLZmUgxvwL480BvJnZBH6eBoDP/6U/EI7f9x5S9TGzpG589n//UzaPnj/JRHdFClk4a4GMerYAcSJZ1dbasnPZOX0N7AW5nP/08/w0VsHsVnDGSqCWMSQMfVp3Skc3SvMc5aJd4D9+/ykNezI8X3ros6bUq8Vat3ZXZNSY6Bhx8HsXRO7A0O9tv3O6rxng1Qmpv+Iumz6KyfmfO+BNdHHe+GO65DWrY0QZwAgUFRC0TOvR7VrChQUYfvuJ5TdOhDcsou6w4dxcNbsiGMURiMNxoyO+LuzFedEiPcTeJw2ln49kREPfIjiImzy/T+jTUtl5Pw5HJk/n11ffA0iEfWuHom6cS1mTr4ViVTOja/Nx1DJJp9CpaP72Ic5sr38HlS/3QDaDryRYnspJYIMa1EBkuOHSKvRAO1Jm9k+X4CFvxyMir5C2CRqyaI8bprQGkmMjWaZWk3dy4czrkMH7NnZEAqhSU1BlZREmwHjWP7dyzGvt27vAfxpvQuTogbeoOu0BLzHZWfL799WOL916TSadh+BShc7NfZiEiix4py3JPakP4BwJA9zegP2rJlDz2seiyngCYZo3XMsmYc2k3dkZ9R0aWHWaa9zSopcvP/y+og2uIf2lpCYoubOR9vz+hOryh5Vw69qhDFOiTFOGZFBdZP8CyZt647jpFpiEQK3NPwWnbRqvGkuJZzWIjwuGyJBjEpnivodeV12sg9tY/HnT1OQsReJTEGLXlfSdfQEdObT81Fx5OaybeqHMed8Dge56zZcdAFfsm8/P3btQdAXXv+EgkEO/jKLnDXruHrtKnQ1alT5e1aWXeRxluLzuAkEgmfcitYoT+OWhl9T6svH7S9FKdGjlSailJy/bIL/AtUCvpqzQqWPo1GnwdRo0pGA34dMoUauOrsvY8jnx7NxG8Uvv1cmvMJGdCuIe+kx5K2bVVijKJj0GO6+Ecu7n/5jQoRw0w3MmJ6Jzxuk1OK5oAK+qggFg2ckON1+OxsLZ/LV/lvKxlbmfkySsiH3N1tIXFoD6rTsztJvX4gZKdz+50yaTPiaYLB8N1veoRWCvmrEhFoiIBOJ8FaQOXEi+g4g9ZRw5dU1+OrTcC3WgD7xBN79LObrAgVFyEQCWQt+qzArY9Nb79DomqsumFlaZQhGA6rLeuOc+3vUnDg+rlITu4ooLcpm2vNjsOSFUzA9jhLuefBxgoIZsdxIUpoBvVFe5pZ/susxwG/Ts3h44iv4fnyAvCPby8YTajZh5EOflJWBnCkypYbe1z5B5p61OCyRbZ26XfFATHd7n9OJWCY7L2mMtesbaNkhkW3rIz9/crqGzg1ElDz3dy27RIzxwTtRdGlHIL+IoDV2yyzv7gNorhyO9cNv6PP6JGyZx8tStWv070fv995GVysyMnZw87IKr+/Q1uW4HdZqAX8eUCcn4a+tIvG2y4AQW3fNI/+PcO25Bxs7//yFLiPurbSGPT69EcPufZdFnz2FQmOg3fA7KNUn87NGwgGXFxHQXG1irNtLurUQtT688eV2+jmwO9p74QQH9hThcvqiMsXcgSDeUBClRIKuZg10NSMX7s16jGb36t/IOxKZWdCw+xDy1cew24topx2DQnyakbMQNOp2A7XbXknB0fXsW/NLhPFqKBQidIrMt4tGIBCROfZPxKUuFCo9wYCfoD9y0z9gteHPzCYweyFt7XradLgD92gTv37xELbi8o1Opeb0N7OPHrBEiPcT5GU72LOtgC5901m9JJMR1zWi5j/6wp8gWdWEZ9psZkfxfHaX/E6isiFdkm4gTl4Dmfj/x6DO53WTd2QXiz57ktzD2xEJYhp3Hkqva5/EmFj+N59zeAfTnh9T9pz3e91sXvwNx/dv5Oqnvz+tDNCA14vP4ahw3nrk6Dl/nnPBa7OxZuILZeL9ZBw5ORxbvITmt1Z9FkxCesMK5xRqPccz3CxZuIs+Q2qTkKw+be8QAKVEj1Jyfspr/6tUC/hqzomq2IUPlFgoefvjaOEVCFDyxockTHkJsTm2q70gk6Hs3glRWir2H2cTys+HGjUI9h/Aj3PzOXLACoBEcm59xS8kAZ8P27EM9v/0M3kbN5HcuRP1Rl6OrkaNUwoaqzeHr/ffGjWe69rHvMxXGHj9o5RkHqYk50iF5yjI2Y9C3xBkUpTdOqC/5RrEulMv/lx2C7biXI5sXQFAnVa90ZgSUWoMZccYJWJGxBuYkR/tTNrDoEH/98LZ53KxduILSE1JTHhoFLN/K0AuVL44C1qtuHKiewKfwJmfTyhQ9QaLZ4Mgl6G7ZiTBEgvu1eXmVJLUJEwTH6nw770ysg9uLRPvAHlHtjN/yjUIEim1W/Sk4YT3I1rdqf7hxuy0+3jr+f2MvfEVOo8JIhVb0cUloTEmojlL07JA0I/Fm03IANe/MpvDG5eyf8MiVPp42g++hbiUOhHGeKXHjnF47nwO/TYHdUoyre65G0O9ulXags1gUnLX4+05uKeYhT8fxO8P0qNvKk1MbkLvvFt+oD9AyRtTSfhoEiGHs/KT+vyIpBKU5jhGLpyL12IJt8Y5gzaF1Zx/3E4rmxd9FTOyCrBvzTzaDhhXqVGTQq2lafeR1GzWFa/HRbFYwWuFPnyhcKZLCNju9HLYI/CKRsaJbWOZXExCspoj+y0xz5uYrEGuKBfvdn+A4x4vv+ZbKPD5aaRSMMSsJ1EmRXJSGYguLpmxT35D5v4N7FrxM2KZnLq9+5OnOsrMvMcQi6T0Tb0PqXDqmvWCPAfb1uex6BcFDptA4xYjGfbQLaz76XGyD4TvUy37XHVJRt8BfDIZkvQU/JnZMedlrZpTvHoSWlNyRGQ3UGrH9v0vOGYtLD948w6kiWbGTpjKl69fRcDvRR+fhsZ0eiWCPl+A32cfqnB+zfJM7n++E2NuaorOIEddgdeOSCTCrKhF75S76ZF0O2Lh/3PZXpR1gG+evrys1CoUDLB79WyO79/EDS/NQmdOxWEt4vcvnom5SZ9/dDeFWQdPS8BLVSq06enYMmOvF5I7XtyOIR6bjaA/QI2+fchdv6GsbOYEh2b/RpNx18U0RT0X4tLqoTUlYyuOzlBrOeAOFswqZNv6An6ffYiXPuxLSnp1BP1i8v95J6jmX0WwqCQqNbVsrriEgLW0UkEjaFR4UmrwZ81+qBvA8RwvK5/bXdbLMi5Bic7479ipDgWD5K5bH9Hb9sDPv/DXs88zZtnvJHes3IhoZ8mCmC1nANbkfcPgto+jdVYegZZKFSjSU9F//DqCUX9arT0cpUWsmvE2Gxd8UT749UQ6DruDLqPuK2srKBMEBpp1qMQCP+WXYA8EUQgiBsfpGWzWo5aEF7Du4mIO/ToLd0kJ+l+mM+yO8cQl1MahVBByuWNdAqhVxHfry95vYrtNJ3fuhER9cV2TT0YcZ8TwwO0Eb7qKYLEFkUaN2KhDfJblCvvXL4w5HvT7OLJtBT63I8Jgsnm7xCgvSofNxxdTDtOtfzq3PdSr0rZpp8LqzePP3M9YlPkGroAVtcTEVW3fZVjP91HJjVG17yUHDjK9W0+c+eURq91ffUP311+jxR23IddVXUTaGKekfbdUmrVJIOB04/n4C9wr10YfGArhmL8M9RVDKjyXSKUkBKgG90XQ61AJAipz5eUG9dr2qzDtuV7bfihO2vSqCkr9fkp8AbbZnUhFAi00SgwScdn37b+CIJYgV1a86JSpNAgxatf/iVgiQx+fhsVSyMIiF74YgsIeCPKX1cFojRZBJEKukDD86kas+yMr5jkvv6YhCmV4U80VCLKsxMbXOeUR+2NuL0tLSnmhTioN1ZHPM60pifrtB6BrUouVuZ/ws/U56oh6MbjGy7SJ649ZfmoTQkuxm68mb2XjqnLxu2qpi/Wr8nnspddYPHUMSq0x7O9yBpG3C0mJWo1wyzXw/JtRc0KtNDxJ8fh9Hgbc/CKakzJ/AvkFkeL9xHheIbLlm2nScQhH96xh7FPTYmYMnQ2hEOzbUcSqpcd45KVuqE8jQeL/Vby7HaUs++6VmD4ppQXHydy7gabdUvG5HeQd3VXheY5sW0mtZl1P+X6alBS6vfYyC64dFzVnqF8fY8OL50HiKirCkZWNrmYNgj4/Le64DevhI6x++lmC/nDJnTqGU37Q5SZosRLy+hAplYjjDJV2ToqFLi6FayfO5Oc3bqUgI+wjIUikNO89DrGhD9vW7wfC64Tpn+3krsfbld2zqrnw/H/eDar5d3GqdLzTSNczxilp2a0WLzy4Ao+r/CEgV4p58IUumMxnX5NeXOjC7fIjlQrojPKwid55wp6dzZwrxpaJ9xP4nU7mXnk1V6/5s9IUcLsvup1Q2TlCHhCJMCSkk9aoA8f3RhvjCGIJccn1UCbEI4k//ahr7qHtkeL9b9bN+Zi6bfpSu0X3sjG9RMJgs57OejWeUAiZSIRBIkZ6UqmASCRCLA9HjKyHD7P+sQcoWfMHnQcMxjM72s1UnBiPoNehrN8CXc2alB47FnmASESPN19HaTrzyPb5RKzVhJ2C0889rV9nSq5wTqU1IRIEPAEnpd5cXH4rMqOaZ6a2Y9KDWyK+M/FJKsbe0vycxLvbb2POsRdYkTO1bMzhL+bz/eO4LO0Rhtd8HgnlAt5TWsrKRx6NEO8n+PPRx6k7fFiVCvgTKFVSAk4btq0VLwoDuXkIKgXKvt1wLY1209dcdTm+rFzUA3qddrmL1pRE+yG3sGHe5xHjCrWevtc/g+Isy5FiYfX5+TanmOWWyCjOtYkm+sfp0P6HRLxCpaPDsNvJ2L0m5nzHYXecUSmYVyJjr7fiVNwdniBDg0GUfy+kU9K13PZwG758b2tZK0exRMS4e1uSWqv879vqD/BtTnS6vT8EU4/nM7FOCoZ/mLxKxXLSdM3pL3+Fxp4gC4scHAmE0Ho1qOUC5lP8mgtyHRHivewzegL8+n0eV034isT01NOuM74Y+Amx1JzA4KfvJ/TZ9wRy80EsRtqjE45rRpEp8jP8vsnUbNI5YhPCueKvCs/pW7mevlMmEtDI0MWd/meXSsX0HVanQr+NDt1T2b4xj8N7LaxcfIzhVzUs6w7yX8PrspO5u9x7JS61Pm0H3oDGmIggiHE5rIRCIURiMVKFCp87dkaU9jSzIwBqD7yM/p99wqrHn8RVWAgiEXWGDKb3lHcvaJldMBAoK9lxFhTw13MvsP3DcoPobR9+RJ2hQ+jzwRSW3HEXAC3vvD3iWeMvKML62TTcK9eFW5tqNWjHXYGyZ2fEZ1j+GJdSl2ufm4mztBBLYSkuj4I1K20s/3F/xHHr/8zi+rtbVgv4i0i1gK/moiPEGRHJ5TFduQW97rTrr+s0NPLmFwPYsi6Xg3uLqdfIROuOSZgTz6723W7zsn1DHt99uI2ifBcSqUC3/jW48qamxCWcn0iuMy8fZ15ezDlbRgaugoJKHy5NTQOYkzEx5lwtTTsUEi1qpZnBd77ON0+PwG23RBzT/9rnMKbXQXUG4t3ttLFm1gcVzq/77SNSG7SJSFkUi0SYK2m7pUxIoPltt7L2xZfKxg7Mnk2jUSMx9O+Od9lf8LfLvLRuLUxPT0CSkkSy0kifGb+x/dWJHJkzm1AgQFyTJvSa/C7xzZud9mf6N9Kkw1BWn9T66GTaD7yJgCrIzMMP8WfuZwRCfkSIaG4awuszpvDHTDu5WXbad0ulYbO4c/77LvXl8UdO7PZWS7LepVfyncQry6OC7qIiDs+ZV+H5Mn5fguk8RUVESiXShnXxbNgac17WujlijQb9rdciSUvG/vN8QnYHkhopGO6/nZA/gO/QEXx79kO92ojNRkSnKHVRagx0u+IB6rcbwNrZH+KylVC3dR9a9BmLIaFqzYl2OFxR4h1gWl4xTTUyGkr+fd4g50Jqg7Y07no5e1ZHmg026TaClPptzuhccqkcvURMfgVmlAaJGMlJQlGlkdJ9QE1atEskJ9NOMBAkOVmJNuhEbrcSlOoQ5HKOuj1UVOxz3OPDHghiiHH7tPoD/JDn5A+LvWxsp8NNokzCxDopxFdyz928JrbQBNi5pRDFhDbozJd2DatGLGZDIMiOxBSueP4REvx+AhIxfwYFVpZ6mFQnieTUQdEvjGGCe4KQP4BcpUUSd+ZlA3UaGmnS0szubYUR4/FJKlq0T2TBz2HTsMWzDtLzspoYzyHQ8G9GJP4fe2cZ4MTVd/FfJu62WRcWW9zdpUUKFEop9ZaWujwVylt395a6u1AXKO4Firs7C+sW10neD1t2SZMstlDacr7tvXcmk2wyc89fzpGiMVpxlB6gWbdh5HU7h0WTX6SicDdIJDRo3ZvsZl0w2DLocPYVLPs19tkiEQQatut/1K+pslhoOe4KGgw6G7/DgUypRG1LQmk8+d9xX1UVjr372Pj+B7gLi2gyZjSZfXpTsXVbFHk/hN1TppJz9lmY8/Joe8N1UQJ7YmUVFY+9RHB7rT1q2OnC/sbHIBHQnjMQyTGKzmlNSWhNScyZsYEfP98ad81/M9R0euEMgf+PwFNaiuvgQYpXrUaTkkxSq1boMjORngZel1KzCeOtV1P1wl9UQSUSTHdeh9R6dCXFglRCcrqOwec1ZnA9XNeGlcW8+mhtWW0oGGb+b3vZt6uKe57ujcla/2X5YiC+p33tfOKNBoBN1Yimxj5st0crtUsQuLjxJPTy6tLepMymjH9+JjtWzGD3ugWYbFm07X8ROmMKOtux2QCKQT8eR2JxJo+jnKDLFV/xNwGkMhmtrx3Pjh9+pHzTn9nRSIRfrxzH0A/ep9E7z4LHh0SlRDAakJqqgzzWZA1KdWN6vfYmPZ5+EklYRGU2ok/796n1Ho6w14t09XaGXPYI0794NKpqpUGLHjTvOIip+U8x/zBSHSHC+oopeEKV3Hz5j+gV9RfgcAZKiSSgIGIkiCtUjo1aAh8JR+KKKh5C0Bu/xeZwuIJlVPgPsKVyDgqpmuamgRgV6UdUuhW0agzjxlK6ah38xSVBotei7t4JAKnFhP7CkWjO7lvtHiCBiidfIbhjb+16pRLrk3ejaN7kiCReY7CS26YPGU06EAoFUWr0ca3HTgSOkMhPpVUJ538rqyJdHkCv+O/06etMNgaPf4Kuw69lw4LvkUgktOozGlNKAyRSA4UHnAQDIhqtHHOSuk7FZaNSyfk2A9+U2qkMilSGokuAhyUZoyqLoNoG1ZasxuQopuKFtxD3H6QKQC7HcMUYNEMHHPfmuMgfiCLvh1AcCPFbmZ1LUi0x13MIckXi9ymVSk7bsvnDYZHLuCUrmUd3F/BiTadV9f/kpkwbFmV8HQB17264f54Rd07VvROC7vgCmmarmv891I2Na0qY8eNOQoEw7bul0biFhXeeW1lzm/b7RMKnqzBgPUOMiEQiIjKhtgJLZ0qm64jrWPL9JFr1HcN3z11d+wyLRNi7fiGfPXge456ZSpcR15G/dTkFO1bXHC8RpJx3x1vH3N4gSKXoszI5lZ3cfrudDe99wKL/u6dmbMcPP9L57omUb9qc8Lgtn3/JqKk/o7HZoqrRxJKyKPJ+OJyffYuqWwdktuPTrOjaNzMhge/aNwOd4RRawJ5BDP5+9nYGJx2uggKmjxvP/lm19ipyrZaRv/5IRq9eSOV/bwmMRCFH3bMT8uzHcX79M6H8AmQNc9BfOBJZRurfYvtVUerl87fWx53bs62KkiL3SSHw2tQUpEplTAk9gFynQ5Ncd2bcqEjhumZfsbDoPeYcnIQnVEkTQ28uaPQCGZqWNeskf5bSdx52De0HXYFUJj/uDZpKayS3dZ+anqm/IjevG/L4FuB1Qp+VyejpUylYupQtn36OQq+n7U03YM5riqKOCgGdXoFObwVOT6Glk4XI6i1kmnVce8937Nm5Ar/PRYMmnVGXePCFq1hY9G7c43Y6FmMPFqFXHJ9QXTwopHUHaxRCdKZJaTKS1q0rhX/EWtgBNBhUbTXnFz1EIiKqv5ByR6CYr3fdwfLSr6LGx+a+SK+0q9HITHVejywzHetT91E16X3EguoKGEWLpphuvzbK0k8ilSKzWQn7/FS99mEUeQeI+P2U3/8sye8+jyz16D5PhVrHydoGiZEIjlDiH59DlFAVKPtPEXgArTEJrTGJjKYda8ZKCt18+PIfrF1eRCQCWp2cseNb0nNgNnpjfOJX4c9HGd5EL8V6jIZmKOXN+aJESWEgyMUpZjKU8f+zYkkZZRMfI+I/LGAbDOL44CtkOVlktW6OAHFDYFlKOfoEQYU5FbGVFocwr9LJ8CQT1gREvVOPdCa/H7+VpGPPdDS6f8Z2sYlayUtNsphaZmeH10eqQs5Im4l0pRxlgs9NlpmKslMb/Cujn/kSTXVw72h0YBLBnKSm99k5pGboWL2kkM3rSvnhsy1Razr2TEOn/3eTIVewglLvTuYXvo0rWEon21jyjP2wqLKQSCS06DESmULN8invxm2bdFWVsHfjEtr2H8sFd39EZfFe9m5YjNaYRIM2vdGbU/5W+96jhaugIIq8H0LI58dXWZXwOF9VFQqdLqaVLLgnsXBv2O5MrBl0FEhK0TBgeC5zp0SLHuuNihNuszuDE8eZT/9fDjEQYNXLr0aRd6j2uvzpnHO5bP1qLE2Oz7u9PiFoNCjyGmP+v5uJ+P1IVCoE1ZFVc08W/L4QZcWJlad3bimnacv6J4ia1FR6Pvk4C+/6v5i53s89gzYtcZ/zIZiU6QzLfoDeqdcQiYRRSrVo5RZcwXJKfbvY6ViKSqonV98FoyINpfzEyqWlMjkdB1zC2rlfEvBGZ3+UGgOtOpyDxFd35UAi6DMzyLtgDI1GnosgCCfFVux4EQyIVFX4CAbDKJVSzElqBOHvyVIJajXaC0ZQcf8zMH85ubnZSJQKgj9/TcDjJfjJLYiRxP+DSl8+mdrW9XY9ermNFHUTir2xvrKZ2tbo5dE2eWqrlQGvT+LrHr1jqlCaXXIRijQLa8t/YW7B64jhAD1TrqK5eSBmZSYAmypnxJB3gG/2TCDP1I8cfd2l0YJKiap9K2wvPkLY5QZBQGrQIyRwXwhX2fHOXxx3LuL3E9y976gJ/MmERirQSquOm5UFaKb2s8sxjyzdsT8DHIESQmE/gkSGSXnk+9LpjIoyL09NXEhhfu3n5HYF+ejVtSiUUvqfkxsT4Cz0bOOF9f2xB2pLzw3yZO5oNQetoikGqRRNAn0B77I10eT9T8jaNKfKVcWuV17lsivG8WlF9P9NLpFwY4oZfSSW2kcikbhieocgRqiz7tWarOG8y5rFZNxMFhUXXtMKS9LpIwBaF+SCQIZKwVXpVnzhCApBgvIISQCp2YR5wg34lq3B9eM0Ih4Pyi4d0J0/7LgsPePBatOwYvFB8ndH21GqNTLOu7Q5StXp82yrb7iDlcw6+BJT99cKd66rmEKSqgF3tZlPkioHnTmZ3Da9mfXhQwnPs2v1HNr0uwCdORmdOZmsZnWL+p6O2PXLlLjjJatWkdWvDwWL4z9XGo4YFtfRpE7HGpmUcAJR46OB3qjkomta0a1vJlMmb8PtCtKpZzq9zs75R9oy/9vw771jnAEA7qIi1r/1Tty5kM/H/vnzMDbIQSo/PaK/gloF6r9fMV4mF5DJhRqhob/iRETx6oJcrablVVdibtqEJQ8+QuWOHViaNaPXk4+T2rXzUduGSCVSzMqMmr/tgWK+2X0ny0q+PGyNjKuafkQ76yhUMh1hj5ewx4tEEBDMxmPKyOuVZi6/42Pm/PwyezctBomEpp0GM2DkXQQKy9i7axm6ogL0WZnHJRAjq2e7lBNFZZmXX77expxfd+P3iRgtSi4c34ouvTMSZutOBpwHD1K2bj35CxZizMkh8+HbCf08i+BhomyyzDRkWisShIRl7QbF8bUYiJVVhKscRPwBBIP+T9cCFSZlGje3+IkX1vfHEawVpjMp0rmh+bcYFLGbYmvrVly2ZiXLnnya/HnzUScl0fnuu0gb0ItPim9ifWXtxmebfQFp6mbc0XomMkHJtPznEl7j/MI3uUz3DlLJkcXapBYTUovpiOsiwRAIArJh/RB7dIFwGOmCpYTmL4aQiFgea5P4d0ApCIyy6Vlid8eQO4NUIE9lp8JzbME1d7CS3Y6lfLfnbg56NmJRZjMi5yHaWkbE/b/+E1CY74wi74dj8vubaNslFautlsA6AsW8vXlMFHkHcARL+GDLedzddhEaWeLfVHDrzpgxiVZDZGgfJg85B1OjRgw791waGw38EoIyMUyeTMIQmQT5M5MQb74K4S/ClxKJhP5mPQsTBGt6GLUJM/cAWr2CYRc2pV23NGb+uBOH3U+H7mm075ZGWuY/zy5KLgjI/3y7AacTT0kJIZ8PhcGALj29RjjsEKQWM9qhA1B17whiGIlei1CPzx2LTc29z/Zmzq+7mf3nc6NTz3TOH9eClPSjkKD/B6MycCCKvB9CmW8v0/Y/xYWNX0UhqJAr1OjMNqqK98c9jzk15x/RylEXAo74VTIHf19M53v+D21qKu6ioqg5pdFIu5tujLv/k2VnINFpibhixTQVPTuz4vU3aXPLjRiys47reo1mFW27pNK0lRUxFEGjlceILboOHqR0w0b2zZiFPiuThiOGo8tIR675ZwT9/qk4Q+D/5RADQYLuxCq59j178HmcaI3/rXLjI8FoVtJncE5M6RBU+/k2zDt5auZqi4VGI4aT1q0rot+PTKVCfQRbqiNhbdlPUeQdQIyE+GDbFTzacSO2fB2OT7/Fv24zgkGHbvQ5qHt1OWpLM6lBi3rafgblnEtk8K1IBAGZxsi0K6+laEWtz7kxN5fzpk05aYJkpwJOu593nl/Jmj9qH7L2Cj/vPr+KgF9k0KhGdfbN1hfse/by3VmDse+u7X8T5HJGfvcNhrQUxJ170Qzqi6p7R4JqOR2SRrOq7LuY86Spm2FSHHtQJZhfQMVjLxHa/6ctliCgHTEI/cWjkJqNpGtb8ED7lRR4NlHo2Uy6phVpmuZYVFmIwSDugkK8paVI5DI0tmR06WlYWzTn7Hffwm+3I8jlaGw2NlRMiyLvh1Do3cofJV/QM/VqXMHShNdZ5S8gHAkeFYE/amjVRN58hsmBMEs9AQQJ9B49gnPPH4bw0HMomubW32vVAV9FBZFIBJXFknBjmyKX8UCOli+KA2z3BpAA7XRyRli8fLvjIv7XKn5G6HA4qvxUlHmpKHPhzJzBZ7uvqZmr8O/nk+3XcFb6bYxs8BhqWf27BZxMlAeCbNma2L2jqsKH3+lDVIWrHSOoJuoHPfG95Et8O3EEizEqExN4RfMmeOdFZ9oU/brz+4svEQ4GaX/NeMKvfYSlvJKrB/RCTLIg3bWX0O/LCIdEPHN+xzhubMx5s1UKWmtVbHBHl83qpQLnJZtRHCETrTcoadZaSePmFsRQ+F+RFXbs38+CCRPZ+cNPRMJhVBYLPZ54jLyxY1DHEaaTmk6eiJk1WcP541pw1rmNiEQiaA0KVP+Cz7guiKEQK4snJ5xfXPwJw3IewKLMQmdOpvt5tzLt7YmxCyUSWvcZcxKv9NSg4fBzWP7U03Hntnz2Bef+/AOrX3qFHT/8SCQcpvGokfR88jGMuQ3iHiNNspD09L2U3fc0EWftXl+W14hgx5asGnk/+ka5tL3+2hO6brUmfqutY99+vh80lMrttSr1CyfezbDJX5E7bCjyE2g/OYO68e++c5wBco0GY24u9j2xRBTA1rEtETG+gu7phMif2aNTFX1VKGWMGdeC/bvt7Nxccdi4lHue7YXFdnJvSs5AKS5tOWFNCK3Mwom8mt1fxPQD8TOUESIsKfqEfh+Z8a9aj6JTG+jVFq9cSvD3pRj69DyqDY2g1aIbfQ6+ZWtwvvcD0q7tmT/1xyjyDtUBox/PGcGFi+ajS/9nlt1WlvuiyPvh+PbDTXTulX7czgdHC7/DwYIJd0WRd4BwMMivF17MFRvXYbLZEPQ6JBIJMuDCRi/jCVWwpWpuzfo0TXNubflLnWQjHkKl5ZT93xOEKw7LNIfDuH+ejmA2oB97Lk5nCI/diDbQlXb63pj0KuRyKb6qKnb+8BPzb7+TgLM6G2HIyWHY5C9J7tgBuVaLXFv9+QXDfuYXvBXvEgD4veh9uqVcRp6pPytK428S21pHIBfiV/UEwmFCkQgqQUD4y70l4PFQGYYDgSCucIRcjQqzQo5eJqVCq+OBwiqc4p8VDRGY5fazSi7lsSfuQWo8uRlL18EC9s6cxfq33iEsirQcdwWNR52LPis2y6KUKchUipylXchoSxOQwM7KX/hg05tc3uQtjEcI3lSWe6sDVkuLuO2FxvywP87mGphT8BoDMm75RxH4skCIF/YVMyA1caZIqZLC/v3YJy/EeP0VSC0mgmLdooqBcOL2KwBVl/Y4PvyKiO8wrZMGGeQvXASALjUV8ff1RDxewj9X+5Mf/pQO7csnEg7H6MOY5DJuy05htdPD1DI7vnCYbgYtg61GkhVHv92TyQRkslOvPVPfcBcV8dOI8yhbX9vb7quoYO5NtyCVy2l19bhTrrEjlQonff9QXxArqxDLKhBLypAmWZEmWY5aVBjAXVzM3hmzcHdILHIrRgJRPe9NOw8mf/MfbFz4fc2YIJVx7q2TMNgy4p3ipCMQ9uHwF+IMliITlOjlNkzK47OaMzbMJefss9j3l7ZWqUJB88suYerYi8jq358hn36EIJOR3LEDptzEAWGJIBAyG+Dai1AGQkTsDiSpyRRt3cK888cg+v1s+/Irml18Yb1bsQY9HhY/+HAUeQeIhMNMvegSrtq2CVOjRvX6mmdQizME/l8OXXoaPZ9+nN8uuixmzty0KdqcDNSG08sb+3B4Skoo37KNjR98SCQcptXV47C2bIE25diU0o8HVpuGiU/2pLTIzc4tFZiT1DRsasZiU5+0zU04EqbAvZEPt49jv2sNABZlFpc3eZumxr4ojyAOFg9iJIQ9EJ9wApT5diOYB6B65BbWb5zFmq/uIuBz06hNX/q2ysGq09bpViBW2YkEQ0gUcrSD+6Hq3A57STF7br4u7nr77t24CgpOCwIfDkdwOwMIUglanYKyYg/bN5WzbUMZGTn66tLZZE3U/7tgvyPh+dyuIB73yQ+IecvK2PXzr3HnQj4fZRs2YBo1MmrcoszkuuaTcQaKqfQXoJUnIZcms9VroCDkIVMpxyqXHVWQLLT/YDR5B2QNswmfM5zKpBQc+S5WLS7gx8+34vWEUKlljL26JX2H5FCxajUzx0dnAxz79vFt/7O4YuNaTA1r1emJRAhHEn+eYiREJCgyPPtB1pT9SCgS3VdskKfQ2nJOzHHOkMgBf4AppVXYQ2E6GjT0NOlI/tNmy19lZ4svyHOlDvyHbS676TWMz7Qxu8JRS94PQ0VQZJXVyDCTgUA4TFVIrA4QSATMcmm9BCBdBQX8cv4FFC1bXjNWsno1a157gwvmzIhL4k3KdDolDaDIs50tVbNJUaVxf4dlmBQZKKWJyasohpn18y7WLK2+f8j0btwl8dsDIoQprtyEYbMbWVYG0iTL3yJAeizY5vGyw+vnvAZm1FoZ3ji/3YGDMpAuWoD39z+Q52aju+BcdPIkpBJ5XF0JAWmMxsNfIU1OIum5B6l44hXEkj8txvwBVBYLQZeLsm3byW2QRXDz9rjHK9q2TPjZmuUyBloMdDJoCUci6KVSZH+TNsffDfuevVHk/XAsvv9BGgwZhD4z8xRf1T8DoaISyh95kdCe2nJ2WUYq1ifuRpZ+5IBv0ONhxTPPs/mTT+m28FXmEd9WtJV5COrDREZ1JhuDrn6cHufdwoHtq1GqtaQ1bofOlPy3iNS5guUsLHyXX/Y9SihSHXBLUjXgxhbfk6VthyA5Rou2lBQGf/Ih27/5jtWvTMJXUUFW/360uf5aVjz7PI59+9n08Sds+vgTAFpefRUDX38VWV2ZbImEOffci3PfPpQmE+7CQkK+2iocuU53UvSDPCUlbPvq67hzEVHk4O+LzxD4k4gzBP4/gPQ+vRj43pv88eBjuIuKkAgCuSPOod09t2HOaYhU9veq0CeCu7iYOTfdys4ffqwZ2/rFlzQYOphBH7yH7igE3Y4XvspKPCWlBF0ubCYjOYPTUOhOfp9auW8fz67rjVesJYkV/nwmbRzOfe2XkavvfMznVEl1NNJ3Z4t9Ttz5lrI+SHq05fvJD1Kyr9bGZPuqmezesIirn5uOLSu25D3scOLfuBXHJ98SKihCnpWB4eoLkTdrUv3wqENQyVtSknDuVKGs2MMf8/P5ffZ+LElqxoxrwTN3/469sjYrJlcI3P9CH5q2tCL9k8QbTIk1GiQSUNRhx1RfCAeCddqueUrL4o7r5Uno5Uko5E2ZVVRKXsSNPlBJUKHiq7CKYak2clWKIxLN0P4DUX8LZw8kv2UvPvlkNyWFRUgEaN81jdsf6c7bz66gstzHp2+so1ljBcvuezD+Ob1etn/7PV3urs3wyqUqeqWOZ0PltLjHtNePpGTjehp1Oot72v3OlztvZbdzGRIE2lnPZUzD57GqcqKOcYdEfimt4ofD7NW2eHz8UlrFk40zSA2FKPf4eLrcRegvX+E/nB5yKxxUJvD9Blji8NDNpOOHkirmVjoJRiJY5FIuT7XSXq9Bn0DY7GhxYNHvUeTd1KgRrf93HYYmDXFUFqG0WeJaNhoVaRgVaeSZ+h71a1WV+5j2fW2/tvQIevlyr4Tyh19EolSQ9Mz9yBvlnNYkfrm9OlP+bcDNzc/25J37l+K01waB2ne2MbiTktAz1Z+367upaM7qg8GcytkZtzP9wPMx5+yXfgMGRd0BZolUQJHXCNsrj1VrSISCCFYLHW67lQUTJrL2vfdo8ulncQm8RKNG3e3IfvXGE/ye/RtQsmZtwjlPSUmd7YX/ZYQdLiqffyuKvAOEDhZR/tjLJD19H1Jz3ZV57qIi1r7xJuFgEHFNGXkt+rLNvSBqjUJQM6bhczFVO2q9GbXejC27Wf28oRPApsqZ/LD3vqixMt9enl/Xj0c6ridJ1eCYz6lLS6P9/26h6dgxiIEAO77/kakXXlJTkXY49s+eg6+qCl0dBF6TlET7/93MzKuvxVcZG2Btf9utJ6UfPRwKEQ4lfhYm2oecQf3gDIH/D8CQlkHjseeT2r0LrtIiJDIZYQWYGzbFkHR8ZUAnC6FIhIpgiL1eP5qly6LI+yHsnTaDAwsW0uyiC0/KNTj272fWdTeyb8ZMAASZjJZXXUmPxx5Bm3py/cTXlP8QRd4PIUKEn/Y8wPUtvkEjO7YePY3cxOjcp3lqbbcYETOjIpUmdKDEVRBF3g8hFPCy4KtnGXHrqyjVtQGMsD+Ae9pcHB/WRl+Du/ZSfv+zGP83HkXzhgnt8AD02dnH9B7qG6VFbh69bT6lRdUb+C69M3j/pdVR5B0gGAjz/H2Lee7DQSSlVD8Ak9O16I2KqI3+IbTrkorBfPJFGBUGPfrsbJz744v9pHbulPBYjyhSVl6E5rfXmDlvMmIogEKlpc2w6zjY+0IMKekkKeoO6smya7NW0hQbJe378Pxj62rGImFYvbSQ/D12Lr+5LZMeq7aHKyusomJrfLtBgMKlfxAOhaKyBY0M3Wmg68Je1/KotWZFBu3lw/nt7TsY/0JHGiR15n+tpuIJVSEgoJVb45ZzV4TEKPJ+CA4xzGeF5dyplrHOF4wh74fwa5mdGzNtzEsgFqYWJCyucjGj4rAgXFDk1fwSbs200cesjynXP1oEXC42vPt+zd+NLxhN4+su5Pepr1G6YCuCTE6LHufS9+K7MSVn4fOFkAoS5IrjI3OiGMHjqs0yF2yTkJXZlnz3upi1WpkZk12LGAwSCQYpv/9pbK8/jSz59NVXsf1ZVr7HH+ArRYSbXuuL4qAdR5GD9DQ1yl1bEJ//AP4MloWdLiKiiFKqYVDmXRgUKfy2/2lcoXK0MjODMyfSM/VqVNKjC/ZKreaokuS8iy9k7/QZ7Js1mzWTv6bdDZcRmPwr4Uo7UF3lYp54E9LkxJooYY+XsMNZLcSmUR+RaP2bUZdwl1SpRJrAF/6/DtFuJ7Ax/n06tGc/4Sr7Eb9X/opKwsHqe8eSa+9lyE/v0TJ9AL/7PsMjVtHSOJjhDR7Apmpc79dfX7D7i/hp7wNx53yik61V8+iVetVxnVsikaBLSyMUCFC8cmVc8g6gsliOyuq5wZDBZA3oT/7ceVHjeRdfSHK7tsd1jUeCQq/H0qxZwmd6Vp/eJ+V1z6AaZwj8fwRagwVtSwtGT0MiYgiVzlRv/eRBvwdXZSkBrxO5WofWmBRF9o4WYiTCdo+Px3cXMlgpRTvptYRr17zyGg0GD4prq3Ei8JSUMPXCS6L8qMOhEBve+wBBJqfPC8+elEimzxvC7fWwtWpewjX7XKvwi65jJvAA6dqWTGgzm8933EiRdxsSJLQ0D+biRq+iXXSQ5VsSv+6uNXPxexzRBL6iCsdnsYJoAI73vsD67nO0vfkmVr/0csx89oD+aFNPfgtEIoihMLN/3V1D3gGyco1M/iC+B7LbFaS0yF1D4C1Jau59rjeP37kgquQ2PVvP1Xd2QKM9+RUtuvR0+r3yIr+OviBmLvecIegzE/cKepxVrPn4IXavnF4zFvC5Wfn9y7QLeHGPvuMoCHwGgtVMuLySyNChfPHVvrjrSos8eN1BklI0lBV7KCz0YWrcmJLVq+OuT+7QPqbUz6RM58q0t9jomM4y19eEwgHaaofTWj6ImS/ci8dZTihQ3ZOsk1vRyesmjKudibNuKx0ewlI1xXUc7xLDJMsTPzqHWo28czC+qN5nRRW01qmxHuHzrQuH7tu69HSa3ngZ379xfU21SzgUZOPC7zm4YzXD//cFn76zH41OztDRTcjIMWAwHRthUatlnH9pE8rLfKxZUcLUT4u48fn3+Mg/FFeotq9VJlFyfdpH8HJtlU/Y7kQsKjmtCXxfs54f/wzm7AsEeTtSxaOUYf32Q8J2B2FRrFkry2uEbPQQDq5fR8DhwNKiBX0yrqFTh7EEIz7kEhVGZfoJiSXq0tIY8tnH2PfsZe9v0yhx28l99n6EkIhELkcw6OrUJAkVFmN/7wt8S1dCOIIsJxPTLVcjb9rwb7Vl/buQ1LoVSqMRv90eM9dy3JWnpBXvSIiIYQiHkdRxTznVOJJ3eNhdt8YDgExbWwUk+v3MGnoFKV06c+71NyLTa8jNGIBVk1jMNvxn8F/4G4MsoUiAUt/uhPN7nSuOm8AfgkyhoP2tt7Dtq/gaLp3/b8JRiRjr0tI454tPKd+4mY0ffYwgl9H62mswN22Cxnb8lqa+kBNnsIwIIiqpEYOi9lzalBT6v/Yq3589OOa4zH590TfIiRk/g/rD6XPHOINTApWmfsWVnJXFLJr8Auvmfk1YDCERBJr3OJeBVzyEwXpsJe4VwRBP7ykiEIkgi4iEPImFgoJuN367vd4JvKuwKIq8H44N739Ap7vuwHh4j249wOnwM/uX3ezYXEbaZYmj0WZlJjLh+B5mSqmGZqb+TGy7AG+oCkEiQy9PQi0zInZORrlnRsJjFSotksMMhEPFZYSKiiFBGXHE6yNS6aDzxDsRZDLWTnqNkM+HRCql6dgx9Hnu2RNW1T8eHCo5dzoCLJwRTThFsW6vVM9hVluCICG3qZnnPxzE/l12igtd5DYxk5qhw3yS7AXjIXtAf0ZPm8qCCRMp37wZpclE+9tupc3119b5+YZcFVHk/XBsmPYh7QZdAUcQu5HZrCQ9ez8Vj72MmJ7B3h3bEq7dvb2SjGw9ZcUelq9wMf7Rh/l5xMiYdVKFgmYXXxT3HIGiKgq/Wki/XpcjkUk5OH0Z368bB5EISo0eqfzoqx7itK7XIAxEZFKah8LEVxiATKUcnVRKJ72Glc7ojWxfkw69TKAiJMY9tiok4g0fvy+vQqejzfXXsX/OXFreNJ4/Zrwdt1WlsnAPRbs3ULBfoLzEy7L5Bxk0qhEXjGtBMBjGafeDRILBqMScpIobzBXLKpBt3cnAAwuISKWMuqk3O+wqPnv8IJffOYcy5QqKIstICzeglawPwjtzEbdFi6WKVbHE6e9AQPQSCHtQSnXID7uHJsll3JRp460DpUSA8qBIeXoaSVIJHE7eW+Xh69WBX0eOJOCoraxofvmldLhvAstmvYcxKYMWvc7DkJQeN4Ad8vkIejzItVpkdZASbUoK2pQU0rt1Pab3GCopo2ziY4iltaKroX0HKLv7cWyvPo6i6X+jFzUo+rEHC3EFyxAsMs7ftpC5I8ZTtGJlzZqsfn3p+uB9dfcVn2SITjdiUTGuX2cRqbKj6tkZZfvWyOqorjhVEHQ6EISaypOYedORxdA0yTbSunWN2k8VL19B8fIVmJo0ofmC2GcAgFheQWDLTtzT5lY7mww7C0XThnXae4qVdhBFJBo1gqb+/qcyQUGSKpcyX3wR6BzdkVtZjgaWvKZ0e+gB/njsiajxvIsvJGtA/6ixUMCHq6oUZ8EBCESQCXLUSVaMObloU1PRpqaSNbD6mBNN0pV6d/Pt7omsKf+JCGEyta25tPEb5Og6ovhTOyWtWxfGLpzH/NsnULJ6dbXl3a030/bG69Em/zNtRf8pOEPgz+C4EfC6WPDlM6ybW1tGHQmH2fz7T/i9Tkbe+jpqvQmvs5KA34NEIqAzJcf4rx5CgT+I588HxmZBweAxoylcFp9M5ww+m6WPPk73hx/E2KBBvb0nR/6BhHPhYBC/PbGA2fFi24Zyvn5vI1KZhEFXX8pCJhEhdlM+PPtB9PITe7gbFSkY/9KfKbWYaHvWxayc9UncYzoMugKNqTrqGtx/kLK7HsN02zVx1x6C0xlClmSkx6MP0faG6wg4nci1WjQpyadES+BwiHYnoYOFuH+bQ8TrQz2wDzfd1JQXn9+E11MdhAiFwugMClyO2LJ4iQTS/+KDLAgSbKlabKknV22+LiiNRhoMGURy+7YEvT4EmRRNamqdgoMA7vLChHNiKEDYG7+U76+QZ2WQ9NwDVDrD6Ax74n52ACaLir07qgDoMTCLtG5J9HnhORbf/2BNi4XaZmP4N19hyInfWmHLysNRdpDFn7wUM9dlxPXozEe/UWhv0PBFcXzbsDZaNWi15HjKsMlllMYJUl2ZaiFZKefGTBuFgSALKp1IJRL6mvWkKOTs9CTOXgmA/AQ3Vek9u5PWozvWtq2Y/1Fihf6inb+TlTuC8pLqQOjMn3bRY2AWrz+xjLLi6jFzkopb7utCXuukqDJ7sayC8kdeILjjsI3rkhU07dqJUSMGMe9rB1fdfgkmxVgqnnyVwLo3iReykGedvBatSCRCRVAkEIkgl0gwy6VI//LZekNOSrw7mHHgeYq928nWdeTsjNuxqRoil6pQSwV6GnW00KrY6PLhFkX0eg2WZx/E/up7BNZVtxVJRw7ip0GDYlqCtnz2BfpGOdhDB9gw/1t+/+4Vht/yCs27D6/RIQh6PFTt2sWql16hfMMmbG3b0OGO2zA2alivFkuBTduiyHsNwhHsH36N5YHbker+vvvVqYArWMHyki/5fs89+MPVlTZJqgZcM+MrVDvAuWM31pYt0GVkRGUlA36RqgovbmcAtc1NZWQnq8q/QSnV0y35EizKbLTy+hP7Dbs9eH6bg+PDr2rGfMvWICRZsL3wELK0U1MZ4AqWYw8UUeTZil5uI0mVi0mZgWA2ohncF8+02Mo8Vc/OCEfhTKO2Whn6xaf8MHgYVTtrdTR0GRmM+uVHtHE0jEJlFVQ8/jLBrbXr/cvXoOjQCsvEm2JsbcVKO/5V63F+8wvhSjuKVnkYLh+DNDMNIY5n+rHCqEhlVM7jvL8tVgRaKdXR3DTwhF8DqsvkO9xxG3kXjmXPb9MI+fw0HDYUfVZmVDA+FPBzcPsaQqUOltz5AKVr1wLV1Vj935hEzlkDUeh09VJdW+HL57n1fan01+6JD7g38Ny6fjzQfjk5+o5AdVA5s3cvRk+fQsjjQSI9un3IGZw4znzCZ3DccNnLWD//27hzu1bNweOqpLxgJ7M/eZSD21ai0pnofM542p99GXpLbC+5I1Cb5dzm9XPRiHPRT3odZ35+1DptairZAwfw47Bz8VdV0e+DSRgt9WMvIqujpE4iCNWR6XqEo8rPtx9Vl26LoQjzv3Fz8QWf8W3xNQTD1URAgoTBmRNpajx68aljhTElh2G3vExZ2Q5CXi87Fs/EUXaQlIataT/ocqRSGaLdQeVzbxK2O4h4fQgmI+E4GTZpio1dBSHKdudzzpgmGBrk4CgvoPzgTvbsWYotqxmm5KxjIl3HC9HuwPHxN3h+qy3t9f2+nOTGudxz79U8/GC10v/cKbsZdWkzPn8rVrF4wPCGGMynb/mp5hjLQDV6U53zKuXRt4hIzSbMhjDDL2zK1+/F+mILUglNWlj54dMtnD2yIb3PzkFhlNHo2gvIHjUIT2ExCqUWTUoKhoyshME9Q1IGlz7yHd8+O47Kor1A9e+x3cBL6TDoimMS4rTKpfQ36ZlXFR2oUEokjEu3olIrMQeCPBQM8oE7wDqPnwhglkm5KtVCnq6adJnkMkxyGc210SQsS6VALUjiZtp7GHUnLC6mS09nxLdf46woRq0z43XFV4VX61PwuqNV0n+fuZ/MBsYaAl9Z5uOpiYt4/qNBZORUZ9UikQieRcuiyfufEJetpMOIs+g4pAtqjZyIKKLu3qmG6B4ORcc2CJb6rZA6BEdIZJndzeTiCipDIlqpwIgkI2dbDJj+LEUOin7Wlf/M+9surzlun2s1S4o/4vZW02lmGoBEIkElFUiTKkhTHrbhz0jF8uAdhO1OkEjY/MtPCfU8NrzxHj0+fI79m5cCMPXNO8lq1gVLWi5iKMT+2XP45bwxNRVAxatWsenjTxj5y080GDIo4Xf+WOFbFr8tBarJfcTrh385gd/lWMKXu26NGivz7eXlTWdxQ+vVpLbtiE0Zfa+wV/mY+eMufv5yKzc/1YT5rlvZ5VpYMz/jwPMMyfw/hmT93xHbc44WYnllFHk/hHBZBY5PvsF0+7UIqpOrpVLlL+DT7dexvnJqzZhebuP2VtPI0rXHcMVYJAol7t/mQDAIUimas3pjuHJs3EBQwOdBDAVQqvU132lTw4aMXTAX+549VG7bhrFhQ0yNGyds8fKvWBtF3mvOvXojgc07UPfqUjMmOpzY3/8S7+za/5VvyUp8y9aQ9MJDKFskLs8/FrS0DGZUzmNM2f9EjcuJRZnFzS1+xKKqPy0flcmEymTC2qI5UG3hFwkEEcsqEMwmJFIBV2UxoSoX00ZdElUJ5Coo4NfzxnDRkkWkd+92wtcSDIiUVdjppLuKVXxMhb92Dx4hzPd77uWGFt+gOcw94ETK9M/g+HCGwJ/BccPvthMJxy8VNaXk4CjJ58vHL6op8fS5qlj0zYvs27iYIVc8iVKhRZuWhvTPSGmmPHoj81pYxoRZMzn4xhts+/IrCIdpNGokeRdewOwbboZIhN2/TCHv4Hg8KidpmhNXLPUk2bA0b07Fli0xcw3HnI9gq9/yNp/HS3FBrRjW0hmleBypXHPZUtyKPSDzk5faEYMi5aT6K7vlVeTnbmet8WfUMhN9+00gXdEKg8KG3pKKp6QEX1k5kpFnodi8A/eUWZhuu4aKpyZVP9z/hESlhOuv4+u39uP3ifQYkEnQtYcvH70Qj6O2Z9aW05wL7/0Uo+3kWviE8guiyHvN+M49mHetp0XbJDavK2PTmlJym5i5+b7O/PTFVg7uc2K2qhh1WTO6989CqzvxaP6xQgyFcBcU4C0pBUGCJjkZXXr6Cat660wpmFJyqCqO7VvPadkDnenYvuNSqUC/oQ3YsamcVUtqs/syucBtD3VFb1Tw8udDMJpVKNQRttvn8ebm8/GJTiQI4I3QL3ATI8SHMEgTB3Vs2Xlc8cRPuO1lBLxutCZbtd7GMbYFGWQyLkuz0Mmo4aeSKpyiSFudhuE2IykKOeWBIE9XuOivFBhv1BCyGQlJJIiAVaVEewTCZZXLeCA3jcf3FOI7jMQ3UCm4LM2CSnriquy69HQ0KSl0HjaehZNfiF0gkZDe4iy2fxytYh4KhRH+YikmihGm/7CTK25pi1wuJVzlwDNjPoqxQwm3aYQv4EYpVyPdW0zomxl4p8zC3LZF9ctIpagH9AKFHOcn3xK2O0AhRzO4H4aLRiE1Hn/LlhgKEbDbCfl8VG3bwb7ZszHk5pI9/BzmSVV8XlSbbXaLYb4urqQ4EOSq9CS0Uin2YCGf7oi1shQjIT7cPo772v2BzKnD4RDZvL4Mv0+kZcdq20iDUYlUr0Oqrw7YVm6PJRWH4C0tRamufZ+RcJi9G37HkpaLu6CQ6VdeHeMYEQmHmTHuai5dsRRDPVWQSVMSb6ClJuNp7QZQH3AESvjxL2rhh+APu9ltn8Y23+WcazOh+POzCIcjLJ2bz3cfbyavlYVi3Qx2lS6MOX76gefomHR+vRF434o1Cee8C5dhuPrik0rgg2E/0w88F0XeAZzBUl7ccBYPd1iL1ZKDYfzF6EYPJeL1IVGpEMyGmOvyOCso3b+VP35+C4+9jIbt+tNmwIWYbFlIBAFdehq69DQyevao85pEhxP3lFkJ512/zETZoXVNiXy4vDKKvNeeSMT+2odYn74P6VGU+h8JenkSgzLvomvKZTgDpcgFBXp58nH7wB8JYZcb/6ZtON77glB+AYJBj+78c9AMGUBl8T6KFiyNIu+HY/EDDzHiu8nH3VoqhsKUFLmZ9t1ONq4uxmDsz9ALRuFLXs4PJTfVVIbudPz+px6T6Xjf5hnUA84Q+DOoE86KIqpK9lNZtBdLWkOMtiz0luqMXzyrokPoMOhyZn/ySNz+zP2b/6B833bmX/E/znrzNXIGnY1co0HtdNBRKWOVv7pstSIo8rpCzs3DzyG5dSuQSNg3azY/jRhVo24aCYcJ+j28vGEQ97Zbill5Ypn4QFIS3b77hmVjL6J8U62oWeagQTR58kmk2vrLwAd8bop3rSCzgZ4dm2qzaOuWVrBuaQU6g4ILx3cipeHJVWkt8e7k6bU9cAZrhbe2Vs2lZ8o4zk9/hoO/L2bOTbdStmEDEqmURiOG0+POOwkcKED59GOEVqxCVnCAUFYD/M1a8u6HeynMdyGTC4QCAb5+/JIo8g5Qum8L09+9l5F3vIFKc3ICE5FwGPeU2Ynn581n/ISJvPGGiFwh0KiFhZbtbLTpnEooKCJIBczW+P3BJxt+h4M9v01jzg031wgwaZKTGfr5J2T07oXsBDZ3eksKF973GV8+dhHO8oKa8aSspgy/5RXUOtMxn9NsVXPD3Z2pKPOyc3M5OqOShk3NmK2qqNLsEu9OXt04rMY/+5ArwryCN8jRdaBX6tV1vo7OnILOfOLlpSqXk6YlpdzmdKIwGlEblGj+zMBucvvY4wuwxwfYo8vhR9mMXKxVESmvwLd8DUQiqLq0R0iyIjVU3xukEgmNNSpebprFbo+fslCIxmolKQo55noUqhKkUtqddRn7Ni9l34bFNeMSQaD/lS8y+7cqIn9pYe3WJ51Fs/YjEYia27O9koBPRC6XQiSC/OrzmDPnbbY/c3fNmvQm7Rlx70Pw29Ko3lipUY926ADUXdoT9vmRKOQIZuNxl7BGwmHse/ay8YMP2TtjFuokK80vuxSZWs3s625g0NLf+VYbn6zOr3QxOtmMViqlwp9PIBxfR6XSfwCHt5iN00v57P0dhz2mNtKtXyZX3dYek6X2N5bRpxerX3k17rnMeXnYKwqixvzu6g22p6QYf1VV3OO8ZWW49+xDazTXi1K8ZkAvXJN/ifvM1Y0ZjmD5d6vRh8J+ijyJtTiK3MvxchHOkIj1T6vPyjIvP3xaHazvNkzPImd8r3KAeQVvkaPviHACIoWHEPHGr+YAqrUX6rAIrQ84AkUsLHw37pwnVMUB9wasqhwEpQIhNXFQ1ee2s+yXt1nyQ63gcMHOtaz47QOufOrXuPazCRGOEEmgHQJUJwoOC4j6NyR2Mwnu3kfE7YZ6IPAACqkamzQXmyq3Xs6XCJFwGN/yNVQ++0bNWNjhxLNkBb6O6WjSkylbvjbh8SWr1xB0e+ok8JFIJOGe5sA+Bw/eNBe/r/r/cBAnW9aX0W9kS84e/BAzyx8FQC9PJhIKw+lbmPifwBkCfwYJUVG4m68ev5iq4lqrKnNaLhc/8CXm1AZojEk0aNObvesXxRyblJVHyb7YLPYhFOxdjyEri1/PH8sV69dgbdkCldfLiAP7ychuyGx/GE84jATQWqx8N/icuOfRpKTgVbqpqMynzLfnhAl8llLBszItZ33/PZ1dDgLl5ShSUtii0hJMScFYTxtwX2UlTnsxC764jxFjP+SlTbFlsMGgSNsuJ9e2zh9y8/Peh6PI+yGsr5jKIPt1fNN3QE0GKSKK7PzpZ4pXrWb0p5+ydYeLmavMmG1plC31svPD2sxC87ZJiEE7rqr4nu8718zBYy8/aQSecLhONd2IP0Bqhpb7XuiNIJGg1Z/6LHsiVGzdym8XR/fdeUpK+PGcEVy+fjXW5s1P6PxJmU0Y9/QUqor3YS898GdwLvOE2hoMJiUGk5IGjU0J16wu+7GGvP8VU/Y/QWvLUIyKYxO/PFY49u9n1rU3sG9mdbZHIgg0u+Riej/7NPLUFOZXJtYAyAkEcP40Fc9PtSKAjo8mozlnYHVp6Z8bRplEQrJCTvIJqM0fDfSWFEbd8RaOkgPs3bgYtc5MRrNufPtpAUvmFkWtbdXeSporn7G23Yx8tjvPv7ilxokhI8eAQllNTEIKKb8v/oztK6OFLQt2rOHHL+5n7PWvxSiaSwQBqc1KfRSDl2/Zytc9ekdlmfbNnEWbG66nw+23EdTp8ScQAowApYEQ6UpFXCJ7OEJBkU/f2xUz/sf8A7TplMLAEbVipamdOqJLT8dVUBCzvuMDd7Fk0YdRYw3a9Przguq+hrDHR3DHHqRd2tW57mggTbZimnADVS+9HUV0VD07o+7d9W8JRJ5KyAQFyerGFHhi2zkAkjUdWBeMJojBgIi90o9UKqFpOx0z9yXWuHGHyglHwidM4P12O3Rug0IMEf59BaH9B6PmFa2bIdHWv9PN4QiG/QmDWwBldSivHw5XZXEUeT8Ev8fBrI8eYvSEd1Bpjy5wJOh1aAb0ittaACAO7I1dqeAQNZWo6nheSyTVInyHX2uwnGDYh0LQoJWfnNaeE0W4ohL7O59HjcmuHsbODk5+qjifdF9LWjfJS3i8PjsbaYJnTmW5l/277SycsQ+FQqD/sIakZuhqnElcDj8fvrK6hrwfjvk/F3HP0FHMkTyJGAnR2zQeHGH4d3fknPb4d9dUncFxw20v5bvnxkeRd6hWN/7xpRvw2MtR60wMv+kl0hq3i1qTlNUUS1ouQh19qUq1npDHQyQcZv077xEWRTQpyRz84gv0d93JhMI9POwp54r1K6havZrss+KLhXR4ciJzQ+8BUO6Pb2V1LLAqZExokMpKhYaH5QaeTWvIKwo9GanJNNbUT7jRV1nJ6lcmUb5jK/bSA5Tt+IHx/2uMVl/7eaVm6Hjg+e7YUk7uHdIdqmBlWXwdg97aq1hy38Mx5Z8Azvx8irdspmmmjIoyL3/MO8jOLbUlrYJUwsXXtcZdlrj0lEiEkD/xJuJEIZHJUJ+V2IdU1bMTgkGH3qA8rch7wOlk2eNPxZ0Lh0Kse/MdxGB8EnwsMFjTyG7RjdZ9x5DRtMMp0SQoTLC5Bqjw7UcMx3c2qC94Skv57ZLLa8g7VGc9tnz+BYsfeJCw251QZM4kk5JXUhJF3mvO+9scAttjyWB9IRwK4cjPp/CPZeTPX0DV7t0E3NUiXTqjjfQm7elx3i20P/tSkjIacv649gw+rxHWZDVZuQauua4p144wIL72JuLPU1C8/Dz/d1dzBKkEiQTOuaBJTaWEx1XOxiU/x72Okn2b8UgC9fL9iwdfZSXzb78zbono+rffIfusAciPIOKv/bNFwaLKRi7Er1QxKdLxlCcmSb98tY2qitp7kz4riwvmzyazb5+aMbXNRt83XqLYvZPygztqxhu2748hqTqQrElJQZHA0UFlsaA2mUCpwDP3d1xTZxPcsx/RcXQikn+FoFaj7t2VlA9exnTXDRhvuALbm89guu3aOhW8/y0wKFIYmfNY3DmFoCbXOIx0pRz9YRoUMoUUrV7OmBuz2B2cRXPTgITn75p8CTLh+ANyQa+X4tVr+O2Sy/m8Z2+mvfgs5R3zUI2/qJpwAsjlGG+8sqZ142RBIdVgkCe+32dp2x3VefbESd7Uzi3E56o66muSSAWU/XogTYlt4ZJlplHZujkbXbW/SUWrZiDEv1crO7ZBMFS3tbiCFWysmM6rG4fyyKo2vL7pXLZVLcATPD0cMg5H2OWJ0hWSdWjJpvbFfFR4E5X+A2ytmkPWpUMStsN0e/A+NHGU3yvLvLzyyB88ddcifp+1n7lT9/LgTXP56t0N2KuqkxxuZ5Ct68tjjj2Eneu8pGta0towlExHU3asiO9kcwanDmcy8GcQF257OaX745coFe5ah9tRjsZoxWjL5ML7PsNdVYKjvBCdKRmdJQWlWk+r3qNZPy/W21IiCKRmtGDZxmrRq/ItWxB9PhR6Pb2efILlTz3NguEjEAMBBLmc1teO5+z33mHtm2+x/q13CLpcGHJyaP/kBPa12s1Oe3X5aIqqSb289xSFnNuzk3GEREIR0AgCFkX9/VTse/fyx2NPMGLOzyCRsHbm22S32MCtE28hIpiQSiV4KndgMbsQpKciaxJ/R5wttGbx72/EnQPYu3AhGXIdDzx5Fl9/sYtlCw4SFiPkNDYy/vYOZDYw4ChOXBGhUGlR1NG/HPb6iASDCBoNkuMU/lI0b4KsQRahvdFCiBKtBv2Y4fWiVFvfCLjdlG9KTHQd+/bhDgQJI0EtFVD+g3pb84z9WFz8cdy5TF2bhISrvuApLqFg8ZK4c5s//Zyu99/LOclpMfZwAH1UcoQvEm9aXN/8irJlHkI9Z8/EQICDi35nygUX4ausrtQRZDK6PvgA7W6+AbU1ui9XECSkZui4/Ka2nDs0heCqdUgXTya4s1aULlxRhWLJQnr3b0rHvtmkpNcSBr/XlVDbBKBk5ya0aiuGrKx6fZ8A/qoq9s+O1aw4hKJly1E4XTTu2IOdccqQzTJpTZuCUZ7KpY3f5OPt0W0ZAlIub/A2q7+P75oA4LD7+etHYG7ShBE/fIuvrIyQ31/tLy7xseX9+xBkcjR6C11GXEfrPuejNVaTEG1aGme/8xZTL740+mQSCQNffRllUKT8niejSqbV/XtivP7y4yqrF1RKhPQUZOl/v7/534E8Uz/Oa/A0v+57hFCk+vthVKRyUZPJTK/UclV6bf87gNmq4tyL88huX8X7+fdyU4sfWVc+hUA4+vefrGpMY0OvE7q2wqV/8P3ZQ2oC4r7ycqbfeBNtxl9NuzHnICmpwHDpaGTpJ7fqDqoDWCMbPMZnO26IGm+s78kg5S1YS5Nxug6gTUuLEVmMhEKI5ZWIZRU1Xu1xEYnnpVM33GYT4lP3IZ/zO+F5v4NEgLP74OjVlWedflpE3PQ06RAkEqRmE8ZbrsY+6YOocwgmI8Ybr0TQagiIXv4o/pSvd99RM7/D8TvPr+/H1Xmf0NV2CVLh1NEg0eWuFsYMBhG0GgSrOZqMy6MDROExXfm57MLa4yMh5kXep99Xr7No3ARC3j8DGhIJnSZOiAowHkIkEmH5ooNsXV8WMzd36h56D8rB2E4FR9hqKqUaLkl5lcL1a5j28V0MuPzBo3/jZ3BScIbAn0FcBLyuOueDPjdiKIjbXkpYDKE12kjOaRG1ps+FEzmwdQUVhYeVY0kknH3JI2x6s7bkML1H9xo/Vl1aKr2fe4aOd95OwOVCodWhSU1BrtHQ/fGHyL5mOE5vEQ6hlCmBd8m3rwUgU9O6XhVBNVIpmnpSB/4rNn34MQAFsxfRuN0Adq6Zw/7Ni9m/ubaP1ZbdjNY9vzkpr384NDILHZPGsLz065g5T9iOJjk5xgXgEPQNctCNHYFBr+OG/7Nw6fVtEMUIGq28pixLa0qmYbv+7F4ba0fT8/zb4roRiA4nob35OL+bQri8CmWH1miHDkCaYkNyjAJgsiQL1ifuxjNjfrWNXCCAulcX9Beci/QUWfUcK+QaDebmedj3xKqAt3v8MUzjxvFKcRVlwRBN1EpGJptJU8iQ1xORDwcCRJwukAgIJkO9il/lmfqhk1lxhWIj/efnPotecXKVbN2FiS30IqKI3+4gJzuHHgYtSxzuqPksqUCkjuxo2OkiEsd27kTh2LePH4YOr9H9gOqM/NKHHyGpVQuajD4v7nEySQTp1F+RGw1IB/Yi1KIJnrmLibiq31dk2XLGPT8MZYoVqaz2f6xU65AI0oQkXqMx4y4siiLwblcApz1AJBxBo5NjNB9nIOYIO/5IJMKed99n3OtteFmuovywkmiNIHBfbhqWP4N9cqmKjknnk6FpyW/5z1Di3UG2rgNDsiYi7A1g0yX+XzVrnYRKE7s9UlssqC3RdmJjJr5PwOepFuwyJUf9XqRyOQ3OGcLFC+ax8uVXKd+2DWvzPDrdcCOG1DSqJj4eU2bvnbcYZetmaIedVfeHUQ8QgwFcVSXYSw8QCYcx2jLRmmzIlX+fP/qJQCe3cnbGbXSyjaXCX0QwIseHBVfYxtUZ6piWFqlUYMDwhiwqfZNA2MtPex/gphbfM6/gDTZWTkcmqOhiu4ghmf+HRXX8gqvuoiJmX39T3Gq29R98SPs7bsPcsCGC+uQGMA9BkAh0SDqfgOjll32P4BXtXGJ5Ce18keVP3MX84mLUSUl0ufduml12SY2fdyQQxL9hCxWPvkREDJH9yPUJXyOnVY8jls97nJV47GU4K4pQ6y3IVGY2bvIj6trS8q5uqIwKvhXDrHD66KSS01YhqeGZglqFpl8PFM2b4Jk6B7G0HGWXdqg6t0P2p6CjPVDEd3vuifvaX+38H3nGfljrcd9YF0IFxVROep/AmurElWA2YrzusmoNlT9V/QWjHkX7VjVr/Nowbnd0e+VK1w94GtkZtOJTlPlS8IrYWrdGk2yLW+3jqPQz48fElZAzftxJ05ZWdHoFrTsls2Fl/JbHpo1V/Hj3NQR91dygUbt+x/wZnEH94gyBP4O40Bgs1WVdcXr45CotaoOFhZNfYOW0Dwl4XSRlNeXscY+S0bRjjSq00ZbBZY9+R/G+zexcOQuFTEtO465seecTdn77AwAytZoWl18atemRq9UYc2PFQhRKDdYGTVh54GfmHJxUY+nR3DSQK5u+j1Fx8iPXJ4pIJIK3rDoSuu7VNxn2y9f4PU7yty2vWWPLbsYF93yMznTybTlUMi0jGzzG5spZMaSq0lBKhwm3s+D2CXGPbXH5ZUj/LFNTqWWo1LG3E43BwvCbX2LRty+zft5kxKAflc5ErzG306rPmBj7r7DLjevHabi+/LFmLLhzD+4ps7C9/CjyBsee9ZPZrOgvHoV26AAikQiCXoegPP0y74egNBjo/uAD7P0tOtvb5OqrKBt7MW9UeMhRKegjF1GE3Hx1MMjwFCstdWpCZRWIpeWEK6uQpSYjWMxHrcQbCYcRi0pwfjcF35KVSJRKtMPPRtO/B9Kk+vFAtqpy+L+2C/lg2+Xsc1XbXunlNi5s9DK5ui5HOPrEEa+88BAkgoBCr8ckl3FNRhKDkgzMLHcQiEQYaDbQTC2Hzu0Ibo/fH6rq1Lbes+8A277+Joq8H46ljzxORu9ecS18wh4Pyk7tcP88nbDdiezCgQgX3YbPXYHSHka+cCcKlSyKvANoTTZa9zmf9fNjA4ipua2wb96JvlPt6xUfdHFgn4PyUi9/zMvHaQ9wzYQO5DY1o1AcWxBUaTaR1b8f+fPmx51P69aVFc88h/6553jijTc4KIbZ7Q2QoZTTUKMkSS6L6vVWywzkGrowPu8zAmEPKqkOhVSNQ7+f3LQCzEkqKsuidTKkUgkXjm+JRnt05dIqnQlVHcKPSoMBW14ePc4aSnjEKKiwIyxeTdCWlLBH3vntr6i6d6r30ncxEMBdUIinrAxBLkOUivz6wYSaajupTMFZ4x6hVZ/RR927fLpBIVWTom5IirohXlFEjIBGKiAkaI0xGJUYPSYA9rpW8s6WC+mecgXj8z5DjARZW/4LguTotsphMUJluRe/v1oY1WRRIZdL8VVWRXmh/xXFq1djbdki4fzJgF6exID0W+iQNBrR72fHi5+z+LDWLW9ZGQsmTMS+bx+9nngMhV6PWFZO+UPPwZ9ic7J1u+nY/1JWzfsi6txylYZBVz9RpyCqo7yQqW/dxe41c2vGrBlN6HvlG0x6bx+flHrRGxU89FJH+pbsZ8/b71BaWcnGsReQM+hsDNlZCFoNioY5yG8eB2IYyWEaRWIoRJX3QE0lxl/hFe24gqUnROAj4fBRBbhDpeWU/d/jiKW1e6xwpZ3KZ9/A8uhdqLtVe6pLdVrM/7umZq2M+PuUze45bHbP4e7ui2hirLsyJByO4PMkDlZ6PUFEMYxWr2Dcre158Oa5eFzRz5th52eye8XkGvLebeRNp6Tl7gzqhiQSOYLKyn8MDocDo9GI3W7HkKB37b8An8fJvM+fRKa0kdKkD+Ew+J0HWT/rNfpefDfLp7zLvo2LY44be++nNOl0dsx40OuleMUqpl50SU0WzNS4MUM/+5jkTh2Ryo4+lhQQvdgDhXhDdhRSLXq57bQVJYFqf1SPvYxQ0I9SraNq43a+7VedXZHrdHR55D6snVvjcVZgSM7AktsEQ9LJsSiJh0gkQplvL4uLP2Jt+c9oZCYGZU6gob4r0ooIs2+4mV0//1KzXiIInP3BuzQ9fzQK/dHZRAUDPtxVJYQCfuQqDXpzalz/42B+ASXXxA8YKNo0x/LQnSe9P/B0gN/hYNdPvzDn5lsJuqofmkM3rOOJiIqbTQLi1iVsn/ExAZ+bnM5DaDrwUrIlGirufiJqk6Bo1QzLPbcgtcXaH4XDEeyVPsJiBLVGjtxZQekt99dkaA9BntcIy8MTkFnr7zfmDJbhCpYhhgNo5GZMigwEyclvBXAXF/P9oHMoW78+Zq7JmNEM+uA9lIfd90PhCBEiNdUNocISSm65L+YzkmjUJL/xdL2XLofDYaZecBE7fqgNaEmVStrdchNZ/fsh+nyY8/LQpqdFZYbDPh/uX2fheP9LJDot0qeuZrLvSTbYpxEhgkLQMCj1dgZk/Q+DMvaaqwr3M+fTx9i64rcakpmZ15neQ25lzhU3cOHCeahTMziwz8HX721k05oStDoFfQbn0Li5hbefXcGjr/Unu5HpmN9z2caNfNW9d833/hBajb8abUoKEqlAmxuvR5d2/GKHkUiE8s1bqKgK89MvZaxeXkokDA2amLj6f21p2Mwa5Z5QHwgVFOP86ke8C5ai6tWFSCCIb9GyuGslSiVJ772AIk4/8LHCU1pKyOslEo5QvGo1M668iuCf+gmmRo3o8/ZLLJj+MkV7NtYcc/njP5HdousJv/Y/BSXeXdy/ommNK8bhaGs5l2uafXZEG1dHlY/Fc/L5/pPNOO0BlGopQ85rzNAxTaDiAB81TUzQh387maZjRp/w+zhe2Pfs5ePmrRDjlMRLpFKu2roJU+NGOH+ajuOtTw6blKAYfz6VJli26As8jnJy2/Sl09CrMCVnx33GQ7UDz7R37mbjwu9j5sxpubQY8gZvPb+TMRdlolv7JVvejXYH0GdnM3bBHIwJLBgDTid7pk0n0FrOq6XxK5QAHu6wlixd24Tz8RB2eQgVl+KZNhexvAJ1764o2rVCdliw7ZAFbMDuQKZWI/cHsd//bFxRXVlWOknPP4jUXHt8qLSc4K69eGVePpE/xiZ7rL2eVmbm4Q5rj1h5GgyIfP7Weqb/ED+AdMv9Xeg9KKf6vYUjlBa5WTh9L+tWFGM0KRg6uiHS4E7++PkZ1HoLXYZdj6BpgMmWHOXUcQb1h6PloWcI/F9whsDXomB/FR+8vJaNq6sVym2pGi6/IY/sXIEPJ/SMe4wpJYcrn/w5rtVTJBzGVVCAt7wcQSpDnWRFm3r6Z81PBI6yAuZ98RSbF/9MWAyhM6cw4NL78azZz+IJh5V2SSSoLRbGLpyHtcWJKYwfL8RwCE+oCpkgRy2rzb54SktxHThI/rwFKE1GMvv0RpOWikJbXfYV9vsJV9qJ+ANIVEqkFnNUJPxY4JoyG/trHyScT/n4FWSnael7fUMMBHAXFuEpLkZQKtiXnYvdUU7BR3eTvyFaPEhjsHL57R/jv/9V+EsZt6pvd8x3XIugri2LrSz3snRuPlMmb8dh99OinY2LLmmI+rsvCa+M9Si2PnE3qs7tTsr7PNWw79nDr+dfSMma2vfZYPAgzn7/XfSZdbtYRCIRQgcKsb//Bf7layACyk5tMV57KbKs9JPitb3q5VdZcOddAEgVCoZ9/QWbPv6UXb9OqSXX/foy5JMPMWRXb+ZChSUUj78TRBHZxAt51/wY+9yx/9eR2Y8xNPueGHGusChSsmEt7qpSgiEfMpmS0j9Ws/q5Vzn7nTdpNGok+Xvd3H/jHELBaNLTtKWV3oOyWb+yhJvv64xac2zCX2FRxL5nD+vffpd9M2ejtiXR6a47seTlISjkaFJTjyngmwiRSARXQQEeh48AaiRyBVq9HHPKsWee3Y5yXOVFFO3ZgNZkIykzD72l9jqr/IV4Q1UIEimaoA51WIt/xVqqXoxvWyZr05LAVdeQ2TwlRj0+LEbw+YLI5FIEmUiFP58NFb9R4N5EE2Mvmhh7Y1Xl4Hc4KF6xkvl3TqRs/XrUSUm0vnY81pYtmTHuasKh6vuEQq9nyJQvmfzqVTWvkduuL+dPeK+mmu7fDr/oZmXJN3y0I1ovwaRIZ2KrudhkDWKcFw5HMCjy69fbmPz+ppi5HgOyuPLG5kwffW5c/Q1BJuOqbZsxNjy5FmV1oWjlSr7s3D3h/IW/LyCjZw+q3vgI9y8zY+alNgvSPp1Qn3s2amsyUnl15jgUDmAPFOIKliMV5OjlNoyKVCqL9vLWrb0StumMuPN7Xn2mhDtvTGLGsP5x17S96Qb6vvh8XEvVohUr+bJLd86e+SkfaG+N27JlVeZwb7slx+TnHvZ48cyYj/3tTwEQzh+G/+y+rEQgoFTS0agjKSKy/5NPWfLAQ/irqpAIAg2Hn0OvCRPxT/qQiNMdc96UTyfVlP3/FaXe3by04WxKD3MGUEp13Nl6Frn6zkflilB80MW918/G7YzOrKdl6njw5b5Yk6MrxzxrNuFYuQnB5URcuhxZkxwiuRlIPH4iMg3flOYSighcdVv742+XOoOEOEPgjxNnCHw1yoo9PHDjHCrLYyOGD7zQlXnvjsJZEb+f9IbXfsea3uhkX+JpD1dlCV8/eSnFh2U2DmHk/14nsLaAJQ89QtDtJvecofR6+gnMeXn1sjk9VRDLK3F88QOemfMhGEKiUaMbMxztOQOPS4TJ9ess7K9/mHA+5aOXT4nIz+mIdU4PoR3Lmf7kxXHn2/YZS1dvQwLz/oieEARSPnipJjtsr/Tx1jMrWPNHtM2YIJXw8CPtML37SlQWH0Ddpxvme289KQT174CnpARPcQm+qirUSUloUpJjepvrQtjtIfxnFl7QaU9K6fwhVO3ezWdtOxJ0uWh/6y24CgrY8f0PMevSundj5M8/oLHZ8K3eQPm9T4FCju+VS3j64NC451ZJ9TzYZiUp+li/5qDHQ9WuXax68WVK123A0qIZnSdOQJ2ZS1Ci4NsPNzF3SqxOA8CtD3bl+4838+ArfbEkHV8/tRgM4rfbkSqVKI+y0ufvgLOiiCmv38HudfNrxhQqLRfe/wXJjVuwy7WUz3feWLMBb2rsyxVN3iHJa6P0lvsJV1RFn1CQIH3wPp5/5yD3vdAbq636u1WTHZuxjw2rimnezkLbUVVM2jIkqkxYJ0/i7laLcM3awq/nj4253obDh5HSsQNLH328Zqz7c4+zuXguRXs2AGBObcAVT/6MzvTfKJMV7Q6qfl+Au5mOpcGfqAgfpLViII09zeHln1A0b4rhijHIEniilxS5uevKGXEtuABe/mwIStcBJvfqWyNEeQhDPv2IJmPOR67++3QHyjZu4tPW7RLOX7Z6Bcnt2+FdtIyKJ16Ju0aWm03SM/chNVU/993BSlaWfsO3eybiE6u1Q1LUTbm++dcoy5S8PyG+sxDAwPFvsnRZKi0rvmPj65Piv55Gw1XbNscEXQMuF9Muu5JdP/9CcpeONPv4Vt4ruyrKvlQuqLirzVwaGRIHLeLh8ApB4aJRLOnbiy9d0XvkNhoF5+3exuzB0ffcpFatOOfRx/G/80W1eJ1UStjjxXjNJciyMwjuO4AsyYqsQSbSJEvUs7bSf5BCzxb2OldgUzUi19AFszIT6VG2dkQiEYoPuvnx880sW3AQmVyg/7BcBo9qTFJK9LMrHAxS8djL1QHqOBAsZnaPuYFJk7bx5NsDady8ftrrzqAWR8tD/zlM4QxOKbZuKItL3gEmf7SDAQPGs/S7J2LmBKkMxDMxIYCqkv1xyTvA3C+eYtxTv9Lk/NEQiaAwGqJKd/8JEB0uKl/7AP/SVTVjEY8X56ffEgkE0F86+phV3pVtE5cZKlrmIdH9+8vnEyFHpWD+otiSw0PYunIaXa94Df5K4MNhIoeVRpYVe2LIO1Rn9j75fC+3nTMUPon2okWtrLU6+hdAk5xcZz/8kSBoNSeVtB8OQ04OY+fPYerFl5F91gB+OW9M3HWFS//AU1KCxmZD8mfpqtRkpDiY2NPZJzqpqNyHSZoWk22VazTYWrfmrLffJOhy4Q/LWbOygtlvr0Qigc69M7jt4W68+8JKvO7oqo+t60tp1taK9ARcNKRyOZqkEy8hP5kQgwGW/fpOFHmH6hLhrx6/mKte+o1XdgzhcD3u7fYFPLuuN/e3X4H+8fvxfPAZwTUbIBJBlpUOl1zC5N8qOLDXgdsZqCHwB/bYeeiWeXj/7Gc95yozb28/L6bH1xUso+jAelbcdnfca949ZSptrr8WQS6v0VaoXL8FQ6uMGgKfktsaheq/Y/IsllXim/QlMoOO8568F8cvvyHu3E1o30IAvAXFBDZsIenFh5Elx34n3c5AQvIOUFrkpk3nZly6ejm7f53K3hkzMTVqSOtrr8HQIOdvJe8AalsS1pYtKd8UW0FgyMlBk1J9r5TnNUawmgmXV8as04+/iJBC4FA+eLdjKZ/tjFa5L/Zu5/l1/Xi40UqkciViMH5/utacQSAQqFPlPhwIEInEtjwEXS7KN1a/j5Llq1BN/II7n/6ZdcygSLKTLKE13bOvIknTMOG5E8G3ch1Q3eLi6t+TLx2xe+T1ngCtcxqS2q0bRX/UPovLNm7Eq1EivPkMuyUCIhI6K6U4n5pEcOfemnUSnZakp+5F3iS3hsSblRmYlRm0MB+fsKVEIiE1U8fVd3Rg7PhWSCQSjCZljP5J9VoBSR3VJhKVkmCo+n62ZM7+MwT+b8S/I51yBvWOdStiN/iHsHtrJdbMVnHn8joPRZSH2Vewgn37l1NStA2nN9a+4r+Agh3xI5gAzvICQgEv+swM9FmZ/zjyDhCuskeR98Ph+uG32MzSUUBqMaEbMzxmXKJUYrz5KqSG/y6B1woCQh3VGYIgi7KjOgSJUolEU7tB3LCqOOE5dm+rJJSZE/vaQwfGlPKewamBIJWS0rEDYxfORZ2cHFfJ+hA8xdUKwtJUGxKlkrDLjVmWuERUKpEhun14HBUJ18hUKjwRDU/e/QdvP7uSnVsq2LG5gi/f2cAvX27lugkdY45RqWV075f1ry+vdFWVsnrmp3HnQgEve7cuxqaKJQrOYClbKmdTFNIwP/MsfA88gv/+h9k2ZBxPf1jC4kXVv9FDG2ynw897L66qIe9yhQCGUjyhqrivLXVLcB04kPC6yzdtRn+Yi4CxWSNcFdXPfIkg0PP8//2nCLx/XTXh0/TphvPLH/DPWUxoX/TnJ5aUEdiwJe7xSmXdZcxavaKaNDVoQPtbb2bE99/Q7+UXSWrVEsVpEJTWpqQw/NuvYoKaKrOZkT//gC69+h4iS7Zie/4hFIcF2gWLGeWtl7Fk1Xf8+vrtOCuKcAZK+X7vvXFfyys6KBJ20XHwlXHnUxu2pbBAxvaN5aQPGZnwmhuNGonKHKvLItdqsTRvVvP3/qmz+K3dKIQ7t9LkZRvZ01OwKRogOw77uEM97IoOrZhbB32aHZHS8KYbYsaLdu3khZCEl1wBdgPOj7+JIu8AEZebsvufRixLfE8+XiiVMqw2DZYkdVzyDiCRSdGdOyhqTJqShGJoLxRDeiEMG8LchX/u6c9sCf5WnMnAn0FcpGYkfqiYrCrMKdkIUhlhsTbzYklvRK9LJ5C/ezl/fP46VcX7kMqVtOg7kt5jJmC2nRq7jtMFBmsdG2e5EkF2+iqhHw3EkjoCM4EgYVesl/aRIOi06Maei7JjG1w//oZEoUDeqAGavt2Rpp58Vf4jIeDz4K4qJeB1oVBr0ZpsCTe6waBIVYWPoF9EoZJhtqqQHqMN3uGQSwXaDbiYDXNjLf8AWvUYReSP2IoP3ZhhUWrWdfUkS6USBMJRbl6acwb+63ylXSERhyjiFcNopAImmRT1SbKNrC/oUlMJOl0J3UGgVmVfajFhmngjlU++irlCg0mRTlWgIGZ9J/MF7Fk0j9RhdeturFpSyP7djpjxPTuqKC/1kt3QyP7d9prxDt3TyMj55wUljxWiGCToS3yfs5ceRGuygG9XzNzGymm0TruEKb/m8/03e2PmG+aZ0RuqM2FuR5Dtm2o39HKFFH84sdWrIJfV+T1RGI2EPNXXLVOrSe3dlfmvvoXOlMywm17Eknbs2UlXsJxA2IuAFKMiNSrg53VWEgr4kCqUaPSnX8ZOoq7+nOWNc/EsWJpwnXfRctR9eyCRRd8r9EYlzdpY2bo+ttfaYlNjTooOZMmUiTOcfxeszZtz6co/KFmzjpK1a0lq1ZKUjh3RZ0c7v8gyUjHcfSOOfbsIedy4/HZ+n/UmBTuqXUVkciX9rrmfQs/mhK+1rOJLLhz1AmIowJrZXxAOVVeCNGjTl44jHuG5B7eiNyrRNcgm++yB7J81J+p4hV5PzycejRv8UOj1dHvwfnZPmVozFgmH2T97DhJBoPP6u5AeY2XgIag6tsH5yTegUVNVB4F3iWFkhtgWQiErC4dYHYDtRYTg78tj1gBEnG6CBw7ErfY4FZBlZaAZ0h/vvMUI/3chhSnl/O6vdiXpab2WftYkdm+voufA/9ae/nTDGQJ/BnHRvX8W3360iTgVSoy8pBkyexljb/6AogMbcTnLSM9uTVr7Tuzfs5zpL06sWSsG/WyY/Q2lu7dy4X2f/aesJ1IbtUGuVBP0e2Pm2vQfe0ps4k4mBEPdfal1lWHVBalRjz8rFXHU2RxcvBhVyEW6z4M2EPhbSw2dFcUsmPwcG+Z9Q1gMIUhltOo9mn6X3hvjZ19V4WPadzuY9sMO/F4RrU7OyEub0W9ogxPKSlrTG9Kq92g2LorugTbaMuly7vWIH/xIUJBAOIJEqUR3/jloRwxCIq8l7W06pSTc23frl4neoiHQtQMSjRrtiLORZaYhNf57yFhZIMg7B0pZ7ar+XQpAT6OOK9KtWI5TfPFUQZOSTJPzR7Pju9hWipTOnWpKXSVyOarObUl++1k8i/7gtgHfMunAhVT6a7OKefp+9JBcysKNT6Mcczd+0YMjUIQ3ZEcp06GX29DITDjtfuZOSVyGv2LRQdp0Tqkh8Jdc35q0LP2/PvseFkWEMBiTs7CX5Mdd06BNL8xCFgrtHewKrWZe1fs1WfMUVRP0egV3PNKd5+9bjHhY65nOoOCmeztjMFXfQ/8qVeRxBTFJGiNBElWefwi7pavJGTyIfdNnxMzJVCq0qSm4i4rQpKQw/LuvUTVI4ZoXZqM2mNFb0o6p2sYbcrDftYZvd9/FPtdqzMoMhmbdTYekMShDaop2b2D+V89Snr8dc2oD+lw0kYwmHVDrTx/nGFWbltglEiIeL4JehxhHaAxAsJggThBWb1Ry071deHLCQooL3IeNK7jnmV41bRCnO/RZWeizsmh0bmwV3OGwu8v47sM76HH2eJKSchg44nY2rJrKhiU/sXnJz/S68E6SVA0p9m6Pe3yWrh06czIDr3iQriNuwOe2I5MrObB9Fa7yHdx2bxLuyoPM//p/DHruMfJWXMDqV18j4HDQcMQwOtxxG6aGiYNM5mZ5nPPlZ8y+/iYCzur+e5XFwpBPP8JYx3FHgjQlCWXXDog79tA1HCI+/YaWCinlCxdEjSlNJmjSlKpgdauFEAzGrZg7BFfJfsLBBmjlpuO+3qNBQPRhDxSwy/kHzkAJjQ09sGoaYLj6IiJXDuD9fVezvbBWNHddxRSaGgdw96TXsaX+d6p0TkecEbH7C86I2IErWEHAL7JjtZ9Jjy2LUhnueVY2V9zcllDBLj5t3Y6kNm1QmYyok5Np/PzVzHv+YSqL9sY97+VP/kR2s/+ONY0YCnFw+yq+fvKSqCxNRl5nRk94B4P1+G2QjhWRSISKkEhVMIQvHMEql2GUCSeUdRTLKyi94xHE4tKYOUX7lljuv/24LN/chUXMvO4G9hwWQZdIpQz9/BMajhheo35/KuH3OJn27j1sOow4yxRqslt0o0GrnrQffDkqTfX9wu0K8PFra1k4fV/Mec69JI8x41qgVB4/UXTZSynZs4nlU98n6PPQoue55DTrTuXaLdjymqPRaJGIIhKNBqnVFEXeodr3dfHs/bz34uqocVuqhode6UdympZIMASCpKaX+p8ElzOAo9KPzxdCq5NjsqhQqqo/b0dI5IV9RWxyx/Yu9jHpuC7DhvoEqiROBZwHDzLnxlvY/euUmrG07t0Y9tXnGHJi2x8iIZGA28HBqs3YxWLcYjlmWQblm7ez9PNJjL7rXZJbteLX/Y+yqOh9xEgICRJaW4ZyWeO3kfuTefKuhezZXhX3epq2tDJwRC77d9vpO6QBSSkatLrTq7qoyl9ImX8PRZ6t2FQNsakbY1FmHvf5XAUFbP7kM/LnL6DBzWOY/sn9UfOCTM7Ft32ApTSI54cZhO0OZK2awKV9mex7ivXOaTzWaRNpmuYE/CLlJR6WzMvnwF47rTqk0KZTCkkpmhoi7XT6+eKN9axcUoDTHgBgyMUZ0PMzFlfGKtlflvkGjcrbMv38y3Hm5x92XTJG/PAtusxMCIfRpCSjSz8x94S1ZT/z+uZRMeOjs58mY08Dprx+R8zcwCsfpuPgK5Er/97e70MIe3x45i7CM2shqi7tcH76Xdx1tjeeQtE4sVp8RZmXonwne3fZSc3UkZ1rxJqs/te1H5XsWI/0YDmhL3+rbjWQy1D06kTorI589dp1XPzw1+TrNvPu1ljBVZlEyWOdNpGsjhU6dpQVsH/zH2xZ8gsaYxIdBl2BKSUbtc6Eu6SEiCiiMpvjKs8DhP0BwpVViMVl1c8vkwGn3U7Q50Wbkow2La3ONrSjgVhRhXfxcpxNGvJYRE7JX1xfZBJ4zKhiZstWNVaY6qQkzp76C28aktkXqF7/sF6JbeKjhJ3xK2mEF6+HhqmkaZrFna8PBEUfm6tm8+bm0VEif02Nfbi++bdsqZzJ+9suj3vsdXmT6ZIylkC4umJPDgj/EpHbvxtnVOiPE/9lAu8KlrHLsZQp+5+gwp9PL+sNdNbcRMEuH15PiIbNzJgtKnQGJd7ycmaOv67GH7zJ+aNp9vwNfHPXJQnP3//y++kx6pZT9XZOC4ihEM6KQkr3bcFZWUxqw9YYkzLQ1nP23Rkopdy/n40V01FKNbSyDMWoSEMjMxKORNjjDfDM3kIqQn9Gf4HBVgMXJJsxnkDWMbjvAGX3PhUlaiPLycT6+P8ltEWpC5FwmFUvvszC/7sndlIiYdyWDVjy8o77eo8XFYV7eOvWnjVp684XXE9al46s8/+GL+Kii+0isg0dMCnTKDzg5I7LpsfNcMvkAi9/NpjktBPvewz6vbhLS1h46wR2/vRzzbipUSNGz5iKqVFiJwiPO0h5iYcF0/dSXuqlc68M8lpZY+xk/mkoLXLz1rMr2PSn9aVUKmHgiIacf0VzTFY1+b4At2+Pny0VgEl5WaQpTy/yGQ++yko8JaX4KitRGgxokm2ojyD45qwsZuP8b1k792sqi/aSnNOcs8Y9QnKj5vx44EHmFb4Zc0wjfQ9ubfkL6xa5eeuZFQQDsRmjm+7tTN8hDerrrdU7Sr17eHXjUIq822rGTIp07mw9m3TtsVt2ugsL+WXMhRQuWYqleXMGvPkylcFi5n/9LK6qEiSCwNj/vYtpzpZYjRCpFPkz11GYbqeZeSBKad2/t4DPg6uiiF1r5uKsLMWc2QWH28pHb+zB5wkx/v5G0HgZs0qepsKfT7qmJUMtE3H8sYtt86Yw+JLHkFQGODBvAabGjckdNhR9ZmZCAnSsqPIf5Mm13aIqOw7h5szvmPPwvfg9zpg5qVzJDZMWYko+fUpww24PYnEpYZcH51c/4l+9IWreMP5itOcMRNDVHUAWxTCCIPnXkfbD4V23kYq7n4op45JmpeG6tC+WZm2QW/XMLXid3/KfRIxUk1a93MaNLb6nob5bjHXl4QiHQkik0mP6DMMuN555S7C/82mNlapEqcQ04XpUXdojqI/8nfd5nEiQoNTU/XyORCKEqxyUqZR8V2pnod1FKAKttCrGpSdh83nw5h+gYutWNMnJGBs25A+1jncKa9tg2qgV3L55I643YzU0pC0bs/P6FPRJDWhrrbsa4kRQ4t3Fgyub1fx/DseNLX5gRv6z7HYui5mTSRRc1fw35IquzCh3EopE6CWEyfY4MUbC6LOy6tR1ikQieJ2VSAQ5Ho8Uv09ErhAwWVXI5f+8pEF944wK/RkcE7whB7MOvMzU/KdqxqYWPsxvPMrENvPoYuoTtV5ttTLwrddJat2KNZNep3zTZlSCvk5l0b+WGf8XIJXJMCVnYUrOOvLi44TdX8QXO29mdXltdnjy7js5P/dZ+qReizui45HdBXgOK9cKA9PKHaQq5JyTZEQ4zs1GxGrC8vyDREorEEvLkWelI01Oiuq5Pha4i4pY9dIrCV4swrbJ39H9ofvjz59E+FxVteR97A1Utq7kmwPn1sz/UfY5ObqO3ND8GyrKNIlaTwkFw7gcAZLrofgiaHfy64gxlK5dWzMmEQTCoRBzbr6NoZ9+mFBpXaOVo8k1ctmNbU/8Qk4TVFX4ePGBJezZUVUzJooRZv60C7lC4KJrWmMPJVaKDgMeMXFJ4+kEldkcV8CpLujNKbQZcBHNug1HIpWiVGlRGyyUenezsOi9mPVtDOfTR3c3qxZU4XNFmPBED3ZvreT7TzbXlHs3aGKkdcfTVx/BFSznw21XRJF3gKpAAa9tGs7dbRcdkw80QMXWbRQuWUrPB+6ncYfOhD7/jTS9lkuueAXRqkdhtqB1Ryhb+nnswaII786gxRN3IT8K8r5j5Ux+fvXmKOHClNzW3P34a0z+pIAkfRpNkq6mY8pwPO4KDmxczpqP36WicDdpjdpiadwUW1ZTmo4efUzv8WjhCdnjkncAuVcel7xDdWuds6LotCLwglaD0DCHSCSCOfMmxJIyvMvXIKhVqLp2QGo1x3WecLsCOKv8eL0hIuEIfyw4gNMe4OxzG5KcpkNnOP0DgscC0e7A+e6XcXuwxPxCrIIZjTEZpULLkMyJ9Ei5gkr/AeSCGqMyDZMiHUFSd6b2eLLkwb0HYixoI34/lU+/hu3Np1E0jK1OOgRHeSF7Nyxi7ZyvEASBDoOvJKtZV/SW+Pc2iUSC1GwkBbgqRcpomRaXI4AcAZM3jM5iRmc2Y2vTuuaYNv4gmuKqmn3Yem8Ab/cWyDiP8BdzCNsdIJch79cJ5/nN+fLgeUxIm33Mn8OxYHPlrLjkHeCgez3BcPy9/NjGnzPT0YS1rlpB3LVArkLDuJJ8Di5aTPPLLomrUWAvPcjWP6Yg02RwoDCDX7/Zj9MeQKmWMmhkY4aNbYLZenpU5pzuOEPgzwAAZ7CE3/KfjhmPEObD7VdyT9slmJTRrEOXlka3hx6gzXXXIAaCRMxyWvQ9lw2zv405j1SmICuvy0m7/v8y1ldMjSLvh/D9nrtpYRrIgVDTKPJ+OH4oraK7SYf1GLPwAZeLiq3b+OORxyjdsBFb2zZ0uf9eLClWFGbT8bwNACJiGE9JScJ5x77YsvRTAYW6+kEkV6pJ69KBb/LPjVmzz7WKeQVv0NYYqz4bdS5V/dx2PaWlUeS91W0TyBx5EfkHAkilUOmWInhDqNT/jdt8ZZk3irwfjpk/7WLI6MYYTImzPhI47cvnjxceZwUHtq5g0eQXqSrZjy2nGf0uuZdkWQt8YUdU+SRAJ+Pl5BbeznPP7UYU99aMd+6dwZ2PdefHz7dy1oiGtO2SgsV2+m62nMFSdjh+jztX6tuNI1B8zAR+x/c/kjt4EI2btsT39mEkffMOAOTXXEKgjrrG4I7dSDwBMNX9Oq7K4hjyDlC8ZwP5675g4pP3oNUfIpRZmFUZGNok07BhLwSZHI3egsYQXzDOHw5TGRRxiSJKQcAoEzAcB2mSSur4PR3htyQ9TUVcJRIJUosJqcWEolnjOteWFLp4/+U1rF9eRCRS3fM+8pJmRMJw73VzGHNlC4Ze0ASd/vR8r8eDiM9PcOeehPOSbftQ9q1O+ChlWmyyhtjUx993fjQIuz04v0xgsRqJ4P5pOvJbxyOJs89xlBcy+anLKNlbK7q3f/My8rqew+DxT9Sp2+Sw+5k3dQ/ffbyZgL86OJyWpeOOR7uT3dAYVUGQrJDxRKN03jxQyk5vNTFeDnhb/EHLJ89CJxoQZRHm+L5hyYH/Qyu3YFGevMQPgDZi5K6Mn5FF5OwKrmKm/U3sgUIAtlTNoWvyJeTvWRt1jEWZBfJ2rC0NxpxvTyDE9pR0fHffQ/bAASiaVP9+gmE/dn8BrkA5PlclCqOVrVuS+enLnTXH+r0iv369jbJiN9fc2TEm8BWOiHhDdqSCApX073duOB3w39jZncERccC1Ia4YDkCZby/uUEUMgYdqr97D7Wh6jbmT0j3bKNq1vnaNTMEF9378n8zAn2w4AsXMPPBCwvn5hW+Ta0s8bw+JhMLH1kUTFkX2z57DL6MvqInCO/fvZ/evUxjw+iRaXj3uuMXmZFoNad27UbB4Sdz53HOGHNd5TxQag5WMZp1Rawys8U1JuO6Pks/p0PQiUtK1UWJGh9ComRmDsX4UiIOu2vN3fWES28Q2fPDIjpox4f39jL+9PT0GZqPRxt9oBz0eQl4vCr3+uJV5TwbC4QiCcGxVISWF8YWnAIKBMO78UoyCnkZqBbu8gZg1HQ0ajIepSwc9HiSCUG/lxn8XAj43q6Z/wsKvn6sZy9+8jM8eGMWoO98mtUN7JAhEqCaKAlK6qe7kmWd2xCTZViw6SMt2Nu57ofdp1+ceDwGxbicMdyjWz/pIUJqMdLrxRvwfxwZNATxzf0c7dEDiEwhCXCG0v2LfpiW0H34FKrOZit272LF0OmKo+nu7dvbndD33etDXZoQlgoDBmnZEbRV7MMRPpVX8Vm7nTztnctUKJmSnHHP7iE6eRJ6xH9vs82vGtDIzWpkFp7wCoy0Te2lshl6tt6A1/bMFbStKPTx+x8Ko+47THuDzt9Yz/o725DQ28t0nm+naL/NfReCRSJBo1EQ8seK8ANIk6ym+IIgEAoQKY7V4DiGUX0DE748h8JFIhO3Lp9WQd4kg0Gn4HWQ0H0z+Ph+b1rnIzdNisqpQKGJLu9ctL+LLd6JbLQrzXTx623yeff/sKJE3QSIhR63k/tw0XKJIOAI6qUBIfysvbxxMsbf22a2VWbit1W+YFBnH9XkcDUIlZTT6wY135jQIBGnXogntr57M18qnWe+chjtYQcek85lf+CZlvr01x7U0j2KpUwfEv7cuRMaYyy8nf+48zE0a4wiUMLfgdWYeeIFAuPo7c3vmaqY9GT8ItHTeAcZe3SqKwJf59vBH8Resq/gFjczMoIwJZOvbo5f/s4WgTxT/znTDGRwzZELdDxhBcnR9KRZbA8bc8xGXPfkjA654gHNve40bJi2iQcueSOX/oofYaYKQz4MzlNjOzR4oJE2RmAglyWXIj5EouQsKmXXtDXFL6BZMmIinqOiYznc41BYLfZ5/ttoC6S8w5OSQ1uXvqeLQGCyMuu0NrFlN8UVi7bQOwSe6qBT3MP7hLIyWaKJuS9Xwv4e61ShLnyjUVgsSQcCYm4vYoCszpkbbhIXFCO+9uJrigliRHL/dTuGy5Uy/4iq+H3QOCyb8HxXbtiEGYontqURZiYclc/fz6qNL+WjSGvburMLtOrprqisTLJVKkAe8+G++lzs0Chqro/8HbbQqrk23oZVKcRUUsP3b7/nlvDH8ev5Ydk+dhvsEvtN/N9z2Mn7/9qW4czPfvx+pCzok1ZZYNzH2Zv1Cb8I2kF+/3kbAl7gV4XSCRmZCJkn83DErj32D3PTCsahUiQlMaE8+ipbN4t7DAFS9Oh/RwcMVrEBooWdLu5XMS/8U59lBznvuQzJbVN//gn4vkfCx/w/ESITZlU5+Kasl7wB7vAEe3V1IeSA2q1YXtHIzlzZ+E6MilVRVHjdmTmac4k2GVt5KhqQFw297BZki+ncpyOScd+fb6M2nb+vF0SB/ryNh0PDXr7czcHh11nnpvPiaG/9EiA4nge270ZzVJ/4CQYKqa/tTe1GARKVC3ihxibw8rxGSOIFYr7OCNbO+qPn7rPGvsz2/C49N3MpHr+/lpYdXcecV01m7rAi/L7rUvLLcy+T3Y21bAdzOINs2xloKAhhkUtKVCjJVCkxyGUnqXCa2mc//tVnAxY0m8b+WU3iowxqytO1Omo6CWFZB+X1P450yB/78zYc27yB49ztcpLwfnTyJixq9jE3dkIltFjAi+yGsyhySVA1oZRlGqA7ptGAkgkShIOB0IoaDLCx6jyn7H68h7wAet4i/jmdISVHt76rYs4Mn1nThp30Psse5gk2VM3l542B+3PMArmD8z/i/gjMZ+DMAIF3bAplEQSgSu1nO0rZDJzv6qKrRko7Rkk5Os271eYlnEAfyAhfNtH1ZWRW/fKy9ZSRJCjVGmTRu/+9FKeZjts7ylpXiLYsfNBD9fpz5BzDmJlbqPRKS2rRmzJyZzL35f1Rs2YJEEGh83ij6PP8M+qzjV44+UZiSs+g+8mYs/oUsL4vvxd7CfBbb7PPZ7Lifq597g0hZExxFcrIaGEjN1NdrubEmJYUWV16BMqsxM2cnziRO/34n197VEZmsOl4bdLvZ8sVXzL351po1JatXs+Hd97hg/hzSu/89v9vSIjeP3b4galM8/fudXHZTGwYOb5iwiuAQrMkaUjK0FB+M3VT36p+OfMUfhJwuhNse4P9efASnzoATMOk0GGUyDAoZroMH+XnU+RSvrBUf2/PbNBoMGcygD99Hl3bqq4jKSp1UlnupqvKQlKxGZ5JiNhnrFIE6HFXF+wmL8fscPY5y/C4nFzV6GU+oki1Vc9DITFQVJ96gVVX6CB9j1c7fBaMijYEZtzHjwPMxc52SLkAvP3YSqc/KJBiKkDCsFImABIw3XxXTkyu1WTBefXGdglrekIN5Ba/z876Ha8YKPJtYVvkVN13zDc6nC1DrLShUx+7GUREM8VNJVdy50mCIgkAQq+LovleHkKrJ485WcxCLXfzwxHjc9upnw0KgVd8xjH9+BtuWTePg9lWk5LakVZ8xmGyZCP9Ah4vDsWNTYvJQUuiusVH0eo4tKHI6I7B+C5VPvoLlgTsI7txNYHNt1hhBwHzvrUit8ds2TiYEtQr9xaPwLV4Of703yWVoh52FRBb7fYtEIjWBsLRGbSmpzGbRrGjLzGAgzEsPLeGlT4aQnl0beAsFw5QWJa7w2bWlgl5nHZ3Gg0mZjkmZTlNTgsBIPSOwcy+h/ILYCVGEz+Zxz4Q5GPXVezirKpvh2Q/SN+0GQIJeYcMt9bDeFT+A2UUSpuinn+j9+KNUBQqYnv9szBqZou7nx6GKFZ/o4sd9D+AKxu43Fxa9S7/0G9HJT33Fx+mCMwT+DAAwytMY1/RD3t92WdS4UqrjqryP0Cv+26UqpyuCM5YxbOT/WGv/NSb4YlKk09wwELNczmMN03lpfzH7fNVrlBIJF6SY6Wg49k2gRFr3beNEbVoUWi3Z/ftxwbxZBBwOBJkMdVISCn3dWatTAa3RSq6/C5ma1hzwRJfOKQQ1/dJu4u0tF+ATnbxfOJiROY8zovsDJ+VaFHo9vZ58nAOb9lDxTuxm0mpTM3R0Kk3y5FQU7ERjMKEzJeMuLmb+bbHWTmIgwIyrrmHs/NloU08tUfX7Q3z/yea4Ga3P31xPh25pRyTwliQ19z7Xm+fvW8zBfbXiWR26JjO6v57QM/MACDtdhCe9j7lpQ+S/zICQiPS2awif1Ztdv0yJIu+HsHf6DIpXrkQ34uQpAsdD/t5KnrtnadTn0qarlSvvbE6KzYxceuTyfpm87ooPQSrDrMzk+uZf4wgU4wlVUdgtlcWzDsZd37SlFeU/RFdBIVUzOHMCKqmOGQdewCc6kQsq+qRex9Csu4/LY1llMiFkBJE1zCa0e3/MvGDQI+h1aAb0RNk6D/f0+Yil5ah7dEbRuhmy5LrdAhyBYn7Z90jMeDDs44eyBxk46mqyc7qiMRzdxjUSDtfYxAXCkYR6KAAHfEFaH2N7adDhROVR8vnTl9SQ90PYuOA7/G4nI+94k24jbzht+96PBynpiT8otVZGMFBNDLv2/fuCzvUJsdKO4+PJEI5Q+ewbGMZfjO68cwhs342g06Ls2AZZdjqCsn4qzI4Vsow0rI9OpPLldwlXVAHVvu3miTcjS42/f9XoLbTudwFzP3uCvF7jmPxVcdx1kTAsnrOfC65qWTMmlQlYbGoqSuMT2TSbhIOLl2Bu0jihmOzfBe+SFQnngmu3kBK5EZmsdr8lFWRRLbQttWqylQr2+6P3nBaZlK5eB9uVSvRZmVSKJfjEWCHLfeF5NGvTka3rK2LmLDY15qTq55onWMHq0gTaBsDq0u/J1rVLOP9vxz/jKXwGJx1yqYp21pE80mE98wvfosS7gzxjPzonX0SSqsHffXlnkADy5CTkb87h7hun8k3VI+xwLkZASgfTKEYa7sIcrN7kZaoUPJybhkMMEwhH0MsEzDIp8uPw7VQnWTHk5MQVlFMYDOgy6qdvS5uSgjbl9CuzNCsz+F/rqcwveIsFhe/gF120NA+if/rN/LLvkagHVoek807qtWjTUsnSmWj6+2qWltQSrkbNTFw23sTSb+5j/ffVgQZrRmPOueEFJFUhwqH4GdnKbdvwVVSecgLvrAqwaFYsGTqEVUsKyMg5sq1nWqaeh17uR1W5B8eBcowaUG7diPjMBzXWQgARfwBkMvizKqXqzY+hZRPWvfVOwnOvmfQ6WQP6o9Aee9DreFBe6uHpuxZT/pcN4vpl5fz48S4uuikXm+HIvzVDUjpKjT6uIrg1ozFqQ7WavU6ehE5eTS5NbTxYbeqY15ZI4NIb2vyjenoNihTOybqXHilX4hfdKKQajIpU5MLxaxsokm1Y7rmVsomPEbbXfq4SpQLLI3chtZiRSAUEbTamG66IItFHwh7X8oR6NPnudeR07U2KuukRzxMqLsW/aj2+leuQpqeiPbsP8hQbKkGCL0EFRbry2LLv3ooKNr7/Idr2jXBVxic/O1bNxF1ZjCXt+KuyTkfktU5CoZTWiJcdjv5Dc1kyN58W7W2kZf79ged6QShE6EC1wFnE78f+5sdINGpkWelEvD5ChcWY77jub7s8QaVE2bkdya89iehwgkSC1KBHak3s1iERBNr2OJ8USwO0toYoVYmfQQX50fdPs1XFmHEtePf52ICvWiMj0+Jlcq++NL/sUvq+9Dwa2+mTBKvLJUjQaxO2/xyCVSHjgYap/F7pYlaFEzESoasUeoS8hDZsYOAbk1AnJeHyOuNW9s6vfIbxt8/h/Qf9URVzeqOCe57phdVWre0RgYT3QoAw/wzXmJOFMwT+DGqgkunI1LXm4savIYb9yAX1v9rL9N8Ade+uOD79FtV9JYw77wbE5o8iCYPw+w50Db0Ig2tJj1Euw3hs+7O40KWnM/TzT/hu4CDMeXkojQYqtm3HV17OkE8/QpteDx5ph8FbVoa3vIJwKITKbEKXfmyq0ScDUqdAG/tAmhl7IVepUSj1bCqdxW7nHzVrLm38BuaTrCILoNGrGH1FS5YvLEAUI0gEuPy6NH59aSRBX22JX/nBnXzxyBiufPwXZBoNIU+i8r9TXx4diUQIBRM/jD3uoy9DNVlVGM0KXBuW4Hj2C+J12qk6t8W/vlZ1mGAI0eUmHEz8OuFgkIh46nq/iwucNQRarhDIzDEgihHy99pZOquIwRenYjtyTAOdOZXz7nyHb56+IqqUXq7SMPK219HFERNLStbw8Kv9+OT1taxeWkgkAunZeq6+vT1ZuUfxoqcZpIIcqypxj+zxQJ6Tie21pwhs3kZg83ZkDbJQdWiD1GaJUWA/WvJ+VK+r1B6xfD6YX0DZhEeiggvu76ZgeuEhhlttfFdaFXOMSSYl4xhF7Kq272DVS6/Q+c1HEi+KRAj642cp/8mw2NTc/0Jvnrnnd7zu2t9Vu66ptGhvw5amoXu/LEyWf7YIZg2kUqRpKYgFtXogEY+X4LZdAGjPHfR3XVkNJBIJ0iQL0qQjl/GHvV6Ce/JxfzwZ1a59kGzl/y4YzrqqNN57Z1vM+rado5MJEomEzr0yKD7oYsrk7TXWmhabmhtvasCqieMB2PL5F7S9+cbTisBr+vXA9dVPcee05w5GajYe8RxWuZwRNhN9zXrEYBCl2w2iEt3ll9asMSpS6Z5yJYv+YlPqDlXyQ9VVTHz6VyqKwhzc7yMty0BWrgFrcq0wp1Zmpp11JGvKf4x7DR2TEttjBv1e3FUllOZvIxwOk5zdDK3RhkJ9agLwpwJnCPwZxEAqkSI9gkftGZweEJKtWB6ZQMUTrxD+YCpQTb+Ug/qi6trhpAVgbO3actnaleydNgPnwYN0uP02rK1boc/MQCqvhygB1aSufPMWZl51DUUrqku+DDk5DHzrdTJ694rrMXoqUFWSz2cPjcbxF3XlziOu5dazf6IovJO21uEYFWmoZaeG7KRl6njolb68+8IqkpJV7Fv3SxR5P4SwGGL5b+/R7LKL2fjuBzHzpiZNUFlOfQ+jWiunedsktqyLr63QofuxBW0kgoC6dzfcP05DLI0u05ParMjzGuH8MnpToBBkNLvkIpY89Ejcc7a8ahxKw6kjr+UlHiQCjL68OQ3zLOzaUoFULnBJXmuWLzyIzxsgHAkf0VNZKpOR3bI7170ynw3zv6V0/1Yym3WmWffhGJMSl/emZOi45YGuOKp8BANB5NIAMqmTkA9QH19lTDgYBDGMoPp7ymzrE+7iYip37OTAkoXoMtJJb5yNTCVHdoItRA31XZEgiZt5ytF1RCur+/cpOl1UTXo/irwDEIlQdc+TDJn8DpUhkXmVzpr8VZpCzt0NUhAixRS4HYQjQdQyIxZldsJnSMjvZ/Wrk/CVl2O0Jv4eKVRalJp/SRb6MMhkAk1aWHn+o0EU5btw2v1kNjCi1smQyQQ69Uyvt+evI1BdiiyVyNDLk1FIT711o9RiwnDFGCqfeT1mTqJUourU7qS9dlXo/9k7ywCpqjcOP3PvdM92sLBLd6eghHSIEiqKXdgtdnfX38AAFSwEA0VJke7uhu3e2em8/w8Lu4wzs8CyIOg+n+CcWzN7597zO+d9f68fh7/ibjXIRYwR8tlPBkmS8GzYRskzb1S2+e0OeOcD2lw0jCHDG/P7r1XmgyaLilYdwic6jWYVo65uSd/B9cnZeRi5KBHIPcC628dRunNn5Xbbv/iSlO7dKj5LiZvyMg9eTwCjWYU5RoVSdWalmJgQW+HR8b/JIe2Klk3RDemH7AT9KQSZDLNCDgo5aMPvSaWo4aIGT1HmyWJL6e+V7TGqNG5s9hXxgoW0BnradYt8fLXcwOiMl9hl/ROnvyykr3vCeGKiTMq6HTZ2LP+FPz59hKC/YlJeJoj0ueJhOvQfj8ZgPqHPd7Yjk6Rq7AT/g5SXl2MymbBarRjP4GCtjjpqiuTzESguxX84h6DTiaJhAwSLCdFwegSu3+2uKCN3yZiQUOzYVq24ZPYvGOufmHHL8bAePMi0jl1xl/7NoE0mY9yKpSR3O/OO9D6vm7mfPc7G+dMi9t/4xnwS01tF7DsTlJW48bnt/P7hTWRuXxlxG3NiA0aMf4UfevYPaRcUCsYunEdqr55n4lLDOLCnlMdvXRi2Et+mcwJ3Pt6t0hTqZPDnF+L4ZQ7OBUtBJkPTswvqbh0pffsTgkXHCHu5SOLnb+F0u/ju/D7YMkOdo2NbtmTUnN8w1Dtz+ax7dhRxYFcZ2zYWsvLP0Mmii8c3p9eQBNLqnbyQDvh9iPITm2Rz2cvYsXwWC758Fq+ropqBJSmdS+77mMT0VidsRBYos+I7mInj5zlIbg/a/hegbNsCefy5aUBkz83lj6uu5fCChSHtXR6ZSOcH7kNzCpNgLr+NRbkfMOPAwyHtSkHLxPZLaKDvWO3+vuw8Cq4P97g4SsyT9yF174jVH8Tq96MWBHSCD4d3J/Oz32Ft4ff4JS9mZQqXpL9Iu9gR6BXhn8drs/HTiIvJ+msxPV9/kT1lKzm0Pbz8Z58rHqH7yFtP+J6rowq3385B+xq+3nsnOc5tyGVKuidexUX1nyRGXTvv2ZMhYLXhmDUH2zc/VaYfCTEWYp+6D0WTjBMWfieKLxjkgMvLB1mFZB7Jt26oUXJbvQTqq5WI1UyQOO0+bOUeAgEJrU4REgkRKCqh4M7HCZZEMH8VZMief55776sIjW/RLo6bHuhEav3oesCWlcUXLdvitYWnKQG0vuE6Bn46icwDVt56akWlR4tCKXDRFc0YfEmTWqtOc6IEnS4CxaW4V64jWG5H3bUD8tSkasPra4rdV0y5N58i90H0ijgsqtQTrgAiSRJF7gMszvuETcWz0MktDKr3AA2NPTAqI3sL5O7dxOcTI5ccHv/MDBq0Pq/Gn+VMcKI6tE7A/406AV9HHdVTtn8/U5q3jhhu3PLaq7nwf++h0J56BMeG9z/gzzvvjthX/8J+jPjhO1Rm8ymf52QoL8rhwzt74ve6I/afN+ou+l75yBm9pr/j93mZ/dEDbFk0PWJ/WotujLr3Y6w797H6pZexHcokuUc3Ot57N+ZGjf6xevA+b4DcLDvff7aVresL0BmUDBvbhB790rDE1nzFSfL5KlYjZRAoK6fw7ifhmHtXTEpA9ejNlPutOMtLSIhrwq6p37AZj5oqAAEAAElEQVTjq6+RyeW0vflGml1+6RkV7wDlZR5W/JnJ529viNj/3IcXkN5Qj0KhIVBYjGfjVrx7DqBs2hBVu1aICbE1Ct0O+P3YSvKwFmbispUy47UbwrZRqnXc9OZCzInHFxGBMivWj7/CtXBZSLs8LYXYlx4950S8JEls+uAjFt5xV8T+cauWnXK5S4evlFznduZkvUaJJ5Nmpr70TplAnDodURa+Wuf3eHAVFoIkIUoyyu55CskROUXG/OCt6I4pA+YNuNht/YufDz3FAdvqsO2vazqZ8xKvCVtNliSJdW+8xeIHJyLXaBgycxrbd8xh+8pfCPp9qHUmzht1J237Xo7OdG79jc8Wdpct4bXNvcOiMVK1rbi3zVzMqjOfThZ0ewiWWQmUlCFTKhDMJsRYy2mJ9stye7l/T2ZIyUMAtSDj9SZpJEfxbMjLsjHl3Y1sXJ2HJEFqAwM33NuRxi1iUKnl+A4cpmDCxKjnNTx8B9a05shVSgxm1XE9P3wuFwtuu4PtU76M2D920UK0LToy8cb52Kzh9StueqAjFw5vWJeyWg2BoA9noAxRpkArN4f02bxF2HwFuPxWzGIKiz95me1Lf4p4nIx2vRn1wCeoz+KooBPVoXUh9HXUUcdJkbt8RdRc4Z3TvqHHU09gSk8/pXME/X4Oz18QtT9/7Tq8dscZF/ASEgF/9LrkZ0Oup1yhpMuwG6MK+J6j70Yfm4i+ZyLDvv0av8uF0mBAHqFOLlR4EEiShCY2XBAGJD9lnmyyHJsp8WTRQN+RWHU6JuXJrwwrlCL1G5q4/bGuOO0+BFGGOUZ9yoMamUJRmRMpMxhI+N+L2Gf8hnfXPhTNGhO4pBdfvTgeW0nukR1ktO93BaMW/o5So0cbH1+rOcwnw4Jf90ftmzNjH9fc0RQy8yl68DkkV8WkkhOQaTXEvfYEysYnZxwW8HvJ2rmW71+6mvNG3cmOFb9G3M7rdrB33Xw6D73+uMf0Z+aGifeK9hycvy/EcOWoWl+5O5048/NZ//a7Ufs3ffAxiZ061bhMmiRJqEQ9jU09SdO1xye5UYvGqGUDyw8dZu1rr7N18hcE3G4yhg3lvAcfQPp+Nv7d4fePsmmjkP9bvXl4As6I4h1g5sFHaGnpj0UVOoElk8loMmYUq19+FXdxMbNHXk6z8Zcz6tr3QJQR17wF5tQMxONULakjMjZvEd/uvztiKkW2cxs5zm3/iIAX1CqEpATkSafXWd0bDPJzYVmYeAdwByUWlJQzLikmbBW+KN/B03ctorS4apI9+5CN5+77ixc+vJBGzWPgOL9Nr8OOouwASZ07n9C1KjQauj/2KPtn/Ya7OLQiTPrgQcQ0b8q2naURxTvAumU59OxsQqEQEHTaastMnq24/DZc/lKQydDL42o9zUMUFBiEcB+BIvchPtkxjn22FQAMirsfR0Fm2HZHsRZmVSzAnMUC/kT5Z0YlddTxL8JWkkfega3k7NlAWUEmfp/nn76k04qzoDBqX9DnI+iL7HB+MghyOZamTaL261NTEU+yXnFtoNYaadIpullPi/NGnMGriY4lMYMht7yKcOzgWSaj55h7SG7crrJJqa8QqJHEuy07my2ffsaMQcP4of9g1r35dkhoeUDyc6B8NU+ua8172y5i2t7beHFjd97dOpwSd/QX6PHQaBXEJmixxNa+iaagVKBoUA/THdcR98rjKK4azvevXV8l3gEkiY0LpvHb5w8h6FSnTbw7HT5ys2wc2FNKfo4djzv0dxPwBykvi/4sKS/zI/kkSp55s1K8V34Ep4uS594mUBwhRLQayotz+eb5K/C6HZji0yjK2hN126xdVaWIfEEPQSnchFAKBnH8Nj/qMRxzFhEsKz+pa/ynkQIBPFZr1H53cTHBGpgduvzlZNk38/W+u/hw+2gW536C3V+EXhEbVbzbMrOY3m8AG//3IX6nEykYZP+sX/l28FBkY4cg+5spnXZIX4S/GVT5gi5ynNuiXpfVm4c7YI/YZ0pP5/Jli2k4fBgBn4/tn3/JhhfexJJQH3Nyep14PwU8QTuH7ZGjbwC2lc47g1dz5nEGguxyRo50A9jqcOEOhD9ztm8sDBHvR5GC8M0nW3DYvQhGPfKMyNFDMrUKn1J+0u8ec+NGXLlmBV0mPoSlaVMSOnZkyFdTGDT5U3SJiRzcUxa2j0Ip8NiT7bi6VRnWR56jYMJDlL75Mb5DWUhRKsWcbQSlALnOnXy+6xomrs7g0dVN+HrfHRS6Dpz2c9u8hXy849JK8Q6Q599NQtPoaYypTTui0vwz/km1Td3TtY46akjA7ydv/yZmvnEL5UUVJbwUai39rnqCVr0uRqM3/7MXeJpI6Rk5f0iu1XLeq8/hV/o5vG0FaoMFnTEOnbn6usfRaHXdNax9/U2IkOXT+tGJeCwWzrTVokproO/4RzmwZXGYSVzDDn2JSUo/w1cUGbXOQOsLRpPR7gLyD2wlGPCT1LANOlP8CRlK2XNymHXJGPLWrK1sW/zgZjZ98BFjFy3AWD+NMk82b28djOdvg/tD9rX8ePBxxjf5ENVZaoYpKJWgVOLYf4iy/PByiACHti3HUVZ4Wn7HxQVOJr+7gbVLc5AkEOUyBlzUiIvHN69MF9DqFbRsH8ey+VkRj9G6vRElAeyFxRH7A3kFBK3l1ZZR+juHti4ncGQC0laSiyWpAUWZuyNum9GhD4WuA6wr+oFd1kUkaZpxftKNxKoboBKPOP1KElI1zv74fBF/32czKouF9MGD2PHV1Ij9zS4bi/wk01DcfjurC7/hqz0TKts2lczCqEhkYvslJGoiT2ZmL12KdX/4Krvf6WTdpE/oPu5iXN/9ghhrQX/5SNRd2od5oyhkKozVRMyIMjlyWfT83JhmTRky7UvcxcVIQQmVxXxKHgB1VCDI5KhFQ8Q62gAWZfU5xMUFJbhsThxluQhSGfH1m2OMTalxZMiZRinIsMhFsj2Rnx/xCjkKIUJax/LciNsD7N5ajNvlRxdvwnD3dZQ9+iqS85ioOUGG5o7xlEheEmpQu92UkcF5zz1Np3vvQiaXo4mtSh1Jaxju7H7zLU1JnDMd/6atlW3uxStxr1hHwrvPoWhYu5UzTgdF7gO8sKFr5X3qlzwszfucbaVzebjdMmJPo1dDuS8/LHJoS9lsBva+iy1zvw9LdRREOT1G3o5CdeZNIE8HdSvwddRRQ8qLspn61NhK8Q7gczuZ88kj5OxZ/w9e2enFmN4gzOhMrlYz9Kev2V20nEn39eWrJ0fxyb19+eb5cZRGEUjHPU/9+gz77htEVejgscUdt1PUpQfvZhZi85+5sl5HiUluyI2vzaN9/ysxxCQRV68pQ299nRG3v4XOfPaUilGqtVgSG9C8+zBa9hxJTHLDE3aDzl66LES8H8V64AA7p31NMBAgy7El6uBydeE3lHsj14U+m3DZq1+h9jhrf3W4vMzDe8+vYs2SnErtGvBL/DFzLz9N3YnHU7HyolLLueTKFsgV4a9po1lFtz4NEMscYX3HIp1kNMyxkxmbFn5H58HXRdzOkpSBuUMTnlnfjh8OPMSWktnMy36Lp9a1YUvJ7/gCFZMAMlFEO7B31POpL+iOzKhHCgQIlJUTsFVMBtmsHg7tLWPOj3v5a85B8rLteFxnx4qUQqul22MPI4/g82HKyKBe7+ifNxrlvjym7rktQns+3+27D5c//D4MeL3s+vb7qMc88Mcc5D07kfDJ68S9/hS6Ab0RzeEiQqeMJUHdCLUY+dnQJf7yagU+gMpoxJSRgblRwzrxXksYFYmMTX+dmy1TuNP4PTdbJtPc0BcAGQJtYodG3M/p8LF9Yx7vPLuRx+9cw6fvl5KZE8+25X+Su38zUvDcqJ2tFUVGJ0SffBwRZ0b5t+gomUxGYkr0MmEmixpBkGEryeOXH55DeOwGlFeOQN2jE8qL+qN87k7+XP8tYoIBXQ3L1YoKBdrExBDxDtC4RQw6fVUkjVavoHFcIES8V+LzUTZpKgF79c/3fxpf0M3crDcjjgNKPVlsKZlda+dy+ErIcWxnZf409lqXk+fcTUDyMybjVRLUVWlBQQL8WPY0wx9/n7i0ppXtluQMrnjqeyxJZ/+kyIlStwJfRx01ZOeq2fi9kXOeF339CsmN2qE1/vvMe3SJFbnT695+h80fTcJnt9P16cfYuOFHDmz+K2Tb/ANb+eGV6xj35HfoT1LcKnQ6zIMH0XfDOry7duN3OjC2acNqpYZZ7iDgotwfwHCKJWVOFkEUiUlpyMAbnsd92YMIgljjKIOzEa/DwZZPwkvMHWXblC9pdf21lAWzo24TkHz4parwb6/bgaOsCLfTilKtQ2eMRX0WRKjozdWsPCpUnA6P17ISd9RyefNn7Wfo2CYkplSskianGXjmvd5MeW8Te7aVIJNB+26JXHVrGxLrmQnk+SpK+EQS6koFgunk8vzqNa8yXyvO3oPLXkb3kbey+rdPK8vxaI2xXPL0p3y8d1zYwE0iyOe7rubZztuJE9MrLqNxBormjfHt3BuyrWAyYBg9jGCpFee8v3AtXoVMrUJ2+y1MnZbJyr+q7i+ZALdO7ELXC1LRaP95N3Nzo0ZcsXoFSx95jAO/zUZUKml13TV0mfgghrSTNzvcbV2CRGRhtaXkN+y+orCSlDJRRB0bXSyrTCYEpTKqSaDNW4jdX0RQChKvacyNzabyyc4r8ASrREMDfSdGpb941kbS/JvxFJWi/dbOmpeexF1cjCY+nu4Tb6Xr4NFoExIwR1iBDwYlNq3O5e2nV1W2Hd5v5eM3rAwf24TyolnoLYmY4s587vzxkIJBpEAA4ZgytBkaFZcmWJheUFrpBCAA1yTHkhrFwO6CQQ345ZtdEQN7LrqiGZZYDSW5uezbuJB9GxeS2rQTsUkZ2EvWceCVp5CCAVJbdCa9fa9a/XxxiVqeeqcPrz22jMI8JxlNzIhbtxBtWtK7YSuSwwX6s7duucNXyuaS36L2ry2aTvfE8ajFUwtZt/tKmJv1BovzJnFDsy9ZmPMe64pmEJB8pGpbc3H686wrmsG6oh8A2GtfxjT/Pdz3xBxw+Ss8fPQW9JbT69twpqkT8HXUUQOkYJDs3eui9hdn78Hv/ffmwutTU+j1wnN0vOsOgn4/PrmfBfdGXnkqOLQDR2nBSQt4gHJRzjM+OcYmrZELMkp9ASRP1ZvZHiEH7kyhUKpRxJx7ZjPHQwbV5n3LBAEZkKHvioBIkPAoCJMyCdWRl7a9tIAl099k4/xpBAMVw5WM9n0YNuE1TPFn1tn97yg0Ohp36s/edeF52u0vHEfe/q3Ua3ZiRkYnSnFBZHdwAL8viMtRFTIqV4g0bhHHxJd7Ybe6kcnAYFaj01dEpQgWE4bLL8b21Q9hxzJcOQrBYj6pa0ts0AJTQhrWIyZAf33zCq3OH8WYBz9FrlCj0hnRWxKwq8vIcmyOeAxv0EW+aw9x6nQAxFgLsU/ei2vZGhy/zEXyeNGc3w3diIEgFym863GCJWUAKJo1Yt3q4hDxDhX5qx+8tIZGzS3USw9fRT7TCHI5ca1aMmTqF3isVmTI0MTHRTWCPB5/T0M5FgmJoBT+GxNEkXYTbmHb5C8i7tfx7jvRJoZPUEmSRI5zG5/turoyxzpR05TbWs7g8Q5ryHRsotyXT7q+M/GaxjUypKzj1PDa7ax5+VXWv/VOZZursJDVDzxL58IHaPX4lREnVUqLXHz+9saIx5w9M5snXhlJeWHWWSXgA+U2Ajn52H+dh1RuQ3N+d5TtWiJPiMMgFxkRZ+ICi569Tg8qARoE5fhtPgqtNpx6BZZYNXJF1SR+XIKO2x7tyocvryEYqBor9LwwjS69KiY9RFGBqFAR8HnI3r0ubCxniK3970cmk9GgsZnn/tcPa6kbQZShXlFK1F++Qg5nuSu9KMjRys2UeA5H7NfLYxBlNZ9wLS/KIf/gNg7uWEZcbBwPnLeAD/eMJd9VldaV7dzKJzuvYEKL6ey3raTUU5FyVl/fEZ0pDnWMgTJvNoXeQ2SWbiVWXR+9PB6t4p9/j5wqdQK+jjpqgEwQSGncnl0rI88+xiQ3RFSe2bqeZxpRqcSQlgZA3v6t1eayOqzRje+qQ3tESJYHgkTQiejEuiyg2kah09F2wi0cmhvZJKn19ddSfigT58H9TGw6h23CIn6zvkxAqlpLGJX+EmZlCj6Pi2Uz32X9nFCRcWDjIma8fiOXPToVnemfi14QRQVdht6IzhTH1iU/EvB5UKp1dBh4FbEpjVDrav8lb65m0kcmA5Um/LVsMKowGMOfJ4JahW7EAMTkBGxf/kAgrwAxOQHjNZei6tQWQXVyudiG2GSufHo6cz59jH0bFoIkcWjLUhp3vJC0Tt0qv49ye+S8+6P4AqGRSWJsDLoRA9Gc3x2kIIJBjySBddLUSvEO4O97Ib99lxP1uH/9cYgrJ7Q9qc90OlEZjahqodxsM1PV5KdK0GFUJmL3FeEKlDMq6WksZTo8+3ciU6kQLSaEI2W7TI0b0eWRiax56ZWQ49Xr05smY0ZHNOIq9hzi1U0X4PBXpY/ku3bz1Lo2TGy3lK4Jl5/y56nj1HDmF7Dh3fcj9q1/8x1aX38t6sbGsIlWu80b1fgyGJAoKQ6ikp89YdkBmx37zNnYv/mpss29agNiYjxxrz2BPDEerVxEKxdJVinJz7HzwUsr2Lm54vmj1sgZfXUL+gzNqKyjrtbK6XZBKs1axbJnezEul59mreOwxKoxmCq20ZrjadtnLBvmhftYyJVqkhu3P22f2RKnwRJXkYPtE7tg/zJyGoz2wvNPOoLqTGNQxDMw9T4+331txP4LU+9GIdRsHFyad4hpT4/BWpSNwZKE1hiLvKk5RLwfRULij6xXOT/pRmYdeoaOcaO5rNGbqEQ9h+0beG/bCMq8Fe8VGTIuSLqJi9KfPecnJ+sEfB111JDmPYaz5Ps3ItYE7z1uIrp/Yfh8NFRaPYIor1xh/Tv6mKQaHdckF+mg17DBHp6q0FanwXSGw+f/jfhcLpx5eTgLChEUCnSJCSR370q93heQ9dfikG1jmjfH0rwZX3frUdnW6NKLufmZL/mo4EqMyiRGZ7xMu5jhCDIBa1khG+Z+FfG8uXs3YS/JP60C3h+UkAvRVzEKDm3H7/cS8PsYeff7FaZrUpBtS39ix4pfueb5n2vlOsq9+ZR5cyh07ceUkMI9rzflq1czKS4Iva8790rBZDm5VVzRZER34fmoO7RG8geQyeWIMeYaX6slsQEX3/sBzvJiAj4vKq0BQ0xypRj0WK1Q5sWiTKXUG55GIUMgRdcyvF0mQzzGAT1QVIJ70fLQjXR6rKXRfQkK8xxIkvSvq5dsVqVyQdIEGsj6obI1piDLQ2ySkkb1NajmrKNw5kNwxNleiLEQ+/T9KBpnoImJocuD99P88kvZ+c33+Gw2ml46BkvTJuiSIj9zt5b8ESLej2XGgYnc2eoXdIq6PPZ/EldhIVKUSgZBn4+ifTvxKfwkZoS6bQti9b8LpUrElHj6TMVOlmBRSYh4P0ogvxDbtz9juvXqCsNRoDjPxvP3LaEgt2oCwu3yM+3jLWj1CvoNb4hw5FmvUstJTNWTmBo5dFuhVNNrzL3kH9we4lckV6q57NGpGGOTa/FTRkeMtWC45lJsX4SKeDExHsMVl5z0BOw/QauYQbSNGc7mktCSo/1T7iZJ2xyrNw+7r4iA5Ecvj8WsSkGQVT9uczvK+X3SRBq16UuzNgMo3bEbpU6P0deYDvqL2GD/JWyfA7bVXNd0Mt3ir8CoTEAjN1HkPsjrm/viClR5iEhI/JU3iXhNIwbWu/+413I2Uyfg66ijhuiMsVz++Nf89OYE7GUFQMULoM8VD9d62O3Zjs4cT5s+Y9m04JuwvnrNu9TY3E0vF5lQL54PswrZeIyIb6vTcHta/BnPf/+34SouZuvnU1j+xFMEPBUrN7rkZC6aOZ0h074kZ/kKNv3vQ4I+Py2vGY8mLo45194Qcox93/9EQpt2vHTvfkS5HLMytVJgeV02Av7ItW8BrEVZYYPQU8UbDFLo9fNXqY3DHi8ttGq6mfTEK+UhNYPtpQXM/exx4us3p2XPi1jy3esUZu5CJog06TyAyx79CmMthJoWuw/z4fbRHLRXmQLGqdO54/Ufef/+LIoLK+7rFu3jue6uDmh1NQs5FGNOzG0+UGpF8nqRiSKCxRSxBrtaa0StjbyynLNsOUufeIrRU5/js8IbwupUD0l7GKMiQui2349MXjXkkAH8bXJFOHSApi3j2bI+skdAxx4p/zrxDqBXxDJQ/zKvPryC7EMVXgGNmlu4p38A5/TQKK9gSSlFDz1HwsevIk9KQG2xoLZYiG97/MiEoBRkR1n0sn6Z9o14gk501An4fxK5tnqXbEGp4Kd3bmP80zPQmeMISgHKvQWo9SrqpRvJOhhueqjRyrHEyM+qhQXnklXR++YtrhCx8bEE3R6y95aEiPdjmT55Ox16JBMbf+JeDca4FMY+PAVrwWFy9mxAH5NEcsO2GGKTEeVnxmdD0OvQjRiAukt7HLMXECy1ormgG8o2LaJ6V5xtmJRJXNv0Mwrd+9hjXYJOHkMjYw8MikSK3QeZtGMcBe6KZ5peHssVjd+nTczQME+PY3GWF5PepAeuVfv56c5hldGdgkJBjw9eRN8phiW2KSH7aOVmNHITFlWVN8SB8tUh4v1Y/sh8la4JVxCj+mfT+E6FOgFfRx01wOt2sGv176z65SP6jH8UtdZIMOBHpTUQX785GsOJl276N6BU6+g9biJSIMCWxT9UOt1mtO/DsFtfP6VBQ5xSwT31Eyn3B3AEgmhFAZNcrBPvtUD2kqUseejhkDZHbi7T+w3g6i0baTpyJPXPO49AmZXc7duZNfrSiMfZ8Oa7tL72Wgz1Ql+GSrUOmSAiBSOvJukttRvC5g9KbLW7eOlgXqUl2JpyJ98XlPJcw1QaaqvC+fw+NyW5+ynJ3U/Boe10HHQNpvh6yGQyDmxezI5lv5BQv/kpXY/TX8a0vbeGiHeAIvdBpuaP4fFJCynYU1H33mRRV4aBng6CdifeHbuxTpqK/3A2gkGPbtRQdIP7gsVEsc/PboebbK+PJhoVDdQqYpWhQwRHXh5/3f8QJTt3YnltAXc9NJO5rvfJdG0kRl2fIYkTaR5/IWp5ReinFAgSKCjCtXQ13m07kTeoh7b/BYiJcQgmA9r+F2CfUSVQpbnzueyeiWzdWMTfy8pb4tS0bH/2VHmoTWxWDx+9tJ7sQ1WmgBcNTSY4/YOI20tuD+4NWwl0aYHTWohCrUVnjD3ue0eQCSRrWwIzIvbHqNKQn0LOah21gyYhgZgWLSjZsSOsL6Z5c0qtWRRl7sZlL8Wr8bAi/0sW5X6IXh7L9Q/P4LX7NuFyVkXDCaKM2x5uS1J981lV3lZyV+MT5PcT8AfIPWAlVuHlwPboaXhlJW68npOvSKM3x6M3x5PatNNJ71tbiAY9okGP4q4bIBBEdg6Oa7TyGIwKOzp5DHmu3egVcYgyJZ/uvKpSvAPY/cVM2jmOie2W0MQU3SRQkiR0fgMr3wl9/gV9Ppbd9CBDV85kpfAtvmBV9OuFKXeFTRznOrdHPYfdX0wgeG77VNUJ+DrqqAG2kjxmvX8PSBK/vn9PSF/X4TfTd/yjyBX/7hz4v2OwJDLwxhfoOfZePA4rSo2+1tzGDXWCvdZxFhay/ImnI/b5XS72/vQzbYYMxzbtB4SLBpO3Nrys3FHcpaUEI9T71pniaNlzJNuWzAzri0luiCGmdkMVS/1+3jycH+bn7Q5KvJOZzzMNUzArKl57gqhAo7fgspdSmneQBV88E7LPsFvfOOXrsXkL2FLye8S+Avc+fIpC2nc7/YNHSZLwbNhCyfNvV7YFbXZsX3yPd89+hDuu597cUtzBqtX0WIXIMw1TST7G7dnvclOycycAuyd/R+asBXS4dTy924zBnVME3mwMD1alRPgOHKLogWeRXEcGWivWYf9+FrHPPICqYxt0IwfhWrKKQEHFinuw3IZ+7i88/twlTPl0F5kHypEJ0LF7Mlff0Y64xLPbDT3o9REss4LPj0ytQow9sYlcm9XDzi2hUQcWs4JAYUnUfTx79zN/w+RKA8bU5l0Yedd7WBKrL5PUPeFKZh9+MaL55PD6Txy3ZNx/CaffitWby/bSuXiDblpZBhKjqodecXrSfgLlNoJl5cjzCrjs++/I27GDhQ8/gnX/fgC0CQlc8PGb/Dq1YtI1oJf4YOtFHHZUhIGXeDL5QRrDfe9/yuF1WnZsKiYt3UCv/mnEJelQa86ukGxNzy44ZkT2EVJ1ac/yFcV8/tEOXnuiEYmJ0aMSNFo5CkX08YHP48RZXoIkBVFpjGgM5lO99FpHJpPBOTjGCUoBDthW8uaWAZWCeg5gUiZzXdPJfLprPHZf6LPtx4OPc3vLH9EpIj8f5ZKcbe9Hr4Rz4LMfaXvnMNaVVkxENjX1pnfyLYhCqKRtYIj+brUoU1EI53Y9+DoBX0cdNWD/xkX0H/swGQ07I3h8BFUK9u9dyeJf/8eG+VPpOuKWs8rp9Uyh0uhRaU6tZEgdZ4aAx0Pp7nBDmKPkr1tHo6uuYskdN7LJ6eWS886Luq25cWPECA7cSo2eflc9jstexv4NCyvb4+o1ZezDUzDERBcLkiThyMnBXVqGqFKiiYtDbaleEBV6/biCkc0Uszw+ygNBzEf0qN6cQNeLbuGvr18O21ZUqGjQpme15zoRPEFHWIj5sdh8NTN3PFkCxaWUfRjZrdyzfC2ycZfg/5sJZbEvwPuZ+TycnoxCJiOIhFyjQWU24ykrA8BVVMTG596u3Kf/pI+qzllaRunL71eJ96MEg5S89F5FCHhiPKaXHsf652rEdWuQqVV4u3bF6/LRd2g6GU0txMRrMJrVNU4tOFMEikqwTZ+F8/eFSB4vYlICppuuRNW+FUI1paBsVg9eT4C7n+qOSi1n384SZn23i8IiD6bkBAK5BRH3C6bGUbroIEqNHktiA2zFuXz73BWMf3Zmtb+rGFV9bm/1Ex/vuAxvsKIiggwZ/VPvoYVlwKl9Cf8inL5SFuV+zMyDj5CoacoA9e1YC/biU5YQSGyKKaH2Qm89ThvOsiKCBUUEV2zGN3cZktOFJj6Wy3/5mW1zfkMVZ0GVGssf3z+FrTiHpIw25Ph2VIr3o2Q6N/CusxMXd3uOuy55AJXi7K2UIk9JRNmpLd51oRUtZCoVwpjRfPXYFoJBCSkgUT9GQqdX4LCHTxQPvqQR5pjICyZl+Yf569tX2b7sZ4IBP2ktuzHwuueIr98MUX52TWici5R6snlv24iQ1XAAqzeXXw8/S9/k25h1+NmQvmzHFrxBJzoiv89FmRx7dvQytY4DOfSNfZAGpk40N/cjTp0eceIxTdcekzIZqzc3rG9k+nOYlGfG6+B0USfg66ijBjRtfB6eSd/j+/y9yraG7VuRds9kpr19XdSQ4Toi4/MFcDv9KJQi6ggu3OcCruJi7Dm55K9Zg8psJqFDe3TJyTUuLXW6EVUqLM2bU7Q5cjmwxK5d+cTpY1V5xSB/ePMWGOrXx3Y4vGTM+a+8hD458svQGJvMyLvfx2ktwl6aj8ZgQWeKr7Ymq8dq5eCceSy65z4cuRUv33oXnM+ATz7C0rRp1P18x6nbHjimXxBF2vcbR8HBbexYPquyXanRc+kjX2CshVJCGtGMXKbEL0X2AYhRnRlDKcnhJFgc3RxO2neQ2IxG5HtDTSh3Oj0UeH1MzSvBL0n0tRgYtm41s7v2wF0c6kQvKBTU79e38v9Bqw1/ZmRHecnpIpBfhDwhDllsLLmNOrDPloTHB8u/KKAoP4fGzc10vyCF2KSz24kZKnwFSl5+D++WnVVteQWUPPcWlsfuRntB94j7FeU7ee/5lZWu2gCtOsRz95PdmfnVdu68+GL4cFLYfjKDDl89C137XYdWNFK0YSva5okYmqZjL86rVsArRQ2tLAN5tvN2Clx78AYcJOtaYlRUGD/VUUGBex8zDz5CO90w+pVew+qrnmH9rl0AJHfvxoBPPiK2ZctqS24eD0mSKM7Zx4IvnmXv+vnIZDIat+9H78dvQ/p4BoFD2ZQ/9zap917OjE/vxVaSV7nvwJue59fC16Iee272G/RMuhYVZ2+Or2gxE3P/BFwr1uKYOZug3Ymqczu0l17EC6/uwGGrEOvlMi3mGdN49PGree3V7ZSVVInF8y5IZtDIjJBSckexFmXz1ZOjKC+qEoOZ21cx5ZHh3PD6XOLTmp3+D/kvp8h9AKe/LGLf3vLlDKr3UFh7nDq9Wnd6pdFIUrcu7J0ZWcTX630+jePOp7nywmqvLUadxoNt/2TSznGVJTOVgpYR9Z+kXeyIc95P5dwcKddRxwkQdLkJllkJOpwIGjWC2YSgO/kQTJ83gN3mRRQFjGYVgXIbnk+m4ztmsAbg27gNhSBj0JVPoVRHX3Gpowq/P0hhroM5P+1l56YiYhO0jBjXjLR0IzrDuTM77sjL48+772P399Mr20SlkmHffU2DQQNRaM6+UC1tfDw9n3+Gny+6JKxPrtWSOHRIpXgH+CQo5/7ff2PzXXeTtaBiNV0TH88Fr75MWt/eYccIOZfBgtZgIa5ekxO6trzVa/jtsnEhbVmLl/B97wsZt2o5xvppEfdLVCoQICyEHsAoihj/ZtimtyQw5OZXOP/SByg8vAO13kxsSiP0liRE+am/Hk3KJPqm3M687LfC+lpZBmJURp/EqE1kR2sKR5ngkBn0IeHzx5Lv9bP5iIHkdoebVJWCu5b8xS8tW1duI9doGPnzTAxpVWIhmot2Zb+3YlJDrZHTokMSifUMbFqVx/n9VbTrkkhSPT2W+HPjORooLA4R78di++oHVO1bIv6t3JzN6gkT7wDbNhQilwu07pDI+jI/Xa++HO93PyIdMZmUp6Ugn3ApDnxse+5jCtcf46Kt0TD8x++wWRIxxEWv/CEXlMSpGxCnrj7c/r9KIOhnYfb7qEUDgwK38dvgUSH3c+7KVXzXqw/j16/B1DCjxucpKzjMlIeH4XFWGG1JksSe9fPJ3L2Wq++aTOCxdwmWWjHJDJgTG+B1O0hIb0mfKx4hsUFr1IeiR7spBS0yzv4yq2KsBf3wAWh6dqnIAdfryMxysXtbWeU23/2QxS1DB6Gf/BFP3XYpVoUJh8NHQrwaU4IeU2LkSb5DW5eFiPejBPxeFn/3BiNufxNlXcTgKeGKIt6PEpDCIyYuavBMtWkoSr2eHk8+wb6fZ4W9R5QGA0mdu7D25VdpO+FmtAnVv0OTtM24t/UcbL5CfEE3OkUMJkUyCvHcT3GtE/B1/CsJlJRSPuV7nPOXVJTfkclQn9cZ863XIJ6gu2cwKFGQ62D29N2sX5GLRqtg2NgmtG1nwbdjT8R9fOu30vSmK9AY61x8T4RDe8t46s4/8XkrJNeBPWWsXZbD+Fvb0v+ihmi0/2zYbNDrA1nF6mI0pGCQXd9+HyLeAQJeL7PGXMa1O7ZgaXJiwvVMk9rzPPq88xZLH3kMv7NCrBvq12fY9G/5Vq4FqlY6SnwBXhJVDP7gI4b53JgDXrSWWHQpyQgRnMxrirOwkL8emBixz5GXR+7KlVEFvEkuMibBwvcF4avNN6XGYYmwSqMxWNAYLMSnRV/ZrylKUcPgtImIMgULc97DG3QhINI1YRyjM14OGcS4iosJ+v2oY2IQq7nfaoJgMqLq0h7P6g1hfTKVCme9VKzl4eUwtYIQlgCQ7fGxOTGOa/fuImfxErTxccS2boUuJQVRWTXpJhgNCCYjQWsEF2BBQJ5StUqs1shJqW8ipf65uQLs3bU3vFEQUI27CHeMkSVPPQMyGc0uuxRTwwy08fFYSz1h4v0om9bkM3h0YzauzkN1VX/M/c8jaLUhUyrwq+SsmPsZ7gV7QsQ7VHhX/HrJZYxaOg+N0YxceXZG/5zt+CUvZb4cehiuZNvTkyJORnmsVnb/MIPOD95/0it59nIvwaCPDfOmVYr3Y3Hby9i6cQ6tO7XGu3YLstJyxkz8HL/XjUKprczh7p18C8vyp0Q8R8/Ya1F4VXCO6BTRYq78t1rrQ64Q8PsqxgXbNhfzrSmRsTfchLhyGfFFhaS2boa2QzfEhMjjuaDfz66Vs6Oe7+CWJbidtjoBf4okaqNHMejkMSEpZKJMwUUNnqaRsUfUfY5ibtqE0fP+YP5NEyjbtw+AhI4dOe+ZJ1nyyGMUbtyITKGg8wP3Hfd9aVDGY1D++wxQ6wR8HecchV4fOx1utthdpKoUdDXpsBAk6HEgV6iQI1aI9zmLqnaSJNzL1lDidBHz6F2IxuOHZeZl2Xl0wnxcjqqw0g9fWUv7Lolce8N1BD/6JOJ+gjdyLfQ6QrGWufn41bWV4v1Ypn28mS7np56QgHcVFSEFg6hjY2tNSAaKS/Hu3o/z9wUgE9ANuxBF44yI9bUdefmsefX1iMeRAgH2zPiRrg+Hh5HVNi6nrzLkUGdQnNB3p46Joe0tN9HoohG4CgsQFMqKGe2EBLL2h4c/2wNBfgjALr2J21IEDOroq3w1JeB2Rw3rB8hcuIhml46N2KcRBYbEmcjQKPk+v5QCn590tZJxSTE0UCsR/oGQOZMykZHpz9AnZQJuvw2VqMegTEAtVgwcHbm5HJq3gPXvvIfP4aDJ6Etoc+MNmDLSa+0aBJ0W823XUHQoi0D+MXn3chHL0/exQqHk2Mmao4yIN/FnqS2sfUGpnb6N6tP6umuinlOMtWC68zpKn38nrM9wxSUI5nNTrEfiWPFxFNX1l7Lq+2/Z8/PPNB59MUkX9ODQ0kUEZv1K+ztvx2Gv/lmlN6oYP6EdSpUI6CCxYgAq83po0qofM29+KeJ+fpeLwg0bMKWnY4g5eQHvCgQp9fvZ5XDjkyRa6DRY5CL6c9Bgq6aoRC3tY0YiFAfYvSq6CDw8fwHt77gNhfbEIvuspW52bili1je76NXPQub6BVG33b9zOa1ajoe1W5DXr4fKEL4oEK9pTO+kCRy0rWGE8T4svjhQCBxmJwlSc3Ys/YVOg68950KFzTFq+o9oyB8zqybGli/JZ9WKAoaN6cAl9zVBY9JUm74gE0V01aRpaQwWBOG/c0/XNv6glyL3AXIc2+gafzmrC78N22ZMxqs0Nvbk4XZL8UseYlUVueoq8fiRVQqNhvp9+zBy1o8Ub9uOIIoU79jJ/Am3V+bHr3npFVpcMQ5jgzOTina2USfg6zinyPF4eXxfDlZ/xYx4K42cpo58Nsz7guydqzHGpdJjxAQ05sghy94NWwmWlR9XwLtdPr7/fGuIeD/KxjX5lAxpS4xBT9Bmr2xXtmyCslVzBNO/Z2B6OnGUezm0zxqxTwrCgd2lJKVGnx235+Rw4Pc/2PS/jwh4PDS7/FJaXj0eY4NTCwsNFJdQ8sK7eLftqmxzr1yHsmMbYh68NazethTw47FaaX3z9TQYOQRkYNt3kM3vfEzZ3r2U7omwOleLSJJEXpadbz7ZwuolFS+2zj1TueKWNiTX0x938CZXqTClN8CUXvW9SZJEP4uGybmRc7d7GtyoZKcnLUAmytElJ1fmvv8dS7PwlXJfMEiZP4A7KCECGWoV99evWOGVZGCWi2hrMUrgZFEIauLU4aG2jrw8/rjmeg7Nq6rNvfrFl9nyyWeMW7EUc6OGtXYN8uRE4t54Ct/+Q3g370BMSUTdsQ1iXAz9kCHJRWYVWbEHgsQp5Fwcb8bqD7DB5gw7VpRo+xBkgoC6Uzvi3nya8snf4juQiTwhDsP40SjbNEfQ/HtWhxWNM5CplEieit+LmBhHqctO0c6djPzzV7at/YUVG79AqdbRutvFOKxFmMzRB50yGRiMyiPiPRS5UoVGaybgjfzbBHDmFUANNJvDH2B+iY2v8opDIi+GxBoZm2DBpPjvDBnbxQ5nXem36JKTsedE9nIwpqcjHIk6KfcHKPX5yfP6MMtF4pQKYo/5vmxWD1M/2sziPw4B0KyVrlo3dI3ejMzpQd6gHvLEyKuHBkUcg9V3oM4vxfHWN5VVC5q3a4FsnInlf71Js25DMMTU/kTr6USllnPJ+BZIksT8WfsJ+CVkMmjfNYmBoxrjVBdQ6CipmAxVxKNThE9uyGQyOg64ig1zv4p4jm4jbqnWh6WO6tlfvpI3tvQnKAW4tumnxGsa8Vfux9h9RSSoGzOm4Ss0M/VFp7BgUdXcT6Zg/QZ+Hx95othrs+G12yP2/Rf47zyN6zjnsfkD/C+zsFK8JyrljPUV8PMTYwn4KvIDCw5tZ++6efS55F4a9z8P//zlYccJlpZB/dRqz+Ww+SrFUCRWri9nZPPGeNZsRIixYL7rBnx7DuBeuxHvzr3oRw5C2apZxBXbOio4ngaozo/MnpPLr5eOI2dZ1d93+ZNPs/njT7hs2V+YTkHEu9dtCRHvR/Gu34J3+x40vbqGtMt1Oi7+8zfWLvqSmVPuJBjwE5/WjF4fvsC+z38gfdDpdXYuzHPy+G0LsZdXDejXLMlm+8YCXv6kPwnJJx8iKJPJ6GrUsrDEwSFP6CRWC61II40Mo/L0DAp1yUl0eWQii+66J6xPkMtpOGJYSFuZz8/sIiu/FVtxByXUgox+FiPNtCpyvT6SlQqKfH56WwxYzjIBUrx9R4h4P4qrsJC1r75G7zdfR6GrvTxweXws8vhYNN06hrRbgEsSLPSxGPBLoBRkZHu8fLq/KOJxzjfrMIjHz68VtBpUrZoR89T9SG4PMqUC0WQ87n7nGmKchdhnH6Lo8VfA70d1yxUse+55Lvj4DWZ8eGtImPSirFfZt30xF93xHm06J7BlbbjLfOeeKZgs0Sc41GYzhrQ0bJmZEfuTunZBqTr5+ybH6+PLvPCw/t+Ly2mn19LFdHb9fk4nseoGdGx8GTkPJ5E/9saI27S/fQKiXE6x18+7mflsdVRFscQoRJ7ISKG+ukLgFxc4K8U7wIq/irjupus4tDV8jALQpdflCCszMT/zYLXjCFWOnfLn/xfS5t20AyEzj/533U8wcG5GBJpj1Vx5S1uGXdoUp92HWitHZfCxoOhp5u9+tzK3upmpD9c1m0ycOj38GIn16XfVEyz86rmQ9iadBtCs65Bav+agw4nk9SHTqBHU50juQg0o8+Twya4rK/8Gk3dfTzNTH8ZkvIJWbqGeri0Jmka1ci59avSxuqBQID8L/YXOFGe/w0UddRzBFgiw01nxgmymUXGPPgiFh2jV62LkytAf8V8/v4Osb+QakEI1q+9uZzlOW0X+rFwe/eeh0ChQNEoHQYblvpuxfvgFtmkz8O3ah3fLDkqef5vSNz4mUFJ2ch/yP4TeoCStYeTBvEyAhk2jlwzLX7cuRLwfxZ6dzeYPJxGIUJP8RAiU23DMmhu13/7zHwSdToIOJ/7CYgJFJfiCbn798iF2rJpVOVgqzNzFjx/dQbMJV5LS69TLkUW93kCQv34/GCLej+Kw+fhz9kECgUiWbscnQaVlYnocNyeraa1T01avYUKyjOuTIEF1/JX9miKTyWh26RhaXnN1SLtcq+Xi337BkFaV/17q9fFtXgkzCssqTdjcQYnZxVbW25y0M2go9Qc44PKysKScLLcHVw2/j9PBtilfRu3b9d10XLl5UftrG1EmI06pIEmlIEYhJ1WlpIkmfBAaqxDprLNRsHcDmz/9lOVPPsOh+QuirlICiAY98vjYf6V4B5DJ5ShbNSPx09cxvPUYue4cknt2Z/2SbyLmOGfuWElZwUFue7gLHbpVTYTJZNClVwrX39MBrT56Cow+JYXzXwsvfwiQ0KUzlsZNUGlPbuLOFwzya1FZ1P4ZhaXY/f+t6ipx6nQyzh9Ix/vuDmkX5HIGfjoJY8OGuANBpuYVh4h3qPAMeW5/DsXeinfR+pWhEUXFBS6KrSm06HV52Hk79L+amEYdUN90NYrk6KvEAWs57qm/hLQpWjVDecVI5EN6YwhqUahO3rj3bEGlkZOYoiejqYW4FCULi19mTvYbIcZou6yLeH/bSKze/LD91ToTHQaM55Z3l9Dv6ifpOfYeLn1+Go2uHM4m92yK3eEVVWpCwGbHvWkbJc+/ReF9T1P61iR8+w8T9ESPkjmXsfkKKfVkhbTtsi5iyu4b+GD7qFotj2pu3AhdUuTFguZXjkOXFL3ixr+d/850ah3nPH5JQimTca9ZwL5hNvPnTMbjtJHephdjJ05m0dcvkbtvE1BhLFaQt5e4+JjKsDIAecP6EXMv7aUFZO5aw5rfPsXvcXHe6PvpNbA+C345EPFaeg6ojyKpEfFD+uL8dV5oXukRPGs34juUWbcKHwWTRc2Eh7rw9F1/huXBX35ja0xR6rr6vV62fPJZ1OPumPY1He66HX1KeNhWMBDAaSsBJLSGCDnzQQnJF33FQkxJwp9fRPnkb/Gs2Yg8JQnrNX0ozY1wn0gSy37/kMsejS7SThWn3ce65dGF07plOQwZ0wSjqWarAYkqHX0sMjoZnATxo5RpMCoTEWSnd+5Xl5hIn7dep8vEBynasgWVyYSleTP0KSmVhjUlPj/ZHh8LIuRoAywps3OB2YAgg+4mHQfcHjbaXLTUSSQpFWjPgpze6jwbZKKIPyefYGrqPxJuHqOQ81B6EmvKHcwpLscvSXTWB+ii9xFYvYXvh44neMxEmaVZM0bP+e2UU1jOVWQKOfKkBMrLDrFxyXf0GnonS96K7hGwZckM2vWRM2a8lisn9MXtBr1Bhcmirla8HyV94ACGffc1ix98GNvhw4gqFc2uvJzujz+Ksf7J54T6JYkSX3SBbvUFjlum8d+IITGZ7k88TtubbyZvzVrkajUJHdujS0xEodOR5/GxtCxyGG+JP0C+10+sUoEYIWJlyv/2Mebqyxn54BUUH1qMDBkNOw5ApUtAY4pBq6v+PpC8Pnx7K949MoMO9a1Xs3fpYra//ALBQIDml46hdVoqnCOmun6fm/LCHLavmEVR5m7S2/QivU0vzAlpWL25LMx5P+J+WY7NlHoyMUWoBa7WGZFUIPawsDJnKpklz+MprPh7mZUpPNRuMWYhGUdZIY6yQuQKFTpLPD6/kYN7raxdmo05Vs15/dKIideG/U2CbjfOuYsonzStsi2Qk4d78UpiX3wEdYfW/NuQItZ5OaZfqr2JPkO9eoye9wczBw8LqQ1fv19fej7/7An7T/wbqRPwdZwz6ESRW00C2yfdT/b2qtXX7ct+Zs+6eYx+4FN+fPMWPM6KAb1MEVqGTJ5Rn9gn70O0hAp4e1khsz96kD1rq1Zef3rzRkY9MptNq7QU5YfmgA64uBF5ehmv5BQzQaMgeeGyqNfsmL0AVduWyP7B/NuzmfQmZl79bCB/zNzDzs1FxCRouWhcM+pnmKKasMmg2u9TEMWKpay/UV6Uw5bFP7B54XdISLTtcylt+ozFYEki4PcgylUIRj3afj0p/yx8Zl6mUaMfeiGFdz0OR1ZVxKQE9m1ZHPVacnavw+dxwWkqZS1XCOiN0cvt6QxK5PJTWylXybWo5Gf+Jam2WFBbLMS2aB7WF5QkFpXaSFYqog4lgoAoyFhaZme301PZLpfBgw2SaKvXoDyFGs61QesbrmPblC8i9rW4YhxisRXJ6YJ/KF88RiGnm96B1zkLENhd/BPB0tuYP/zqEPEOULprF4snPsrAzyahrMWw/3MJKRhk3dwvObB1KRde9SSiXIHf64q4rSCIrPjpffYeMTJrcd4IBt7wPFr9iT0s1BYLzS4dS2qvnnjKrQhyBdrEBFSGmkU5qASBDgYt2x3hZoYALXVqtCeQNvFvRG02ozabiYngv+GVgtXKmRJ/xYRwxx7JfP3xlpA+SYLpXxymR980bn34blTqkxuSywQBIdZCsKgE1U1XMOvuuyjetq2yf+ULL7F96tdcunhhjSZ1ziQBv49DW5fz/UvXVEaybVsyE43ewlXP/4gnxos3GPm3BFDo3k+6oXPEPqs3l0k7x4W1l3lzmHHgYXo7r+Hn124j6PchyBWMmvgrk97ZSNbBqsnhGV/s4Mb7OtJrQP2QsUmwtJzyz8IN3AgGKXtrEvFvPYMYGz2a8FxEL49DL4/F7g9Pt1EIasyq6lNUT5a41q0Yt3IZtsxMnPn5mBs1QpechCYueim6/wJ1Ar6Oc4YYuUiyNZsl28NDp31uJ+v+mEyb3mNZ+/vnyASB5DZdMTzfk0BJGWJiHILJiKgPH1gWZe4OEe9QUSf0j/9dxf1PT2f/XljxZxZag4KBoxqzzyjjreISJMClEqtP5j4Rt6eTRAoGCZaUEThSnkk0GRBiLNU6sgYDAew5ObiLikAQ0MbFo0+tubFIbSGXC6TUN3DVbe1wOX0olOJx3dMFhYK2N93Avp9+jtjf6vprw2qDlhflMPXpMSEr5Zk7VpHatBNrf/uM/IPbSGrUlnb9LsfQ7zzEWfMIFITm/+ouHoLth1mV4h1AcjoxGKOXJ9HoLchO42q1Rqtg+GVN2bIuPI8WYMTlzdDqogv8c5VCr4/fi63cklp9aRgZsMfpobNRSy+THlEmwxOUmF1kJU2lJFH1zwoSc5MmNBs7hl3TfwhpNzZoQMY1E/hydin9GnipJ7irzYk+nVi9ufx84HYAYlRpOPdnEvB4Im6754cZnP/SCygz/p0CPuh0EbTZICgh02nDzFAlKYjP7QRJYsOf0468jyJHCzXq0JdZ71WFZu9YPov6rc6j06BrTio9RZ+SEjHa6GQRZDJ6mvX8VFiG429pJnJZhUeC6h+e8DobUQsCakFWmcLzd1KOmNxZ4jRcNK4Zv3wT6q9iMCm57IZWJy3eAYQYM4ZLL8Lx+0Jydu0IEe9HKT90iJ3TvqHzQw/UarnP2sZWks+M128Ky9l32UuZ9d7dDHvqXUSZImJdcah4NkVje+m8qH0bin6kV8x4gv4jOfVdR/DnXFuIeD/Kp2+up2WHeFLrV41R/JnZEAggb92cwKihSCYjsqIS5NNn4dt7gGC5/V8n4M2qFK5qMokPd4wOaVeLBm5u/i1mRe2PLQ31UjHUq92JgXOdOgFfxzmDTCZj74pfo/bv2/AnI+9+n7W/f07f8Y+htyQiiAocfi97v/mGwk2bSenVk/SBAzCmN0AmkyEFg2ycPy3i8Wwlufz82khufmsRvQY0ICBIfJxbzLJyR+U2yyWBxn16EJwZudSMol93Fkx7geSMNtRr3gVTfL1T+g6CHg/eLTux/fArgl5HsNSKPzcfy703o2zbEkEVLta8djuH5s5j/i234SqqEKX6evUY/OVkYtq1RB/zzzuxKpQiCuWJDS6CpVaMfhnpgwZycE7oxIu5SRNaXX1VyEBFkiR2r5kTIt6TG7Wjbd9L+faFKytf3Ac2L2bVrI+54olvSXnzGZy/L8A1fzHIBLQDe6Ppcx7Fj7yIsk0LJJcb376DeHfupcmVd/DXz+9EdN3rMvxG9ObT+/1mNLXQb3gGC38NDePvMzSdRs3/XQMHgIAkUeIP4AwEyfb4SFcrOegOzzVMUyk57PJwZ1oC+10ePs4uxBWUMIoCg+NMWP0BElUnUKbQVoqjvBivy45aZ0Jnij/p/OJo6BITuODF52k+ciSbvvoKn8NB2kWj0Xfszcsv7aG0yM3SBVn06FuPa+/qgLkGZcFOFblQdU61aMBTWBp1WykQqNYd/VzGn5OH9ZNpuFeug6CEonljzLdfhyIjDdmRtA5BlNOm9xh2rZrNhrlTuezRr9i7fh5l+aERPa17j6Hg0A68bkdI+8qfP6hwDbf8M3mdCQo5LzRK5dPswsqc7oYaJbekxpOkPP5v5b9IjFzOyHgz3+WH/y6aaFTEKCreRXqDkouuaEbHHsn8Nn035WUeOvdMoXufNBKSazbhJZPJ0FzQDb9cZMdrkcsKAmz/ciqtbrgOXcI//66HCq8Zye1BJgoIZjMyUaA070DF5NffMCc2wOd1oXCr6JFwFUvzPw/bJk6dTqwqeuqOJxCe4iCXqegf+yiN5cMRnAlc9OBcyrLXYU5qxI/PhOfTH2XdshxS6x8T5SIIiBPvYG1GOjNdPsr8AeITU7jsoTtovmEzCOdWCb8TQZCJtLIM5LEOq5l16BlyHNsZ0eAJ4tWNWFP4PRuLf6J7wngStc0ipjXUUTvUCfg6zimU6uihvKJCiVJj4OrnfyYurSlypZrspcuYOXBI5aByx9RpqEwmLl28kPi2bU/onCVFNoyxerw6keXloQOu1Q43Y4dciHrJypBcewB5m2Zk2w6x6ucPAdCZ47nq2R+JTa25O2egoBi7vZzsWDVZa1cT27AhjW8eh/Wn2cTExyKkh89Cl+zYyazRl4a02bOymDloKJcsmk22fAP1W3RFYzh7xZ7bWY6jrJDcvRuRSRCfkkKfJx+n6PLL2Tx1akUZuZEXk9a+PRplaL63217GlkXfh7R1GXYTcz97olK8HyXo9/HTW7dy3at/YLxyFPqLBuL2OQnIJBx+N/67x7J9/Tx0+gQyrh+BNHs5/L6CEde9zK9THkEKVq1cpbc5n/YXXnnaVz1MFjVX3NyGQRc3Ys2SHCRJossFqcQlaDHUMPf9bKbM52eN1UFrnYZfCsu4My2Bj7MKKTjGuyBeIefu+gmstjrY6fSwzFo1gCsPBPk+vxR/UCJdo6w2jN5amMWs9++udIqWCQIte11Cv6uewBhTOwMTbWIiCRojfa68jtwGbZg2LZN9v4au0q34M4veQ+rTun0MCtWZdd01KOJJ0bYix7mNIvcBYjq1irqtsUEDlIbTlC/yD+LPL6Tw/mcIHmNK6tu5l8J7nyThfy+hOOa5m9y4PQnprSg4uI1Z793NoJtepDTvIAc2L0GlMdDqglEUZ+9l0dfhgstRVoQU/OeM4mQyGWlqJQ82SMIeCCIhoRNFjGeBX8TZilyQMSimIi3v5yNmmgLQxajl+pQ4zMdUvjAYVbRoF0/D5hb8viAarQLhFAWeaDGj6d4RQRF9gkVQKM6KWvBBpxvf/oNYJ03Ft2sfgtmEfvQwtP3Pr0g1O4amXQfTof+VFGXvxe914ymzMjLhaZy+MtaXzKzcLlHTlDtbzcJcTamylpaBzDj4SOX/5TIlN6b+zoLPBKYv3QfsA6BR83rcOrEFKnUJXk/k36Htb4axgcbpzC6x8ZutKvWk0Ofnfauf8R3aMdTy73kelvj82I4YWepFNRmGLtzU/BucvhJ+OfwMk3dfX7nt4rxPaG0ZwnXNPsOkTP6nLvlfTZ2Ar+OcotX5l7Dy5w8i9rXrdzn1mndBra14YNoyM5k1amzYipDHauW3y8cz9s956BITad//CrYt/THiMRt3Gcn0L7LQ6IsYdVc7RBn4j1loDQIvOrw8/MKjmFasxf/XcmRKJbK+nSkzSvzyyb2V2zrKCvnto/sZ+9CUauu/RiPo9VKaeZgfRo3GXVo127/q9Te4aOpXqJauxpiSWFmXFirqZK58/sXIx/P52P31dEqTyykvzKLjwKsQFWdfuLWzvIRVsz5i+cz3Kttkgkj/sRPJkMXSZ8RoJFFE2rEX97yPMXwQOjCWCQKCGDq4Uag0OMvD87cA7GUFOK1FKNU68jK3sPCr5yk4tANTfD06DroGXVI95k95CmQyRlz3MokbCklck8eEN/7k8J51uOylNGjVA1NcGjrzmcnRMphUGEwq0pucvZMwtYVPgnklNu5rkMgrB/P4X1YBVyTGIsqg0OsnSSUnXa0iIEk016mZXhB5xfjXIiv9Y40kKiMLeIe1iBmv30ju3k2VbVIwyLbFMxAVKvpe+zR67akPzkSDHt3A3pSXupn85Foy94e7lgPMmbmPYPkyGrbthiXxzBnFGZUJTGjxHa9u7oPdV0RuzH5S+vQiZ9HSsG17v/k6+pR/32DNvXZTiHg/ihhjoSS3HLurmHKrl7hELaaYGC579Cu2/DWd9XO+ZPZHD9F56PUMu/UNdKY4dq+Zw6JpkZ/J9Zp1QlGD8m+1jV4uoq8T7SeMSSFySbyZPhYDzkAQlSBgkgtoo0zeqlRyVLU4t6qKjaH97bdyeP6CiP3tbr0FbXz16UZnAu+O3RQ/WvV+DpZZKf/sazzbd1Hv5ssrvGskiZa9LqZes85Mf+W6ypD6v755hWbdhzPuuncY1fBFyrw5aOUWTMokTMcpaRqjqk/H2FGsL64Q/j1jbmfp13LWLw2t8rFvZynvPb+ea+9sz3vPr454rI7dQ59v5WoNv9sjl9ucXu6iZ3IcZ0fcQ83xBYPscnp4L7OAoiMT5bEKkTvTEmim1eGy7qWjdygdLUPYKS1lqe0LXAErW0t/Z3vpfHokXvUPf4J/J3UJTXWcU5jiUulx8e1h7ZakdLpfdGuleAewZ+fgLikJ2xagZMcOXIUVD924tKY06nhh2DaGmGQadb2SNcvyWTT7IAGrl16m8NDZYl+AB61ufCMHE/fy44h3jmPmn2/y/f9uxe8NNQTK3L4Kly3yNR0PV2ERc26/M0S8AwT9fmbfdDOB+klI7tDcVJ/DQdHmUOOcYyndsgNTbBp/fv0ittLIedT/NLn7NoaIdwApGGDedy/ibpqI988VeGfNx7f3IDK1CuFvPgdqnYnOQ64L3V+q3kU1GPCzb/0Cpj09ltx9mwj4vZTk7mf+lKcoydlHx4FXgyQxa/LDyIach6ZhBiZLCu0vHEePkbeR0rjDGRPv/zUEWUWd8pkFpTzUIJF6KiX/yyrgy5xi3MEgaSol0/NL2WF34ZekqBYVXknC4Y9+H9hLC0LE+7FsXfQ9trLIg7aaIOh1oNNFXfUB8HqC5B/YzhePDKesIHL979NFiq4VT3RYyy3Nv6NMW0jvKW/T6aH7K1fbLc2acfGvv1D/wr5n9LrOBEGfD/fK9WHtYmIc3rvu5YV3D/HohIW8PHEpD1w7l3efXYk/aKbHyNu59qXfuOmN+Zx38R2Y4lORK1WkNu2EISZccMgEgb7jH0OjD6+S4nZYcdnLTsfHq6OWUAgCCUoF6RoVySpFVPF+ukjq1pUGA/qHtSd26kjDi4af0WuJRKCkDOv/Jkfs86xYh+j00XXYzcgEkTYXjGbu50+E5cPvWvkrWxb9htpdn+bmvtTXtw8T787CQkp27qJg6w7y92aSfagcX7mOKxv/j2uafEKipimtleNYtTBymPyhfVYSkjUoFOHy6NLrW2GyqFi7LIdNa/IoyHXg9ASimhh6JKlyxfpkCHp9+AuK8OcVErBGrrJyJsnz+nl2f06leIeKce+X2YWU7djBquseY0G7K1jYfjyKx/Zzp/57kjUtAJiX/RY2b+29K+uoom4Fvo5axeErweYrxO4rQiM3Y1AkYFSe3Myv22nDfWSwotGbUR0jyjUGC90vvp1m3Yey7o8vcNtLadnrEuq36IYxLjSEyu+K7lgKEPRVrMzrzQkMv+1NDm1bwdrZn+HzukhvN5TEpgN5/5X9BAMVEmDVvMNcemkTNttdlPztoXxxvBmdQo6oUZG/bS9FWbujnjfgr1mNcre1jMKNGyP2eW027GVlWI7JgQ86XAhSRR3N8oMHI+5nbNIQmzUfn9uJy1aMOeHUcvRrG5fdytIf3oncKZORV7Cfho/chqeoGLlajUqri1i2r36r80hr2Z3M7SuP7CqgUGsj5tyZ4tPQxsWzY+Pv1G/Vg8PbVoT0b5g/lcse+Yr1c78ESeJg9mY6jb4aUXdmQ5v/qyhlMobEmvgmv4TD7gIujDEyIMZIQJLYaneR7fGxxGqntUGD8jhho8pqwldtJblR+4IBP8U2Kzp/oNbCiw1GJd171+Pnr3dF7O/UXU/OmqU4rEVsW/ojPUbefkZNqWLVDYhVN6BLQkU6TsLzLel4x+0EfH4UOi26xH9nrqNMEBFjzWHt0hXjefX1HRTmhT5DtqwtYNrHm7np/k4YIqRZmOJTGf/cj8yf/BR71s0DSSIurSmDb3yJ+LRmIdvaSvI4tHUZa/+YghTw07bvZTTpPDDsXXcySD4fgZKyihxklRLBYo7onVLHuYU+OZlBX3xO/pp1bPrgQ4KBAG1uvIGUXj0xnAWGtUGHE392XtR+34699BxzNw3b92b/xkURPWUANs3/FHO93mg7NEapqpIwwUCAos1bmHvDzRRs2ABATPPmdH75bWZuVDLgkub0aHEdbWNHUHRQJBg4GPVaivJKePZ/FzDto+1s21iATq/kzse7sntbMfddM4ej8/8KpcCER7rQK03D0ijjzereMZHwFxZj//4XnHMWIXm8KJo2xHTrNSgaNUCozbCNE8QXDPJrURl/n4YQgBsCLr7vcT5eW8UkgyRJHPz1D/KWrWL8ki953TUUt99GMGzvOmqDkxbwmzZtYtasWcTExHDppZcSd4yNf3l5Offccw+ffx5uMlHHv59STzZf7ZnA5pIqo7kG+o5MaPED8ZqM4+4vBYMU5+xjwRfPsndDRShY444XcuH4J4hNa1KZw6U1WNAaLCQ3bIckBRDlkQcfhvr1kYkiUiD84aEym1HHxlb+X29JIKX5AJTbEtGKsHydi22fbg/ZJyhBokrBi41TWW9zstLqwCgKDI0zkaJSYjgykE9o0DLqZ9RbElHpalbmJ1hNfXIAfzCAoFIR9HrxZ+ZS/sV3+A/n0PXOOyKG1skEgSZXjmX6RzcDIMrPPpOigM+DvTTCTLlMxrDrXqV47hqmfnRPZZpEWr++DPzsE0zpoSHGhphELrn3I3L2bmDD3K/I3beJC696kj8+eThku/OuvoeE7u2ZlnMntiaFNG19PqPlE/jz3WcpyanIk5OCQWwluah1JtwOK47y4jrxfgaRyWQkKeWMijczq8jKz4VlANRXK7kuOZaPswsB0IsC+T4/8Qo5hRF+O401qmrFt94cXZDKBJGgSos3GARqR0SLcoH+FzVi0e8HsZaGRtIkpepJS3OzcXqFy/SulbPpOODqGqXi1BaiQoEhLbrz878FmSigGz4A5+9/VrWplJSpzRTmhZebBFi+MJNLr2+FWhPZ7DAmKZ2Rd7+P01ZCMOBHpTWiN4dOdNtK8pn5xs1k7VxT2ZazdyOrf/uUMU9MwaoqwKRMxqhMRCWGesM47F487gAqlYjOUPV+DJRaccyai33mbCSXGxQKdIP7Yhh38TnvlG33FeMPugm6VRzc4WPVX1lYYtX07F+fuATtcaub/BvQJyejv2g49fv3Q5Kks6qco0wuVoRPRXHrFww6tAYLjTr0Zcui6VGP4ygrxOX0Yi31EJ9UJWHKDxzku/P74HNU+RSV7NzJ/LEjGPLHYl55dBkvfHQhyfUScevtiKKMQCDytSTXjyejaSz3PdsDt8uPKMrYt6uUGV/sCNnO5w3y/rOreHhSP1bKXCHplVDxTjKcxCSrv7iU4idexX+g6rni272fovufJu7NZ1C1aHLCx6ot3EGJfc7wqiOdtUoOffBhpXgP2ae0lOzvFtBiVH/SjO3QyWPOxKX+5zipEPq5c+fStWtXvv32W1555RWaN2/On39WvdRcLhdffBG5pm0d/25cfhvT9z8QIt4BDtnX87/tI7F6o7t6HqWsMJMpjwxn7/r5FbOvksTedfOZ8tgIynIOhG0viGJU8Q6gTUygy8QHI/Zd8Nor6P5WeketVZCdGeSHrzLZtiE85Kdzz4rt45UKBsWamNggqSIHSKepFO8AOlMcbftdHvG8A65/NmL45Imgjo1BEy2PTSYjplVFyJJv70EK73wMz6oNBHLz0RzMo+9rryJXV7lJK41GLpzyERtXTyfg92JJzkBrjI187H8QldZAvWbhtV2bdxlK6Z8b2PTuByEeB5kL/+TnkaNw5IXP9BtiEmnWdTCjH/qMXmPuoeX5FzP+2ZnUb9UDvTmBCyc8Q1GrIt7aNZgNxT+xt3wZswtf5qPCK+h731MhkSBylQb/kQiOhu371P4HryMqJrmISpTRSKPivvqJPFA/kYfTkxgUY8QblLgk3kIPkw69KPBjQSk3p8Zh/Fv96niFnNvqxVcv4GMSSWjQImJfs/MuYq+oQ1HLZbUSknU890E/Bo1qhMGkxBKrZtiYNG65N4EFn06o3E6p0SPI6wLoTidup42S3P1k7VqLWy3DcNOVlX0ynY6S4sil9ACCAQmPq/oJV5XWgCWxAbEpjcLEO0DWrjUh4v0oJTn72Lzke2YceJjH1zZjbtbr2HwV7yuH3cvOzYW88fhyHr5pHq89toztGwtw2LwEPR5s02dhmzazQrwD+Hw4Zs2l7IMpBGzhTt3nAjZfERuLfuGtLYN4dn1Hvjh4NbLEfbj9Dn78aicPXDOXZfMP43LWLPLtXESh1Z5R8V7iyWJH6QIW5vyPHaULKfVkh20jmAyoe0Su045cRNm8ceV/G3cOTwU4Skrjjhzc58J/TPpTMBBg25QvQsR7ZZ/Px65P3qNrjzgW/X6QYEDCZFFz/sDIPiIZTc3ExFd8dzqDktgELaJcYMYX2yNuL0mwbvYhuulDJ9GMosB99RNDTAyPh/9wVoh4B5Bp1Kg6tcO1dPU/8htVCjKSIlRraeLzkDd3ftT98v5YRhOxO32Sb0Uu/Psnz/4JTmoE8PTTT/PAAw/wwgsvIEkSr732GhdddBHTp09n8ODBp+sa6zgHsPnyWVP4fcS+LMcWyr251ZaTCPh9bJg7FY8z3MDJ4yxn09xp9Bx7Lwr9iZdvUur1dLznLmJbtWTF089RfuAAsa1a0evlF0ju1hXxbwNgjVbBlbe0YcemQux/cxodeHEjYuNDV1nVYuTBu0Zvpt+Vj5LcsC3LZ76HrTSPxPRWXHj1kyQ3aldjN1h9Sgr93nubpY8+gaVJY5yFRRSsr8jN7HTvPWgTEwmUlVfkmR0TdeD9bQGpXdtz5dw5OAJe3M4ygioZqxdOJnPnKlRaA6Pun4T+HypdVB0KlYbzRt3F9uW/hDjGt+o0nLkjr4m4T9Hmzdgys9AlRZ4oUSjVlcdu0KoHYydOxudxYVUW8un6W8K2t/uLmVv+Dq36j2b9L1NQaQ0Iohy/10VSwzbEnEJVgTpqRlOtmhy3j8f354T13ZgSS5xcpNwfQJTJmJJbzE2p8TgCQQp9PpKVSuQy0EX5/R5Fb47nkocm8/MbN5O3f3Nle8POg8gY+xBBgxnTaTD6SkzRc9Wt7eg/NIbs3es5uOF//PjKgpCQ0m4XTUAVZXW3jlPHXlrAwqnPs+WvHyq/996X3EuXSa/g27QTyecjuUUyEDndQakSUZ/Ciq/X7WDD3K+i9u9dPId2rYeyp3wJPx96ikRNUzrFXMraZTl88GKV6LeWeHjm7r+46f6O9OoRg8/rQTl6CNLhXHzrNlWuhrqXriZ43WWIhnPrnnL5y5mf9Ra/ZVaZAm6z/s526x9cc/lMcvebOLTXyidvrKdlh4T/xCr8mSbXuYs3N19IqbdKtMeo0rivzXyStE0r2wStFtNN4/HtPUAg/5gFEkFGzCN3IRyT+pbWvBuGmOTwNCaZjI7DHmDa5HIGXFI1fvPZ7WQuWhz1GovWriZt4K1s2FKC1xdArZFz+Y2t8fuCLFtwuPLR2qJtHLc/1hWTJbRcp98XpLggekpmXqadCQkxdDLrOez20FijprFWRfxJll50rznGc0Umw3DlKBQZabjXbkYqK8e35wCkp0VMEzxdqASBi+PNrLCGTo44RTnqmOhRO+r4WHqm3YBFXf90X+J/lpMS8Nu2beOrrypeKjKZjIceeoh69eoxZswYvv32W7p06XJaLrKOsx93wIYU1coDSr25pNE+ar/HaWPfhoVR+/dtW0LnQdeelIAH0MbH0+KKcdS/sB9Bnw9RparWjTWpnp6XJvVn8ZyDrF+Ri96gZPjlzUhvbEZvPPH8I505nk6Dr6VZtyFIwSByhQqt6dRWuGWCQMMLepM66VM8m7YjmQ3ImjXE7rAR36E9KqMRf04+vr0Hw/b1rd6Ib/VGLA/fhr9TB/atX4AlJYM2fcaQ0faCk6pPby8rxGWrMNLT6M3oLafXY9WS2ICrnp3Jbx/eT1FmhbeAQlTjd4bnrx+lZPcekrpEme3/G2qdCbXOxLLM6APmLdbf6dvpJjbOnsagG19i/R9f0GXYTXQfOeEfq9n8X8YklxNUwpBYI78Xh076rSt3kqFVMS2vhGuS43jrcD5vHM7HLBcxy0VKfeVclxJ3QuLbnFCfwQ9/ibWsCJ/Lhqgzsx0NmUo9TaTd/Hb4d1paBhCrSj9pr4/qUChFYuJ1rJ01h/0bQlc5Wp8/ipRG7WrtXHWE4vd5WPnLh2FhvH/9+Babls3g2pd+RW+OJ1jqplFzC/t2hlc5GDq2CZZYdVj7yRDdfrEi1/TY7p8PPUX9wGAmv7Mh4vZf/m8TLZr3YMXP03EVFdPgggto+egdBKb9XLniFywqhXr/fK70yVDuy2d25sth7RISvxTdzeCrZ/Lxk1YANqzMJSXt31PS62zA6snjg+2XhIh3gBJPJh/tGMN9beZjVFaND+TJCcS98TS+3ftxr9uEPDEBTc8uCHExIdVzTPGpXPH0DOZNfpL9GysmL2NTG9N99FPM+8PLxeNbEBNXtaAiqtUY0xuQs2xZxOvUpaRQbpdIyzBVmtNZ4jTceF9Hxl7XErvNh0Yrx2hRYYgwzlNp5GQ0NbNhZeQc/pbt44nRKumtUwE1v8dES5WBpfHaS/Fl5mCbOqOyzTl/Mcr2rVDfP55MxV7i1BkYFQmo5af3vk5RKbijXjyTsovwHpntWBUUuO2B+8leEl6NBKDTffcQYz5z1VL+i5yUgFepVJSVlYW0XXHFFQiCwGWXXcYbb7xRm9dWxzmERjQiyuQEpMhhgwZF9QMDUa6otg65xmBBqN44vFpO1GBJJpORkKzjkqtaMHhUE0S5rMaz9jKZrMbh8pHwZedS9OBzBIuPGTDKRRKffxiV5ch3J8iqzTOTubxYEhvQecj1Efurw+/3kr9/C7Pev5fi7D0AxKQ0Yvjtb5LSqP1pK0EnV6qo16wz45+egctRhk8Cnz2AoFAQ9EUOi5QMCWxclUfT1rFodSf29wtI0UMsJYIYYpO56Y0FR1buu6M1xSFX/PvqrJ8rWJRyxiZYaKVTM6OwjFJfgMYaFQNjjWhFgQUlNuYVW3k8I5nVVgcH3B7ilQpuT0sgWak4ofB3uSAjOSYBlSEGqz+ALxiks6ycuYfuZVZRxYTPjwcfo41lCFdlfIR/TwlFW7eiS07G0rQphnqpyGoYZq83xzPohhfoNvxmti75EZlMRqvzL8GS0OCUJwPriI69tIB1f0ROBSwrOExxzj505nhMFjX3PduDT99cz8ZVeUhShanV4FGNGTK6CQplzaMzlGodHQZcxcHNSyL2N7lgEOvcv1f+v8C1F5vVh8sR+f3rcQfIP5Bf6YVSuHEjm6dMYexPPyJ8MJVguQ2Z/uzJlz5Rchzboi4clHgOo0+oiqRz2LwRt6uj5tj8heQ6d0Tsy3JsweYrCBHwAPL4WOTxsWh6Vr/gF5eaweBb3qE4Nx9buYvCAvjlx3IGjWpM6w5/O6ZKRcd77mLntK8jHqvpLXfz6U8lPPra+YjHRF5pdAo0JzA+0OoUXHp9q8rf+bGoNXLOuzCtxpGVIcfq0Ynyz75BMOgRE+Ion/xd2DbejdsIrFjFjLRXybRvYmC9BxhU734MtTiB/He0okhPs55Weg0F3opnTIJSjtaips1NN7Dlk89Ctu9w953Et6ubZD7dnJSAb9++PX/++SedOnUKab/88suRJIlrrokc0lrHvx+dPJGu8VezoiDcwLCBvgt5PjPJgWDUsHOV1kD34bdwaGvkGdQuvS5HqTxzRmGiKKA3nj3OvAGbnbK3PwkV7wD+ACXPvEHCx68iT4xHMFbkmbmXhedOIpOhateqxtdgzT/MV0+OJuCryv0sydnH1KfGcNMb84mr17SavU8dnTkOnTmOTLeX31x5NLlqPLs+Dy9LY0hLoyxo4d2HlvDYGxfQtvOJTd60jRnKjwcfi9jXwtQftaIBxpg4lKq6+shnA+XeAvaVL2VTwTcMtYyisXkYOxxB3j1cQLJKwbMNBHJcW9heuJq2hvO40NIWg9yIRXlyky6CTEacUkGcUkG2YwtPrWsbts2W0t9ZIX5B+R3zyF1eUbVAHRPDqDmzSezYocYiXmeKQ2eKI7Vpp+NvfBLYSvIozt7Loa3LMcalkt6mF4aYROTKU1s1/jfg8zjxe6OHy5bmHaR+y+4AxCXquOuJ7ljL3HjcfrQ6BeYYTa08I9KadyG1aWeyd68NaY9Jbkhi53bsPvx4ZVuKthVC9AV7AIS/uWF7y8tZ8sIL9BkzDv+qTSGrf/80HqcNR3kxJTn7Uag0mBPS0FsSw4xWFUL196twjMFk+661N5n+XyDo8RAstSL5fMjUasQYC7K/jd9cfmu1x/AGokfJHQ93wI7GoqC+qQnWEg9J6UG69VWg8JUTtBfjV8YgP8aV3dKkMRf+7z0W3nVPlXGxTEbb+x4ix5fCDfelEp9U80mqlPpGHn6lF5NeX1cZTp/W0Mjtj3QlPrF2Jr/E2BjM99+CZ+M2XEsj16EHkP22lvPvu4Kp9vX8kfUK9XSt6Z44vlauIRpKQSBBWVEqsZLEBHq99ALt77iN/b/OrogSHT4MXUoympg647rTzUkJ+FtvvZXFiyPnmYwbNw5Jkvjkk09q5cLqOLeQBA2dkp/AJ3lZV/h15ax4U1M/Bjb4iGkFAi0M0QV8MCCRmNGWDv2uYMPC0FnUjr0vJ86QimA6c+FvgXIbks+PoNUgaP75QW3QasO7OfJMt+Ry48/KrRDwWg2mG6/Eu3NvmNg33XEdosVco/MHfF7W/TElRLxXXpvfx6pZkxh0wwvIT1Ic1QSTXCQLgW4PP4y7oIBDv/5W2Wdu1Iien37Nex9W5M598d5GnnyrN6aY4/8NLao0eiZey7L8KSHtKkFHf8MLPHnjKh559Xwymp7bbs3/JDZ/gFJ/gFyPF6MoEq9UEKsQT3r1wu4rZsaBiZV/q7VFP6AUtJyfcg8vNHoYUSrgna0DKHJXmV+qBB33tpmLSdENQVYzgbUsP7pJ61+2Txh7x8RKAe8uKWHGgMFctXEdxgb1CfiDlBa7KS504vMGiU/SYrKoUWvOrBmdtTCLb1+4sjIdBUCQKxg7cQrprXuekd/w2YxCpY1aYhIgJqVhyP+1egVafe3nVhtikhj94Ccc2LyEdX9MJhgI0Pj8AVjaNuOz3NAIqqvSP4TduVhi1ZQWu8OPZVIiKwv3izi8YCE89hixwwaeNS70jvJiVv3yESt//gApGERvSaRhp350GnwN8anNKj1MABI1zVAIanzB8M+cpmtHzpFbvGX7OBJSzq38/n8Sf1EJtqkzcM5fDD4/gsmA4eqxaM7vjnhkHOYP+pAhIEOIGAUhyuToFScfKVTmyeWAbRV/5n5AUPLTK/EGmln6oCkRODDjd9a//S5em41GI4bT8d67MGVkIBMEVCYTLa65igaDB1KwbgN+n5+4du3xK40ojSbMsWrk8tDxZ2mxi/IyDz5vEINJiTlGjUod+Xms1shp3y2Z5z+8ELvNiyDI0OglfJp8dts2YVDEY1QmYlDERdz/RBA0ajS9uqFs2xLr29G1lOR0oaLKNO+XQ8/QwtIfk/LMT1JpYmPRxMYS3zZ8YruO04tMkqIUW/yPUl5ejslkwmq1YjTWrNzXfxFfMMhn2UUoZC466Bz4g1bkgp7dLh2/lchorlVxX4NENH8rqeGweynMdTD/l/0UF7kYeVkqRp2dfSt+A0miUYueaP0KDC1a1Fh8ngglhS4O77eiFzzE2XLwzvyVQEkpypZNMV45CjE1GeEkDUlqE9/BTApueShqv+XRu9D27lH5f39+EZ7N23CvWIcYH4tucF/ExDgErTbqMarDZSvl62cvDzHzOpb4+s258unp6Ew1f3mdDPkeH68fzmOIKNGwsJTSfYdRWmIotKv4/vu8EMOZ974dSkLyic2Ql3sL2FG0mAX5r2P3FdNYcyFdtBP45tVC9u0so03nBO59pgc6/dkTnXGuUOLz81FWAetsVX8bk1zk8Yxk0tVKhJMQ8Zn2jTyzvkPEvvGNP2RD0Y9sK5sb1qeVm3mq4yZia2CsI0kSk3ZczpqiyGadSkHLzeUfsnDYDSHtl/z+K/X69WfH5iLefmoFDntFqoYoyhh1dQsGXdIYg+nMiGav28nsjx5k25KZYX2iXMmEd5dgTvxvmw75fR6WTn+TZTPeDeuzJKVz1XM/1mpq1IngspchBYNYxQLe3TaMQvd+oGJSalTGi3T0Def3cdfQ5KkPePv13fh9VYJKFGU8+ExnDrx8Nwdnzw479vW7t2NucubLU0Vj+7Jf+PHNWxDlSvre/hSkyFnn+RlJCtIr8XoamDpjViYD4A242VT8C5N2Xh7iGaAWDdyYModvX7DT9YJU+g7NwBJ35iL4nA7fEWEYQKNVYIlTh4Run80EysopeeEdvJvDXddNt12Lbnh/ZKJIqSeLuZlv4AiUsDz/y7Bt+yRNYEzD11HLT3x1usyTy6e7xrOzLNQP6ZaErzh059Swcrgqk4krVq/A0vTk7l9JksjcX86bTy4nN6vC2V2uELhoXDMGj26MyXz8Cf9i9yE+3nEZ+22rKtsaG3txc/NpxNSCcZtjzp+UvTkpYp98+PnM6rOElaXfAhWTJS913U+M6t9f1vO/wInq0FOe+rfb7QSDobNvdcL3v4dCEBgSZ+L+PTZml6iAozlKEiAxJjEmTLy7HD6WzDnE5Hc3VratX55LXKKWZ965HTNOkAkIMebTKp4Lch08f/9f9Dovngu8W3DMmVfZ516yCvfytcS9+jiq1s1P2zUcD0GvRbCYCJZGDllzpqVi8/hIPFLuQ54Yh3xAb7QXnl/j8N1jkSvVmBLqRRXwxrh6ISsjp5tElYLH0pOx+4Ns2eLlmy99eD05SH9bCFBr5AjiiQtDozKBPb80oaHubdR6OLjNx0t/7iVwpMDr9o2FuJ3+OgF/kviCQWYWlISIdwCrP8Az+3N4vUm9k3Ls3VT8K6JMTnvzWFqpLkVAYKf3J9aWfo1JmRRRvAM4/WUUuvfVSMDLZDI6xF0SVcA31vekZE7478OWlU1RvpOXH1oSUnc4EJCYPnk79Rua6HrBiZtIngrO8iJ2LP8lYl/A7yV7z/r/vICXK1R0HnojboeN9XO/RApWhOMmN2rHJfd9dMbFO1SYhQJoiWFiu6XYfIUEJC96RRxmZQq2fYfIW7kS+RsP8/gTL7FmnYPMbB+pyQq6ddHh27MER074Cnz9/hdGL036D+CwFrF0+psA9L/7ORZrvmR7TpVo21DyE81Nfbk25m1UKw6h7tKOVvEX8nSnLSzOnUSeaxfNzX3pFDMGhTuZh16WYbaoo74D7DYvHrcfuVwIcx6vKUX5Tqa8t5G1y7KRgqAzVORPn9evPkbz2R/dEigujSjeAcq/mo66RyfkCXFIksSCnPe5pukkdPJYluR9ijtgQyMauSD5ZlpZBp2UeAfYb1sZJt71ijhUh2Vh4h3AY7Wy4ulnGfDJRyhOomxeUb6TZ+5ZFFJtyO8LMvPLHcTGa7hwRMNqo8JsviI+2XlliHgH2Fu+lM93XcetLaejU5xaCLmqQ1vEpAQCeQUh7TKdluDQ9qzJuaeyLUXbCoXsn48UrePMUiMBf+DAAe644w4WLVqE210VuiRJEjKZjMAxJazq+O+QqFLwQINE3s8swHXERE0pk3FDahxp6vDBeVmJmynvbQxrL8p3Mvn9zdz+aNcTNiCrKS6nj68+2ERhnpOeXUwEnpwXvlEgQNm7nxH3yuO1kifotlpxFRTid7lQmU3oUlLCStr9HSHGgmnC1ZS+9F5Yn9ivJ4sR+GN/Ni82SiXuGCFUG+IdKkqu9Rh5G7tWhq/gAPS4+DaUZ7islVkhx6wAeeckvvJuDBPvUFH+z2w5uUGTXBT48dPMiH16oxKZcOpmNf8EzqIi/A4nMlFEm5R43HuuNinyulhQErmGrT0Q5NARg7kTRSeP4Y7UlSz/xcWUeUUEpQCde1/H7WPuxs/Bave1+4pP5tJDaGzqSayqAcWeQwiIGJQJeAMOvEEng9V3seiDG8L2Se3Zg7Vr80mqpyfnsC3MBGn65O00bxuH8QRWfU6VoN9PMOBHJog07TSQ+hntcbtsbFr5E+WFWTitRcc/yH8AvTmeflc9RrcRN+Oyl6FQadAZ484K80CzKhmzKjmkTa7RoE1IIGv+XLL/XED9/v3p1CAD58JD/PHwXEZM/w7b4dD60kqDgT5vv4HKbK5sc+TnE/R6ERSKqGU4TycBvxdrYRaWpHSc8U6254aLtp3WP9mlXkrj+Qco/+xrtCMGEnfFSIbFPkJQ8iHKVGhVFsRqxg4up4/M/Va++XQLB/dYiU3QMPrqlrTqkHBKIru02MUrDy/h8P6qyhgOm4/J72xELhfoN7xhmBfB2Yb/cFbUPsnmQHJWTMKqRQPNzX2ZsvsG2sYM56omHyPKFAQkL6sKvqaVZdBJndcTsPNnzv/C2hsZupMzOXqFoj0zf+T8V146KQG/b2dpWKngo/wwZTsdz0sJcbn/OzZvAXvLI/s17bQupMRxGFEuotbXfLwoT4gl7tUnsM/8Defcv5D8fhTntSd4aQ8+Kr05xHR3dMbLp9XE7ljcATu+oAuVaEB5HA+KOk4vNRrBjR8/HkmS+Pzzz0lMTKwV98U6zn3UgkBng463mqZR4gsgIRGjkGORi5WOz1ZvPkHJh0owsHVDUdhg9ijrludgL/eedgFfXuZhzdJsEpJ1yA4eiFq4x38oi6DdUSMB77B78XoCaHQKPHnZLLztDg78MQckCZXJRI9nn6bFFZejiYsefi4TBNSd2xHz/ETKP/0a/8FMBIsJ2ejhZHXryDdlLoLAVruLPjGn5zuLTW3CoBtfZN7kJwkG/EeuS6TX6LvJ3bsRt8NKWvMuaI1ndpAbG6/hgefP4/UnlleulAO0aBfHkDFNkCui5zvb/AHK/QHsgQA6UcQoFzl/UH1+nLoz4vZDxzTFXEsrNWcKr91OwfoNLLr3AQrWr0dtsdDh7jtpc9ON6FOSj3+AWsAd9FWWn4lEvvfkHKKbC5fz3MTlIakSS2bnsWl5KU9/2RydPAaHvyTivinaFid1rmOJUaXxYNs/OZC9AmO5GeuOfehiE4hr3Iqt73+Cu+SYc8pk9ProFdz1/ATk8+jXXCBNfh4rZztYOLOqHFFhngOf9xRKbJwESo2Oxh0H0G/IbbB0M8EFexCMBlpc/CzZnlwSmnU8I9dxLqBU61Am6Tg7MsOrR5+SQo9nnmLBrbcjBQIcmjOnss/cqBHxHdox9NtprH31dVyFRTQYPJC2N92AKSMDAFdxMVl/LWbpI49Tuns3xvR0ejz9BBlDh1RbdrW2USi1JKS3IrFFG1Y5v4263SLnFzTqcxt8kYXfomX76l9ZMuMtbCV5KDV6Og+5ns5Dr49Y4lOSJLatL+C1x5ZXtjntPt5+eiUXjWvGJVe1qPG4ozDXESLej+W7z7bRoXsysQk1S2M7UwjVjXEEGTJVRfSZVmFmTMNXeWFDVzaVzGJTyazKzerrOpKibXlS5w1KEkEpfPEvKAWQqRRoExKQJAlXYWFIv6hUwklqkAN7wks/HqW02I3PW/0ipCsQ+W98lOKSAzgLc0lu1A5jXOQKTB63H0GUoahmfCJPjMN04xUYxo5AkiRsSivv77uUUm8OA+o9TSPzEGJU9YhTnn4DSqe/jBzHdmYffokizwEaGroysN79xKsboRDPrTHRv4UaCfhNmzaxbt06mjVrVtvXU8c5jlyQEa9UhK2mlXsL2Fr6B7MPv0ipN5uu8eOwuO6JehwpCMEopdBqE78vWHGugATHW5E8idVse7kXr8dPcaGLaR9tpijfyYTbMlhzy6WU7tpVuZ3HamXR3fciV6toc+MN1a6YC3od7nat2fHw3STKJOzALz6J7WVVImZluYNeZgPy0zDLr9YZadv3Mhp16Efe/s24bKUY41PZ+tcMti39EYCeY+6hx8W3ozqDq/EKpUjrzom8PXUIu7cVYy1106x1HPGJ2ojmdS6nD6fdh18vZ1JeERvtVd9fS62aO+vFc+vDnfnolbUhE0ytOyXQe3CDkwrJP1mcRUVY9+9nx1dfIwUCNL9yHOYmjdElJBx/5yjkrljJjIFDKv/vLi1lxdPPkrNsOUOmfoH2FI59wkgOjKJAeSCyUG2gOvFXkSRJbFheFCLej1Je5mHnMhmj2r/EV3tvCevvGHsJxlM0+tFYNeQ98jNLfqjKI1eZzQz77mtKd+zh8Nx5yESRwX98zfr6C5i04b6Q/QcPeIYRxgHMmlKx0pWWYUSpPjOVDfSWREZc+iRlD71YuZIGwJYdpPbvieG8c6sOeB0VyASBpmNG4bPbWfns83htNgDS+vZh4KeTMKWnY0pPJ6V7NwIeD0qTCVFR8Y4OeL3smPo1i+6puk/LDx5kzrU30PmhB2h/+63oU1MRxNN/j2oMZvpe+Qi7tsyJaEx3FF/QDQoRRec27JfnMf+TqlrwXped5TPfpThnH8MmvI7GYA7Zt6TIxadvro943Fnf7uLC4Rk1FvAH90V3ZS8v8+BxRy7zdzYhT0lGMOgJ2sIjptTndUEwVaXIpmhb8liH1cw88DDbS+ejlhvok3wbfVNuxaw6uWeJRm6gV9L17LIuCmk3yhrS4u7HUPa6AZkgIzEGdk96l33ffwNAy2uuRpMQj8flp7TEze5txbhdfpq3jsUSp4noL5Le2Bz1Oswx6uOWgNTJo0/ryZChDGpY9PVLdB56A4069MWcUJWbXlzgZNvGQpbOPYRKIzLokiakNTSG5N17ysuRiSJKnQ6ZQoEYVxGObyGWu1vPodivYmqelelZbgRc9DSJXJ6krEyhrG08AScr86fy9b47K9tynNtYUfAV97WZTzNz79Ny3jqqp0YCvkuXLmRmZtYJ+DpOCKevjJ8PPclfuR9Xtq3I/5KbWt0WdZ9GzS3oToOz79/R6BTEJWopzHPir9cMURAgGC4ylK2aIRiOL0pLCp1sXpPPvF/2I0kSnc5LYcjoJkz9cDOBoqwQ8X4sy594moyhQzDUO04urAx+8ATI9UauWW6RyzmN+hKfx4mzvJilP7yFo6wQx99CbpfPfJe2fS87owIeQKkUSUjWVWtWFwxI5GbZ+O6zrSQ3NbOvh5mNjlARuN3p5t2sQu7rW4+3Wsexd2cJcQ3dqCxORDmgKkGSUk5L1JGzoIAlDz/KtslVTuebPvyIhiOGM2DShzUKaXXk5bHwjrsj9h2aNx9bZuYZEfAq7AyPVfF1QXhfqkpB/ElYCrgcflYsiB7m+du3+3n8gou5qbmBGQcepsRzGI1opF/KXfRNua1GzshHCfj9bPpoEnt+CDWB85SV8cslYxi/YU1FqKNOz37dBv7c/kHYMf4oeoqbz++F9gcFTruPy29qg8F4ZnJjgw4nzsnTQ8X7EbzzlyGOHgH/cEp0MBCgvCib/RsXkbt/EymNO5DR9gJM8fVqLSXobCfg92Mvzae8OIeAz4MpIQ2dKQ6lOvrzTRMXR4e776Tp2NF4yqzINWo08fGoLVViQ2kwgCG0mosjN5dljz0R8Zjr33ybtN4XYMvKIqZ58zNSGioxvRV+nxevWgzLMT5KF81IhDX7kQ3rxZJPJ0TcZtfK3+gz7qEwAe+weSM69QNIEmQdspFUr2YVb+KqWV1XKIXjCsOzATHOQuyLj1D0yItIdkdlu7xhA0y3XIWgrQotlwtK6uvbc3OL73D7y5HJBIyKBEShZmO35uZ+1NO1JctR4SUyOO5Z/OsGMnHysopFFipMGS+/6i5aJyVz+OcZdHrgXnw+GasWZ/Lxa+sqtwPoNaA+V93WDvPfJvIbt4hBZ1DgsIWPo8Zc2xJLbPUrygZFAm0sQ9lSGp5S2NY8nOy1qyjK2o3WYGHzn9/Rc8w9iKKc4gInz937V6VxHsDqxTlcMLgB4ye0RXAUcWjefLZP+RJBqaTDnbeT1LVLyLvfLhl4bF8WniOrCwFgsdXOFoeLlxqnnlQq2olS7s3ju/33hbUHJD9Tdl/PxHZLw9J66jj91EjAf/rpp0yYMIHs7Gxat26NQhF6w7StKydw1uN1O/E4y5EJInrz6R2xWX15IeIdwC95yBb/pGu/jqxeGDqqF+Uyrr+74xlxZo6J03DNHe1444kVzPo9jzHXjMc/OdRRVabXYb7rBkRj9aK0pMjFa48vY//Ossq2fTtLSW1g4OYHO1Hy21dR93UWFOCLMKj+OxaFnBHxJiZlR85VHRhrPGFxGfR6CZaUEbTZkSmVCCYDojl6KJbbWc6S6W/SoNV5FBzagSEmmdZ9x4BMxuEtKygvzEIKBinO3ktMUvoJXcOZJC/HzqO3LMDt8vPQ1c34wVEWcbsdTjcOJJJSlNgMh/h011WUZFfkj5qUyVzb9DOamnqjEms3FLJg0+YQ8X6U/bN+JfPPy2g+7vKTPqbXZqN09+6o/VmLl5LYqXZrjEdCpzATy2LGxnfht2IJZzCIDGirU3JlokiM4sR/64JItaXX1Bo5yqCJbgnjaGbqjTfoQi5TYlIm1XhgeRRnbh4b3g53JwfwO53kr1lLiyuvwOEr5fdtr0Q9zlrXR/QadD9NmyeR0eTMBWkHbQ4867dE7Xev3oCiYYOKbR1OAmXl4PUh02sj1oGubSRJIm//FqY+PbqyjNvG+V+j1Oi56tmZJDVsc1rPfzbg97k5tG0lP75xCx5nRaiuIMrpNfZeOg26Fq0xuogWFQqMDRpAgxM/n6uoCJ/DEbEv6PfjsVqZc/1NDJ32JXFtWqNNSMDrcOAuKUEGqCwWlPram7BVaQ1ktOmFwZnKEuvnIaUgASyqenQSBuLb9D8Y2ROP0xb1WCU5B4ir1zSk7Xhu8CpVzUV2/YZGdHpFZaWJY+kzOP2kjPKcRUW4i4oJ+nyoLGb0KSlnZAJLJggoGqeT8OHL+A9l4S8sRpFRH3liPGKMOeI+WrkJrfzUw7gtqlTubj2b9UUzWFs4g3r2S3jr09DyuYGAxLQpB3j6jTvo/sCdGNPqcXi/lQ9fXht2vKXzDtO6YwJ9h2aEtMclannqnT688fhy8nMq7n1RLmP4pU3pekHqccdQOoWFq5t+zNd77mRjyc9ISMgQ6GC+iN7CDcz66Q5kgohMENixfBadBl2LWh/DvF/2hYj3oyz+4xAXDm3A+psvI291Vf33w/MXkDFsCAM/nYQuKQlvMMjPBWWV4v1YSv0B1pU7GRxX++H0+a7dITn3x1Lo3o/DX1wn4P8BaiTgCwsL2bdvH9ddd11lm0wmqzOxOwcI+H2U5h1k6fS3OLB5MRqDhW4jbqFJ54HoLadnJe6wfUPE9l8LH2T8ld/QoUsHfv12L+VlHlq2j2fMta1IrnfmVnBbd0zkkVd78dUHm5mjtzDg6SdQLluMVFyCqkNrNBd0R0w8/iTH9g0FIeL9KNmHbBzcW0Zaw4zwnY4g12iQq05sGbKrUcf6cidrbaF1iq9OiiFBeWI/6YC1HMesudi++wWOrOYrGjbA8thdKOpFDn1zlhWx7o8pNGzbmwH3vEgwWWCt+0ckgnQcOAF1kYoF7z2JXHH2ObR7PH5+mrYDt6sihNF7nBQDRyBAUfAwb2zpH/LisnpzeXfrcJ7suJ40fbtauz6f08n6t96J2r/uzXdoMHAAmtiTWz0W5AoEuZygvyp0M6FjR5pNuBt1YgrGemfmpWtSJtLa3JWinE+4I2kQCGbksiABfw5xyo4ohBMX8GqNgqFjm7B5bX7E/mFjm6I/sqJ9smGcxyPg9eKxRg+TLduzDwB/0EO5N/L1AdiCeVx2TSPMuhhE+RlcVT7e3N4RgeDPK6Ds/cl41m4CSUIw6DFcfzmaXt2OO5F5KthL8/nhtRvCarB7XXZmvHEz4176FrtQwraSuShFDa0sgzAqk9HK/z2Vb6yF2Xz/4lWVPiMAwYCfxd++RkKDljTrOrhWzyeqqv/tydVqnAUFOPLyEZRKPNZylj3xFHtnzESSJBpfPJKeLz6HpUmTWotMsmdnY924g1tbfs0a/4//Z++8w5wouz58z0x6T7b3hV16L1KlijQVELFgx94L6mvvKCqi2LG/9oJdUVHpRZr03tlle8mmbXry/bGwS0wCCyyI38t9XV6XzDMzmWSTmeec55zfj2U1nxIOh+hpOJ9+sgvgiU/qrk069LNG9bfVdwC9QUlOvpG9O+p+x6kZOhKS1VSVu7FWuUk9xNwjHAgSstnrfhNGPcLfFq8sSRoefKE/T9+9MEIkrV3XJMZe0RZFI5ID4XCYqk2bmXXFVZT99RcAmpQUBr08jdzhQ1EeJ5enUK2bcDCEqNMgiCKy5ERkyU1jCxsKhaksq2XdilK2b6qmWUsTXXqlkZisibr/mZUZDE6/lW76y3ntibVxzzlr5j5uuu80AOb9sjvuft9/soUuvdIiVuEFQSAnz8TjrwzCbvPi9wbRm5SYzCqUh0gOR15nJuMzX2KQ6jr8eJCHVRQsW8yPP91C0O+lVY8R7F67ALlSjSBJ2Gq8zJkZ/zpn/7SHrMwMWB65fffMX6hYuw5taiquYCii5e/vLLO7GGjWo2ryROvhftendND+CY4qgL/qqqvo0qULn3322SkRu38ZlUXb+e99ZxHw1ZWQ1dqr+Hn6PeR3+42zbpp6XFbjlWJ02V8rwwAGSFeiskNG2zCnTeuHPyii1spQa06s37pGJ6dzzzRyW5jxeYNIkoCpWwuEUBBBqWzU97vW5eOPH3fFHV84ay833dwepdEYMwDocN01aNIaF0yZ5TJuykyi0h/gL0ctalGkq16DWSahaUSfYjgUwr1gKY6Pvo7Y7t+1l8p7JpH08pPIkqIDxerSPRAOo0g1sqj6eTYV/1E/tpYfydf1YcTdz2FOjZ+o+Kdw2X2s/rNBOEwRDCNAXNFCiwz+KHw1ZtY5TIhfCp/hipbvNtkqfCgQwGePv5LkczgigvDGok5OosW4sWz9vM76rPe017ElduGDH8qoKK0gJaOWi65R06FbynGveElU5TAiayJ2fwW+oAuNzIRBcXSVDM1amulzRhZLZke6BXTumUrbLsevokimUWPIycG+d2/M8bTePQFQy4y0Mg2kojT2PaG9aRhmgxlROLEl4aJOi7JHZ7zLYidVVT26EKyqpvL+yQSLG34vIYcT20vvIKqUaAafftyuz2WrxFEVbXcG0PPiG5mx7z9RNn7nNXuG/qnXoZX/G+TmDs+G+V9HBO8Hs/CL58lsdRraJlTDVycmYm7VKmZ7lyYlBa/dAeEwnupqjHnN+LxPP9yVDRVg27/+hsI5c7lk5TKMh0hSNxbrjp3MGHgGzqIiBFEkZ+RQLrr8IdL79saizsDx4rt4K60gCMiqXeS2P509GxZFX7shAVNStC+2wazk5gd68M7Uv7ji4mw0ZQVIxXsJ9c9C26kTakvs+2CgvBLXzD+o/W0+BEOoB/VBd+4IZKkNCx+iKNCshZln3z2T0n1ObFYPmTkGzIlqdEYZlZ49bK2ZR6FrLc10p5Fn7EuCMjtijuEoKODL/oMiBDFry8qYeeF4zp/7B1kDm7bfOGitwbd1J65vfyHs9aEa0BtFr264VXq0OsUhRdYay94dNTx++zzctXXf63m/wCfT1/PwiwNo0dYSNccSBAEhqIqpc3KA8mIXPm8ImSxEeUnsChKAGquHYBztFXOiGvMh1OYPh16dRGmZn59evJmDBXNMKdl0GXY5X0+5mhHXPYtGb8bjcRPwxxcr9fmCiLLYc981r08nc+AAREmGVhKpjL0YjkESj0sLZYq6JTJBQSAcLTabrMpHKzv+rTWniOaoAvi9e/fyww8/kJ+f39TXc4rjiMdZwx/vP1ofvB/Mjr9+p7pkF1pjYpMnZDJ1HSJ+/BdZnkMzJ8Bfkx7AXVGBXKej8y030eW2W1FrTrx1zQH+3icFjUsk+EIhXIHDqEgLsK1AZPDn3zHvsvMjJkDNzhrJaf+5G5mi8SvXRrkMo1xGnubI1T+D1TU4Pvkm5lio2kpgd2HMAF6h0mBKyWFfYAOb7H9Eje9wLsHW/Er05n/ubxgPQRRQa2TYa7wAbFxYQrf+FlbGaFtor1Uhx80ex4q459vrXIUn6GiyAF6h19Py/PMoXhzbmiZvzChUR9F/qtBqOX3y05StXEVyn37spD0/vtWwClBW5OKlx5dxyQ0dGDqmGUql/LiWaWrllmP2x4W63+qVt3Zm+Nh85v2yh2AgxMDhuaTnGGL8jpsOXVoa/Z6bzMwLL44aM+TmktC+HQAKSc2wzHtYWvYJgbA3Yj+NzETP5IsRhRPfDxtQi4TvHoOnvBOKsAL5hjLCn80hZHOgHTsCMcGMf+eeiOD9YOzvfY6yY9t6UaWmJuiP7UaQlN2amqQqVpR+GTX29e77aG0aTDP5acflmk4koWCA8oLNccdrygsIBo7MseFwaFNTOfvLT/lywBl4a2rqt8s0Goa88SpLHnkcgIS2rdnx/Y8Rz64DeKxW1r/7Hr0fe6ReHO9o8NhszLnlNpxFRUBddUD2RcMR25hYVPlfzKZs2t87GrPvUmS1sH3zInqPvYWaikJqyhqSagq1jgsf/Bi9JfazKKuZkQfvzKP6vkmEnS4OpEucOi3qKQ9D88gehEBFFZX3TiJY3FBV4/ruV9wLlpI07QlkB1XoiaJAYrImoh8+HA6z1/kXz68bjCfYkKjVyszc02k+mdq61hC/z8ue336PdLM4iIX/uZ9zf/nxiCux4hGssVHz6n/xLGrQGvBt3o70zc8E75zIj8tsDB2dX+fSc5TzQmuVmxcf/bM+eK9/HW+QqQ8tYfJbQ7AkRQfRao2MFm0tFO2Nndhu3TERtUaGKIl07Z3GioWxE3+t2ieiVjftolDI4yVUYwN/gBYt+3DTq3+yds5nOKpLyWjRFaXWwE+v3E5Gy27kdugHgM4gp9eATGb/FHsVvmc3LRvejf38D/v9EA5jlEmMSjTxyr4YYjLAyERjveNTU2JUpHJx/it8uD1SGFYS5Exo9f6p8vl/iKMK4AcPHszatWtPBfD/Mjy1Dvasj85UH2DrnzNJSM9Da2yasqkDGOVpXNP6E97cfAGdDaOQf29j6UMN/aF+p5MVzzyHbfduhrzxWoTozokg4PHgKi3FU21FrtGgTkps9APSEwyxylHLN+VWBo3IYfPa2L3pPfplsOC3AkLBIHcsWoKvbB/uigrMrVqhTU05pIWctcpNSaGTjWvKsSSoad8tGXOCulHleDHx+QhZ45cB+3btQdWjc9R2Y3IWrfqPZJnjc9qbRmKSZVPp385W2xzC+9eyF5S/RdeUsej45/2SD8ZkUTH8vHw+eKWuJO+3z7Zy22n9QKPmr1p3/Up8Z52aGzOT0MmCpKpbxRVRSlbloxSbrgdeEATyx4xm5ZSp9ZPXA6gsFjpdd+1RT4wVyemM/nUWLr+c+26OPUGY8f4mWrcJsWPZf+k69HJMKdmoNCd3WbLRrMJoVtGqfdPerw5HzpAzOPOdt1h47/14quo85bPPHMKQN15Dn5EBgN8fROnO5uHW25lZ+R+WV9RZYrU2Dubi/FdIUOWesOu1VXtwuwNIegeLq95gVtGUeoXvZrk9uOrF10io0SHPzEDS66jdGr+SKFhRRfgILf+OBI0xEUmmiApSW595DrPt78U9bl7R62S3fBtJPKopzUmDKMnIatOTbct/jTmelN0GufLoVwzjkdihA5euWs6eX2dRunwlxrzmJHfpzLJJk6ncsIHc4cPw17op/D3al/0Au374ia533HZMtnOeikr2/vZ73T8EgSE//5cf9FPZUb3f8q0GhD0TubzF23RPuYAczUDKCzYz9Oon8XtqqS7eRUJGPmn5nTEmZsRNRoatNdRMejFCpA0g7HRR9eSLJE19FMnSMA/xLF8dEbwfIFRdQ+2seegvGYtwiOq3Gl8xr20cExG8A7gCVqZvOp97Os5DGdBQvH0VhXPnxT1Pxdq1BGrdNNXjNVBYHBG8HyBYXom0cD5Vlfks/GErZ49Mg6pqRLUKMcGMlGBudKLXUeOt7zP/OzXVHqoqamNWXiqUMs65qBULfy+IsIeFOkHAM0fn1Zffd+iWgtGixFYdmSwVRLjomvZomlAQOVBeif39L3DP/xOCQaSURIzXXUbvYddic1WwdvbnOGvKGHX7KyRlta5vTVUoZYy6uDXLFhRFedC3ap9AakKYpX979h+gw3XXIFPVJaY769X0NGhYZo9sMzo3yUR6jDbMoLWGYHUNoUorYqIFyWI6YjtkhaTmtKQLydZ2YVn5p5S6t5Koas7gjFtIUjU/onOdouk4qqfdOeecw5133sn69evp0KFDlIjdqFGjmuTiTtG0CIKAKJMTCsSuv5HkCtwOa5MH8HJJRQfLCJ7ovhFfYTU/TD4r5n7bvphBn8cfPaEBfG15OWtefZ2VU6YS8NRNatN692LER//FlJd32OPLfH5eKCgjDIxuY6ZZazO7t0R6jGbk6GnZLgGHL8jgEbkkpOmQWjWu1LCqvJZn719U368HdSqsdz3Zh3bdknBSxA7bEoprN5Cj60au/jQSVNmHPqlcjqDXEnbEfqjKszMJhPw4/ZUIgoBenowoiOjNKXQYeBnpjgv5c1YVxXsDZLaUM3TQ8/xiv5Ptznn4Qx7CnBhP6yNBEAR6D8xi+YIiNq+txO8L8fLEhQy7pBXnjshB1MrQyiSMMgmdTALknJl5J3+Wf1SfnDiYs7IfRCU7OqXieBhysrlw4TxWPDuFTR99TDgYpOUF59PrkQcxNMs9qnO6bS48JdXUlttx6RKjJkIH8HmDOB1+Vv/2Eat/+4ihVz1Jx8EXnXA3gaPFY7VSW1ZO5foNKIwGLK1aok1PP6bVwHioLBbaXXk5uUOH4K2xIamUdWrfJhPhcJjyEhezvt3BysXFqDVyRox7itHdXkXQ1aCVWU5YqbfHHWDn5mref3k1pgQl7a9dzk/lT0bss9uxnGm+cdzXeTEqZd33WUqLr4UiqFWHt9s8BnSmJPqOvZ0FX06J2K7Q6nEGYidHAWr8JYTCAaSjm9KcVLTqMYKFXzyPzxN9fx508f2odaYmf01BEDA2a0ar8RdhaNaMv16YxpKHH0Wu1dLxhuvJHjyQ4j+XojTHf22l2YR4BFVksQj6ffWlyNnDh7DJspgdNUsi9gkT5oPt15Bv7EOauQ06czJ+r5twKIii7+jGvY7NTrA09ipmsLiMUI0dyWKu8x0vKsY9J/7Ch3vBUrTnDD1kUGT3lWH1NQRnkiDDoEjFE3BQ6t6KK1CNo3Afi75+idxmbeKeR5+djShvmu94OBzG9cvc+OOLlnDpY4MJ/jKLquvm1v9dRJORhCfuRp7fvFGiloHDVCfaazxsXOOhXefkqCA+JV3H02+ewcevr2P9X3V/r6xmBm649zSSUhtaM5NStTz+8iD++8oa1i4vJRyGzFwDV0/sSnpO0z2ng1VWqh6ZQmB3QcO2skqqn3wRy2N3k9y7G0OufBTC4ZgJjpR0LU+/eQa/frODZfP3oVLLGHZuPqf1y0CqrUCXmYlzX6TDSmqvnqT2aKguMsllXJ+RxNjkAMvtLuSCQE+DDov8wNylgUBpOdWPv4B/V0N1irxFMywP3xlRNdIYQi4/qioV+Ts709F4BqnJHTDI05DEk0/z6H+Fo7oT3HBDnW3HE088ETV2SsTu5EVjsNC2zyg2LPg65nh2217U2mOXbh0rSklLmqYN5e61cRVvAWy79mA5AfaEQbuDcCBA8eIlLHv6GcIHfWfrfLNHcuHCuejS44tgBUJhfq2y1Yd3bzpruP7+rtg2Wlk6cw+hYJjuw3JoeVoym0I+ul2YR4JW1WjRKp83wDcfbY4I3qFOhXXqw0t47sOBPLO3Y0RWXy9P4j8d55OmjT8JkCwmdOefg+O9z6PGRL0OoXka3+5+gBWVXyATlAxIu54eyRdhkNIp2avi2fsbrFrWLoffvhS57dmXcaovoGfyJSdtP5Q5Uc0dj/aiqMDB4tmFqNQSp/fNIFGtQK+L7ntMUuVxTetP+GDbNfhCddlumaBkfP5LpGvaHZdrNDbLZeC0qfR86H4IgyrBglxzdCv9weoa3B9/g3vWHMRAEPlTzx1yf0nWUCL52/uP0LzL4H9FAO8qK2PRfQ+y8b8NCv5yrZZR335FRv9+yA4j0nU0iJKEPisLfVZkj21ZkZMHbpgdYU/0xuS/6NwzlRvvOw3tcSzv/zu7t1l5cuJ8wmEYfX1zvqqOrYpf5d1LmXsrZmVd9YCiVR6CSknY443aty5QMR23a5Yr1XQdNB6TPomFP7+BtWQ3lvQ8UlPb0dI/gBXVX8Q8rr36TOTSiftsjyfGpEwufeIbvpt2E9XFdaKIar2FYVc/RXJu2+P62iqTiZyhZ2Jq3hxncTFBn4+SpcvYNfNnej36MJn9+1HwR+xV+O53T0RlPDYFbKXRVK8xkXfD+XzgvCfuvisrZnBOziMAR16VEMeG9QBhf9141caNbP7gI9oo4r8vQamoF3+M+3KhulYtmaDkgqQnaUNPwgUVCEYtjtQA8oCKOd88Q+HmZZx+202seu7FmJonPR+876jsROMSwzL3AOFwCE3AhfX3OZGH1Nio/M8kkt98LqL/Px56oxKlWsLrjo4LJJmAUbRRNOsnahPPQ9WieX2pfnWlm6I9drZtrKL/8Bwm3NEFSRJQa+Qx1fzTsvTc/kgvHHYvoWAYtU6O6QhU/xtDoKg0Ing/GNtbH6No1byuciNOu4EgCKSk67j4+g6MuqgVoiggiQ58tcWEVQrGL1/Epvc+ZNNHnyAplXS++UaanzUyah56oIUy/xAtlEGbneqnX44I3gH823djfe51LI9ORDI0LrnhqC7lx1dvZ/faBfXbZAoV4+59n5y2vZEpTowN6ikiOaoAPnSIH/0pTl7kSg2nn38nBZuWYq+MLNXpOeoGdqyeQ8+zr49zdNMgUx/6QauyHN/VqWB1Dd7V63F+8wthtxtTxzZcOmc2M6+/geotW+r3s+3ahXXb9kMG8P5wmGJvw0TAHQozzW4lp4WC0+7uQL5aSY5Fgzcc5gxJi0UmHVEfmc3qZf6ve2K/j2CY1at2Y0pLp9TdID7k8FfwztZLmdjhj7grfYIkoR06kGBZJbW/zIbQfn/V5ERMj93JSyXj2e5YWL//jN33sKz8E65Ln8PLTyyL8FkF8PtCfPhsAeMnPUfr5I7/SG9vYzElqDElqGnXJXLiEayyErI5CPv9iEY9osWESqGnW+J55Bn6UO0tIBwOkqDMwaBIRSE1fRnrAWQqFfrMzEbvHwqFsVV7CIXCaHRy1Bo5Ia8Xx6ff4p75e/1+GqMSS5Ka6orovv+UDC3u6u0NG8Jh9q5fREL6yV0eFw6H2frlVxHBO4Df5eLbs0Zx5ab1mPIPX0nTFHg9AWa8vymmt/CaZaWU7HMc1/78g7HXePnglTX1ukoKXRCXNX5ydp9zPa1NgwGQEi0kTH6AqoefiygxVvbsim7McIQmWgGMhzY5jVbNe5M6xIiQYCRcYUVWLWeg+SpWC99FaQoY5Cm0lPoe12s6kYiSRFpeRy578hvcdivBoB+13oLenILYCJHSY359UUSWqMNt87Bl3U9ocxLpctldqJITSNF2ocM1V7P+nXcjjmlz2aWk9ep5zK+tS09j8Ksv8d05Y5DrtbgC8b+zVm/snueDqaly47D7CIXC6A0KTAlqRFFANBpAJkEgxmKTXIZoMFBbWcmsK6+htqKCNi+9AnHsF7WjhyMZDx0IGRWpyAUld2R+jemd1QRWvgFAWBTQJ1rQ35+PXKFGrTVRZt3H6MVL2PTSS2yf8SUhvx8Egc633ESzkSMO+54biyAIaIYPxD03dluVqn8fPHNjVx6EPV686zZjFbR43UE0WjnmRFXM+Y0pQcVF17Svb187mLNHZ7LltRfY9vEHbJg2hQsXziWxfXvKS1w8ddcCSosaLNcUSokHpvQjqV20KPIBNDp5k5bL/x3vxmihxwMEi0sJexrXXiSXS6iUHnb89QdzPn4Kp7UMmUJF5zMups8Nt9D+mqsQJemQrZWHI1Rjx791Z8wx34YtdVUmjQjgXfZqlv/0dkTwDhDwefhy8uXc8NJCzKlH4Ft5iibj319vdoojwpiUyXn3vMPeDYsp2PgnKr2ZVj1GULRtJXKFGo2p6XqXqyvclJc4KSt2kZapIzFVizopkYx+p1O0MPrBoE1LQ5fRtLZPBxO01mB94U28K9bUbwsUlSIuWsHZb7/Fp0OHEXA3BDeVGzbWK74GAz6c1nKcNRUIgNaUhMaUTCutig2uSFHAvR4fe/HRQwxym0xP8lFaegSDYfy++Mkye7UfVWbkDXhYwkTayc5gxVdvEfR5adVzBObUXLR/cxeQzEaM14xHf95ZBKtrENRKRJOB31xvsdOxhJGJd9FDcQ4yZ5CwSsZuYRPVVbUx/W0BKkprSRMGkaA6sf3Ix0o4FMK/u4DqSdPqexwFpQL9ZePQDBuIzKAnUZVDoqpxDyh3ZSXOomLKVq1Gk5RIQvt26DIyokq5w+EwgbAPmaA4JtHI6ko3i/8o4Jevt1Pr9NPxtBTOn9COZI0f1y+RK2U+q4Or7ujCq5OW19vpQd2k55rbcln44VWR+3sje+xORlwlpayYHHtlOeT3s/OnmXS747YTci1Oh49lC/bFHV/4WwFtOh4/hfyD8XoC7N5eU//voFeGUtLhDUZ7EAOkaFrU/78gSSha5df5QBeXErI5kOdkIlpMjV6xOVZkzbMxqVU4v/sV/7adMHwQJT/O4dbLZ/BD7WR2Ov9EQKST4SyGym5GbTt+CbV/Cp0pGZ3p+Fi7Hgp7ZTGfTRpPZeG2+m1Lf5jOGZc/Quchl3D65El0vPF6tn/9DeFQmJbjzsWQk3NMwcbBZA7ozwXzZlO6ax35nfqy3Rk7iOyUcHbccwQDIfbsqOHVp5ZTXFBXoWayqLj2rq6075aCwmJEd8EonJ9+G3Ws/sLRiGYjnj176m3cKivLsHTviH/luoh9FR3bourWMf51BEPYa7xI4WRuafUj5m924V+5EUGnRX/xucjSUwnZ7IQdtQy68El29ajgj1+qsFZU0rL7LZz78GQ821eiTU3EktUcTXLTfh/k2Zkou3fE+7f3JVpMqM8ZRvUt98Y9tnZHAW/8HGbj6grMiSquuKUznU5LQaOLLKmWyyX6D80hIUnDl+9tpKjATmq6jrPPSUazbxlLP65Lvnpravjtmus5+8eZvP/ShojgHeravJ69bxFT3h8aUT5/Iokl7nsAQamsSwo1glAwyJZlP/PzG3fXbwv4PKz85T0qi7Yz5s43UBuObS4ejiHQCyClJAJC3PGDqbVXU75nI6t++zDmeCjgZ++GxRiS0/GF3MhFFbJTJfUnjEYH8C+//DLXXXcdKpWKl19++ZD73nbbiZkwneLIkcmVmJKzcefUUGuvoqasgLmfTKbX6Bto1WMEQbkaWyCIRhQOq2bpCTix+8soqd2MTFCQom6BQZGGQlJRUujgqbsXUFHaEASkZep44Pl+DHv/Xb4eOgLbrgaxJJXZzLkzf0C3XwQqGA5j9QcJhMMoRAFzjNXrQCiAzVdMja+YQMiDRZWDQZ4SVxk8sK80Ing/QMjhJDxvKW0vvoh1775fv93cok6k0et2smPl7/w8/Z76vkSlRs9ZN7/Ime378WOFgC8cuSotAuenWFAfgx+nSi0jPVtfPwH5O83bq1jqblg1PTvpQZQrgnz7/dX125b+8AbNuwzinJtfRGdOibxGjQZRo0GWUVeSV+MtYd7O6dyW+QVJXxUQmPMmwf3vKy8vl6prDx1oCqGTd+U9HsHyKirvfiLiYRb2+rC/8ylSogXNoMav7DmLS/j92uvY/XODAJVMo2HMD9+S0e90JIWCQMhPlXcPy8o/ZbdjOdnazvROuYIEZQ5y6cjK0GqqPbz8xNII4cRl84tY9WcJk14diFKtJuRomARJAT/ffrSb2x7pSeFuO+UlTrJzZWRm+lnyxS0RKs4AuR2On11YUxEOBnCVlMQdt27dFnesMThsXryeIKJYJ5p3uPYX8RAePnL5ibOME/e7LhxQfl78vZO+429iTmV0G4VOnki6pn3ENkFqWh/oI0VUKBBzMjHeeAVhjwdBqaBDXhbfn30up984nlG974YwFH8yj+2FHzLo5Rf/kev8/0bA72XpD9MjgvcDzP7wCfK6DiYpqxXqxERSunY5Lteg0OvJHNCfhPbtsEjdeWbd6YSIXClPUbckWxf/9SvLann89nl4PXXHiZJAyzYmqKnBXyahSDOjGz0cWXIi9o+/JlRZjZRkQX/pOFS9uyGqlBEl7L/eeBNnvjSNlD4TYPUmhHAY5aA+qNu2QrKYYl5DRamL2TN3sei3AkRRYOCIXBI6ZyEt3YTp5gnYP5yBf1vdHEga1J+VaRIfvdfwue/eXsOcn/dyzxPtSFDV4vnwa1Q3X4mobTrxVMliwjzxBrzrNuH89hfCHi/hbt3wdj6NTducZCRYCJZVxDzWn5lN+fy654u10sO0x5Zy37On06VXtCK5zqCkfbdkAoEQmclQMn822556gMoNGyL2K122HIcjyOqlse/p7toAxYWOfyyAV7RrBXIZ+KPbGzQjBjVaHM5pLWPeJ5Njju1ZtxBndSnaYwzgBX1k+5tmSH/UA3sTKCgiHA4jqFWEXLWH/D7ZKgrxOGvwuWMnfgFqKgqYVzKdFRVfkKZuzRmZt5GkykMp/TN/o/8lGh3Av/jii1xyySWoVCpefDH+w1IQhFMB/EmOxmAhr8tAUnLbEgz4ECU5YUMi29xevt1TSk0gSAedmpGJRlIUcqQYK4ROfxVzi1/jx71P1D9c5aKKCS3fp4VqGFMeXBYRvAOU7HPy0uPLuO/Z07lgwRxqtm2nfM1aTPl5JHXsiD47C0EQsPoDzK6yU+UP0EGvQSaAQZLIVCnqRTr8QQ/bbAt4c8uF1AZqgDpLizG5T9Iv9Rp08uibX+3shVHbDuBbtpr8K8bUB/DqpCQS2tb1kVcX7+S7aTdF7O+tdfDN89dy9fO/82ReHi8VlteX01vkEjdmJJOuOLYCF5NFxZW3debpu6OvO7+NGbdhK+7Suv54tWQgL9CdH76/MWrfXavnsm3Fb3QdetkhX08QBLobzyX5tyr8s5dHjAV27sHkdyFXiDGrArR6+XH3ET8eeNdujJuJtn/4FYpWecjSD99zGPT7Wfv6GxHBO0CgtpZvR57DFZvXY2renD2O5UxdP6ReBXx99c/8um8Kd7T/lVamAUfUflBS6IjpeuD3hfjkrQ1cPexM+KphhUmxZiUJCTk8d/9ispobyM03kZcv8dXk86KELdv0HYUh4eS3hpFUKhI7dqRy3bqY40frmez1BijYaePDV9ewbWM1aq2M4WPzGTomH0sc32C9UcmAYbn89l3sssX+wxpXxVFT7cHjDhAOh9HqFRiO4ndlNCsZOiaP7z+tK/n8a1EZvUdcTDfTPlbVfFYvzGhRZnNb+58OL3z5DyEq5KCoq14xt2rJub/8yF9TX2TJSx+gNJvpNvEOcu86s8lXJv9XqbVXseaPj+OOb/5zJklZx1+jBkCdkEBmUMO9nRfx2Y5b2eNciUxQ0jvlcs7Ofqhes+HvhMNhFv2xtz54N5qV3H9/OzTLFxH64CMcgQD+03tiGD8a9bCBqHp0IRwIIMhkSAkNbWcqsxl9VhaOwkJCgQCzbr4FdVIS2YMGIshl9Ll0TMzgPRSqE7J84o55ET7mX7y7kYXZeu6//x4c775TH7wDBAaewScPRN/D/L4QH721l3sus+Cdswj9Jec2aQAPICWY0Qzqi7J7J3y1Pspqwnz18RZMZhX5l5yP84XXo44RTUbspjQqSiPL4r94ZwMtW5tQKwUEpSJCyE2rU5CRbaDk9x9Ycut1ca/H7wvyt/WQCBw10docJwop0UzCE/+h6tEpEToKivat0Z1/DkIjRVO9bge19qq44+V7N5OSe/Q6OzXeYuzyAlR9u+BfvBrdeWchKBRUPfJcfcuk/Z1P0V98LroxwxHjVFZtWvIDOlMKCRktqCraHnMfQ14OHxfcgd1fzg77YhaVvcf1bb6kc8KoU6vxx5lGRxi7d++O+f8nI6+99hpTpkyhtLSUTp068corr9CjR49/+rJOOg7YW7gCQb4qt/JDZYNY2j6vn9nVDp7Oz6CZOnoCucexgu/3PhqxzR/y8PaWi3mo42rKS2IL1e3YXI3N6iUzNwN9RgZZgwZGjDsDQb4oraaHUUdhjYOXCssIhCFHpeDSVAutNSo0Mokq715e3ngWwXBDJjQY9vP17vvI0LSnY0K00r1wCPVkQZLqhez02dmcO/N79FlZ+Dy1LPnmldgHhcMs/2E6I294nieap+MIhgiFwxhkUsyKgaOhZbsEHpzanw9eWcO+PXYUSokzzm7G2Re1YI7j8fr92puGs+OPWXHPs/ynt2jZYzg6U/wSXr08mTM0V+H9eWrMcfHXnxl/9VA+fGND1NhVd3TFnPDvE5HyxekRg7qeNn9hMYJafdjMem1pKatfeS32eXw+CufOw6/w47VZuSn9Sxa5PuQv61d142E/b265kEe6rMKiyop5jlisWBTbcgaoU+y9pFtEAB/89Xcuuf9enA4/m9ZVUbjLjq3awvn3fc+631+lYNNSNIYEep97M/mdBxNyeHE6ilEnJx0XNfemQJOURP8pz/DNsJFRY9rUVNJ6H11f7t7tNTxy61zC+3NVbleAbz/awsZVFdw1qU/MXnaFQmLU+Fb8taQ4YuIOMOis3MOuGrmcPkr3OdmzvYbff9hJdaWbZi1MnD+hPVnNDChVjU8IyuQSw89rwdb1lWxZX0U4BK89tJ2zLr2Fh0Y/SK1YhkZmxKhIxaQ8fm1LTYkoSZhbtGDgtBfo9ejDiDLZMVmWnSKacDiM3xu/tNbjrDlxF0OdfVWeoRe3d/gFT8CBKEjo5UmH1CDx+YJsXd8QHE28sw3q6a8QOMgGzv3bPDx/riD5laeQpaXEOg26jHSGTH+Nb88eXa/A7q6oYOuXM+h65x2oLdFirdYqN5vWVrBne03UPQCguMDBhm1u2rsbglDRYmZ3iTdKW+YAhbvseBTZyMJhQjYHZByfxKqk16HWQ24K3P5wL4KhMAqfG+mGy7F/8CVhd13SWZ6XS+DKq3htWmRAN/KcLM7sbcD77oe4yytQdm6HemAfpJSk+kA+PUuPuv9pLIl69TqMzZuj1sgwmpXYrLED9ezmxyaUeCwIcjnKDm1Ieft5/Nt3E7TaULTKQ0pOPCJrNplciSCKhOPoicn0CWxw1JKqlJMglx3RXLLGV8LbWy5hp/1PJl72LWajDkXrfKqfeilyx3AYxyffoOjQBlWX9jHPFQ6FWP37x/Q+92Z+evWOqHFLWnMCCUHsRQ2ODmHCvL9tAk9020BCI1sPT3F0NHpGMHHixEbtJwgCU6fGDgBOBF988QUTJ05k+vTp9OzZk2nTpjFs2DC2bt1K8qksfUysgWBE8H4AXzjM20UV3J+bhv6g3h6Xv5of9j4etT/U/Xjnl02nS++LWT6/NOY+Xk90+ZHPG0BAwBYO0kan5r3iSkoOynDu9fh4ek8pT+al00amZknZBxHB+8F8v/dRmht6Ra3Ca4b0w/XjbzGPUZ/RD0XrVlyycmldL/5+8Tq/t5bKOJlHgMp92/F5azHrzZiPQ4yj1sjp2D2Fh6cNwOsOIEkCRrMKuUJiuOluLKosfi54CoWgxeu0xz2Px2WLUNqPhSiIaIN6PN7YQizBxUvpM+Yssp7vx4z3N1FW7CSzmYELr2pPVnNjo9X1TyYU+bnE6/SWUpMJllYQSko47MM56A/gs8f//Cu3bGL17h8o3LwMmUJF97HXkNuxC1+XPQiA01+J3V92RAG8Whv/CydXSMhSkpA6tcW3dhNQ1xogfvwRtz/wHxw+karyWoxmFaZEFefc+hK+WieiTE7A6mTlU1PY/PGnALS97BI63XITxpyT82Gc2rMHZ33+CfPuuAtXad09J7N/P858ezqG7CNfWbbXeHnvpdX1wfvBlOxz4AyWU+OooMqzF7MyE4syC5OyblKdlKrliVcHs+rPYpbMKUSjlTNiXAuy84wYTIdeSd+3x8ai3wsjVvDXLCtj7Yoy7nu2H517HJn6tCVRzZ1P9KG8xMXG1eUYTEradUnGrFOhVB5fNfPjiUylQpd28leHnAhC4TDV/gDuUBi5AAaZhOYYhO4UKh25HfqxZ33sarW8rkMJBkIn/F6vlyeilzeunUMuk0jP0bN2RRnZzY0Yy3bH9HAPO1w4v5+F8erxcVdOM/r3Y/yfi1j84MOU/bUKfWYmPR9+gKxBA1H+TXHfZvXw+tMraN0xkVVL4rf1zJ+9j7YdOsDOPXUbGhGbHViRFjUnRutBrpCQA6j0aM8+E1Wf7oQcTgS5nFK7wFOPrsJa1aD9029gKsOzagg88RIH0hbeNRtxfPkjSS88hrxZ3X1YoZIwZaXT9c7bWfViZEApiCJnvDINxc8zGX9xF6a/tjnqurr2ScMUpwLqRCHIZchSkxulwB8PjTGRlqcNZ+uynyO3GxLoPPZ2VC1OoywUYr7VSQ+DhqwYi2jxKHSuYattHgAv7BvDtaPfI+fDeCkTcH7xPYqWeYja6M+1bZ9RLPthOsXbVzHyhiks+vol7BX7EESJlqcNo8fFN/BiYfRimTfoxOrddyqAP840OoBfvXp1xL9XrVpFIBCg1X7Lr23btiFJEt26dWvaKzxCXnjhBa699lomTJgAwPTp05k5cybvvfce99133z96bScrm1zxM+5ba724gsGIAN4f8mD1FsY9psK7HWNS7EmEJAno9A1lNdWVbnZuqeaPH3ah0cnpe3M7/EI4Ing/QBj4qKSKe3OSKXLFVoQFqPTsqi9RjnjttGTUQwfg/m1+5PakBPQXnBPTF1Oh0pKc0zZmTyBAcm5bFMqmLWmLhcmsgr+JyhsUKQxOv4VuiefVqYb3XsDOVXNiHt+88yBUuuggNBDyUe0tYE3l9+xzrWes9l4EpZKwN3b2WyMF6XhaKs1bW/B5gijVElrdv7dMStm1A4JaVb+6cDC60cOonTUPec7hFeHlGjWmFi2o2R472ZPYtRNr/6hLHgV8HpZ+/ipnJD5BpqEj+1x1pZPxElLx6DUgk6/e3xRzbOCIXIxJWsQH7yBkrSFYWY1oMiBZzEgWEyYgq1nk90Gp1mHfW8AX/QZGeNGufP4Ftn4xgwsXzTuqgPh4ozIaaXn+ONL79sVbY0VSKFElJsRcIWsMHneA3dtqorZrdXJum9KMdwrOZt9B958UdUtubz+TZHWdZkZiioahY/LpNzQHURJQKg//mLVWuvG6g/z+fXRFSDgE70z9iydfH4w54cgmryaLCpNFRct2TSdOeoqTA2cgyF92Fx+WVlMTCCIC3fQaLk+2YAwLR3VfVuuMDLnyUd6/dyTBQGQiNy2vK0XFOnTJtaSkn7z2kqIkMOSc5vz6zQ7atjMiXz2beEXXnsUr0F9wTp31VwwUOh1pPXtwzjcz8DtdiPL4VR/lJS7WrSyjZbsEZIfQu5DJRIRQQzI9VGUlO02JIBIzaZiZa0BZVoTYLLtOPf8EI8hldXOj/fMjRZETuy3yEx01Mo3AY49GHRuudWN96R0SnrinXgBTaTTS4/57Se/Th2VPTcZZVETqad3pfd+9iLOXUPvnKlqPU3LbxPZ89uluKkprUWvq/NKHn5d/VC1FJxtKtY4zJzxOVfFOKgvr2pxSmnek9+1v8rlXxqbddQmn1hoVzdVKtLIAlka4fwRDARaVNjhEBMI+Smo3kxXdaddwTGU1Yb8PiH62GJOzadN3NKtmfUhqsw6cft7tqHQmRElOUk5LJu3shSMQWyMhFD5lJ368aXQAP3fu3Pr/f+GFF9Dr9XzwwQeYzXU3PqvVyoQJE+jXr1/TX2Uj8fl8/PXXX9x///3120RRZMiQIfz5558xj/F6vXgPClbsh1hF+/+KcNgUcOS4UtKTreuGtTp2CW8LQ3/0eamsS6+hrDiylH7IqOYY95efVle6mfbYn+zZXoNCKeH1BhhyXTuW+6ODqQNsrfXiDws0N/RmbfVPMffJ0LRHIUYH1ZLRgPHq8WgG9a0TbKl1ox7QG1WvrnHFmuRKNb3H3Mymxd/z98YsQRTpcfZ1/6gHpiiI9b2AYscBGJIysVdEKmHLlWr6jr0tyis3GA6ww76YaeuHEwjXTdaCZg+jzxmC/6tor18pPQVpf9ZZp1fAUQhSB/xenNZy/F43cqUanTkZmfyf+/ykpEQSJj+A9alpBCv2WxbJJHSjhxMOBPHv3IPYiNI4bWoqA55/ju9Hnxs1ZsrLQzArcdVEPuhWff0+A2+/lo9dtyIXVRgVR7jCmqTmwqvb8cW7GyO2p2boGH1xa+QKCRR6JKMeeW7Dyn44HMZRXUKtvZpwOITWkIDOnIIgiGz9YkZE8H4AR2Eh22d8Tdc7b4/oazxZEEQRfWYG+szYfbFHgigKMbUeRl6exszamyOCd4Ay9zZe3zSWS1v9jE6egkkmoZJE1JrGl+T4fUGKC51xez8rSmtx2n1HHMCf4p8j6Pdhry5lz7qF1JQXkN22F0nZrZtMW2K9083L+xruKSFghaOWfV4/V/hVZBjVpGcf+U1aZcxh7H3fsvKnqRRsWIRKa6BNv8tIbnE2L07awsXXahl+XovDn+gfJClVy8QnerNhWTFhVfzWLkGlxGt3IIoCKpMpatxVWopt9x7K16zFkJ1FYvv2hEOhmPfAdSvqgq6/lhTTe2Amew5ygTiYYefmoVi/i4PTtdKCeYy/uDuffhyZwJPJRa69Og/ZT59hfvjOIyrTPlpcDh82q5fiQjsanYLkVC2WRHW9QKclSc19z/ZjygOL8XmDJCSpkRXvi1sO7t+8nZDdGeFgoUlKouW4sWQO7E/I60UMQ80djxHcL7oa+upbWuSt5oFLRhC0NEOVm4E5RYdMfuTVJeFAkJDTCZKEpD95Ek/GpEwuefQLrKV7Kdq5jtR+43iosBp3qCEe2VLr4fmCUibnZ2LZ/zgJ1tghFELQaRAV0Uk6gcjPaG9gPae1PwvitAsqOrRBiFPZoTUmMPSqJ+tX4lfMfIcWpw2j85CLEUyK+tBAKWrpYh6NSUim0L+J7Y6FWJSNryY8xdFxVCpbU6dO5bfffqsP3gHMZjOTJk1i6NCh3HXXXU12gUdCZWUlwWCQlJTInqaUlBS2HOTxfTCTJ0/m8cdjl4P/r9BWG/8B11ajQvc3JXW1TM+o3EdZV/0TYSJv2kpRS0/T+aht6+h4mQWbuSXvf7iHvbvtjBjbghHjWqBS133tdm6u4sJzMzALZsIuFyQnI/n9GA5hxaGTRARB4LSkC5lZ8BS+UHQB9LnNnorrgS6ZjEhdO6Bo2xKCQQSN+rD9RZa05px/7/v89NpE3I66IE9jSGDUbS9jTsk95LEnEmNiBpc98TWLv36Z9fO/Ihjwkd/lDAZf9lBMn84abzGvbxpbH7wDLLfOoOfgsaS5Tsc3awnsfyjL85thefB2RJMBe2UxLltlXfBnTERnTkGSHT5YcVrLWf7TW6z85T38XjcyhZruw6+k5+gb/hG7JNivtp2bhemO6wj7/YT9AQSFHPf8pTi/nomic/tGBfAAGf1P56zPP2H+xHtwFhcjiCLNzh5Jx//czA/v3hm1v7VkNyapLuAc1+w5jPIjC+C1OgVDz82nS+805szcja3aQ5/BWbRom0BCchwnBr+HfVtW8v20m3HW1PWtqXVmRtw4haxm3dj6+RdxX2/LZ1/Q9srLUSf8/17NNZiUDBieyx8/7IrYntdF5NfS2JZW+1zrcfqrWFwOZytliJKIpNUi6RunxCtKdUmDw+1zin8HgYCPwk1L+eLpy+pXspd88zLm1FwufvQLTMnHVsli9Qf4sCS2CFaJz0+tTsuUBxfX+WYfoWJ3wC8y5bFC+p95K4Ou+w8eT4iFc2rY8MFGwmHYvrma4cd09ccfpUpG115pNG9lRlWgxrdkRcz9pL7d+fb8C0k9rTu9HnsY7UFtlvaCAr4bNZbKtQ1CbUqjkfN+/5WUbl2jgvgD/uO7t9cwanwr8lqb2bnFGrFPpx4ptGiXgKHdaMK1btxzFkMoRGjuPPpMbE/Laf35/ovtVJXV0qqdheGjckiQ3CjuvSVCYO94UVPt4eM31rLwt4L6bVq9nHsnn05eGwsymYhcIdG2cxIvfDiMgl02/P4Qeu8uDrn0FSe41+y3HvSsWEPYEal07t+5B157AxEwvv4MMvmRJS/C4TDB0gpcv8zGs3QVokaNduxIlB1aI5lNR3Su44XOnILOnIKyeRd+qKzBHYrO4HpCYf6osnGJQU1w2WocX/9E2FmLsnsndOPOQpaagrB/ji6JMvqnXcvKyi/rj19bM5OxZ9yLMHMJYc/falHkcnRjR8ZMBNRfoymJ1r1GktuhL8GAH5XWiCSTEwqHuLzlO2wp/oWB4kWIM1cjlNug2+Voz3gXhfyUPsnx5qgCeLvdTkVFdNlERUUFDkdsy6uTlfvvvz+iv99ut5OV9b+VOTLJJS5MNvNFeeTDRi0KXJuRWK/8fjCp6lbc3n4mH26/jur95fTpmnZcmfwyPPIRjq11QocqpZL7J92HJzUTvVmNfH8G1Wn30Exhw//yS4TsDd8Z8fReDJh4Hd9V1BBrMWpkghGTTAJZDvd0nMfbW8ZT7qnLLGplFi7Of5UMbafDvmdR1fhVX4VKQ37XIVzz/O/U2isBAY0xAb059aRbjTQlZzP06kn0O/9OwmFQag2oNLFXYazewnoF/wOECfFK0XjOPPMmzhk3GcnpQ1CrEI0GQhoFe9Yv5PuXbqlXUFVq9Ay7djItuw9FGed1oM6Kb8EXU9i0+HtkCjV+r5uAz83SH97A7bRy5oQnDnn88URSq6B5DvYPZ1D7+wIIBEAQUPXpjvGmKxvtfa0ymWh5wfmk9+2Lz25HUiqxOYr58vkJMW1YTCnZBAQvd3b4jVxdd+TSkYsA6vQKdHoFV99hJhQKI4qHDvJqygr57MnxhIIN6z9up5Vvnr+W66fMRaaOv8IrU6sOKQT5/wWFUuLcS9uwZV0F+/Y03JsCQnSysLm+FwnKZiCEyHcaSPtkBp4lK/GEQig6tcF0wxXIsjMO+7kZTCoycvTI5CIBf/Rkt1kLE3rD8a1UCYdCBKushGx2CIX3t1yY/if+5k2Ns7qML5+5MqoM3Vq6h9/ff5RRt76CUnP0q4HeUJjyGFZWB9gbDqDVydnwVzmDzmp2ROeWyUUMJhU/zYjdJpfX6vgHko3F53HhczuRZErUelPEmEwukZisJajIwRejdU7WpgUuo4aylSspW7mS1B7daXflFXXndTiYP/HuiOAdwGuz8c2wkVy65i8M2ZHzxIM1Kt6c8hcTbu+Czxfkr8XFiJLA0NF55LYw7a+iUWO6ZQL6S8YSrvUgalSIJiNJGjW5rSz4fUHUGnldFdUJIhQMs2FVOUI4TOuOiWxZV1d37XL4mXTXAqZ+MJTktLrvrEwmkpSqrU8O+feJcQN4WVY64mESmYL60M8+oRHl438nUFxG5e0PR1ip+p56CdXpPTHdOgHJ9M+J4f2dMGG21MavPN3g8mDbswf/lAZHgNqfZ+Oes4iklydFtPllaDvQyXIOa6t/BCBEkP/WTOSayS8SfuOnevcDWfNszHdc1+hefpU28vMSBZE2mgHk7VNjn/YGIY0a4bprKRNNrPmoGGOSjR4Ds7AkqA+p13OKo+eonsznnnsuEyZMYOrUqfXq7suWLeOee+5h7NixTXqBR0JiYiKSJFFWFilYUlZWRmpq7NUtpVKJUvnv76k5FrSSxIhEIx30ar4vr8EaCNJJr2aw2UByHCs0paSlvWU4D3ReijNQhRgWkW+pIPjQNwQO8g0Ne73UPDaF5OnPIpM3TFjkTjv2yc9H9R77Fy1FdXpPbu7YlteLKiLW99tpVZyZYNhvayejmeE0/tNpIc5AJaFwAJ08EaMiDUlo+gmnKEkYEtMxJJ78is1yhQp54uFLib3B2E4BYUL8VvUqvXKuJjuzc/12677tfP7UpRgS0mjRfSg+j5PCzcv54aVbuHLyT2S0jK9/UWu3k9VpHKbml+D1hkhJlbHrr69Y+/vbrJv3JX3G3vaPBfBQ54drvP4y9BeMIlRbi6ium1AdqWWPIAj7y7j3f/7lsqjWiwP0u/Bu8jOHojnClYV4HC54Dwb8/PXrfyOC93rCYVb88QGdb72Z4iWxBW+63H4rKuPJM+k5niSmaHhw6gAKd9lY9WcJ5kQVGSkyxBKJEEFytKcxyvg6W5cHKNwcIjtPR037MDKnu361ybd2M+W3P0zy688gzzr0fUOhlEhO1XLFLZ14b9rqiK+MWiPjhvtOO6wI3rEQ8vrwrd+M9bnX6wJ4QNBpMd1xDarunRAPkdg5RTQVezcT8MXWltm+8jdq7VXHFMDLBFCJAp4YK3YAZkQcNh+rl5XQr28ColqJ2Mh5jtGs4sKr2zP14ej7gEIp0a3PP/8M9Ps8WEt2s+iraezbuhK9JYXTx91BRstuaP7mny2ZDBivuRjtyDNw/jwbvF7CHVpRXVXBr5c2WKsue+oZcocPQ5uaSm15OTu+/T7ma3usVmq2b48K4E0JKq6Z2JV3XliFxx3gjWdWkJKupeeATEacl48lKfJZIqrVMX9Xao38iNpvmoKwP4C/pJwWu5fSJliIv3MOvku78O6He9myoRqfN8imNRX1AfzfkcxGdOPOxvnV39oaJQnTHdcedsVbSk5E0GkJO6PnJPL8XETj4ecGPl8QW7UHnzeIUiEiW7gsIng/gGfRMoLjzjqpAnidJGE6hPikUSYS2rojanvY48X27qdY7r21XoTOqEjh8hZvsce5gt+LpuEPuemdfBkySw6GSffVfSbhMKJeh2Q6Nk0FqcaD/ZX/gigi3nM3L767jz07GlrwPn93E9fe3ZW+g7NPBfHHgaOKdKZPn87dd9/NxRdfjN9fJzYmk8m4+uqrmTJlSpNe4JGgUCjo1q0bs2fPZsyYMQCEQiFmz57NLbfc8o9d178BvUyijUxN8xwl/lAYtSTG9H//OzpFGn4hiWqPD1+iCctDdyB99yvBgzzXw65aAkUl9X3mwWAImzNIYOJdyNy1yP6YhX9dgxiX/5W3af/Ss7zcKpv1zlqcgRDt9WqS5TJMf8vEmpRp9QrQpzgyktTNERCj2iAAtDIzOlnDRCgYDLB+wVeMvuZF/CU1lMxbgioxl943Xcem1TNZ8OULjJ34ZsxJqdcbYPtWP68+VVTvzSsIMHT0cAZcms/8j++ta01IO7KVoqZGVKsQD7MScKQYEjK45PGv+Oq5q3FUFQMgyRT0Oe828ruc0WTBe2Pwe92U7o62ADzA7jVz6XPf9eSOGMaeXyItCXNHDCOjb9/jfYknFZZENZZENZ32r6x5gk76pF7Jtpr5jFS8x9RbtuF1132fly+A7+Qi994/jhSXi8D2/eX3Pj/OGT9ivPnKwwZQliQN3U9PJ7+NhTkzd1Ne4qJtpyR6D8464jLoIyVYWk7Vw881lLrK5cjSUnB+/j2ylGQULZsTctUS9vsRtdqjWhE74muyOwhVWfFt24mo1SLPb1ZXEaA4+SeCrkN4PIdDIYKBY/OyNslkDE8w8F1FtHuMQhBI8wmUFjnp2SuRmuffQLKY0F98bqNX29p0TuSSGzrw5Xsb67UgzIkqJj7Rh8SU4y/aejhKdqzhk8fOr09GOqqK+XLyFfQafRN9x92OShMZmEhGA5LRwLqZP1L611KKX52KqyRSKf6A3ztAwO2J29MN4CqNVrVXa+T0HZJN646JLPhtL9UVbk47PYMWbS1RwfvJRDgUwrdlO5X3Pw3+wP7e/JXIvv+RG++5i+fe9lO010FxQfzqWlGrRXfBOSjat8bx2beEqq3I27bCMH4MUsbhW8OkBDMJj9xJ5YPPwEGVJaJBj/k/NyMdRrzPWunm+8+2MPvH3fi8QbQ6OaPH5tDzhmsJTn87av/aeUtQtKnTcQgG/DitZfg8tcgVKrSmpCi9oKbC4/Zjs3rxeYOoNTLMiWokSUQrkxiVZGK1M3bSb7RWRfj32M4Q3uVrCLlcESryRmUqnZTn0Mo4kBBBNDJT3YAKpEYkQxqLf08BBIPI+/Tg16UO9uyIvh+9/fwq2nZKOhXAHweO6ims0Wh4/fXXmTJlCjt31pUv5+XlodUe30lGY5g4cSJXXHEF3bt3p0ePHkybNg2Xy1WvSn+KQ6MURZSNrAr3hUJsdHp4oaCM2v0PO5kAF50/ml45mYTe+6x+35Ct7uZfU+1hzsxd/PT5NlxOPwaTkjFjz6Fbn771N9qwqxZ92I9KKSdNefJkSf+/YZCncGbmnfy2L9r28YLmL2A8yB864PWQ16I/f1xybYTS+uqpL3P6C5NRJhvwe2tjBvCVZbW8+NiKCJ/bcBhmfVdE9p0dSMhogUL1z987jhVnTQW1tkoCPg9qQwK6/ROB9PzOTHhmJi5bJUG/F40xEZ0p+bhNEqCu/89ZXELQXYuoUKBNS0OuVJGU1ZJ9W5bHPMaSnocqwcKw996hauMm1r/9DggCHa65moR2bdHGqWJqLMFwGKs/QKU/gCcQIlkuwyiT0Map8jnZUEk6zs15kgJVAW/fV1AfvB8g4A/x8kubeeKO8+DZhkS2d9U6Qs7aRq2AWhI1WBI15OabCQSCyBVSoz2Aa/1WbP5SttUsQBJktDD2x6BIQS079OQ37Pfj/H5WXfAuCugvGoO8ZR6+zdv391aG8W7Zgf3dTwlZbSi6tEc3Zjiy1GSEY7AsOxTB6hpqXn0Pz+KDepflciwP3Y6ya4dD9myeDKTldYw7prekoVQf28qXTBQ4K9HEHrePNQdN+lWiwM0GM99PWY0gQN8eZryPr4NQCO+qdSS+8HhcwdaIazQoGT62Bb0GZmG3epDkIkaTEnPi4TVjjjfOmnJmvnF3zEqipT+8QZchl0QF8AdQmU1s/+bbmGPJ3boi09QF2gqDAZXFgqe6Oua+Ce3axNyu0crRNDNyyfXx//4nAz5fkOoKN2uXl9KumRxp8ksRgTPU2Y7y5luMv+hGnp+yiZbtD619IhkNqHt3Q9G+Ffj8CBp1oxPigiShaNuKlLeex710Ff49Bag6tkHRoU1Md6CDcdrrbD+XL2gQVXY5/Xz64Q68FzZnwOm9CSz6m4j1/tZHl62StbM/Y8m3r+CtdSDJFHQceD79LrwbveXYnnd/p6q8lo+nr2PpvH2EgmE0OjljL2vDgOG5GExKmqmVjE0y8U1FTcRxoxKNZFRW4au2xj6xJBLPi1AlO75VjeFA3TMwcHp/Zk8piLvf8oXFjLnkxDso/H/nmGZOWq2Wjh1PrhvVhRdeSEVFBY888gilpaV07tyZX3/9NUrY7hTHToUvwNN7SiLWbwNh+NjhIbtXdzJm/k6wrK6PSp6TSa3Lz5fvbWD2j7vr97fXePnwve04zm/GoAGnE5i/CCkpAfkhhPVO0TSoZHpGZN5LprYDP+19kkrPHjK0HTiv2TM0M/REEg6anIdh0+vvxbRJW3TXA4xbMjtuQLrg170RwfvB/PpdORdcdAcaQ+M8fk9WKvdt4+sp11K5r85uUJIp6DX6Rk476xq0xkT0ltQmnxDEw11Vxe6ff2XRAw/h3LcPhV5P51tvpvMtN9F95NWsmf1pzNWlHufdDkot2lQ92tRUMgcOAEFAbAKdB38oxJZaD1P2luEK1r22CJxt0jPCZCDZ8O/4vRuVaRgCKsqK/og57rD5cMj06EQB9pc3i3o9wiGEOWMhSgIKqfGPZ4evgpkFT/NH8bT6bQIC5+Y+zYC06+OKegKEPD4Cu/YCYLz+cnxbtuP4tCHIcXz6LdpzR6BsV7e6Figsxj1rHokvPo4iL/eI3ldjCPp8OGfNiwzeAfx+qh9/geR3nkfMSMPqLWKfax1rq37EqEile9L5mBSZqI/zpLUx6C2p5HUbws6/or8nZ054HJ3l2OcjFrmM27NTqPL62Vxdi9IfRusK8uMzq9i91cott7dFOXsWwf2/9WBFNZ4Va9CdNaRR51coJZLTtCSnnVzJVY/TRnVxbEVtwmGKd67Fkt485nDOkDNQJybiroz21er/7OR660ldRjp9nnycOTffGrVf1sAB6DKO3e3in8LvC7JxdTlT7l9MMBjmxafaEKqJXjkFCFZUkW4WsCSpadaicdoHR6v0LshlyNJT0I8dcUTH2azeiOD9YH78bi99HxoMfwvgNYP6EvB7+WvWByz84vn67cGAj9V/fIKtqpjRt72KxnB0VqTR1+jhxcf+ZPvGhoRQrdPPx2+sAwFGjmuBXiYxJtnEALOeDfuTcu11aqTaAD5bbKs2APWgvojGf0ZdX948p66cUi7H446vyWGrjt/ff4qj5+RS4GoibrnlFvbu3YvX62XZsmX07Nnzn76k/3eEwmHmWO0xiq/rmOELET5nKADKnl0RLWbsVg9zZu6Ouf9P3+8lMGAQAPoJF54QxdVTgF6RRJ+UK7i30yKe6bmbiR1+o71lGNoDJVf78VZVs/XTz2OfJBymfMnKmD3soWCIooL4+rSV5bVktx+A1vTvDeBtlUV8/Mh59cE71E0EFn/9EpsW/3DIUsymJhQMsm3G1/x6+ZX1dnA+h4PlTz/DnJtvQ6tPYvTd70UI0shVGgbcMJW58iQqDy5flKQmCd4BKv0BJu0uqQ/eoc726ocaB6usLqorowXi/inCoRAhrzfu3y0Uf54CgN8fgv0r04Jei/L2y6myFbN3wxIqCrfVC0A2JbsdyyOCd6gTRvpmz/2Uurce8lhRpUSWm4WUngKShHtudO+z69tfkOVmIuwXowp7fdhefZ+gvelFa30lZdR+92vswVAIz9JVVHsKeXH9UF7aMJJ5JW/w/d5HeXhlW1ZUfI4n8M8L6WoMCZx1w/Ocfv6dKPevBidk5HPhAx/RrNOAJlvFNsgkmmlVDDDpaCNX4N3n5OzhaUyZ1J68v34huCDSNcGzcBkh9797Mn044ViZPH51hiEnhwsWzCGtV8OcUJeRwajvviaxY4f6baIk0eqCcZz51nQ0+5XpJaWSjtdfx/CPP4jrBX8y4Q3WUuHexZaauWy3LabKs5dAyI+1ysPUh5YQDIYRBCB4aL9uuRjmkWkD4jqb/NNUlMZ/dvi8QdzhyOSpeugApJREnNZyln73Wszjdq2eG2X7eixUV7gjgveD+ebDzVgr6wJ2rSSRqVIwPNHI8EQjmSoF87/ZxbJ1LqSR0d4PYoIZ7UXnEqysxvnjbzi+/BHfjt0E7HZslUWU7d5ARcFWnMfwXkJeL4HScvwFRQTKqwgf9H2RzAZ0F5+LsGsXrdrHT3Z063OqzfV48O+oXTzFSUcwHKbA4487XubzE0pLRXfBKHTnDkcy6rHuqSAcJ5bx+0K4gjLSb74S1Wmdj89FnyIuRuWhV4fDwSBBny/uuK8mdpAuSiLtuiSzYmFxzPHcFiZ0TdiT9U9QtnsjLlv0ig7A4q9epFXP4RgSTozwk7OoiMUPPBRzbMe339HzrenMSm5P/ydnonRV1/Xj6hP4xadglcuPpsbJ+NSmWXU4mD9tLgJxPM5/9rhIc4cxmlVI0onLKfs8QWqsHvbtseP3B8lrYcAQcOGevQD/9t3I83PRnNkfWXJSRN+13qhAo5NT64y+/0kyAbM2DP5AXfD+5G18/f5dlOxYU79PVtuejL79NYyNEJpsDC6/lZ8LJ8cd/33fi2S3/gC5GLvKQZDL0I0ZjmjUU/v7/Jj7ALjn/4m6d3dq96t5+zZtI1BtpdZuQ5uWhtgE5fRBnw93eXncFUGAkE7Oz4WTKa7dFDX24fbraGnsT6qs1TFfy7Git6Rw+vkT6TLkUkLBADKl6rjZZWr1CrR6BSlGqH76ZXyrN8RMrgt63b/eVUCtM5Oe35nig35TBxAlGSnN2h/y+IQ2bRjz0/e4K6sI+f2ozGa06WkIgoC71o/T7iMcBq3eQPurJ5A7Yhh+pwtRoUDQWwiKykY5fvyTuPzVLCp9j2/2PEAwXHefUkl6rmn9CcrqrvW6BuEwuGUaVAo5+KLvZ4JahTE3CWXayfuM1hkO3U6jSjIhb52PqNOiO3cE8vxcJKMB755C/N7YPecA1rK9JGU3zX1k3974ixi1Tj/u2thZ4VAoTNFeO8sXFKGa0JHO93VEvnAegsuFv0Nn3HltcLhDeG9psO6Wlw2ktIWWPz57Grezruw+IaMFY+58jZScdkfknBSsrMb+yTd1zwV/AFGvQ3/JuagHn45kNCBqNOhGD0e5p4BL22h59H5rVLVlbgsjGbmnyuePB//uO/kp/jFkgkBLjZJVjtjZz2yVAkPHNuh6dqkXPFJpDv11U6cnou2bd9z6Kk9x9Cj0elK6d6ds5cqY47kjhsU9tlufdL58b2PMgGf8tR3Q6U/uftbDUbZnY9wxl62SgD9+4qOp8dbY8Fjj9MoBPo+XlU4vi8MgUBeoh20AdX+bIq+PcDjc5H2ue9zxRbsqfAHcoSBOuw+j+cSU0rtr/fy1uJg3nl1JwB8iJ8/AXZcnUfH8i/W9oN6Va3F+9ROJT92PomOb+omPOVHN5Td3Yvqz0b+FcZe3QResRbj8fIT+Xfnq9ZsjgneAwk3LmPnG3Zx75xuodaZjfi+BsBe7L1pU6wA1viKcjgqCDg9ypRqdOQVJFikoJKUmo+rbA/f8P+OcBUJ2J1JSZB9s1abNfHfpZXSdeAcdrrnqmHUSfE4nFRs3Ys5vhn9H7GotX5dMFm97P+451lXPJFUTOfH2BBzY/WVUeQpQSTqMynRMinREIXIyGwoGcRWXEPC4kZQqtGmpSPKjF1+SJNkJdS6RDHo0g0/Htzq2WKVu9LATIkB4PNEYLJx101Q+fGgM3trIaovh1z2D1nj41XF1QgLqhMjvckmhg0+mr2PlkmLCIejQPYUrbulEWmY6Tlx8/8kWVv25HqVKxvCx+fQenIUl8eR0Z9jjWMmM3fdEbPMEHby2cQz3t12NJBMI7s+o/vRLKRedfx6BT6Ir7AzXXYoisekTuk1FOBjErIWEZDVV5dHBeLsuSZgyLagn3YcgkyJ68uVKdV35dxyXGK3x0D3/R8KhvieiJKBQxp7ziqJAh27JLF9QxAfv7+ArvZwefXqhSRHZstCBYlkBN3RpWNmXkiw4Ombw48vXRpynqmg7Hz08lmum/oE5JadR1xy0OaieOh3fqvX120IOJ7bpHxEOBNGdOxJBJiEZ9Uid2pFtc/HkqwP56I11bFlXhUot48zRzRkxrsV+68RTNDX/L0voT3H8EQSBfiYdijgT/QuTjMhloYjJgtGsIik1dhlWbgsTxgRto4L3oMOJf+8+nD/+huuXOfj3lRCqjZ9JPcWxo05MZPAr02L+fTIH9MfYPL6CfFKqhsdeHkhuC1P9NnOCirsn9SG72b9fpDA5u3XcMY0hAZnsxCUoZIcRDZICfvLUdWJq4f3/HUwn3fERqWqjiX9dWSoFtnL3CRXHqihx8cqk5fWe6+MvyCb05jtRQk4EglRPfoVgVUNSRJJEevTP4OEX+pPfxoJSLZGTZ+TuSX04c0w+xr5dMVwyFk/YS/G2VTFff/eaedTGqdo4UtQyE61MA+OOt9IN4LfpD/Hm7f15685BLPpqGs6a8oh9RJUSeXY6yi4d4pwFFO1a4d/dIFQk79CGwqXL8FitLHn4UebecRfuOKJfB3BXVeGurCQcZ9IsV6spXr0G2ajYfdpSUgKoFfhD8cvAnf66FgVf0EOlew/7XOv5ds9DPLiiFVPXn8FTa3oyaVU3djuWEQw3/L1rKypY89obfNz1NN5v2ZaPOnZh+VPP4CqLnxw5GVF174SyZ9eo7dqxI5Fl/3t7tw8mKas1Vz//OwMuvo/cDqfTafB4rn7+d9r2GYVCpcFZU0Fl0Q6qS3bhdsRPaB6gvMTFwzfPYcWi4voqwfUry3jiznmU7HNw/7V/MPfnPdisXspLXHz42lpefPRPrFUn37zD5a/mh4LHYo6FCbHM+h6dejRoMCxZWMpSbybSxNuR5zdD0KiRt8ojYfIDqPv1OqkTPoGSMgJPPcM9d7eNstlMy9Jxw72noTMokfTaKEE9jSGRvC6DY55Xn5COvgmr5lIydHFtQHsPysRoji9w2rlXGlp9XRLR5fAzd9Y+Zn5fwM4tVi68NI/w7w06G9LQviz45Y2Y5/G5nWxf+VujrzlUbY0I3g/G8em3Ec9EAJVRS37bRO5+qi+vfjmSFz4axoXXtCfhJHZg+Ldz8v4yT3HSk6iQ83jzdKYVllHmq5sI6SSRS/QSO758li22cs6c8AT6/YI9lkQ1/3n6dJ64cx4OW8OqpDlRxe2P9sJwiJvYAQI1NhwffU3tT783bBQEDFeNRzNiMCGNhNVXxE77Epz+CvINp5OgysWgOD6li/8rhFxuEnObccP2rWyaMYPFT0xCrtHQ5bZbaH/VBLSHEIkUBIGcPBMPPt8fh81LMBBCZ1BiTlT944rGTUFq8w6o9ZY6K7y/0efcW9GZT5yApjopiZyhZ7L3t9+jxlQWCwa5jEuTLDywM7qlQS+JdNYfn4dtV6MWTZm13q3iYM5RaJAk53H1OT+YYDDEb9/vitiWrAsRiqPyG6qxEaqxw0Grz1qdgvbdUrgv34TPG0QmF6OqBzyu+GXgAF53tEfx0aAQVQzNvIs/yz4iEI6sdFBLRtoKA/lqxRVA3SRu0YwXqLVXMviyh1GqG8SPRJUK3Xln4Z69iLA38jyiQY+yQ2ucX9R5YwsaNbLRQ1h13rj6fbZ98SW9H32oXgjsYJxFRez+ZRbr3nybcChEuwlXkD9mFPrMzIj9ZGo1ba+4jFWvTafrLVfgm/EzwbIKEAQUXdqjv/YSAiodzfU92eVYFvPz6GAZgSfgYl31j8wpfo2OlpHMLn45Yh+7v5yp64bweLf1JKmbE/B4WPPq6yx9YlL9Pl6bjT8ffwJHYQEDXngepfHfkWyULCbME68jUFKOe+EyBIUcdf9eSEkJRy0wdrIhiCLmlBz6nnsrPc66BkmmQJLJ8fs8FGxcys9v/oeqojrB1YzWp3HW9VNIzGoZ8bwJVlsJB0MICjmrl5VHzEkOcNrpGXz1waaYJc7bNlSxb7f9pFtd9Ic8VHpiV68AlHg20aHnNaxaUlq/7dOPd/FHmpYHHruV5AQFglKBZDh5y+ahrjfb+eWPBPYUonn7NR6//VLKfCrKyz1kZqhJTteRdAgBRpXWwIjrnuHLZ66gfE9DO47OlMxFD32CIaHp+rYTktQ8OLU/T921AHtNw721ZfsELrm+Iyp1/CqfpBQNj78yiDeeWcHOLXXPKEuSmqvu6EJSwQa81TUNO6clUjFrS9xzFWxaxmkjrm5UGX2gqDTuWLjWTbg2dvWt3qBEbzgxz/L/dU4F8Kc4amSCQEutisezEimpLsMf8CPYK9jw9osUbqoTQlLrzQy54tF6hfKs5gaeeftM9u6ooajATk6eicxcQ6MEUhx2L6HVmyODd4BwGPu7n6Lo2JotSVt5fdO5ESsrrYwDubb1J5iUJ66U8WTDXlVMdfEurGUFJGbmY0rOqU+sHIpwMERgXzG2dz/Fu3wNSCLNBvalw+ZN+OUS6qTERve+GkzKqCCt1l6Lu6wUT1UlklyONjUZXXr6vyqwNyRmcOkTXzHjmQnUlNWpeQuixGkjr6Zd/7FN0hvcWFQmE0PeeI2vzhyObVdDkCrX6Th35g9oUlPJRuD+3FTeLqqsF61rrVFyY2YyScfJYztJLuPxZmm8tK+cfd66cn2dJDJOq8e6qop+/TIPc4amI+APUVr0N6GzwwkNxhF60hvjT1TU+kOUngpChJDgsZKkyuO+zov4ePuN7HHWlfa3NA7gHOP9zH95clSZ6OrfP6HnqBsjAngAWVoySdMep2b6B/jWbgZBQNmjM8YrL8Q58w/krfIQWuRCpzbMvPU23BWR4kjVmzaT0CbSYstZVMx3o86lfNXq+m3lq1ax5tXXGff7r+izIv/2phb5pA/ox69PPk73G2/EkJoKokRApUBIMKGTG7ko72WeWdOHEJF/lzx9b5LV+VR6d/H2lou5JP91ZhZMIha+UC2ba2aTpG6Oq6SUlc89H3O/De9/wGn33fuvCeABJJMRyWREud/r+v8rgihGWJBWF+/i48fGEQ41fC+Ktqzgw4dGc/WU3zClZBO02vD8uRLH598RrKhGnp9LtwvGUXJWJr/M3Bdx/tYdEnl7auwqGoAFv++lQ/emT9CGQmEqy2rZtKacXVut5LW20LZTEgkpmsP23islLZmajth8sQOwPH0fTh+Uh9suMvPLbbgcfrKaG7j0xo6okozIEv4dK6YhZy2ev9YBECgshmefI9WgJ92oJ1hlRWidT+jRuxBV0fdovz+I0+ZDkBK46KFPsVcWUbVvB8akTMypuU3e9lK3iGFk8ttDKCtyYq10k5FjwJKkPmzbmCAIZDUzct+z/XDavQQCYXR6BeZEFbW/7yAi1Vptw5Scjb2ymE5DriO11SAC/jBC2MXmBe+QktO20T3woukQfeuCACe5lef/AqcC+FMcM76irfx4/1kxx9bO/ozeo2/EtL/vRhAEElM0JKZo6Na38TdJvz9I8eZSzN/+FHcf17e/snrUqojgHWCrbR5zil9jdM5jSOLxCVBOZioKt/LpYxdElM1a0vMY/9An9X+XeARKy6i47SHCnv2PiUAQ9x8L8K7ZQNKLjx9TcFpZUMaOGTNY/siDBPZnc7VpaZz1xaek9ep5TL2nJxJBEEjObsPlk76n1laJ3+dGa0xEY0pC+Q/42xubN+PCBXOp2ryZ0uUrMOXnkdrjtLrVTkHEY/WQL4pMapaGB5AE0EsS+iO0OjsSREGguVbFQ5kp2PxBHE4/gjuA0h7A0EmNrXQNIV86Kn0S2uNUBXAAuUKiXedk1q9s+D04w0r0ahXhGArdglKJaD7y4E1rTKB554HsWjMvaqxN73PQGI/NecHqLaK0div7XGtJUbckQ9uBO9rPwurdRzAYRuGR8eUDF8UUWAyHgrjt1ZCaG7FdkCTkzXOwPHwXYacLhDobPFGrxnjtpbjLy5l1/Y3suf2mmL2jqhir74Vz50YE7/XXv3Ur27/5li633RKRsFPq9bS8YBwZ/U/HvnsPvlAIY/PmaFJTkKvrEsGZ2g480GUpX+66m+22BahlRgan38qAtOvRyRL5Ye/jhAmjkydi9cW2mAIoctWViHqqqwl44pTlh8M4i4owt8iPe55TnHhsVg9V5W4Kd9uwJKrJzFaw8IvnI4L3A3hcNjYvnUmPwZdj/3AGtT/Prh/zb9sFk55jxK03sn2XmR2bGypxAoEwcoWIzxs7gadWH58p9N4dNTx+xzzcrgNzmZ2otTIee2kguYexc1PLjIzOfYKNa6LLpRWimt4pl2JQqxlzSWv6npGNzephx+Zq3p+2Br1JweU3dSInz4TyOL23pkKQJESdllBlQ+VbyO4gtN8ZQzTqCYTAUVGLKAoYzSpEUaC8xMWv32xnyZxCJElk0FnNGDSiNR1bRLedNOn1CgKJyRoSj1LNP9YCiLJzO8RES/1nEPp9Kf3H30LYmM9Xn1Sz4b06u0W1VsaoC26j9emN638HkFKSEM1GQtboSjJljy5IhwrwT3FCOLl/oaf4V2CrKIw7Fgz48HmO3SLKWumhrMCGMY7aOdSVxOkEU8yxucWvMjD9RizKE7fSdzLgqC7ji6cvi+p5rS7eyQ+v3s75/3kftT72hCDk8+H8amZD8H7wWGU1nuWr0Z195lFdV3VFLcVLV7Dk7jsjtrtKSvj6zOFcvHoVyrScE1ZW3RToLSmNqmo4Eegy0tFlpJMz5Iz6bdZKN4v+2MkvX2+n1uWn02mpnD+hLcmZemSyEyOHkqRVkgTUSg5271nK7E8ex1Zed/9QqLQMve4Vkpv3ICnNfNyuSRQF+pyRxfefbqkvjf32x2ImXDKewDvR4miGGy5DspiO+HXUejNn3fQCs955kG0rfoVwGEEUadtnNGdc8QiqGLaLfyfocBKqsROurUXQaBBNRiS9lnL3DqauG0KVd2/9vlqZhYntZ7P4izAzv9jF/ZNy4rojAPVVUbGQ9FrQRyafRJUSSa8j6PXGDN6VJhPGvEj/ba/Nxvq33437Ohve+y+tL74oypZLplJhzM3FmJsb8ziFpCZX352b236DN+hCEEQM8hQkUYYn6KLMXWfpaPXuI0XdgjL39pjnydXW2YnJ1Icug1YYTk1WTwQOfyWBkBeVpEcti/+ZV5XXRvlq33hPPkXb/op7zN71C+neZVRE8H4woU8+57zLb+XZgwL4vxYXM3B4LjNnxP7+DByRe5h3dORUV7qZ+vCSg4L3OtyuAM8/tIQnXht8WPG8NE1bbmzzNR/vuAGHv65KJknVnGtaf4JFWXfNTruPN6esZNPqhiqa0iJ45Ja5PDJtIG07n9xWeZLJgO68s6iZOj3muOqsobz78lpWLCpGo5UzYlwLuvZO49Fb52KrbpjTzHhvI4v/KOChqf1PWqu8A1ir3AQDISSZiDlBjSw5kaTnH8H27md4Fq8gUFiMOaMnk59cT8m+hhYttyvAF+/vxphgZnBaYqMqHKVECwlP3U/VfU/VJ0UAZM2yMd08AVF7cn9W/wucCuBPccyYkrPjjskUKhTqY1+FdDp8bNnuolXrVlAW29NS7NqK3b7YAh7uoJ1QOLZVx/9HQuEgVm8RtdWl9QHS3ynctAyXvSpuAB92uPCuWBP3NTyLlqM5o1+UOExjqCooZ8PU2NZXQa+X9R9+RnWrcQwZlYfJcmKUyf8/U1Pl4aXHl7J5XUNAt3TePv5aUszTbw4hu/mJLQ8u2LiA7168JmKbz+Ni5qvXMubemQSCMrJyj981JaVoeezlgbz+zAr27rCxekUFbdrkccbTD+H+7GsChcXIMtPQXzYORX4uwlFWgxgS0jjnlmnU2irxup0oNXq0xkSUjQjeAxVV1Lz8Lt7lDavXym4dUdx3Ge/uvDwieAdwBap5dfM5jGz1HQF/iKJ9Ask5bSjfuznq3IlZLdEeRQWAymzmzLemM+OMoTgKGgTtZGo1Y378Dl3636qqDjNRPNJWmZDXR6i6hpDNDnIZKpMRbUJWxD4KUU0LQz+21MxhYek7nJlxJx/vuCnqXDpZAipre8pxoU9OIqVbN8r+ig4A9dnZaFNPjsTcv5lAIERVeS3rVpRRuMdGq/aJtGqfQGKKFoe/gm01C/ipYBI2XzG5+tMYnfsEqerWKKXIQMHjDvDpm+uifLWtVX505pSoZPUBknLa4t8bf7EhVG3Foo1MTGkNcoafl8/qZaUUF/xN8f68fJJSj21uEwwGcFaX4nM7kSnUaE2J2Gv8cb3NK0prcdp9qFQyal1+BAH0JiUKxd+8zmV62hvO4b52XbF7KyEsolckYRBTkIl10/7yEldE8H6AcBjef2k1D73Q/4S5ghwtqu6dUPXujufPSEcQ7QWjmftXLfN+qbtHuhx+1q8so7KsNiJ4P0BxgYMNq8sZMCz3RFz2EWO3eVn9Zwkz3t9IRWktSakazp/Qji690jCkpWC+63pC11wC4TBbS8IRwfvBfPHeRjr3SmuUsJwgCMibZ5P02tMECosJllciy81ClpJ0VAntUzQ9pwL4UxwzhsQMErNaUlm4LWqs69DL0ZmOffKjUEgsmFvMOU8PhSXLwB9pSSbotKj692DrzotiHt835UqCIT8rK2Zg85XQXN+LBFUOBkXTT8yCNXZCTheCKCLodXWrWSeQYDjAHsdKXt94HpfIYvd1HiBwkA9qdaUbu9VDwB/CYFZhVggIOi1UVMU89mg9hYPBEG67E+vWrXH3cWxaS5n2TDatLqfPGfETRKdoHMWFjojg/QB+X4hPp6/jtkd7odGemJYFl62ShV++EHMsHAqyd8331MrHkZisQa05PtckSgK5Lcw8OLU/TruPUDCMVq9Al6hG3TIHvD5QKppE9EulNaDSHtkKbtDhjAreAbx/rcNTs5edjth2b1bvPgwZLkRJ4NtPi7n9wWn88dZV2Csbysj1CemMu+ddtKbIFTaHzYvL4SccDqPRyeNO3k35eVy0eD4V69ZTsuRPzC1bktGvL7qsLMS/3Q+UBgMdb7iOfQsWxjxX+2uuQp3YuERC0ObANfMPHJ9+U+8WICUnYnnkTuR5ufW9naIg0jvlUmbtm0Jx7UY8QRfn50zjx30P4wnWBWEZmvaMtbzHfx8toVsfuOia9oz89CNmnDEU576GPmhVQgJjfoqRmDjFEREKhtmxsZJJdy+s9yCf9c1O9EYFk97txXz7s/xe9GL9/uuqZ7K++hfu7DCLtuZIRwKb1cOfcyN71QHm/17FRRdfT+m7t8S8hg4DxiFWHNrSMznTwMXXdyAYDNG1dzoJSWr0RiUPTe3P1g2VLPy9AK1OztDR+aRl6Q6pgXE4XLYq1s+fwaIZL+KttSOIEm37jKLNmY/FPaZluwQCgRAvP7GUNctLkSskBo7IZfTFrUhMaZhnuF1+li8s4s3nVhKs9+XeydAxeYy7si1Gs4qNq2MnOgAKdtlw1wYwHrpa/x9Hspgw3HY16nGjcC1aTlCUEerYmV21MlJVajS6vfX2tW06JTHnp/jifvN/2UOPfhnH7ZlztPi8AX7/bidfvtdgV1tRWsvrk1dwwVVtOfvCVijVasT9VUR7FkbPww9gq/bi88RuB4mFy+HH5lZQ4klAl5lOgkWN2XByiTb+L3MqgD/FMaMzJ3Ph/R/x3Us3U7S1LhMqiBKdzxhP7zE3I1Mcexm0waQku7mRtz8q4LoH7kX87NO63jVA3qkdxhuvwJMoJ6W4ZX355AFaGQfSLfF8HlvVMcJ+KN/Ql+vbfIFZ2TTWOmG/H//OvdS89A7+XXWZX0XHNphuuQpZdsYJE2ar8RaxofoXLm85Ha1oYcxT77Bj7q9smP11ROmrJFei1BoJBcPs3m5l6sNL6r1U5QqRCbd3oefYkdjilKjpxgw/KosZSRIJhGWYW7WidPnymPvo23akqtLHD59vpUP3lGOaKJ0Cli+MnvAeYM3yUmqd/hMWwAcDfmwV8a/HWbWbatFBrct/3CdTRpMKoykyUJX0OviHBZhDNfao4P0APq8j5vYDuIM25AoRa5WHV58t5LLr3kMhluN37yEpqwWJGS0jFJZDoTD79th5Z+pfbN1Ql6zLyTNy3d3dyMk3IVdEayPoMzPRZ2bSfOSIw76XzAH9Se3Zg9Jlkb/1hHbtyB8zqtH3Re+qdTg++DJiW7C8ksp7niR5+rPIUhucRhJUufyn03ze3Xo5xfZt6FbcxGWd5iNoHEgoKN0u8eYzZVRVuPG6A4w8vyWWli0Y/+ciqjdvpmLdeiytWpLYsQP6rKy/X8opjpDqUjtTHvqzPng/gNPuo8pVEhG8HyBMiI+338B/Oi3EpGz4vvp8wYOC0gZKCp3UuPLoOuwqVs16r367IEqMvP45jIkZSEoPglIZ5bIAoGjbEmWikdEXR7fZJSRr6DM4mx79MhFFEKVja+8JBgOsnz+D2R883vB+Q0E2LvqW1gNuQiYX6y0uD6BUS4yb0JZHb5lb35Pv8wb57budrF1RyqPTBtaXgFeU1QV4f+e373bSplMSfQZnoTPEFyGTZAKS9O8Qki2xwhMPbCKrWQbBQIgdX6wnGAiT3dzIFbd05o1n6j6HYCCEXBH/76ZQSocVCGwswWAIa5UHl92HTC6iNyowmI6umqGm2su3H0dXUQF8+/EW+g3NJTmtYR6WnB5/wUiplpAd4jOIfF0PH7+xloW/NVRaqdQy7nm6L607JCCTN41mjtNWgddpQxBF1DoLar2pSc77v8CpAP4UTYIpJZsL7vuAWnsVPrcTtd6M1piIQt00tjUGk5JbH+rJk3fO54kXdjNq9Pm0uECNKImoMy0osiwoBYGJHX7j2z0PsqLiC4LhACnqlozPe4lJq3tEWSztsC/ml8JnOL/588jFYw8QA8VlVNz1GAQaMpy+dZupmPgYya89HTHBPJ64gw622xfxY8ETAMgEBacPmMDwzlP4deo99UF8z7OvRW9OprLMxRN3zMfjbmgx0OkVWJ0VOIanIE2/HWHRFkLfLybkqCvN0o07G3nW0a9KJeelId55H6Xjx0aNSUolacPGsOmx7SSmaAgEDqMQforDEi8QTkhWM/ySFPyaYqo8CvTyJBTS8c2wy5VqknPbULgptg2YJasrtlIZAX+IVX+WsHdHDVnNDOS2MJGQrPlXORQcLfEsegDUXiUKUY0vFNuDOkmTjXd/ErOyrJYXn9yKzijn6TfPJyUtui2hotTFI7fMiei53bvTxqO3zePZd84kM/fY+r916emM+noGhXPnsnb62wiiSPd7JpLWswea5MbdE4PVNdj/FrwfIOz24F29AdmIBk9nSZCRq+/O3R3n4vf7+eDzvXz9frR1IoBMLtZX+uszM9BnZpBzZmwf+lMcOeFgiMoiO0579Oq3JUlNkXtN3GPLPTupDVgjAni1Ro7OoIh5vvdf3cm0D2+lx1kTKNqxGplcRVrzjmhNSShUGsJyNZaH76Dq0ecjnCVEowHTxOsOa50mkzeNLoezupRFM6KTFgAb577FmEuv46v3I4O204dkM+enXTEF9cqKXGzdUEmfwdmEQmH++GFn3Nf+9qPNtO+aRPuuKQgi9b73B0hIVnPhNe2QyUT8/iDyJgrUjge1Tj+fvLkeh83HpjWR7QA11R7SMgQeeKY1AX+Ywr1eTj8zm8/e2hDzXCPOa4FSdewhkcvhY8XiYj56bW39d7RZCxO3PNTzqO6lNqsnKvF1AL8vhM3qIfkgu7zcfBManby+8uBgho5uXEtiKBRm0R97I4J3qGtfmfyfhbzw4TBS0iPn9u6qKpzFxRQvWoJcryO9dy+0qanItbETCgGfh5Jd6/h5+j311buZbXoy8vpnScxs+T/xnD9WTgXw/6PUuIsJBD1IogKzpmmE3TQGCxrDIayTjoFAaTnaRUt4+PY8il1y9hZ5qQyqad4iAdNBk/oEVQ6X5b/JmNxJBMN+NJKJDdZZUcH7ARaVvsuwzHtIUB1bmXbI48Xx+XcRwfsBwk4X7vlL0Z1/dqMtPI4Wm6+Md7dcRqFrTf22QNjHvMo3kZIUtO57NrtXzaPXmJvpMuQSZAoV61bujAjeW3U0MXailp+td/Lruj8REGjb6UwuGPoUhqWVqDq0QUxKQDyG8mJLogZHs3b0nvIiKx6NVKHv+8Z7zPi6inAYOvdMRas/ZVcSD487gN8fRKOVIx1iVaj3oCy++TByQti1fyJnXAW/WG/kh9V/IRMU9Eq5lHOyHyFB1Xi12iNFrTMx6OIH+PCh0VFjCrWOzDZnktMlmUdunktNdUPFjN6o4JEXB5Kdd2y98UG7A4JBRJ32qHvbjzeCJn6PonJDJcO63cWPJdH2aD1NF2IM6qMm5q07JKE3RJ8zFAyz6Pe9UYJZUGe39/2nW7hmYtdjntjqMtJpc+klNB8+HMHmwLN8Nf5f5+Pr1RUpOSmuonEwFMDuL0PtFwiWxC/59W3bhaJLewSZDCnBXP88MCpSQAFDR8tZ9Wdp1KomwJBRzU/6Xt9/M8GaGmqrXbHHAmFk4qE/e1GIDCDNCSrGXdmW/768JmrfFu0S0BhMGM2pJGREuwYIchmKTm1JfmsKnsXL8RcWo+rcDkWHtshSEvEEXQRCXtSSAUk8ftNjn9uFtzZakFeh0uKrrWTAWAMpaT348r0N9T3Pg89qxtP3xG5FAVgyp5CeAzIJhcKUl8T+vKFOCM3vD2FOUHHLgz15ddIywuE6uYqLr++I0axk3s97+OHTbXTqkcLQMfkkp2oRT8IVeXdtXW/732nZzsT4CWaWf3UXBRsXI4gSLbqP5PQL/sPOzeksXxiZzOvRP4OcfFOTXNO2jVV888EmRoxrgSVRTWWZi3m/7OGx2+Yy+a0hR6ybEKsC6lDjCckaHnqhP8/8Z1GE53z309M564KWjUrI1FR7+OHT2C2OAX+INctKGXZuw+/LVVbG/In3sOXTz+q3CZLE0HfeosV556LQRyfGqkt28/Ej5xEKNjx79m1exocPjubq5387pLbWKeo4FcD/j+Fwl7PVNp/vCh+m1L2VZFUeZ2c+QlvjEEzak7PPL1BWQcWdjxKqrgEg3WIiKykBYbsac/sbEcWGG2KwugbR7sAQBNFgQbKYsXrjl+v6Qm6C4UP3xR2OYChAyOXEtyF+T7fnr3VoRw1FOArBtyPB6t0XEbwfzMKqd7h/whIGXnIfJksWkqwueNm1tUF1V64QOf/ORF4v6lPfbhAmzMaa33jOuYKHh/2FvgmCO41WTrMO2TizLiX/nJGU79gHkoTNq+aDbyso2G1HqZY46/yWUQI9/xb8/iAOmw9BqKsgOVSAfaQ4bF4Kdtn48Yut2Kq9dOyezKCzmsedaCUkqTl/QltmvL8JAKNZyZlXyZheOKjeS7uL8QI6ileze6sdu7YKs1mLKeH4fF+Tc9ow9q63mfXO/fVK6UlZreg7/jkctXo+f3lFRPBe9559THlwMY+82Ae1yovGkHhENobB6hq8azfi/PpnQi4Xqp5d0Y0ahpSadNwTa0eKaDKi7N4J78q1UWOqnGb02qtCkzWZmdVTcforUUl6Bluu43TbMEIrV5PfxlIv8pWTZ+TyG9pRu2c7PpUKTUoyCl1d8s3jCUTY6f2dresrcdcGmmRlKmh34Pl+Fs4vf6zf5vzkG1T9e2K6aQLS36z6rN5iFpS8yZziVxifMJnc9BSCxdGTdYBQcgKOfUUEPvoG080T6nri9//egjY7LS0+XnosD69MxaZdXj79ZDe1zjr/675nZCOKAo5AkGp/gA0uNwpBpJ1OhVkmQ92Ev9v/SUJhkswSghBtXlBT7SGBNkiCnGA4esWwub4nOnmkRoIkifQ9IxtBgBnvb8Jp9yFJdc4S46/tcNhkjKhQIGamIb+wIYHo9Feyr2Yuswqfx+4vo4NlJH1TriRR1ey4rATKFCoEUWqwvBME+l5+J+b2LVjr+YU/fE8zoM91PNq1L4TkyGUSYcJotHJcjujPCcBgVCKKApIk0um0VFYvje0D36JdAmqNHKVKRve+6bzw0XBWLCwiKVXDpjUVfDJ9Xf2++/bYmf3jbia9PpisEyx02hgEAVQaWcRnolBKXHJ1Mt8/P4aAb/8cJhRk2/IfKd62jCuf/JGBI5ox+6ddyBQiQ0fnk9XM0CRJPJvVQ3GBgzGXtuG373ZQWuQkI9vA+Gs7sGurlc3rKo84gDealCSnaWMmZZJSNRj/5tQjigLNWpiZ/PYQKkpdOG0+0rJ0GM2qRrciBoMhbNbYi15Qp6lzMLt++CkieAcIB4PMmnA1qad1J6Fd24gxn8fFwhkvRgTvB/C4bGz+8yd6jbrx1Cr8YTgVwP8P4Q96WVL2ITMK7qnfVu7ZyXs7ruCs9AcYbLgBY/LJ1e8XDgRx/Ty7PngH6lSI9//bt3YTsjNOJxwM4t+5F+szrxAoqntwiUY9xlsm0LXdWXzD/THPn6RqjkI8OpE5u6+cktpNzCt+g+7qUWSZjQTLY9s2SUmWo+oXP1IqPPFL53whN17BRWZSp4gbY/PWFmbvF3fpOTiFZa5XI7QCDuAKWFlW/hkjsu6NWhU5GlQaGSqNCTAR1CXz+TvrWTZ/F+FQmE49Urjspk4RpWH/JspLnMycsZ0/5xYik0QGjWzGoLObHbUH7MG4nD5mztjOtx81rKjv2mpl1rc7ef6tgZgkH/6dewgHg8hbNEMyGdHqNYw4rwXd+qQz+6ddtOiqZLbt1vrgfWzya1j/7MoLnxbh9xUBW0jL0jHx8T5kNTc0+YNUqdHTutdI0vK64HJY8XnBVatEpU9AKxMp3BXbLrK8xEVpQQnz3r+czoPH02nwRRgSD594DNbYqHnlXTxLGtSKXd/+Qu2seSS9POmY2kGOB5Jei+mOa6l54U28q9bXb5d1agOZFgLXTKVTpza0H/sRwQQZkieMNGM1/j/fR3P2UK66fRC7t9eQlaNH5iji+96dcBYVI8pktLzwfPo98zT6zEzkchFLcvyWCZNF1WRlw4HC4ojg/QCeBcvw9jkNzaC+9dtsvlLe2nwh2+2LAPjV+To3jn8cpn4RdbygUeNPMvHDmHM57513qbx3EslvPIssNYlAeSXWZ1+NSK52atOCDpOvZk+VSItWRkxyHzU+OR+UVLHA1jBJFoAJ6QkMNOvRHkGi6BSRCColqsKdnDkik99+jk6m+6t0XNnyPd7delnEdrVk5PKWb6OTJ0QdYzApOXN0Ht37ZuB2+1EoJIxmFaqj8C6v9Vv5pfA5Zu2bUr9tr/MvZhe9zP2dl5CubXuIo48OrSmRtn1Hs3HhNwAMuOY+tmas4M99/6nfZ0HZm7Q3jeSinOmoSEZnUDJyXAs+eDU6qQcwZFRe/X26W990Zvx3Y1SwL4hw4VXt6vVOVGoZ6Vl6Rl/cmoJdNl56PLqtyeMO8N9X1zDx8d4nXTWc0axi6Jg8vv1oS/22PoNT2DTv7frg/WCcNeXsWTeXbsOvoFOPVBBoUrvSQCCEy+njw9ca/kY7Nlfz6lPLueSGDthroq/pcJgT1dz1ZG8eu31eRKWUWivjrkl9MMewFBTFxnnO26werJVuyktcmBPUJKRosCSqUSgkMnMN7NsT+znctlODAKqrtJQVz8UXS97w/n8Z8PxzEdu8tQ6KtkZrNBxg99oFdB8+4ZBWp6c4FcD/T2GtLeD7fY/GHPu1ZArtnP0IWt1YWrU8wVcWn5DdgXvB0rjjtb/PR9WnOyGrjcq7n4gQpwnZHFifepmEqQ/T2jCQLfZ5Ucdf0PyFiP66xmL3lfPFrjtZVv4pmdoOFEgdyLluHOJTbxGqtkbtrxs9/KgU248U8yF87iVBhl6eHBWMdeyejFojw10bILutjKXueXHPsaH6Fwan33JIj96jITVDxw3/6c6lN3QiHA6j1SnQ6E7O8ubDUV7i4qEb50RksL/6YBOL5xTw8AsDjtlrtqbKExG8H2DosHTCi/+k7INPILS/VFgQ0Iw7i0DHVsjMJjLSUrhmYjeqPYV8vHwBALm67oS29eCH/0Yq9JYUOnns9rk8+86Zx2yXFAtBFDGlZGBKiRSR3LG5Os4RdXjcfhxVxSz4YgobF33LxY98cdggPlBSHhG8HyBc68b+3y8x3319vYrvyYIsKQHz/bcRqrHhtpfjUDhYF1qEVPst7Vrl4V+7GdZuRgBC+/8DUJ/WCXMrC9nNdKx87nmWPNxwzw8FAmz55DNcRcWc9eVnaJKSGDmuJYv/iG2vNebSNuiaYNLuczpwfvdL3HHnVz+h7NoRyVhXalnh3lkfvAPsc61jQ/Z2ul4+ltrPfqx3IZFSElFcfSE/3zkRZ1ER1spytCol3nWbEDRdsT7/RlRllH/zdhQf/JfOF5+L9a6nsY8byZo+vSKCd4Aw8F5xFW00appr4gfwDpsXe40Xny+ITq/AlKA6qfuGTzSSXoexQz5nKYvJTG/Jt98VYK30kJ6t56LxubTMS0SRMJrHuq5jfumbVLp30cZ8Bl0SxpKoyo1/XkkkMeXYE6I1vpKI4P0A7qCNL3dN5Lo2X6CRNe3qs0KlZfClD1JTXkhN6W4ULRL4s+jjqP021PzMGsUvlP3Rg1HjW9F7UBYrFhdH2b+Nu6JtfbI77A9gkdy8+nJ3iss8zPi6iDUryknL0nHtXd1Iy47d579uRewVe4ANf5XjcvqbPIAPBkME/WFkCvGoBOQkmcjQ0XmsWVbK7m01AOS3VLL5l8Vxj9m6/Bc6DrrguASHPk+QHz6LXYn5zYebeeiFAUd13uw8E1PeHcrmdRXs2maleUszbTomkRDn+x+ssRGsqiFYVo5kMSMmJSBLiLQUqCyr5cXHlrBjU8N8NSlVw/3P9SMjx8ClN3bkmXsX/f3UmBNV5LVuaJUNBQK4SmN/d4zNmmFp3RpXeTkqsxlpf8uaJFOgM6fgqI5zXFIGonQqPD0cpz6h/yEcvkp8odjiSMGwH5fcxvLxlzJ21kw0SUkx9zvhiELsPlVRRNmpLfJuHUEm4V7wZ0xlWQDHh19x4wOf8Hnp/Syv+Ixg2E+CMocLm79AK+OR3VDDfj8hm4NQwIYsqOD6jDmUbtSyZq6bbSoHQ6+/i1TrXkJvvVsXRIkCxusuQ5Z+YnyEE5S5JChzojyiAXomX4ophuJ+YoqWR6YN4IVH/qTWFkafnUyFZ1fM8xsVacjE45OFV6nlqNT/zqD9AMFAiD9+2Bmz/Kyk0NkkXrOb1kZ796o1MgZ10+CbNC1yIBymdsZPKFMS+GrcBXS/5y7aXn4pgkpEJ0/A4a+gr/ZuZnwY+0HqcvjZvqn6uATw8TCYFDFVmKHO/k2jCRHen6CoKtrB3o1L6DBg3CHP6V4YWzAPwLNkBSHX5ScsgPcEnDj85dh9ZcglNQZ5MiZl7ASEZNAhGXTssxfx1Jq6e5VZkUH7qz6G+3ZF1STLMlKRN69rcXEVF7N88rMxz1s4bz6u0lI0SUmkZem44pZOfPj62oi++bMvbEl+m2PXNHFUl7Hnr7mk2uKr54ccrghRsQ3WWVH7fFb+H/J6LESZeDlyQQKZSHVBAYuuvY6a7dsBqNi4EUNSAv4de1C0zse3dlPM1/Nt2AKBAKJeh6NtS35wxF8Zm1Vl4zp1ElKMKpSSfQ5ee3p5fbuCUi0x7vK2DBzZDIPplHPGAWQ5WZj9AbrPnE2HCZ3AaEQmhLC0TkaWWBccZ+o6MD7vJYJhP/LD9MU3JRutvx1yrDZQfUQBfDgYJFhlJVBUQsjmQJ6bhWgxRQnkGRLTOf/e9/B4bHxaMTHu+Za53qBdUkd+/34n513eltsf6UXJPgfL5u9DrZHTe1AWliQ1Or2CYHUNrh9/w/ndr4Rr3Wj0Wm6+YBTinYMJqDWYLPHvcYeqsmrqSmaPO0BZiYvZP+6ipMBBiw4J9B2STVKqBoXsyJJfliQN904+ncI9NpbNLyIlQ89unSlucKg1JiFJx2ee4bB54wrOuWsDhP/eQ9JIRFEgKU1LUpqW/oeZPwTKq6ie/BL+Tdvrt0kpSSQ8dV99pVmty89701ZFBO9QZ0/37H2LePyVQbRsl8CtD/Xgw9fW1s9n2nRO5Pp7ukckzuR6Pel9+7B3VsPvSJAkBk17AUEmY+N/P2T1S6/QfNTZdLj6KgzNctEYLPQZextfT7k65nvoNmxCfYvnKeJzKoD/H+JwgZccJeWrV1NbXn7SBPCi0YD2rCHYXv9vw7bePfGPOJvfl1mp3BJidGE12k3xvS8DuwpRBVRc2uINRuc+TiDkQyXp4k6a456nrALnd7/inrMYRIERjzzBM49uoKSwoWx+1ZJSeg/K4LIpk1CVFqJo2xLJbDzuwYHfF8RW40XAxO3tfuXVTedQ7tlRP97RfBbn5k5CKUVnbEVRoHkrC0++NhiX008L1X+Y7ohWhwc4M+POEzq5+rfhsPtYOi++5sKCX/fSs3/GMSUqwqHoSUCvfqnI5s0hnppDaN4y2l08nrm33UF6n94kdu3EGem38d3eh9GJqVRVxO+D3rW1mj6DT1xrjdGsYtT4VlGiewCDhqexe9U3EdvWzfuSVj1HoFDFTzIIcUqgBY0a4fpr2bzby4qPV6EzKOk9KJOEJM1xqQCx+8qZte95/ih6kWC4rhwyQZnzf+yddZhU5R/FPzN3umdnu3fp7m6kJQUFUbG7xS7sQkVB/YmNgYWiqJS0dHf3sl0zs9P5+2Nhl2FmlkZUzvP4PHLf98bszL33Pd84h7sb/UKaulnURbRJkUG2tj37K1ZS7sllhvJDBr90G8FP5uA7cBikElTdO6K97kqE2ErS7bFYq8QhI8Gybz9xTZqg1sjocXkWLTsms3tbKX5fgHqNY9HHyFFrzi5Y53U7WTptAoX7t3J5k9EQhVDLWzdFpKl+NkUqmwYodu1nzdUP4yoPr3ICMGZnE/hrA7J62QSdkVX6jyHo8SJc1omS/QewZtWPOq/U58cfDIYR+NJiBy8+sJjS4urzuJ1+vpm8BY1eRo8B56d/+p8IsUKOvHF9JKlJBB0uEMSI9VrEitB3iVgknJP2rNNBIBjdFztIMKxvvyYEfT48u/ZR+swbBO3V9568QyuM992MEBOaBVXrYxHUclwFlqjHdPmtyJTw85Q9XDYwG1O8CkOMggZNQ9doAbsD6xff45izqPp6KuxUfPotGrsD3ejI7/RjaNo2Ad6PMtYm4ZxU4kDlWmXDqgLefW5F1d9289pCfv92F0++043a9WKQnGY23hirxBirpGnrxMoNzjv5bdL9Eee27n8j4vNUDSmcpN0omiBdIBDE7fIhlQpn1bIUsDuwfPB5CHkH8BcWU/r068S9/RyCyYil3MX6FfkRj1GYZ6esxEmt+jF0vCydBs3isNu8SKVitAZ52O9AodfT+ZWXOPznvKrAesfnx5GzcBF7fp5eNa9k61Y2/28yV69cirFuXdIatKXNgJtZM/PTqjkisUC/W1/FkHj+RHT/TbhE4P9D0IhjiFfUoihCn7ReloTvQOVLxO88/T6dk6HC58cdCCAWiTBIBMSnuLARiUQoO7XBMX8J3l37EZo15nCbfkx4YiNdL4+n4zUiNvIRHdKSIUqSTUiMB5kUuaBCLmSe0fX7ikooGfsc/uLKTIu0dXMWzCsgP8cWNnfFwlx6D8umUa+uZ3Su00HA5aK0xM2M73azaG4OHrefdt1SuPv+efhkpdh9pRjlqWilCWikNWfTjr0E9Z7OdEu6g8X5of7vQzNeIFEVfaH7T4XH78Tuq/S/VktMZ2WjJhJViuhEg0wuRnSWXrONWoRbbxl1EtgXvfQ8UGZG3TgTgA2T3qP3x5PpnHgTO8zzsQeKMMYqKC+JfN9nniN13lOFXCGh/xV10Bvl/PzlDizlbrR6GX2HJJGRmsefH38SMv+YIFRNUHZrj+2HGaEbRSLEjzzEpCn57N1ZXSr405TtXHdXU7r3SkHurFSsF6lVSGLPLhsdDAYpcu4hW9uO2xv8QL5jOwvzPqDUfYjxm7ozrtUmYqMIROpkCdzZ8Ed+PTiOlUVfs9LyPUXaQ9w47n1iAwkIEhligw6xvDrrK1GriagcdhSqhOqqIKVKilIlJTEl1FnCYS3DYS3B63Ki0BrQGOKQyk+9bNlmLmbj/KkEfF58wx9DHGMI0TOByh5pzfDLEctkOOxerOUu0iyjuDuuK/milcwtfxabtzJIutQ/lTYP3snqZ18JO5fcYCAuuxbu2cuRNa5P0BsukBRyXpmMoEFL8exZ1LurEasiOIgAtNKqkEUQOTxywEp5jgs+tQABAABJREFUmYu2nRLp0zMOuVTM4Vwnv87I5YdPt9G8bRIxcRdXW8bfDcGgB8PFJYbWOKYvPx54OOJYA/1lqCSGUz6Wv6SM0ideDasGdK9Yhy0jFd11VyI6IcOskGhpHXsluyyLI1+D+nL2rPPgtPvwRqhKqjq32YpjbuRj2H6aibp/T8Q1WNkaTUoGjarHb9+FloCrNFLG3N38nAU0y8tcfPDK6rDHktvl56NX1/Do211JjD271oiUhn0Y8cxf2CocyGUBjmydyYa5H9Jp+P3EJGef1bFrgsGoQGeQh6i/H0NsQrjgXDAYpLjAwbL5h9m8phBjrIIBI+qQmKo9o4CJ32zBtXJ95LGCIvwlZQgmI26nv8bAlKW8ci0gFoswxaswncTtM6ZBfa5c8Cfz77oX6+HDxNSvx7Knnw2b5yovZ9nT4+jz2ceo9bF0HfkwLfvdQO6utUhkCpJrN0dtiEemOPvWmP8CLhH4/xBM+ixurfU1b+/si9NfLU4hE6u43vA+6295DkEuR2E6d1ZwLn+AQy43U/JL2e1wo5cIDI7V09WoxXiKom5CbAymcWPx7NyHWWVi0mMbadTSSN3BB/jfkRsQI9Cm+yL4eUF17+9x0F43/KT+rjUhGAjgXLS8irwD+Nq2Z1GUsmOA+b8epEGThDPq6zpVBFwuSg+X88qLm8g7XF2eumpxLmuX5vHK5F40rNPytI+rlcVxReYr9Ey+hx3m+QgiCQ0Ml6GXJaI8x72AfzeKnfuZmfMaq4u/hWCQtvFXMyDtCeKU4S95p91LeamL9SvzsFk9NG+bRGKqJsRX9ZiozqcTNkQ8X7/hdZDLz+6xa4hRMGhUXX77rrrq5MBhB4E6dSBKJYpQK53CrZViaBU5R/B7PBjUydze4DusniKGXCfiiwnhGW+lWkK9JrFh288GQZ8Pf0kZrk3b8ecWIK1fG1mdLCRx1VlXnVFOn6G1adMlBa/bj7OiiOU/PMWfM8MXqG3634RUVnNViBAfi2pQbxy//Vm1TdqqGQs3uNi70xw2/6sPNtOolhzZc8+A14cQF4P+7huRN2uEWHX6pMzjd7LPupyv9txRVRmTqWnNdXUm88vBp8mxb2K/dWVUAg+V+haja7/HwIxn8AXcyAU1BllK1CyvKj6OWkMGs++XX8PGNKmpaNNrrqooLzjIL+/cRd6eyt+yWCKlVb8b6DTsXtSGU6vQ8jhtBHyV/eo/f/4wVz7yLpI/luFZsQECAWStmmC49VokifGUlzqZ+tEW/pp7qKqUP7teHW56bC5fFPXH6i2k3HuYxjd/iONgHls/n1IVnNAkJzPoi88ILFpF3PhnEOJjCdgdKDq2jqh9IG/dDPf2XYgSTBz47nsuv+tO1onAd8KiVicItDpqv+f2BzD7/Ox3upGJROTsK+e555uj37iCwHufEXS6aJqdQePbruTXZU48nuiZ3QsJn8uFvbAQ25FcRCIRmpQU1EmJCLJKgmDzlmH25LK1bDYATWL6Y5CloJYaazrsvwYGWQo9k+9hQd57IdvlgoZRtd855b+D21GBZ8v2qK189hlzkVzWA3FsDEpVKBluahrErCOvU+YO1aJQSQy0kN/MK3/tqSwvryE4HCgrjxqsw+slYLVBDQReo5UxZHQ9WrRP5LfvdmE1u2neNpFu/TPPqZhsfq4topc9QO6hCiwW91kR+KJ8OxNfXFfV1iISQYce3bn57TvQGgSU6vO3hlEr/Dwwri2vPLospAVMJhe4f1z7MMG53EMVPHv3AuzHebYvm5fDtXc2pdfg7LDfyckQdHkqfwNSKbI2zQnqDZBzBO/Wyne731yZpFNppEhl4qjl/qdtdadUktqtKyMWzMXv8bLxvfeizt3z83S6vvUGMo0GhcaAQmMgNoLl4yWcHJcI/H8MqZrmPN1kDVsLZ3LIvYHEQG1q+Vqx7uaXKdu2nXZPP4k6MfGcnW+3w8ULB/I59lox+/x8WVDGDruLu9Li0Z1iv5NgikHZKYY9awvwuP30uc7ElKKhAATwM93+JiOevhPfm98TdBwtaRQEtNeNQN6gzll9hkCFHeeiFaEbxWL8J672joPPFzj6Mj1/BN5fbuXA7rIQ8l415g/yzYebeeC59mckPKOWGlFLjaSoG52LS70oUeI6yGubOmHxVAdi/ir4hE2lv/Fki1UhZMrp8LJs/mE+fqs6uj39q53UbxrL/ePaE3Pci7l15xSWzD3Mnm2lIedr1zmR9EQpQa/vrBwJ1FoZg6+uT5PWCfz27S7M5S6S0nVoBvbEMmceQdcJC0iJgNCtPTtfqRQzy+zbB+lRn3GdLKHyv25uzEVBfvtuF35/5e86LlHFwy91PGvRveMR9Adw79hD2VOvhyx0hbgYYl59CtlxavBisQhTXOW5K8rseFzh1S71O1xOfObJf6OCTovu2hGourTHNn0mgQo7wdFX8+cTkQMtAMuXFdO3cX3cG7biLy6j7Lm3iH1rHPLGp1+FUuDcxYQtfatU/wEO2tbyyc5ruK3Bd7y7dQBH7Jtoy8gajyMTlMSeYhWRXKej58QJ2PPyKFhdrfirSUlh+Ow/0KaE62EcQ0VZId++eDXlBQertgV8Xtb8/jEKlY5Ow+8/pR5FmVKDIJHh93kwFx7mizdH06zzCBo8NQaxWII6sx4iqRJfqZlNq8tZ9ufhkD78/bvK+Wycl5tf/hW0paSqm2GQp9Dt7Tdp8/ijVBw6jEyrRWWMQaFUIrRqhRBjAEDQqDHcfSNmQcC1dDXHzK4V7Vuh6tuN8lcnIW3RmIZXXcm2++/niXcnMhUZ+5xuREBzlYwbEk3Ey6Q4/QHWWO28l1OEH4iTSnimhR7hf5Px76wOmnn3H4LX32ToY2ORKf5+ITu31cren39h/t33VrVTSDUa+nz6EVkD+uOWOfn10DgW5f+vap9pBx6lV/L9XJ7+NFrZuQ3eXYxQS40MSn+WpjGXMyvnNSq8JTQ29qV78p3EKrJO6Rhet5P9mxZjOhjZhQaOCmaW2Dm430PLdknIj1PMNynSebTZEmYfHs+Koin4gz6a6YfRWfMYXzyfT8Af5OrbmoS8Z06E6CSBRZH85OsArV5Ooxbx1Kofg88bQKmWnFP7UyCitsnx8PrPrE8cKm0Jxz+5jMP7q1sSgkFYviAXhUrGDfc2P+Nj1wSX2UzJ5i2sfPFllInJPP/qE2zY4uLQfiu1G8TQvntamOCircLDZ++sDyHvx/DNh5tp3Tn5tAm8WK1E3OcyXB27M2tJCYU5HhrUaUa7kVcjfP4ZkoTKwKshRkG/K+qEVVsANG4Zf8aWeuqjVV0icfT1jUgkOo+r4v8WLhH4/xikcgUJ8rpo3UZSF6Sw8YMP+WPJCyjj4uj+7gTqj7oKyTnq1zZ7fXycV0Kkx/GaCgelXt8pE/hj8Lj8iEQgUpfjtlQv6Ndbf8WmKWXom4+jsyjQEoM8KQWxUYdYocBtteIsLsHrdhBUiynU5KCUaDEpMjGerBdeECOShz5IJZs30q5ja/6MYIsD0GNAFuLz7B/sO5jDuk2RPWEBtm0owuX0XXTWLxcDAkE/q4u+DSHvx2D1FrKy8CsGpD9R1Y9ZWuQMIe/HsHNzCQv/2M/QaxtULXRiYpU89Ewb9m/OZ9FfxUglInp0iiHOcgTHPWPRTHi+SmjsTKEzyGnWJpE6DU34vH7sUhEv5xZy72tPI3nvM7x7KxXlJekpSEcOZPFLL+JzOlEYjdS9ckRY1lZnlDPsugb0HJiFpdyFTCagMypqXDCeCXxFxZQ9/1ZYlspfXIb5nY+JeeYBJBFKbLUxiYx45BMK9m1m4/ypCFIZLftcT1xa3VPOBgsGHYKhIdL6tcDnp8wOjgiLp2Ow2v2I5KElj9ZPvyXm+YdPq6LH5avgt0MvhJD3Y3D6rey2LKauvivpmhanfMxThTYtjaEzplORm0f57j1oU1PQZWaiTY1O3gHMhYdCyPvxWPXbZJr1HIU+LrrjxTFoDHE0u+xq1s+ZAlQSnbXzvyLv4FaGjn4Jx4ffUr56AyK5jAZdO/PKqz145dWtISKQ+Tk2FPa21MloV7VNrtcj1+sx1qk5OCvExqC77xa0148EpxOxWgkyGWWvTSTo9uBZvYHGY2+j6JVXWHNZL4Y8/hja1q0QAzFKAwmyyiVSidfHxJyiqneZze9HF3Rh3RlFe+Xb79A2fxr4e0voy3bsYM6NoUJRXpuNP0Zdw7Xr11CUXhhC3o9hXt67NDUNpKGs14W61L8VWlkcjWP6UUvXEW/AjUqiPy2xVru5mD8/H8fVV74adY6QEIuvYC/yMge2jNbIs0Lvn1hFJiNrv0XvxEcpOGJj5Vwz42ftRyYXuO3hVjRtU7MQrmDQIyTF488P1zOR1slGrD915xiFUhL20w34/fi8biRSOeKzsFVMStMgFkQEIhD1mDglWt2ZrVeCgQDlJc4Q8n48Fs8+yNBr6pOQrIk4fqbwOp3snPodC+6+t2rbrm++JrVbV659czwJLetHrJKyV3jYtiFckBYqgw47NhWTlHp6laN+rZ49dbvy/uPVgenNa2HGrxKefelO4o4GN2VygYGj6iIWw6yf9uJx+xELIjr0SOXaO5qetQBn3RFXsOa1yAKqda8cgSLm3FX5/pdxicD/R6GKi6P28GEkdWyP3+VColSiTko6qwfziXAEAuS5oy+Qd9qdZClP70GRkqmLqoi627aUN2wDkYhkPNViNWmaynIxy8GDLHpgLPt/+51gIIAmJYVWrz/Gtkbb2OiZyYONZ5Ogim6dJ2jUqIf0x3OcMIh3+SoGPD+AVcuLw/qd6jYyXZC+Yb/Zgk4T/QGv0kjPut/6n4SKsgIc1jL8XjcqvQm1Ph6pPHIk2emzsK5kWtRjrSv5iS6xN6NXV1oMLl9wOOrcWT/tpceAbIyxCqzeQoLBAAqvi9Tv/8cN9epAIID34y0ELJVtK5aPv0H7wC3IE07SWHYKqPTzlWJ3e9nj8jJOKmH4w3fRJOBH5/NSsG4dS+64A/O+faR278Zl709Clxk5eKBQSlAoNed8gXM8fAVFBCvsEce8W3fiL7dEJPBQSeK1MYlkt+iOCPEZCxGJ5XKQgwovTVonsHFV5FaYNs10eL8Ntdbz7DlA0B1NKjAyXP4KDlREV8E/ZFtPhrolWdp2UeecDVQJCagSEkhoeeoBgtK8cJ2UY/A4bXjd0cXxjodUrqTziAfxuuxsWfITBINIZEqGXvsyzqffrQrkBJ0ufHPmodiylQceuJPnx4V6XRcXOKjTMLKwXTTYKzzk51Tw2w+7KS1y0Kh5PD0ujyfOqML05P14dh/AMWchwbVb6fe/93HYKihYvwF1QT6xTZugSUmpsj1aUGYNCUQny2U41m6Oem7f4Vw4sRLmAsNuKWHVy69FHgwGWT/hXfzPRM8wzz3yJtm69iiE8/c8OB9w2S1YS3LZvmwGbqeN+u0GYEqtjcZw8uetUqI7o5BLeeFBKkrzsEpcqJMT8OcVhs2RDu7NvKefpmjDBtJ6XUb/Lz9HkxRqYSsVK0jQZaBJ9xA/3E3/wQFUGikxJiVioeZ3uWAyYnr+YUoefZmAuZrECvGxxDxxD4IhOoH3+7w4bWbEYjEqXeh95vO6sBQdYeP8byk8sJWkWk1p2nMUhrg0BOnpk+2YGCVX3NCQaZ9uC9kuEsGo+5ujP64lzWb14PNV2jNKolgzVpQVUnhwG7tWz0KffVMNnzGIy1GzLsaZwFFQwOKHTtBQCAY5smgxvw4czOg1KyMGTAMRxGiPh88XxO/z4nZUIJHKkClPfh9arF4mv7slbLvT4eOjD3fx5PiuHPsVGIwKRtzYiF6Da+F0eJErJOiN8tPO+keCLjODxjffxNZPPwvZroyLo8ML45CqL5yrzb8Zlwj8fxiCRIIu7fypSwuIEFPtT3wi1GcQLNAbFPQfUYegLYhC0OLyh5ePqyQG1EdVjCtyc/mpVz/M+6oXpLbcXBZfex89f/6IbWkLeG/7UB5usgC9PHrrgLxpfeStm+I+tmjz+ZH+8C3Pv3cvs38/wIbFecgVAh0HZ1G/XSJB7clvLX/Ah9mThzfgRCpWHrVoO/WHpzQlkY4GKTN+jjze/4ra6I3/fiujYCBA4aFt/DT+FsyFlURbIlPQ5aqxNL9sNCpdeLRXLJIgr2FhqhA0HPhtFmnNOmCoU4fy0ujCjh6PH7+ylAV505mfOxGXv4Km+v70fuYWeHMG/r2HkTZviOTyzngkAbxigYryIg4vW05Kl07nxPFBLxHTRqdildXBR97KTG+8VMrAVm1oM/0nkpVy1CYTCuPf29fqN1trHA+4Ti6gKUjOTUWJSi1l9O1N2LKuMKwdJi1LS4rMFqJ7ASDEGqMq2keDwiXjodjv8fgd5LGP3y1vhzhEGGUp9Ey5D5Mi/cw/zDmGMSF6hYhEpkQiO3Wao41JoO8tr9D5ygdxVpjR6mLxTZkRsVfYn1eAsTyXJq3jUSqlOJ1etm8sJi7x9No4XA4fi+ccYsqkjVXb9mwrY9bPe3h+Ug+y6sSg7BCDok0zQIRIIqAB4luFa4YEgkEKPKELf1cggL+mKgypBM5zBVZNKHbu50DBMsx79kadU7ZjJ3GeBlHHK7wl+AJu+AcReGeFmTUzP+GvH96q2rZ25qekNWjH0Hs+IGBzUrB6DYFAgKR2bVAlJCDXnXpmOhpEosrv+revn2b0vR8hTFuAZ/1WCAYR67XIhvVjz8Z1FG2ozIzmzJtP7l9LqXfVlRGPp9bIzsgBQpqRRtykl/AdzsV3JB9JZirS1OQqZ4pIMBcdZuO8qexY/hsSmYI2A26mVsueaGMSCfj95GxfxXcvX0vAX3kPHNi8hFW/fcTVz3xLeqMOp+20oFBK6DU4m4w6BmZ8uYOSQgdptfUMvL4ByelaDDIJlnIXu7aW4LCUERfnJHf7n0gkYhp0HIg+LrXqnV5Rms+Pb9xM/t4NiCVS+jcZE/W8giBCoTr3lMd66DD+aLoHBQU4S0siEni1RkZ2PSP7d0V21Kjf2MCS78azb+NC1PpY2g+5i4TMRhHXM8eQc8AatUXhwG4zFVZ3SHZdJhNOWd/A6/VjKXfj9wWQKyQh2j8nQmky0fmVF6l75XDWvf0OrrJyag8bQv3Ro9BnZp7S+S7h5LhE4C/hvEEnEdNap2K1NTxbIwB1VaffZ6PSSBl6TX0OHihhRNL/+ObIdQSPy42IEHNjvS8wyCrL4ku3bQ8h78djw+Nv0nPabXxT/iAV3qIaCbxXr0fz0B1oDh/BMWsBiER4bx3D80XlZPWJ57I+yfiAlQEP35QWM1JsZHiiMaJ3MFTaSS0t+JRZOa/j9FuQCxp6Jd9Pz5R70MtOTYNAkpqEeuYSxtxYmy8/D12o1WsUQ4+B2ee8f+1ihKUkl6+fHY7bUR3M8XlcLPz6ZfRxqTTqPDRsH6VER++Uh9htWRLxmB2Fq1n31Ossyc/nmrWraNc1hQW/H4g4956X6/DZ3mvYY60+1tKSL1gr/MQTD89CNm8PuQl+5n9yJy57ZWYkJimbXlc9zY6vp9LklpuQaU9eKue3WAl6vIjEYtx6LSW+ACvMNhyBAG11aq5PisXqK2KHo5IEF3l9/CwReLZ2bUynWelyviBJTYo6JtZqEF0gL/ZjSE7T8sqHvfj6w81sXVeIXCHhsoGZ9GmjJPDGm2HztaOGVvVYnwzBYBDf4VwqJn5KcOtOpECt9BTuv3MyP2onsLHidwB6Jt9LnPLUem0vFIxJWWhjkqgoC7caatl3DBrj6VWPyFVa5CotJFUqdRfVkL2WbVrHqGu6sHXJV8jVcYwYPZjY+NMjCeZyF1++vzFsu9vpZ/IbayszUQY5olOo4hCLRLRUK1htra4cyXV78Taqh0QQQvzrj0HVszPiCBnPYCCAKIKi/blEmSuHNzZ3o7GyD8am9SnbuTPivPhWLVHp4iFK23aTmP4ohX+WaKm5+HAIeT+GlMzmbPvsC1Y+83yV1RUiEe2feYoW992D0nR61R0nwpCQgUSmwG4u5ssJY2jTcwzNRj+Or8SKrbiIv957jyNL/grZZ+Ok98no2weF/tz+jSXxsUjiY6F1s5POLS88zJQnB2I3V5dy//G/saQ1bM8VD31IwO9j+oS7qsj7Mfh9Hn555y5ufH0WOlP0Z3o0GA0K2rRPIqtBDB63H6lCQKORohIEKixupk7eTK06Eqz7P2XZ59UZihW/TKJpz1H0vPYpVNoYti79hfy9lUGRgM+Ly7yTtGwdOfvDA8Vd+2WEZPfPFU4W0D0W3DkROoOcWx5qybh7F4aJyfUZms3uFV+zavqkqm37Ny6i/ZC76HTFvSg0hojH9HpPIpx5kqx/NJSVOJn5427m/roPt9NPcrqWMXc3o17j2KjuBKr4eDL79iG5Ywf8Xi9yvf6cVvhewiUCfwnnEUpB4IakWA448yg+ztJHDDyQnoDxNPvfj0FnUNC0RSpm6wAyTWv5M+9t8p3bSVc3p1fqg8QraiM++tDMPeGleTzKd++mZbAyMurwh/dNeQMBrH4/xR4fvxaZKfH5aJmUTN+xdyJ1BdhicVDi81Pic7LmhH1/K7XQw6QlThb+cHP7HczOeYO5uW8dt83GHzkvY/EWMDL7bZSSk2cFhBgjpn6dab9hF03Gt2TtejN2V4BWnVNJytCFKZ7+W3Foy9IQ8n48Fn/7OhmNOkYkHNm69rSKHc66kp+qtiUo69AncSzJljrkZ6Vj2b+fZc88S9f3PyI5XRsmGGiMVaBKL2TPjvBAgMtfwUL3V/TufTd/PjUMj6uaAJTl7+eXj+7jilvew1FYVCOBDzicePcewPLRN3j37Eds1CMfMZCSti35yezED/xeYqGhSsH96fFYfX5y3V5MUgmJMikm2UX0mFerkHVsjSeSMviIAQR156a0rqK8EFtZIXZLMbrYFDT6OFT68EW6VCaQWcfAg8+3x+nwIRaJ0GjE+DdtpcwTWiqv6t8TRbtTd3XwFxZT/NBzBG3V37vvcC489SlXTniCPc7lXJX9FvHKsxPZPB/QmZIY/dwP/PjaDZQdK6cXiWjUeRgdhtyFRHoWASGxuFJwyxr5nkWtZOO8j9m16g8A1s/+kP63vY6q6xXIT6GMFGD/rrIQMbzjcWC3GVuF57T6PJup5OgEAetxZP17b5DrH7sH32sTQxbGkoxUtNcOr7L0Czgc+AtLsM9djL+4FGXH1sga168kWcfBX24h6PGCIEYwGhCdYfB1n3U55e4jrPb9wH0Pfcf+aTOqSetRiCUSWtx7N36TDMWh8Eo2paCnU8KNCDWIUZ0PWDyFWDz5mN15GOUp6GVJ6GSnHiza+Oc3YdsSshqTld2BX+8YEvp3CAZZ+cJLpHbrSnrPHmd13RpjHAPufJMZ796D21HB0t/fJygSc2TiL1VZ9xPhc7kjBn8uFHxeN6tmTA4h78eQs30lBQe2o41JwFkR2Z7UVl6Iw1p6RgQeKsXM4gzhhLq02Mm2DcW0auFnzcrw8sLNC76jQYdBJGY1Zv2cL0LGVkx7npvvmcpXHwns21l+9DzQvnsqI29qjEJx7n/P2vQ0pGo1Xnt4a5guMxNlbHQhyIxael7/pDe/fruT7RuKMcQoGDK6HrLgDn6fGN5DvvLXD2jWc1RUAp9RyxDRObR5GxPXXZeKULKXzXPXozAaiW/ZEk1KMhJFzUENS7mLSS+tYvtx/fp5hyt47bGlPPxyR9p0rllP5VSSE5dwZriIVnaX8G9EglzKS7WS2etws77CQbxUQnudmli5BPlZZocNOiMGjFyv+RhPwIFcUCMVhz6MdDWU68h0OjziymylTlotEOMLBinyeNluc1Lu8/NdYXWJ036nhxnFFiYkJJLnid5P5QgE8ESxdLF6CpifNzHi2LKCz+mf+tgpEXgAIdZETPc26MutDK4Vg0guQ9D8s/qLrJ5igvhRCUakwukTg7x9G6OOlRccxO+LrMOglyVwTe0P6J36ECtyv6JF3FAqPIUsLpqM2++gydt9uNzzIMuueRjBa+epN7sy/esdLJ59EK8nQN1GMdz5RBv+LHso7Nh11F0YKH8Yx7ocDhf9yaDR47F5Slk47bUqIu9x2sjL2YxSYcBQu1bUz+DesoOyZ8dX/TtQbsH58Tck79jLmGtG8Lmtsnxvu8PFUrONIXEGap1BdcuFgDwpgcD1VyJKisc7dwmBChtCYjyyoX2Rt2iMMoq4jS0vH8v+/RRv2Yo+KxNTwwZo09Kqyjf9Ph+2wznkLFqMoXUDfvvoIcry91ftn96oI0Pun4TOFFmw8sSS1WCrpiR8/Ba+w0cIOJxIa2UiNugRtKd2bwWDQZxLV4eQ9yoEAgg/ruSlB7YgVxmRCRdnoC02pTbXvfAzFeVFuGwVyNQmgiItbq8GdTB42qWzx2AXK1Bc3hv7p+FkCyDQoTEH/jchZNusjx4jo0mnUybwEdxEQxA8zUyUKRjgeaOKTx0etjoq77fD/iCO2lkkjX8W3+79+MstyFs0RpqRimCqbFUJOJ04FizDMqm6F9T11yqEOBOx459FkhRPwG7Hs203lo+/wXc4F7Fei2bEIFS9upxytcfx2FI2C6gMCi9SfkHPaZNZccdTOIoqxc00ycn0+/Jz9NlZiOUynmy+gu/3jWW7eS4AjY39uKrWW6eswH4ifF4/FVYPIpEInUF+ylaqxc4DvL99CEfs1T28mZrW3NlwGqYa7BWPIRAI4LBWO380bDeYpu2HU7HrAGUbtjPk1585smgxa9+aEMJw1rz2BgmtW51VKb1EqqBum77cPH4uq3//iLL8A6hi46k36qqoBL7BtaOR/40tTbbyUnYsD7eYPIZNC6bSffTjNR4jGDj3AYiNq/Jp39XErmVvRJ2z6rcPGXDrG3hdodWdDmspsyZdTb+BY4m/oytimQGtTo7eqDhnPvYnQpOURN/PP+H3kaNDfleCTEb/r75Akxw9wCGRCqRk6Lj5wZY4bV4kUjF+Twnv3R69FeDA5iXEpkYO+uqNlQGAX76pVpfv2iOeYZdrWffC0+z5eXrI9Q2Y+hWZ/ftVudJEQkmhI4S8H48pkzaSnipDr5f87e15/0VcIvCXcN4htvgonpeLZ1cZW0pdbA4EuO7u5qTX0p+1JzZUWitFWwSnX9YDQSbD7wkXnmpw9/Ws8H1Pm9ir0Emro/yHnG6e3pfHIxkJTM4Nry/0BINsc7tIlUshipaTSixGHmWBa/eV4Q9GJpVBAlR4i0jg1LNyIqkUSfzZlQD+HTC789lS9gfz8yr7xpvFDKFXyr3EKrJPixwkZjeNOmZISMfrCz+W3+elorwAp7UMvcxE39gHmHrkAbZaZ1fNyWEjy2Vfc8dPU0AsIjZBxfX3NGfYtQ0IBIIo1RK0OjmyPaGkrp6qK/2L72D+sGvwHdfTndC2DUPHT2La+7dXeWMXFe6mYetBUa/fX1qO5f3PI48tXUWLkUP46qh/dYxU4KDLTYXfj/4MBd7ON0QiEYq0FIIDeyHr0IqAx41ILkeIjYlqX2k5eJCf+w2kfFf1okRhMjFi3hzimjWFYJDC1WtYcO/9dJo4nhkfPUh5fmi7w+Fty/nzs3EMvHsCctXJSaBIKkWSGIck8cz0CYIeD+714WJCx+DdvhejR4mgvTjJ+zG4fRo+nLCNPdtK8fsrHTf0MXKeGt+V9Fr60ybx5aVOJr20iisG1yW2QT38O0JtjGSDL2PPwdUh1SrHsH/DQkzJ0QNdAF6/G7Mnl5Q6yoiZKIC0bB2ao84cFWUFVJTlYzeXoI9PQ2OICxPxgkoh08TScu7PPYI3Kx2Xy430yGGEqdNx1K+N/o4xBCUCjsIi8levQiQWY6xbB5VEjuW98PvXX1yK5fPvMD54K67Vmyh/rbpUNmCpwPrpVLz7DqC/5yYE7en1oJsUmVX/v8Y2jZL0Q/SZ/yqqCg1isYTE5CYYUjOrvrtkdSNub/gdDm85iESoJUaUkjMr6y7KtzP3l72sWHgEqVRMryHZdOyRTkxczb9zq6eYD3eMCCHvUGm1+NmuG7iz4U9opDUrV4vFYhp2HsrOlX/Qovs1mDzxzOgxKCTr3nDMdXSf8BaLHqgOulYcycXvcsFZ9sLLlRoSs5sw4I438XldSOUq7E3z2fTBh1gPHQqZq8vMpNYVV+DOKyZYUECg3II0Kw3BZESIIuJ5ruHzBREL0d8TgiBFrtIilSvxup1h43KVNuK9crYQBDEKBZjt0fVSgn4/3jIrWY27snVZaJbeWVHGX98+xbCxH1G/RfR367mCIJOR2b8f121cx4aJ71G+axeJbdvQ5LZb0WdlntIx5HJJ1Vq4vLBmob1glMQQVOq6XD6yHnUaxfLzl9uxmt0MGRLP4d9/DiHvAH6Ph9+vuprrd2whpm50Eee9OyJXYECluGjB1t389e6L9PlkMrqMs3PXuYTTw8W5yruEfw3MpU7eHrecPdtCHwLP3L2A5yf1oF7j8+szq0lJYegfM/h18DB8zuqXUGq/nsSO6YRJcHN5+tOopAYALF4fHxwpRiOIKfD4ogrwfW+z8kxiAjE2gbIIfUeDY/UYo3h9y8Q1CzLVJK72b4HFU8Bnu8aw3Tyvatv8vHdZUTSFp5qvqtEV4ERkNOqMTKGOuOhv0e9+dm33EntcENxZYWbb0uks/PplPC47Kp2JruOeDSHvx2D25LFa+JXhcR2BSvuV4/1cKyxumqhGMZ/qiooBirFh5B2gcPUa9n35E/XbDGD7isrMh9GUUaOlSsBeWX4bFXv2k1W3LkPkPoT8XRQuW8K2uBTqtemLNiYJmeLcebifK4gFAXVyEtSQmTgGV3k5f956Rwh5B3CVljJ9wCBGr14BIigtshP/8P9wyXxh5P0Ydq2eSY9rnzwlAn+2EEkkCPHRn21CjB4u0iDLMbicPqZO3sLOzaG/P0uZm5cfXsIrH/UiNr769+V1OLAXFOCtsFV6sycmhGV2dm8tZdv6YvZsK+POu64kc5AL6ab1BOQKlF07sHf/IhZ++Xbk67FHtoc6Bo/fxU7zfD7YfgUdDHfQf/RIZn6TGzJHIhVz28Ot0ccoKMndy/cvX1MlfAmQ0bgzg++bGFYW7C8zEzRbEW/ejnzHLnTNm+ArMyOMuRJ5kwZ4vR62f/YNSx5+lIC3MjiX0LIlA594OnIUgcpMvH/MlVgmfxlx3LloBdrRV5w2gW8bP4rfD79QpQ1zwL6GyfbrARiZ/Tb1UgaGBV5UEgMqieG0znMiivLtPH3n/BALwK/e38xfcw/x2KtdaiTxFd5CDtnCbToBdlkWUeEtOimBB0ip25L4jIbUrd+DGX2Gho1v//IrEtu0xli3LuW7Ky0AU7t2QXYOhOyOQSKTI5FVVpLp0tO5avECtnz8Kdu//ApEIhqOuZY6Y27Cm2fG/saEkCodWdOGxDx2d42ic6eLYCBAwGYHkSjktxQUaanbfjjrZ30Qcb/G3UbgdbsYNvYjfnjlurDxPje/hMZYs7XdmaB5+0S+/qCA5g17UHhwW8Q5rfvexJpXxtP07hvZs2Eebkco2Y9NqUty9sk1AM4VZBoNcU2bcNkHk/A5nUjUaoQzfL4r1XoyGnfk0NblEcezm3atcX+dXk7rTsnUa2wi6PdRsmIxWz7+NOLcYCDA7u9/pP0zT0U9Xk1CyIIgQhAFODxvPr8Ou5IrZv1W5QV/Cecf/36Fq0v4W5Gfawsj7wDBAHwxcSNWy6lZ7fjLzHhz8vDlFeCPVJYaBYJMRmq3rly/fTNDZkynz6cfMXrDKnp+NonkjJZcmf0mBnn1Ys3mD3DQdXKbqFKvH7lUzFOpCaTJq0tvBaB/jI7eJl1UATutNI40dfOIY/GK2milZ28vdrEj37EzhLwfg8Nn5rfDL+L2n5pNFYBIFsvl932DLra6F0uQyGgz6EHK7HVZtTg3JGp9eMdK5nzyZBXhT2vYnnX2X0KOKYgk9Dc8zCP6mTTN6UT5th3Y8sMFvUqLnWxbBJ2MdwEQp8jGvv5AGHk/ht3ffE/9Zn0BEEukNO46DKUp+mJNJJUQ1TcREGlU3KgOsHH8dfz5xg1snvUZC798kcn3d2Xv+nkRMyf/JDiLSzg8b37EMXt+PhU5Odi9Ur6dFeD7qUewlobbNx1DMBDAGyHIcz4gEgTUg3pHHdeMGoqgv7h7Ay3lLlYsyIky5qYw11b1b1t+PoseHMsX9RvzVfNWfNGgMUvGPor96D0TDAZxW+wUHDKjVEnwuP28O2E7z757hE8K6vPxvnRee/8gXmn07GN285r7lM2eXN7fPhRf0MNf5RMxdFnOPa/WplGLWJLSNPQYkMGrH3YnOa4y8/7di1eHkHeAQ1uXsuCrl/A4qz+bv7ScstcmUfLoi9h//xP79NmUjRtPwFKBvHljBKOe0m3bWXT/g1XkHQCxGE9p9OwVgQD4/QTKowcmvAei21dGQ4w8jVvrT0VMqMZMC9NQ2sZdXaUPcy7h9fqZM31vCHk/hoN7LOzZURphr2o4fTUHZyK5zUSCzpTM1U9PZd93v0Sds/WzL6g/ehQAglxOywfvO2kf8NlAl5FO++ee4eqVSxmxZDG5GVdgLnDjee2tsBYbz+btWKdOJxChYvBM4CsqwTZ9FqVPvELp06/jmL8Uf2llS6BEJiWz5QiMiZlh+9Vp3YfS3D38756OVJQVcPP4uaQ36oDGmEBm0y6MeelX6rbpjyA592XpRpOSzDpGMpsPRakJL8tWamNIrtUMR34+S+4Yy/A7/0fDDkOQq3SoDXG06XsLfa58Finnxq3kdCDIZMj1+jMm7wAKjYE+N7+MTBHertVu8O2oT1FAVKuXI5dUJpdseXlR55Xvje5UAZBdLwapLPIzo03HeHJ/r7TjLd6wAVtu9PNcwrnHxZ0CuIR/PDavib6g3r+rHJfDh04fPcIXcLnx7tqLedJn+HIqHw6yFo0x3HMT0hoUrY+HIJWiz8w8LfuKMp+fRJkkqg1emlyKTCEQL5HzVFoCtqM97zqpgFEqQVFDf79WFscdDX7grS29KHNXL9D0skTuaTQjJKDwb8XKwq+jjq0v+Ykrsl5FLpxa9lgsFvhtupf2Qz7GFOPF7/MgEvQsnmdm6bwDXHVzo6qMk91czKKpr9Z8PARuj/uKnHHT+OOHwVXbDbVrM3TGdGIa1K++1uV5TP/sCCPuGsOtLYZxRLQQd15kWxgAn8uFCDFKjZGhD3yAITmzRj9zsU6LvFVT3Gs3hQ9KJQhZ6Wz58dVqobGjCAYC/DLhLu6ctDTiAu2fAq+j5kCOq7SUfFs8+3ZaEAsi1IbMqHMFqRy56txl2k4GSWI8hgduxTzx05CmbNXgPsibRLfvuljgcfvx+6OXaxblmYk3laOQ61jy0CPs+u77qjG/x8OmDycj1WnpcP/9uJavxbV6I52Meto82Z1lm11M++EgFRYPG1cVAJCaqSOtYQdEYiGstzazSRcMCTVbnu4wz8cfrC4//a34EYaYnuHq4b0pXp9L+bqf+OW574lp2JCuU97BUnwk4nG2L/uVbqMeQabUVGoZ/LUKz6btYfNsP8xA2bkNflECq14N91sv3b4dUa3oJaXSutkgrZkAic9Az0QuqGluGsLLbXaz17ocp99CHV1nDPIUtNLzU/Fms3iiBnsAFv5xgJbtkpDKIwvXamq4LhFiVBIjfn8AS5mbQCCIXCGgjbJukCu02I7kRhyDSmsvRUwMcc2a0fvj/6HPzo4691xBkEjQJCezZ1spC2YepnNSDEFH5OCqY+5itCMHI044O3tRX1EJJQ+/gL+wun+5fOdeZE0aEPPEvRhNRnIPxtDh6o+xF6/lwPoZSOVKGnQYiNfj5M/PxxEM+Jn14SPc8tY8Rjz6GT6PC6lciUJ9/sr8NVoZA6+qR2GejaGPTmPd7xPYs7ZS16FBx0F0G/Uo2oQUag0dwqIHHuK3y4ZQ95qR9B3wFH63h30//MK+jWbS3u503q7xfCM2pQ43vzWP9XOmcHDzXyh1JjoOvZuErEYoowjYRYJEpSLg95PYpg35K1dGnJPZt0+Nx1Ap3Tz8UgfeeHJ5iN1qSrqaAT2VzBlY3SJk2b+fhJYtTvn6LuHscInAX8J5RU3kXCoTn1TgxpeTS8njL4eo/Ho2bKVk7PPETXwRyVm+5E6ERhCToZBxyOVhmcXOkDgD04vNIXMkIrgrNR7DUeIVp5JxuleRoKrDE82XU+jcTZ5jO4nKuiSq6hMjr3mR+m+BRBQ9Oi6IpJUZiFPUs9MbFXTtm8E7zx3/gqrM/AkSER17VP9N/T4vJUf2hOyfs30lXa54mtV8C0Br/QhKPljMvh9CBX7Me/cyrXc/rl65rMrXVSQWEQzCj+8fRq2R0qbbYBo3cwAvR77WrCxiM+py81t/ojUm1NiDCCBWqzDcfQPFj7xIoOS4bJ5YjOqJ+ygW/OxZPiPivsGAn0PbV/6jCbzCaIiq8Augy8riq8mV/aUBf5BDByGlXltyd60Om9uq7/WnnL04FxCrVSh7dETerBGe3fsIerzI6tdGbDScshje3wmlSoJSLcFpj9yTadC7+OyxIYy893N2ff9D2LjCaKTRwEGU3P8sgeMV5xctp8vI4dgGpDJ7ZjWJ7to3g9jkOG56fRY7184krklj/Eo/SpUBgyoNlbpm8nl8MBQgVpFJxoE6/DaoZ8h2e14exXvCCfkxBAN+vO7KwFGg3ILtl1lR59p+n4fq5lFYDxwKG/M5HORt2UxiyyZ4T9RDEAT0d91Q6RHevBGejeGlwiKFHElaZNHFk0EmKIlTZhOnPP/kFABRZXtC1T9Fld9nmy4p+H1BNDoZDocXfRQCr5XG0zxmMBvLwp9lHRPGILEl8vPvO5g1bQ92m5da9Y1cf09zMmobUChDn6ESpZLMfn05NPfPiOdK7tSBrMsHUG/UVajizu364WRYtzwPjV4GNVVmeL2VbgRngaDfj332whDyfgyeLTvw7DmA0mSkdgMTB/cKeP0dyW5RgaUoh8XfjQ+zj1w763P63fZaGHl0FBcT9PtRmEwIJwlGnQ60ejlavRy3W0/8HW/Rx/MciECpMVRlpmsPHcya197AXlDA9k+nsP3TKQBINRp6vjvhvFZVnG+IBYGYxEx6XPMk7mEVCFLZKQt4Hg9BIiG2cWNaPfwgv48YGTauTkoisXVrClavQW4woIyPQ2EwAGC3lHBwyzKW//wuhoTaPD/hYQ4dFFGcYyYzTUBhO8L8KweGtKZqUlPP+DNfwunjUgn9JZxXNG+XGLUCuPuALHQ19NcE7A6sn30X0bsyYLbUKBJ1ptBLJdyVGodMJGJReQVB4MH0eJprlGQoZPSJ0fJ2nTSyT8FXOxD0U+Y6QpFzL2XuIwSCoVklozyF+oYe9Ey+m4bG3v8Z8g7QRjOihrErkVhPz2KwUYt4ho2pj1io/rGpNFIef71LSM+6WJCEEVqHtRTvIQtNdP0AaCcezs5PIitk23JzMR9XctaqY/UC227zsuiPw+TbdcQ2jdx/1/XN14mt2wB9bMpJyfsxSJITiXvnBWKefgBV/8vQ3TKa+E/eRNOqKUaRv0oQLxKclppLVy92qJOSaPvUExHHag0ehNwUh/+458OPU3JoNeRVarXqW9V6IEhktB14Gx2G3o1UdmEXdWKFAklyAqruHVH36YY0PeUfQd4BjLFKrrg2cqVArfoGnGXb8XlcuMvLI/Z5N7/tVvy/Lwgl70fh//4neneP5Vg1d1yiik6XpSOTydGmpeDtKOP90iuZeHgIr+/sxhvbunKgYnVIhv1E1NN3D/l3N8XNbH52Utg8Z2kpWkNkwUQAiUyJTHm0vSEQiJopBQhabUjkchLbto44vuCxxxEN7oX+rusREmIRKRXI2zQnbtJLSGtlIKhVGO+7BSHuhDYaqYSY58ZWqdlf7NAbFfQaXB0suPXhVkhlAhPGrWDCuBW8+OBixj+5jMI8W8T91VIj19T5gHZxoxEdXZYKIgmdE25ioOlt3n9lLdO+2I7dVvms27eznHH3LmRvhNJ8kUhE7WFDImqLiCUS2j/7NIbsrAtO3gHkCgklBQ5Ij/6uFxv1iBRnYdNIpRCic164tekxOGbOJ+D1olBJqN80ljoNNKyd+Skb/vwqjLwDWIqPEPBVl/Xb8vLZPPljpvXsw/edu7P8mXFYDhw8q2uOBLlcgkavQx+Xgj42JaSsXJeRwcili2hwzejKKjaRiKwB/Rm9avkFqaq4EBAkUlS6mDMi78egTUvFULtWpfNEVrWzREaf3lz+/VR+6tOfqe068nm9hsy54WYqcnNx2S0sm/YOv0y4g6JDO9i9+jd+eL4b+xfeRsfOHg6/OZaF1wzHVVYdiNJnZaGr4Xd9CecelzLwlxAGZ2kpXpsdkViMKiEeQXbyXiKf04m9oACf04VUo0adnIwgkaDTB3nzk+aUFJhxOqXM+a2MHZtKScvSMfSa+shk0YlawOHEszN6f45rzUZUfbsjEp/bOFSmQs5bdVOZVWJhQ4WDEo+PMUkm1IIYvURAegrns3qKWFH4FbNyXsXmK0UrjePytCdpGz/6tHxt/61Qlitprx3NyoqpIdtjFZm0948A9+nZ0+gMcoZcXZ8e/bMozLUhk0uITVBiMCmRSKq/L40xni5XjmXGxHtC9l80+WV63/8ynevdjCJPHRJVPhGW/ftJ694NgJhYJf2G12b2T9W/0ymf5/DIpCkc/uxtdk79loDXizY9nW5vvUHaGfoNS+JMSOJMKLu0C9kuVUiITatLSc7uiPtlNO54Rue7WCDIZDS55SakKhUrX3wZV2kpEqWSJrfdQptHH0GTGEfPy7PZtaVyIe90+Bj/7E56D7qDwQ8/iFrtxxhvQmOIRyq/uBXfLzYIgpjuAzIB+PmrHTgdPkRiaNMpgQFD1Pwx8e7KecrIQZHMbt3wvvtF1ONL9++hedtE6jYy0bVvZlWgbY91Gd/vD7VmLHcf4c3NPXmu1WbilZGV6JNUDUlS1iffuROAeCGbXVu3hs0LeL1Yd+wjuXZz8vZuDBtvN+hWNEcrNUQaNfLWzXHO/yviOZXdOyBRKmn9yFh2fvMtAV9ogEEkEiHE6FG3b4OySzuC/gBilRKxujqoKElJJHbCC3j3HMC9eTuSpHjkzRsjUqngPPSrnwm8bid2czHFR3YT8PuIS6+PWh9bRSzEYhGdeqXz19xDxCdpyDtcwbwZ+0OOsWd7Ga88/BfPTeyOMTb8XjTKUxid8TGDDO8REHtRaPyo5Tpy9rjZtj48kxwMwufvbuSZd7phMJ5gH5uRwcili5l/1z0cWbQYgNjGjbnsw/cx1ons8BL0Bwg4HIgEAbHq/DwrWndJ5rtPtpJjlZKRmY7/YLjGge6GkecocBMhcyKVItaowgQ01VodKXVbsWvVzIhHymraBYms8m9iy8/nj1Gjyf1radX4mtfHs/WzLxi9chn67DOzHzwTGGrVotfkD+j0yksQDCI3Gs7KDvDfCEEqJb5ZMzTJySS2aYPP5USQyclZvIQZQ67AVV7d8rfv1xkgEtF14njWzAwXvis+vJPfJ4+l9+NPhHz/xrp1GfrbL2iSz6xi6BLODJcI/H8MXocDn9OJVKNBIg+N8nqdTko2b2HRg2PJX7ESqUZD09tvpeUD91eVDEeCLS+PVS++wtbPv8DvdiPX62nz2CPUHTmCP398mT1r5xAMBFDpTPQf/ig33tsTrdFETISX+DEEg0Hw+TE+djcEAvgO5+JYsgq8XvxFJQRdboTE+HNO3gEkYhHJchljkkw4AwFkInGNPe0nwuWzMTPnVeblvlO1rcJbzHf7H8TsLWBQ+rOn3N/9b4VOkUCjpS1o3qk/K4PTcAUraCrqTaqlHnvfnUrGB+GZs5NBqZKiVElJSI4era6wuEHVjFYD7mHDnA8J+CsX3Eq1nnhDPVJMLalw5iDTavFURBZOMtarV/X/Gp2M4WMa0LpTMr9/v4sKi4eWHZKIrZNJnQ8m0eG5Zwh4vEg1mhr9YM8Uh4Kb6TDmPn575d6wLGh6o47o4//5JW2quDia33MXda4YitfhQFAoUCcmVj2/mraOJ6O2nkN7K4Ww3C4/v/+Yw7qVGp55uxvG+HN/r9ktpTgrSnHZrciVGpQ6ExpDZUbP47Th83qQq7TnReTpQkJnUHD5VXXp0DONCrMNe/lhcrb8wW8TvqwSSCwu3kt8y5YUrT9BRTxIVAV2AKVcxN1PtkWtkVVVzlR4ivnlYGRFZE/AwabSGfROfTDiuFGezANNZjP94FOsKf4esz8ffVZWldr48Vj5xPOMWLmAJb9MIODz0qDlAGRyNSK5lNSm7ZBIK39bYoUc7dVDcS1dTdAdKtAmJCcgb1yph2GoVYvhf85m7k23YjlQ6YJgqFOHfl98ij4r6yiRj07KBL0Ov0GHv8yMZ/MOLB9MQazVYHjgVuStmiKOEiS5EHA7Kti58g9mTn60qtpHJBbTdeSjtOo7BqW28nOZ4lQ88UYXzOVuXrh/UcRjFeTaKMy3hxF4S7kLq8XNolkHWTE/B78/SMeeqfQfoePQXnPUazty0IrL7oMT/rQikQhTg/oM/vlHXKWlBPwBFEYDqvjIwXNfYTHOhctwLluDWKVEM/xypHWyERt0p22VWBNiYpWMvLkRkz/azbPP3I5q1i94V62FQBCxTov2+qtQdmx91usasV6LqndXKr7+qfLfBj1cdw0VugRKStwk1I5DsPoxmCqfTzKlhi5XjWX3mjlh+hMKtZ4GHQZV/R2KN24OIW/H4CwuZt3b79D1rTfC1pbnE1K1Gqn6n1HV9HdCFRdXVXVStnMnC+66J+K8fb/OoN1z0VXpzYWHMDSqy/Xbt2DPy0NhqrSAjWYDewnnD6JgTaaC/0FYrVb0ej0WiwXdvyiS5zabKdu1m7Xj38Jy4CDJHdvT/J670GdlVWXY81et5ruOXUJ8UwHiW7Zg6O+/okkKJyDO0lJmX38TB/4Ij9y2enwsJYYSdq7+I2T74Hsn0rjbiKgvxoDTiXvjNsyTPiNQWo60Xi10Y64kYHfiy81HmpqE32xB3qwR0oyLj6AUOffx9Jp6BAjPIgsiKS+13nnhehMvUngdDvb8NJ3FDz9K1tB+iDVKihauxp6fz1VLFkTNkpwtdm0t4dm7F9K9byKdLzPgd5ciSORU2JRIVbG06ZyK3+tl1cuvsvL5F8P2N9Spw1WL5kck4y6nF5+30h9eOI2Az5nC6inizc09SJU2oQOjWPPNh+Tv24RCradx3yup33MwGUltzvt1XAwoLXawfkU+82fsJxAM0nNAFm26pGA6h+Q9GAhgLcvH47Szfs4UNvz5Nf6jZaWZTbvQ/7bXsZmLWP7zRGzlRWQ17UqL3tdiiE9HLJxeS8jFiNK8fXx4b+ew7SqdiSE3v8OS2x6ibMeOqu39Jn9I7NaDeHdFrqKKn/wG0szQkssy9xGeXF0LXzCyCnebuFHc3uBb7BUeXE4fIhHoYxQh95vbZ6fCVwRBEUXTlzP7muvDjiPIZIzZsQVBKmXLx5+w6YPJeCwWUnv2oOPrr6CrWw/N0Sxs0O/HdyQfy+ff4V61AZFMiqpvdzQjBiI5wSrQlp+Pq7QURCKUJtMpL2q9OXkU3fEo+E54Z4hExE18CVndv+99UXBgC58+HFnoavSz35PVLNTW6shBK2OvnxP1eLc/2pqel1dnaS3lLnIOWPnk7XXk54SW2BtiFDwxvguP3/onwQhqsoJExLvf9CcuMTKB8/u8VJTmc3jHSsyFh0mt34bY1LohNoG+/EKKHxhHwByqhC/r3h5Llzr4JEGS67RAZzo32UV7hYfCPBsLZx4gK0NFy8YaFEIQiU6NEGNEdIbvjnKvD1cggCASoRMEJKVllDz6IkGHk8Ajj/LWpL3kHqoOSqdl6Xj01c7EJ1X+7bweF/l7NzHzw0coza3UiUmp34YBt79BXFo9RCIRAb+fP0Zdw55pP0W8BmVsLNduXIs2JXrC5xL+PhR5vGy3OUnetoXfu3aPOu/Kv+bz5YTwnvljuGPSMkzJ/+017PnEqfLQSxn4/wC8Dgc7v/2e+cdF3IrWr2fLR59w5cJ5JHfsgLOsjEUPjg0j75VzN1C+c1dEAu8oLIpI3gE2vfsBfWZ8FUbgF37zKplNu6CNScTv8+KsKEckFqPWVy6GvAdyKHvuLQCk9WqhvWoQZS+9Q9BZbc0lrVcLRftWp//HuACo8BZHJO8A/qAXs7OIgDkeQ4wCufy/eQtKVSpqDxtCQquWbPtiCpYDB2lx3z2k9eiOLiP9vJzT5w0w+6fKhcmiOQUsmlOAIIgIBIIEg5CeXUS9xnHoDHKa3Xk73ooKNr73Af6jlj5JHTsw4KspaJKT8Hs82HJzyV26DOvhHFI6dcRYr27Ee+R8wR/0UOo+TJ5jO3vly+l+8+20kN6GJ2hnhW0q+8q2c0fij+fFNupigylORe/BtWjfLZUgoNXJzmnmLBgIkL9/M+vnfoVUJmftrM9Dxg9u/ouf37yVZpddzb71CwAoPLCVdXO+4IZXfiM+o+E5u5a/C2p9LPXaX86ulaHPc4e1lD07FzJ87kzsh49g3rkLbWIiKkGKskNHSp94FbyhOg2qvt0RRygTlorkJCjrkusIL30HaGkYyYHd5Xz1wSa2bypGrZHRb1htLhucXVXRJZeokUsqCaKqj4a2TzzGmjfeJOivfCYrjEYG/fQDUrmCGcOvpGBVteDh4bl/cmThIoYu/wtRi+aoBQGRICDNSCXm0bsI2J2ACLFei1gWXl2hSUo67WdA0OfH/se8cPIOEAxinfozMY/efd7KumuCz+th9e+fRB1f+tM7JNVuFqJKLlcIyOQCnihtUInJajxOO36fF7lKS0mhg307y8LIO4C5zMXKhTm0aJ/I+uUFYeMde6ahM0TO9vp9Po7sWsO3L47G762unjAmZjJ63A8Y4tMIuN1Yv/k5jLwDeBatRN+1BV9MugmNMYHR475HH3f2CQO1VkZ2vRgy6xgJ+IMh4n9nAqc/wC6Hi0/zSshzexGADnoN1yXFEDv+WSz5FsZP2hdC3gFyDliZ+MJKHn21MzqDHI8gRVm7JcNen4PEYUXkcaDQGFBpj7tPRaIaWyrFUuk5fe5ewrnDEZeHZ/blYvUHePEkyUlljAmxRBpRXyelXuvQ38Ql/G34b7KH/xjsBQUsvD+87NDv8TDnxlu4avF8/G4P+Ssi20wA7P9jJmk9uodtP1YuGAk+pxORL7zAo6IsH6/LgbnwMOvmTGHnyj+QypW0GXAzDZr3puLz76rmakcOoXz8ByHkHcC7ax8VX01Df8+NiC9gudapQCauOetXUQrP3TmHywZmMey6BhhN/87e3IDfR0V5IRWlBfi8LvSxqaj1sciUlRF/mUaDqWEDur7xGsFA4Ly0QxwPnz9AhTU0s3e8TZa9woPfVxnAUick0PGlF2h29124y8uQqtUo4+JQmkz4vV5yly5j+oBB+I8rq41p2JArZv523gIQJ0ImVpGubs4e61LK3Dn8XPh0yPjoWpP+E+T9eESzljpbWEvzmPr8VVx+19vMmHhfxDmFB7ehMyUjSOVVhMHrcjD7kye58tEvUGoN5+XaLhQUaj19b34JuVLD1iU/EfD7EEukNOt5NR2H34fC4sH6+mT0SjnUrUX5LdcwOyiix7svIv7pdwJbdiLW69CMHIy8SX0EbXiri1YWx7Csl3lv25CwsThFNnpzR566e37VfWuzepg2ZTub1xYy9sUOGE54lqpiY2n75OM0vvkmrAcPIlGp0KSkoElOImfJXyHk/RgCXi+rH3uCHt9NRR1XnWEXq1SIVee+HSPoduPdsz/quO9ADkGXG/4GAu/3ujEXhivsH4O1JBefxw3HJcANMQp6D8nmjx/2hM2PiVOi0/uY9uYtuCrKaT/0XvJL61dZCUbCioVHuPuptmxYWRCShU9K1TDy5sbIFZGXsRVlBXz/ynUh5B2gvOAgcz55ig5XvU6cUsC5aEXUc7NuJyn1WnNg02KWT59E7xteQCI7N88YsVh0UheeU8F+p5sXD1QLz/mBpRYb+5xuns9OxmGTcGD3uoj77tlehsXswqwQMSWvhE02J4IIuhg0XJWQiuqEIJVYLKbRLTezc+q3EY/X8IbrUUZpU7iEvw9Wn5+JOUVY/ZU30G6lmuSuXchbEq7tkdm/L6r4BAbd/Q6/TrwnpA1KoTFw+R1vVrXNXMLfi0sE/j+Ash07CXgjK1WX796Nq6wMmVZXo11TtN4xZWzN1j5ieXi0ViJTEiTI548PwGGtVpGd+eEjxD78NcLeg5X7xhgJmC1h5P0YHAuWor3mCsSJF9cLQyuNI15RiyLXvrCxJFUDCvcJ+LwB5kzfh9vl44b7WqBU/XN7ZT1+J2ZPHrsti6nwFFHX0I04aTZle/fy85u34rRViqSIBQmdht9P6/43otKZQo5xvsk7gEIhYcCIOlw2MPtoOWCApfMOs2555eKnebtE1Nrq36tUqcSQnQWEivLYcnP5ZeCQEPIOULZ9O0see4I+n36E7AL05KmlMVyR9Rqvbwova1YKepqaLg/b7nL6cNi8CBIReuM/12bnQqNg/2bcjsoMls9Tg8BhSS4qrZGKsmpCkrN9FS67+R9P4AG0MYn0veVlOo94EI/LhkyhQWOMR0CM+aNPwOUCjQrrLdfyrMWBLwgzRCK6Dx9KvREBlFIpDZNiESTRlx61dZ24Kvstph98Cm+g8tlvlKVwd/YCPhq3NaI3/e5tpeQfsYUReKgMFMo0Ggy1Qks+9834Peo1HFmwEKe1AuJqfr+dC4hkMiQZqXi27oo4LklNQhThPXohIJWrSGvQjpwdqyKOJ9dpiewEhWypTGDQqHpYylwsm59Ttf5PStVwz5ON+GX8UCxFlZ7xu1fPQpFeD1kUezkAmVwgLkHFhC/7sWZpLqXFTpq3TSSjloGYuOhBjZIju/C6HBHH9q6fR/3uRdjcCvRE7yIVBYNVGeXNC3+g0xX3oYu9eMrDLT4/n+eXRBzL93g55HKjcUR3bgCwO3w8v+cInqNflC8IC8ttbLG5eKlWMnHHkfhgMIhbk0rWsOEcmB5aRm+oXZvMkWMuGuHFS6hGhc/PPmf1euUnj4jHP/4Ybr01hMSn97qM3pM/RBUbS902fbn17QVsnDcVc+FBspp2pU6bvuekCuUSzg0uEfj/Ak6ichAMBlElxNPktltYP+HdiHNqDRkUcbsmNQVNaiq2I0fCxtJ7X0bOgfDIb/NeV3Nwy7IQ8n4MJfn7SI4z4Tt0BLFGhb88vLStCj4/QW/NL6e/AwZ5Enc3+oXxm3tg81a/XHXSeK6M+ZIPX6te3C+ec4hh1zb8xxJ4j9/B1vI5TN5xVZXFkwgRj6cv5oeXrgkpwQr4ffz1w1vEpdenQYeBF/xay0udbF1XxJ8z9uNx+1GqJPQanE3Ljsl89cFGBo2qeSF5DMUbN0VVqd8z7Se6vPoSsqwLo8Sbqm7CXQ1/4pu9d2PxVP6u0tTNuLnel5jkmVXzfF4/+bk2fp6ynW0bitHq5QwaVY/m7RIxxPx7iLzL6cNS7uLAnnKCAciqY0QfIz/r+6u8oDILKRYLiAVJlfjhidAY43HZw59ZwZM9hP9BkCnUyE7oOfaXmfHsOCoWd+UgPnd5OVZ85QkGmWt3MbfyX4w3GdHWQOA1UhM9ku6iZewVWD1FSMQytNI4fOUGdm0Nz5gfw/qV+TRodurWYIoaROWkajX+C1QKLJJK0Azui2PWgoiWqdrRw0JU6y8kxIJAs56jWPPHx1WihccgEgt0uuI+ZIrwazOalNz0QEtG3NCI8jIXarUUudzDjy8NwFqSWzUvZ8dK2jZ10a5bKhUWN1l1jLicPtavzMdx1DKu77BaVaJ3g6+uf8rXHml9UYVgkIDfy8o1Ti5v1wbf0shZ+GCr+uROrmzn83lcXGySUZ5AgAPOyHoRAJttTvrq5IhEkfUkRWIIqgQ81vDBEq+PzTYnl8VUPzsddi9ff3GILiMepseIazjwzSf4nQ5SBo5A0ag9n35yhIdeTI/a1nAJfw+8J3z5jkCA1zxihn/0Cf2cNlR2G9qYGFTxlVWGADKlmvj0+vS+8Xn8Pi8S6d8TRLyE6LhE4P8DiGlYH7FEEmZxA5VRU6XJhCCT0eqhB8hZuIjijZtC5vT55CM0UURJtCkpXDHrd6b16oujsLBqu6lRI7q/+zZTJ1wbMj+jSWfaDbydz58Izw4CrFkylSuHPoHv3S/wF5UgTY8e7RbHGBH9jeq8NSFF3ZhnWqwjx76RHOtWtJ46KOx1+OyZfFIydIy8pTFyhYC5zI3Hc3qWaRcTzJ48Ptx+ZUjPf219Zw6sWhTVn/yv798kvWH7Ks2DCwF7hYevPtjEsnk5VducDh+/fbebvlfU4rVPehOfcGpZc/txv/MTEfT7q3rmLwSUEh0tTMPI0rbF7i1DEEvRSGLRykKJzOEDVp65awE+b2UJnaXczf9eW0OHHqnc9GBLdDWUnvs8bmzmIvxeN1K5Cm1M4gWpmDhd2G0e/ppziCnvbyJwNEsrEsNVNzai99BaaHVnvqhMzG4CwN7182nQYRDblk4Pm6OPT8PjtIURndT6bVGq/90lhyKZFCHWhD+/CF+dbPa4vKgFMQOMUEdpwxdwIBYbWGZVsLHCQbaq5u9CKiiIFTKJVWRWbSsRO2rsrdbqTm+BWWfEcFaOez7y2M03obyAPuFCYjwx48ZS/sYHBO2VWWORXI7+zuuRZvy93sqG+DSue2E6v73/AMWHKy36DAkZXH7nWxiTogcq1VoZaq2MpDQtAH/9+E4IeYfKEnyFtJDE9FZUdE1l6/oi1Foptz3cioN7zWzfUEzLjqcmHhfw+7Hn5eEsK0MslRKf3iDqXI0hHoddYNH8w/R9+XLEm7cSsIb2iEs6tCCnaGdV5U1G405n5cd9PiAG1IIYuz+Cwh8QK5Wg1yno0COV5QvCkyxdemewnujvqxUWO10MGmRHn/disQiZXOCTyQcwxipo1eUxpBL4ZZuNQ7/upk7DmHPSFnAJ5xYaQUAnCFj91c9ORyDAV24QidVMbF0fU5QqH5FIdIm8X6S4ROD/A1AlJND1zTdY9ECot65YKqXPZx9XKeVqU1MZNvM3ynbsZP/vM1EnJlBryCA0KSnINNFfXLGNG3HNmpWY9+7FcuAgMfXqogyI8Hz1C1ePmYDFZ8Hhd5CQ3RiNKYmAzxvJoRSA4pxdWE0S9FcMwD59Fn6zBWndbLy7w3sEdTdffY78Us8PTIp0TIp0klw9GffwQizle7nz8Tbk7Lcw5b2N2Cu8JCSr0RlkxMQq0ZzmAvRiwNayOWGCfQZpEtYjOVH2gPLCw/i9F47kAljN7hDyfjzm/3aAy6+siyA5NVKa2KZ11DFNaioyrfaMrvFMIRKJMMpTMcojl7ZVWN189s76KvJ+PFYsPMLQa+pHJfAVZQUsn/4eG+dNxedxotKZ6DrqERp0GBjWBvF3I/9wBZ9P3BiyLRiA7z/dRt1GJhq3SjjjY5tSamNIyGDLoh8Z9tCHOG3l7N+4qGrcmJTFoHve5ZcJd4bsJ1Wo6Hfbq/+K8vmaINao0V49hNItOxAHgxgEMfenepl98A7mWRYCIBHJ6ZJ8P41V9xLm+3UK0BvkdO+fydxfwluTAFp1Oj2VcF1qCl3efpO/Hno4ZHtMo0bUu/9e1BcwOCxWyFG0bkb8h68TKDNDIIDYZESIMSCS/r3VWWJBQlLtZlzz3I84K8oJBgMoNQY0xtO8n4LhgRepQoVcncCrj/5FeWl1q9yx59L949rVaDd7DG6rlYOzZrPgnvtxllRWvfX4+D1qtejJvg0Lwua3Gfo4M2eU4Xb5Gf/Obh576kk0m1bjWrGuUiywZ2uKBAuzP3sMAEEio9cNz6HQGE7vM59nGKQSBpj0/FhUHjYmBlrpVKjkUsbc3RylWsaiWQfw+yqF83pensXga+vzclnkEnwAnSBGOK4SRamSMuDKOmxZV0R5iYt5s0IDMgNG1PlHrmP+7TBKBW5MNvFuTlHYWD+TDr3kn++S8l/EJRu5E/BvtZFzlZdTun0Hq197nYqDh0ls345WD92PPjv7vHh2BlxuApYKCPgRqVUIumpS4/d5Wfzt66z45f2I+455+VdS0hrjN1vw5eQhSU7E9sMMHAuXgc+POMaI7uarUbRrEVEI6WKDpdzFK4/8RfO2ieTlVLB6SW7YnNsfbUX3fllVfsj/FPx84Elm5rwasi1L24ZuOdexeMorEfdJqtWMUU9PRaWLuRCXCMD2jcU8H8WbGODVjy4ju96pXY+jqIg/Rl1DzsLw413+/bfUu2rEGV7l+UFhno37rp4VdXz07U0YMro+/oAXp78CqUiOXKLGYS1jxsR7Iy6Ae9/4PK3634QgXBwxYI/bx/uvrGblovB7C6BJ63jGvtARpfrMyVB54SFmfvgIR3auocOQu0iu25KAz4dCrqV03RYCTg/yWglsXvUTDmspKdktaNn3euKyGoTZyFV4ivEGXIhFAnpZ0r9CuTlgrcD2y2ycDidHRnbml30DyXfsCJs3KP15OmsfwGQ6/fdrSaGDVx9dwpGDodnSWx5qSede6af9/TotFiyHc9gx9VuchUUkDx1CbLOmaJOT0Usvjt/2Pw228kKKc3aza/UslGoDDToNRhebjLnwMJ8+3DtkbtPLbmJPfl9WLYlc1fTmlD6kZepDtgUDgUpRP4mkygmgcMNGdnz5NT6Xi/0zfsOWl4cgk9H/52/ILd/Oujlf4HZUYEzMpPWgR9m1N4Hff6x+VvS7ojbX3dEYkdOJx+tix4Y5LP3xbZw2C1lNu9B99OOYkmshXISZyDKvj4k5hWyxVQdABGBsRgLNNSrkR23p3C4f5jIXLqcPhVKCwaQAt4N5JRY+s0UOqL+SmUA9Xegay1zm4otJG1hxQka/RYdEbn+kNXKpA6/LgUgQUOvjECT/zPbAfxvsfj97HW6+yi/lkMuDSSrhCpOWVkoZpn/AOvq/hFPloZcI/An4txL4Y/DYbPicTmRaLRLF31d+bik+wjfPXUl5wcGQ7Y27DqfXDc+FlVcH3G4C5RaCXh8ipQLBZPxHLXoL82wU5Np45eFw1U8ArV7Ga5/0JvYc+lZfCOwon89bW3qFbKul7cx1uk/5dtzleJzh1kBXP/s92Sd4B59vHNxr5rGb/4w6/vaXfUnJOPX73ZaXx7q3JrB58sd47XYMtWrR9c3XSe3eDYXBcA6u+NyhKN/OfaNnRvRRBrhvXBvqdPCxJP8TdprnY5Cl0DftYfS+RD6+rUvEfeQqHbe+Pf+iEbSxV3h49dG/2LO9LOJ4SoaWce92P2vhPpfNjN1ait/jRq7WIQnK+LnPAIo3VbYdqRISqHv1CGQxBko3bMFrqWDQ9GnIj75LHD4rhyrW8uOBh8mxbUQvS2JA2hO0irsSvezMKwQuFgQcDgJWG/sVe3ljc6eIcxSCljHqxaTH1SEx5fQXjmUlTg7tNbNmaS6GGCUde6Zhileelc6BJxDAfbT/XHtCNspW4cFa7sZh96LWStEbFKg054eUOKylOKyleN0ulBoDamM8UtnF1Sbmcfsxl7lw2L3IFQJ6owLV0cBJRVkBP75+E/l7N4Ts0+Pap2jSbQSLvxvPpvlTq7YPfOAHXh9XGFGYEODq2xoz9JrqUnhfQTHOv1bhWrUesVGPZlh/Ajo1m76YwsHZc5FpNdS96krcZjNLHn4UgMunfU9il7Y4HS62rDcz59cSDu+v1qmQSMWM/6wPyenVSYZgIIDNXEQwGECu1CJXXdiqqmPwetw4K8qwlRcikcpQ6WLRGMNFey1eHyVeH7sdbjJEAga/CLVcgv4krhzle/ZyaM9efsuuz8YTWvkGycX0EvlIrVsn/HzlLgpybSyadZBAIEj3fpkkpCixFW9n7R+fktnqSoJCIl6/jNSsOIyxatSaiy/48V+D22olPycXW3k5jiNH2PPWBFylpVz+3TfEt2yBuAZtkku4cLjkA38JEXFMkffvhj4ulWuf/4kDm5ewZfE0ZEoNbQbcRHxGw4i90WK5/KJTmz8dJCRrOLA7vMztGCosHuwWFyaTHJHwzylnSlI1IEXVOMS3uavmMb54P5cr7v2GxV8/RFleZcmrQq2n/fCnUJtOXYjoXEFvlJOYoqEgNzygUKdhzGmL7miSk+n0yku0uP9eAl4vUpUK9QX0gD8daPUyWndKYc1f4dlpo0lBcssynl/XFXfgmAPFWjaW/crg1Odo2n8Um2d9F7af22Gt6g29GKBQSqjfNDYqga/byHROhCIVGkNIGa2juJhgoDoy4igsZOM71ZVFqd2qA1XBYJAd5X/yvx3VFRpmTx5T993LQdtaRmZPQC29eFuCTgXHrNZKCqJXfLj8FSB18cvXO7jpgRbI5Ke3DImJVRITq6RF+1O/32zeMiq8heQ7dqKSGIlTZGOQJyOIKs8tE4uRReigKSly8PGb66pszkQiaNs1hRvubRFVAd3r9WMpc+P3B5DLhYjK+JFQln+AXybcSf6+ymCQIJXTbtDttB146wXVC6kJ5jIXf/ywm1k/7cHrCSASQcuOSdx0f0tiYuVsmDc1jLwDLPz6ZWq37EmPa56gfrv+rPj1A5wV5WhikvD7o1vIed3V95b3SD4lD44L6VV3LVmJfEhf3EXF5C1bBsDB2XOoM2I4HV98nmVPPcPOL7+m9uBBKLSQkKbGYa+2XEtIVnPXE22JSwwNnIvEYrQxiWf8dzpbOP0ByjweNpebsbtc1JFqKN00n13zvmb42I+IS68fksDQSyUo/CCUu5j2xSb27SrHFK9k+JiG1G4QE9VeM+DxsHjYcHpP/h8D2ndgkyBDFgzSzO+m8OtvcTRrAhEIvN6oQG9UUK9x9e/y4NZlzPviBVoPm8D7b+zFaq6uvuncO53r7mr2rxJMPRsEPF4CFiuIRIjVSsTKs7OItLgLCOBDIlaglUZ/VhSuXcfPl/UJ2/5Dj16M2bIBQ3Z2hL0u4WLFJQJ/CX8bdLHJNOs5inrtL8eDg4AQQCb5eyLdFwIn86gWl5ViXTEfdb8eSOIvjgXbyWCQJ3N/45nMOPw8Kwu/whf0oPAms2XdPgrznAy+aiIJiX6CAT8er4rZM0pB7SLhHCdu/RYrAbOVgMWKWKdFbNAhGKpLL40mJY++2okXH1pMeUl1qWFCipp7n2l3Rv7hErkcXfqF8Xw/GyhVUq69syl7tpViLgu1ZLxjXC2+3X/TceS9Gr8deZ6xPWezefb34RLGIhGSiygzKEjEXDYom7m/7MPt8p8wJmLgyFCHAa+3MovodfuRKSQYTQoE4fSF+aRaDZd9/RG23CNY9+xny8SPMO+r7tFuetutVdl3syePb/dF9pBfXjiF/mmP/eMJ/DGYFBlRxyQiOfjkrFx0iCtvaoQp7vwuQyyeAn7YN5ZVxdWZX5XEwL2NfiNL2x6JOIqPuKVS6HHruuq+0WAQVi3OBUTc8WjrsEy8ucxF7iErNqsHi9nN5tUF9Lg8iwZNY1HVkIG0luYz9fmrsBRXlyX7vW6W/zwRpcZA24G3hbVhXGh4PX5m/ribGd9W290Fg7BuWT6W8uWMfa4B62Z9FnX/LYt/4rIxz1C7VS9SG7TF7/USQEWT1rlsWRvemwuVwQGAgN2J5aOvwoTmANy/zqHRE3ex/oP/ETwq0rVn2k/Uu2oEMp2O2KZNEAsCMgGatErgxQ96YrO6ARFavQzjKQZYLhTsfj9Lym18mldynHeFiI4N+9ElNpWvnr2CW978M6z6adfWUl55ZElVpVVZsZPXHlvKiOsbMnBU3YgBTLnBgDI2luU33oxMpyOhVUv8Hi8FjRpQa3Cl89CRv5aiS09HnZIc1f7Rbi5m7idP0WHk67z1wi6cJ1jXLf3zMCnpWoZcU/+MnrP/dNhtlVU8LqcPpUKMpiQHid+Hr6AYkVyKrH4dhNiYSv2F00CFp5gt5TP5/dCLlLoPkapuyvCs18nUtEYlNYTMdZaWsvSJpyMex+dwsPen6bR+ZOyZfsRL+BtwicBfwgWHv9yCL68A57I1IJdCh/qsCvzGnPJJtI4bSe+UB4lTXhgbrguJhGQ1ao0Uuy1cnb1BUxPS7VuwffczzrmLiX37uQtG4n2+AOUlTooLHXg9fhKSNeiNp269FaNIY3StSQxKfwZ/0Ie3xATsoyjfzifvhgtODR4dXR34jK6/uJTyNz7As3l71TZpvVrEPHU/koRqJemUDB0v/+8yCnIr2xlS0rXEJ2tOSSTpn47EFA0vfdiTjSsLWLssD6NJQfsuKjSmcvbvj+zxHCRIjm8LxsRMyvMPhIzVbnEZKv3FJWIXn6jmuUk9+PD1NRzaW1kim5qp4/ZHWpGQXO0wUF7qZNa0Pcyevhe3049aI2Xw6Hr0GJB1WiX21pI8lv70DpsX/oDf6yYuvT6dP3yFA1Oms+vr70ho3YqU4zLwDp8Zsycv6vFybJtIUp3be+PvQryyNgZZcsTP2854A6t+t18QvQ9/0M/Sgk9DyDtUfhcTtvTh+Vbbor5rrGZ3CHk/HquXHOHq2xqHEPiSQjszp+1hwe8HcDp8xCWqGDiyLru3luL3B2jbJXrUsixvXwh5Px7Lfp5Ig06D0f+N/uPBYJDSwgpm/bQ34vje7eX4PH7cEVqmjsFuqRZLU6iqy0Kvu6sZT9+5IMxdoF23FGKPZsYDtgrcqzdGv769h4ht0oTijdVzDs6eS2q3rtS/5uqQuccqOC5WFHt8fJIXLiy33OGjQXJDdKYkjuxaG0Lgy0ucfDR+bcQ2qZ++2k6XvhkR3+fq5CR6TJzAbyNG4rFayVm4iK7jX8d68BC/Dh5WVV0k0+kYNO17Urp2iaiZ5HbZ8XqcFORLwsj7Mfz+/W669cvE9A9rEzxblBY7+Hj8OjYcreK5clQWPfR5WL76Ho5Vb4nF6G4Zjbpvd8Sak7vhOEtKcHjM/Gl7j3kF1dbPh2zreXtLb26p9xVt469GLKoO+vmcLsp2hGuSHEPe8hUE/P6/PVB4CaeOSwT+EqrgL7cQdDhBECPW6xCfBxVef1k55W9Nxr32OKu6qdBsRA8Cne7kp7znWVv8A0+2WBliIfRvgNGk5NHXOvPy2CUhixVTvJJbxmQQfP1bAPzFpTjm/YV25BBE5zla7XH72bq+iIkvrKx68YoFEVdc14B+V9Q+5cy0TFBiEiqzbnajh2ZtE9i0OlyYSKmShPQani38FTbM73wcQt4BvLv2Ufbyu5hefBRBX71YNMWrMMWraNTin9uOcaaIS1DTe0gtuvfLxFx8kM8f7U3/ZybUuI9UqQ5ZbAPEptal762vhG3/uyFIxGTXNfL0m92wVbgJBkGjlaE/rmzTYfMydfJmlsw5XLXNbvPy7Udbsdu8jLihIfJTKOmuKCvku1eupfhQ9YKo+PBOpn94DyPu+4jagwaT0qkTmpRqZXSJuOYeUKVEX+P4PwlGeSoPNfmTidsGUuKqDv400Q2hoftu3p27i75X1D7vftEWdz5zj7wdccwTcLLHsiQqga+wRBb2iktU0eOqOvi1Esq9PoxSSaXTw7sbWbesOmBRXODg83c3cs0dTVg+P4faDUxRiWNxzq6I26FSd8HncUUdP9+w5eez9+dfEOp1iGrhB1BhE8hq1pW9ayNrjTToMDDi9pQMHa9/0ptfp+5k89pCNFoZg0bVpWnrBPSGo/dukMhG5scQgXgE/H46jHu2xioph89MhbcEf8CLUqLHKD89J4NzDX8wyJxSa9W/E2QShiv8xHsqCHjdyMQxZFz7DIXbltGo89CqebYKD8UFjojHDAYgZ78lot6ESCQivXcvRsyfy5KHH8Pv8SAoFGx8/4OQeR6rlemXD+a6zetRJMWhPiF4K4gFdKYUigqiu8vYbd6ITij/NviDQcq8PvY73RS6fcS5g7QdlMnenZXtXZ0aSHC9+m3oToEA1o++RtagDvKGdWs8vjUnh9ljbqTZO2OZb5kYcc53+x+krr4bMYpqC0pBIUdfq1ZIkOt4xLVofom8/8NwicBfAgGXG+/ufZjf+xzfoSMgFqPo0g79TaOQnOO+c9eKdaHk/Sj80xbSvM2t/CFosXoLWVrwKYPSxyFEKW/8J0KQiKndIIa3pvRhx8Zi8vaVUTtTQYrag/jdt/FXVGcvnAuWou7fE8F4fhf1JYV2xj+1rMo3GyDgDzLti+1k1jbQpsvpZ33UGhk3P9iSFx9cHLKokMrEPPpqp3NashiwWCP+nqCSxAfKLSEE/hJAKhewleXhdTsp37+fjKRWHLKtizg3OViPhGH34LJZqCjNIyGrMQlZjTHE/73e1DVBZ5SjM0Ymhhazi7/mHo44NvPHPfQalE1C8sk1Qkpz94SQ9yoEg/w1411GP/NdWIWCRhJLXX03dlsWh+0mE6tIVjU86Xn/SUhWN+ThhksosORgthejFaewc6WXiV/sJiZOyYAr6yCVnt8Foz/oxe6LrIsAUODcHXVMowvPWPa6qjb1B2Uww23nu0N5mKQSRsQbqGMlhLwfjxnf7uL6e5rjipKZBIhJit57KlNqEKTnN9ARDfaCAmZdM4achYvoM+svxIIo5F0RApGSHtc8wYGNi/H7QolcbFo9ErObRNxNIhGTnK7lpgdbYK/wIpGIwwI7IrUKWZP6eLbsjHzqOlmUbN0asq3h9dcR26xJ1LLvIudevtlzD9vNcwkSxChPZXStSdQz9EQl+XveGYFgkFJv5e8kTSblNomFReNvY+VRsV+xREqby2+lYcdBIfudzHtdGkng4SjkOh3pPXtwxZw/8FTY+GXgkMjX5vWy7asvsSa7qNXyMtIbtUd91EpUpY8lIbsxysTo73ZTnBKp/N9NEP3BIPscbl44kIczUH2fpMRLufvNzuyYcwBh3lyihTFsP8xA+vg9uDx2zIWH2bJ4GoGAn8ZdhhGTXAtJUML8O+6meOMmysR5BIl8L9q8Jdh9ZcRQ/Z5WxcbS8flx/DpkWNh8QSaj/tUjz+qzX8KFx3+vGeUSwuA9cJiSR1+qJO8AgQCuxSsoeexlfMWl5+w8/nIztunRxY2kc7fS1DAAgDXF32PznbtzXyyQSMTEJ2no0iOZ/tLtpH77AcHX3sBfXHbiRDjJS/lcYPHsQ1EXZNOmbMdqdp/RcROSNTz/Xg8ef70zw66rzx2Pt+atKX2p2zgWySn6rZ8Kgo6aM1MBW3hv9yVUikiKxGI2/vo1Q03PIRWHV9v0ir2XvQvn8PObt7LkuzfYufIPlk9/D/jnuD+ciPISV9REns8bwF4RSjwc1lJKjuyh8OB2rCV5BI722B7vAX8iCg9sxeMOz4appUbG1PkIvSxUHEsQSbir4U/oZRenCOLZIFaTSpqqBQZbe2Z/4mDHaic33Nuc5yb2IC4heqloMBjE6fDi9UTP+J4KZIKSOEV0cpytblv1nZ4InUFO3UbV1pJ1GptIG5jOO+Zy9js9BIBir4/vi8rZs9cc9RwVFg8yuYBEGv25F5dWF5UucktKk3434dFEHgsGgwTc7qre73ONsp27quwy8/6YRpsOkQP6cYkqjLFKTMm1uPH1WWQ16wYiETKFmraDbufqp6eeVBROLpcQE6uMWJUhaDUY7roBkTy8ikXapS0Hly3F765+V2VdPgBTwwZRyXuZO4fxm3uwzTynigSVu4/w/vZhHKpYWzXPUVxMwZq1LH/+Rda88SalO3bgslgiHvNcQCoW00pXWWJ+vcrL7BdHhjj1BHxeVv36AXl7NnC8gZRGJyM9O3KwXyoTk5x+8oCEKi4OQSbFevBg1DnWPQfwOG38/OYtrPzlA1xHhUylciXtBt6G0ejCFEXcceQtjTGaLh7dlPOBcq+Plw7mh5B3gFy3l5liF01amgiWRl/X+otK8VRYmT/lBT5/fABrZ33G+jlT+PLpofzxv7E4bOUcmDkLv9uNTFRzIkQQhQcgkzt1oMtrryLIqu8jhcnEsNl/oMuIrltyCRcn/j3pzUs4I/itNqwffxOxPM1fUIR3934kceeo1zUQqJlQWZ0oRZXl1TKxKqR/598GsUKOvGlD7D/PjDiuHtT7vGeOfb4AOQeiL0aK8+14vWe+MDTFqTDFqU5LLfp0IVarKqWho7AysU5DecEhcnatxm23ktagHbqYpBr7tx02L06HF5FIhD5G/q8U3VHpY2nV70bWzvyUdR9P5oFbfmOFfSr7navQSRPporkB1/Yils54EwCbuQibuYhGnYfh9Ssxl7n+kYrCSnXNrzyZonq85MhuZky8r0oVXKUz0evG56nVvCdqQ1y0QyBTqBGLIz+7ElV1ebL5KvZZl7PTvIAEZV1axA7FKEs9aYn9PxXHFKvrNIzB7w9WWY5FQ3GBnVWLj7B2WR46g5wBV9YlJV17RkKTelkiQ5Nf4OP914aNxcjTiLElsmzRRGq36okxMROFupoE6QwK7nu2PZNeXMWuraX0HlOPL+3hImoufxCpNvpnEokqCVa0qhAAuTGJwc9+z+zxN2EuPFy1Y/2uI9B2u5pfS23ckCxHLAri8dsQxArERVacK9biXrMRITYG9aA+SJLjEatP3kN7qtj1/Y9V/7/zk8n0/+UKKqxGtm+udlSJT1Lz+Oudq9oDEjIbcsXYybidNkQiMWqd6Yz80/1mC77cApwLl4FYjKpfD+LeewXbzzNxb9iKWKdBc9VgxNlpuD74H8Z69ZBptbR88D7SevRAnRDdlvFgxRrK3ZE1B37YP5YHm8whWOJi4V0PsP/X36rGNkx6n+7vTyCtR3eUWsNpf6ZTQUutitpKOc6Da3HaIjvXLJv2DrVa9EAilaPUxaA3KrjryTaMu3chCoWENj3TUOhkHNxaQtdeGYhE4PH4kclqXlNJlEpimzShYPXqiOOmVk3ZXrgIgJUz/keL3teiOGqxp49LRZDKeOL1bCa/tYU92yqTEkqVhKtuakTLDkn/KOvfM0Ge24vdHzm/vtHhYlSqEbJrwf7IVWCyRnWxWArYvOiHsLE9a+bSoN0gYurXp2znThRlMpSCDqffGjY3Td0MrTT8HaU0mWh+713UvWo4ttw8BLkMdWIi6uTkS+Xz/0BcIvD/cQTdbjzbTygjFARkbZtDSgIeawXnquBZpFEjb90M559LIo77OtRin/MbAHom3V2jHQaA1+3AVl6M22FFplCj0seiUP9zyqVl9Wohb90U99rNIduldbNRtGt53s8vkYhp0CyOdcvzI46n19YjV1zcjwixQYeiW3tci1aEjclaNsHsLOXjsZcTDFQHIuq07sOAO95AYwxd4Hk9fvIOV/DN5M1sXVeESiOl79DaXDYoi5i4i194J+D2ECg3E3A4ESsUiA26qKq2CpWWziMewJRcm2U/vcPPj91Ei37X0rPXFFRKI989PSpMVEssSGg76E4ev20pqZk67n+2fVQrrYsVhhgFCclqCvPCA4m16hvRH83+WYqP8OXTw3BWVFfGOKylzHj3HkY++TWJ2U0QicUhFnLH0Hbw7TUSfJMiHZMinbbxo87BJ/rn4FSeJflHKhh3z0Is5dXZ1FWLcxl6TT0GXV0fjfb0iKDbakUy38zI1q/zh+WNqqquOpouDJM9xZo7XyDumq589mg/el73DC36XFdFSADiEtU8/HInrGY3ToME8+HwZ6UjEECWoESlkeKIIFDauFU88clqFDV8fmcgwFdBA90e/ZZYtwWv04bcEM+6oIJ3yn10Nvgoc+9nZeFXHKhYwxj1K7gf/4CApTqg4JizCP2d16Pq0+201ayjQaKqfu75XC7mDR9Ar3EvM3xYJ8rK/cRnxiPzWkjJCH3vKtT6kGDI6cJfZsb83me4lq2p2mb/dQ7Kvt3R3TgK3ZgRIJEg6Cq/qw7PPUuL++5BLJGgNJ084bCjfH7UsRz7RqyeQnJnzK0i7xKFgk5fvYW3voS5fIHo8BS6Jt1Guro5evm5DVDHyaQ8kpHAulXbo86xmYsoPLSdmR88TKMuQ2k36A4ystMY//0ADrs9zK2wUREI0Kx3AoliOeOfXkbTVokMHFm3xsCr0mSi82svM61n77AxuV5PXIcWFLx7tO86GKT48E5ikqo1JDSGeDQGeOy1zljNbrzuAGqtlJhYJcI5rLy7WGGpoRImABS7Ayi794DFf4H3hJYaqRT1oD7M+frxqMdYO/tT6oy6gtXPvcKmJ9/h+k/+x8fFN+APVj931BIjt9T/Gq0s8jtIqlKhz8pCn/XvE4r+r+HiXp1fwvmHSIRYryNgrszESurVQjyiP1umTiX3l+9QJyXSRikQ27QJypiYkxysZojlcrQjh+BasoqgO7Q0W0iIxdZAw5EjW6it7khjTfgL5HjYyotY+uPbbJj3DQF/5YOwdste9Lv9tb9Vrfd0IMQYMI69E+/eA9h++xMCAdT9eiBrUGkpciHQtmsK077YjssZ3p959a1NTnvBfKEhVqsw3HYdZpEI1+IVEAiCSIS8fUu0t43m3Ue7hpB3gD1r57L1rw60G3gbInH1oiL3kJUn75hPwB+kYfM44pPU7N1ZxuZ1hTz0QoeLzm7oePjLzFT8MAP7739WLgzEIhSd26O//VokUX5Lan0srfpdT712/Qj4vAhSORpjPB63g4H3vMusDx+hLH8/AIaEdPre8hpLF7mxWT3s3FzCzGm7GXVLYyTnuY/5XOKYneALDy7GUlb9DIpLVHHfs+2rsryHtq0IIe/HY9G3r9F+8J30veUV5nzyZBWJNyZm0mfUcwh22Py/jzA1aoixbl20qf+M59HfDafDy7cfbQkh78fwyze76Nw74/SfR8Egez+dhuhLgZsen4goRoaAjOK5a5g3fgyalBTij7aELPjqReq26RNC4KGylF5nkJPrii7QNd1jZ+yrnXjjkb9CbAwTktXc/GDLGtsFAORiMTKRmPesQUToEAQdPiuAD6NEoI++kBfXd8Xlr2BI7BN4P/49hLwfg+XDL5G3aX7OCHyDa65m/dvVQpdeu51Vjz6ASCym2T13sXdaES3uufucnOt4uLfuDCHvx+CcswhVl3Yo2jQP2S5IpTVm3E9EvLJ21DGtNA5vsYUd735Rta3b9+8xJ+UzdpdVJx82lP5CA8Nl3FzvKwznmMTHyqSkZTdhbZRxjTEBp7Ucp62ctbM+Z+eKP7hmwiJm2/3MKKmuqtvjdDNbEPPA022YdPdiIMhVNzUOsdQ8EQktW9D/6y9ZeP+DuI6We5saNaLzu6+yYPrrIXOlUaxEtTo5Wt3fo9vwdyItQovHMWgFMV6Ll8++yeHOpx7F9+kX+HIqdTMkackYxt5BIEaDyxa9KtLttBPTtlIrpWDZKqQPaXjotRnsYDHFwmHqGbvTKKYPMfKL3972Es4elwj8fxyC0YDmigFYP/sWcWwMgUE9+L5ff3xOJwDFmzZxcPYcOjw/jpYP3FflaXymkCQnEDfpRSwfT60UH5NIUF7WkcDw9ix3vs89Sd+QVGpC64l+Hq/bybKf3mXdnCkh2/eun8f0t+/gysc+R63/Z/ioCzEGhLYtkDVrBIC4hhfA+UBcgprnJnZn0kuryD1UuSDUGeTc9GCLqD11pwu/z4O1NJ9DW5ZRXniI9IbtiEtvgM50bhY9gsmI8YFbCd52HYEKG8hl4PfjLCrkhvumcODAOpbO/B8um7lqn1W//o9GnYdW9WXaKjx8+f4majeI4YrrGrJlXSFHDlpJSFbTokMSZcXOi5bAB9xuKr6djn3G3OM2BnEtWUHAZiPmiXurMlUnQiQShfWmyuQqMht35Opnv8NhLcfvD+ALqPjp62LWLK1WFP/z1/30H17ngtkC+bxuXDYLYkGI2i98KkjN1PPq5F7kHrJSkGsjNVNHYqo2RCH88Lbwio5jKDq0HbFEwv4NC7nqiS8pPLCVYCBA7Vpd+fXyodgLCqrmatPSGDF/DsY6dc74ev8rsFk9rP4rN+r4uuV5pGWd3jNJrtfT5NabmT3mRo4sCBcPbPbgXezZUU3K9m9ajCklMrnTSgTqKOXscYYHGHK9XpLqJfDmF33Zva2UwlwbdRqZSM3QnVKVilIQMyLeyLoKB0HAd1xHUF9jgOn778Tlr3w+N5J2w7dhSuQDBYN4tu5EmlJzv/mpQpeZQcsHH2D9hHdO2J5JVr++rHzxZQy1omsMnAn8FbaorWUAtumzkDWuh1h55s/jZqZBTDvwKP5geOC6a9JtmJ15uMoqA3gx9etTlmVmd3l45eAO83z2WJbQJv7cC4Cl1GmBUmOMWEbfesDNbF74fdW/HbZyyrw+ZpSEl1Pb/AH+8DnoOiSb2T/uoffQWiQkRRfqlOv11Bt1FaldO+MoKcFuLaG4aA8zf3gGa0n1/SmRKYiJcq9cSNi95VR4iyhw7EItjcEkz8AgT0EsuvAZ/xiphGYaJZtszrCxkXFGks0BbnigNXKTAsP4ZwhUVFaCibUaBKOeYDBIg46DOLIrPHgFkNWwE7q4JOqNvIrdP04jZ9Z8cuYspPXDY+n7yLsoY/8Z695LODe4ROD/4xAJYlS9uuDevAPqZjLniaeqyPvxWPHcC9QfNfKsCbxIEJBmpGG4/xa8ew8QdHlwb9kO3y1lAE3xblqG+porEGIMUY9hMxex4c+vI47l7lqLzVz0jyHwx3ChiXvVeQURWXWNjHunO1arm4A/iEYrwxirPKmy7anA7/OSs2M13718LX5v5cJ3+c+VGd3R437EmHB2keKA242/qBTnwmX4cgvQDL8c+6+zccxeBEfL2bIa1iHzvs+Y+t6tOKyVGQW7tSSk/Nnl8GEuczHqlsa8+fQyvJ7qsXkz9nPfs+3IrG04rTLA0mIHuYcqOLTXTFKqhow6BmLjVee8DzBQZsY+M3JJqGf9lkol/igEviYY4tOYPcPM9K/2RBx3OX34oylSn0MEAwHKCw+x5o9P2Lt+HnKllraDbie7WTc0xjNzyThmJ9i0TeTx2LToVj662BQclhJ2r5nD7jVziEurR+eB9zFz5LUh5B2gIieH368azfC5M1HFRS+rv4RKRPKxPga/78x+a+k9exLfojlFGzaGbDfWrUtc59YsmvRh1TavO/zddww6icA9afE8sy8P63GlshIRPJKRiF4qRZIkIz7pzHrQUxRS7kqN45PcEjxHNT2UYhGttV7m7FlZNU8UDNZoqRb0RK8UOF0EpBoa3vMgqf0Hs+uLT/CYy6k1bBiquFhyly5n4I/foU48N8GCKvgDBBzRv4eA3UEwSp/xqcIoS+XuRr/yv+3D8QaqhVCbxwwhSVmfLda5JPfsxN7vppMxagAr/d9HPdb8vEk0iumLSmI4q2s6EbrYFK594Semjb+Z8vzKwKkgkdGy7xikMkUIyUvIaMiGiuh/s40OF306JSHyBPB5ApQU2tHo5CiUkSmAWBDQpqWhTUuj8NB2fv7kbtyO6ooPkVjM4PsmoTH8vXasFk8BP+57mJXF31Rt00hM3Nf4DzK1rS+4jtKxZ8QvxWb+LLXiCQYxSgSuSYyhtU6NNv7465EhGA0h+4tEIuq168/KXz+goiz0XaLUGKnbqBc/9R5A45tvYvD0afg9HmIa1EeTmopC/++xIb2EU8MlAn8JlRnMR+7EciSHwnWR7aQIBslbvhxj3XOTSZLEmcDro/ytD/FsrbSGESkVaEcPQ9mpTUhp84nwOGxhNjXHw1qSR0LGv8uS6XxDH6MI8cs+V6goK+CH166vIu/HYC48zNxPn2LIAx+ElayeKoIeL+51Wyh78W0IBFF274hjzkIcf4SSWe/2PUgmO+lz1ZP88slYAFLqtkYqr84ci8Qw8Kq6fPn+phDyDuD3B/nozXXUbmAiNuHUss0FuTZefHAxJYXVauRqrZRnJnQjs7bhnJL4gN0Bvui9d/6SMqQZqWd07EYt4pn+VWTrprRsHXLF+V8gleXv5/PHB4QsIH+bdB91Wvfh8jvfQm0498G6um37sfCbVwj4wnua2w++MyT7VZyzCxkKzHv3RjxW8caNOIqKLxH4k0ClkdKkdQJb1hZGHG/Z8cwqdjQpyQz57RcOzpzN5skfE/D7qXXlYGI7tuCPKY+FzM1u3r3GY6UqZLxRJ4VtNidb7E7S5DLa6dWYpBIkZxnwVAsCXQ0ammiUlHl9iBARIxXw+faHzDsU3EHD2pl49x6MeBx5kwZndR3HYLd5mDdjH1M/2opKI6VFm9uRy2HfwSBj+jcis18fJPJzXyYt1qpRdGqD7VBkkTlll3Zn3SIgFRQ0NPTixdY7yLVv+T97Zx0dxdnF4Wd31n03biTB3R1KsZYipQKFCqXu7l7q7u5OlZYKLVCsFHd3DXFdd/v+SEnY7m6aoIEvzzk9p8w7Mzu72Z1573vv/f1wBsw003Rjn20Fn+68AkEk4/Z7f2Lf9D8Qy6UEQvHdWPwhD6G6Vp4OE5FIRHJ2OyY/OR2ntRK/143f52Ljgu9Z9ftHtfuJBVp0HUK5TAFE368AwlSLDc47YOeuy2YjFovoc3oGE6/uFNMf/lCSs9py9Utz2bFiJvs3LyYhvQVdh1+MLjETiezElckHQwEWFX8UEbwDOAKVvLxpOI9330SiMue4X5dJKuHSVBNnJ+rxh8PIRWJMUqHez3x9UiaTn/qFZT+/zaa/pxEOhWjd40y69pvIX1fdStDrZcM777LhnXcBuGT1iqbg/f+UpgC+CQAEgw5R2X9kgY9ySZIkPQXTlLsIWW2EfT7EGg2CSY9IWrdKsVShjisgBZzwVeEmainP34HfE22pBbB77Txctsr/DOCDVlt1qVk4XF1qZqiuAglWmal69o3qvndAeVpvqp5/O+Y5AnkFJGvHIJEpCPi9DL/s0QgVYZ1BTnK6OsK3/lCcdj/mSne9Ani71cvbT6+ICN4PnuP5+xbz9PvDSDiKonhilRJh/Hn4O3bB6wujFIJIFv1FYOGS6s/sCNwMMrJ1ZOXqyN8XXZp52U1d0RuPrRK9q6oSe34BIyc9za7N89m+8o+axbtdq//EXJp3TAJ4nSGFiXd+zE9vHZJ5EonoPvhimuf0oDRnc406vUgsEHDF/t4cJOCue7wJUGtkTL65Cw/fMA+vO3JBauDwZkf0m9FmZNDpmqtoef65OC3l/PHJ/Sx4852IfdoNGFuvtp4kmZTBJimDTUdfMFUqFpMsE5Msq30G2jCRqe5MgbNa7HSm5XXaXfcFPPBB1MKd6qwhiOuoXmsIlWVuvv6g2lvd5fCzZEFt+XRqhoYLr+54VF7n34gEAfWIwbhmzCVki+zzF5uMKE/rU+cCf32RiGUkKnJIVOTUbCtz7yYYDhAMB/gp+CTnLfyOgq9n00UykjzWxjxP3+RJqCXGI76eeGiMKTWCqztXzWbzwmk1Y2KJlHOufZ3CH+fQprsFiL2g2lmtZNkf+1m/ojqrGwyGWTq/gC3ry3n63WEkpcavGhGJxRhSmtFn7HX0GHkFgkTaKNTkrf5i/ix8OeaYP+imxL0jbgDvC4XwhsLIxSJkR+G79G+kYjFJssM/ryGlGWdc8TgDxt+G3+lk1RPP89uj50ZVx4olEuQGwxFebRMnK00BfBM1KExGUnr2pHR1DOkUkYj0/n2P+msKOg2Cru4V4H+jNiTSts9oti37LWrMlN4Cran+YjZNxCcUDOIwl+Bx2hCkMlS6BJQaQ4PO4YwjBAYgkSoIh4Lkb1vJ7rXzUKj1tOp1JlpjCnKVlnAwiH9/PpZXPsC/u7qEUJKTheH2a5C1zMWfVwC+QzIOYSL//S/CFWYyWvVg4AV3kNSsbcSYVCqgUNa9cBQI1C/LYrN42bkl9vs2V3owV3iOagDvEtQEu/ck7HQjkUpZst5BUNuHIbd0QfTzz3W2o/wXpkQl9z9/Gt9/upnFcw4QDIRJy9Rw+a1dadn+2AkthoJBLDt3seihh9n76wwEmYxWF17AhNs+4bdP7sJhrs7Sblv6K5ltehz11xc5XGimL+eyaz/AHnLgczsxJWbByq2473yegU/cTu+zr+XA1uVIZAqS0jrEtTMUS6UojlAAtC78/iDWKg/BQBiZQmi0Wg31ISNLy/MfnsHvP+xkw8pStHoZYy9qQ9vOSTH9wRuKMiEBmV7LyOufZ/G018jbvASl1ki/c26iebchR6StcKzQyZKY3OoDXtgwiEDYR6U3j2+kT3PRK1MQpq3Av3kXgkGHZuJY5F07HFa7TCyWzottdQUw97e9jBrf6pi5c0hSk0l6/QlsX0/HvXAZIpEI5dCBaCeegyQlupLF6g/gCoURRKATBBSHaf3ZXNuX9oYz2GqZw27XEt6XXsnpV11Nr+yLWOL+ikpvXsT+iYocuieef1gBrddT3bbldgVQKCXojXKUqrqfQemtutO6z0h2rpgJQK8zrmT3e9+we9p0pLktGDx2HH95Ixd1FGIRF2i1vPJ9dF+1tcrLyr8LGXVBq3q9B8lh2AEeK4IhH66AJWr7cOMNDJJfhHxuCTb/Tyh6dkFISUQw6PEEQxT7/PxabqHA6yNHIWNMooFUmRR5I7OLlcgUqHQJFOzfjT0vP2Zra4errkSd2jTf/X+lKYBvogZlYiLD33+H7waeHnWz6PfYo6gaoPJ6LJErNQy/4nHcTgv7Ny6q2Z6Q0YoJD3weZQ/WRMNxOyzsXDWbeZ8/gdtehUyp4bTz7qB1tzOrLftSkuvlG2po1iH2gEjEeXe+x+/v3EX+thU1m+d/+SRnXvU0nQZfgMTipOKOxyIcCwL786m45wmS33+RsNsTdU4kQtxycm3LNpw7+F00cSy+DCYFaq0Upz16EUAqE9c76Pb54pezA7gcR69H1VNWhff7X2H2fAS/H0EkYnDv7thHnseijTbGPvXAEQXwAIkpKq66vTsXXN6BQCCEUinBcIyDROuevUzt3Q+/wwFAwO1m26dfUDh/IWd9+irT3roW4LD8petDOBTGv24rrNuKQiFHKZXidzhrAvTwqq0k3XgZSVltgGq7so5XXs7mjz+NOle32245+n3C/1BV7ua3b7czb8Y+vJ4gqRkaJt/chbadE1FrGs9kOx5+l4tQIFCjrSJIxKRlaZl8c1dcdj+CRHRY/u91IUhkJGW1YfQNL+F12hELkmNSxRGLqgo3ebstrFhYgN6kqKksUGnqDtyaaboxpft6ZhW8wC7rYrxiFxVJDprfPhmpR4xIIiAcQaVNLBz2+PcpjztQVxv+UUGSnorhlivRXTYBALFei1gW+Z32BkPsdnv5qKiCAx4fAtBfr+HiNFNEFUN9McjTuKrt5+y3r2Z+0duEw0HS07qgUpi4t8tC/i7+kKVlnyNCRL/kyQxKu4YERcN1XMyVbn78bCvz/9hHMBBGJIZ+Q7K49IYudYoeagxJjLz2efqMuZb1c6fSpvOZ/HDtMwCsve8B+icl07lfP+ZKlNgCQbqoFZxh0PH1s6uxW2P/PVctKmTI6FxU6oZ/XicSqViJSd6MKm/tQtMo050M2NSNwGfv4AE8gP2LH5D36Y7urutZj4iX8ko5+NXd6/bxl9nB/TmpdNOqEDeCyoJDcZjL+PGN6xn7yEvIjQb2/Pgz4VAIQSaj3eWT6PXAPUjVh6e50cTJT1MA30QESZ07cemGNWx4533y/1qIJiONnvfcTWKnjkcsYHc00SWkcd4d7+G0luOoKkWpM6ExJh+V8vlwOEwwHEAiPrkeaEeTA1uWMeOt2wHIaNWDQaNuY+Or77LqsgeRabV0uelGOl5RbcNUFzaVkdyeZ7Jv9Z8R23M6DaRk76aI4P0gf378ENkd+yOftyHKbhAAfwDHtN/QnDcyYrNn+RpUgwfgmhutFiykJSNLS0UwRJY6hoJhrGYPYUBrkHHl7d1588noa7rwmo7oTfULJNRaGXKFEGEndSh1lSs2hJDXh/unGfhmzK7dGA7jX7EGtc1BUv9xWEMKjkZDiVwhISn1+DwuAm43q158qSZ4PxRbXh7WjTtJzm5PWd5W2g8455hcg0ihQNa+Nb6tOwl7vIQ9kd9DRZ9uEf+W63QMePpJNOkZrH3tdXx2O3KDgV733kOHqy5Hqjr6mUqr2cPrTyxn+8aKmm0lhQ5eeGAJdz/Vn16nNV77OldZGWXrN7LujTfx2+20uehCckeNRNcsCwCZTECWcGz1FWQKNTLF8Zv8Vpa5ePbeRRHtKD9/tZ3Lb+nK6SNz6gygJGIZaep2XNLyHdxBG1KRHJXUUD14jN5C70GZzPllb8yxLr1SUR5GwGe3evF5gwgScZ1+5AcRy+WIk+Lfd/M8PqbsLaoJyILAIquDnW4Pj2YYCZbsRqVNQGVIRF7Pv7VelkaXhLNpaxhGmDAKofa4sdmPMST9RkCEVpqEIG74PdHt8vPdx5tZ8Pv+mm3hECydl4/b6eemh3rXacGmMSShMSSR1a4PRcuW11b9hMMsveJKdNnZDL78MqQJJtqPHIE8pMda4Yl/Pr0MiaRxBa71wSBP5/ycZ/hoxyQAZGIV/URjCXz2XtS+3hVrMXu8vF1u59/rTiHgrfxyXmqVSYKscYVEblsVHoeZH9+5ka6DJzLmhumEvD5EMgnbN87G6a3EQM5xux6r2YO50kNlmQtjggJjovKkrvg62Wlc39YmTjhiiQRjq1ac9vwz+Ox2BLkcmaZhJe7HC7lIhlSkRa9RVPcBq4+sD80TcFDlzWNxySeUunfR3jicLglnkyDPaRQ9X8cLh7mM+V8+BYBcpWPQyNuYcda4mqoMv9PJ0kceZe+MGYydPg1NWmTfqNflwGWvJBwMkiRX0/GKZ0jI7sCmmR/jddkwpbdg0KSH+fGZSXGvYfPfP9LNlY5IraoWZRKJ8G3dUeN97Nu0HdHF56MaORTXzPkAuOYvwXT/zYTDIQL7CwharIQqzUiyM0mYchdCQuT3o7LMxcJZ+5n7214C/hB9Ts9kzMTWPP7WEL77eBMF++ykpKu54IoOtGhrQi6v3+3SmKDgvEvb8e2Hm6PGBp7ZDJ3x6GQUQ2YLrhlzY44Ft+2g1QQJXne0TVJjx2M2s/+PWXHHC2bOJ310F5q174s+8dgEqYJOg/76Sym//dEajYWDSHKykOZkRR2jTkmhzyMP0vHqKwi4PUiUCjQZGfWqVDkcyktcEcH7oXz+1npatDNFWOM1Flxl5fx1171s/6pWfKrg70XosrOZsHAeuuzsE3h1xwa/P8hv3+2IqSXx2Zvr6dQrpV4ZUJmgRCYcn79pVq6O5m0N7N1uidgukYq55LpODcrYes0OfBY7FaUuliyvZOMmC2df2IYuvVLQGQ5PR8MeCPJ5cWVUQAZQ6guwtbKcrS9fh62ymO4jLmPg+NvjVl/FQi5EL7oJYgkGefphXe9BrGYvC2flxRxbt7wEu8VbLw91kUiETBs9N7Pl5bHh8ScA6LB1I1qdnDETW/P649EL0wCjxrdGVs9nWzyCgQCu4mKCfj8ShRJN+tGxiP0vOprO4uIWbzF9/0O00Q9GMncL8epGLP4Arji6SbZgEGswQEIjC4nEkurrCfq9rJn3BWvmfREx3nvstcftWipKXbw6ZSm7t9XaGqY303LfswNJzWycMcKpTuP6tjbRaBBkMpQJja8f8CCB4jLMr76Hb8M2AERyOZqJY1GPHl4jctYQ/EEPm6p+54PtFxH+Z0qwoeo3fs17jPu6LCJdHacU/BQk4PdSVVydeek88ALWv/BGzP6rkhUrqdyyNSKAN5fsZ85nj7F7zRzCoRCJma0ZeOVT2AZeTP/+45ATohyBCkGE+xBf9n/jNJejGHAO8o5t8a7eAKEw+usnE7I5sH40FXGCEbFKie6yCcjatcL+7S8QDmGV6SkffB55e22kpqlolqvFpBcj+ZdKa1W5i2fvW0T+3toJ9Z8/72HZgnye/WA4dz85AK8ngFQu1GsydShSqcCwMc3RaGX88OkWrGYvSrWEUeNbcea5LY9aaXPI5QZ//J5/UVUV8tzoQPNY4bKbcZhLKdi+EplCTUbrHqgNycgUDcs+iyUSZDodFBXFHJebTPQ8+xq0pmSU2mMnHiXJaUbSK49jee8L/Nt3I5LLUJ01BM0FZ0ctBh1EkErRNTsya8T6smtrZdyx8hIXnqO8eONx+3E5AwiC6IjECyt37o4I3g9iy8tjzWtvcNpzzxwTdfMTic3iZf6MfXHHVy4sIHNy43BOCXi9OItL8FaUc/uD3fhrTjFzft2Ly+mnS+9ULrq6E+nN6jdhD4dCBEorCGzZARWVaNZu5ky1miHXn80Lr24lb7eFcZPbH1Y23xsKscMVP7O8LSwnObsd1vIC1sz8BJXWyIBxtyFITmxlndPuI1SH/aalykN6s/rNYVTJySR06EDlli1RY2l9+tQ4X7Tvmky/oZksmx+p7D/qglZk5hxZZaWzpIRNH37CmldexWuxoG3WjIHPPkXOWSNQHkPtDwCNNIHT066jS8LZEAyBeVrcfeOJHtfucJQv7iig0iWgT87CWpYfNaY1pR43u2SH3ccHL62OCN4Big7YefGhJTzy6un1qqhp4ujSFMA3cdIRqDRT+dCzBAprfTLDXi/2L34AuRzFmDORKRr2kLb6ivl4x+Sa4P0gzoCZz3dewy0df0MjbbwLGkcTsSBBqTXhtleRmduNP/+MrfQKsPP7H8gePgwAa0UhXz5yPvaq4prxioKd/PLUhVz49G+8SxJFPj8dNUqu1CnI7tCffRsWxjzvwJHX4Z42D/eCJTXbXHP/Rt6tI4Zbr0JITECsVoEa1CMGo+jdjXI7PH3vEsqKnTXHqLVSHn1tMNm6cEQVxY7NlRHB+0HsVh+/Td3C8N4BDM0y0SYdXjCmM8gZdnZzuvdPw+cNIpUKGBMUuJxeygqKCCNCoTaiM8gPu7pDrJCDWBSVIT6IPMWE5hirxB/EYSln3udPsPnv2gmUSCww5saXadN3FHJl/YW1BJ2JdtfdyJI7bo053u6qa0nJOTpWWXUhlsuQtWtFwhP3VustiEWIDXrEh9FbeyyoK4gWJCIkkqMjyuT3BSkudPDjZ1vZur4cvVHO2Iva0LlXaoMnbV5PgC2ffR53fOtnX9DzrjvRZjbe8v/DIRwibksNgN129HQxjgSPxcKuH35kwe13VjsriES0mjCBx194FrnRiEojQ6Wu/wJkoKQM1+yFeJasBEDRryey9q2xvf8ed9x2A/ffv5bhY1s0OIAP+HyEPD60ghhbHE94PUE8h1hPrvjtfboMvRB90uFZah4t/kuoTi4P4vO46rXwqU5JYez0afw0YhTWfbULRMY2bRj19ZcoE6sDPINJwZW3dWPshW1YuagQQSKm92kZJCSp0OgOf0HZYzaz8K572f71NzXb7AcOMPOSyQx9+006XXs1guTYhhmCWFKjQ+Do3wvPsthWyPpQCKVYhDvG81IriNFJjq9nfH3QmlIZd9eHfDVlHD5P7bxGKlcy7p6P0ZiOjbbKv7FZvGxYGdves2C/DWuVpymAPwE0BfBNnHQEi0ojgvdDcX77M9bc9sjSkkhJr39ZT4FzE4Fw7EnUHvsyHP7K/5sAXmNIpt+5NzD/y6cJBHxI1Wq8VmvMfeUGA56gE6lITv62FRHB+0HCoRBLpz7DQ3d9SEChQSWIUQkCQy99mE83LyEUrM0UChIZfcZejyQkxuP1RInSeddtRnFaH2Stm0e8hkus4J0XIoN3qLZue+6+RTz9Xq11m98bZOGs/XHf/8qlpXRQW/n9/HFMWDifhPaHFyyKxaKa1wwFw1QWH2DDXz+yd/WvCBIZrftfRMvuwzCmpKNQNvxWLDboUPTvhWfxyqgxIT0FVWYScsXxucXvXjM3IngHCIeC/PbW7aS16BKl+l8XTocfof1AMoYOo3D+vIix9jfcTJHTQJY3iFR+fCZcgl4L+qOj7H00adnOhFQmxu+LDmAGDM1Cnrcby/T1yHt0QtoiB0ni4WXD8nZbefSW+QQD1RNfm8XL28+s4rQzmnHZLV0bJDLn9QQJ+eIHq6FAgEaZCotBOBQiaLZit5dTuGc9xfs3kZTdjpxOA9ElpiMWan97SpWE9t0S2boudstDzwFHVpZ9tCjfsJE5115fuyEcZtd337Hnxx+ZvHk9qpQ29T5XoKyCqkdfJJBfW0njyP8VIT0V/TUXE96yjvadEynOt/+nF/mh2AsKWf3iS5j37uOMl1/lxxjrIiKgq9jLr9tr740+t4OAL37G/nihM8rp1DOZTavLosayW+gp2fUXWlVXkrPr99wxtmrJxMV/Ydufh3X/fgwtWqBt1gxNWmRwpzMo0BkUNG9z9LLirtLSiOD9UJY89AjNx4w6bhVJnmAIW/s2CEkJBMv/VZ0klWCQS7nBoOeVA5Gfuwi4KTMZo7RxhkMpuR255pX57Fm/gKKd60hr2ZkW3YehT8w4bq2d/1XN1VgWIP/faFy+CU00UQ/8+6PLiQ4SsjvwOdw8fMP8qGCuLnyhun2aQ+GTr5f43/h9HqzlBVSV7MdhKY+7n1gQ6DR4Ah0HjWPHhtm0mXxR3H0zJ5zJG5tHs6r8W3avnhN3v4IdqxH73STKpKj+6QlOzGjJ5c/OILNtbwDa9B3DxVO+w+O0Mf2L+1iTVIrwxI1Ie3eOOJdr5oKocji71cuOTbFLis0VHszltS0AIrEIeR0Bs0wuEPJ5cVdU8MfFl+Iqj/9Z1RdzaT7fPjWeFdNfpDx/ByX7NvH31AeZ+c4N2CqiFz3qg1ilwnD9ZGSdIoNjIT0F0+P3okw7TuV1ljKWTX8r7viG+d826HwSiYhffi6n3SOvct7ceXS+6Sa63XUXY+YuxtZ+Alt3eBGfhKJLRxtjgoJ7nhmARBr5GM/K1XH+GQYcT7+M85dZVD36IhV3P06gJDpg+C9sFi8fvbqmJng/lEVzDlBVEd1aEw+/143TaiV9zAVx92k5bhyKRty6dZBwOIz/QAEVedv57PHz+eWd21j5x0f8/u5dfHDnEIr3boy4R6m1Mibf2BUhxve2VQcT6c1O/AKR12Jh2ZTHY46FAgE2ffQpoWDdDhuH4lmxNiJ4P0iwqAT/njxk1iratdY0aJHRWVrKbxdMZN0bb7F/xu90Ksij47/6t8XA1QYJO357N+JvIFOokchOfJZQLhe44tYutGhriNieka3lipvSWPXzM6z84yMC/voHRZr0dNL796PdxReR1qd3VPB+rDDv3B13zGux4DVbjst1AFgDQZ62e/A9/QCSwf3hn3mGpGNbxC88SoFaTXetmhdaZtBPr6aZQsZAvYaXWmXSSatEaKQ6R2JBwJDSjB4jLuPsW16j58grMaZkRywQHmtUaimCEP/zMSac+N/V/yONc8mpiSbqQJIWX1dbJJfjDwvVPYd/7OWCyzog1KOUtJmmW9yxREUuKsmx67U9Htgqilg87VU2/vUDQb+XxMzWnHHFE2S06R6zvFljSObMq57CaSkn7PRTOH9RVJ9dl4duZY1kBjutCxFEEjok9o37+mpDEmJxZMZUIlOQ1qILE+7/DL/XTen+LXw1ZTzhUPUkMX/bCtb99S0X3PQOWrOdwK7qEsGwx1Pd73YIPm/dE0uHo7ZXXCIVM+KcFixfUBBz30GDTORNfRGA8g0bcJdX1PQSHg7BgJ/1877BVh79esV71pK/Yy3ahNT/LK2MhZCUgOmROwhVWQiWVSI26hESjHF7tI8FoWAAhyV+cGgpyycUCkb9/eOhMyi45q7u/PTlNraut5DT6nw0WhkhVxKOgIUzzslFaGSevScCqUygfZckXvliBNs2VlBV7qZNeyOm8v2En38RfLXf+WBxGdaPv8F453WIlfWfbLmcfvbttMQd37KujOwWhnqdq6p4Hyt/+5ScjpeTMWQohQvmR4wrEhLoef99SJWNT3jv3wQrqrBt3sivc1/CZYtcOPR7XPzw3OVc+cJsdAm1+iCZOTqeeX84X7+/kU1rylBrpIw4ryXDxjRvFErOfpcL846dcccrNmwg6PUiroejQtDuwDUn2g3kIJ7la1GNHo5vt4iktPprZFj37qNk+SG2o6PGcNY7b3FOv/7sEmQYjXqy3BVs+vEFdi3+KeLYXqOvbhQWs5YqD1vX7uO8853IdW0xV/nRG6V4zDuZ/fbFOK0VVOTvJODzNCrf9VjIjYY6xwXFcWrhMpcSsFRwtd+KgySKLh2P4aLzEIXDbEfMr24/lwRCtBLEtFApuCUrGU8whEIQIxc3PUv+C4NJwdDRucz5NdqVokuflCPSRGni8GkK4Js46ZA0y0Cs1xGyRvcwS4YPZs5f1WWKKxcWMvL8VvW6uehkKQxLv4V5RW9GbBch4tKW72GQHx9V1WOB3VzKd89eStn+rTXbKgp28s2TF3LxlO/J7XxazOOUGgNKjQGA82f/TtmatWz/9jsUCSbSLh7KNtViZltfAWCndSGj+t7Dml8+IZZBcN+xN6COY/Gn1Brxe1389uZtNcH7QULBADO+eIhLLn4ZXqgO4BUDeiH+l/ruf1m3Jf/Lui0jV8egM5vx958HIrbnttLTLtPD7MWLa7YFY1nZNQCHpZLty36KO75r+fe07D7ksAJ4AEGvQ9DrkOYen1LFfyNTashs04u96xfEHG/ZY1i9g3cAc4WbN59awYF/NAoO9t4tmZfPrY/2aVK8PQSpTCAlXVPTLuRatALzq6/H3NezeCWhqy5qUAAvFokQiWL+pIHqapX64PM4WTztNXavmUObAZNpfu9LZI1dzp7PP8DvcJB51hjaXXkVqsyTQ4E+WFaJP1FLef6OmONOSzkOc2lEAC+VCeS0NHDblL64XQHEIhF6k7zRLEZJVCpM7dvhLIndnpbcoztCPcUFRSIRorrcFwQxocwsuqbpG7R4UbhoccS/g14vy666hsROnegx5SkSevVHFAZzXq0DiEgspuuwi+k58soTLmAH1QF83l43Vb5f2bP2T+QqLT63I6KVLL1lV6TyE7+o81/ocrJRmEx4qqqixjJOG4gy6dhXgVUU7OSH566oEd5FJKLt6RcQPPdOXjMHOLjUn62oXQyRi5sC94agUEoYf0UHxBIR837bR8AfQiyI6Dckk0nXdzkiHYUmDp+mAP7/BIs/gDkQxOwPkCCVYJAI6OvR8xP8x7ZL+FcPqD/oIRD2oRC0x91iTUhKIPH5h6h4+HlCFbUPDkmfHpR3HsDSJzYA1d7VYnH9rk0lMTCm2SM01/XnjwNPY/EVkqPpxbm5T5GuahzqwIdLVeGeiOD9UOZ88giXPD7tP9VMtRkZaDMyaH72GPbalvPypmH4rLXls8FwgCXuLxh2wxTmv/dkRCDett9o2vUbU+f3xGmpwO0wxxkrx6sSEAFinRb1yKGI/iU4Y6jDum3A8Kwo6za9QcGkG7swdExz/vxlNz5viF5d5egDxSy4pLZlQKbVokg4sp5BsViESBR/siASi+tVJdJYUah0DLnkAfZtXBjV2qA2JJHbeVCDzldc4KgJ3v/Ndx9tpn3XpMNe7DjVCVVZ6hgMEQ7UvwQaQKOX0q1fGmuXRrd5iETQoVv8aqhD8bmdFO9eR8DnYc77VzDkirdxtuxH9qO9UShEGDN0aDMSj5pDw7EmZLcTDMZ3gIDqTHws1BpZo3yfCoOB/o9P4bv50QtxgkxGxysuQ0R19UE4GEQklyEY9NEnAsQaNeqzz8C3NXZGXzX8NEQpSbQ2ahsktqiOURqe2rcf7Z96hw8/zaf4jTkYExVceMXrjGotgZAHtT4BtSEJZGLK3XtxBSwoBA1aaTIqqaHer320EIlELJ5Xyr1Trmf3mll4/uXGIpZI6THisojFhlAwiKOwCL/DgUSpQJWa2igqVbQZGZz7+6/8OHwEfmdty6I2K4szP/nomKvQ2yqKmPrYBBzmQwTWwmG2//U9KkMyvfpPZoXTRxuVnMRG2uN+smAwKbjk+s6MvqA1blcAhVKC3ihvehafQJq+0f8HlHj9PLe/hHxvbU9Va6WcO7NTSIqjqBwsr8S9Yi2umdUPc9XIISj79sClC1Lo2szcgtdxBqvomTiergnnkKA49pkTl9OP3xtEqZYgy21G0utP4imuwFZQRchoYvlmB989uaHGomXU+FYNEljSypLok3wh7QxDCYZ9yAUtKknsCcrJxP7NS+KOlefvwGmzU1ZWbZdmTFTUmRESiURYfEX4QtG9r8stX+NL9HDNG/Mp2bkRr8tOVrs+aE2pqHR1P8hD4f8OLFQjBqO98FwkqdFBw0HrNpVGyrRPt2KzeFGqJIw4vyVnnd8SjTZ6wqw3KtAbFTRvoWLXjz+z/N7HsO3fH7HPgKefRJ12ZNUXan0inYZcyN/fPBdzvPPQS9Hojl1W2V1VhbeqinA4jMJorFEmPpokZLTkksemMeuD+6ko2AkiES26DuHMK59ssOrzzi2xhb4ASoucdap5/78j69A67pgkIxWxqmGTfpVaxuSburBnWxVWc2QlymU3d8VQz9JJQSpHl5SBtbwAW0Uhv7x4LmktupDSvBdmnwNzkZrm7R9t0LWdSCTpqcgPOJAqVDEDdZFYjC6xcQjTNYTETh0Z8dknLLj1dny26kU0dVoao775Cq0pEfs303H8PIuw3YkkJwvD7dfgS1QTDAcRJFI0xpSahVp5lw7IOrTBtyWySkHatiWKPj2QpBgafH0ZA/ojlkoJHbTPFIno/uwbPP3Mzpo2KnOFh3df3IVIBE+/N4zMVCM2bzEz9j7D3yUfEAxXH9vROJJLW71Xo2B+vDCYFIhEIubOdjPiuvdY9PWDNW0YWlMaZ9/yJvqkWvtPV3k526d+w/InnsJjNiPIZLSbfCn9H3sETcaJdWsQicWk9uzB5E3rKVq2DPPOXaT17kVi505oM2vv+0GbnZDNAaEgIrUah0iB2xlAIhNjMCmQSg9PkLSyaHdk8H4Im2d9ypBBE1EbdVyYYqpXwqqJupHLJQ0Sh27i2NL0jT7FsfoDvJAXGbwD7HR7ebegnDubpaD5VzYzWFFFxcPPEzhELM765j5cUid/5s7mr5L3arbvsi5iZv7z3NdlEUnKSGXwo4XD5uPAXgvTv9pGRamblu1MjL2oDSkZeoKCil//sPLXzA0RZZ6de6XQsUf9skP/RieLPi7o9+MsLsZTWYUgl6NMSjyivujjibaOvj+pXMn+3TZefmw1ao2USTd2ofegjJgB70GyNF0RISZMtPp1oW8zsgQdnU4f36Br1OiTkSnUEVYpB1Go9WhzmqPtPgBfSMBa5gSLBYnfi0yjQKzTIjdq0RnknDG2BT0HpOPzBJH+MzmQ/MfkQK5R03zkGQScdlY89QzO4mIMLVow4NmnaDZ0CIL0yFaYxYJAp0Hj2fL3NCoLI0V/cjoNIq15pyM6fzzCoRCVW7Yy94abKFqyFICUnj0Z/t7bJHbudMTv61CkciXZHfpxyePT8LpsiMUSlFojCnXDPYZNSfF7YmVy4ajZo52KCIkm5D274F29IWpMf+PlCCZDg8+Zlqnl6feGsW55MWuXFWNKUnLGOS1ISVPX2/5LqdEzcNwdfLP1wpptxXs2ULyn+jqvfnkugqTxZaXjITbokSyuYNDZtzDvh+ejxnuNugrVcfJoPprI9XraXnwhWYNPx11ejkgQUCYlodJoMb/8Ht6V62r2FQ/pxfZdi1n0+jtYywvQJqRz2oQ7ad3rLNT6BIQEI6aHbsO3bRfO3+cCoBo1DHn71oet0aHOyGDs9Gn8eu44QoEAWUOHsmaDO6YGSmauDrXPhn31Lv7QfMGC8ncixjebZ/Letgnc0uHXmM/8Y4UhQcFND/TipUeWUlZsZPQlX6FSehCJxCSkpZDaLKtmET3o97P18y/5+577ao4P+nxs/uhjrHv3MvrbqSd8HiKWSNDn5qDPzYkaC4fDBPIKML/6Af7t1c8+ITUZ8WWXMm2Bm1WryjnznJaMntDqsHQgqor3xR3zeZw0E8J0Tk9sKpc/AsLBEMHyCrxrN+HbuQ9Z2xbIu3ZESE5AdJifq9dmw1VSStHS6nlJev/+qFJTkOsaPl/4f0YUDsfrbvv/xGazodfrsVqt6E6BL1Oe28udu2KLdQG80TqLDEXkxMnx+1ysb3wcsU0kl+N59SKeKTwr5nkGpl7JxS3eQiYc3bIujzvAnz/vYep7GyO2C4KIh185nfZdk7CaPZQUOPhr5j4CwTCDz8ohI1t31Hwp3VVV7PjmOxY/8BA+e3VLQWLnzoz+5ksS2jf+8npzyX7evWVgVH85QOdhV7K7ZATLF9auYt//wkC69YmfdXYH7MwvfIPpeQ9HbBdEEu7qNJ/Whtg99XUR9PvYsuRnfnvztqixc+94l7b9xuBxhSjYWoKheA+hb78jZK62tpN174T+5iuRZTRceTfo8+EsLsZVVj1Zlet1CAoFYkFAnXp0lXwt5YXsXT+fTX9NQ5BIaTN8DIbmzTEmZJN4DCpYLHv28lW3njXf2YNIFAomrV+DqU38bO2JpLTIwV2XzY5pjzZyXEsm3dAlSnm9iVqClWacsxfg+OkPwnYn0pa56K+bhLRVLuIjLLv1+4KIBdFh9W277GbWzv6Cv797oabVQpDIGHXDi7TpMwq58uTK7ASrzNh27KDAvJu/Z7yFpTQPrSmVAeNuo22/Mf/ZlnQiCQeCBKvMhKw2RIKAWK+rM6j27dlP+Y0P1PxbelovdjTz8vcvb0TtO2DcrfQfdxsyee1CXMhdbd/WEP2FeAQ8HhxFReTP/wtZcjo/LFRGWbLpTXIef6At4edfgMcv4QnzWXFtYqd0X0+WpssRX1dD8HoClBY5mfnjLvL3WclpZeCs81qRnKaO0JWwHTjAF5261VRD/JtLN6wlqfOxWQA+GgRKyii76UHCjn8tzItFCI88xEPP7sZh89HrtHSuu7cnWl39KyYB8jYv5asp42KOyVU6rnl1PvrEE1ulcLLj27mHinueJOyprcASqZQkvvgIspa5DT6fu7KS9W+/y7LHnqgVVxGJ6DvlEbrdctMxb7s4GahvHNoUwP+LUy2A3+Jw8+jeaCuXg7zQMoMWqtqHatDuoPLBZ/HvjFSblHfryILJZcyuiH5gA0hEcp7pvRuTvGHlsv9FaZGD2yfNqimLP5TUDA2PvzkEwzG2sNg57SdmXDAxarsyMZFL1qw4bj6nh4vf52HfhoX8+OLVEUI5qS260fO8l3jz2V30H5JMWrqEysoge3d6uG1K3zrF/xz+KvId6/j9wNOYfQU01/ZjZNa9JCpaIBMO7+/hcdmpyN/Boh9epapoN0lZbRg4/g4SMloiV2nJ221BvmsrwdfejDpWSE5E//IUZoTFFHv9dNGoaK9RxG0RgWqbm13Tf2HBrbfjdzgAUKWkMPqbr0gf0B9BdnQzgmZvAd/tuQMTWYREQTbYZ1Du2YtBls79XZce1SA+FAyy7LEnWPHUMzHHO193LYNfexnJcVIJbggBf5CtGyp44YHFEUF8286J3PZonzoz9E1UEw4GCZmthEOh6l5lfexnmT/kxe4vh3AYpUSPUnJsn3letwOXtYLyAzsQS6QkZrREbUhGKm9838P6ECgtJ1BeiVcOQVGIcKUVtVyHrFUuYnXj/J6GHE7cS1Zhfe8Lwq7qVighJQnTQ7cibZkbU3zOMXM+1tc+rPm37LEb+fj1Swn6owU+xRIp17+xCGPKsW+rCwZDfPPhJtTiAN06aBAFAzgDEuw+ETmzviKwbRe+1y/j6bKRcc9xc/tf6Jo49phfaywC/hBeTwCZQohZRl65ZSt/XnUtPrudyq1baXbmMDrddj2CXoVam4goEIZgCGViEuq01EZ3P7dPn4ntvS9ijkm7d2ZBxnB++mE/AC9/MYLM7Ibdf+yVxXz+0FisMRxeTr/4fvqdexPCcbRbO9UIVFRRcfsjBMujRQqF1GSSXnmsQdU0jsJCyjduYvqos2OOX7BgLlmDTz/s6z1VqG8c2vTNPsUxSOKXD4uhxpM7glhiYyIRwTq80EPhQHyp4iMgf681ZvAOUFLowGH3HtMA3llSwuIHHoo55q6ooGjpskYfwEtlCnK7nM71bywmb+synJYyTJk9KC5WsWxhFXc8lMqmue+w569tGJKyGXveDQR9DiD+56qRmmhnHEaOtie+kAeloDvi6guFSktmm56cf+f7+L0upApVTVbO5w3gLKpA8eOPMY8NllXg3r2fOcZEKv1BFlocGCQCT7ZIJ10eOxAv37iZP6+8OmKbq7SUH0eMYvLm9ZhaH90M9Q7LX6yumBa13eIrYlHRh/STXAzBEGp9IlrTkWX/fXY7B+bMizue/9dCvDZbo5vwAUikAu27JPLKF2exd6cZa5WHlu1MJKaomuxq6olIEBASTYR9foJVZtzbdhF2e5C2yKm2GtRqqPQcYHbBCywu+RR/yENH4wjGN3+BFGlrsNiqXT7EAoKh7uxsQ5ArNciVGoypOUflfCeSQFkF5bdPIVQVKb7pARKeewhFt44n5sL+A9+uvVheeT9iW7C0nIp7niL5/eeRpEW3XAn/cv3whDwxg3eAUMCP01J+1AN4sz9Asc/PTqeHRKmEVmoFCRIJ40YlY3/rY/w/VtucGtQqMi8ZBz06Y9+2C1lYEbflC0B/Ah1mJFJxXKu4YJUFlT/M8MuuBrUSWcc2lDkL+fu3Nxky6k5mTZiMeUe1voAgl9PnoQfofP21J7yk/iBhfwDfumhR2YMEd+8jt1/tnKGkwN7gAF6bkMbFU75n+ivXU7K3ukpTLJHSa+RVdBt2cVPwfoSErLaYwTtAsKSMkNVW72eDefcetnzyKWVr18XdZ9ULL5HSswcyzclVjXWiaPp2nyKEfD5CVjuEQaxWIVZX3xh1EoHuWiVr7dGiY4ON2qgAX9BqUI8ahmXHnojtvh176C4/l7lE9pEdpFvieaikR997WvSfKvJ1jzv8lfhCbgSRBL3sMEqsvT4su3fHHS9etoK2F0Zn5xsbUpkCY2o2xtTqSdXs6bv5e/Y+zhvn5ucXr69ZfLGW5ZO3ZTEjrn4O9eBxBP0+JDI5MoU65nmVEj1Kjq7Qn1ylQa6KvIH7fSHUChGB/PjVJOGtO0gbkkalv7pVwBII8n5BOfdkp0bpPHgtFpY+OiXmeUJ+P1s+/ZyBTz/Z4B6vYMCHw1yG01qOSCRGrU9EY0zBh4fFpZ/GPW5VxbckmpP5+4Nn0SVlMuH+z0jObn/YDg+CXI46Pf7EVJ2aUm9LqBOBRCqQnKYmOS32966J/ybk9eJds4mqZ94Af61iumrEYJSXncPLe4ZR5qm9t20yz6TZ/g6cUTgS23tTCTurxdmElESMD9yKrFUuIknTlOEgvu27o4L3g9g+nIr0mQcQDI2rii9os2P79LuYY2GvF/filWgviM6OSVvmgFRa8z0ShLqrkySyo7vQVuHz8+z+EvZ7qsvglWIRYeCdJC3uR54jWFJbQh92urB/8CW6aychbduS8KKd9Oo3jpXmH6LOm6JshVF2dKsGjwbBiioqn3gZ/47aSkifWIzp1ksZMfFRfh1xPp7Kytr9vV6WPvoYmowMOlxx2XF3BoqJREBIi68tIE4wYrbWJoU0DSyfP4gpLZeLHv4ap60Sv9eFUmNEY0w+KWz4Gj3+yKSdSKtG3rEtIMK3ZQdhf/yk3qF4LRbm33QLqb164iyJLToI4CwqJujxQFMAXy+ansanAIHScuzf/Yp77t+E/X7kvbqiv+piJJlpaCUC12cm83lRJcusDkKARARDjFompphQxuhllPfsgrRlDv7d+2u2hZ0uDCUyuprOYX3VLxH7KwU95+U8hUKo/48uWGkmUFCEd9P2auGlLu0REkyI/lXynJ6lQpCICAais/AZ2Vq0cfwn3QEb+Y71/LjvPvIc6zDJsxjd7CE6mUY1SLBGLJWgSU/HURQ7cEzs1KHe52pMdOmdil7rZPHXk6IqJ5q174cpLZv5Xz5FwfZV6BIz6HfeTSRltanxhT/eKFQSJDIJYq2GkN0Rc59wWgq2YGSWZbPTgz0Yigrg/S4XVdtjezgDlK1ZS8DjQaqqfxms12Vn5+o/mfX+fTVifEqNkbG3vkFG+55IRfEnKBKxnFCgenJsKy/gqynjufqlOQ1WcD+IVKmk5913snv6zzHHe99/Hwr9ye+w0ERsnKWliG1Oqp58BUKRv2/X7L8Qt2mGMi3y72+UZdDfNQLryx9GbA+WVlB531Mkv/c8kvSjqwtxMuNdvyXumH/PfsK+um3mTgRhn7/ORVDf1p3VbRf/WrgUTEYSptxJ5ZSXIBhEWmbDlNa81nv7EHSJGUe1/98bCvFNSRX5Hh/nJorppHJi8+ahlSbBPmtE8H4ojh9/R3fpeCxvf8Y5/W7BqTOzxTa3ZjxN2ZabO/6K4QRm4GMR8vmx//BbRPBePRDC88aXGF55JCJ4P5SlUx4n56wz0aSfeAcEkUiEeuRQnL/MjlmdGTrrLGb9WAKAziAnKeXwW05U+gRU+oTDPr4JCAYDOMylBLweJHIFGmMKYoMOpBIIhZFdNQ5nkoK1G2YTDofoeMu5ePVy6tNo6K6oJO/POUjValJ796J8Q7TIKkDWkMHIToHW5eNFUwB/khMor6Ti3qciHmLeFesoX7+VpLefQZqVToJUwg2ZiVyUasITCqESizFIhbjKnJJEE6bH78G7bjOuP6ofeOpRw5G17cilui70tExgTsHLuANWOieczdD0m0lU1F/MIlBWQeUjL0So3CMRSHjsbuRdOkQE8T7Hfi66Opev3ot8mEllYiZfn4NYZOPfpd7hcJit5rm8u61W3KTMs5u5ha8hConplnguSln9bhLqtDT6PPwg8268OWpMolTSbOjQep2nsZGYrMTvEOG0Rlp2GVKy6T3mGr5/7vKaEsmyvK3sXjOHoZMfpceZk5Epj39WVBDEKNITCZx1BqEfpkfvIJXibN+WA/boss5AjMmDVKXC2KY1rtLYq8HJ3bs1uLy8omAXv74e+T1xO8x8/9zlXPPKfIZl3MYm88yYx/bRXsSeabWTS4/DQvGeDYcdwAMY27ZhwDNPseShRyImUD3vuYuUnt0P+7xNNG6cJSWsff0t2mW1iAreD+L+fhZnPHgNHznW1Gwbqr8GPvg75v5hrw/XwmXoLjrvmFzzyYikDtFMITMNkazxTa9EUgmStBT8e/bHHJe2yo1ZdSSSSZF16UDKRy/hWb2RkMvJebe+zdSnL4rwMZcpNYy/95MjbgE6FGsgyBKrg1szxGwsfYi3dn8DQI6mJ9fsiH4uHyRUaUasUYPfT/Chj7josivwd30Iq8yKTp+FXpGOXpZKIBSmMhBgh9NDhS9AG7WCdLkU4wmyHQtZrDX2vdGDIfybdpDYsSMVm6PL0x0FBQS9sVsbTgRCShLG+27C/PJ7EdlcYcQZ7AgkUnRgOyqNlAdeGIgpqSljfqJwWspZN+9rlv/8Dl6XDblKR99zb6TH0EvQXlgdqP+9eRrbv56NWp9Ibtcz2Lx5EdIdixk4/nY0xrqTYkFfdeXM3t9mcOHyJWyb+jUBV6T9pkSlosuN1x917aFTmcb3hGmiQfg2bou5Ah32erF/+wuGW69ELJejFASUsfrdY+CyVeLyVBFoYURx31Wo1SakWi0AeqBv8sV0NI4gGA6gEgxIhfqXPoW8Xuxf/BAZvAMEglQ+/jIpH70c4fOdv3kBYpuV+58Zz1+zzVSU+WjeWkn/0/Ws/OkBmjV/Neo1LL4ivt5T+2A3yjOZnPwO1h37ODBrNhW6NfQceQXG1JyojHLQ58NRVEzV1q14bTaSu3ah+dljqNyxgw1vvl2jnqxMSuLcGb+gbZbFyYhEKiCTR//8e468kr++eS5mf+OCr56mbZ+RJySABzCYlLjPGoIzP5/A8tU120VKBfJH7uBtf3SPY5JUgjpWlYnBwIAnHuP7wcOixsRSKR2uvLxB5fNet4PF06K/iwDhUJDVMz9h4OS76JowlvWVv0aMZ6u6k+3txPpdb0dsL8/fSdu+o+t9Df9GaTLR9aYbaD3+fAoXLyEUCJB52mmoUlNQGAyHfd4mGjf5C//GsmsXIm38SVWw0oxOiBxPFVoQLFmMrEsHRBKBQEUlqoF9kOZWa3wELVbCgUBTGf0/KPr2wPbxNxCsdfcQmwzoLp+I2KjHvWQVkvQUJJnpSJKOf3YwHA7jqCrFWlFAef4ODElZJGS0QHftJCrveyr6AKkE1en9455PLJMiTk9FM7Y6ONeGw1z90hzyt6+keM9GUnI60Kx9H/SJR7ck3R8O00UjJd/yHmsrvqnZbvOXEU6JX0UkUimRZGeiGNAL39adiOdvIiG5BemduiP8k+ULhMJsdbp5Zn8J/kMWObMVMh7ISa1TBPWYEQwRrisItzvjZinVaWkIssbTGiVWKlAM6EVKu1b49x0g7PUh5GZj9stwrDVzzzMDyG6hJyFZ1TjK/v8P8XqcLPnxdVb9Ues85XXZWPj1c7isFQw5/y5Ktixmx9o5nD7pBQRNZ5YstOPzhOjeR425yodHsR+lxIBaaoj5GnK9Hk1GBtpmzdhVJOOM6TNZ+9BdlK6unsel9OxJz2deIWxoqvBqCE1P4pOYsD+Ae+GyuOPeVesJ253QgF7XysI9/PL6TTX+vDKFmtMm3k3nwRNQ6WrtHTTS6AlJyOMlWGnGu24TIasdebeOSNKSEYyG2n0sNlwLlsR+cX8A37bdEQF8YlZrLLYFqNJKOOsaKZKwCUnQz5IvH6OycGeEAIy7ogLrvn3YTFasvmIApGIFV6d8yp/P3o+torBm301/fc+A8XfSovclIFajN8pRKeDAvPnMuOBCAu5azYAOV1xG/ycfp/vNN2Hbn4fMoEeRnojdVsrqWZ+iS8ogNbcTuoQ0xCeRaIpKZ0KflBmh4KpPyqAif2fM/cOhICX7Np8wASpBIkaTmYRw81WIJl+Af28egl6LpFkGvwZFbKi0Rh1zdUYipjiZlKQunTnjow/46/Y7a1Xok5MZ9c1X6LIbJsDk97qoKNgVd7xs/1akQTmXtnqfIek3s6DobYIhH91V56Ix65n5wt1Rx6TmHHl7hlynQ67TYWzV6ojP1UTjxOdx4bSU47ZXoZBqWffqG/gcDkLnXRD3GGmrHPL9kRk8qdaA//mLWOb4BY/IQ4+E8xHnibE/8TYEAij6dEfRvXOdmedTFb/XjdNaQSjgR6bUoDEmIyQlkPD4PVQ9+Qphrw+xVoPxruuxvP0ZwaKSmmPFJgOJzz2ENPv49Fp7bTbsB/Lxhd389vFdEfdztT6Rix75Fv3t12B9+7OannaxXovpodsRkutf+i4SidAnZaJPyqTjaecf7bdRg1IsZqjBw5db34rYXuU9gCtHhUKlrFHTPxT12DORpKVgvPsGwm4PSCQIem3kOQIBnv1X8A6Q5/HxTUkV12UmHXcPcZFSjiQ7k0BebPtfaae2cVv6+j78YJ3aJ4eDudJNZZmb8hInyWlqTEnKBnm2i2UyxKnJEfO6VGB0y8Yhtvf/jstSzupZn8UcWzPrM3qPuZaVf37GwAufYs3GbJbOr72fbFgF6c3KufnpVnxju4xJrd7GGMOJSp2expA3X8NmC/LhZ/sQiUScdf1rdMyoTirmFQZ57f1yrrzNiimpqf+9vpw80UYT0YhFiHTxv+witQoa4NdrqyjiqynjcJhry4p9HifzPn8clc5E58ET4h4bcnvwLF2N+aV3aso27V/8gKxjW0wP3oKQ8E/wHwhAINqP/CBBsyXi38kdOrJOP5uXCkbUqMjKxWomTXqLbmWT0BiqFXNdFRUsm/I4G955jxGra5W++xgvZssvP0QE7wdZMu0VkloM4+kH9tKirZHbbs7il3POJxyMvL4tn35Oaq9edL7+WgwtW2CtKOT7Zy+jbH9tD6RUoeKiR74lo1W3kyaI15pSGXvbW3z92ASCgdgeuf+mMaySK5MMBBM0CDnJSP+xrBvuD6BVSJleZsEcCNBCKefS1ARylPEXr+QGA+0vvYTs4UNxlZYhkkhQJSehSU9vsHidVK4iKasNltK8mOMpuR2QypUoJDr0slRa6wcRDofZtXQmP792Y9T+akMSyblHHsCHAgGCwcBJa9PVRN04rRUs/ektVs/8mFAwwKBz78TncFC5ZQv+BD1ig56QJXphS3fVRYQ1M6C8+t8js+5jk3Mu84per9lnQfm7tNEMYvJjUwg8+CGeZWvwbdtN0htPIkn5/5l8WysK+fu7F9n890+EAn6MabmcecUTZLXrg7xrB5I/eInAgUJEGhW2D6dGBO8AoSoLlY++2GDLpcPB53Cw/ZvvsBbsJ1+8M2ox1mmt4LtnLuXyZ2aQ8tFLBCvNiKTSamcCkxFRA+YLh0vI4SJotuBdt4lwMIS8czvEGjVCYkLM1zdJJSRJwniC9qixqeYHue6JVwk+8SUhW+24YmBvNOeMQCSVIJJKQBUdcPo9HnY6PPjiuOcssTq4MNVEsuz4BvCCQY/+hsuovP/pqDFJ82wqAhUM+egNFlx1C7a86ueNWCKh+x230Wr8+Uf1GV1a6OC5+xdTdKD2s83I1nLfcwNJSW8KtE4FXLYqwqHYc/JQMIDP40IkEqE09WDp/GjdoKIDdpbNshDsE+LLXTdwVZsvozLxIpGIZsOGUlbsxPzJYgC+/uJA1Ln27TLT67SMI39T/yecHJFGEzERCQKaMWfgnhO7b1Fz3kjEhvoLVZXu3xIRvB/Kwm9eILfzoLi9bcHKKswvvhMlVuLbvB3HjLnoJo1DJAiIlEqE9BSCRbFfR94h0rprr2cl88ojfb+9ISefFlzNY903Iv6nLcC8Yycb3nkPAMuSLeSe1ot99lV0Vo5k9tI7477nwm3zadGmOyJg67fTooL3g6x87gVanDsWmVHHgi+fjgjeAfweF98+dTHXvrIAffJ/Z1q8oRDWQJBguFpR13CC+u0yWnXj2lfns27OVAp3r0MsEkjKakN5fvSNWiQWSDkKWeEjweGvpNS9i/lFb+HwV9ArcQLtjcMxKZoxIkFPb52aYBjkYhHaOiwUDyLIZOiysxuccf83cqWGgRfcwa7Vf0aNicQCPc+6AkFSW44pFVcvLOR2GcRpE+9m2fS3CPg8ACRnt+O8O99Hn3j4DzK3w0JV8V5W//EpLlsFrXufRcvuw46op76JxkUoGGTD/G9ZOaPWEqxw/zqyRgylcssWZt18C2M/+YTQL3/i3/yP3VRyIvqbr0DWIofTJFfT0XQW68qn01zXl1c3j4h6jR2Ov9mQtIyu7Vri37abkMWKZ8VaNGOj9z0VsVaa2b9tN4k5p5FVWkjelsWYi/fx3TOXcuHDU2nRbSiS1CQkqUn48wvxbY1dvRQsKSNothzzAN5ZXMK8G27i7Lk/s+jtT2LuY68qxm4uQde6e0RW9HgQtDlw/jwT+9SfIrarRgxGddYQJFkZCNroFi2tVIlWmoTdXx6xPc+1hne5mTvf+AlZhZ+Q3Y4kMx2xQY9QR3Ij6PNRvHQZpWlZxHOyCYRj66ccD2RtW5Dw7INY3/2cwIFCkElRnTEI7YXn4hR8eJ1Wzp0zg4DNSdDrQ52Sgiol+ajab9nMXl59bFlE8C5XCOgMcn75ejsXXt0RnaFpYfhk57/U+iVSGT1HXc2fs2OLBwMsmlnEhNMv5pvSi7H7y2KW0st1OtReCXqjHKs5dotIVm6TsG5DaArgT3KEjFQ0F56D49tIZXhZt44oB/Rq0Gps0e71ccdsFYUEfPH7sjxLV8f1gXf+MhvN6OEIiSYEkwH99ZOpevTFqP1kHdogJNdmduy+cn7LeyLmOUMEWVE+lfM0TxMKBFj/Vq293eZn32Xc8A94z30pYoQade9YBP1uBIkItVaGY198uzhHYSHhQBCntYKtS3+NuY/P7aA8f8d/BvAVPj/flVbxt8VBIAzpcilXpSfSRqWI6QpwLBEkMkzpLTh9/F3Y5i8kvKqIsy58lK9fuzKqD37opQ+j1p+4zJvTb2Z2wUvMzH+uZtsW82xM8mbc22UhiYqcCOGhoMMJXh8ipQJxjAzM0SYhoyXn3fk+M9+/F4+zOuup1icy9tY30cfxRFbpEuh/7k10Pv0CXHYzUpkclT7xiFScPU4bq/74hEXfvUhOp0Hk9hiPPyhlyfT36Tf2qlPCg7sJsJtLWPZzpG7Cng1/0fuOL9jx5bdY9uzhh3Hj6HHTjTS78yrEYjG6dm1RZFYrVKtRopYaSVd14POd18R9nQX2T+g88kXYVn1/9Cxbg2rEYMSN2IbwSAkGQpQUOvj+021sWFmFUiVn8Jl3M/as25j59mX4PS7mfPoYKbkd0Riqg+Cwp27xsIO2fMeSgr//hnCYYLBuBXy3Pba387EmcKAgKniHamcEWbtWhF0ehJ6do8YT5Jmc3WxKhLZNzbFBKwGTAm1ai3pfh7O4mN8vvIQ+C/8CYi/0JkklKI5z+fxBxEoliu6dkL7wyD/l/wJigw6xTFZt2ppw7FXmrRYP+3ZaABCJYfzlHWjWXM+29dWLKGXFTkQiEVr9qXsf+H9ApU8gMbM1FQXRi4+JWa2RKzVktO5B4PfYi5MAfn8QgeoEhTtgiRoPBcNYzB7EYhHnXtKWz9+KVqFXa6S0bGeK2t5EfJoC+JMYh7kMt70K+fA+JJ7WG8+ilYQ9XpSn9UGSkRrRe14fEjPj98kqtaaIDOK/CZbHtjWB6onLQfE3AHnHtiQ8dR/W978kkF+ESC5HNWoomvFjEIy1K3CBsI8qb3SZzUGKXFsJhgKEfT7Se/emeNlybHl5OEtKWD7uXq7/7FNCIinZnQeQtzF2331u1+F892MJpiQlp/UeCF9+GXO/5G7dkCgVeD3xy40AHJbYljYHqfIHeHpfCQe8tSXrRV4/T+4rZkpuGp21h2+lciRI1Gq0HTtS9v43yPe14Mp7v2XtsmkU7NuAzpRGv3NuJDG77QkRsDNXuLFZvYRMeRHB+0GqvAf4/cAzXNTidWSCkqDdgX/3fuxTfyJYUoqkeTa6SeOrMzOqY5cxkCs1tOk7iozWPXDZKkAkQq1LRGtKrbMkXyJTYEhphiGl2VG5DntVCSt/e59z75nOxg0ivvqiHJ83SPe+Z9GmMoza4EKmODHfsyaOHkGfJ0IBHIBwmNnfTOGs6V+x/aOv2P3DT6x85VXsk0rpec/dNcF7zTnMVoJCGGcg0o3iUNwBK2Fl7b1fbDIiOknahA6X4gIHD143F6+n+l7vdgb4aeoBNrTVc+7k15jzwbVUFu7C763tvRZrNRFe6f9GnHDsJ6f+fxYJRP4wUrky4voOxZB8dO41DSHk9eKY9nvccdfcRch7dUHavBmCyRAxJhaJ6ZU8EXfQyu8HnsYXqn6fLXX9ubLNFxjlDatWMu/eg7u8HP+a1bTp2Z8dvuhn+uXpCXH1U44XglEPxhOTlfS4a1XjJ9/UlR2bK/jhk9rKwz+m7WLY2blMvLoj+qZM/EmLxpDMuHs+YupjF0RU4GpNqYy752PUhuqkzekjPCz/K7oVFaDHoAR2+aorfpQSQ8SYucLNX7P2M+vH3TgdPm59tA+jxrdi1vTdhILVSb+kVBV3Pz2AxCOwEvx/5NR+Cp+ihEMhSvO28vOrN1BZWJ0VURuSOPvm18ls1xu54vCCrIzWPZApNfjc0aUy/c69EY0xJe6xit7dcP4aXT4MIG3XCrGidpVWrFah6NUVacscwh4fCGIEox6RNHKBQCZWkaXuwk7boqhzysRKxhsewbdyA87f55EFZL3yGhaXnT+uvY6q7dv5o+/5ZJw+iMFvP8vU7RcS8EVOZpp3HYwg9jF+chZfvrcPxaQeqFJSYlqLDXrxOZSJifgrfagNSTgt5VH7AP9ZYl7o8UUE74fySVEljzeXoT9BkwZJZjrJbzyF9cOpeB99mx6n9abPueeibNcGRcLxzbx73Q7cdjOVlRJef3IdpkQlba6L/f0CWFb6BWdnP4LBn4xrzt/Y3q9diAmWV1G+cj2mKXei6NO9wf3tDUEQJOiTMtAnnbg+rt1r5jL0yrf4/AMreXtsNdsXzi5i9ZJynnp3MOnNmh6UJzuCTIFCra+p9jhIVfFevnvzSiY/MZ2BT1ZXMCkTEyNsEYNVFjyr1uOYNgOxVkOPW86Ockc4SAfNMMQL8zm4BKsZewaierSnnKy4XX6+/WhzTfB+KHu2WwmIWqExJON12yP0TsRGA5pxo6Kq4QCUg/tXeyofY7JOHwTA9k+m0mPkZSz/472ofVr2PLNmUn48CfsCURo3hxKy2iAUJhxnAUQrTeTMjLvok3wxroAZqbi6rD6WoO5/cdDCauX1NzJpziz+TkxjoSeILxwmVSbh0iQDnTSnlq2Zz+PEZaskFAjUCDHWhUYnQySCxBQVMrnA8gXRonrzfttHn9Mz6dKrcQlbhsNhLL5iJAExUmcYiViOoNdGzTGbqCYxsxVXPPcHFQU7qSjYRWJWaxIzWqFLrF3wzW2TQOsOCezcEpms0+hkDBqn4+2ir+loPAudtPbeYqly89pjy9i+qfaYVx5ZxrCzm/PiJ2fgtPuRKwT0RgXGxFPr93Y8ODH1QU0cEdbyAr596mJS23Sm/+Tb6TLyEoJ+H98+dTFFO9b89wnioEtIZ9Lj0yIf7iIRXYdfQqfBE2r6zWMhzW2GJCtGWZdIhP66SxHrtFFDgtGAJC0ZSXJizBurWmrkvNxnY77eTWlfIX5tBlVTXsK7cl31f+9PRbt6G2M+re39K1+3nqAsxOTnfqFd/7Go9YkkZLRiyKQH6XDa+Ux7/lJM6u30H5rJyg1+LvhrHpn/TIIANBkZnP3TDyR37waA1pjK0EkPxbymrPZ9I254sdjkjJ0RAcj3+vCcoJ47qPYIljbPxvTQbaR88irGKy/B0KfPcQ/e3Q4Lq2d+woHdBTxz70pKC51IJGICxP/sgmEf4XCYkMWK7eOvo3cIh7G8/hGhKsuxu/BGgkQqx2JPiAjeD+J0+Jnx/W583vhVJE2cHGiMKfQ9J1oAEUCh1qExJaPNzESbmRkZvFtt2GfNx7VvL0LLbPx788i1tCBZEV2CLBMrGam9meCclQDoLp+IcJRVrhsbLoefdcuL446vX+0mrWVXup4xKaLVRSyXoTlvJNorLqwWkAVEcjnqcaPRXzsJQXPsq5c0mRm0mTiBPT/+jMGfwMBzbkOhrs7gClI5PUZewajrnkepPba9+LEQqxQoenWJOy5r14pAaTnUNc8Q5CQqcmim6Uaaqu1hBe8ApnZtEYnF+J1O/hw0hPQnHuWBqiKmuKu4ascmOgU8qOtpu3syYCnL5/d37uLdmwfw7i0D+OKRc9m9ag7uqtiJCAC9Uc6AYc3oMyiTxX/GFmcFmPHtDtyuuls2jic2XxnLS6fiKTqA+8MfqLrhEcquvxfrh1Orv19NxESXmE7zroPpPeYamnc5PWoua0xQcscT/bjytm6kN9OSkKxk2Pnp3P5aNt9WTqS1/nQmt/oAlbT23lJ0wBERvEN1p+3cX/fy2ZvryczVkdPK2BS8HyaicPgERgyNEJvNhl6vx2q1oovjtXmi2bb6D8KJAkscn1Pg2USiLIdB2qsoWLiU0q0bufDhryMs3xpCOBzGXlWCo6oEr8uOPjkLlS4Bhfq/P4tAWQX2qT/hmrcI/AGkzbPR33Q50pa5ERn4huAO2NhqnsPU3Tdh81dnxrslnMPkkjuwvRCdXQBQXDWRmc8/Q0K7dvR+5H726Dci2ummdPNGUnM74vM42bZ0BiV7NwKgS8zg7FvfRipTYEjJRAiKcVdUEPT5UBiMqNPTIrQE3HYLe9bNY/6XT2OvKkYiU9BlyEQGjLsNbULdk9vZFVY+KIpdrqoQi3i9dRaJMinO0lI8VWYIh5AbjWjSTu1J86Ec2LqcX9+8lXYj3uWDV/cC1f1R172m4cPiM2Ie09k0hmvaTkW0OT+2x/E/JL37HLLmRyZY19ipLNrPJ28XsnpxbKshnUHOcx8NJyHp2GThg4EQ5koPPl8QuVzAkKBAOM7aDscKu9WLxewhb7cVjVZGRrYWY6ISieTEvD+HpZylP77O6lmf1bT2JGS0ZPw9H5OY1Tpq/1AwgLlwL+tnfUnB/g0YEjLoMWAC8o15hAa3Zk7gc5ZWfYk/5KWTaRTnZz2FfmUVIqcXeY/OCCYDYvWpXb1RVe7m7stn43TEDkpGnJNFpuEP+p17A1pT9H05HAwSrDQT9ngRyWUIJsNxzfw5S0vZO+MPVr/wEgmdO9Lx5qtRZaQg1+jRmlKQyE5cuXOguJSymx6M0gMQyeWYptyJf38+mvNGHtMqKai22lv5zHOsej5ai2fs9B9pcc7ZjcJx5WhgqyzmqynjMBfvixq76O7PyW7fL8pe7yDmSjc7NlXw4+fbOLA32s0CoHkbIw+9NAiNThZz/GgSdLlwlZZBOIwqIQFBHzkvdfmt/LT/QU6XTETywNeEzJHXLCSZSHzlcSQNsEtsIpJwOIzN4sUX8IDCji1chEqiRydNRi2NjDs+f2s9f/wQ31737e9HN5XNx6C+cWhTCf1JRigUwpPm5d09FxCiesJW6NrMBssMJg54CW1VBgG/57DPLxKJ0CWkoasjEA2HwwTLKvBt3l7t256bhaJHZ4SkBPQ3Xob24vMhFKxWnD/CskGlREe3xPPI1fXBFahCEEkx+BNwfPRW3GNCy9YybsavSHRapGo1xnBrFvz5NBvmfcMWmRKNMQm33Vyzv62iELetnKkvXUPzLoMZc/NrdfpmK7UGOpx2Ptkd+uPzupBIZKgNSfWaGHXRqhADoRhjZ5p0aEUiSlatZvblV1G5dSsAhhYtOOOj90nr2ycik3Yq4vM4WfbzO+gS0inMr201cDr8VO020DZlBNsdsyOOkYvVjM99DqVEh/c/sianyqSsLlQ6E3J5/AyiXCEgPkafg6XKw5xf9vD7DztxOwOotVLOvbgtp4/MQW88ub+7lioPn72xjmWHlJLKlQL3PD2Atp0SkcqOf8ZOY0ji9EseoNfoq3HZqpDKlah0CXHLY0v3beGLR86raScqYBWbl/7MyEmPk/n9Ms6UtGXkTZsRSSUoJQaUEh3EXjM7ZdEZZAwdk8tv38YWbRp0Vi6p6begiVOGLhKEExogqFNS6HTVFTQfM6q6XFqrRd5IkhFCShJJrz6O5d3P8K2r7qeWtW+N9sJz8G7dhWbM8GMevEO1Inbba29G1aoTW15/EXt+PomdO9P5vkcwdO54Sj0nSvdtiRm8A8z74XkmXvcmOn37mOPGBCVd+6SyZ4c5KoBXqiQMPzODESNSkTstBEUqBO2xs5az7t3L1k+/YNu33yESi2l/0UTaXnwR2qwsxMrqZ4s9UEalay+ydbsImKMXHILlVbgXr0Jz3lmn1N/4eCISif55lisAA0lkxd1XrYm/cCmTCzT9CY6MpgD+JMPqL+brgltrgvdD+an0YW4/5zcEybFdCQ3kFVBx9xOE7LW98ja5nITnHkTWtiWSlMjJS8jrJVRlJex2VyuCG/Q1N9z6IBaJMckzMcmr1d2DNjthf/wS4LA/iEytRlCr/zleIL1lV8665llU+kQsZQfQGlMJh8Ms+v4l/F43Hkf1zX7vhr9Y++cXDLzgDoQ6hJpEItF/ZttjYZIK3Judyot5JRF/wbYqOWOSDLj27+f704cScNeWi1v27OHHM85i0rrVJHY8sTZux5qAz4u9sgiPy0a7npHf469ezuP6J56jXfLZLHe8jStgppNpFGdl3keSsjkAQnICIqWiWrn3XwhpyYj1jWMieyxRanSccU5zlsyL7lkEGD62xTEJpl1OP99/spl5v9VOFp12P1Pf34Td5mP85e2RK07OR04oGGbhzH0RwTuA1x3kufsW8/LnI0jNODG+yHKFGnmq+j/dBZyWCn5969YoLRCA2d8+xdV3fYNvyjuYrpj0f+Xz/m8kUoGR41qxdlkxhXmR3uPnXtKG1Ezjcck2HinqlPiaNScKkViMNDsT00O3E7LYCPv9hH0BxFo1mvatj2kAeChV5W4eu38dMrmRobe/S45eoKjYz6sflJOetYm7nu6PVndqqKvv3xStIXSQsryt+Ow2glZ73Cy8Qill+NnNmffr3pqqlJR0Nffe2RrpjF/w3bWGslAYWae26G+4DGl2JiLJ0b3P2/btY9oZI7Hu3VuzbcnjT7L12+85/9ef0bduWf1+3LvpIB0Ey6JtcA/iXrgU1RmDYtoVNnF06TM4kx8+3RpzbOjoXHSGU+M3dqI4OWdT/8c4/JWYvbEn5v6QB7/Gf0QWVP9F0Gyl6pk3IoJ3gLDXS9VjL5P09jNIkmr70oJVZmxfT8c1awH4AyAIKIf0R3/FhQiJh1fmL9ZqUA4/Df+uvTHHlcNPq1YEPoSU3E78+OLVlOXV3kw0xhTG3PgKlrJ8Niz4tmb7qt8/otsZk+qsQjhcZGIxXbRK3mzTjO0uNxZ/kHYaJclSCVoRLPnwo4jg/SChQIBVz73A8A/eRao6uUuOKstd7N9lYeOqUpLSVPTon44pQYFYHECmVNOsfV9W/fExyck+NDoZDlt1Jj7gD/HWA7vIbd2ay+79hdRmKtQSEzKhtn9KMBkx3nMjVU+9CqFDuoPkMkxT7gIgWGlGbNAhOoV6HP9NRraeoaNzmf97ZOYlp5We085shlg4ukvfoWAQc4Uj6vUO8se0XQwf25yU9BMT5B4p5io3v30XOyMb8IfYtLr0hAXw9cXtqKIiP/Z7CAX8VJkLSRoxOKZeyf8bCckqHnp5EHu2V7Fk7gHUWjnDxuSSkq5p9MF70OfDXV6Ox21HFBYhU6hQpaQgyBrPdQtazXEL1mNhrnJTUVpdxv/V55GLNFazF7vFe8oE8Dpj/HmMXKUDtxeCgbj7ACSnqXnq3WF88+EmVi0u5PZbWyO8/FJEltu3aTvltz9K8tvPIm129ERcw6EQO7+fFhG8H8S8Ywf7fv+dzulXI9aoUQhafHgQ1dGyKVbIETXyli5PVRXOklJK165DrteR2LEj6vQ0JCeZdacpUckl13dm6nsbI7anN9Ny9oVtTkjV2qlEUwB/iiFIj+0PPGS1EciLvYAQstqqRcL+CeBDbje2z77HNX8JQqKJsNNNyGbHPXcRYZcbw53XH9YqqEgkQtm/J86fZxIsjrRtE9KSUfTvGVEe5XHamP3RQxHBO4DDXMof79/LeXe+y6wPV9ds97pshP7jgXYkyMRiUuRiUuSR5UUei4XCxUvjHle8YiU+u/2kDuDLip08ecdCyoqdNdumvreJmx/sjKfkZ1RqNd3OvJR1c6ey5Nv7ueORd/nwtTxKCqsXjASJiC69U8lIbIYuxsNMJJUg79GZ5PdewPnbHPwHCpF374jytL645y/GNWchAMphp6EeOfSU7YXTGRRcdG1HBo/M4c9f9uBxBxg8MocWbU2YjrJgjNNawaaFPyLRDyAcqzeE6iDXYfORcuzti48JoWAYuzW2ewRQ8/1szPyX3E0oHEJ30bkNqo46lUlIUpGQpKLngAzE4pOj1tNnt1Nu2cVu21I2hGchDSvp7TufFFcLkjPaI9M07kWm44XTFv+3DEQ5EATDYSz+IGHCKMRiNCeRC0Nup9MQiYWY9rc9Bl+MtNSOqHfd8zCRSER6My03PtALX6A7ovl/YY9Roo7Pj/37XzHcciXioxRsuquq2PbNd3HHt30/jdYTLkClUZMgz+YH72y6j74Ptu+Oub/6vFGIVY1XNM1ZWsrf99zPti+/qtkmUSgY/f03NBs+DKmy8V77v1FrZAwbk0vX3in8NWs/lioP/YZk0aKNEdMx0uD5f6IpgD/JUId1mORZVHnzo8ZkYiVJquaHdV67rxyLr4gDjrVopcmkqztikGUgEUd+RcKBugPbsMdb8/8hiw1v20S8YydR6N+GXkgm2Z0CH/6JZ+lqQlda6x3AO60VBAN+pHIlSo0Bs16P+4n7UP61BOYtrt5p2EBcgwfg1Wo5VKbMZatg7/oFMc9rqyjEXlWKIJERDFQ/1JOz2yGVH/+bpESpRN8il+Jly2KO67KbITmJbt5WbwmOQCWhcBCNNAFlKJkv39kQEbxDdXD09rMbeejZ4fz8/Gj2bVrEJY/9wKwP7uevTy/nokvuQ5PcCYncgDFBgyFBUWcptlghR5ydif6GyYR9fkJ2BxX3Phmx2OP4ejrueYtIfGnKKR3E6wwKWnVIIBwKIxwDoTW/18XyX95h+S/vcvbdc+rcV644eSa9h+J2+QkGQ2Rka6NKqg/SrkvjLzlXagwYUrKxlEYrSovEYlI69vy/Lp2Px8kSvANY/MW8X3wpRZ7axeo1TKOPdiLn2B8jWdP2BF5d4yExJf68QyYXUGurqxUsZg9emYiFdid/VFpxBEO0UymYnJ5AplSKrdzNxlWl5O2x0rqDiXZdkkhMUTWq/mq5XMf5d7zH9NdvJBSoFWbMad+fzu2Gocpqjlhev+oMt1TEFpuDdivWxt3Hu24zIYfrqAXwIrEYcR0ikGKpBLG4+tlikKczvvmLlOoKSenRnuCayKSNYmAfZG0Ob458PAiHw+z6cXpE8A4Q8Hj49bzxXL5tU536TI0RtVaGWitj8k1dT/SlnHI0BfAnEYHyStQVfi5Lfp03CiYQDEcG0xdnvII21PCydIu3iE93XskWc604mFys5taOM2ihG4BEXHvzFGs1iFRKwq4Yll5iMcIh5fM2lYMvUl9nZ1FtD5ZS0HPr3d+ie+Xvat/XWNZzh+C0VZK3aQmLf3gFa3kByTkdOOuaZ1mmSuUri5su/fsxcGBfABaHRGywuOkRquK2rGRU/5RI+73x7ccAPA4LUoWKoKM6gB9++ePHtA0hHhK5nB6338b2r2LYoAG9H7y/0YgR1UUwHCDfsZ6Ptk+ixF3di6aXpTGp+XvINPrYxwTCFOaH0Cdlsm/DQgRByoQHPq/52yk0RtT6hlkGiQQBFGI8fy6MqtQACJZW4F604rioHp9IxGIRHm8Am8WLIBEfVt9ZOBzGWVSMq7yccDCAMikJdXo6Dks5K3//CAC3ZRepGZqY2eicVga0J2G/W2W5i6/e3ciBPVbGXtSGd59bHbVPQrKS3FaG439xDURjTGHMTa8w9bEJUdm4QRfeizqhcXk5N9EwfB4ni0s/iQjeD7LC/h19DRdjcmdHLQKHgkGcJaUEPR4kahVybbX466mMziin35DMKE0LgLMvbI1SI2HTmlIqXD7mp8BWT23GfqvLw4O7C5nSLJWP71tM0YHqRb05v+xBrZHy2JtDaNY89nPuRKBLzyDodnPNc39SvH01Tks5mTmdUbpFaFIykaTV7Qd/EHsgyLsFZWiBtnVoyYh1GkRHsUJBaTLR5fpr+PPq6HsvQJerr0b+z8KjWCTQXNsXsyyfwM2tUBUPIzRvHSKxGPVZQ5BkpiEYGs/f5iC+UIhyXwB7UTGrnnsh5j7hYJBd036i9wP3Heera6KxcurOWk8xwn4/7vlL8G3aRsKnW3k4cx6DTFfQTNONnsbxPJA5i5azQgi2hinQB0J+5ha+HhG8A3hDTl7bPBLLIf32Pm8Qm0iBevIFMc+lPn8k4n9U530hD3+UvsROR6SAijto5fXCCYSvPfM/7Yi8LjsrfnmX6a9cR3n+DnweJwXbV7JyzldscngIA+tdXt5y+HjL4WO9y0sY2O3y4g7WlovKVVoksviZa40xBa/LhiElmwkPfkl6y64R4y5bJbaKIpzWytgnqCdV/gB7XF422l0Uen04AtElbYZWLTnjw/cjVpxFgsDA554huWt8D93GRKUnjxc2nF4TvANYfcW8s/1cBkwQx+0hdTqCSBXV34nda+fi97pJyGhJQkbLBgfvBwk5nNW2hnFwz1tMyO6MO36y4/cFObDXynvPr+b+a+by1F0LWTrvAFZz/e8TQZ+PoqXL+LrvAL7q1pOpPfvyZefu7Pj2e/wOV01WZ+Uvz3DtndnoTZGBekKSktun9EVvOLlKs20WD28+tZKl8/Ip2G9j7w4zV9zWDYOp9n106pnCo68NJiH55CgHTG/VnatfnkOH087HmJpDdscBXDzle3qceRlyZVN59cmM3VfGMutXcceXu74j4I8sHbcdyGf5E0/xbf/T+H7wMFY8+QzlGzZi2bOXUDC+UOzJjkYr47JbujJ6QquayiC1Vsol13di5LhW5O2y8PIjS1FkqiOC94OEgM/Kqxg0rkXEdqfDz2uPLcNadfhOQEcbkViMsWUr5DIdOe0G0mXABSRltEHXtRvyFjn1FpyzBoJsdLjZ4fXjHz087n7aC86Osnc7UrLPPJO0fn2jtmcOOo2M00+LWICXiKUkKZuTlt4DY4/+JNx7E6a7b0DesW2jDN4DoTCbHR5u35mPzefDUVgYd1/zzviWbE38/9GUgT9JCFpsuJevRtG1I8EVmxGv3cFZp3UjmH06QpmDwILvCLrcMPb8Bp3X5itmQfE7Mcf8IQ+7bEtIVObitPtY9GceX723kQsm5tLnlhsRTZ9OoKAYISkB0cTBOHpkoVPK/jlvCYtLP415Xk/QTqEynyRj9zqvzWmtYPmv70Ztd1eVkCQK0VMrJVfuwxWSsMwGVf8ExCapgOyQskeNMYW+Y69j8bTXos6V22UIcm02o+/4DY3eQEpmJnJV9XtwOywU7VzLX18/R0XhbkxpOZx2wV0kJrVAIpKhSk1BVs9MxQGPj2f2FVPur62a6K9Xc2V6IkZp7c9QrtPR9uILaTZsCJVbthEKBkjs2BFVSvJJ0b8YCodYUTYVX8gVNRYmzELbswwa8zB/fB39kMptpWLnrAO154rRs9dQRCJx3R7MUgmcRCWyB3HZqnBYyqgq2oNKn4ghOQutKS2qdHPfLguP3bKA4D8LWpYqD68/sYKhY3K55LrO9RLksh04wLRhZxL01rbHeK1WZl16GRcsnIvakITTUo6tvICFn13NjXc8g8Olp7TIS4sOmWQ1Nx0zz/ljibnSw7b15TX/nj19D606mLjo2k4oVZIaD3i1pvGIg/0XUpmC5GbtGH3Di3jdDiRSBQq1DpuvlCrnZgIhL2ppAgZZOhLxyfO+mgAkAv6wN+6wHzfCIaXS9vx8pg07E8vu2l7hDe+8y55ff+PMD98jHAxgbN36mF7yicSYoOSciSmMGKNCJJYiVRrRGXU4bD4+f3MDmdk6doX8cY/f5/YxvmV0QFiYZ8dm9aI3NZ4FS5FIhDbjCAVIHFU8LLPjqChAnN4W2aXjcX85LWIX5en9kHfteGSvEwNtViZjvv+G4mXL2fThx4jEYjpfdw2pPXqgaRbfxuxkwBwI8MqBEkJAgUggpUcPSlatirlvs+HDCPr9CHXNaZr4v6EpgD9ZCIfx78lDe8HZ1f/2+/HPXwnUeopLcrIQqRvWIx0I+/EG4wswVXiqVaX37jTz6RvrAfj6yz38naNj7JgrSEmWk5Qj4/2qiyjL282UhHUY5ZkEQl4CdUwmLDIzbpeTQFUFMq0WVVJ072VV8V7CoWhVLIe5hDGJDhaUfsT24jlopMlMTr2TynAbviwNMz7ZhPaQEi6JVE7PUVchSOUs+/ltfG4HYomUtv3PJ6fHtTx48yb8vurXOe9SJ+dNaodECLF1yS/M+uD+mvOU5W3jx5euZuDYW7HN3oTKkEDfRx9GnVq3XU+Fz8/je4uw/CvjvtTqJFEq4eJUE9JDVpClKhX63Fz0ubl1nrcxUr3oszjueIF7He1yo287nXsmYCteTcBXnbkwpGSj1BiO+HrEGhXqc0bg27w95rh67JknVA35cLBXlTDjnTvZu65W10FtSOLCh78mJadDTRBvM3v56JU1NcH7ocyfsY/RF7T6zwA+HA6z9fMvI4L3Q1n+2FP0ueFa5n/7NADm4n38/vpFqPWJpLfuQcvzX0RjOPmCdyBKq8FgUlBR4uLd56onVy98fMZJFbwfilSuQipXEQ6HKXBs4v1tEyh2V/9G5GI15+Q8Qf+Uy9BID6/ypYnjj1aRTDfjuSwq+yjmeL+ESyO0Xfb+MSsieD+Io6CAA/MXoNiwkW633HRSi6bGw+O0sW/jQuZ+9ji2ikLEgoR2/c5myKQH8QWNHNhrJbuFHiXxF3clIgjHuLcC+P1x1DxPUqzlBcx+4SpK9taqiQ8Zfzdd330W25pNhL0+DH26I0tOOOrZd4CQy43M6iRxdzGnnz0esUaNMq0ZMl3jy6g3lDJfAPc/jjmzQwK3PPcMJcPOiNpPnZqKqUtnlj/xNG0mjEffovlR/22avYUccKxjU9UfmORZdE88H6M8E7mgJhQMYjeX4HPZkcgUqHQJyFVNjiUnkqYA/iRBpFIia9+aQF4B8t7d8K5cF7mDWIThlisbXCIkE6tIUjSn3BPbkq2lrj8Om49pn22J2F6w38Y7b9kAOGN8BurTE3D4lxP8Z8VaLqjRy9Kw+opjnjcl0IIvu3fHXVlJUpcuDH3zNZK7d4u4IUljlL0rtSb6Xn8bz23sjSdYKyi13TKXAam3ck/WvbRTR698q/WJ9Dv3JjqdPh6X3U5psZ9li+z8MGUrwUDtQ/i3b3cydHQucsHCvC+eiHnty2a+z/ir3+HXM85BqtXS/4kpddp7FHr9UcH7QWZV2hiZqCdZdvJ2swT8Ifz+IHKFBIlYTpqyLVvNsQXNEuTZtOuYRm5rK/t2WtDoZAwfk0r79i5mvvUYUF3yN+r6F9EYj46PsbxjW2TdO+Jbuzliu6RdK7w6NaLy8poFpFA4TCgMkkaalQ/4PCye9lpE8A7gtJTz9WMTuOKFmRhTqiUcnQ4febtjKAX/w9YNFWTm1H2/CHq9lK6K3XsIULllK0O7vc3KWR/isETqDAy55EE0huiFuWAwhLnSQ8AfQiYTMCYqDlv0yeGvwuYrodC1GbXESIqyNfoY4puHQ1KKCo1ORpcBBk4/T09FeCthQiSJO7D4Vzty5ckpyncold4DvLjxdJwBc802b8jJ93vvwijPpFfShBN4dU3Uhcdpw+O0ggiUGiNypYazsu5lbdWPEX9PgGx1T7L1PWv+7bVa2fbl1Ljn3vf7TNpNnoTHbD6lAviA14vPbid/90p+eunamu2hYIAti6dTmreFC+7/BrVOSmaOih5GJd857cQK0/uqVKydHi0mrFBK0DZyq8GG4HZYmPH2HRHBO8CCaS+xedXvdLr7C7aG5UxMMSEcA3X+cDiMd90mqp54NfK6AM34MWgvPu8/2zEbM/5D3EEq/AH+Tm/GkB+nsfr2O7DnV3+/MoYMpssbr/OeSs95ky9l5kUX0/eB+2h+9pijYg/pcdmxhUp4besoyjy1i3rT9z/E1W2/or1qODsWzWDhty/icVgQicW06nkmw694DJ/GR6FzMwqJjhRly6P2/G3iv2n6lE8SBI0a/TWXUH7HFIy3XoWsbQtcs/8iZLEha9cK3ZUXIcnJbPB5DfI0xue+wLvbxkeNpShbkaZqh98epKQgfp9wWV6AnuqzOFN3C879pXjEleiTsjg/52k+3Xll1P656l4kVJkY/8P3iLx+kEkpWL4SQaEgtWeP2mtLaYZUocLvqS3H7jzqQv6wPB8RvB9kSckbnJFxLTpJbA9SQSJFn5RJaWkFzz4YW5U+4A9ht/oISCojXvdQgn4vfnwgErH+rbfpcsO16HNy4n4+pb74ZXi+cBhfKPYqfmMgGAxhNXsJh8IolJIadV4Ap91HcYGDP6btpKrcTZdeqfQflsXQ9FuYX/Q2YaKzEGOzHyPblMWDLybj8wYJh/y4zDuY9/kLKLVG0lp2ZdDEu0hIb3nU3oNgMqC7+Uoc6zbB6o0QhnC3dpjtFmaePpiBzz1Dm+uvozgQZHalDXswyAC9hnZqBYmyxlWq5rCUs2H+tzHH3A4zhTvW4Pd6kCjTcbnif+8AZPVYNBJkMhK7dGH/7D9jjhtatUSdkMQVz8+kPH87ZXnbSMpqQ1J2O/SJ0b9DS5Wbeb/tY8b3O3E5/BgTFFxwRQd6DcpAovZg95dR5clHKdGhl6VhkKXHDe6t3hJWFn1NZrA5RjTs8S9nqv0mLmv9Ec11/SLENxuCxVtMhWcvhaqt3PtxF/a5l/B23j2EqF6EEyFm9NgnURjbAyd3BmK3dVFUsHeQ6fsepLVuEHp5k7hdYyLgdmMu3c+8L59kz4a/EIlEtO45gqGXPkRyWkse6raKPwteYV3ldGRiJaen3kCf5AsxKmrnByKxOKKc/t8IiupFtVNF3DMUDGLdt4+1r75O8sBe/L0odttgRf5ObBX7eeLVjqz54z02Tfudq0beyMdmX0QQnyaTcrZGy2Ozox0dLrq2I8aExlM+f6S4rJXs3xS7qq48bxtJLjNnZyYRDlcRDicedQX+UKUZy1ux2zEdP/2OevSwkzqAT5VJEVNbSTvPF6aqWy8mL/qLqsoqRFIZ2+RKnvOD3eFhl1jEgx99yKyBg7hsywZ02dl1nP2/cVjK2bF+NqsTfosI3qG67fGj7ZfySPtVzP7oodrtoRA7V87CXLKPDjdN4tOiawCQCxpuaj+d1vpBTS1Yx4GmAP4kQtosg+TXnsD68TeEXW40E85Bmp2BkJKMJPnwSx3bGoZydduv+GHvPVh9xYgQ0zXhHCa2eAWDPB2X309mrhZLHGGW7NYqMszZ/PryDQR81arhMoWaic98w+RWH/DT/gdx+CsQI9AzYSKXaJ/C9e43+NZuqjlHetuWSEJh3GYzSqMRALUxmfPvfJ/vn7u8RjU5pWMXtlc8Gve9bKmaRaa6A05/FVZ/KSWubaglCSQpmmOQpyMWCcjqsCADkMoEhP9YQRQECYTDBFwu/M7Ygf5BMuuYKKkFMRKzhaBUgpDYcAeBo03Q78dVVgbhMEGpmoXzSpjx3S4cdh/tuyYx6frOZGTrCAZCLJi5jy/frl2V37ahgl+/3cETbw3m5va/8v72CTW98IJIwtjsx8nR9gKIVEJP682FD00lGPAhU2qOiZhW6Y7tzLrhOnLOGI5IJGL/p+/UrG6vePJptKNHc7+zdsFhlc1FikzC483TSWpEQXzA5yHor6M1pTSPZT+/zaBJL7JhvYwO3ZLYsq48aj+RGNp2+m+nBZFYTMcrL2PtK68SimEh2f/xKSiMRhQY0SWm06Lb0Ljnctp9fP3+JhbOqp30mis9fPDSGhx2L+qBC/hu/82E/5kqG2Tp3NLxN7LUXRGLIgOJYDhIqKKKzr8rCP75M2Gvl67tW9Htyo/5Me8VjG2ySFLUvwXF63bgslbgtFfgFFnYF1zDQusHjMt9jml5d0bsGybEjJKHaJ8yEAOD6v0ajZF99pVxx8o8ewiE6/bLbuL4EiyvxFJRwGdPj8fnrm59C4fD7Fg5kwPbVnDlCzNJTm7BxBavMLrZQ4hFYrTS5Kjfj0yrpdstN5E/P/ZCdpuJF6BMTkKdcnSqoE405p07+bp3f/wOB6NGD41ppXiQgm0rKdixij3r5gPQzu/jsTHXsz6swC7I6alT0VylQOYKctUd3fnx862Ul7hIb6bloms60r5rEhLpyV+dcxCfp26R16CziPe3XE4g7GNg6lX0SBqHSX70+tJDdgehKkucwTD+A4VI0k/eRUa9ROD8ZCPTymoXUlsqFbxjd7FLooUw4Kmdm7hDYbYpNSR06IBtf94RBfDBYID1c6eib5vLWvNPMfcJE2Jb1TwSs1pTkb8zYqz8wA50riSUgg530IY36OD1zaN4suc2kpUtYp6viaNHUwB/EiGSSpE2z8b04K2EXG4QiRCM+mq7rCNALTXSJ+li2uhPxx2wIxHL0EqTUUqqs0sqjZQJV3Rk85roh71UJmbg8Cym3ndORL+6z+Pky7vO4fo3F9Ox+1o8ATtSsYLgpjJc3/0QEbwD+LfvJvzldFT33wzV8TsSiYzsTqdx3esL2bzwR8rzd6BLSIOK+O9FLBKweov5Zs9trK74ofY9Skzc1vEPsrU90BvlJKepo3pcATKytegMcgQS0SdlYi2PtplRG5LwV1SXJgtyOVJV3boDqXIpaTIpxTEy8eeoZIjf+YxKs4WEx+9GSDhxQbwtP58N77zHpg8/JuB2kzN6DG1vupuliXJsFi+b15Tx4PXzePKtIWh0cr56Z2PUOVwOPx+/to67nhzKEz23UuHZSyDkI0XZCq00BYUktuifSnds33fFxk04CgrY/OlnUWPuigocTicQ+Xcs9QX4uczC5ekJERoFJxKpQoVSY8TtiJ01NaU3x1J2gDW/v0J27wfp0a8jz963BLs1MhC7/Nau9RZZ0mVnc94fv/H7RZPwVFY7MUgUCga99ALJ3bvV+9qtFm9E8H4oP36xjVt6dagJ3gEsviJe2jCUKT02kKiInKT4K0rxPvEpwf21v8/A1l1w317Of+FO9tmW1zuAd5jLWPjN82xY8F3NQmF6y65cf/O3zKl4M+5xMw88T7N23VBITt4sfKY6vrOFSZ6FIGo8i1cHCYfDhCqqCFZZCLndCEmJCEYd4lOo1DsWQYsN22+zWeNZXxO8H4rbXsWWxT/T75ybkApyjPK6RcvS+vYhd9RZ7PtjVuT2fv0wtWtLSvdup0QG3muzsei+B/E7/lnw8AWQypVx7WWVOiPmQwL8bfO/Zvtf35HZthfDz7mJVln/KLDrJQwZlUvXPqkEA2EkUnGEQ8XhEvAHcTkDCBJRTI0Nh92H0+5DJBKh0clQqY/tb1Sh1iMWJISC0Qu4ACE15BVV+8J/t/cO/i75gDs6/YlJnom9oICqbTuo3LIFU7u2mNq3Q5fVwOD+P8ryRYqTz570UJSCmNGJenKVMr4vNVPuC9BcKeO3CkvcY/bKVbRv2ZxgjEX1huAwl7Li1/c4s/1zUbbUh+IKmmO2tAI4D5SRlNaCA87qtt5g2M/GyhkMz7ztiK6tif+mKYA/CRGrVUe9ZEgkEmGUZ2KMcy/Maq7n1kf78PGra3HaqwPRxBQVNz/cg91LPo4pNkc4zJJprzPquhcwqatv2naFG9vK9TFfI7BzLyJPZHZRKpOjS8lg4MQ7ESHGE7TR3nAGWy2xe6y7mMayoPjdiOAdwBmo4uVNw3m8xyYSE3K45+kBPH77XzhstYGN3ijnzif6/fMQVnD+XR/w1ZRxEQ96QSrnrElPsfL2xwHodM3VqNPSYn9o/2CSSngkN40388vY5qquYpCJRIzWyBmwdx/+f/QMvFt2ohoUbZVyPLAXFPDTmaOo2l4r9rb7h+85MGsmV86Yz5NPOvD7QoSCYX77bgfd+qYRjlP5v219BU57kNSM7KjA60RhaBW/JF+q0eCXyiBGxfkCs53zk40kNBKNAq0xlYET7mTOJ49EjSVmtcbrsuNzO8jb/Df9h1/L8mtu4N6nX2PbfjFbNlkxJio56/yWpKSrUarqN/GTKBRkDR3CpetW4SwpJRQIoE5LRZ2aikRR/wlreYwFs4N43UFC7tp7mkQko0fSeDqZRuHyV+GRJqAQaiszQvuLI4L3GoJBRF/MR3tr73pdU8DnZdnPb7N+3tcR24t2r2fmi3fS+87bWM6XMY+t8h7AH/agOInL6NsahyIXq/GGov82Z2dPwSCv+952vAkHQ/h376NyyouEzP/oO4hEqMYMRzdpXKO0iTpaBCur8CsF9q1fFnefXav+pMeIy1Co//tzUKemcubHH1K+fiMbP/iQUCBAmwnjSezcGWVSEpq0kzereShei4V9v/9R8+9dX0+j0+njWDs/2nJPkMoxpuZQVbQnYns4FCR/63LCoRBZ7XqjUNcKtRkTGiYcHI9QKEx5iZM5v+xh3fISNDoZZ09sTasOCeiNCgKBEIV5Nj59fR3bNlQgEkHX3qlMvrkraVmamKXrXm8Ah9VHOAwqtRSVpuHBvtqQRJdhF7Puzy+ixnK7D2ZXYGnEtmLXNnZYFtDG2odpw0bgKKi9T6tTUxk//08S2rWr9+uLdVqkLXLw79kfNSZSyJGknfxVIjqJQF+9hnZqJYFQmDBhkmVSDsSwMARI83lwl5ZhaH5kQsdBvw+P04qtoJAMU0cKXZtj7tdc3ps/C7+POabQpNNCPbgmgAcocccWDW7i6NIUwDdRL1RqKX1Pz6RNp0TsFi9isQitQY5C7mHhR7PiHldZuAu/z41EVr0yIP2n9Dwe4UM8uas8+Wy1zGV1+feopQkMSb+RVGVbLmzxKs+s7xfVBz8843ZEIhHzCl+PeW5v0ME+2woSFTlkNdfx/EdnUFJgxyT1ohX8SKUCEk2QcDiMSCQitXknrnllATtXzaJgx2oSU1qQld2DlY88Q8WmTbS5cCK9H7yvXkFMilzKvalGzJVV+AIhlD4f0mm/Epj7d80+rpnzUfTuhvgErCgXL18REbwfxGe3s/P91xk4+CoW/FktSHhgr42O3et+aIYbWV9/YqeOKBISajLIh9Lx5htZIFWCP/ph6Q+HI7LCR4LFW0yRazOryr9HKdHTL3kSJnk2aqmx3ucQCwIdTjuPoN/Hkh9fw+uyg0hEi65D6DXqKn55/WYAFCodtj17KVqylKLBvUnt25eBZ4ygzQVXktKi/q936Otqs7LQNjR7cgjq/5g8Sv5JNhnlmVzW6iNWlE3li53XEAwH6JZ4HudmP0GyshVikRjvig1xz+PfuIMM8SX1uiaHpZS1MSamAJWFu9G4DMjESnyh6GxdC11/FMLJG7wDJMibcXfn+by19RysvhIAxAicmXk3XU1jT/DVRRMsr6TivqcIuw9p5wqHcf02B0lGGppzRpwSWeNYBPKLELm8KOpw51DpEhAk9Q/S1KmpqM9KJWvoYMKA5CgIYjU+qnv5DyYZ9v/2Bxc/u4qywp0U7KhtIZHIlJx36zvYSuP7cMsUKsRHWPEYj5ICOw/dMB+Xo3YlefvGCk4fmc2lN3TBbvPx8A3z8Xmrq4TCYVi3ooRd2+bz7AfDSU6LrG4rK3by0xdbWTTnAMFAiK69U7nkhs6kZ2kRJPX/jcgUKgZNuAuJVMbaP78k6PciEgu0HXg2Lc8dybv5E6OOEZtDzJh4UUTwDuAsKeHXc8czYeE81Kn1WyAS9DqM99xA+d1PEHYcstAoFmN88FYEU8OfZyeCkMtFsMKMZ8lKgnYnyr7dkWSmI5gMNfvoD6k2mJhi5MW80qjzSETQ3e/GNWokqiNscZHIFKgNSWz45SvOuedR3j0wMWq+01Y/FJlbGbOVQq7SEZY2o6VkJAuoFRlsrT/9iK6rifrRFMA3UW8EiZjEZBWJybWZMr83TEpOeyoLd8U8Jq15Z2Ty2v0FvbbadztOgCcYqle2Kzz7eWnjECo8+2vGVpRNZUTm3YzMepBHu69jccnHbDbPQitNYkTmPWRpuuIOWHAHbXHfQ6m7WqRDJBJhMgho8kswP/setn96rMQJRoz33IisQ2vEMhnG1Gz6nH0dvcdci8dsxlFYyGnPPI0qOQllcjIKff0zPmq/H/cTryApqA6EowqWBIE6XGuOGaFAgG1ffR13PH/2TDqcexML/tExqyxz0aaO/uncVoYIsbvGgDYriwvmz2H66LERk4o2F11Ih5tu5J04+g7dtSpUwpEHBGZvIe9uHc9e+/KabX8WvMzoZg9xZsadqKX1byFQ6xLoPeYaWvU8g/L8HQiChLyty/npletqSms79j+fXVNrq1BKli+nZPlyTK2ak9Li4iN+P4eDKUmJMUGBuTL6s27VwUgR1UJJl7R8my92XUOVt1bheXX592ypms0j3deQrGxRc5+IhUilRCapX4WSz+OqsS6MhauiAoMykzJ35P1NIpJxRubtSMUnt1iVWCSQo+3Fw91WY/UV4wu5Mcoy0EqSkPkEwqIAImnjmSb4tmyPDN4PwfHtLyhP64OkEWiJHAuEBCPBz9bQe+LFTN8R2xmiz9nXIZU3vDrvaChZN1aUiQm0v/wyNn/0MQC5o0ay4dV36NBuKH2HX0154Q5UWhNadQprHnieQS+/ELdkvNfoa5ApYreBHQlup5+v3t0YEbyLBRGhYJiFM/M495J2/P79zprg/VAcNh9L5+cz9qI2iP9xT6kodfH4bX9RUVqrz7NuRQlbN5Tz3IdnkN6sYQuPGmMyQyY9RO/RV+N1OxHk8v+xd9ZhUpVtHL7PnOmu7WBZurskBaRERAXEDsTuVuzu7u5WVEQQKQGVku5cFrZruuP7Y2WXcWdwgV3Cb+/r8rrkfc85c2Z3Z877e5/n+T0sd3/J2/suIFvbjWg0wh7XKsLR6vu3eNIoX1e3xA6q/Qg8pWX1FvBQ3SI5+dXH8C1fg3/NBqTNMlEPG4CYZD2uvp8SEXF78Py6CPvrH9aMub+diaxtSyz33hTX/6i9RsWkZBPfllax/7euESVco5VjqHSRe8lFR9whQmdKYdCkW5n11h3s+v4Xrj3jO2ZWPsku51I0UjPD0q9nUNpUbLt9ZHUYyN6Ni2vOVWlNjLzqXT5+t5Az76jdNNTLUmihP+mI7quJ+nH8/+U3cVwjU6g56czr2PTHj3Ui6xJRSs/RlyLKahcHotmIclBffAtj0wAFhRxlv55IzEaCER+z9z4ZI97388u+Z+ibfB5Z2q6c3uxBTsm8BZkgr6lDDUUCmBSZVPnjpNcCufra1NpwYQkV9z4Zs5kQqaiiYtoTJL/+BJJmB7j2CgIqsxmV+fAXhxKDDvXooTjejt++RztuBJKDtKNrNAQBhdGYcFqu1RIM1v6MBgzPxmhWMHpCS2Z9E+taKpVJuOyWHrEmdccBgiCQ1LkT5y79HVdREf4qG/pm2aiTk/BqtLT3l7DBHSsM5ILAeWkW1PWMuLht5bgd5fjdDtR6M2qDFZXWSCQa5s+Sj2LE+35m5j9Kd8sZhyTgobqjgtaUwvaVvzL/44dj5tJyu5CT3ZsZvzxR57ytH39Mm7PGI1OrcVT5cdj9+P0hdHoFRrMCuaLxHgkmq4o7nhjAQzf9FrNQtSSpmHJHe14qPJfmul7sda2NEe/78YbtzCt4mfGZjyPt2xs+/jbu66hPG47CXL/IhFyhRpQpEhoDKsx6zk59lu/y7q5JL0xTt+Pi1u+SpPxvmPRUl09lYFJkEA1HCJeW4fl1Ns61mxBTk9COH4U0PfW4cHoO7on/vQ4Qsdnh75pQT1kZntJSgk4XSosFdXISikPYbD0eEVOTifr8JAe1dDppPOv/+D5mvvfYK0jKbntsbu44xFbpo7LMy749dtIuv4/0085mxe3X0fLMM5h7xVUE3W6kajX6Zs0IOBy4Cqoj73kzZtFz9BSW//RmzPXa9R9Ham6nRrlXtyvAqqVFpGRoOOv8NCzmIKGAB6nSwrIlDkoLXaxfWTcau5/VS4sYMb5FTT38+r9KYsT7fvy+MN9/tpkpN3ZH8S9mvv9EJldiTKktiWtRNIaLDGPYstyLRAKjeqvYHp3OrxUPIfoO/swMug9ujAcQ8XohEkWiUSMIAtLUZLTjRqAZO/yYZNk47X5Ki9ws/nUPAX+EAcOyyGyuIRqswuu0IcrkqPVmNIa6wY1wWUWMeN9PcMsO3LPnozvnjDpeVnqpyOlJRoaYdJQGgsgAkwD6cBBVs/idlg4VQSKhTb9T8biq+P2bFync+Bf9xk7mzHYPYE7NxaLLRRREyiLlmNvdTvcxt2Mr2YFabyFEMh++VYw5RcmOwCyguu30xa3fw6LMbpD7a+LgNA8V9T0AAQAASURBVAn4Jo4YU2pzJt35ITNfuxm3vdphTmdO5bTrXsKYGvtBlqjVGKeejy0cwbdkOYJCjnDRhbjSm7O5NIJ5k4u0Zkr2OuPv3gIsK/uCLG1XRIkMnSTWfd+kSOfMnMd4d+uFdc6zKpuTpm4PQMTvx/nVjPiZAOEwru9nY7jqIiQN6EAuSCSohvTD8+siQnmxAkXRsyuylkdWz3S4SESRLlddwaYP46cSt7xwCgv/rC5XSMvUcuaF7dEblZxxQTs6dk/h+082Y6/y07azldPPa0tKWsNHKBoKbUY62oxYcycFcGN2Cn/YXMyssOMJR+iqVTMxxUSKon6//6riPL55agqlezbVjLXuNYpRlz9OWBNhfuErCc9dXPwOzXQ9Es4nQqnR0+2U82jZfSibfv8Rr6uK1r1G4d9dyuwJF8QtVZFqNAiiSNFeJy88uJS87Tag2oxy7KTWjJ7QCoOpcaLKgiDQrKWRp949hV3bqijMd5Lb2kRmcz0Kg49ThJtwBErZYpuf8BprK3+kv+4m5n5TwKkXn0/og9g6VlmbFmhPG4lQz37EGlMy3Yafx8pZ79WZM6XmYFOVUebK47oOMwhGfRCNopGZ0csbp+7SYfPjsPmwV/nRGxXoTQoMxqMX5Q/m7aX85vuJ7vci2bAF79zFGK67FPXwgUgOwfOgMZC3SexlIaZYCYT8OHfsYOakcyld/XdNpiDQdvLZDHr2KbT/4ldyPCNazVgfu5vyux+n39hB9LhtEjs2/44ok9O6/6nokjJQHSS9/v+J8lIPz937Bzu31Jp9GswKbv9yNvLKHQQ91eI25PFQuXlzzLn+KhsDbrmPlj2GsXb+F0hEKV2HnYMlo2VccdYQRIGs5nouucrCvHcvx/G3ea4gkdBx8AVYrDejNyooKYwvfA0mBbK/0+KD/jBLFybe6FqzrBi3K3jIAv5A7FU+5r7vZ9Ev+TVjP7wPwyecwvmjW6Pym5HIZESCdY1lBIkEdVJSwmuHK20Etu3E/cMvRAMB1MMGoujVFWmSpeb8o43D5uebDzbyy/RabwSDPkhV3kb+/O7J6lI2ILlZe8bf9BpJWW2IhMO4i4qJhIJEi0qRNsskFGcD0v3jHNSjhyG11C0FUIoSlKKk3uuQw0Gjt9B33JV0HHgmPpcNqVxZHYDQ166tk9O0fPuRl8/eKcFo0uL1uvG4tqFQiVx7Xy/EJCtDcy5FK7OglR1+R6wmDg0hGj1IQfL/IQ6HA4PBgN1uR69PnKbZRCzRSARnZTEeZyWCIKDWmdGaUxP2BI24PYRtduwRJS8+voptGytr5lQaKdc82pJZkUvJd9dNFRyWfh3ntHwp4b24ghWsKPuS6XnT8IRsQHUdz4Wt36ppbRG2Oyi/7eG4X6gAspbNsTx+F6K+4WtcQ+UVBNZuxv3LQgSpiGbcSOStmx/TWi5veTlLH3mM1S/Gum6n9u1D/9c/YNYvVfQckE7z1iYsSbGROJcjQCgYRqWRHdGi4FgTjUaxhcJEiKKRiCjrmTrvqirl0wcmUr5vW525TkMmMviSO5m2tm0dz4b99LRO5PJ2X9Rp9XQ4RKNRVr/8KgtvuCnu/IRff0HTpS/3XjWfirK6dd0XXduFUWe2IuB3Eg2HUWoMR23BFAz7cYXK+XTHtayp+D7uMVmaLvQrf493H9vJiFEZjBxqRbphHYLbTbh9R/StMwmFvYRcLmRaDZq0tH/1qHBVlTD3w4fYuGR6zaZHcrN2jL/1DUSzCp086aikypeXenjlkWVsXlvbZqNlOxM3PtCPpNTG3xQL2x1U3P0EwR27606KIinvPnvMDaNCpeWUXXdPdbT9Hyiumky5ScKfV9xF1datdea7XHkFg557GpmqYUzHjgXRaJRweSXhwhLClTZkrXKQ6PWI+oZvvXmi4nEHeePJFSz7rW4tu9Gs5ImX+jD3kgvYOy/+RuHE+b+SdfIQoPrnDTR4b/N/4vUE2bt9JzOeOwOfyxYzJxGljL3mRbxCb569N76B4UOvnFxT1hYOR3j9iRUsnpMf99iMZjruf3HIEW3ULlu0j+cS3MvdT/embP37sKacda+8WWe+85VXMOipx5Hr6q6twlV2ql54C//SVTHjYnoq1ifvOaJ2yUfC5rVlPHD9wpp/d++XyrmT1XhK9yFIRQr2rGH1b5/jdVaiMSZzyf0/suWTz/nr2efxVVZibNmS/tPuwuQKEpwxN/biokjKhy/WbFAcDSLRyCGvN6oqvKxZWszMr7fhdgXp1ieV0ya3ISVdi0Q8BrWf/2Hqq0NP3NV2E8cVgkSC3pqO3nrw1jX7kWjUBKVyvn1xTYx4B/C6Q7x69w6uefkZ3nQPqXNuz6RJB722VmZhUOrldDaPxRuyIZUo0cmSYszCBLkcaUZqQgEvZqYhNFI6u9RqQTpsAMp+PUAiHPOoFoDKaqXvvdNod/65bHz/QwJOF+3OPxdrp45o09K4smvic7X6/0b9pCAImA6jns5ZWRRXvANsWPQd/SfeSEfzaFaWxXdx7Z18ToOId6h+D60nnMmWTz+jePmKmLk2Z0/C0qUTO/IcccU7wHcfb6ZTNxULP7yBoN9Du36n0X7A6RiSMuMe35DIRAUmMYPhGTckFPDD0m/ih1eqBe6c2QXM/bWQlm3NqDTJnNpRxfqHH2TLRx8SDgQQFQo6X3k5ve+8/aD1llpTCqOmPs6gSbfgcVYhV2lQ6y1ojYmjRA2NyxngradXxoh3gB2bq3jxoaXc/lh/9I0ciY84XPHFO0A4THBH3jEX8NJkK9Zn7qXq8VdqXKkFlRL5mSPYHdyH3p0RV7wDbHjvfXrcdssROzcfSwRBQJpkOaqL/RMNh83P8sXxjehslT7KHRIGP/Mkn/U+qU6EOLVPb0xta8sQGlu470ellhF0bY8R73KVln4T7keX0oUqW5RWnfSMOrMFs7+Ldcg/66J2ZDSrFcOiKGHkGS0TCvhTJ7U+IvHucgSY8UX8zxjAL9P30C4rQOaIXvQymVj38hv4bTYUBgM9br6RTldMjSveQ5Eogd35dcQ7QLiwGM/s+ejOO/OI2yYfKqFghNnf1ZYK9huQxKn9Pfw68XJsO6rHMwYPYvxDLzL7q/vo0Os0Ft12J9u/+qbmHNuOHcy8ZAqnvPQiKR3bENywFTHZimg1I6alIPmXVsQNgTfkoNKfz+8lH1Dpy6erZRytDYMw1zPd3WRRcfKpzel+UhqRcBSNTo5ccXR/F03E0iTgm2gUKoMhnKEwYUAviphkIuI/Hob2Sj+L5sTvC+31hHDu02JQpmEPFNWMtzeeQrKq1b++viiR/l2HE//LSaJSopt8Or4/4psB6SaMRaJoXGF6NL6060so6McfdiNJ0tD74WkoNQZkiuPn/o42Po+DcDCAQq1DKjv4Ro6zsjjhXDQSJuTzcnqzB1hXMaOOm3m6uj3NdfVreVZftOnpjJv+DSV/rWLDu+8jVanofOXlWNq1RZ2UxN75iRdfTnuAiqIC8jdVR1eKdq5lxc/vcOGjP2BMPjp1bRnqDgxJu4qFRa/HjHcynUob7TBKCmudoyPhKNs2VjB+Qia7X32IXd/V1sWH/X5Wv/gyQbeHIc8/g1ybOEqp1OhRavQcK/szR5WPtcvj17hu31iJw+ZvdAF/sO4gANFwXQOtY4EsKwPdfdfj2LMTIRjCLwRZ+NunFOxaw4BeUxOeFw4EanqBN/HfJeAPE43T1XY/pcUecge257wVS1l8593kz52HwmikyzVX0fnyyxq0fZ4jUEKJdwcry75CJlHSO3kyFkWzuJ4nlQcYAcsUKk694XN+mu5lQJ8QhmApOz79jf4d23Hq271YtzVM0B+hc68UjBZlnX7xaZlaxp/flu8/ie0s07VPKt37HVkZSSgUiWm/+09cjhAyhY6fP7iTFl1PZtKfCxEFGVKVEk1aGqKsbjp4eSDIFoebtjPnxrliNZ45v6E+dXjcVPPGJByO1Hi26I0KRvSXMGPsaTHflwW/LaJq4hZO+eodJFEJP3x1X9xrLXn4ESZ/+RWSUYOp2JtP+abNJOUkIy0vR6fOarQNI1/IxYqyL/lo++U1YyvLv8akyOS2zgtrMlPrQ2OV2DVx6DQJ+CYalFAkyk6vnxfySygNVhsK6UWRKzKtdNGqUR2QlhwKhgkFEz9pA1VaBnWbyqLit1GLBoZn3kgX8zgMB9SfuoIVeEJVCEjQSM2oZcZ636s0Mx3jLVdif+U9ov7qB5KgUGC88TKkmf+NHrj1wW0vZ8XMd1g24y1CAS8IAq17jWLElIcxWBvGLOV4xmGrNqdZOHs33Xpp0cj3smLGa3gcFTTvMpieoy/BmJyFRIz/dak/yM9IlMqRq7TolBnc020l0/PuYV3lTyhEDQNTpzIs/TpMiob/GWvT09Gmp5MzaiSCICCR1t57enbilCy1VkY0HGt+5KwsZtmPbzLsovv+dTOjIdDJkzi92UMMTJ3CstLPCEb99E46h2RVS6RRJfe81wa7twwpKgq3SPjh3SK6dFAw+/r4pnYb3/+A3nfchrzl8Ztm7HLWrRU9EPe/zDcEEq0GaVY6ob2FdScFAXmr3Ea/h/rixc87T02MGRNlCrTZiTNFRLkcue74/RtoomFQqaWo1FK8nrou8tXzMuyOMEldOjPmi08JOpzVddmpKQ3aIs7mL+KDbVPYUDWrZmz2vqcYnn4DY5vdg1YWW0+f0rxjzf93HnYZC+YEOH2kjsUXTcCxpzbQoc3M5Mw5s7G2a5PwtbV6BadNbkP/YdksW7gPvz9E74GZpGRojliAaXQyuvRKpWjvjrjzHbpoKNvzFzpLOt1GXoi+WTMUqsSfu4pAkAd3F9FWLqVdJPF6MBqJHIsmPSiUUk4alsW6lSUMHZ7EhmceiLvZ6Sktxbk176B/Q1KViqBezbejx+ItK6sZV5rNTFwwl6TOjWOSaA8W8fH2K+qMV/n38e3uO7mkzfsoxabvxhONJgHfRINSFQphC4W4MM2CKAisdnrY7PayrdxNelAgw6RC/FvEK1RSDGYF9sr4DtAtWltplnUng9OuQCJI0cuTa+ZCkSAF7nV8vP1K8lzVUfR2xuGc2/IlUlVt67WTKdGoUQ05CUXn9oTLK0AQqlOazEaEOLvE/0VCQT8rf36P3799sXYwGmXb8lm4bCVMuvPDGOMeryeIyxEgGgW1RopWf3y5zR+MaDRKhX8Pm6rmsLlqHmmadvS0TGbNwhAfPr+Z0ydnUrDmA9Yv+KDmnPJ921gz71MufmwGyc3ax72u1pRCSm4nSnatrzPXZdi5aI3JSCVS0jXtubTNh3jDNgQk6GTJSCWN+3cWL9qRnWtAZ5DjtNeNopwyNo2tv79aZ3zD4u/od8a16C1HxwRMJ7eik1tjzP3sgWK+23MHi4rfJkr1Qi8zrTPXPfMhkh2JI6vRcBhfVSVwfLrGV5R68HqCCELiILhcHsRRUdSoP3/RbMR441TKb38E/hFt104ej+QgrfuONkq1jtTczhTvqjU7DQf9uINVmNu1q2NMBtBx6pRDal3VxImJyaJk3Llt+PKdjXXm2na2si/PgcGkwGxVoTQYDqkV7KGwoXJWjHjfz9zCF+mRNIFWhgEx46nNO6AxJuG2lZHRfiRRWYTlN06NEe8Arn37+OnMCUxc8OvBS4N0crQ6Odm5Dfv+ZDKR0RNasXBWHj5v7CaJziBn0OjWSKOPotQY0JpT/nUttsnto9AfJBCJcsawQZAgK1J9cn8kjeBJVB869UghKVVNdqacVSuWJzyuZPEycs8al3C+5y038fP5F8WIdwBfZSU/njGBs5csbBSjza22BXX6u+9ndfl0JuY+3STgT0COvp1jEyckEbeHcGUVEU/82lmAqmCImeV2Xsgv5Zn8Ep7eU4wnHOZ6i4U9L2/mldt/Z/rHmyktrnZSNVlUnDM1/o5j89ZGktI0yEUVRkV6jHgHKPPt4PE1/WvEO8Bm21yeWNOfCn9evd+XRC5DmpqEomNbFB3aIE1J+r8R71BtwLZsxhtx5wq3rYpJDy8pcPHmUyu5/txZXDf5Z56e9jt5222EQgfJVzyOKPJs4uFVPfho+xWsKP+KH/c8yAOrOqLrtJVOvc106aGIEe/7Cfo8/PLuPXhddY2zALTGJCbe9i7ZHfrVjAkSkS5DJzNw4o0xpQgqqQ6zIguTIqPRxXsirClq7ntxCMkHdAsQJDBkVCZtWlewc3WcNMZj4HXqj0QoCQTZ7PZS4HUzd99L/Fb8Zo14B9jnXsenFWdg7nnw2mzZQdLnjyWRcJTfZuexdnkxvQbEz8To1NPK7lXf8dXjF+KoiBMdb0BkrXNJfu1xVEP7I6YmI+/QBssjd6I9Y/Rx0UZuP2q9hZGXPYogiY12Lfr+WYZ//CapvXrVjAkSCe0uvIA+0+5GeoCBXTDgx1a6l8IdqynJ24SzMnGbriZOHKQykb6DMjnzgnaotdXfsaIocNKwLE6b3IZvPtiIsyrxOqYhcARKmVPwXML5+YUvE4rEbqDqrRmc/+C3JGW3JRgUyE6LUrZmTdzzK7dswVNaFnfuaJCcquGR14fSpXcKglD9/Og9KIOHXx1KWpaJpOw26CyJTYz3E4xEWGyr3nwtD4bYl5mO2KFuZoHEbEIzbsQx6/luTVFz/4tD0JnVaA4isBUWI9YOHZAnMB4ztWlN5ZYtcefsu3bhLS1tiNutgzcU30AXIEKYSPT4KI9q4tBoisA3cVDCTjeh3XtwfPod4cISpM2z0J97JpIkM6LRUGMoEohE+LHMxozyWpETAZbY3dj8IToPSOOrl9exZ6edOd/v5OHXhpKSrqXngHSuiPbki7fXY6/yI4oC/U7O4twrOmE0x0/1CoS9zMp/ilC0buTeHapiRdlXjMy8rcGMwf6rRLw+Ai4HQf9BNmWKdpPavCNlxW7uv24BVRW1vdK3rKvgnqvn8cTbp5CZc+jRuXAogr3KTzQaRaGUNqoZnstVhiu/gMvDbyEopFSqC/nZ+yylvp18XnQhV09dRcnKeQnPz9/4Jz6XDZU2fjTDkJzFWbe9i8dRQcDrRqk1oDFYD5o6eCzJbm7goVdOxlblw+cNYTIriQRK+eCOCXHFesdBZ6LWH70KcWcozK+VDr4sqSQUhVszQ8wvjN95otyXR5mwm1Hzv2LumAsJ+Xwx82n9+h60bdGxxGHzsXBWHhVlXm64rw+CCMsXFRCNgCBA977JjD1DxU8vPkvQ72X7sl/o0u8MRL0eoZ5dEuqLKxQmJEhQZ2dgvGEqUY8H5HJE7fHZFjKleUcufWo2Cz99nPzNS1HrzPQddxXGFi0Z//OPeEtLCThdqCxmVMnJKA5YVHuclayZ+xmLv3qWUKD678WYks1Zt75DSk4HBImkuitFoIAqfwGeUBVWZXN0suQYM9Qmjk9EwuTvtnHZzd0RRQGJRGD10mJeeOBPgoEIVmvjZo5FomF8YUfCeXewkkg0BMQ+86yZrTjv/q+x2wUq19fNIDiQ+vRSbywkokBWcwM33t8PlyuAQLWZrUp9aJvSEkFAc8D32AtOP3fccDnJGzYj/jyXaCCIcnA/NKcMQppybL/Dk1I1GC2t4Y7bmHvZ5XUPEAQ6TZmCMSeX8TO+57uRY2KeRbqsrH/tiBI8SIDsSGhrPDnhXLa2Gyrx+MmuaqL+NAn4JhIS8QfwLvwd+yvv14yFS8spW74G441TiYQjqHp2RpqSRFUozKyK2geWXBDorFOhEGCXN0hOjyQECUQjIFeIVHj3UlqxhwLPelr2789DPQYQ9kuRyiUYjEqUqsR/mp6wjW2OhQnnN1TO4uS0q1FKj026VX3x2e14S8sIut0oDPp6tbxqCCL+AOGCIhyffw8jeiFIRKKR+Duwur9TdtcsL44R7/sJBiJ89/EmLr+tJ8pDaCFXWeZhzg87+WX6TrzuIG07W7ng6i5kNTc0uLOpt7ycDW+9zfKHH695oBpyc7nosxf5XHU3+7zrCMgL/71d2r9UZah1JtS6E2dxb7KqMFlrI5Jum4+WPYazfcWcmOO0phT6nHblUal/389mt5dPi2u7U0jw4o8kXrDmuVZSnLqNoT+8zZyRF9SMW9q3Z8ynH6OyNk7/5iMlCkQiUULBCC8+tIxr7uzKmPEmPC4/cnmUwk2/MuOFN6q9KYDV8z4l26FFbbSi7N8LsQFquu3BENu9fqaX2rCHwnTQKhlnNZJiMiI9Si7ch4NMriS1eUfG3/Q6AZ8LiUREY0yuifqpD/I7z1u3mAWfPBozZivJ5+P7zmTqc/PQJ2Wy17WGlzaeij1Qm4XUN+l8JuY+jUHRlIp/PKMTA2SkKHjpoWV15nr2TUYraVxPCY3MRCfTGH4rrttGDao76cjF+BktGqMVmSpMsMyKRColEqpbyy9IJKiSjv13mlorq8lyOBxEQWCkRc+iv6Pw/miUh+xeclq1ZsjNremoVpKUbG5Qb4IjQSYTaTF2DEWXXMTG9z+sGZdIpYz68H0MzZsjymSk9evLRRvXkb9gAVVbtmJq2waZRkPQ40GUywkH6pavSaRS1CnJdcYbArMii66W01lT8UPsayJybotX0MmPzw3uJg5Ok4BvIiGRKhv2Nz+pOxGN4njvCwxXXkDZ9feS9MKDeIxGgn9H7k6zCHRUlbOh/AM8oTLGmyZgVvcmOU1DwB/misfTeK90OLaC2nRQnSyJWzsvIEXT4V/vSyYo0MmSKfflxZ03ytMRJcd3azNHfj7zr76OXT/PgmgUUaGg2/XX0uOWm9CkNG6rpsCmrVTc/ThEosj1Ktr1HsOmpTPqHKe3ZqC3ZhDwh1i5JHHq7oZVpXhcwXoL+KoKL8/e9yc7NlWS0UzH8NNykUolfPrmOs69vDMt2zVspDdvzq/8Me3+mDH7rl38OuJCJv7+Hs97x2PzFZPWsm/Ca+R0HohKa2zQ+zre0BiTGHPF0+wdMokVP71N0Oeh3Umn0X7AeIzJWUftPuyhEF+UVMWMRVAhl6gJRDxxz7EomzF775N07TKW89espGrrdgzNc9BlZR7Xdc96o4LBI3P45sNNhIIRdm4pZUfJU+zdtDTu8ZFImGgohO35tzBrNagGHFkHA1cozLelVcw8YPO1qDLIoioXj7bIIFd9/Htc7O8gUF9ctjIWfv5k3LmA10Xe+t/JGTCYZ9YNxRuOLZtZWvYJFlUOQ1KvwqBIQSIcH8KiiVjkShnD2oZRnJPLjB/z8bpDyOQShgzPYOxJarSaxv29ySRKRmTdwrKyz/CFY9OXzYosOphGHvR8uULElJ1O+8umsuGN1+vMd7jkItTJjSP2jjbpCjmjLfqYAFCeL8BStYL+VtNxI973o0lJYfAzT9Hz1lsoWrYcuU5LSvfuqFNTkKmrN2VEmQxDbnM65TYnGo3iKSnBU1qKqFTR87ZbWPbo4wBI1Wo0KSl4y8vpdMXURvud6uRJXNDqDdobhzOn4FmcgTJaGQZyZvPHSFO1a5TXbKLxaRLwTSQkXF4Jwfg71RG7A0EuI2Kz4/x2JorLzkMAxphB5f+YN3Y+UHPsqvJvSFG1Ysp931Kwy8+3VRdjC8QKQmewjFc3ns7tXRZjVBzcxEMjMzM66w5e23RW3PlhGTcgkxy/C09PSQkzzppEycq/asbCfj8rn34WiUxG3/vuQdpIPejDFVXYnnsLItWbLYE5Sxh0z5W4bKXkb6mNVuiTMjnnnk/RW9IIhyMYzYnvR6dXIIr1j9QV73OxZ4eNq+7shccd5I/5+QT8Ybr1ScPvC+Fy+BvMHM9VXMwf99wfdy7gcOBYuoOMrh2wiG1Z/qeHbqOuZvXs12KOU6j1jLj0EZSaxjE7Op7QmpJp1/dUmncaQCQcRqkxHPUFlD8coNgf+72zxKGmf9q1LCh4qs7xFkUzItEQ3rCDBSWvcW2HH0ju0uVo3e4RIYoSTj41h99+yaOs2MO6v5yMG3dGQgHfsedYWFXdBtD+7ufI27dGNBsP+/WrQuEY8b6fQDTK2wVl3N08DZ1UxOcNEvCHUapkJ3zv33AoQFVRgn73QOGO1Wh7Nqsj3vczv+BlMtQdsSqbk6Pr0STij0NEiwmDVcPAJb/Q99ZhBKQK5ISR/rkErbsdEnPbf7/IEZKkzGVat+VM3z2NNRU/IJXI6Zd8IaOz7/y7xe3B0ScbOem+u9EkW1n9/AsEnE5kGg3drr+WbtdfF1MSciKjl4pMSjExxKRjQZUTXyTCYKOOTKUc8zGqef83lGYzSrMZS/t/F7+CIKBJTa3ZSO52w3UYW7ZAqVBhMFug0oYiJxtFs6w6rU69niAeVxBBImAwKWoMoA8HgzyVk9Ovobv1LCKEUYo61NL//prmv8zx+elo4vjgX1OKq+e9C/9Af9HZ9NSq6Kop4bVdD9Q5tMS7nVWq1+nd+RK+i+PWDVDq24kzWPqvAh6gpX4gg9Ou5LeiWAO2s5o/QYqq9b+efyxxFRZRsvIvDLm5dLnkYnSpqZRt3sy69z9g1fMv0mnqFAw5OY3y2hGni3Bpee1AIIj/sbcYdd75hE6/GUfQgTYpHb0lHZ2l+oEjihJGjG/Jwll74l7ztMltDqk1zaqlRVx4bVcW/ZLHxtW1Rjx7dthZPGcP974wuMEEfCQQwL478WLdvnorg0dfg2u3ioWz9zB+8ijG3zqIDQvfxWMrI7fryXQeejbGpKMXgT4e+OdmRZW/gD3Ov1hZ/jV6eQonJV+IWZF9SG0b64M7WEWBewOZyubs9NZ6XCxzBLgu4wpOClXwZ8n7tS70mk5MaP40H26/DIBQJIDf5yISCR7Vmv0jwZqi4YGXhvDH/L0snpOPMb0Xyc3aU7pnU8xxptQcWrfsi/+jVwAIFxYTDRxZKvBGV+Kay21eP25XgKJ9bn74bCslRS5atDVz2tmtSUnXIpMfG+EajUZxFxcTCQYRFYpDzlgSRRnGlGbYSuJ/n6XldqbYE99oCsAbtiMRRJ5fP4IHeqzFomx2SK/fROMjCALyDm0wmY24vvkJ6fbdSNNT0J19OtKsdCTyxs/Qkwgiaeq2XNrmAzwhW7WQk1mRS+r/rNSmpdL3nrvodOlFhLxepCpVdS/1A+7fUVFEWf5m9m5ZgTk1h+z2/dBZ0hClJ44Zr14qRS+V0lL93+8xrk5Kos2o0VTc8yT+v1t2+gExyYLl8buQZWUQCkUo2uvky3c3snZ5MSqNlJHjW3DymOaYkw7fTFQQhHqtr5s4MWgS8E0kRLSaEdQqonGMNcRkKxF7deRGkEhQBvxclZXCrL110732s8L2If1Szzvoa/rD9TNm0cuTODPnUYamX8tW+0KkgpzWhsEY5KmopMd2Z7q8xMPOLZVsXF1KSoaWHv3SsCSraxa8tp07OfnJJ8hu3Y7wvN8Jb1iGuXk27b/9luVvv0XAkdgx9EDsZfvYu2U5u9cuwpTWnHb9TkNvSUemOMhDME5Na9TnJ/DutyCR0OyJu1G2rlvGkJqhZfLUjnzx9oaY8T6DM+ja59BSlK0pauRySYx4309FmZe5P+zinMs7IpUduUAQZTL0zZrVacWzn5Tu3WmVPBFFspG2H1RHRbQ6Oa269SIcCqBQ6Y67FL6jTYV7Ny9sGkORt1bUzNn3LBOaP8Wg1MtRyxpuF98WKODLHRcytsU8XiyoHY8CrxREuST1Ae5Km0qlfy8yiYIS73be33ZxTZ1yH9M5zH/rQSoKd9J12Lm07j3qqLW+O5BIJIpEUv+sFGuKhrFnt2HI6BwkEoFm0z5h2/LZrP71E6KRMB17jaV1y34En/u45hyJXocgPbK/TfEgNe59lSrWLizgvedX14zt3eVg8S97uOe5QbTvevRTeD2lpez4/keWPvQIroICTK1bM+CJR8kaPAiluX4bNlpTMoMn384PL15TZ06u1NC8yyAKpIkFvEGeijdsxxu2U+TZ3CTgj1MkCjmSnCwM100h6vUhKORIVEdfICqluiPy49n/DIPqiGx5uZ9Q0IdKLUOUVPHxPeOxl+6tOV4qV3LOvZ+T2aYnErFpiX+8EXY4qXr6dUJ7Y7NQw2UVVNz3DEnP3k+hXeDuy+cSDFRvVAf8Yb56bxOr/izm1kdOivGuaeL/l6ZPdxMJEc1GTLdfTeVDz9WkXAMgk2KYeh6OT74FQD18EKJej0EmEo0kdl4NRrzoFBZEQUY4WjdyJCBBL69/NEUjM6ORmcmoR9380aK4wMVDNyykoqx20+PT19cx7dlBtM6UEq2ykdS6DaaiKnyv1y7GA1XrYfUGel97MTJztRGa3xfCXumjpNCNIBFITtNgtCiRy0UqC3fx8b1n4LLVth1Z9MXTTLj9XXK7DEEqr45guwqLCNjtSOQyVElJSHVapFnpdR4eAIhiQqdXjU7OyPEt6T0ogzXLivH7QnTrU70xoTceWrS8Z790Pnx1TcL5JXPzOfXs1pgb4CGlSUvjpIcfYPaFl9SZk+t05A4bhVZmAcASs7MtBY6ftlnHCl9ZMXNKn44R7/v5ZvftdDKPaVABv75yNuW+PArtH3FhysV8XRbB+/d3T5JcSo7CgLfMztflt1Lhj92USVW1werI5I/fHwHgl3fuZtWvHzN52qdHRcRHIlHKiz2s/L2AzWvLyW5hYMDwbKwpakIBJx5HBUGfB4VGj9aYHNNeEEAiEdAb9wuMNHqMupi23U7B89ufsHxjTeR9P9oJY5GYjId1r8FIBHswTEeNkivTIhhENxJBxk6fmtmVAo5wmFFyNU+88medc8PhKK8/sZIHXzm5QT6j9SXgdLL88adY9cKLNWNV27Yx48yJDHv9VTpOuQSxni1Ac7sOYdDk2/n9mxcIh6oNpXSWdCbc/i56SwaRkASzIptKf36dc0dk3sqGitkA2PyN29bv/5Wg34vbVkpJ3iZCQT+puZ3RGK0o1Ye+OS9RyEFxfHvi1IeyYjefvL6O5YsLiISjmKxKJl2cS4ueZ7Dq59oOHaGAjy8fu4DLn1+AISnzGN5xE/GI2B0ENsTfIAwXFhOqtPH5e/tqxPuB7Nhcyb48R5OAbwJoEvBNHARBKkXRrRPJbzyF68c5hPL3IWuWibJvD5xfzSC0Zx9ishXN6SNr+nN2t57JvATtnjqYRqJXJHFK5s3M3lvXRGhw2pXoZSeuMYvbFeC9F1bFiHcAc5IKc8hOxS0vEy4pw/LyI1T8PL/uBaJRAl/8iK5XN9zOAIvn7OGj19YSDlULGLlC5IrbetClp4lZb90RI94BopEw3z17BVe+tAi1ysS+RYuZf+0NOPLyQBBoPnoUw157BdOtV1F220Pwj/Rb441TkRyknna/42xG9pFlOGj0soP2hxUECIeCOCpsiKIUjfHIHFJzRo6k7733sOLJp2rcX3VZWYyb/g367H+vRfx/JWyzYy/ZxmLbhwmPWVn2dYNuoEX/7kc7d9+DtDet4Pr0u4kKFkQhisO3mlBhe167ZxdXPjKDrdGvWOP6DIkg0s9yAbn+nsx+8taY65Xt2cyuNQvpOuycBrvHROzZaeOB6xbi81a7Ri9fXMD0jzdzxxMnUbj2VVbNfg8AiVRGj5EXcdIZ16E1Jf6+EwQBjTUVWYdOVHw5O2ZONXQA6lMGHVYruRJ/kB/LbAQjbnqpNzNv99U1hqAt9P25Ked1vi43Eyz1EgrWXUQClBa5cTkCNQLe73AQsDtAIqBOTq63kD4UPCWlrH7p5bhzi++4i+ZjRtX786zWm+l7+lV0GnQWbns5okyOxmBFZ67OJjKLmdzaeR7vbrmQnc7qTQyFqGV0+t0k2UYS2TeIPi1vIkn736hDPtaE/AGc+wrxVlYiUckoLt7ArLfvIBL6+xklCPQddxX9xl9zwpTGNCQVZR4ev30xBXtqs/Oqyn28+cwmrrxtDMnN5saU3AS8LioKdzYJ+OOQqK+uA/2BRBwuNq1K3A/+zwV76dSzcY2OmzgxaBLwTRwUiVKBpFkmxqsuImJ3ECqrwPnx1xAMYrjmEpR9uyNNrm1nkqpuQyv9QLY7FsdcRyoomJD7FDq5hREZN6OXJfNz/mO4QhWopUZGZt7GwNQpx33rt4PhtAdYt6KkzviUS3IRXnyBcFklEoOO8M49sRkN+5HJkI4chK24iGKPh/dfWhMzHfCHefmR5Tz+5skUbPur7vlAOOinbM8W1EEd3489vXYiGmX3z7P4btSpnPX7r1hffwzvL4sIbtqGND0VzfhRSNNSjkptoFIlY9jY5ixduC/u/IARWaz55WVW//IuemsGAybcRPMug9AYDr1tjre8nK1ffoW7uJixX39B2B9AYTJiyG2OMTf3SN/Kf5qI3Uk0FCIYSVwn7QlVJZw7HDqaR/Ft3p0AbKr6mU1VP9fMtdUPJ3XVQ1SV+3jiqi30GjSS626YilIJf370EtPnXhr3mqt//YQ2fUY3ahcBW6WPlx5aViPe9xMOR3nhweXccMdI+FvAR0JBVsx8B6lMwaDJtx20PZ8glSJv14rkN54kXFxKxOlGmp2BxKg/rBZypYEgd+8sIApcnVrAmxtOJUrtd9FOx++8t3kod3VZQVHFwb8LqjfaQlRt3caSu6aRN2s2Mo2GTpdfRrfrrkGX1bC+Efa8PKKR+BsKAYcDb3nFIW3IyeRKjCnZGFPin5Osasl1HX+k1LuLSDiKvUDB/PfsfLxoMwAqtZS7n21FpO2hlUs0UYu7pARXQSFCMIToCyDbtotQ39bMfP3m2AOjUZb+8BqZbXvRpveoY3OzxwC/L0RpkZvCfGeMeD+Q7z4p4LwLrmbee9fGjHudtqNwh00cKhKtBqQihOK37pWYTUhECRB/XqM/8bNJmmgYmgR8E/VCkIqIFhOixYTsnpsgHEbQqOtEUg3yVK5o9wXLSj9jXuFLeEI2OhhHcHrOQySrWgGglyczPOMGelonEiaETFCiEHWopEfe0/hYEgpGiP5DlytUIikKH+Gy6p7W0VAY4kXNJAKqGy5lwVNPYujUjWXCiISvM+u7nbTqPZZNi78Gqvt095x0GcYWzQlFAygNSeS/N7vOeWkD+tL9rWl8Wngdm+zz6HbSOPqPOY90Y1fkqqPbBzS7hZFufVNZvbQ4ZjwpVU2PnlGmP/ka0UiEioId/PDiNfQ+7QoGTroFpbr+GzyRcJgtn33BghtuAmD92+/UzKWd1I9x333d6C37TmSi/gDChnzadjyZzY44GSNAd+sZDfqaJkUmg1Knsqj47Zhxhajl7NznKLCbGHeOhuatTbRqb8ZqVuNz2SjZvSHBFas9Og6W8dEQOO1+CvPjL7A9riChqAlRKq9J1wZY8fN7dD1pAgZDMqI1cVRREEWkydaYjdLDIRyNsrDKiS0U5gyrhPl7740R7/txBcvZYptDjnokUpkkbhQ+JV2DVi/Htm0bn/XqS8hbvcnjt9tZ+fSz7P55Nmf+MhNdRsYR3fOB/NOh+Z+IjdC5QyuzEnDJeP6elWzbEJtO7/WEePquP3ni7eFYkpvKbQ4VV1ERsy+ewtCnniS85C98M+ehGNCT1b+tSnjO79+8QFbb3v8XUfhIJMrG1WV89/Em2nRM/NkvLXKj1NeNtCdnN77TfhOHjsRsQHPaCNzTZ9WZUw7ojWjSM2xsc2Z8sS3u+QOGN2UNNlFNk4Bv4pD5NyMYoyKdUzJvpk/yeUQJo5IaUYqxiy+JICJKZBS4NjC/8BXC0SADUi6llWEgJkXDLfqOJmqtDEuSKiaFXquVE62qjVJG3Z7qHVi5LCaFXd6rCxtm/sSeuXPpM/J0ylYnjnoW73PTfnD1w9mUmsPQ2x7ii4pb2VewrvpaxWpOOfV6+uoeYek19wCgMBrp9vbdvFB+BsGID4Dfyz7k97IPaaUfwJXtvsagOHr9so1mJVfc3pNtGyuY9c12Av4w/Ydnkdvcw69vXlon0rb8p7foMfLCQxLwroJC/nzgobhzRX/8iaugsEnA/wOfzYantJSA3Y5cq0MMKznLeA9POJcQisam/rXQ9SNV1bCLRK3MwvicR+hiOY3Ze5/CFSyng2kkQzOuxapsTtZgkb6DYxerKp2JbiMuoOj1tXGv2WPkRfVqARgJhxAk4mGJ/f1lLokIBCJIRJHwAQH6UMCLr7iQ4FPvYX3kDsQkyyG/7qHgDkf4015tEpopD/Gna0XCYzfafkE1P8i5F/bho3djuziIUoHLrmuPRhnhl/serBHvB1KxcSOlf61qUAGvzchAZbXiLS+vM2ft3Bl1cuNsQnqdAts2VMadc9j8VJR6mgT8IRKNRtnx3ffIVCqkbh/emfOqJ/RabJVFCc9zVhbHbIL9l6kq9/L6EyvQaGWYLInXXAqVSDTijxlr02cMmoOU5zRx7HC7bShOHwZKBZ7ps4j6/CCToR41BP05ZyAadIw+qxVrlhWzd3esp9TES9o3fdc0UUOTgG+iUZAIkoO2q7AHivlw21TWVf5UM7ap6leyNF24vuPMoyLifS4bwaAfmUJ1WOY4/8RsVXHxDV159p5a4yeH3Y+QnBMT53L/NBfD5Rdgf/V99ofshb7dWTf5bACc2zeS3bw9RftccV+nZXszLbv244+vYOCVd/BW0QU4grWp+4GIh5mVTzB58NOk9O5FyfIVtL36Qmb5XqwR7wey3bGEYu+WoyrgAUwWFX0GZdKpRzKRMATcBbx+7ZD4B0ejlO/djjmt/mnvQZcTX1XiFO+KDRtJ6d7tEO/6v4tz3z7mXnE1u3/+OzIgCLQ8fRyD+z7E3a1/4/uSh9lkn4daauRk6xUMyLysUf5m9PJkulhOo5V+IKFoAJVoQCYePLraottQUnI7UfKPFpVpLbuS02kAttK9FO9aj9dVRXrLbujMKaj11YLZUVHIvq1/sWnxdJRaI91HXogppRkqnane96zVy9Hq5bgcdcWFKAoYDBGC/lihK5HKkApSgnl7cc2cS+SkboQDQdRJVtSNsLEkAIq/NyecYQGjPJ0Sb/woT7KiJXtn/oS2dR533XM+8xbYKSv1k9NcxeCBBnwb5+PPHkne7F8Svt6WL74k97SxDZb9oElP4/QfvuOb4SNjNg2UFgtjPvsYdVLjCPigP37a/n7criNr5ff/iKekhFUvvESPG68nMH9JzXgkv4jsrt3Yvfa3uOelt+qGXHViZ+rVF4fNX/NfWpYOmVwS19hs2NhmFG2pNhRWagz0OnUq3Uecj/oQvr+aaHwCXhd7tyznr18+wmzJwZrZkoyHrkGnsSDVapGYjNWGi4AlWc3dTw9k93Ybf8zfi84gZ8ioHKypGrS6phT6JqppEvBNHBPyXatixPt+9rrXsrLsa4Zn3NBoaa/ev1NuF331DFVFeSRltWHQ5FtJymqL4hAivPHo2D2Fe54bxMevrWXPDjtqjYywzoCiVXOC26sjWf6/1iHRarA8eBve5asJF5ciSbEScFan4G7/7BNGzLiC5b+XEP3H81oqk3DK6S2wWAUufnwmebI1OKrq1t0D/OJ8gcm33UfJxBWkntKXLxwvJLzvleXf0MY45Ije++Gi1lQ/kMqd8Wu+9iNTxjqv2it9+P1hRFHAaFEi/qM0QVQqEUSRaDj+dbXp6Udw1/8tvBUV/HLpVPJ/nVszprJYSOvXF/u+YqSlCs5r9iyRTAm4vBiNzZGpGtfA61B6zOstaUy680Py1i9h9a+fIAgC3UdcQLOO/SnZvZFvnp5COFgbpWrdaySjr3iKcDjIZw+eTWXhzpq5tfM/p98Z19Jv/DX1rps3WZRcfF1XXnl0eZ25UydksX3pZ3XGO/Ybh/Bndeq/Z9YCXASYfe11DP7gC8jqxIo/StBoZZw0NAtLihqN9sgWbjqpyKlWAy/sLWWhXcEp6Xfyzc66vgECAv2SL+A355XsefUFdnzyPu0nno0qPQvn+g3MeWg6Z/7yM4JEglynI+iO3/pTZbE06He4RBRJ6d2LizasIW/OXCo2bCTtpL5k9D+pps1WY6DRyVCppXg9objzKen/H4KyIYmGw/htNkJuDzhrN2OCG7bS5pzrWTrnXfye2JIUQSIy6OxbUfyfCPgDmfH5Vq6+qzdvPLkCv6/2edahexLjJrdHJmbSb/wViKIMjSkZ8SDt40J+P+6iIsrXrcdvt5PSowfq1BRU9WzD2MThUb5vO2G7lyyhDXnvz6JK+TtcMBFJqo5mPQdi+EenBHOSGnOSmh4nNa1TmohPk4Bv4qgTDPtYUPhawvlFRW/SJ/mcQ2opV+/X9vvYuHg6v7xzd82Yq6qE3esWMf6m12nXb+wR9U5Va2R06pHCtGcGEQxEkIgCRrOSyH03Y3v5Pfwr1kA0ivfPlYjpKegvmoSgkOGprMLYsiW2HTsIulzsePkxrr/5Dj7+cC+Vf6fkp6RruPzOLmitQRQqMxmtu7N89/SE91IVKECRWv1QDgeCyNUq/JEEi22x4VqBHS5qrYnU5p0o3r2+zpxMqcaU2hyorinevqmCj15dy748B2qtjNFnteKU03MxWWpFvjo5mdaTJrD18y/rXE9pNmNs3arx3swJhresLEa8q5OTGfXR+/w+7T4W33FX9aAg0PrMMxj8zNPI9Mef+7bekkbnIRNp3WskCKBU66kszuPrJy8mEo4VX9tW/EJ6q24oNIYY8b6fP6e/Qvv+pycU8DZ/Ia5gOVGiaGUWjPIMepyUzv0vDObzt9eTv8tBcpqGCZe0J7elgm8ej23H1rLrUE7qdTa+x94EIBoIIFEoGPrF93w6I8DOLX/UHDv9ky1MmtKBkWe0POLoSwetii5aFWtdXgTTYPqkTGVZSa3fgFSQM6XNx1jVuQx57hm+HDAYv93O5nfeqjkmpUd3TG3aoE5Ooss1V/HHvffHf61LLj6ie42HKJViyM2ly5WXN/i1E2G2qpg0pQMfvly3RGPgiGwM5oavvQdwOwO4nNUZHVqdHM1RjLzZyu24nEF8HjcyaRCjWYVab0GmaJg+6gqTiZxRI9n23XRa3XIH/jUba+Yi707nvOvfZfY3j1G4Yw0A5rRcRl/xFOa0Fg3y+icCeqMCvVGBw+Zn87pyIpEo197TB0eVD5cjQMceKSSnaf5u41q/tmJBr5f8X+fy06RzCPtrNzTbnX8eg555sqmkrJHwOm2EbG4WT7kZ+65dNeN7Fywke+RwrE+3buoY0MQhI0Sj/7Td+v/G4XBgMBiw2+3oj8NF6n8Bf9jNq5vOYFPVr3Hnk5S53Nn1dwzyhk/PtZXm8+YNgwkF6qaSK7VGLnt2LgZr46TvR9weIjY7EZ8fiUaNxGyMcX3f8f0P/HjGhJp/J3XtSoebpyHLaI4y3Uy+sJiFjodpoe/PWblPoJNZWV76JW9tmRz39XSyJO5svYjS7//A2LENi01fML/4lbjHPtB9LZnazg37hg+D0vwtfHzvGfhctpoxQSIy8c4PyO0yCFEqZ8XiAp65548653btk8o1d/c6oJc2OPfuZcaEsyleXlvvqzSbOWvOLJK7d2t0c7MThcI//uSL/oNq/j301ZdZ88prVG7eXOfYjpdczODnn0FhOPabPv/GipnvMOe9e+POqXRmTrnkQX586bq4831Ou4LhFz8QMxaKBNjtXM67Wy+oab1mkKdxQYs3yJH3wKBPx+UMEPCFkcokGEzVf4uuqhKcJQV4ivai0VoQN+cT+Go2BKvTr+UDerFXLWGboi/Tv94b936efGc4Oa2OPC3WFgyx1xdgbqWTbpogzRVO9rlWoRK1ZOk7Y5SlIROVBL1eyteuY+FNt1C0dBkyrZbOV0yl+43Xo8usXmy6Cgr48axJFC+LzTzoe989dL/xepSmxkvj9VZW4tpXQP68+YgKBdnDhqJJS0VRj+d2MOwlHA3Vu+uJ0+5n1Z9FfPnOBirKvGi0Msae3ZqhY5tjNDdsT+ZIJErBHgfvv7iajavLAOjQPZlLru9KRjN9ozve78uz8d4Lq9m4utprID1bxzmXZmG1ushs1Qm5smFqcCu3buWT7r0555fZhF79iEilrWZOYjYhGz8U+nQEUYJSazxoy8X/IpFIlLXLi3nyriUxmXgmS7V/TOeeKYjSQ2sjWbltGx+27xw3K23oqy/T5aor6jwTo6EQ4UobEZsdoiAxGRDNJgSpeFjv6/8RV2UJyx97ijXPxm+xfOr0L2gz/qyjfFdNHK/UV4c2ReCbOOooRA39Uy5JKOD7JJ+LVnpkjsuJcFaWxBXvUF0T77FXNJqAl2jUSDSJFz+ZQwYz5vNPWHTL7bgKCylft468b9+nw2NX81rVKTiD1Yu5Iu8WBqVNRSezkqvvg0rU4w076lxvdNadJJlbkzKl2mhslC+LjfZfKPFujznutOz7MCkatuXT4ZKU1YYpT89h5+r55K1bjDW7DR0HnonekoEolVNZ7uWDl9fEPXfNsmIqy30xAl6XlcXpP07HtW8f5es3os1Ix9SmDbrMDARBwFlZTNDnQSKVoTFYkSkadjF+oqAwGWv+X5TL0aSmxBXvAJs++ZRed95+Qgj4qpL8hHNeZyVSeeKIot9b14Oi3LebZ9cNqzH0U0g0NNf1ZkXZl2iMBsp3bCarfV90+tg0X60pBWVETuVbPxLcuDWmQZCgViE5uR/sLGTB92UJ72fBrDwuaQABb5RJMcqkdNCqEKjuN99M167OcTKVirS+fRj/0w8EXW4EUYI6JSWmx7s2I4Nx07+hYuMmtn31NQqjkXbnn4suKwulyUQkGqU8GGKTy8sub4AWagXtNEqSZNIj2jxzl5ay+I672PTBRzHjA554jM5XTEVpNMY9zxkoo8CzgbkFL+EL2+mVdDadTKMxKw/u7KwzKBg8KodOPVMIBsJIpZK4ZTsNQWmRm3uvnh+Tsr9xVSn3Xj2fJ985hZSMxkshLyty8+ANi3DYaqOzhflOnntoE3c/3gFXVQnmtOYN8lrGFi04d/mfrHr+RXpeP4XovD/w/b4CIhFkOZnoe/dGlpWBIP5/CkWJRKB9tySeencEM7/aSt4OOxnNdJw2uQ1pmbpDFu8A277+JmFJ2YonnqLl+HExpWURrw//X2upeu4tom4PUP19ZbzpcpS9uv6roXET1YQcHnZ8+V3C+W2ffEWrsacjkTZJsibqT9NfSxPHhNaGgWRqOrPPvS5m3ChPZ0DqZYiSxvnT/Lf0+CNJnz9SlEYjLc46C0mzLihlHlyKfayLzuWlqgn4w7Fi4o+SD8jV98GsyObWzgt4eeNp2AKFAAhIODn9Kvomn49EqH3Im5XZ3Np5PjsdS1le9jk6WTKDUi/DqsxFIzs+DG8EQcCYnEWPkRfR/ZQLECSxixSvJ0h5iSfh+Ts2V5LT0hgzpklJQZOSQkqPHrXXcdvZs/535n5wP/ayfYhSOZ0GT2DgpFvQW///as7USUlkDOhPwZLfUZhMuAsTO0FHgkFCCeqejzdyOg1gxcy3484lN2tfJ7X+QDoMGB/z73AkxKKit2rE+6lJd9Eq0o9di+YRcLlw9tuH2ZhDZcEO0lp2rXM9qcWE5e7r8cxbgnvGHKI+P9LuHZEM6s2s666nwx334/Ukdth22PxEo9EGyxqR1PM6KosFlSWxQ742LQ1tWhrNhg+rM5fnC3DfzgK8kb8T/SpAI0p4KDedHNXhp57vW/hbHfEOsOTOu8k+eQipvXvVmXMFy5meNy2mReEW2wLMimxu7/IbVmXOv76u2dq4G3zhUIR5P+2KW2/v9YSY+9MuJk/peFjirT6sXlYYI973E43AjK9LOXOy2GACXiKVYu3QnsHPPU3A4UB62TnoLzsXQRCqN7u1mgZ5nYYgHI7gdYeQyiQoVUdvjaBQSMnONTDlph74fSEUShG54tBf3xtyUOrdSeX2rQmPcRUWEg3Hmu6ECkuofORFDuyPG/V4qXrsJZJefhR5q4b5W/ivI8oUEElshhkNh2lKhm7iUGkS8E0cE0yKTG7oOJOVZV/zW9GbhKNB+iSfy4DUy7AqG8+QSGtKRqkx4HPb68zpkzKPWX/ZsM1OxObAhppHHtzE1Ptz+MJ3bsKa9f1u8hJBQra2G9O6LccRKMYXdmFSZKCTJaOS1k29MSky6Zk0ge7WM2PE/fGCs7IYn9uORJSi0plQ62J/H1KpBEFCHXO//egN9RMF+Rt+59unp9T8OxwKsGbeZxTnbeDsuz4+YdM1o9EolcEwJYEglaEQGQo5ZpmI4V929lVWK6M//YhZ511I8fIVaNITd5CQyGSIWhVLSz5FJ0siVd0GoyIDUTi+HifeigqsaS0xJGdhL62blj74nNuxpLdEplDVcYjPbNsLa2asR0Ig4mGXcxkAo623o1wR5Ycfamuxty6diTWzNaMufwKf2x63dZ1oNaOdMBbV8IEQjVKyaSPfnjKCkM9Hyp+L6Nh1DH8tjR+FP2lo1glV8lEZDPFUXnGteP8bdzjC03uKeaRFBibZYYiR8nJWPPF0wvnVL7/KiHffQpTH1oyXeXfFiPea+/TnM2ffs0zMfQaZpHHq2euL2x1k7fLihPPrVpQwbnIbdAYFYbuDaCiMRKNCojzySGg4FGHtitKE8zu32BAkDd+DWqHX16vs4VgQjUYpLXLz2+w9rFlWhN6k4LSz25Cda0BXz2dNQyBXiMgVh5+JsMu5jM93XMdpQ66CD+saagKk9OyBVF27QRXxB3B9MyNGvNcQjeL8agamW69Aoji2n5kTAXVyEm3OPZu/nnk+7nzHy6bEZDY10UR9OL5WXE38X2FSZDIs4wZ6J58LRNFKrY0Wed+PzpTC+Jte48vHLiQaqU0lE2UKxt/4Kjrz0W2lBhDcW0DlYy8R2pVPdNrd+L1hNvzhpdOI8ays+jTuOf1SLqz5f0EQMCkyDqn13vEm3gM+N/mbljLrrTtxlO0DIL11d8Ze/RzWzNY1wkVvVNCzfwYrFhfUuYZMLiGnlfFfX8tlK2Puhw/GnSveuQ5baf4JKeCj0Sj5vgAP7S7CFqr9226vVnBjdioW+cE/W/rsbMZN/wZPaRnRaARLhw5UbNxY57gOl1zEbP9LzN9abUSpFHVc33Emubp+SBv581tfbDt3MfuiS7Dv2s2pX77P8t/eZ+faBUQjEQxJmQyYeBPbVvyCSreCS5+aze/fvsSOVfNQqLT0HDOFDgPGozXFGjrJJEpSVW3Id62irTCQ73+YWud1y/dtY/PvP2JJbwEJAoiCKEFqqc54MbVrS7cbb2Dl08+w9YN3GTvjItatqqjTLiqjmY4WbRp2c9HuL6LAs5GVZV+hkhrom3weZkWzBsvGsYfClAXjZzgUB0LYQ+HDEvDhQABPWeJSA3dREeFgsI6A/7P044Tn/FHyIaOz7jwq7UsPhkwmwWBMLIgMRgWSUADPwr9wfTWDsM2OonM7dOecgTQ9BeEIRIAolZCcljjqbbQoUWuPT6HdWBTmO7nn6vl4DmgVuPrPYsZNbs24M5qh0SuQqI/vsit7oIQvdt5AsXcrql5ZaFJTcRfX3SQa9PSTMZk2UZ+f0J66z9n9hPYWEPX6oUnA/yuiTEbXa65h6+df4SqI/ZlmDh5Ecveux+bGmjihOT5WW0383yIRJBgawW0+4euJUrI79Ofy5xeweu6nlOVvIb1lVzqfPOmYuICGyioov/2RGgMfsbSEzBw9f/xSwm3jb2GTcyaeUPVcK90gBmhuRxPOQufMwiMNotb+N3Zty/K38uVjF8Ts9hduW8VH94xnytNzMCZX1+ir1DIuvKYL+btslBTUZieIUoFbHzkJkyV+JKqi1MOenXbydthIz1Qz8PwX+POruynfu6XOsfu2riSzTc8GfoeNT2UwHCPeWyhkXKCQYHG4iK7fTDAtCdFkQKJKvOBUWa2orNX+E6d//y2zLriIoqXVUWdBIqHteefQ7Naz+KZ4dM05vrCTF9aP5KEeG7Gq6p9S6Q97cARKCEV8KKU6jPKMBokwuwoL+XbE6Bq335mnTqT91IvpdtnrKJMsePw2lnz7AiW7q1u4dT75bMZc+RRelx2JRILGkFSndANAKpEzPOMGbIEC8v5clPD1Ny6ZTt/Tr6rXvaqTkugz7U46XXYpzvx85EZ49LUhfP7OJtYsL0ahkDLstOaMmdgac1LDCYUqfwGvb5rALufSmrFf9j3DqdnTGJFxMxrZkW8WBCIHTwkNHmbKqMJoJOvkIWz++JO4881PHYNMXddrJBSpmxq+n3A0SJTY+wkHA7iqSgj6vUgVKrTGZKTyxhUrKrWM085py7qV8SPhY89uRfCb73F/93PNmHfBH3iXLMfy9L0o27U+otcfempzfv56e9y5EeNSMSf//wh4jyvIh6+siRHv+/nxi20M6KYh8OsM9BdORJqZdtzW6gfCLoo81X4mn7lv5dJf3mXNzU+zb95CAPTNmjH0lRdJ7tol5jxBpUCWm01wZ17c60pzMhGOoAzm/w1DTjMm/76IzZ98xtYvvkRUKul67dU0O2UY2rTEGW9NNJGIJgHfxAlPKOjH66xCECRoDNa4i+8DkckVWDNbMeyCewmH/Ehlyn89p7EI7c6Pdd/9eSbnn3clTzy6jg8eLGHqfQtZ5Xsbs7QF0h0n88Hb+ThsWxGErXTpncqlN3aL24c4Go3+/TMRUOmOfn273+Eg7PejMBr/NTXM67Kz4NPH4qbq+Vw2dq6aR49RF9eMJadpeODFk8nfZWPTmjKSUjV07pWC2apCJq+7iCrMd/LQjQupqqg1L9ToZNx072ss+fgyKot2xRx/LLIwGoLiQLBGvHdSyrjS7SB6z0tEHE78QKlEgnbSaWjPHINo+PeFuLFli2oDwNIinPZiIjqBPPlaijQ7ObvFC3yx4wYif9uxBSJedjj+qLeAL/fk8/2ee1hR/jnhaAiDPI0zmz1JV+vYI44AV23fGdOqJ+B0sua5l1nz3MsozWYGffJSjXgHKN+7laTMVsgU/+6ubVW2YEzWNLYs+jrhMQG/55C8NORaLXKtFmOL3Jqx6+7tg9cTQgD0JgUyWcOJg0g0zJ8lH8eI9/3MzH+U7pYzGkTAG2UiUgFCcXS6XBAwHKaLtUytpvddt7Ptq69jWmFB9QZUyzPGx90I6pN8XtwUeoAe1glopLXv2WUrY+Ws91j+01sEfR6kciXdR1xI3/FXozM17oZzTksjp05qxcyvYoX0qZNakZ0qjxHvNQRDOF77kMi0a1GnHr4YsKaoufqunrz51F+Ew7W/uIGnpNNjQDZa0/9Pr3CXM8C6FSUJ59dtdNCnuJSy6+4h6dXHkGUdn94pEkGGXKIiEPFS7tvNq6FzGPLcVEZEriYSCmG0ZJPdvE/d8+RytGedimfeYvjnZpwgoJs0ril9/hDRN8um15230enyKQgSyUG9RZpo4t9oEvBNnNBUlexh5cx32fznDCRSGd1OuYBOg86qlxGZRBSRiA3TEudwCfxjdztUUEzq+sXcftcAPv54N49fvp0xky/BlGvh5adrW6FFo9Wu64/cvIgHXz45JjrnKC9ky7KfWb/gKySiSPcRF5Lb9WR0ln8XpuEqGxGbg4jXh8SgQzQYkGjr/zPylpdTsmo1K556Bm9JKdmnDKPL1VdhaJ6DJEGEIuj3ULxrXdw5gN3rFtPtlPNjRJE5SYU5SUXXPtWL1XB5JcG1O3AvX41oMaHs3xvRasblE3jxoaUx4h3A7Qzy+tM7ueTyu5nz5mU141K5koxW3ev9fo8nKg9IV75EJhC+96maNmUARCK4vvgBWbNM1EMH1OuaDk0lj9kHV2eBVP09WALdrWdyes6DTM+7p+bYMt+uuNf4JxWuQl7beib57r9qxuyBIt7ffiGXCh/TL+W8I4rEV2zYkHDOV1mJTIzN0lBp679hoJRqyNX3QewfYv38+CK+VY9TUGiOLFKp0crRaBun77cjUMKCwlcQENBIzQQiXgKRWmPIRcXvcIGux0GuUD+MUpEzkkx8XVpVZ25CsgnjEbShMrZsyeTfFzH/2uurM0QEgeZjRjP42acx5MT3UElVt6WDcSQbbb/EjKulRk5rdh+Kv58FAZ+HP757iRUz36k5JhTwsfynt/A4yhl52eMoj/D3ezD0RgVnXtieoac2rxGQnXulYLSoYP6ChOcFt+3CVbAHQaNCpTMe1mur1DL6Dsmibeckdmwqx+sJ0rqDGYNJgcHceO73xyXR+OXfNdPRKCAQ9ftxfv49xuunIFEef4JWL09hYOpU5hVWtzBzhyqZaXsSAFGQ8nBq3Sy0/YhpKZgfuBXbM28QcTgBkOh1GG++HGn6ibnRfayRiCLqpKRjfRtN/AdoEvBNnLBUlezhgztPxeOoqBlb+OljbPr9eybf/Qk6S/0iEZXBEK5QGEEAnShiPIy6zMMhFPRBWt12eZE588jcupU7zjkdOvZDkIk8esviuNcoLXKzN89eI+Ad5QV88sBEqop21xxTuGMNaS27MvGO9w8aXQ4WFFH54HOE9lTXoCMIqE7uj2HquYjmfxc5vqoqlj/+FH89V2vUUr5hA+vffpdz/liMtVPHuOdJRClaUyp+jzPuvDk996ARzVBpORV3P05ob2HNmOODrzDeehX23E7kbbfFPa+izItM2w4EAaJRRJmCSXd9VK+NjuORDEW14GulViBfsZpIsG7qJ4Dzk29RdOuIeEDruHj4Qk6m755WU8JxIGsrZjAy9RauyfwGISJQFtlNjrFvve6z3J8XI94P5Lu8O2hrHIJZefjlLMaWLRLOyTQaIkKtP4BSY8CUmnNI15dK5CQ3a0966x4Ubot9HzKFiiHn3oFCdfyKnSgR+qVeS67xdMqCImqJgDRaxC/5t5DvWokrWE4kGjlinwyFRMJoi55kuZQvS6ooD4ZIlkmZnGqmm06NvB5ZTx538G/3bSlqTW0mjyiTkdKjO+N/+gG/zYYgkaC0WA5qhmaQp3Bpm/dZXzWbuQUv4As76Woex7CM67Aqa7Mf3LYy/pr9QdxrbFg8nYETb2lUAQ+g1cnR6uRk5sQaIboO9mwSBIIBDx5H+WELeACFUkpKujZuZtf/E2qdjA7dkti4Or7fQucOBoLTq9tU+lesIepyQyMJ+FDQj6uqFJ/bjkyuQqU3o65nZp1MomBU1u3scixjt2tZzbgoSLmq3bfIvDL25a9ElMrRGJPQmVNrNlAlSgXKnl1Jeu1xIrbqVrUSo766D3wjtFBsookm6k+TgG/ihCQcDLDy5/djxPt+SvM2sXfrStqfdNpBr+EPR9ji8fH6vrIas6VMhYzrspLJUSmQNrLjs6uqDJciiKjTEnHGtokL7dmHzl2BLktPaYmH0qLEbbu2baygS69UopEIG5d8HyPe91O0Yw17Ny+jff/T414jVF5Jxd1PEC4+oPYyGsU7fwkSvRb9lHOQyONHBP0eJ257Gf7iqhjxvp+gy8X8629k3HdfozTVXXRojUkMmHAjP7x4TZ05QSKh85CJid46EX8A52ffxYj3/fdue+Z1As++mPBcAFFu4pRLH0ZrTCK9ZVe05lREaf0in15nFW5HBeFgAKXGgNacgngM2xBaZCKtVQqMUimyvL3Eq/gVtBro0w6XxIEyJEMhTWxa5Q5Vsbri+zrjKlHPlZmfs/mb79i44DtCAS9J2W1peWkvAi1dyA8iXiPhCDsqlyWctwUK8QadcASm2pb27VBZrXjLy+vMtZ9yIRtXzQSqxfakuz9GZz70lGidKYUJt73D+t++YeWs9wl4XbToPpSBE29usDZbjUVUSGKv5Dw+za9tWacVs7i6+ZfM33MJfZLPbTCTS4NMylCznm46NaFoFKkgxDWuC/jD2Cp9+P0hlEopGp2MwnwX33ywkb27HaRmaph4cQeycg0xmQn/1uKuzv0o0hiQegldLGOJRMMIGCgOCcwurkAqSOhn0KAU5YnbC0ajuO1lmNNz488fgL3KR1mxhw2rStDq5XTslozJokJxBG3IFJ3b12w4/hNZtw5s3vIHbVJOPAPO4xGtTs7F13fjnqvn4ffG9k4/ZVQGio1riPxdsiSoVdBIZXhuewWrf/2YP757qaZbRma7Poy79kVMqfXr2GNSZHBtx+8p8+1iu20RenkqLXX9KVq1mjdeGUAkVL3ZqzUmc9bt75LesmvNprkgSpAmWSCpKd27iSaOJ5oEfBMnJB5nFZv/nJFwft2CL2jdc8RBTYeKAkEe2V3EgX7P+/xB7t1ZyHOts0hTNK5BXFVxHnO+eJAJdzxJ8OVPCZf8LTgkArLh/ZEO7Fn98JRKUGmkeN3xF5Wpf0dKPM5K1i34CgCtKQVBIuKsLKpZ7K3+9RNadB8WNzoYLimLFe8H4P55HtozRiNJrbswdHg9VPgC7AyKqJIzGPXXCrY8/gT7fp5FNByuqVHdt/A3fFVVcQU8QPMug+g5egorZ71bMybKFJx+w8vorYmjsRG7A8/cJfEno1E0YhiZXFLH1RtAkEBSmoHUnlPinHxwKgt38eMrN1CwdSVQHckdct5dtD9p3DHxHIBqsXRLsxR+LrcTbNsK5sVmbYinD8Q5qjmzXa9Rsu05MjWdGZN9Fymq1ijEukJeQKiOxPxDK5yd8ix/vPIcpbtqHerL8rfw2QNnc94DX5PTKXF6fiQKemlip2+pIEfKkUWxdFlZTJg/h+9PPR3n3tr2ca3PnkiXa69i46qZtOk3hqy2vdCZ0w+pXj3mdcyp9B13FZ2GTCQaiaDU6JErj5/e1fEIR6MssXtZZIvtN+8KR3hhn8DdzV8nU1m3/d2RcjC3+apyL9M/3cz8n3YTDERQqqSMOqslyWka1iwrJhqF8hIPG/5ayNRbuzN4ZE5cr4tDQSdLwhYM8UZBGcsdteUD35RWcapZx0mXPMzyTx/DmtWaVn0mYMroSTAoIJeHUer/XSBXVXh5+ZFlbFxVG70VJHDNXb3pNTDjsHuJi2YjuimTcb7zecy4xKBDmDCMNa9fRZcxFyY4u4lDJbOZnqfeHcEv03ewbkUJOoOc00alkOHeR3T6AuQXjSfSPBXBoMMrBFBGgtgDRYSiAeQSFUZ5+hGVA0XCYbatmIW7vIyTxlxDRdlutiyfyb7Ny/jsobO58OHp9c40NMhTMchTaak/CYBNv//AzBduiDnGZSvl0wcmcfnzC+q9OdBEE00cG5oEfBMnJIJEQCpLHCmVyVUIksQPTl84wjelVcRrJx6IRllQ5eDsFDNiI0bho5Ew5fu28eUHNzH0vJuwGNLBFyCiUbBu1c90DjpQk4HRrGT0Wa347qPNda4hV4i06VSbht+8wwCGn3U3ju27iYTCGNu1YuvaOaxe+CkIAoneTbg4cVsmAkGivkCdYUcozPRKFzMqnH9rvBBSmY4rXnmVbju349+Th6hQsOblV9m78LeD/iw0BiuDz7mNnmMuoSRvI3KFGmtW67+dnw8Sjg2HY+u8qV7MSsb2I9IyBam6nIuv68rbz66qc+qwsbnoD9KyKRGO8kI+uf8snJW1rXh8bjuz37oTldZE+/7jDvmaDYVVLmNiihmhVxcqNWqi7mpxIvbqyI5T4IN9Y2qOLfZu5a/yb7im/XQ6W8YiEWJFkVpqpod1AsvLvqgZ00otaJ0G/E47o85/BL02lUgwiEStYPPqmfz6/n2ce/9XaAx1S0MApFIJzXTdUEg0+CN1s0p6Wy5A1wBdKZI6deKcP5fgKizEb7Ojy85CnZKM0mhkcKt2R3z9/UhEsY6pmc8Xwu0MIJEIGExKJAf5Hjra2EJhppfa4s4FolFKQul0UhiP2v24nAE+eHkNSxfuqxnzeUN8/8kWho/LZeCIZiz6ZU/N3Icvr6VLr1SSUg99o8TjDhDwhVGopKjUMta6vDHifT9zq1w82XE42ZdlIEttz0fv7mb9X9WGchJRYNCIEJOnWjBZ4ncFCIcjzJ+5O0a8A0Qj8Opjy3nuo1GkZ+sO+f4BJGoVimH9ieSmE/r1D7C7iLbLIdQum28+uJWuw86p0/6wicNHIgqkZmg594pOnD6pBZGt2wm8+RqCTkP05nP46ZvHKPx2NQCWjFYMnXofc6Ovsco2HaM8nfE5j9DVMg6t7PCi187iAlQuBc5v/6JoXwFJPboy8aq3WDr/HXavX0RF0e56C/gDcdnK+O2Lp+POhQJedqyaS68xh76xfTxj8xfiDTuQCjK0siRU0v+fjgpN/DdpEvBNnJBo9Fa6jbiQ+R89FHe+x6iLD5oK7Y1E2OlJ3FZos9uHPxJB3YitYUxpzZFIZVQV5/HtW9U74RJRSiQcQm/NoM/E64Dq/rwjxrcgf5edlUtqU8VVGil3PD6gpv5djEqx+lOZMWw8kdDf0XpBoOvN1zH4jFuxtGiTML1ZTE+86BMUcoQ4tX2b3T5+rIitWw9F4dUSGw+YzcwfOhyJTMaQ558lfeAAlOaDOxgrNQaUGkN1D+16IqhUyFrmENyRV/0+hnTHPbkLP9ifYrd7BfqSVEb3uptHPxrEMzdvoKrch1YvZ9w5bRg8Kiemrra+FO9eHyPeD2TBp4+S3b7PMV1Eq0QJ0dQkkp65j8rHXiK0t5Do2SfxeUndkpIoUT7Yfhn3aVdhVmbFzCmlGsbnPMxm2zycwWoxkqpui2tvEaPPfoTfLr8R244dAEhkMjpfewVpAyYT8LkTCngAnSSdKdk/8O7e8fjDtaUjOZq+jM26D7WiYaLY2ox0tBlHzxk6HIpQUujimw83sWZpMUq1lFFntmTAKc0wW4+PXtHhaBRHOJxwvtCfIHW8kXBU+WPE+4EsnJXHjff3jRHwAX+YynLvIQl4tytA/k473360ieJ9LrJbGDjzovbsVdR9ryJwuwp+v3gKzc48hxlfSdm5xVYzHwlHWThrDzKZyAXXdEGhrLuEslf6mPVN/FZs0Sgs+20fZ1xw+JtICrOFoBAmv/cuSnaup2Dz51TM3c5JZ15H55PPbvRWd/+PyGQixmQdYWVrwg/ehjPk4L2HzyTgrf3+qijYzrcPX8IZj7zLRvFXbIFCPth2Kee0eJEhaVcjSg5tuR1wOtny0ef8fnetUWjlli1s/+pbRn/7CfaKAsr2bCKn40mH/H4ioSBVxXVL7fZTtDOxqeyJhjfkYKt9IZ/vuJ4K/x4EBDqbxzK5xQskqf69FKaJJo5XmgR8EyckgkRCxwHj2bRkOsW71sfMtR8wHmtWm4OeLxcErHIppcEQuSo5J+n8yCURdniV/OkIkSqXIW/kGniNIYmhF9zD3PfvrxmLhEMIEgmnXvVMjOGcyaLiytt7YrvMR/5OOzqjgvQsLSaLClFaXXtn37WLhdffHPsi0Shrnn2JUV99TFbbuq1i9iNNtiLNTCO0r6jOnHrcSCRmY8yYMxTmm9LKhNdbIlWRc+oYdn43nblXXMV5fy1HaTQmPP5wEY16DFdfTPmtDyLNTKfs7Fxe3Duqpqez1+vgg+0XMyBlCo++/zhBpwqZTILRokQ8TBOegm11o/n7sZXkEwok3hg6WgiCgCy3Gdan7yVid5Kv34O/Kr6PgitYjjNYXkfAAySrWjKt23KWl37O6orvydR0okVOf6YPGYOvsvb3HwkGWfP8K5yU9gjSk2PFajAYRkBAKqv+eZvMGqLRvtyUs4ICz3ocoSJydD1I1jbDqkmcXn+8U1zg4q7L5+L3VQtktyvIp2+sZ/niQm55uF/CiO3RRCYIpMllFAXiGxy20dRmu/gjEVzhCAJgkIqNko1UVeFNOBcKRgiH6+ZIiYeQ0RDwh/lz/t6YDJyyYg+r/iji8gf60DpdwTZf7ee1r0pG8YvPUbTkdzrc+xw7v90W97rzf97Naee0iWv0FomCy1E3Y2k/FWV1o/6HitaUTPuBp5PdsR/dgn5kchU6c2rCTh9NNAyiXotEq2bzdy/FiPf9RMIh1v/wBX3HnMtvFW8B8H3efXS1jMeizK7360TCYdzFJUhVSnLHnsrun2cRjVR/FiLBIH/cei89H78B02H6bYgyOZ0GTaRF20GIYRFBKmKz72P5r+9iL9tHZusj70JxvJDnXMkrG2u9f6JEWVs5g33uddzRdQlmxeEbpjbRxLHkhBDweXl5PPzww8yfP5/i4mLS09M5//zzmTZtGvIDjLXWrVvHNddcw4oVK0hKSuK6667j9ttvP4Z33kRjorOkMfHODyncvpp1C75AlCnoMepikjLboDEmjgACaKQiE5NN2APFODwzWbrvJXwhB22Mo5iWfTMmeRrSRu4NL1eq6TxkEqk5HVny7QvYSvJJa9GF/mfdEGOEFfL7cRcVYd+5i3AoRMe2bVBZtcg1GqoqvDhtfmTSCGtefDnha61/+U1yh49MOC9aTFgeuYPKx18muHVn9aBEgnr0yejOHINEHhupDkajVAYTR/LKJSLN06o3IKKRCBvf+wDr8880ygJT1iIHy9P34/Ds47PyK2vE+4EsKXmXkVm3kJZx5OnTlozEGQIqnRmJtHG9Ew4F0WRENBkRHIUHPe5g2syqzGFU1u0MTrsSqURO3nezYsT7gax97hXaTz4XTNXibPfWKn6dsQuJRGD4uFxyWhoxWVSYLRoM4dZYbM0QBAGVRopCcUI8juLi9QT5/O0NNeL9QLZvrKAgz3FcCHiTTMr5aWae3lO3v7VJKpKrUhCJRikOBPmmpIoVDg9yicApZj3DzXqs8iP7HYUDAaLRKNK/+0ertQf/rEhlsd8XGq2sup1aAny+EPYqH0F/BJVaSjgc4YOX19Q5LhqFz59fwwUv9I8R8ANDXv589z0Uej1VtvibHADhUBSvJ/68QiHSuoOFrRvqGqwCdOt7+H3aD0QqU2BMrrvpdjwSCHuwB0pwBsuQSRToZMkYFQ3zczjaBANe8tYn8F0Bineso4t8as2/vWE73pANSCzgw043kYpK/Ks3EGqRxdYffmDD+x8SCYVoMe40zpj5I79ecTXO/GrX+6pt2zDq00nKantY70GMSmmm6siCSVfgt9kAMLdrx+iXn2Txr6+Q23XIYV33eMMZKOOrXTfHnavw7yHftapJwDdxwnJCrJi2bNlCJBLhzTffpGXLlmzYsIGpU6fidrt55plnAHA4HIwYMYLhw4fzxhtvsH79ei699FKMRiOXX375MX4HTTQWeksaeksaLXsMQxAkiIcgnjJkdubmXc2mqtq+wEtL32V1xZdM67ocaLha2USotEaadTyJlNyOhAI+5EotcmVt3/WAy8XumT/zyyWXEfJWR6skUin9HnqAnEkX8Oi9aygpcDN2fAby3XkJX8dVUEjIf/DIsDQtBcvDdxCxO4j6fEi0WiQmAxJV3Rp0lUSgpVrByjg1pACtgn5s62ozIyo2b8brrkKjr91YCdudNe77Ep0W0XCYdaFKBWXlxYSaKyjdsyPhcbscy0hTH/nvNLtdX2QKVY0j8IH0Pf1KtMbjzwXaIE9DJerxhh1x5lLRyQ7el1YiiGhk1eZ85evWJzzOXVxMJBikqsLLSw8uZdPaWif4lUsK6dwrhavv6lWdOSJKjgtR2xB4XEFWL62bvbKf3+fl07HHsa1Nrij1sGenDZlKZGqamc/Lbbj+jnC3USu4KFXCnD030T/jYe7dZcPzd8TPE4GvS6tYZndzT24alsNos+kuKaF8w0bWvf4GYX+ADpdeQlqfXhgtJlIztBQX1I1mtulkYffW2h7yggSumdYboyW+J0ZFqYfP317PH/P2Eg5HMVmVXHl7z7gGlgAOmx+FJ3YuNQohr5dIMIjBkPhZIkhIaESnMyg4/+ou3HfN/Dpm8akZWnJaGRNe97+IM1jO/IJXmLX3cULR6swEq7I5V7f/jixNl0M2efN6grgcAaJUb+gc2JWgsfCEw3jDEQRBQCeVYTjIxonWlEy6onXMmEwS+zcbiETwRqLIBQG5243rqxm4vp6B8pbLmXH2OVRuqe3NvubV19jx/Q+c8vYbTD91XI0prSE5C7318EqEipctZ+7Uq2LGKjdvZtaZ53PO8t8xJP03RG0g4mGve23C+c1V8+hqOXaeNU00cSScEAJ+1KhRjBo1qubfubm5bN26lddff71GwH/66acEAgHee+895HI5HTp0YM2aNTz33HNNAv7/AKns0Ov+Kv07Y8T7fvxhF9PzptEv+QL+Kv+GTubRtDIMOqQUuENFqdaDuq6pin33bmaec35M26BIKMTvd9+Donl7goHqxc/efQF69zqJgsXxIwNpffsctEfyfkSDrl5CWiWKTE4xs8rhqWMEqJZI6OJzMeuAezH1as8m7zw6qcegENQEd+/F9uLbBLftAkDWKhfjDZcha56FID30r6VdM38madLBawEVYsP0NVbpzZx127v88MI1eF21AqPdSeNIyelAwOdCqWl4J+8jwShP45LW7/P65gkxGQoSRC5t/SFG+cFT170hJ85gCf6wG0uXDgmPU6ekINdoWLmyJEa872fdihK2baigz+D/xgJxP4IgIFeIhILxxaLqMLwWGpLCvU4eunEhVeU+ADr0TOaK89uQ3EpHMFrF1qpveGP9Y3Q0n8H0MmeNeD+QfH+AHR4fFsOhfY7cJSXMu/o6dnw3vWZs108zSe3Vk3HTv+H2J/rz6M2LqCir3RDLaKbjytt7sWTuHtp3TSI718App7cgKU2NVFo3M8pW6ePZe/9g55baz6Ojyo/Xc/CafqtcSi+Zmr8cHpqr5ARdUfQ5OTjy8gjs3khmjoV9eXU3vfoNycRgSmyu2SzXwP0vDuH9F1ezZ6cdUSrQf1gWky7tiCVJnfC8/yJrK35kRv6DMWPlvt08vXYI9/dYg1WZU+9rFe1z8unr61j5RyHRCHTqmcyF13QlI1tXU0rWkIQiUQoDAT4trmS904talHCa1UCvUZewbv4Xcc/pN/Ri9KHaz0g743C0supN3UAkQkkgxI9lVWz3+MlWyLncUYnr6xlIc7Mp2LwxRrzvx1VQQP7ceeSMOIW8X+Zg7dgRbWraYTnce8rKWHzntLhzAYeDfQsXY2lz6JF9e5UPnzeEKErQmxTIj7BTREMgEaRopRZcofjZMLqolYrCnZhTmyM0csZlE000NCfsX6zdbsd8gCnWn3/+yaBBg2JS6keOHMnWrVupqqqKd4km/s9ZWfZVwrm1FT8SjgZYVvYZ72y9gMfW9KXEG9+YqLEIB4OsefX1uD1/Aba88ixDTq52t123qozM0ych09Q1d5JIpfS+6w5k6oZdOKbLZdyXm0bKAWm1LVQKbpeFWXHRJTVjUpWKzHNG8PbW83EESwkVl1J+8wM14h0guH0X5bc8QKjkIG74B0GXlUXZ3BW01g2KOy8KUpppux/Wtf+J3+3AVuFk4t2fM/HODxl9xVOcPe0T9NZ0vnzsAty2usL1WCNKZHQwj+T+7mvon3IJzXW9GZJ2JQ/0WEdrw6CDLgRt/iI+23Et01a04cFVXQm0kyY0JOx7952ElUZmf5c4E2LWt9vxuhOnJ5+I6I1yhp6auB510Ihj15LJYfPzyiPLasQ7wMaVpbxx5x84S1fz7Oosfsq7CWewjOaGUax1JS6NWVjlIpzg+ygRZavXxoj3/RSvWMn26T+QnqXjkdeH8sBLQ7j6rl48/NpQ7n1+MOnZOs66qD23P96fC6/pQmaOPmGZRXmJJ0a8A4TD0eryDHX8cyzJKkwmJddnJfN622xuMVmY/l0JXadVi82/7rmNqZckk50buxnXtU8qF1zdFZU68aaMQiWlXZckpj03iBc/G82Ln47mspt6HJZ7/omMzV/ID3vujzvnDdvZaf+j3tcqLXZz79XzWbGkWrwDrF9Zyj1XzaO0OL6/x5Gyzx/gtu37WOnw4I9GqQqF+ai4kuUyE6OmPF6nBWWfU68gvXkXNKXwUNoizk97ngtbvYVGZgRgu8fPLdv2Mr/KxV5/EE0kgnf6bAAkbVuyZXrdz8l+dv30MxmDBiJVKhnx3tuokw8v0yvs91O5aVPC+YJFixPOxcPrCbL+rxIevuk3rj9nFjeeP4uPX11LRemRez0cKQZ5KiMyb4k7JwpSWkr68N5tI7GV7Y17TBNNHM+cEBH4f7Jjxw5efvnlmug7QHFxMc2bxy6gUlJSauZMCfpP+/1+/AekFjscdXfbm/hvIpMkTt+VSuSEqY3e2ANFfLr9Wi5s9SZaWRJKaeMvxMJ+P7ZtiTcNnPn5NDNU78FFI/DRJ6Vc8N0s/rrzBkpXV7e2MbVuzSnvvImxZf2d3euLQpTQSavm0RYZ1WZXkQjRgn0snHQ25euqXWyNLVrQ7/0nmB54hAghHN4i5D/tJhonnT/qD+D+YQ6GqeciyA4tYpk7ZjSf9x/E6ePe4XXZJlzBWhEtIHBJ6/cxyI+85rKqwsvyxS5+mS7H486nW28LQ0Zms+CTGyjdXf2e3Y7yg9bJHysUooZMbWfOb/U6gbAHhahBKjl4+qk/7GFG/oP8WfpRzdjXnmmcP/t9fj/nZmw7q/0SJDIZ3a+6imbtOxEJhRNGoqHanCwSOTQReLwjlYmMntCK1UuLKNgT25nh9HPbYE05dsLNaffXEbcAbTtbWe/9MmYsHPGhlEhwxjGPA1CLiVtRxiPo9bL61dcSzq997Q1aTzwLc0oK5iQ17brElnKIogSV+t/jDHt22uKO/zJ9B+df3YV3nv0rZh9UKpNw7bQ+Nd0B1KJIRTTI5rXlpJibMfCN91j98L0smHQqEx54DMMVAwhI1FhStOhNCnT6+mV8GYxKDMZ6HfqfJBQNUOWP32UAYI9rFX1Szv3X60QiUZYu2IvTXtcc0O8L8/PX27nwmi7IGjDq6wqF+aCwnFCcr6qPbQGe7nwKl93ZjpLCrYSjETI69iG6aDXOax+oOa7TgF4YrzaCCiqDIV7dV8qB22NaIvD3mlOIRBAVif+uRIUCS4f2XLBuFfrmh2deB9Xf1fqcHKq2xTdoTOrSpc5YJBzGVVVCwOdCKlOgNiTVlPrt3FLJIzcvqjk2GIgw5/udbN9UwR1PDDimZVISQaR/ysXscixlTeWPNeNSQcHFmW+x+osPCPjcrP/tWwacdUOTCWQTJxTHVMDfeeedPPnkkwc9ZvPmzbRtW5vOU1BQwKhRo5g4cSJTp049yJn14/HHH+fBBx/89wOb+M/RO3kys/c9FXeuZ9Ik1lX8FDO22fYrhd6NrN83m7HNpmGQp8Y9t6GQqlSk9z+JvQsWxp23dOlCYXHtcmDnVhsvv+Vnwp1vMrKDCokkitJkQpPauPdpkkkx/a23yzJE2n56A92cGgSpSKWqkE+8d1Dirl4sqANqAusS7/77128i4vEhHqT+NB6ajHROefM1lky4jSs/eI89xvXsEFdgFrIYmHkZVnUL5OKRLSRslT5eeXQ5G/4qrRmb/3MBfywo4Y5Hnmfm8+MI+Nwo1YdXy3+0kEkUyCT1EyCOQDFLit+LGSvybuF95TWcOv02WgZ6Ei6qQKnREF22lvCCP9EP7MeAU7LZszN+rfzAEc3Q6GI3DiLhMO6i6vp5qVKBJu3EM7iyJquZ9uwgdmyqZMmv+Wj1MoaPa0FyugatvvHrdBMRCMSPqEulEgLERi5Xl73DwKS+fJcgiWSE2YDkENJ2o5EIYZ8v4XzY70+YYXQoJGrTt2lNGUazgifeGc7cH3axL89Bi3Zmhp2WS1JKbEaSWiOjQ7dkZny/j01t0xj90rfoVGEQZSz5rYQWndPo3PvotSX8LyAV5JgUmQlFfH2zonzeIKv+SOwxsXZFMW5Xe4zmhhNg3kiE9e7Ef7uLwiJjv5qLdV8hhmsuwTbtZSIVsRtlviUrcFot6KecgysSpSQQW9KxJRxlaK9usGEroVUb6HzeeeyZ82vc1+ty1RU0P3UM4mGUmB2IJiWFfvffy8/nXVBnTpTLaXXm+Jgxj7OSrUt/ZuFnT+BxVCARpbTvfzonn3c3UdEc1yQSYPc2G8X7XMfc58SgSOO8Zq8wVHcV+d7VqEUT1kg2qz55nz1rqsv89mxYQp+xU1Ec58/uJpo4kGMq4G+55RYuvvjigx6Tm1vbp7GwsJCTTz6Zk046ibfeeivmuNTUVEpKYp119/879SAC5q677uLmm2tdKh0OB1lZJ4azaxNHhlnRjFMybuLXgudjxi2KZvROOodXNsaam0SJYgonkxlpwfyCVzit2X3/GsE8EiSiSIeLLuCvZ5+vMbDbjyCR0P6623jyxdKY8YoyLwqLhaQOjVevfzAUUg0zfE9RGNwIQeCA25YgolWnELCY4O++7f9ENJsQ6mmSFYlGsAcKcQRKCEb8GEZ0ZHzfmRT+upgsoRm9+k1Ebbai1MfPvjlUivY6Y8T7fnzeED9PL6f9oPPYt2kh6oP0QD/R8IWdhKOx6e4t9P1pZ5pAOX6s0VKMT/6I314ddTbfdzOiQs5JQ7P4ZfpOykti0yhTMjT06B8rgtwlJWz+5DNWPPk03rIyDLm5DHj8EbKHDUVlsTTuG2xgLElqLIPV9BqQjiARDqtGtaHR6uQoVCJ+b6yQ37mlipNk41nK+zVj2+zzGZpVRK4yg12+WLEx2qInTXFoG2tyjYZ2F55P/tx5cedbnDkOqf7IvSmymutRa2V4XHVLM2RykbRMHRdf35VAIIJCKcZtIalSyzjr4vasWFLAzi1VvHJA1oLRrOT0i7se8X3+v2FUpHN6swf5YNuUOnMq0UALQ/96XUcqlaA3Jt501OkVSKUN+1kTqG43G0iwwaRWyFCNGEJwbwESgw7VoL545/9OxB6bxemZNR/tmWPAUNeDZpM3gL9fTxTf/Uy4tByT1kDzUSPZPTvWmye1bx9ajBt7xOJ9P9kjhtP77rtY8eRTRMPV3wtKk4nTvvsaXXbt+jcaibB16c/Mee8+wqHq7IdIOMSGRd9SUbiT0dd+zt5dibNWN6wurZNVcyBeZxVuRwUBjxOl1ojGYG0UEa0UdPz1zluEQ0F6jbkMfUo6A868nWHnP4qzspKCLQsQD8NHqYkmjiXHVMAnJSWRlHRw9+P9FBQUcPLJJ9OjRw/ef/99JP8wnOjXrx/Tpk0jGAwi+zv99tdff6VNmzYJ0+cBFAoFioOkLTXx30UrM3Nq9jR6WCcwv/AV3KEKeljPQitL4oNtl9Y45u4nWdkC2foi2mxTsmxQCHugCIuycWtbdTk5TPptPrMvvKTG3EabkcEpb7+BNKcFcnkFHqoXraJU4KwL29Gp57Fzu9bLk7mk9Xs8uXYQoWhsmvzE3KeQqw0oJpyGf9nquOfrJo1Dov73HftQJESecxmvbToLR7B6o04qyDkt+z4GT74Srazhhd8f8xPXya36s5RRY0fR77SLjksX+sNFKeoQBSnhaAidLInz28xguy+JRS5Z9QLXoKT7vS0RHngGWU4W8rYtAbCmaHjgpSHMm7mLxb/kIwgwZHQOQ0Y3x5pcG/n02Wz8Pu0+NrxbG+W379rFzLPPZeirL9Pp8ssabNF6NJHEEYjHCqNFyYSL2vPpG7EZEbZKH7pQR1roTmKns7YW+YPNozin1VfIFf1YYvOilEgYbtGTKpehkx56hDO1by9M7dpRtXlzzLgmNZXs8aPwOCpQqI9MxJutaqY9M5DHbl2M+wAR36qDhbOndEShrP4b+mdbun+SlqHl4VeH8u7zq9i5pQpBAj37Z3D+lZ1JOoZlECcyXSzjOC37/rgu9BZF/Taa5Qopoye2Ytmigrjz485pg7aeZQ31RS8VOdmk45fK+AJ1mFqBLDMV78LfqZq3BGl6CoZrLyGwfgvuH2sFeNQfIOoPoBNF0uQyigK1f58TNHKURDE+cgfuL77H98HXDJxyOV2mTGH9J58QCQbpNPUyUnv3RJteN/ujstyLrdKH1x3EnKRCb1TUy5VfbbXS+67b6TjlYhx5e5CqVGgzM9Cmp8ekkTsri9FbMxh9xZPIFCpCAT/LfnydkryNFO1YQzQSQK4QCfjjZ/mYEnSMALCX7WPGKzewZ8Pf3z2CQIf+4xl20f3ozA27hlFpjQycdAuiTMGcd+6meHf1d6FUrqT7qCvpMPgCoCl9vokTCyEabYD8tUamoKCAIUOG0KxZMz788EPEA75g9kfX7XY7bdq0YcSIEdxxxx1s2LCBSy+9lOeff/6QXOgdDgcGgwG73Y6+Hq7dTfw3CEWCRKIhQpEAn+y4muVln8XMCwhcl/EFKU+uILRnH8IrV0O6hXRN+6Nyf+6SEnzlFUQiYVRmC5r06hTjqkovAV+YcCiKXCGiNypqFqvHilAkSLlvNwsKX2WHYwlmRTYjM28jXd0OtcxE2OnCPeNXnB99XZs+Kwjozj8TzbhRiPWIyJV6d3L/Xx0JRuqmOF7Z7mt6Jk1o6LfFx6+t5acv49cNKlQiz7w3lOR0Y4O/7rHEH3Lz6c5r+aPkA67quJT3S9MoCgQRgOS/zQvVgsDtZi0WiYBoid0sDYcjOGzVGzl6o6JO5LNy2zY+aBPf1V5hMFTXe2YfWjaJrdJHaaGLzevKMVqUtO1kxWRVHReuyMcKh93Pqj+K+PLdDVSWedHq5Yyb3JrBo5uDppJlpZ8xr/Bl/GEnnc1jGZ11BzJRg0KiRiM1IUoO30V/28pfETwhiuYsZtunXxMJBsk9YywtJp/BzE/uYtKdH5Lc7MjbO0bCUSrKPRTucVJZ7iU714A1RX1Qt/hEOO1+PK4ggkRAZ5Af1LDuv0Ig4sMZKCUSDaEQtejlDbcR2RB94J12PzO+2MoPn22NGR80IpvzruqC0Xzov+d/ozwQ5IFdRTGiG+CGDCs9N22h6pEX6pyjn3IugQ2b8f29SS1o1CS/8STSZCtb3F7u31VIKAqX65R0nj2PyPezkPfqgnTMUNCokCiVaNJSQamAaBRpnABTNBpl7y47T971e02W0/5N0slTOzXIz8JtL+eP715mxc/vEP27K4Vab2H0FU+y7Mc32bd1BWff8xWLF2njmpZKRIHnPx5Fakbd57nbXs5XT1xE4bZVdeY6n3w2I6c8glzVMF1j9mMrzeejaeNxVtYtxRgw+UE6DDwXa2rDvmYTTRwO9dWhJ0Ro49dff2XHjh3s2LGDzMzY9kP79x8MBgNz5szhmmuuoUePHlitVu67776mFnJN1AupRAbIkIsqzm7xHK20/Zhd+Az2QBHNtb04w3g3+m+3ENpTXcsn2VGCPPvwjWQOFU1KCpqU2F3pqmCIvXJY6PYgkwgM0+lRSAWOdT6JVCIjVd2aiblP4w07kQtKFAeY/ok6LdrxI1EP7od/8zaIgqJ9KyRGAxJN/Zzy11b8GFe8A/yw5z5aGwailzfsLv6A4dkJBfyQUTmYrP+9+jmFVMOZOY9ilKWzw59EUSDIUJOOfgYNe/3Vi9oshZxiAZJ1dX93/9br3b5zV8I5v92Or7LqkAR8RZmH5+79k4J8B0qVFEeVHwS49ZH+dOyR/H8r4vUGBYNHNaNzzxSCwTBSqQSjRfn3hko6p2TeTJ/k8whEvOx1reLpdUNxBktRiFqGp1/P0IzrDtvzQ29O5d3HR5DTsT+dnroZiSBhz/alfP7CRUhEKXJlw0S2JaJAUoqmQSLlOoMCnaFxv0k9jkrc9nL8HicqrRG1wYJKa6zXuS5ngKA/jEIlRd0ALQorfXuZmf8ov5d8QCjqJ1PTmXNavESOricK8ch/nnJRTZKqOUmqw39m6gwKxp3bloEjm7H6zyJCoQjd+6ZhSVY32u/KKpfxYG46O7w+/rC5MEqlnGzWke50UvHiO3HPcX7yDaY7r60R8NpJ42o2NluqlDzXKotFNifdducR/GE2yivOZ9fGtawcPx5vWRna9HT63jeNlmeegTpBhmpFqZeHbvotxtQvGoUFP+dhTVFzxgXt4paJHArbV8xh+U+xpaoeRwU/vHgdZ936Fl8+dgEQYty5bdi6sZzdW201x0lEgZse6JvQm8JtK4sr3gHW//YNAybc2OACvnzfjrjiHWD1rJdJaz0Ec1IrJOKxL3tqoon6cEII+Isvvvhfa+UBOnfuzOLFh9YCo4km/olBnsIAxdm0dWYiGDSwtZjoDzMIV9pqjpErdOhk9Sv/aAwqgyGe31PCJk+tiJ1X5WSoScf5qWYM9awjb0ykEjk6SfxUdolajUStRppxeKJgj/OvhHOl3h2EInXdio8Ua4qaMRNa8vM3sdGGpFQ1p01ug0wu4q2sxFNcQsnKv5DrdVg7dUKbmRE3inIo+Kqq8JZXEAkFURiMaNNro1f2YIjCQJB5lQ4iUTjZpCNDArryCpCISPRaxDj1l/XFqEhncMY0Ht1TwfgkNQLwWF5xTTd5ATg9yUCmUob5ELsHyP8ly0mqrH8kKRgIs2ROHmf8j72zDpOq+v/4a+be6Y7tgGXplhaQbkXFQLEDO392d33t7i5UQGxQUZCQlm6WXLZ3Z3a65/fHyi7jzMAujc7reXweuefcmNk79573OZ/P+zMhDWkkgtdZgzGzFZvWh3nxoQU888EIMrL/uyssEokEc1riAbVUIkUp6Jm+60l+K3mlfnsg7GHm7pdwBCsY3+I5VGL83ysSjeII1YXQ6kQB4R95/1pzJhkFHdm+Zj7b18wHoNPAsznrjvcQRDl+nwuXvRKt8eg9T4809opivn3pOoo3LK7f1rL7MEZf9TR6S/KVaZcjwNZNNr7+aB0VpW6atTRy1iXtyWmmR6k6sGe+3V/CS2vGsNuzpn5bsXsVz64azB1d5tDK0P+Ajns40OrkaHVy8pob9t/5EGGRi1jkWnrpNfWeFoHdpUScroT9o/4A0VAYlAq048agGTUIyd9Ro6JUQo5SzlkaBfapPyIfMYCVP//Iirffqd/fVVLCzKuvw1NZSY/bbk34DNxRZE/oyA/w4+TNDBoTm6rUVFy2cuZOfj5hWyjgpWzbGnLb9iIttxWGNDV3Ptmf8t0u1i6vxGBW0LFbBmarCrki8YSpo6Yk6bmjkTB+jzNp+4FSvj25ea67topQ0E8gEEKp+vdH26T4d3D0R/kpUhyDSJVKhO+XEdyQoJ61VIqmTcdGO3kfDhY73DHifQ+/25wMNOmaJOCrKz3s2uqgaEMNWXk6WrUzY0lXH9Mz0S30J7Kw8rOEbVnqdkgjh36lVWdQcMZF7ekzKI8Z07bgdgboOySfjt3TsaarcZeX88etd7Dhs4b0C0Gh4OTPPyVv0CAUZuMBnbdm40ZmXnkNxX/X5zUUFDD09VfJ7t8Xj0LJ+yVVzK9tcBP/w+6iq0rOxKpKIg89g6xVC0y3XY3YLPeATdVkghytVEqBSs4LO2ON/KLAN5W1dNWqmyzg9fl5KC0WfNXVcW2ZPXsSiUQIOJ3IdfuPbnDWeilsUcPPb16Bz2Wv317QdTg33HMn61ZUNlrAB/whHHY/kUgUpVqG/jCvxh4LOIPlzC59A6irnzza9D9yxN743FEEAjhrfagssQK+KhBkvt3Fb7a6Afcgk44BRi1WecN9oDWmcfYd7/P9azezY82fjLj8MWpKipj67BVEQnVRHNbc1px5+ztYc1sfoU979HA7qpn2wlVxK5Bbls3kl/fu45TrX0Cpjp8o8ftCzJq+jU9fX1W/rbrSy/KFpdz+eD+69c06oN93iWddjHjfQ5QoXxTdzM2dZqCT/XuMOQ+UmO92P9+zYDaS8dbTSM1GpPL4nHRpKEzEXotwymBW3n5TwmMsfvwp2p1/PoaC5nFtJTuTC1yPK0gwSeWJxhIOh3BUJfYbALCX7+CUa59HZ66bbDJZVJgsKtp23v8knN1fQkST/PuTSKXIVYc+ms2a0zJpm1JjQBDlh7QMYYoUh5tjx2knRYpjCEGvw/R/VyJJYKhmuO4SZEfRHdseDDGjqjZp+/SqWoKR5HW496Zst4sHrpvFk3fM5av31/LSwwu57dJf2LrJdkzX6u5kHo1CSCzGRqpuYvndz2LbkmDyJQFhh4vgrhICm7cSKikj4k1eOkhnUNCmk5Xr7unFbY/1ZdDo5ljT1USjUTZNnhoj3qGuTNb348/FtX07EZ8/yVEbcDkDlO12UbLTQa3NR+32HXx50uB68Q5Qu20bX485heo1a9np88eI9z2s8AbYmJmO2CyX4OatVN76MOHyykZ9H4nQiyJnZBiZY0u86gTwbaUdb7hpA0dNdjanf/8Nojp2tUiTlUXfRx9iyuBhbJ/xM5FQKMkRGogEqvjp1YtjxDvAthW/UrVpCnJF4+7nyjI3H7y0gpsvmMEN507nqTvmsmltNQHfwQ2Kj3VcwWrC0RAGeSY3Z82ho7sfxvkbyVy4EmsZlK6opbqyoapAdSDEI9tK+bisht3+ILv9QT4rq+HBrSVU/SNn2JCex5m3vce1bywiEgqxdPoH9eIdoKp4E58+cCa1+xAN/xY8+wgf3rh4Op7axDX87DU+Jr0dX5oxGoW3n11GTZU3wV77Z23Nz0nbdriW4Q8n/83vC291NTUbN1G5ajXOXbuINPHZcCwjNeiQWhKbI0s0aoR0K2JmekLxXtdHhbxrBzx2W31++T8J+Xx4E0xsAuQXJo9A0BsV9Svf0Ugk6fH3hSDKsexD8Oa3PxFTZvMm102PRCMsrPiUYtaQUdAxYZ92fU9FYzz0E0aZLTqi0ib+m3UeNhFTRtZBpx2kSHEkSd2tKVIkQczPIf31p9BfMh55l/aoRwwk7dUnUA/uh7QJob2Hmgjg24e49kYihBrx0nY6/Lz51JK4Ul8+b4j/3TUP2wEOCI8EFmUzbu88C6uyIadSIWg50/wo/mlbWP7yK3zZbyC1O3bs8zih8kpqHnueiom3Unn9vZRffiu1b30Sky6RCFGUIlc0RDm4y8pY8tTTCftGw2E2T/uGiNuTsB3qvDyKdzh49r753HTedP7vwp959/m/2Pbzr3grEwjvaJS5d99LZVVN0mP+FILI0JPqurvc+BYmFg2NJVshx7YPIV0TChNo4qSPVBDI7NmDC/5awoBnnqLLtdcw9PVXGfzyC3VhpBUVzLzmetylyWtA76F820qCvsTf8Zo/PqWwcP+vu5pKL4/fNofff9xGMFD3GyraYOPBG2axa3vySbN/A3KhbhLlioyvUC7eSvie+4l8NonI1G+IPv4kmT9/hXtXJV5PnfBe6nSz2x9ftq0sEGKRw8M//XFVOiOCVOTPaa/E7QN1YawVe4W5Bj0enMXFOIuLCfn3P/l1vOB2JBZlAESjScOHK0rchEOJf1/2Gl/SkOr9YVQkr22vFHRIJU0P1LRt2sw3Y0/nw7Yd+KRLNz7p2oO1H36Mz2bb/85JiEaj1FR5qSxzY69JPsl6JBAsJsx3Xg//rMoglWC641qE/URbSeVytKePRqbed5i7VKmgJhgi/I/fUm5zPZb0xOkwZ1zUDpXUx86Zv/PTBRcx/cJL2DVrNu5/lFneF1pjGoPPvydhm0prIr/jiU0W7wCOQDm/l7zKd1WP0P/6O8kq7BLTXthjKMMuehDFIc5/B9Bbczjvocno0/by0ZJIaD9gAu1POgeD+cBTDlKkOBqkQuhTHNdEg0EiXh8SmQyp6tCKaolUipiVjvac09CcPgqJTIbkGChppROk9DJo+DHJKnxvSZjljz1B4dhTMBS2QGk0Juzn84QYcmEbRuo7IQAb/yzj96lb8LiCOOx+qso9WA4ij+5wIpUINNf14M5Oc6iq2ITXUYNQK2XjfR+y/tvpAHgqKtgy5Wu6/d9NSKTx4i1ss1PzyPME965JH4ngmf47iCKqc8aiTGvcSkA0FN6nyKzduZOIrRaSrNpUlnt48PpZuBwNg/DsPC27fp2Z9JjlS5bSOph80O6PRIgoGlaA/CvWojllOJIDKAUGYBZF2qtVbPUmPmd7jRL1AaxgSEURb2Ula977EHVGOlt/+BHnzp317b7qaryVVejy8vZxlDqX4WQEvC4EYf8rgNs22yjdFb/iGAlH+fTNldz6SD+0+v2XaToe0cnSKdD2JjeUQ+2798a1h1auQd5uGZ5cC2G5lFm25GG8s2wOTjJq0P/jeRkK+vHsQ8BW7FhHy+7DsG8pYsHDj7Jl6tdIRJH2F11Ij9tvwdC8+QF/vmMFjT75M0UilaJIED4PIMj2/duSSg8sPaaz+RS+2nobUeInfQdlXYNe1jQzUOeuXXw1aGjM89BXU8OvE69EZTHT8vTTmnyNtTYfC2btYtonG7DX+MjK1XLeVZ1o3zX9qPweJRIJ8vatSH/radw//kZw01bEgjy0p4xAyExr1DhBzExD422OLi8P5674EqXWjh1Zo1DzxZZiBpl0DDfrSfs7NcWSpub+Fwby8iOL2LqxblJEJpdy6nlt6NU3jRkXXsD26Q2RFRs+n0SLU8cy/K3X0WQ2zncmv/2JjLriSX7/9HEC3rpnYlp+W07/vzcwWHP3s3diotEw3pADb7iWN0rOYczld9JHchNhnw9RrcYuK0NjPjxlWCUSCZkFHbjose9w2SrwuV1oTBnIVSb0ZhOimFrPTHF8cfTVSIoUB0A0GCJUXoHrm58JrtuEkG5BO/5UxLwcBN2hrdcrkUqRqPZfm/xIIZNKGW3W8ofNiSscO+jKlIvkFO/kl4cfZeHDjzLgmf/R6cqJKP5hFuYMhVkhhPhc48cR8CIBugwwc9PAAbx+2zxqa/y4XIfeCO5QI3fImDv0WuxFRQnbt3zzLR2vuDzu8wOEq+2x4n0vPDNmEezYkkCLZo1yQRfVKjK6d6dsyZKE7bm9exMNJF9FXDpvd714z8rTcvllOUgqiqjKSz5Q0mRno1bIwff3fnIZHeVyQMJKv4/eohTZslXsWSMVW+Tj8YZx2D0EAmE0WhkmiwqhkQMXUSphlFXPrzUO/P9YEZJLJIyxGpAlmChpDFKZjJoNG6jZsCFhu6QRqz3ZLbsmbdNbc1Cq9/9cWDY/ubnS+pVV+H2hf62A18vTuLb91wQ+mZW0T/TXmciH9keCCoHkglFAgiRBuyhXoNKZ8ToTR46k5bfDsXMnk3r3jVmtXfn6G2z94UfOmTuryWUFjzU0Rit57Xqxa32DgZ1Sa6TL4HMo7DYUqSgjHAoiiLF+EmkZahQqAb83fiIqM0eL3qDAGQpTGQgx1+7EH43Sz6AlRyHDuA9PFKMimyvbfs7bG86LEfGFuhMZmnPj3xVaGvD76qJwkpUrLVuyNOlk5pw77iarT+9Gi0gAjzvA5A/W8uu3DRUrSotdPHf/Aq64tRuDTy44KqHPEpkMWW42honnEfX7kcjkSJrgPSMRRfStWnLaN1OZPHQEfru9vk2dnk6vTz/muZCUmmCYqRV25thcPFaYXe8vkZWr4+6nT8JR6yfgD6PVyTFalGz9emqMeN/D1u++p/TSixs9gaLSGek67Hxadh+G12lDkMlR68xoDsJsUiUz0dE8iiWVX+IO1TC5/M667wIJUXuUq9tNRiqp+1uGHU4itQ6i/gBSrQap2YRUfvAGcwZrFgZr00oYpkhxLJIS8CmOS4JF26m87REIBuv/7VuwDP1VF6IZPeSQr8YfSqKRCI7qUqp3b8ZlKyctvx16azYaQ+PzvoTIbm7LDTLTrmKZM4xMIqG/TEI/l43ZZ42v7zfn9jtpMfaUOAG7yuXhzdKGXMsosMLjpUwe5IK7evDaHfPJyjn2y6JJRRGl2QxJBLzSakVIYqwWrthHTngwSNTn4/frbmT0Zx8nnADYG5XFwklPP8nkwcPi2jRZWWS2aYtgNifcN+APsXJxXXijziDnmokZ/H72SHw2G6d/N43lL72SMI+x1913kpadSa6vlHFyDb4iJ3/N2IREKuHMk5vToYWa4Mq1AEgtJnyDhvPW/X+ydnnd51ZpRM6+tAMnjWjWaJO2dLmMx1rm8GZxJUXeugmJApWcq3PSSD+IwZUmKxOFwYC/Nj6qRJeXh6oRkRDW3FaYMptjK9se1zbo/LvRWfYvGkxJyh5BnQP2gZoAHi+YlNlUVycPc444nIgCqESBURY9GxIYaQKMsujRJYj00Boz6Dvuen77+JG4NrXeQkaz9ix/8sWEodbOnTvZ9tMMulx9fJeGVestnHbz6/zw6s1sXz2PvPZ96Dvuehb/+C6Lf3wXuVLNCcMvpOfJl6O3NIS3myxKrrunFy88sIC9589kcinX39cL0SAypcLG93tFZv1c7aCTVslNeRmYkohLhaChs2Usj/fcxDrbrzgDFbQzDSVN1RLDXqU4bdVeNq+rZuZ3dUJ62KktaNXeElcmsmT+n0k/u33zZkKN8ALZm1qbv/6c/+Tzt1bTtXcW1oyjFykmEQQk+wmF3xdpJ3TlwhVLKVu8hKo1azF07YqvfQdejcqp2ctLojIYYkGtm1OshvrnkN6oQG9seHa7qstZ/lLiFBWAv158mbwhg/f7PtuDIMowpOViSDuwFfc9RPyBOtO+YJALLE9T6y9lk2NOfXuUKBmq1hTq+wAQKinD9swbBNbVlW2VKORox5+K5pThCMYDr6iSIsW/iZSAT3HcEbbVYnvuzXrxvjeOdz5DdWL3Y1bAR8JhyrauYtKjE/C5GwZauW17Me6WN/dZQmhv/BEXr6/uQ4+0i3gs+wGq5y5n58cfM/3b74j+wyxox68zMbdpcHeuCYb4pDTxClhZIEQ4Tcvw01ugNx37ztsqi4Xut/0fP55zXsL27v93E2KS6AnBklhQ1zUKRCUStv74E57yikYNeDK6d+PUr6cw6+Zb6kPA8wYPYtCDD6IS5EiT5EUKorR+ADpiVAYrHrwdb1Xd5MqaDz5iyKsvM/vmWwgHGiIiOk28nIKTR6ORybjHZOXlBxewZV3D33TFojI6dLZwxbXXEH39DWSPPMgjd/5JeUmD4Z3XHeLjV1eiUosMHlPQKHEqSCS0UCm4ryAL19+lwzSigOEAw/L3oMnOZvRnH/PtaWfE3L+CXM6Yzz9Bm508T3cPOnMm5z3wJdPfvoutK2dDNIpKZ2bQeXfR8oQhjbqOE4fkMeXDxOWGRp/VCoP52P9NHCzKvj3wzZqfsE3WsS2Cpu5e7aBV0U6tYL2nTpBlyWUMMQbIlUMLdWKRJhUEOg06G2dNGUt+eo9opO5vbcoq4Kw73kcmUVD03fdJr23bDz/S4qyxuJ3V2Mt3ojNnordmozMfWDnKo4XBmsMZt76Nx2kj4HXx0b2nEg7WfWd+j5OF375O0YpZTLj3M3R/vxNEmUDXXpk888EIfv2miN07nbTuaGHgyGZYMzUU+f0x4n0Pq10+FjvcjLQkNz5TCGrSVYWkqwoTttdUeXnp4YVsWNUw6btycTltO1u56cHehENRijbU4Kj1Yy1slfQ86vT0pBOqyQjUOjj5tBxWrnKya6sjps3tCuJyBI6qgD9YJBIJ+mbN0DdrRu64cby4q4JlTg8QP76Za3cxyKRLODnmCFSwzTaPoCe510rI422UIeihJFxVg+Ozr/H8OgeCQaRWM9de/ijrmm/j3eLLkQkqTsqcyIic/8OkyCVcY6fqr02Exp2N5NQg4oL5hOYvxPnJFKRaDZqxI5CkzOZSpEgJ+BTHHxGnk9DOJG7FkQjBbTsRs5qWt3ekcNaU8vkj5+L3xA5EijcsZs6XzzDy8seRKfYfrq8RTcilahaUv01P/3D+OH1C0r6hf7zQA5EolcHkL/FSWZRzLuuIVnd8hArnDhxAmwnnsHHSl0Ddiog6PZ0Ol1yMpX27pPsJVjNibhah4vhwT3n/nqz55huIRgl5kw+IYvbR6Sg85WTSO3fGV1aGNBxB9PhR5+X97UiceOAqCFJGnFbIzO+20raljJlzGlYmNk+ZStDl4pTJX+DaXYJUJpLdrx+azAyUprp8+rVLymLE+x7Wrqpmx6kF9Hj/BdbvCMSI97358t21dOmViSWt8YNgvSigP0jRvjeCKJI3ZDAXrV7OqjffoWrNGjJ79aTjpZegb96s0ccxZuQz7pY38DhqCAV8KDR6dKbMRhsuWawqrrqjO28/syxmlbNdFyuDxxydUN0jjaJdK4TMdMJlseUCkUrRXzoBubHOYMosE7mlWSabPD68wRpk4WXMLH6I33xbyVa354zmT9JM2w21zBhzGI3BysAJd9Bj9KV4HNWIchUagxWtKR1vVVXSkoEyrZYeD9/FlGcup2TL8vrtxox8zr33cyw5icXnsYpKZ0IqiHzz0UP14n1vKnesp3zHunoBD3Vh63kFBi6+sSvBQBi5XEQqSAhFosyodsQdYw8/VNXSW6/ZZyj9vlj7V0WMeN/DhlVVrF5awa/fFLF5fd0z6IGHeiMqlYR88dEZPe+8A01W4yZbXCWl7J47j5VvvIk8EuHMsy9AHN+D11/dhsfVIG5l8sP3m7TXeHHYA0QiUXR6OSar6oC9BhpDFOLS4vZGJpEkdZ5eWf0di1yT6H7mcKpWrUrYp82Ec+rfG0eCsM1O9eMvEfx7JR0gUlVD7f/eoMMd1/BMv2IikhBaMQ2ZoMDnDbF5s4ePpnjZtW09MrmUQUO7c8r9Q4n87xmcn3+Nsl9PxLSjVwUoRYpjhZSAT/Hv49itfkblro1x4n0Pa/6YSv8zb8aYsf8cT4Mih7NbPMuHmy5jl2QtmX16UbZwccK+zUbEhnWLElBJJXiTOIbnaRXojqO615qMDAa//CI9br0Fu8uN0Lo15TI5Orkcl1KOPBpFSLC6LFhMWB69g+qHniO0o7h+u6xHZ/xd2rDq4XuQaTTIGxluCCCRiegLC9A1yyfq84Fc3qi8vbQsDVfe1p1oOH6QvH3Gz2yf8TOarCwGvfgclnZt69uctX5++SZx+gDAzz/soF2PE9i0OrnBnr3GR8B/9Es8yVQqLO3aMeC5p+tMjVSqA3I6VmoMKDXJVxv3hUojo+/gfNp1TmPFojKcDj9de2WSnq3FaD42o3oONWKaBevT9+H46Cu8fyyAUBhZ6xYYrrkEWUFsKK1ZJnKCVsrM3Z8xbXuDa/U252KeWz2US1t/QJ/0CxCksUMNuVKDPFODKbN5zHaV1Uq3m2/k50svj7uuztdfxbwZr8eIdwB7+U6+fOICLnz0G3TmY3PiNhl+r5NtK/5I2r5+/ve07DY0brsgSBFUDVIuQnSfws8TjiSwqGscbmdgn8+Ymd9tpV3XtHoBP3lKJWd9MY0/LpkQk9fd4ZKLaXv+uQkNRf+Jq6SUH8+ZwO55DZEgu+fOw9qlKzc8/R7/e3wjAC3amA7LuyocirC9yM4bTy3BkqZGrhCoKvdw2nlt6dIrA5X64HOxE6ERBcZY9WzcmTg1ZYxVjybBxKndX8oPOx+j2r+Dk8+6Fc2bn8f5EOjy8mh15jiCLhee8gqqN2xAVCgwtmqJJisLUXEYvsfK6nrxLtXrENIshO21RKptON79grQujyFaGyZ0tqyv5rE7Gv7mwUCEX6cXs3mzk5snXk7k5VeJ+o99b54UKY4EKQGf4rgjpFAj5mUT2pXAcEoqRdps327VRxNHVXKTrHAoQCjBSkwiBInACZbT0XdI5+fi5xj73F38PPjcmDBrgNbjz0aXGzvoNooiYywGplba446rkEhooz7+hIraaiVgMDK9tJq5lQ0u4mqplLuaZ9JGrURMsHIiZmdifOR2vNt3ELLZQaWi6PffWXjmWYQDAXrfe3ejwrf/iUQUkGgbb6ao1sjoNywfd5kcU5s22DZujOvjLi3F2im2dm4kEk1aWgrqBqJlRWswmZJHdShVIrL9OFwfSQRRRNAe+jJCjUWpFslS68jKO/Y9IA4XYkYaxhsnor94PEQiSNRqBEPi78MRKOO7HQ8mbPui6GbaGodgUTbeeK7ZyOE0HzWS7TNijbgKzj6VeU+clXAfW9l2HNVlx52Al0ikKNS6pM78Kv0+0nz2Qi6V0s+g4S9n4mihHjo12gOMHolEooT3MTkQDkdiVqXXr7HxedDAGZ//ilVhx1NZirlNG5RWMwqTsVHnLJ4zJ0a876Fq5Qo8y/+gsG07qso9XH9vr5gc8ENFVYWHeTN3cu7ETixfWIrHFWTwyQWEwxFKdjgobLfvFWB3bRWe2ioCPjcqnQm1IQ2lunHPk/YaFZ21Sla5YkV8Z62SdprEz/EIIar9dSVTP3Rey2W/vs32N6ex9YvvQCKh9QVn0+PGW5BptSx95jkWPf5kva+KqFIx+pMPaT56VMKydlWBIEVeP9u8AfKVclqpFVhlYqNSrgKbtyE1mzBcfi4gIVheiZiThTQrA893M4h6Gz5jrc3Hh6+sSHic7VtqqVbkk9a2JZIk30GKFP81UgI+xXFHjU9EuPhi+N8z8I9QcOHcs3Gh4FhdP85o3iFpm8ZgRa5qvOjTyEx0tpxMc11PIoEA5/21kEWPPkXxrNmorFZ63H4rzUePRGWNNQATpRJGWw3s8gdY7GgY8KmlUu4tyMQsP7YeC6FIAJu/mA3236nwbaGlvj952q6YFQ0TE+FolNk2J3PtsSXAPJEIj24r5aXWeWQoEq+aKDLTCYaDLH7vPdZ99DGRYBC5Xs+JDz5Ax8svQZAfmVQCpUpEWZDLsDdfY8rQEXHGdZ2uuDzOvVlnUHDSiHw+e3N1wmMOGJnPwmm30O3ke1BpRLzu+NSJkeMK/zOryykaj1SpQKrcv+O0LbCbcDQ+XxfAG67FFaxqkoDXZmUx8oN3sW3azPpPP0NQKGh/0QVEdHKIJp+sctn3YUp5jKI2WOk28mLmTX4+YXungYknLBLRUasiQy5SHoj9jSulEk5LNyLfa+Xb5w1Ra/NRusuFIEjIyNFisiiRyRMYD+rlDBjRjKINic0Nu52YxYrFZTHbijbW8sxTtdz1eBtmTb8O72QbUkHk7Ds/pHnHfojy5G9od1Ulq954K2n7ti8+ZuIHk9FnpR+23PcdRXYUcoFn7mmYRPjz911k5Wm56vYeuF0BNNrE7wVb2XamPjOR8u11BqJIJHQ86QyGXnQ/WtP+J5jMMpEb8zLY7gvwS3Wdp8EIs57mKkVSI0JRoiBH3ZHdnjVU+3fwfGAsXSeeRternsBs7YZc15ySqBxFwA/5+SiMRnw1dRETIa+X788+l4vXrIxLOdvlC/BAUQmOvXxJNIKUR1pk01y1/1GWkG7BdOtV2F95PyYlR6LToH3wTlxyHXsC+n3eUJzHwd6s2x6g4wO38psvTI7NSTu1EotcTBhdlyLFf4Fja6SeIkUjcNT6mTKlissffADhj1lIiraCxUx42AjWVYi09zhxl/nrHMiPgbrte2NIzyWzsDNlRfE5agMn3IXO1HQzJr08HeRAh1xGvPc2gdpapKKIOj15PVWTTOTa3HQmBMPs9PvRCwJZChlm2bH1QgxFQhQ5/uSF1aMIRfdEJ/wPsyKP2zvPIu1v0yV7KMy3CSIKAILRKKvd3qQCHkCbk8Pgl56n9z13EvJ4kWm1aLKzmmy4dCjI7N2L85Ys5M/7H6R04SI0mZn0uvtOmo0YFpe/KJVK6Dskn1++KaKyLHb1LTtfR6duZhZ9tISA7wH+7/4XefWpLTjsDVEePU/KZPRZrRBlhy6fPcV/C5lk3xNcgrTpvyFNZiaazExyB5wEgM/jwGWrpnWvMWxe+nO9+V3MPobjrzSUIIicMPwCtvw1M+6dMOj8uzGk5TT6WFa5jIdaZPNdpZ1ZNifBSJSeeg0TMs1k7JXG43IEmDV9G1M/XEf/QZl062xgR1kVlZlmWrS3xoWHSyQSep6Uw09TN1O+O9ZHIzNHS7OWRiZ/EG/8KFcIiFJvfcnASCjI5P9dwtUvz8WUkdzXwu9xJKy6sYdoOExmjgZ1Ezw7mkIwGEatljHt0/iSlqW7XMyesZ1zJ3ZEkyBIyFlTzqRHJ8RWwohGWTNnKkqtkaEX3ocob5gsDQbDuJ1BpFJJTCSBSSZikol00tT1FfeTdqCXp3NWwf94ae3JAISjIVY7f6JP+zv4wmZmVUVDalbhScO4fHZvfhs0pF7EE42y+u13Gfj8M/UpDvZgiOd2lMWIdwB3OMJT28t4omUO5v14Koh5OdifeT3OTyPqdON+4gUc194ColhXzlSQIlcISdO5ZGYV71Y72fF32VSlVMJDLbIpVCmQHkNjlhQpjhTHlrpJkaIRGE1K1qyo5va1Nvr0b09Bj+44PVHaR2XItvzCzw9/SNjvp/U54+l0xeUYmmCCdbjRGtM56473mfXJ46z78zuikTBqvYWB595Bmz6jG5UfuC/kGg1yTeNW8XWigE4UyFfFDsCdgUrcoRqiRFGLpphSQkea2sBuXll76l7ivY4a/y4+3nw1V7ebjEZmJByN4thHmGeJb/95czK1GkNBwUFf88EiU6nI6HYCYz7/hKDLhVQm2+dkjDVDzUMvD+aPn7czZ8YOJFIYcnIB/YbmI5PakSnVVGxfzcIvr+Oam+8jTHNcrhBpGTLMabq4MlApUjQFgzwbjWjGHYo3UkxTtkArNr485j9x11ZRumUFC755Ha/LRrOO/Tn33s/47eNHqNjRIBrzO/RHKj9y5lyHEr0li3Pu/pjKXRtZv+AHVFojHfqPQ2fNbrKXQ7pcxsVZFsalmYgSRSMIqP4ROl+y00H5bhfPPN+DyNffEH57ORKFHMmAkyBzOKjjnzWWdDUPvDiIub/sYPZP2wE4sa+R3n3TmDc3saHsmDNz2Tj/g5htkVCQ4o1L9yngt62fT4uzT00YQg/Q/pILUVkOn4mZKEpZ81dF0vY/f9vFmRclNketrSyOK2OpNWWgNaazcdF0eo+9EmN6PtFolIpSN798U8SSebtRqWWMOasVnXtmxDyP9yfc96aF/kQubf0BX229BXfIxsDs25lRm80qd+y7s8gf5F2VnjOefZoFl02s316zcSPhQABRWTdp4AiH2eVPHFlTGQxRGwrvV8ATDBFYE58OBhCpsaGLeti1rRaTRYXBpGDIyQXM+HpLXF+pIKFZVwufuhqiQHyRKE9sK+WZVnlYj7GowRQpjgSpuz7FcYfeqKDfsDzmz9zFvFmlzANuvrUlq267jMq//qrvt/iJJ1n7/gecu2AuhubNj9r1/hODNYcx1zzDwPPuIhzwIVdp0ZoyDsiw61ASiYYpdq/mw02XsdNVZxSVpWrLRa3fobm2JzLhyCcmlHs34Qs7E7att8/EFaxEIzMil0jIVsgoSTLgSJY7eCzhDFZR49vBiurvEaUyulhOxZSei1q2f2FizVBz+vltGXpKCySAzqhAKpUQDor0HHM5f379ClW7NjD91QsQ5SrkSjVypYaLHvvmsH+uFP9ujIpsrm43mRfXjIoJpVdINVzZ7guMigNbGXfaqvlj0pOs/O3z+m2VOzewevZXjLv1Lb55/mr8Xhete42l7cAbUWiMB/tRjhpaUwZaUwYFnQcc9LFkUimWJM7sgfIqTCWbOVO2HulCDYrh/XGHAvj+XArf/kDtihXIHr8rocu3NV3NaRPa0n9AGlu+/Z5dX7/AtBtnM/C9T1Gf35zpP5TirA1gsio55axsjMq1/PHLN3HHcdnK93n9Vbs2kte+B5ZOnaheHZsaZGzZkpbjTjvoie59IZFI8PuTV2kJ+MNIk5zfXrGj/v8tOa0YeO7t+Ny12Ct2Yc5uQfjv0rflu13cc/VvuJ0Nv5fXn1xC194ZXHNXrwNKadLITPTJuJB2xqG4QtUgtGTy5sTf9VZvAO2JJ8Zsyx04AGEvI7tAEpPbPfj3ESWxh2hg3xPnUZebRYuL6dwjE5lcYOy5rdi4poptm+z1faSChCvu68nPIW/c/o5whOpgMCXgU/wnSd31KY47NDo5F17bBYNRya/fFWFOUyGr3Bgj3vfgLitj5Ztv0+/Rh49KOHQy5EoNcmXj892PBFW+7fxvRX/8kYYwyVLvBp5dNYT7uy4nV5c8f/9w4Q4lzrvcw56VeaNM5MJMM//bET9gscgEClTHdkm82kA5Xxb9H4srJ9Vvm7b9Pkbl3sGovDvQyva/4iQI0riBnyCT03PMRHyuWpbP/JRoJEIo4MWc1Zwzb3s3pkRViuOTiM9PxFZLqLwCiVRAyLAimI1IjtDzTioRaGXozyPd17Kk8gt2upbT0tCPEyzjsCgTr7Q6a8rwe5wIogyVzoxSE1vpweMKUl1aHCPe9+D3OFjy43ucefc0am0B1q32o0/LblIZxP8iobIKau58nHBZBXtklUsqwXj9ZURDYfyLlxPeUYx/w5akZbqkgoRQ2TYW3Hhl/bY/Ljuf3MGDuXbi9cj0maR1bsu8r+7ljwXfJzxGXtve+7zO1r1G8vWzV3Lyi09jW7KWLZOmEg2HaXH2qeSNGooy/cAjOhpLz/45/DQ5fiUYoFP3dFTqxEPnPZEFpszmDLvkQb57+cb6FAIAvTWH8x78ih+n2GjXyUTn9kpCYVi42MWmtTWsWFROdYXngD1JBImAWZmHmTyKPP59FuRxIqkv9yfTamk9/uwYYzqdKCCTSAgm8J2QUmeGuz+kWg0SpYKoL7E5b9RsQSrW+daEQwFsuxZwxpkeovLWbNngQacXaNlWidzg5ZtI4t+3ex+RdylS/JtJCfgUxyUmi4oJV3Vi9FmtkEQCzLvy4qR9N37+Bd1uugFtVp1YcdaU43XZIBpFKohIpQIqnQmV7tgIwQyHgrhs5fjctYgyBWq9FZXOeNDHddj9VFV4WL6gFFEmpduJWZitKjQ6OeFomPllH8SI9/rriQb5cceTnGx8gYx0c0Kjo8NFjrpj0ja9LB2VaKz/d3uNiutz0/i4tKY+b6+jRsnVuWlYG1HK7Wiy0T4rRrzvYUbx03SxjKWVof8BH1trSmfoRffT57Rr8DhrkCs0qA0WNIbDPxA+UtQGyvCGahEkMrQyKyqx8aX/jmfCTheeX+fgeO9zCNXd8xKFAuPt16Ds2QWp8siYE4pSORnqVpzS7H4i0QhSSeIVSr/HyY61C/jlvXuprSwGiYQWXQYxcuITmLOa1/ez27xsXPRL0vMVLf+N/uc8hEJrZcQ4DZb0lHjfFxGvl9p3P4/LRSYSxf7ah1gevAX/4rqoK99v81Cf2ANJgnJlAInSjYtnzaJ41iwALl63mq6Dx7MxgYDPbtUNY8a+q8Sk5bXBmNmMqa9dRXbLrrS8dTwgYdumBbS0nhU32XM4yM7T07aThQ2rY6sDiDIpF17bBY0u8YSwIS0Xc3YhfU69hulv3xUj3gEcVbv59sVrGX35K6x5dRI7H/gWQaFg+PmXcu65o/HJjBRtqGH+b7to0cZEm44WrBnqRjm+/xNjNMx1srrygYtEJcu9gRhBr49GCfl8WDt2ZNTHH6BvFms0aRIFzkg38mV5/CT6GKsBY5L7Y28EsxHthHE4P/givq1vbxasdDJ4dF3amstWwTcvXEPA50apNWLJaUmZ18XqaRswZuRz2h2TeD3BPEDmPrxtUqT4N5MS8CmOW+RygfQsDaGADOk+nMKlMhkSiYSg30vxhiX8+OZt1FbsAiCrsAsnjb+V9Qu+Z8A5t2NMP7ol6DyOGlb/MZk5Xz5LwFs3M53fvg+nXPdCXM3kpmCv8fHxqyuY/9uu+m2fv7WacRe24+TxrRDVPjbWzk66/w7PQpZv28oJnRTkNj9y4kgvz6SndTxLqr6Kazu7xbMY5Q0l3rSiwACTjk5aNZ5wGFEqQS8IaBsx0DiauILV/Fz8bNL233a/RHNdT2TSA09hkKu0yFXag7qHjkV8IRdbHPP5fMt1VPiKkCChk/lkzi18kfS/DQ4PhnCtg4jDCaEwEq0GwWI6rOG7TSVYtB3HW5/EbIv6/dgef4n0N55CWtB49/dDRTLxDlC2dTWTn9prsjUaZeuKWXz6wBlc8uQP6K3ZhEIRFs4uRrePsmcSiQStQU5uy/275KeASK0T3/wliRvDYULFpQhZ6YRLK+rKdO1DMKozMlEYDPhra+Pb0tOR67TkNuvNOfd+yq8fPEhNSRGiXEWXoRPoO+56tMbkfh5Ql05w9p0fsmxG3X8lRSsp6HQSwy97BGtOqyZ97gPFaFFy00MnMufn7cyYugWXM0DnHhmce0UnsvOSl7jUmjI459HvcJZsxlFZnLBPadFKvOVbWfPqS/Xblj5wF+k9JtPqoTd574XN9ds1OhkPvjSIZoXGJl1/7bbtrH7lVTZP+hKJIND74gs57eKLeT4kUhsK016tJE0v5+K1q1BZLQk9VuRSKSPNekyiwJflNmyhMAZR4Iw0IycZtSgbUZZQIpOhGT0YiUqF6/OvidhrkaiUCCOGUt6mF44NbtIy6yIRHVUlBHx1Cwg+l53dG5fWH8devhNr0AXERi0OMWkxHOXUwxQpjhYpAZ/iuEeUy+lyzVUUffNtwvZOV05EnZ5O2fY1THpsQozDbWnRSr596TpO/783mPzUJUx4YNJ+BxiHgmAgjL3Gh98fRqkUMZoVCKKUTUt+ZuaHD8X03bluIZ89dDYXP/7dAYc8l5ZW07avQGH3fBb95GLjmrqVgWmfrOeEPpm0aK/DomjOFhIbBxlkOdRWRvj28/VMvKU7CuWReXRoZWbOLXyJXG0Xfil+DneohgxVa84qeJrWhgFxYkGQSP7Ohzt+Hm3haDChAdgeHMEKQpHAQQn4fyvF7pW8uGZU/b+jRFlV8wO73Cu4u+ufmBUHPiEX3FGM7enXCW7ZBoDUoMdw7cUoe3ZFqjn6K74Rlxvn59MSN0ajOL+dgebyc1DommaEdrjwOKqZ+dHDCducNaWUbF6O3pqN3xukdQcLOtVwFn+beGKrZbfhqDTHRsTU8UA0FIZ95CxH3F4kf0draE4ehmQf4kyTncXID9/juzPOjinrJxEERn30PtrsbCRSKS27DSWzoBNBvwepIKIxpiHKGvcM01uyGHDObfQYdQnRaBS5StNkQ7+DxWxVceq5bRkwsjnRSBSVRoZak3y11xEKU+T182ONj7H7yf0OBnxx2yqWLqNN2UbSs3RUlNYJWbczyHP3/8nDrwxutNlo7fbtfNFvAO7S0vptq554ip1fTeGGH77nB6Waa/ZEpaXvewLMIBMZZtbTTachFI0iSiWYRKFJru+CQY927DDEHl0JOL3YnWFWbvPTMtPCGT2bodPX3RORcHLfAQCDVEKaTKQyGEInSDktzchgkw7NMT5BnyLF4eL4GeWmSLEP0rp0pvD00+JEvKVDB9qdN4GAz82cSc8kLE/j9zgp3rAEuUqLs7rssAt4e42XdUuK2bm5mg0bPWzfUsvYc9swcGQ2f3zxdMJ9aiuLqdq9uckCPhINU+4pYoHkf6zR/IBCr2XAbdcyzj2aF27dgN8XZvrUzVzXuhfDc29mUeVnCY/TT3sLn/1UjSBI8LqDR0zAAxgUmYzKu5O+GRcRjoaQSVVH1Rn/UKMWjXQwjuCPssS1j0+wnI5SSL7q0xgi4SjVlR6KNtooK3bSoo2Z3GZ6zGnHvrlfMlzBaiZvvS1hm81fzFbHQsxpBybgQ+WVVN36MBGnq35bpNaB7clXsDx1L8oTkqd2HCmigWB8SPRehHeXUbF1LTkdex91g0yAUMBH2db48pl72LrqD5p3HcnKJRW8/uRixo7PpeOgC1kzOzbCQKk1MuTC+1CoD+438V9CqlYh5mYRKi5N2C5r2QzXlO9Rjx2BmLPvd4wgijQbMZwLVyzjrxdfonrNOtK6duaEm27A0KJFTISK1nTg71JBlB11jw6pIMFs3f8z0hUKM6W8hh+r6+qYjzOm10UxJMgfF+VKpOHEEyS7v5lE+z531gt4gPLdbhx2f6MEfCQcZu0HH8WI9z3Yt2xBMmc2V194OmK0GmhcyVqJRILlIEzioqEQUX8AZaYFVY6AJhQhr5sEqRA7CaBPy0EQ5YRD8ZMfGoMVs8HCk4Z0ApEooqTO9+Zolrx1OQLU2n34fWE0WhlGs/KIjotSpEjdbSn+FWgyMhj25mt0vuoKVrz6OmGfjw6XXkzuwIHocnNw1pRRWrQi6f5l21ZjySmktnIXWYWdD9t1OksrKF28gvJ3XgebjUFDRpJ2xym89c523C4/zToNYe0f8cZNAKVFq5rsUlzh3cLjK3o2OLkHK5i66zYKddO449W3eHTiOhw2P6FQhHRlK85v+TqTttxAhL/zaZEwxHIX5cszqancSasO5qNSM1yQCJgUuUf8vEcCmVTJiLxbWVjxaZwHgV6WTjfrGUlzID3BWuyBYpZXf4M35KCL5VTSVS1jJjgikSjbt9h55ObZeD0NqxxpmWruf2EgGdnHpxAKhD1sdS5K2r7W9gs90s4+oGP7125AyExHqtcS2l0W0+Z4bxKyx+9EMBzdPHuJSoGsRTPC5VWJOzTPZumvH6PPaYbB2vh64ocLiVRAY0zDba9M2G7JbklFqZuXH1lINArTPtvJBVeNZ9S1Q9k4/0N8Lhu57QdxwrBzseQcO6VBjwcEsxHDtZdQfc+TcW2Kbp0AsD5zP0JWBoJet9/jydRq0jp3YugbrxHyeBA1GsR9pLH926kJhevFO8DSiJJ2A8ezfvaXcX0HnXs3UYefIa+9QsWyv9gw6QtC3jqHdalCSSJPtmCgcUZt3qoqNn45OWn7xk8+Y2Pv1az0/cS1Hb4mR93xgPLrG0PE6yNUVoF72gxCxSXI2rZEM2YoYkYakgQGeFpjOoMvuJeZHz4Y2yCRMPqqp9GZM4+Z9KWKUjevPbGIDavqPBIEUcLI0ws57fx2B2xAmCJFU0kJ+BT/GjQZGRSMGknuwAFEw2Hk2gZhIogytOZM3LWJB7s6cxae2ir0luyE7YcCb00Ni598kpWvvFq/reTPP1G99SrXTp7O/57eym0PnJ9UwDdv05tQaTnBHcVIBAExLxupyYBUkTgs0Rdy8932hxKWYStyzieUu5UT+mTQuWcmSpWIRGKgb8ZFtNYMY33pYiLRIBlCFxbPcDFz6k4Azr64A1r9f3egdriwKgu454SFTN56G2ttvyBBSre0Mzmj+eNJnbzdITtzyt5i6ra76rfNKH6adsahXNLmK/xo+cvhwRkK00wpYcJt3fjs6WX4fXWTM5VlHt58egm3PtoPbRJTpiOFz2Yj6PEiU6tQmhoXGi2RCOjlmdQGEq8qpilbHNC1hG12pBotim6dkOq1yPJycH07A/+yutXj4NYdRAOJyxUeSaQqFboLziJsVhFum4nE6UcyfRmhXSUgl0Gfjqx/6kH6nX0THAMCXmtMp89p1/JbgjB6iVSg7YmnMuWTovpFy2gUPnlzGyaLkt4DbiS3o4rOvQowpmsPm+j4NyNv1xrrcw9S+9YnBDdtRarXoTljNOoRgxAtB5aOICoUiEneP/8lVjo9Mf/+wRXi5tP/D601h1U/vYvf40BnzuSsG9+lfM4i/nz5Qfx2OzkDTuLUaVNY8PBjlC5YQP74i/j+s9gJLplcit7YuO9YIgiIquQr9aJSiU8SoMK3hWdWDuS+E/5CItQ9G7SC0Ki89sbgKa8gUFVFaPN2IrtLCK7dSGDtRtzf/Yz1qXtRdGwbt48gU5LZ5mTG3FDA6pmvY6/YSVpeOzoNu46oMo9IBA7R5R0UtmovT905l907GsZV4VCUn6ZsQaWWMe6idsiOwiJHiv8eKQGf4l+HLMELTK230P+s/2PqM5fH7yCR0LrnSH7/9HEUJuNBnz8cDuF11IBEgkZvqZ81du0qjhHve/BWVrLx1afoN+B6JGLigdSw8XehXbWb8o9ebMhllIkYb5yIqn9PpOr4nFxP2MaKmsS+AABLq7/i1MseJs2QQaTWQai0Au/cRSjbtgdbb779fAPV5VuIRmHI6CxGnmbF71zJ1uXrMWcXojWnI8pSs82HAkEikqPpyJXtvsQTsiFBgkZm2WfofLVvW4x434NOXshSZ4h3SnbFuA63zlVw/dP9ef6mP+pF0rrlVTjs/qMm4P21tVSuWs2fDzxEzbr1GFu1pO8jD5He7QSURuM+9zXIMxmZcxtfbbs1rk2ClG7WM5p8PaGqGmoee5Hg+gYjKUQB440TQSLBv3QlQroFybEQkh4JUp3l4afhC9ngmI3WYmXE7VdTWDMKIaJk+rT/EY1EEOXHxm9UIpXSccAZlGz+i/V/NjiUCzIFZ972DjKlgdLijXH72ap9zJhWjMmqpMeANke0CsbxgCdkR4IElbjvHHGpWomiY1ssj91J1B8AqRTBZDgm7uXjnX9OJ0WAF2whuvc6l8EnjqNAJkERFph1zQ1s+2lGfb/NU6ay7YcfGTv1KzZM/pryYDoO+7aYY427oC1Gc+MEvNpqpev11/DrxKsStre45iwmeeom0NwhG+tqF/GVvQ8VgRAnGjRMyDSTpTjwd4Hf4aBi2V/8cdudVPz1F+qMDLpfczWFN16K97WPIRjC9vTrpL3wMMI/Jo1sVV4ev3MZUqmEgSPupE1XkfKSIM8/Xk4kUsYzH4wgLePol96tKvPEiPe9+WHyJgafXFBvzJcixeEkJeBT/GfIb9+bPqdew8Lv36zPTRNEOUMvvp+d6xYy6Kb7mVR2M5cY3kMrO7ASW/aKYlb89hlr536NVCrSdfgFdOh/OnpLFpu/TmI4BWz75htOuuxOdCYD7fudxrr5DcI7vVk7OhUOwHbvP/LjgyHsz72JrEU+8pYFcceUIEGUKghEvAnPKZMqseYImINhbC+8jX/hX3+3/EiHoYNp98ApbNnpJytXQum6qXz+wPP1HgKCTMEp1z1P6x4jkauO3ZdVJBzGaSsjHAygkKlRyFUIOt1hX8HzhsPYQxG8kQhqqRSj2LjVDbVoQL2fgfge5pa+G7dNikCvzP/j0Z3xA4xNfj9rtTI6985k5cKGsPCgP9yo8x1qwsEgRd9+z4yLL63f5qmoYMrQEQx97RU6XH7pPlf3pBIpvdPPY4tjPn9Vf12/XZDIuLLtJExNNLCLBoO4pvwQK94BQmHsL72L5aFb8S9diW7COASzsUnHPhyUeNbwxPITCUXraivZ/MW8576a3pbz6Fh6EjvWL6Sg8wDU+sQ1vY8GWmM6o654iv5n30LplhUoNQbSm3dAZ8pAKspp19nK6qWJ8/pbtDYlrb/9X6TGX8zamhnML/8AqURkcNa1tDKchFGx7yiyo5368W+ki04NpbEl56LAUneAgFbNbRkZOJYujRHvewj5fPz14ssMf+9dlq32Y0kro7rSiyVdxfjLOtLtxCzkisbf9wWjR5Mz4CR2z5kbs735aaNxtfJTU9NQiabYvQKzrD9lgRDza92scnl5ulUu6QdYdrX4jzl8e+q4+n97ysuZ+9DDlJ9+Gr3PHEfgqx8Il1cScTjjBLzD7qe2pu5Z9s3nu/gn9mrfMSHgS4oTi3cAvzeMz7tvM75/EgqGsVX7KNnlxOcJkVdgwGBSJC1VmCLFHlJvwxT/GZQaIycMOI9WHYZQvn0tolyJOaMAjSWdze0W81bZ+TiC5TiClY0W8OFgAGdNGcUbl2LJacnk/12Ks7qkvv33jx9h9eyvOP+hyUQTJbf9TTQSQakWMFo0jLryKU4afxu28u2otEYsljxcT7+TdF/XtOkYb7oC6T9eujpZOn0zLmXm7hcS7tfJPBqJNIJ/yYq9xHsdkd9mwW+z6PPGU+yyFzHvq1g36HDQz7cvXc8Vz80kvVn7pNcW9zmjUew1PqLRKGo5yKVRJGrVYcltc9dWsfqPKahQkK3NwzP7LzxuD8q+PVGd1Bsx/fDUQa8OhviopJoFtS4igBQYaNRyXpYFs+zQPHIj0QiOYHnc9kL9iaxyK4HEony2z8Olp7eoF/BqrQyN7ujU0XWXlvL7DTclbPvjtjtoPmY0hub7znU2KDK5sPVbnOp/kCLnAtSCkea6nhjl2ciEpq08h221eH76PUljmODWHWgvPQdl7xOadNzDgStYzedbrq8X73uzqPpzTiw8D0tOS0Zf/fQRqZvdFNR6M2q9mfT8+DDa/sOa8e2kjfi9sfevRAJnXdoBlTpV8xnqxPsLq4ZT6t1Qv21T7RxaGwZyZdtJGBVH1/ztv4ZJFDjVauC7qtjSemqplMuyrWhEgeXffZ9kb9jx60yIhBg0uoAuvbIIhyKIohSTVUVt1W5sZSE0Rity5f4FrDY7i5O/+IzKFStZ/e77SEQp+ZeeQk1ONZ/abo7pm67qzHxHg+B0hiPMtjk5M93UZIM4V0kJv1+f+Hm+6Ztv6Xn99SAKEAoTTWDutz8OYJfDQvo+VtdlcmmTjOyCgTBrllfwwgML6lPbAIaf2oKzL+uAwXRsRE+lODZJCfgU/xlqt23nsxN6EnS70WZnEwkG8VRUoDSZGDbvE5zBupUffyj5DOvehIMBdqxbwFdPXkyzDidizWsTI973ULlzA9tXzaXw9FNZ9PgTCY/VbMwYMltmoTMoAAUqrRFrbsu689jshCuTGFUB4dIKCAbq8l73QpTKGJJ9HatrfqTcuymmrX/mZXhDDjRhLc6vf0x6bHfRFub9nngCgGiUZTM+YsTExxGE/T9KbNVeFs/djU4SoEDtJjL7N4hEUI8dgbxdS8RDkL6wh1DQz7IZHyILS8naHSQw8736tsCajbim/Eja8w8iZh1aN3tXKMzbuytZ6mjIiYwAs+wuQlG4MteK+hCErEolUrpbz2ZZ1dSY7UpBhysikEzAu8MRFKqGv9X4Szs0ujzRocZbWUnA4UjYFvJ6cZeV7VfAA+hkVnQyK7nagzSfDIeJ+uMF8R6igSC6s8ciVR39QZU3ZGeL48+k7Tslq7nw0a/RGI6vOulpmRoeenkwrz2+mOLtdfeGJU3FVXf0ILfZsTURcbSIRCMsrfwyRrzvYVPtH2x1LqSbYlyCPevw+0KEghGUahHhWEgq/hegFQXGpZvoplfzXYUdezjMCTo1Q0160v52cBcTpLntQZDLQSJFImlwvbdX7GLZjN/565eP8HuctOg6mF6nXIE5swBpAhO4mOvJykKblUX+sKFsrJ3Nu5svwGGLjWzRiCaMqu6UV8WuGC92uBltMaBrYnk2j60K586dSdsrVq0iLd1K1BdIaJSoNyrQGxU47PHPYKVKxGQ5+s9dgPRsDZZ0FdUV8ZGNQ04uaJKJXVWFh2funk84HDs78et3W2nR1syQk+MjK1Ok2EPq6Z3iP0E4EGDla28QdLkgGsW1ezeeiroXms9mY/dXv9FONxQJEtSyxhn6OGvK+OrJiwkH/eR36MuWZTOT9l0+8zO0udm0Pe/cuDa5Xs9JTz2BJSfxirBEpULWujDpseUd2tTX8P0nZkU+17b/mnMLX6Kz+WR6pZ3LVe2+JFfdmTbGQSj8IlGXJ+G+ABGZFEdV/KTEHmpKtxIOJhc9e6i1+3j3+b8I2Ry02DCHyHMvIG/fCvXoIfjmLaL21Q/wzltMuCp5PfSm4LKVs3T6B7Rs3pPgzHihE6mx4fx8GpF9CLYDoTYUjhHvezO/1kVt6NCFq7fU940zait2r6KtKvlnaqtSsHN9DeY0Fdfc1YP+w/MRxKPzGthf7u2RLn0mUSoR85ObvSm6dDgmxDsA4SiSuMzbBpQy7XEj3t3l5VSuXsPuefOxb9lMToaUB14YyPMfj+SZD0bw8GuD0RkUTP1oHZ+9uYqiDTUJB/n/FVzBSuaWvZe0fXbpG5SVV1FTFSswXI4AG1ZX8cpji3ji9rlM/nAt5SUuIpFjZGnzOEcvCnTSqrm1WSYPFGRzToaZDIWsvmZ6y3GnJd237XkTUKU1vP/tFcX89MZtzHjnLip2rKe2spjlv37Ch3efQnVpUaOvSZDJyNV35aTMKxAkDRP86cpCLms3k8/K459nOkFAbGKGWaV3G9XhnXWhMkmQGwxE/QFMN1+BNIFposmi4uo7eyBJ8DqaeGu3hMI4Go0S9IeJ/C2Ao5EIzuJiajZtonbHDkIJ3u/eqipcu3fjs9ka/fkCLhfOXbtw7tqFXgP3PTeQrNxYf5reA3MYd0E75IrGv7cWzi6OE+97mPbJemzVidMfU6SA1Ap8iv8Ifrud7TN+TtpeOn0+zcadiC4tHb2scauyuzcvqxev0Uh4n6vQgihHYTIy8PlnaXnGOJY98zw+m42CMaPpev01GAqSz7RKlQr0556Gb87CBgO7v5Eo5GhGDU4qhkSpjAxVK1SCnjxNFxyBcnSydFrq+2FS5BCRBlD07Irnx8STDwqDkayWXXAuTuz0ndf+RGTy/a/gVld4Wb20nPNHtyH8xR/oLjqLcEkFzo+n1PfxzVuM2KIZlkduR0w7uLxdn9tBRvP2sDzeFGsPnlnz0V10NtK0Q+ei7AonF+gRwLWPNIqmYlbmcWvn3/lp5xP8Wf4RoaifTFVb2mgs5CqcFPtjndKlwIWZFkwDTQwf1gyTVXVU3bzVaWloc3Jw7d4d16a0WNBkHtroiP0hmAwYrr4oYaktsSAfMffYCEsOV9Ugmb+SzgVjWGlPHD3T3jT8CF/VgWEvKuK7M8ZTtaqhPnzLcacz5NWXyGmWTa3NxxfvrOH3HxuMvb6btJF+w/K56Loux3XJppDPh7usDHdZOYJMRJ2RgTY7u1HpRNHoPtKxohF+nLyJZX/UcOP9fWjV3ozfH+aXb7bw5Xtr6/ttWV/DjClbeOS1IeS3aJzvRor9oxSkJLortTk59HngPhY+8ljMdl1eHn0euDfGfNdWto1tq+bEHcPvcTDny2cZc/XTqLSNW2jQy9MYk38P/TMvxxmsQCZVEpZYeHpXmPIE1TTGWg2omjB5GooEmV36BlIi5I8Yws6ff4vrIygUWNu3RzdwIGJOVtx7x1lcTM269Wi9IR5/9SR+mLKNnVsd5DTTcdp5bcnO18WYV0bCUSrL3SyctYt1K6vIztMx5pR0ds/4ngUPPoy3shJRpaLTlRPpecdtaLOz8VZXU7pgIX8++AiO7duxdupIv8cfxdqpIwp94uieaDSKffMW5t//IFv+9jBqffZZ9HviUR58eRC1Nj9uZwCTRYXepGi0GazLGSDgC9dHGSWiusJTPzGRIkUiUgI+xX8CqVyOyppcFCrTLWTq2zM0+xZUYuNCNZ01DTnIm5f+Sru+Y6n8MrFg7DH6UmQKFbIMFa3PPIP8wYMIB4Og1GGzh1g7qxitTk52vg6TVYX4j1VRIScT65P3YHvhbcJldZEDYrNcTLdejZCx71xuUSrHrMzDrIw39ZLK5WjPPBnvrPlEPbGzvVKrGVVuPieNv4XNS3+pN7Dbg0ypptOAMxo14FyztJxW7S0Iy5cR1qgR83JixPseQlt34Pl1DrpzTj0od2SZXIVEIiVhUd2/kY4cToVHZPfc3cgVAll5OkwW5UG5XO9v4KM+xLn+VmUzzm35Eqfk30eUCErBgEZm5L4CLV9X2JllcxKMRilUKbg820pzpRyF7tgIvNJkZzNm0qdMHTaScCBQv10qipw86VM02YevpGMy5O1aYnnsTmrf+Kiu/rtMRD30JHTnnxFnunQ0iEajeOYsIrxoLeMGP8bWtYtxBmPLTp3e/DEM8syjdIWNx11ayrQxp2LbFJveY99SxJZp39DxskvZtskeI973MH/mTk4cnEvP/ke/PN6B4LPZWPfJZ8y7825CPh8Auvx8Rn/yIVl9eteFVCdBK7PSN+Nivt5+T8L2LoqL+GFhLdUVXh679Q+e+WAE0Sgx4n0PXk+I91/8i9se64tWnyoHdzhRGo2ccNMNtDjlZFa8/gbeikpanXUG+UOHoM/Pj+m7t4ntP9m05GeGXHBvowU8gEJQk6YqIE1Vt1BgD4Zop66mPOCK6TfEpKNQ3bT7wB2qYXnVNFyhKq57ZhI1azbETMpKBIFTvpqEvn1bZAlC52s2bmLKkOG4Suoi/WRaLR0nXsWEB29Gn21FmSCnfMdWOw/dMLveME4SDbPh019Z8kDDbyLk9bL8pVewbyli+DtvsfaDD5l/7/317cV/zOHL/gM5+YvPaHXWmQkjvhzbtjPpxP74ahoiAzdM+oIdv87kvCULaN6yeZO+KwB7jY9P3liJ2xmgR99s5v2aOO2geStjk1bzU/z3SAn4FP8JlEYjPe68nd3z5ids7/Z/N5GV1weF2HiX09w2Per/v2TzX/Q57RoyCzpRtm11TL/CbkPJKuwSez1mc92D/JWVMQ9whVLgtsf60q5LWoyQlMrlKLp2IO35h4g4XSCRINXrEEwHv3IiZqaT9tKjOD74At/CZSAIqIf0RzdhHGKaBYtezbn3fsaPb96Oo7IYgLS8Noy94WUMaY1z+laqZUikIIlGUJzQse48SXD/OBPNyEEHJZjU+r9LfZ3QGn6cFdcuTLyUX0vM/HjxL/XmOHKFwA339aJLr8wmGdHsjUGQUqhSUOSND93rqFGib2JeYWOQS5VxkzNpchmXZls4I91IBFBJpYfl3AeDRCIhq3cvLlqzknUff0L5kqWkde1Ch0svRt+8+RELoY+Ew4QDAUSlEqlajbJnV2SFzYl6fSAKSI16pMdIreuI3YHnp5lEbjqFdzdfwiWt32ebczFFjj/Ryqx0t56FP+xCehy82p3Fu2PEu6lNG078+Cl2qtawSDqHSE0h079KvsL+w5ebaN8l7bh0ay5dtJjZN/0fAPknD6f9vVfi0bkpk+xE5k3HJOQjFxJHNkklAn3Sz2du2btU+rbGtOVreqCs6UzprrpKCsFAhL8WlKJUJb8f1q+swuUMpgT8EUBlNqMymxnx7ttEQqGkVTYEWfJ7WhAP3sjRKBO5ONvKGKuBBbVupBLoo9dilYtNfk9IJQIKQUOFbwvvea7kvJ+fI7iygurZf6FqkU3+2GFkFXTF7ZNRtqqSBbOLkSsE+g7Ow6z28f2Z4+vFO0DQ5WLli8+xc/oPjJ89EzJjJyNrbT5ee2JxjNv7sMF6Fk54KuH1bfvxJzzl5XGRD3v47bobye57Irq82HdoOBRizfsfxoj3PXirqlj3yaf0vufuJr2nwuEIv/+wlXm/7OTWR09EEKXoDHKctYG4vhdc0/lvT6QUKRJz7L/lU6Q4RGT16kmnKyey+u3Y8lvdb7uFjM5dmiTeAYwZ+WS17ErplhUA/Pj6LYyc+CR+Ty2bl/6KVBDpPupSMgs6ojWlx+wbiUSZN3NH3Oyr3xfmqbvm8fzHo8jMia8BLlhMh3wlUCJIkeXnYLr9GiIuL0hAqtfWixaZQkWLroO45Ikf8LpsSCVSVHozGkPjXdw7dU9n0jurCJ16AuKfc+rqECch6vcfkEvt3qh0RkZd8SSlK/7E0qszocUNIbpiXjZFshx++HpdzD4Bf5jnH1zAsx+OPGDDLINM5Nb8DJ7eUcZ2X8NnbKVScH1eepONgQ4GuVRKmvzYWG1PhiCXY2rVkr4PP0jI70dUKA5LRYJEBFwuHNu2s/LNt6ktKiJ38CDanH0W+ubNjokycQmJRpFo1JQpiim2reSVtWNpoetDnrYL3pCDDzddRjDipbVhIGnisW2A5CpuWKXTZGZy4tfP8Kb9Qly2unJcZjrgdp2YdH+3K0AodOhSUppKNBwhXFmFb9lKAhuKkLUuQNXzBIQ0yz6jh7zV1fx5/0MAtLnyfDQ3dePVmvMI1NRFQIk1Cs4tfJFe6ROSlpQ0K/O5vfNsllZN5s/yjxAkIt3Ul2N29uOtR2JF/a5ttRS2NR+aD53ikCAVhH0Kv44njWPZ9A8StnXofzpq/cFXUNGLAnpRoFB9cGkoKnScZL6cz903UuPfxav+8aS1bEFm57a4g2sxWXtj9Mp583+LWbGoIWrxu8838tQThVSvjY8MAbBt3IinshLNPwS8szbArq2xoefyqD+pISpAzYYNKIxGPOXxlVt81dV4q6rjBLzfZqNoH5UDtn77A+0vvQhDbn7SPv/EXu3jhy83kZmjparCyy/fbOH6e3sz5aO1bF5bN1Fgsig5+9IOZGTHj/9SpNiblIBP8Z9BnZ5O/yceo+v117LtxxlIBCkFY0ahzc5GaWq6KNYa0znrjveYP/UlVv7+JX6PkzlfPsOYq5+hff9xdSHz8sQvR3uNj+8mJQ63D4eiLF9YyugzWzX5mg4GqVqNdB9OuTpzBjrzgeUlGy1KJlzRifUlHjorlKg7F+CbtzhhX1Xfnkh1B1/v1ZTRDLGnAkk7L5H+vfD98DtRtwfpZZcw7Z34vGuAaARm/7SdC645cEfzDIWMBwuy8NrLCfo8yGVylHoj+gOsrftfQCKVxuSAHm5CPh9F337H9Asvqa9PtP3nX1j8xFOcM3cWaZ0P0tH+MCHV61AN7IM91DD5tNW5kK3OhTH9EpWXO9bQNWsY+Ha85xqm+h/EFWqopb0j+Aftew+jaENis6ke/bIbnXN6OAgUbaP6jsfqIjUA7y9/4FROwvy/e1G0aZnUXyLk9WHfsgWZRkPzm87ghapYc7NQ1M+nW64hV9OJloZ+Sc9vVuYxLOdmepjOY9mfZfz2eiXbNm+K66fRyGjXJbmhYav2ZjTaumeTz12Ly1bBzrULQCKhWYcT0ZgyUKrjQ59THD701ly6DJnAyt8nxW5Py+XE069DoW66uAtFothCIWpDYQSJBIMgYJYfvATwOmqw2nNooxvIRucfAFT6tlLp20p3w1loavUs31QaI9734K7Zd8WfkCfexC0SiZ+0k8gVdQZ6SSb+VWlpdQbGSZD+o7yrzxvE5weFMXmEo9xowO2swkDjBXwoFMHtClLY1szu7Q5Kd7l4/anFjDitkFMntCUSjuDzhvj126107nlkPWBSHH+kBHyK/xQqiwWVxUJap06H5Hh6SzbDL3mYvuNuIBwKIleq0Zr2/+CNhKPU1iQfZJfualwpu+MFlVpG/+HNqCr34LIZ0GnDyFo0I7h1R0w/iVqF9pzTDlnIss6cCWYgvwDNib0gHMYekFFdkdzJt2Sng9DfdXgPBK/Lzq7ls/jt40dx1pQiiHI6DjiDAefejt5y5PO6jyZ+f4jaGh9eTwilUsRgVqBUHf2JDHdpKb9cdkXcgC/gcPDzpVdw6g/fITOYj7m64xJRQNWvF/me5K9ugzwTpXDsr95oc7KxdulC1cqVGPt1YJvz3pj2DY7fGD7wKf74Nr60lEYnY/CYgqNWQSFUVYPtsZfqxfseoj4/9sdexPzCQ8jTEotmUa3C3LYt+rYFzA99nvQcP+58givbfYFKTC6epRIpZk0mbQpVLDU7kcmlBAMNAkcqSBh8SgtMZiVjz23N91/ECny5QuDy/+uGzqDA46hhwTevsfDb12P6nDT+VnqOuRyV7tBFflUGgmxy+1jv8ZGrkNNVp8YqExGlR89U81hCb8liwDm30b7fqfz188f4vU5a9xxJYbehmLOaHlnjDodZWOvmg5IqvH9XHbDIBG7Jz6ClSnlw37tEwh9vPcmA865hYM4VrPT/hIBIV+VY3Bt349FH+HHy5sT7qg1IRZFIKBTXJBEEVJZ43yKtTo45TUVNZYO4X7PBT/6w4ez89Ze4/qq0NPTN8pNGxRhbtUJpbYhoqCr38Nlbq9i60caFl1zD7rnzEu7X5rLz2LZuLtntuiX+bAmQyQUs6Sps1V46dKuLyqyt8TP5g9hoQJNFedSebSmOH1ICPkWKg0SUKzGk5TZpH5lcSn4LAzu31iZsb981PeH24xm1Rva327GBSCSK+aFb8fw2F89PvxH1BVD27YH2nNMQsw7dZ/d7nPi9TiQSKWqDFUEQUboCNG9tZO1flQn36dgt/YDEezQaJVhTQ9Ga2Xz78vX128OhACt//4KKnRsYf/fHaI3HR3mvvYm4PUR8fqRKBVJN8igNV7CKQMSHTKok4tIy9aN1/PbDNkLBCIIgod+wfCZc2am+1nHS8/n9RB0ukIBEr0O6D1OvA6FqzdoY47y9qfjrL0o3lfDVNxsYeUYhrdtbMFqOXHTA/hAz0rA42lFoO5Ei14K49jObP4VRfuybu2kyMjjtmynMuOhSIsQP4AG+rJ7Ajc9N4fdJHhbN2k0U6D0gh3Mu70h61sFH6RwooVob4fLEz49wZQ0Rey0kEfAqs5l+jz9C0dwZVIX/SHqOSl8RgYiHcDBAKOJHKerjJmYc1aWUb1vD2vnfMWrsSVxwV3tqvbUE3DJKNkholp1PeqYGuULg1Alt6dg9g+8+34C9xkf7E9IZc1ar+u+xbOuqOPEOMPer52jesR/5HZKnMzSFYl+AB7aWxJTUlEkk3F+QRVuNEuEoVsY4ltBbs9Fbs8lt04twyH9QEyhbvX5eL469X6uDYR7eWsoLrXPJVBz481Wtt9DxpDOY+dK9aIxp5LbtRTQSZtba+/C5a7nk2aV43csT7jt/oYsOV13N6tdejWvrfNUVqBNUIjFZVVxxa3eevnte/fzrz9PLuPO+p3Dt2knNhg31fRVGI2dM/wFds2ac/MVnfDP2dKJ7VYqRaTSM+exjtH+H6duqvTx5xxyKt9ctoNRqCmlx5ni2Tv0q5hpaTxiPT+5tciqD2ari2nt64ar1k5apwXxvL37/fivrV1XF9DvjonbHTN37FMcuKQGfIsVRwGBScsG1nXnitrlxbSarkpbt/905i1KpBGlGGrpzTkczcjDRaBSpTnPIVt5DoQA1u4uY9fmTbFvxBwq1ju6jLqHL0PMIRgyMv6wjDy2fFRdxp9bK6NG/6avk/upKAivW4yPA7188nrBP6ZYVVO/czKaPv0Sfl0t6txPQ5uYe8XrnTSHidhPcugvHp1MI7y5FzM9Fd+FZyJrlxKRbuIM2tjkXMW37fZR5NpCuasXItAdRWzMIBetWBMPhKHN+3oHHHeSaO3ui1SceNIZKynF+8Q3ePxaAVIpqcD9048ciZh66iZ2wb98h5n5PgDV/VbDmrwo698zg2rt7YjqGRLxRn89V7b/i+x0Ps6DiY0LRAEZ5NmcWPEUn88lNKg/oC7uJRENJ860PJ4bmzTn1m6k4qcSwKZPaQFlMe4VvC2+W9eOhmzZx/lVdIApaveyoR3FEAvu+f8JB3z7b07udQCgSZJdQw1YWJeyTr+1GtXcbk7beiM2/mxa6Poxt9gAZqtbIBRWOqhK+ePx8KndtZNQtT7PaMJP5a88nHK2bDCnI60mvNp8jV9SJIL1RQddemRS2MeF2BfF6gtiqvIiiFI02xJ/fvJb0ehd89waZhV2QKxNP3oVDwUaZqzlCYV7ZVREj3gGC0ShPbS/jhda5WFOpRjHIVWog+aTp/nCGwnxRFm/EBhCIRplrd3F2xoGPNwRRRreRF7Fh4Y/UlBSxcWFDactOg85Gb9LStU8mv30fX01i1q+ljHzpRgw5WSx5+hn8djsKg4Hut91CpysuR66NjySSSCS075rGY28M5av317Btkx1rhhq/Io3Tf56Oa8d2KlesxFDQHGunTujycpFIpeQNHsTFfxumVq1eQ3b/frQ+60z0zZvVH7tkp7NevAO8+9Y2zrvgRkZcchVlv36PKJOQPaw/FVWbmDX1aa58Id4gd2+cNaU4a8rwOu0Y0nIR5EaWzN3Nb99vJRiIYDApOHl8azr1yOCr99cikcKoM1rSe2DuUS3xmuL4QBI9WLeofxkOhwODwUBtbS36JLUhU6Q4FLhdAVYtKeejV1Zgq64b8HU4IY0rbutOVm4q5/BgqNixnvfvGEU4FLvKmtmiC62HPsWKJX46dk/nq/fX1ofiFbQycu3dPclrYWjSy9PvceJfuhLn468h3H8F771wftK+J516M9uf/YKqNWuQ6/Wc9dvPZHTrdsSM25pCNBjEM3Mu9hffiWsz3X0DqpN6IxEEQpEA88re49Mt18b1OzntKbZ+2ZvFs2JXf57/ZCQ5+fHP11BZBZU33kekNjaFRGoxkfbCw4gZhyZ6wbZ5Cx+0aZ8wZ9JQUEDhU5/zxmsNA867nu7PCb2PjTrwe+P2uXGFy4kQQCnqMMqzG33v1gbK2eFcym8lLxMIe+iZdi6dzGPqS00dSaLRKMuqpvLm+rPj2oZkX8/pzR87KhMMyfCW7sJ21X2JzThlMkxvP4I6u/k+jxEOBCj3beLhld0IR2PrcUuQcnvnWby0Zgz+iLt+uxSBWzvPpKW2H7M/f4qF375O6z6jiJ5i4pfKF+LOka4s5I4uczAq6iYlPe4gi+fu5t3nltWH2svkUu5/9gT++OAyKncl9mXJKuzChAe+QKU1Nlx/KEhtZTFr531D6ZYVZLXsSof+p9cJlSRifrcvwEs7ShmnDKH11hKNRAhqjPwUkLPCG+SRFtl00B47E2X/BqoDIW7ZvAtXkpKqPXRqbm+WgXiQ7yBHdQk71vzJ6j+mIFdp6DH6MtLy2qAxWCktdnLXxJkxzvEA1gw1D78yGLNVgbukhJDXh6BUosnOQhD3v77ocQXxeYPI5EKjHduj0SjhYBAxQVTX1I/W8dX78aZ6SpVIq/Zmzr3UytdPjkUiFRh/10fktO2BKCaeiK7YsZ4vn7gQR1WD305hj1G07Hsbzz28PqbG+8U3dKFtpzTUWhkGowKVJjWJ9V+msTo0tQKfIsVRQqOV02dQLq07WvC4goiiFJ1RcVSNmf4N+DwOfv/ksTjxDlC2dSVdR1Ww5i8/u3c4OPvSDqRlqjFZVOiNCvTGukFAIOylNlCGO1SNXKpGL09HK0scLhe22fF+Wle3V0BAEOUJzw2g0Vvx2e1153A4mDZ6LBf8tTjOAfdYIFxjp/aNjxO22V95H3n71ojpVuyBEiZvvT1hv1+qH+LysxbECXiHzU/OP7x/ouEw7p9nx4h3sUU+yq4diUaj+JevgW6diAYCSLUahH0YDO0PdUY6PW+/jSVPPxOzXSKV0uPJF/hkenXM9l++KaJj13RkB1mX111RQcTvRyKKaLMOfELA5QhQvN3Bd5M2UFPppf0JaQw/LYdIZhRBlBB2uYl6fUQk4HO7iIRCyDSaekdnR6CCSVtuYGnV5PpjbnbMw7q7gFs6/UK6quVBfc6mIpFI6GAawR2dZzN1213scP2FWZHPyfn30sk85pgS7wARgxLZ+aMIvP9dXJvs3OGEG1GSTZDLSRdbc2unmby38SKq/XV+IEZ5Nhe1eoeZu1+MEe8AEcJ8vPlK/q/tzyz/9RMA2owcy1s1Fyc8R4WviErftnoBX7rLyRtPLonpEwxE+GlqMe069Esq4Au6DESualgNjUaj7N78F58/fA7hYF00wualvzB/6kuc98CX5LXrlXAiSRIKMMFVxOynbsTjqPuNyZUaBl/yCJktTsSbwKAsxcEhl0rIlMvYkqC0KUALleKgxTvU+QF1GngWbfuMQSIVEGUNv4GMLC1PvDWUL99bw5J5JYiilAEjm3HaeW2xZtRFFxzIO1CtlaHWNk3shkMBXPYKAl4XolyJxmBF8bdJ455r+Sc+b4hN66qRK1tw9l0fYs5qgdaUmXSSwVG1m88fHo+7NjY0vmjpDLSGLPoMHM2fvzdEG035cB1Pvzci6flTpEhESsCnSHEUkUgkWNLUWI6/tOhjloDHxdaVs5O2717/KwWtT2X5wjLeenopgiDhhU9H1Yt3R6CCX4qfZebulwhF64R4ga4XV7T9nHRVYdzxQh43oR3Fdf9YuIYOJ45l1dypcf1kChU6dTqu4uL6bd6qKmq3bT8iAt4XduEKVBImhErQo5fv22wxUmMn6k886Iu63ERqHZBuxRmsjBMaewhGfESV8S7iiWp3R1xufPPrxIVEq8F44+WEK6rwLVwGEilibhbhsgqq73saITMN400TkbVqgfQAQm4Vej09br+FrBN7s+ixJ3EWF5PeowftbryDGbP97NoaO/CKhKNEiF+t93tdeGqrcNkqkClUaIxWtKbMOPHis9nYPW8+8+66l+p169A3b86JD95HwcljUCfJlU6G1xNk5vdFTHp7Tf22bZvt/PrtVp55exDmoJ3aj75CGNqP5V9MYu0nnxLyejG2asWgF54lp38/SqPrY8T7Hqp82/i1+EXGNnsAvfzI+nCoRD2tjQO5oeMPBCJeBImIQZ65/x2PAhp1GqFhvYmm6Yl+/juh4lLEnEwkEwYjdGmFWtO4v6koldPaOIC7u/6JK1hFlChamZVi12r+qp6WcJ9y72Z8URcBn6dug0KK357cYbvcs5FWhn74/SG+/zKxQF8yv4LTXj6fVbMmEfTHOn/LVVq6Dp2AIDQMF501pUx79sp68b6HcNDPtOev4tL/TUdviZ+gEu2lzPjfxUTCDSuxAZ+bP968lVMe+poMxeHxbqgz0/Tj94VQqkSMZiUy+bGbunQo0YkCEzLNPLqtNK5NlMAA06E1vJQp4kWoVJCQ00zPNXf15GJXECSgMyiQH+G/gbu2imU/f8TCb18n6PMgkUpp1WMkIy5/FIM1h7adrXFGkHsYNraQrObZyGT7f1dX7y6KE+97WDt3EsOuOSdGwLuddSktKVI0hZSAT5Eixb8LiQS5UoPfk9jJX6bUE/A35GCGw1FcjgAZ2RCOhJhb9i4zimNXZrc5F/P86uHc2WUeJkVsjnw4GkGq1xFxOAnMXkTf+6+mcvcWSreubDinQsXYic+z7P7Y4wJ4q6rjth1qqnzbmbrtLpZVTiFCmCx1O84rfJUCXS+UYpIBnLCfVRlJXbso2beAlhLbnt/CgMEYv0IZBSSqOuMe0/9dgePTrwlt21nfHli9Hnn71hhvvAzbM29QdfujpL/2BNKCxpfx2RuV1UrL008jvWc3yretx+5vxjPPr8ftih9IDR1bgEIR+7p02yv5c9prLPnpXaKRuvtJZ8lm/F0fktG8Q31aRDgYZNNXU5h5dUOKgWP7dn6+dCI9bruFPg/ch1zX+JQZe42PL95ZE7ddq5cjlpdS+dATKC4bz89330XpooZSjfbNm/nmlNM47duvWdtpYdz+e1ha9RWDsq854gJ+D1pZvPP0sYjO3JxIPznetmkQihCQRlBbczHIMxEkTRtaGRXZ9avkANsciUts7kGQirToOoiiv36DIMilKgKR+JJbQP2kY8Afpqw4sdAPh6L88LWdCx/9jp/fv4fdG+om0vLb92HkxCcwpMWKFk9tNS57RcJjuWzl+NyOOAEfDodY8eunMeJ9b9Z8/RItbn0TDsJQLRG2Ki9TP17HrJ+2EwpGUCgFRp3RkjFnt8Zo/m8YhbVUKbgs28KnpTUE/k4b0gsCtzbLwCo7cjJApZYdkcoefocD1+4Stv00naDbTcHoUegLC1j++6fM/fLZ+n7RSIRNi6fjrCnl9OtepfjbX7j9geG88sxanLUNUXTtT0jjlHNaI5M1bsLBXrEzaVso4EMqif0NSKT8ZyaUUhw6UgI+RYoUhwRXKExtKExpIIhWkGKVyTDLBKRH2IxFY7BywvALEzoqA+R3GsOnX2yP2aZU1T0K7YESZux6OuF+Vb5tVHo3xwl4qUmPOPokAl/+BMEg/qfeZeyl1+E9XUHZrvXo8wsRvVKW3P8UpX/Gu4ab27Q+gE/ZeGz+Yp5bNZQq33bkUhX+iJtSz3qeXz2MO7vMTVprWmo0INVpiTjjB/1SiwmpUU80EkEVNWBS5GLzF8f1M8gzcVc0rMhk5Wm59bG+GBIMnAWNBvXIQXiA4I7iGPG+h8C6TagG9UXMzSJUXIrj068x3XY1UtWBD8T9EQ8bN/xKm+FXolSLcQK+ZXsTLdoaY7ZFIxHWL/iBxT+8FbPdWV3Cpw+eycTnfsOYXid63CWlzLnjroTnXvb8i3S++qomCfgt62sSljsee0oO4c++QKKQ41PKYsT73sz+v9voNT1+ImkPkWiYYBIxmKIBqUSKSZGLKbtpFUgAagNlOINVRKIhtKIFoyIbqaRhAJ+n7YIgEesN6famubYHOkUaQy64l+2r57F55nT6jriY2VVvxvU1KXJJ+1vAq1QyCtua2bbJnvCaRFHEktuW8Xd9hN9dVx1FqTXG5L3vIZkIb917HO0HXcGmjTLWr99Oy7ZmDGYlWp2cUMBHxfb4iac91JRsQRLctzlgU3E7A3zw8nLWrahk1LiW5Bca8LiDzJ+5E78vzISrOqFU/vuHwVpRYLhZT0+9BlswjCgBoyhiOgrv58ONz25n1VvvMO+ue+q3LXjwYc6Y9zMLkhg1lm5ZgbO2EunwYVSIMm58fxBqX5jdq2rIK7RgSVdhMO3/HRPy+/GUl2NKb560j1JjIBCIFes9+mXXRwCmSNFY/v1PrhQpUhx2bMEQH5dWM2evUE69IHBvQSYFKsURLQ0kiDJ6jrmcohWzqNyxPqat12m3sWShr94ZHaBNJ0tD7nvEgzecuLQfQIlrLa2NA2O2KTQGQn27IO4sJbRgOVGPl8BrnyMzGuh0/03IWxaw6p33Eor3wtNOTVgq51BS6lnPKeo7MYXSCdhqUeWks0E6j+m1z/LV1lu5seOPCVc9BYsJ053XUf3AM7B3bqooYL7zenxiiDU/vM2mxdO5YOIrvFV8XswqoChRcGWbL9HUFnLb4xmY01SYraqkbu4SUUDRoQ1Ipbi/nZH083jnLULRvTOh4lIC6zYR8XgPSsBHI1FaDBzKO6XDuPJ/X7FuToRlsxzIZFJOPMVAZkc7UW0l0BCp4LKVM29KvGkY1JkaFm9cijE9D+euXbh27ybgcCQ5dwTHjh0YC1s04YITby4sUBOatAVZYXN2LPsr6e61W7dijSQXnV0tpyGXpnIxDwfhaIhdrhW8vX4CFb4tAGhFC+e3fI2O5tGoxDrDIoM8iwtbvc2Hmy6L2V8l6Dm/+WtEnEEsOS257OkZzPniGTr7T8FtqmGpbTLRv2+QTFUbru/wLSZF3d9alEkZfWYrZv20jXAo9iaSChLGntsGuUJErjCh3kfJsrC9FpXGiEyhigm37zj4EkTrWTx2V1GMQdfIcS056+L2aPRKMgo6sWPNnwmPa8lpiSg/tAZ2tXY/wUCEa+7qyfSpm/n1uyL0RgWDxhSQnqWmtsaHMvvQhpAfShx2P35/CEEqxWBWIOwvKmofyKVS0uVS0v/lLv+1W7fGiPc9+N1OAt7kqSYl1cV8mJFFlScEniqsMpFbOyhIVwbQmvZfws9ns7Fh0hfMuf0uBr31EubsQmpKiuL6dR15Db/NsNf/O6+Fnouv74o6ZVyXoomkBHyKFCkOinA0yi9VtTHiHcARDvPQ1hKeb513xAcNems2E+77nPLta1k//3vUBgvt+p3Bn3M8TP96e32/nGY6rr+3V72DrUyiRC5VE4h4Eh43jTyiwRCSvcIOlWod0dx8ApeeSXTccMLbixFNJpQtCpClpyOVy2g74RxkGg3z770fd1kZMo2GLtdeTbebb0JlObwhw9pdahac/iy12xpc1ZufPoarn/yMNyonEAh7IIGAlwgC8i7tSX/radw//UZw607krQpQjx5CSK9k1scPs2rWlwBE3olw42XfsCE4h5LIeppre9Aj7WzMimaIJhm5zRtnQiY16pEX5OOO7KM4SiQKf4enC2YDkka4Fe8Llc5IwOGjJrCTV4v70KbrEAb0GkuEICt9k/i+ZDn3ZiwmjQZ39nAoiN/jRJAp4vKAAZzVZVSuXs3UYaMY9lbiSJA9yDRNq2fesr0ZiSTeQD8cBmQyIh4P6rRmCfeFOvM0hVxHG8MgNtbOjmnTy9I5MeNC1KKxSdd0PBH0ePCUleGpqkJUqVGnWevN/Q431b4dPL1yYMzzxRWq5q0N53Jnl7m0MvQHQC6o6G49i2babvy++1Wq/dtpLu9Be9kQZj5+N66aci545GvS89sy9oaX8Hsc5Mt7cXrhoziCFagEPTp5epyHQEa2hvueG8DrTy6hsqzuGizpKq65qycZ+xGyEa+P4OatdcaWaSYGn3U7v3z2CACiXElBj/N5/M71cfv9PG0L7bum0WdQLicMu4ClP72XcAX/pPG3otQc2so/bleAgaOa8+x9f9ZPKlSWeZj8/lq69s6kRZsDr61+OPF6gmzbZOPj11aybZMdjU7GyWe3YsgpLY6pcpbHGpFwmJVvvJ2wTSoRkApi0ugRpSkT91414quCIV70ijygkdGYKZ7SRYv5/bobAZh/yz2MnPIx8399nZ3r6ibuRbmKnqMuo8uQ8RgLonTvm0vzlkbSszWpv2mKAyIl4FOkSHFQVLk9/FCdeIXRG4myxeM7KrP+OnMmOnMmLbsNBSASjdBfa6PVCVbKSmvJytGTnqnDZGl4DOoiJgZbr+LnivjVVYM8E6vNQETrQTDGDjRVf4eaBjO9hFu2QKbUxJg+qaxWOlx6Mc1GDCfk8SAoFGiyMhESlLI5lDiLdzNjzPm4du+O2b79m59QZ6Vz0rWXI5EkX9WRyuVI83MwXHE+0UAQiVyGRBBw7N5SL94Bdm9YytQ7Lia3TU+a5bdmwLkT0aqa7swoGPREpVLUQ/vjeG9Swj6qvj1w/zwbAO25pyEYDq7kos6ciZ4GobPR8Tsb+T2mj1zaMMDyhyN4dWkMeno2RMOoPHZWT3me7ct/q++TX9CHaaNOwVNRQe3WbVg6dKB6bYLyRBYL2uzsuO37wmBScvZlHfjqvdjjzVtk46xhA/BO/42M9mciyOWEA/HVENqeNwFdVi6XZr3Piprv+bP8QwJhDx1MIznBejpWZUFMPva/CU9FBUueeY7lL75MJFQ3kDe1acOpX0/G0r7dYT//0srJMeI9T9MFjcxMmWcj07bfx3Xtp6GR1YlKlajDGMgg7Q8TFqmBmh0bmLruw/p9v37uCs5/eApaQxqKvRziM0iekiOTC7Tvms6jrw3B6fATjdaZiZmt+xcQwc1bqbrjMcQ2hQhD+mENODnjxrdYOP1NzFktWfBH8tXNaZ+up33XNIwZeZx73+d8++K19SZfcpWWUROfIC2/7X6voaloNDLe+WRZTETAHlYsKuPsS9sf8nMeCjatqeaJ2+fW/9vtDPLV++vYuKaa6+/tnQq3TkIkFMJTVpawbfu0n2jTcwzrF8ZXjtCaMvAas/DWxt4n5YEQTlHG/qb3vFVVzL/3gYZ/V1Yy/bQJdL7pGvpcNxGZXkvUG2DNq++y6Nc7GfbWG8g1/85nbIojR0rAp0iR4oAJ+Xw4yyvw7KP8T4k/8Yz3kabYvYqntwwgShSN2YzLUUXI7uf6Dt/S3jQCUSpDJlUwKDqeWmMpi+xf1oejpilbcE3aB4iTViC5sXvSc8gUKmSKJCHiEgm63MPjspwMe1FRnHjfw8b3JzHupl8b5fQtEQQkqoa8verd8aGBAMUbl1C8cQk9R12G1nhgpRVEnRbV4H54ZswitDt2MCZr0QypxURoRzHqsSNQdIofgIcdTiJON0Sjf5eb2/+qnlGVS7qykApf/OfKVrdHJ6v7LO5wmPl2F++XVBP8ewlcITFyyUWPo7Zks27mJ+itOQRtLlwlJQD89eLLDHvzNaZfcDE+W4Mjv6hUcto3U9FkN62cnFojY8TphbTtZOXbPWXkuqQx+sxW6OUtCa5ZT/in2Zzy0Yd8f9HFRIINOf3Wzp058ZEHkalUWCmgX/oldDKNIRBxIxc0qATdfqsTHK9EwmHWffIZy559Pma7beNGJg8exvlLFx7WahChSIAix3wAOuhOYbD2QYpWhLCXR+jeWY46zUYw4ovZx11bxfKfP0l4vKpdm/DUVqM1NP13ZrKqMDVCtO8hbK/F/vpHiM1yCQzvy7QxYwh5vejy82l/5cXk9BrO2q8Tl84EsFV7CYUiiDIVzdqfyKVP/IDHaSMajaAxpaM1ZSStHX8wRKOwY0vylKgNq6po2e7YMk2013j54KXlCdtWLi6nusKTEvBJEBUKCk8/la0//BjXtu79jzlj7i+4HZX1q+JQN4E7/K5PeM0rA+INTJ2JDEf+Qcjnw7ZpU8w2v93OkoefBKDluNMRFAo2fzUViSDQ9+EHkRc2VLRxBauoDZSy270WvTydNGVLTIqcGF+MFCn+SUrAp0iR4oDxlJdTu3Yt5lYdqQmGE/YpVB39uvaOQDnvbjgfX7jOmd4fblgtemvDuTzSfQ0WZTOkKiU6r4axq4Yxuv81OKhBKdWi2umFByejvuYSpNqmhTwfTRzbtydtC/v9KIOaAxokKDTJV7079DkVvaAnsHELiDIEox7B0rRQVTHNguWpe/HOW4TnlzlIpFLUowYh79CG4LZdpL/zLFKTEUHX8LeIRiKEdu7G/tK7BNbVDaZkLZrVlZsrbIZEllwgmFW5XNfhG55ZNRhXsKH8j16WwdXtp9SL2l2+AG/tji0P5I9Gedse4v6Tr6Jm20pOOu85qpevq2937trFnNvvYuSH72HbuImqNWtJP6ErLU49BV1eHlKh6d+/Tq+gwwnptGhjIhgIo9LI/nZI1mF96l4CRdtRbNvJRQv/pHTFClylpWT374+pdcuY+vMqmR6V7NCGLR+ruEtKWfzk/xK2eSoqqFy95rAKeFEqJ0fTCX8gQHfXYzx962bCe1aGJ9eFt9/znAr2Wpj7Z0m3fxIK+PbZfqiIev2Etu1EccMl/DjxckLeuuty7tzJovseJbvvb7S5+BlWJjHPb93egkotErbV4v1jAZ4vviFiq0XIzkR22bnQ1Qi6Qy/gZXIBiRSiSeaXm1pD/EjgdYcoTVItAGDD6ioKWh+bof/HAs2GD0ObkxM/cR2NEvJ7adVzBH1Ou4ag34vekoXCnM1jtjrz3USkKfc/WSIqlZhataJieeKJF1OrlpT87YETDYcJuhvKrtr9JXy06QpW236q36YRTdzUcTrNdT1SIj5FUlICPkWKFAdM2B9gw+NPMu7TT3kvwfsvTSaS34gX4OHGFaymxLMuYZs/7KLatwOLsi5vWFbYDEV5CRFvGIVKhRgSoNqFolc35O1aHsnLbjL+SARbMIwzHEYhkWBsnTycVq7TodQ1Ljf9nxjTm6FQ6/F7YlMnhp9zN4W+NKqvvQf+HhAJ6VbM9/8fssLmSJpgwiSmW9GOG4N66EmApD5UXl7YPGH/cHkllf/3IFFPg+gJbt1B5W0Pk/7ak8ia7dstPEfTkftPWEaxeyW73WvI1XQmV9MZs7JO1HnDYaaUx9e0hzpfudlRDQPOepOXn1zHtRNjr7F63Tq+Pe0MLB06YG7bhtbjz0bbxJX3RCQqyyRYzUi1eqStOyCJRmjRtj0yaQRPWTmesnKCLhfqtDQURuNBn/94IuTz4qtOXrKxatUaWowZfVivoW/GxRSET+W5O7c0iPe/KS9x8+nrq5n4fx0x/j3hpTWmIZEK9WUK90aUK1HrzIf1eusRpKCQExCleMrL45pL/vyT0Y9I0GhlcVUcpIKEsy/tgCIapPbTqXh++LW+LVxShu2xFzHceDmaUYORHMBk1r7QGeT07J/D4jnxUUgSKbTvcmBRQocTQZQiFSQJw/6h7jOlSI4+P59z5sxi/n0PsGnyFCKhENn9+9HjwTuY+9Mr7Nq4CKkgYkjL4/yHJlNZq6ajLECxP34Ac4JGhbERZfZUViv9Hn+EaWPGxrWJSiW5gway5JnnABAUivqqI8Gwn1+Kn4sR7wDukI3nVw/n4e6r68clKVL8kwO3tEyRIsV/HlGjxl1cjO6nHzhPJaCWNjxS2qrk3JdtwiI/+vOE4Wji2fU97J2X6lH5mdNuIY/WnswTJaN4uGwIH7R4heCl/RGMByZ4jwS1wRBflNVw48ad3LVlN/+3uZhiawbmdolze7vfdguaJuZf70FnzmD83R8hyhvc39Py2lCobonvs+/qxTtAuKKKqjseJVxZlehQAET8fqKh+FQLiUSCYNDvN889Go7g+W1ejHivJxjCOfl7Iv79l6iyKPPpYhnLmPy76Ww5uV68A/gjUcqDydNByoIhVq9xU1nmYcsuyBkyNK5P9dq1pHXtgsp6+MJ2y0tcvPv8Mq4+63uuPvMHtiwpYu5d9/JBm/Z82q0nH7Ruz4/nXYBjZ/Jaxf9GRKUKpTm54LV26nDYr8GiaIZ7VxrBQOIl4aXzy3DYGlbV1cY0eo65PGHfvuNuQGtKPyzX+U+kRj3qkYMI+ZJHBCy6cSIPvtCPtp0b7u2cZjruf34gWbk6wvZagtt3Ijt7FPLThyFkWOv7Od6bRLg68eTYwaBSy7jgms6Y0+LTBa68rTvGY9A8TG+U03tg4lQrQZDQqv2xFfJ/oETCYQIuFw67l5pKL17Pvt/RTcHQooBh77zJxetXcfJv08i6YhQ/TLqLXRsX/X3uELaybZRtX8sPn22gxU4/p+i0KKV11XJECZykUXNqRIngS54euDdZvXsx+KUXEFUN95QmK4tRn3xYF/nzdyh+56uurDfNdATLmF0aX/4R6hYWatxlVJS62VFkp2y3E4/70H1HKY5/jv7IOkWKFLjLyoiEQohqNap9DDKPNbTZ2Qx84TnKt62is72IblmtcLpCSPw+vPPmYxw9AgxHP0RXIzOjk6XhDFbGtUmQkqluR6VvG/6Qi3X2mXy386GYPkXOBTy3Zjh3dZ2PSXFk89gbQzgaZbbdxXdVsfmebwbgrmlTWXXNteyaNRuoWwE44aYb6XzVFQj7CCvfF1JBJKd1d658cTbbV86hqqSIHv3GE3jq3YT9o14fvmWr0J48LGZ7qKIK/4q1eP9YgFSrQXPqCMTc7CYb00V9PvzLVydtD6zZQNTtBcWBR4OoBCkFSjklCVZqAPIkIhU76iISJn9RzF0PPY+p4CXWffwJkWAQhcFAr3vuosOlFx8288LKMjcP3jALW1WdCOw/OJPSKe+x5tWXY/ptn/4zP5x1Dqf98C2a9CMjAo82muwset19J3NuvzOuTZWWhrVzp8N+DTJBideRXBBEI+Cy1xD0m5AplCiUGvqecQOGtFz+/Ppl3LVV6MyZnDT+Ntr0HhUzgXY4kcrl6MafSmTTZqSiWG8AuDfOHTswq3zc9ng/XI4AkUgUjVaO0Vx3jbawm6Luejas+gqZQkX3888mLajF//okom4PEacL0q1xxz1YMrK1PPraEDasqmTp/BKsGWoGjmqONUONUnXsDYGVKhnnXdmZrRttlO9uCLWWSOHmh/oc947lIZ8Px/btrHz7XapXrcbYoQt54yawcjP0GdqCrFwtMvnBR2LINRqcailTXrsiaR972Q4kdOKdhxbRc3Au15/eApQCQjjKsm938tn6au59ZgCw//ek0mym01VX0OLUsXgqypFIpLjLyph/7wNUrV6NVBTpdOVEet19B6JKhc/jIBj1Jqx4I0HCBdlfsGiKyM9TfybgDyORQq+Tcrj4+q5Y0lNlPlOkBHyKFEcVT0UF236azqLHn8JVXExatxMY8L8nSevSuT7M6kBwB22Eo0HUohFRenBiIRqNUlPlxWH3E42A3qjAZFUiCFJ8YReS4Rn8uf13drqfw1SawxDVNeTUFNBsyEC0R6g80/4wynM4v+VrvLl+fFzbDR2+Y3HF53y34yEuav02P+16IuExqv07KPNsPCYFvC0Y4uuK+BUsRzjC44g89tmnDHXYCbk9KExGNJmZMSsFB4IgyjBlNMM04kIAQpXVlBeXJu0f3LQVTm74d6i8kqo7HiNcVlG/zTv7TzSnj0J3/hkI+ibc/zIZ0n2sagsmIzQiFHJfKKRSzkg3saDWzT8lmCiBvqKSp+fVGde1bG8irWU+zZ5/lO5330LY50ehNaDNyUE4yLJ3yYhGoyz7s6RevAOcdKKWOae/lbB/2ZKluEtK/jMCXioItL/oAlwlJax45bUGF/rWrRn79WT0hzH/fW8K2yafoE3LVBMJ2oGG8GmNwUrPMZfT9sRTCIcCiKIcrTkTiURy+C92L8Q0C9pohJ533s6ix5+Ma+//xGOoMzIQ5HJ0+tiJMnvFLj557Byc1SX123asmU+bbiMYOPEsAm99uU+PioPFmqGm//Bm9B2aj1R6ZL+3AyE9S8NDLw1i+xY7q5eWY8lQ071vNuY0FXLF8ZsTHQmHKZ4zl2ljxhL9u2Tbzt9+Z80brzL0i2m88WQVV97e45Dl+MsUKjTGNNz2+Il7gKzCTrRwG/nz910s/r2Yxb8Xx7SPu6gtGl3jx0+iQoGheTMMzevC3n02G6dM+YKg04XCaESTmYHX52Dl71+y4rfP6T5hIkZ5NvZAScxxuhnPZfvMfH6atLV+WzQCi/7Yjb3Gx62P9cVgPDKTdymOXVICPkWKo4TPbmfBw4+y8vWGEKrSPxfw5UmDOPXbrykce0qTB2mOQAVbHPP5ufgZ3MEaOppGMSTneqzKggMyQwkGwmxcU80rjy7CXlMnDDRaGZff0o0T+mSxyfs7r647vb5/uXczk7y3MDj3WvJzBjX5fIcLqURKR9Mo7uwyl/9n7ywDozq6MPysu8eNBHd3dyhaAUpL3b+6UXd3d6VeKlhpoRR3d7eQQNw2677fj0DCsrshOLT7/IKZe+feTbL3zjtzznumZj/GQfsmTPJ6jMt6E4ffzJQDjwEgFapCTMyO5ZB9E80M/c/WbdcZbzCIzR95Z8/uD7BBJGV0kyZn9B4EIhHi1KQw5/gjyDq3rf53wOPF+uvMEPF+BPu02SgH9T4hAS+UStBcOgzXwuUR+9XjRyPS1KWab+0kSyU8mpnER4dKqPBVTUDjJWLuSk8g0Q0vfjIAmUyMUgt5wRVM3n0f+Y5taCUJXKR7hM6BK9AdtyjR8fH7qn7XInFNyorb5WPNktCJoNDnxOeKbnRWuf8ACW3bnvL9XCgoExLo/twztL3jdpylpYgVCpQJ8WetDjyAKUFJm07xbFoTLirGXpuOWm0Nq2QhEArRmk7dM+FUkSXE0/q2WzA0aczKZ1/AvH8/xqZN6fnSC6T27hkxssTndbN65uch4v0Iu9bPoWOvy1G1bYHwFMtB1oW6iHeHz4zNW0og6Ecu0qKXnZufuzFeiTFeSftu/55yY/b8fP664qpq8X6EgNfLirtuYcSHU/n2o4088HwP1CcgnKOhMSTR+/KJzPr0obA+U2pDDElZdDGqmfHTbizm0BQrtVZK/2FZiE7At+VY5AYDckPNYoSlrICfX7iSktydAAR+8DH0hgf5ueD+kPM6Km7jvSnh3xeAXVvKMJe5YgI+RkzAx4hxrnAUFYWI96OZf/tdJHXogDq17i9vm7eM37MfZlnRpOq2QuculhZ9xWNtV5KiOvGatyWFdl6auBi/r2ZHyG7z8t5zq3juoz5MdT4e8bwFBR8xMPUelGL9CV/zTCEXa2ik68mdLabjC3oQICKIn9c396s+xh/0IhOpQ1zqjyZREd0U7lwiEQhRi4RRRXya/MwbH4mMejTXjqPipdBwbWmrZmjGj8abnUv5ax8ibd4YWbuWePfsjzISOOYvQ9ow64SuL05NQnvr1Vg+/x4CNX+v6suGI21aZT7o9fqxmj0EgkEUSjEq9Yn9XGQiIe00Sl5tlIbF50cIaMQijBIxqCHOpCAQDLCm5Gc+3zmh+jyLt5jJ++8n17aB8Q3era71faJUlDk5sMfM/JnZCIQwcFQDMurr0BurImLU2mM+j1SOQCQKmzAf4USeL/8WpGo1UrUafYP65+T6OoOCmx9sx5ypu5g3Mx+7zUt6lpZLJ6TgNS/HlDr6nNzX8fBYrRRv3MSi+yfi93jo8OD9JLRvhzIhoXrHMRJOSzlbF/0WtX/zhr8YNvFZROdBqlWRcw8/7rmL7eY5BAmSIG/IhIYf0lDXA5nowqk+cr5iLyrGVV4esc+Wn49e7mL7hlKcdu9pEfACoZCmXYfj87pY/PMbVaarAgEN2vVn6M0vozEmogGe/6g/v361jRULDgLQuU8ql9/Ykvik0/c7DwaD7Fz5Z7V4B8jfvZ4GuwZwefM3mFn4EnZfOQIESL0mPO5ws8gjFOXbqNdAf9ruLcaFSUzAx4hxjijdvDVqny0vD1dFxQlNsMvduSHi/Qguv5Vf9z/ILc1+RiGu+yTJ7w8w94/9IeL9aKZ8s5Mm1w4nz7EtYv8h+xYSleeP4A0Gg/hLyhAeOIgwvwhJ/Qxs9RUUO/dWH7NMTFPjAAEAAElEQVSm5Gd6Jt7AvPz3ws5Xi02kqs58nuzJYJCIGB2v54fC8MmRTiyi3mkW8JVmFxWlLgoOWtGb5CQkqTAlKJG1bYn2+suxfD8FvF4kDTNRXTyEsmfehMP1yJ3zliJQKjA+cifmj76JuAuPO3pN6WgI1SpUF/VD3rU93h17CPr8SJs3RmTQIVSrKCt28Ndve1j6Ty5XTEglUW6mYu1y1ClJZPTvhyo1BUkd0goEAgEmiRhTlJB8szufyfvui9i3ovg7hmU8dlICvqLMyXvPr2L7hpqd21WL8mjfPZlbH+yI3iTnojGNWLmwJgx07QYnDceMYc/kyWHj6bKyzmjZtP86ztJS/F4vcr0+LF0lPknHyMuz6N7HiNNuwVmZj0p9kNQOo9EYw6MBbBXFlBdms3fdXBRqPY06DUFjSESmPPO71kfIW7osxGV73v/uAKpqXA/67GMUcZHz14NAMFodNyCIAJHp3Pu+lLlyeG1THyo9NWlAxa69vLN1KI+0XU4DbddzeHf/Do4XURj0+ZFIhac1PUSpNdJh6PU06XwRboe1qnqD1oRcVTMXSkpVc/PEDlx5ayuCward99Ptj+CwlLFx7g8hbZ1HP4RaN4CKlQJu7DAEpdGLSq6CykQEwu1Ryx8ajBe2D0KM00NMwMeIcY6QqGsP6RWeYE7gprI/ovZtrZiN3Vd+QgLe4/aTvdsctf/QASvthI2i9p9POxbBYBDv/lzKHn6hyizpMOKHriRJ14Q8R9ViyqaymdzY5Bs6xI1hXWnNrpFBlsY9Lf7EKDs/BY9IIKC/QYPZ62NWmaU6RztZKuHhzCTipKcvv7SsxMF7z61i5+aaVAO9Uc7jb/Qio4Ee1aXDUPTtTsBsQaBUUPrgs9Xi/QhBh5PKz75HfekwKj+aFHYNRd9uJ3VvQoUCoUKBJCVUBJWXOnnl4SXk5Vh55ImmbJp4I+vXr6/uF4hEjPjlJzKHDkGiPDWDIIe/Aos3wqLEYfLsW0hWNsVVUYHHaq2qcZ+QcFxju81ri0LE+xHWLy9gz44yOvVMJbWehpHjG/PHz7sBmP9PIRMffQRXWRkH586tPkffsCEXz5x+WkrZxQjFXlTEwXkLWPP6m7jKy6k3eCCdJj6Arn59hEf5H2iN8Wj0Jpy2CgSCLBSayIs61vJCpr55Kwd31hRZn//dCwy9+RVa9r60TiLe63TiKCykMvsAApEIXWY9VMnJdTZTtBUUMP/OeyL27Z06jW7PPhVVwMtlapp3H8X6f76L2N92wBVnPZ8/6PPhLynHvWUHvoIiZM0bI0iVcbT3QPWxBPl9/8Pc0WIqKsm5X2i40PDY7TgKiyjfsYOA38/FM2ewbdI37Pnt95DjZDodbpGG3oMVaPWnd8FZJBKji6+9jKhcLkYuP72SyOv1V3kHBUEslIR8VzuNfIC8ii588eWukHNS62l4/I00uvZJY8WCQ8cOSXySMmZiFwOICfgYMc4ZxmZNECsU+JzhpXmSu3Y54VJTtee4CxBwYpMkqUxERn0d2zdGMYBJV4MkcmkuqVBBkrLpCV3vTBIoLafsiVdDxDsAP8xn9FOP8VHulQAECfD17usZkHoP97T8CwEC1BITOmkqBtn5HW6sl4i5MsnEsDg9Fp8fmVCAVizCcIrmbUfjcvr48dMtIeIdwFzu4oUHF/PypwMxJSgRJiVAUgKeXfsIVFojjuU7VBBSSuoIotYtCMSfXmO1nH1mcvdb6NY7kfxfPqPkKPEOEPT7mTnuCq7fuQ1dVib+cjNBpwuBVILQoEN4Au71YkHtx5oE6RSt38Ci+x/k0KLFSFQqWt1yEx3uvxdNWugk0+z1Uenz4/EH0dXX0qFPCusWhedGzvptDy3bJ6DRyrh4QjN6Da7H6sV5eD1+1CkpDJ40CV9lOdaDB1HEx6NKTkKdfP6Id19pOUGbHYRChFr1eV2usTYcpaUsvOd+dk3+pbpt6xdfsfPHn7ly5TLiWrUMOV4gFKLURn/OB/x+Nsz9IUS8H2H254+Q0aIr8cravS1cFguO3ftwFhfjLChgzUcfU75zJ4O//Iz6I4YjPc5CMoCn0kLl/ugpLwUrVhHfKnJ0ktBso1OXMexe8zc2c+jCVoM2fTEln91UhqDPh2f7bkofe6V6YdEGiJISuP/pX3ilYAROvyXknP3WVbgDDlTEBPyJ4DKb2f7Ndyx+8KFq00ihWEzXp59ElZTExg8+rD6200uvs3yNg/G3tEEqu/ClSWmxg5mTdzH/z2w8Lj8t2ycw7rqPWPnrfRTsXUtC4yF8+ejOsPPycqzMmb6Xq/7XmooyV8i7Nj5JyaOv9YpYFjHGf48L/1sSI8YFijolheE//8CMS8eG5KjKTSYGf/k5CtOJCfg2plFMPfBExL52potRiU9s8iESCRl0cQPmTN9HwB++MzH2uhbokpOYVWzE7qsJ3RYi4uamP6KXnjvBG3C7wedHoFQgEAjwlZYTKA93afflFZKywssVPd9hSs7juAN2/EEf60t/p1P85dRTtz8p879zhVwkJEkkJEl2ZhydKytcrJh/MHJfuZuifDumBCU+txt7QQESmz3isdWoNYgH9CGwcTNCpYJg/wEUJzbEbhbQ4DT6iq1eVLWT0aOrhhXjvol4TNDvJ2fOPzRq1prKD7+uWuyRiFEO6IXm6jGI4+r2/dFI4mis7c1uy+KwPpM8E0WOmJ+6dq+e0Hrtdta//S65c+dz6eyZqFNSCAaD5Lo8vJlbRN7hsnVyoYCR1zcis7mJ3z8OLZnn8firv6NqrRS1VhqeI5maiKl5szp9hrNFwOXCs3kn5g++RJyShECrhiBoxo9GkpmB4BQMpM4F1pzcEPF+BJ/DwcIHJjLyl5+Q6fV1Hs9uLmHdrK+j9m9fOo0+V1SVxXOZzQgEAmS6msUPv9mCZ/EK3JNnQGk5uoQ4hk58hEOHDvDXlVdz9Ya1xLdpfdz7EEokIBBU17I+lqONuo7FtW4zvil/MeGuT9m+dR47N81FIlPSseflJMuSkPvO3PM1GAggEIb+DfnLKih76vWwqCB/YTGSL5cw4MrbmFn2WkifTpqE6AJ6D5wvlG3fwcJ7Qw3aAj4fy598mtEzppI7fz6q5BTaTHwU4rO4Mi3uX7G7XF7i5OWJizl0oGbxesu6YnZsLuXxV19k+/zXWL8qss8OwJxp+xlycSMeeK47FeVOigvs6I1yTAlKjHEx8R6jipiAjxHjHCGSSskYNJCrt2xi2/c/YdmzG1PXXiT26INPn47fFwhxmT4eBmkqA1PvZW7eOyHtarGJy7JeRi4+8XzJhCQVD73cgw9eWI3NUpWXLFOIuP7udqRnaVEoTTzZfh3bK+awreIfkpXN6JowAaMs45TL150M/koL3uyD2Kf+RcBqR96zE4qeXQhYIu8CA/gnzaJ7n9dp0/FirN5ixAIpGkk8+vN8x/1c4HH78UdYzDlCeYkDZ1kZmz76hNUvv8rlf8wAkQgimKgJlApyy2G+uzWtLu2B2+Nn3qJS9u3cxKOv9Tyt9601VDn2ikVBfI7wurtHsB06hH1vYU2khteHY/YCfAVFGB+/p05GWyqJkWsbf8Frm0PzacUCKXdnTGXJlRMj1tEu3bKF0i1bUaekUOL18eT+fOxHmRK6AkF+tVq5uWsc6bO1HMyu2SXsPbjeCZU7Ol/w5eZT+fc8BHePZ/Omf6go30JmVnsyyvLRa9RIzkBd8DPJ/j//itqX+89c3ObKExLwwWAAp80ctd/lsGI9dIgDc/5h25eTEIiEtLn9NtJ690JpNGH7/U9sv8yoPt5fXIr/619Iu2QoTceOYf177zPg4w8RHyeUXhEfR9/PvkKa0RifD0Q+B/u+/oj906YikkpJ7Ng+6rkBuwN/YTH+J96jabsWNG1/M3h9BKdtwn1wFkxqW+efR9jYfj/2ylIIBpCr9UhkCvxWG/7CEhyzF+C3WFH27Y6kSYPqBTjfwXyCzsiVGbzrttLuuhuYSaiAH5r+MFpJ+Iqi3+clGAwgltQ9Que/grXEzOqXX4nav23St4yZ+zcSpRKJRntBlPerK9m7K0LE+xF83gB//FbMVTc9wIxfzVHPd7t8BINBtAYZWoMsZlgXIyIxAR8jxjnEaofX38hBpemLptkQlmy1kjN9GzLFTl75bCApGXXPWVdJjAzPeJy2plH8c+htrN4S2ppG0TnhCuLkmSd1f1KZiNYdEnn1y0FUlrsIBILojXL0JjkSSdWORJw8k97Jt9Ar6eaznst4NH6LFcu3v+GY+U91m2fbLmy//UXcCw9HPEecnoKkQSYSuZo4uZ44eXQ35RigUEpQqMQ47eECFCAlQ8PuX39j+VPPALD5hx9oO6wfnj/mhh0runwsU2cUsGFNMQuP6Y47CfdfTyBAhc+P1edHKhCgE4vQHU4f6DEgg2nf76Sw2E9cq1aUbtkScYzUDh3x/RwuwjybthMoq6izU3aishGPt11JtnU1uysXk6hoTEvjRchLJBxcuCjqeXunzSBzyGB22Fwh4v1oZrocXHRFY759aW3VtVJUtO9+4S02BewOrMtWUdYllWmvjiMYqPq8O1f+iUJj5KpHf0QsVKI3KBCKLozJvaiWVAuBSFS1i02VJ0ddnpVShYp6LbtzYPMSjCkN6DLwRtTKOIJ+P56gi9SmHZly0UjKttYYouYtWUpKj+5c+tOP2H7/M+K47j/n0e7OG1j03PP4HI7jCniLQ8Q/++qx4fscgkGQK8QMG/UAPQZehCnViKqWdAx5h1ZYv/kFgkG867fC+pp7lTRpgEB1cjuKlrICtiz6lfV/f4vX7aRxpyF0v/gOxOv2YP2wJsrGtXgl4vQUTC89hjjBVOtiLsEggmNMWzvHX0n7uEtDfl92cwnFuTtZ//c3+H0e2vS/gpRG7SIaEP4XqaxwsWPtIawHcqMeY8k+gEgqDYkYOZ+xWTz4vAGUavFxQ/xXLowcpQawZW0Rigfa02OAnoWzwnPcAdp3S0GpPjNRdDH+PcQEfIwY55D9u8o5sLcyrN3t9PPbN9u55cGOJ+SGqpHE0VTfjyxNF/xBD3KR5pRDwEViIXEJSuKOE9p2LsU7gL+4LES8HyFQVo435yDyHp1wLVsDgCgxHt2tV+MvKsGzNxvHrPko+nRDFG88oXzn/xoGk5xLrmrGj5+GC+AGzQxo1fD3YfEOsOXrSSS0aEHaTePx/b0YX34RknppaK4fzz63jg0frw0bp333ZPSGE6txW+nzMbukkmmllXgOh/lmyqXcn5FIqlxKXIKSq+9ozaxp+7j56Zf4Z8zIsDHi27ZBLZLijjLB9x7MR1K/7gs8RnkGRnkGHeLHVLfZZUWk9+1D0dp1eKzh11EmVuX+74myQwhQ4PGSkKbFGK+g70WZDBhR/7jfzfORoMtFoFUmM964olq8H8FpLeevb56g9UWvY0xKJLPRyZXdO9vUHz6MpY88FrGv1wefUOHVMO2jTVSUOOjUK40mLU21hgzLVToGXP0k25dMp1FmL+bccEt1LroqKYm+775FXIvmIQIeIH/5CrzFpREjXwDweJEIhCR16YREVftimbnMxeuPLyPnqPeUy+ljyuRcJtzWlXZDshDLo39fRYnxSDu0xrNuc2iHUIj+9usQaU88MsxaXsivr1xL4f6a59Cm+T+xc+VMrrn/O5BKwFMTIu87mI992iy011+OJCsj6rhCgw6jsTm3xf2C02ehvrYLOmkK6qPM62zmEv7+4lF2rqhZHNmz9h+SG7ZhzMNfozWeP94S54rs3RWsWVVB/fadKD3mb/MIKd27ItWcvSoKJ0tlhYtdW0qZ8dMuKivcNG0dx8jxTTDGyVFrI88VtLW8v5QqCSAgvb6eRi2M7NkWWjlGJhcx/qYWKJQxAR+jdi6sBLMYMf5FBAJBlv4TfYV6/fIC7NYTL6cFIBMpUYr1F1T+tsdpp6Ioh9ztKynYtwlLWUHYxL42nItXRu2reOczdLdejeqy4QjjjOjvvhHzB19T+el3OOctxfrtrxTf/ADudZsJeOr+Mw/4fVSW5nFwx2r2b1pEReEB3M7ouW0XKh63j4oyJ3a7l34XZTH+5pYolFULSwIhdOmTxgPPdUfkqsBZEmp6OO/Bifz53DOUtGmA6sWHML38GIrObUlvmcz4m1qiUFWNI5YIGTiqPrc80CG8nnkt+H0+VpRb+aXEXC3eAQ64PDy9P59SjxelWkL/YfWZ+FxXbLI0hs/4E1OLFlXXlctpfeMNjJr8E57vp0S9jsigr/M9HYvX7aKi8AB7ts4n8abBDJjyJX0/fQ/xMY73TcZWif2MWnZE4yViMuppeenTAYy5rjlxiReeeAdAKqW8sgC/N7IRZt6utajVXj55dS0Wc+RjzjfUaal0fuyRsPZW9z5ASVw3HrpxLn9O3s3y+Yd499mVPH3XAooLaveJiNOl0qbVcKYOHRFiJGcvLOTP8RNoeuX48LD8YBCv+zg/M7GY1rfegqiWaicel4PCvIoQ8X40077fhcVS+zNapNdhfOA2tDdPQBhnBIkEWftWxL//IuL60cV0bRTu3xIi3o/gdlhZueAbpH27hPXZZ80nUGlFaDIg69Yh4rjamycgj0+mreEyWkiuwLIvkX0bPRTm2XA6qhYEig9sCxHvRyjYu4ldK/8iGMUn4HzG5/HgrSW1qC54/E68fhdOh5e/ft3DqmXFNLz+1ojVdERSKW3vuqPOVRDOFTaLh1+/3sabT65gz/ZyigvsLP47h8dunUvufgsOW+S5Qu8h0Rd6h1zaEL1BjsGk4P5nuzPhtlaYEhQo1RJ6DEzn5c8GkpwWfWHDX2nFV1CEr7CYgCPcADnGf4fYDnyMGEdhcRXj9tkQBAUYFOmIxCf/FXE7rFjLi9iz9m/cDisN2g/AmJSJSh8PgFAoqDVvVa4QI/gX5YXVhr2yjNUzP2Pl9I8I+KvCs1X6eC6b+CWpjdohFB3/9xCMkFdcjccHAgG66y9HPXoI5nc+Dze1CwQpf+l9Er94o8pF/Tj4vR4O7lzN76/fhMteNcEVCEV0G307XUbeilJ3YiaEZxuPy47HaUcsUyCPUorK5wtQnG9nxk872by2CK1OxqgrmjBgRBY9B2bgdPiQykToDDIUSgkWlwSpVovHEuriXLp1K3PuvofRM6aib1dlmqUzyBl1RRN6Da6H0+lFJhOjN8pO2IG4wunml9LIAqPC5yfX5SFOKkGpliBPkmMqtCPSpJE8+Sd8Hg9Bq53A0rUopDJ8rZtXR2kcjVCnQZR8cs74XreTvevnMfWt2wgGanZEUxu156LfvmPmiLEEAwH6vf8u6vQ0vB4/9XxC5EIBrkC4GLjUpCNRLYPjm4ef14g0anzC2sWfw+4me48Zm8WDVn/+R8bI9Xo63H8v9YcPY8P7H+AoKqbhpZcQP2w8D1w/L+z4kkIHv369lZvu74AsQgkrv9WGfe4Stq1Ygs8VISojGGTzp5/TbMKVbPzwo5AuW0U54ngT/pKysNPEqUnI01JQZNYuoItzd7B3a5RdfMBu8+Jy1vLcPYzIZEB96TCU/XoQDAQQKuQI1SdXZjQQCLB50a9R+/dsnE/XCSNhztKQ9qDbQzAYRKzVYLj7RuwNMrFNnUXQ7kCUkoTupiuRtm6Ozxdk28YS3nl6BU7HYed0kYDRE5owYkwWa2sxFVw3+xuadx9V/Y4/33GUlFC2dRsbPvgQr81Os6smkN6vT1g1jNqocOext3IpS4u+QiyQMiLhVRwOL15PgN9nmBn16x+snngX5j17ADA0bsygLz9Hl5V1Rj6TxezG5/UjU4hRqU9tgaCizMk/08OrL3g9AX78dDO3P9YJZYRrxCUqmXBba374JDTqpFELI/2GZVWnBBnjFYy4vAm9BtUjEAyiUkevPx/0evFm52L+4Gu8u/aBUIC8W0d0N12JOCWWuvFfJCbgY8QAnD4LOZZ1/J7zCDnWdehlqQxKvJf2cZdiUp94XrTLYWXLwl+Y82WNK/yy398lo0V3Lr73IzTGRAD6D89i7ozI5XkGXdwAneH8n7SeDvatn8vyKe+FtNnNJfz47OXc+OrfxGVErzd/BGXvrtij5HzKu3dCqFEhkEjA78e9PnIONF4v3n05iOsg4CtL8/jphSsJ+GpCNYMBP8unvk9CvWa06HXJccc4F3hcDsoL9rH013coOrAVQ2ImPcfeR0K9ZshVofmIeQcsPHH7fDzuqkl8WbGTHz/bwuhrmtG1bxrxx+Sqq5KTaX/v3ax87oWw68qNxjDHa5FYeEo7yEG/H7fVTqUvusjIdrppr1UdPt5H0Oqg/ONvCZSWI9TrUF9yEfJrxyGON6G77Rr8hSV49x2oPl+oUWN66TFEdXShPxZreSFT37o1LJokb896DjRay0U/fEd8m1aoU1ORabWUlzj59a313HtPGz61mKk4/NlEwDCDlsYBMYFA8F9h+pTYMLoDui4+jUpLVQTRhbSrqTCZUHTvRkLHDgQ8HiQqFbN/3xv1+GXzDjLuhpbEJ4VPxwJmC97CIoo2bYx6fumWrdQbNJCWt95IxsghCIQCKnftxS+XkPDMA5ROfJ7gUTt1Ao0K/WN3I2tYu4CymUuY+/XTNO4X/l0+glgiRCqrW5SXQChEZDr1VAiBQIBMHn31SiyVgzf8eSDr3Bbh4Xx7kdGA5spLUA7tV1WtRCZFZNQDUHTQymuPLA0x6wz4g0z9dicdu8bhixIxAuD3uS+Yv1VHSQmLJz7C9m++rW7LmfMPhsaNuWzubLTp6ccdo8Kdx/tbR5Br31jdZvdW0rbn2+zZVs6mdeWUlGgY8dy3dNT5EQgEiLU6kjs3OiGD3rpgMbvZsamE37/ZTmmxg8xGeq64uRXpmTrkypOTOlvXFUft27O9nMpyNykRfkwqtZQBI7No1y2JlQsOYbd56NQzlZQMDQZTqOeDUCjAUAdneV9eISX3PQ1H3nWBIK5la/Ds2EP8u88hTrgwFo1inD5iAj5GDGBnxXw+3FEjuMrduUzOvZ9s62rG1nsNg/b4L7OjsZTmhYj3I+RuW86Whb/QdfTtCEUiElNUjBzfmD9+3h1yXFZjPX0vykR0gZVROhlsFcUs/uXNiH0+j5Ndy/5ELrn8uHWrRUnxyHt0xrUstGayQKVEe904hIqql2SwFrEHEKhjKOHOFTNDxPvRLPn1Leq16on6PNuJCQaD5G5bzuSXr6kuCWUuyiV782KG3PQSbfqPRyKr+jnZrB6+fn9DtXiXyUVc92RnvClyVgu8rCkuY7BJSwOFrLrWvEgioc3/bsVyIIft331ffQ11WhqXzJyOpg6TwhMh4HQhrDCjFkmwRTF9SztcUi/g8WD/c16VodaR882VWL7+Gb+5Eu01YxEnxGF64WH8xaV4s3MRJcQhTk9BFG86aY+HvevmRk0F2bjgJ7q8uwit6SgTOgEc3FeJstTD9X45YpMUbxCU3iCrfjvA1/sruefpbv+KckJqYyLtB1/D+jnfhnYIBHQb8zS//FxKSobmtDrs+31evG4nYqkcseTMhfCKpVI4HCKs1kpo1jqO3dvKwqo4+H3BqKLPX1BE0GxFX79+VPNDbWY9Uvr3Jtu9hanf3E3A7yMxswWDBvZAkJZEwsev4tm2E8++HKSN6iNt3hhRwvGjgzxOK3m719H2IjNavSxiGkOfIfXQGasWmQN+P7aKIhyWMhAIUWmNqI1Jp90bRSAQ0G7QVWya/1PE/ra9xyFYHrrzKZBJ0V13OcLDKSuegAtPwIHcpAmrlrJ0bk7UShtTf9zH0MFjyd4U+XfRvPtoFJoLw6+hYveeEPFe076bzZ98Trdnn6o1AjEYDLK+9PcQ8Q6w17qEoV28GH6TU1HmIj/XymcfV/l9aHRSXvq0xWkX7067lz9/2cW0H3ZVt21bX8KTt89n4ks9aN8t+aT+DoXi2s+prSKLSi0l4A8y+JIGSKWiU8ppDzidWL7/vUa8H91Xbsa9djPiYQNOevwYFyYxAR/jP0+58yA/7b87Yt/qip8ZkvogBk5MeGypJcRvzV9f0qT3cLSGFNRaOaMnNKV7/wwWzMrGYfPSc2AG9Rrq/xUT9LoQ8PuoLI7u2lpecoDK/dnHF/B6Hfq7rsfduyu232cStDuQdWmPeuRgREk1QlqoUiJKjMdfVBJxHGnjBnW67+Kc7VH7zEW5BPyRxf25xFpewMyP7o9Yz3nupGdo2H4A+sSqsFqHzcuOjaXV/Tc915XZhgC7bDXh6uusDlqp5dydnojxsIhXJSXR97236fL4o1gPHUKq1aJOTkKdmnraP49AIkaxaSujunXhR2t4iLFaJCRLXjVBD5Sbsf40NeI49ul/ox41BKFSgcioR2TUI23a8KTuyeX0IRIKkBzembSWF0Q91uO0EQiETsp0ehnjbmjJ2iX5/PVbVdipSCQImSxm7674Vzwf5CodvS9/kJTGHVg+5T2s5QUkZbWm3UX3s3hBkIP7i3ny7T7ojSdmahgJv8+DufggG+Z8T97udZhSG9Bp+E3oE+shU5yZfISKMid5OVb27qigQVMjI69owtpl+cyfmV19TKMWxqiTe4FahWfNJlrfdwNbvp4U8Xvb5YnHmPXT4+TvXl/dVnRgGz88O4brXv6TlIZtESfFoxzQ64TuXSgSIxCKWPHrU9zz+Kd88MpeKspqvmOtOsQz5voWyGRiPC472ZuX8NfHD1YJeEBjTGLU3R+Q1qQjYunpjSQzJGXSYeh1rJs9KaQ9vl4z2g25GoFsA76cPIIOJ7JObdFedRnilERcPjslrj3MOfQWBY4dpKvbMTD1HhLkDZCI5AQCQQ7us0S+KLB1XQk33tmFuPQmlB7cFdKn0sfTbvBViMTnv/lYMBhk6xdfRe3f+uVXtL3jNtQp0atbWL0lLCz4JGLfT6Xjue+tv1n0m5Wl/+QSCATp1j+dy65pTkLyyaVO1EZlhZvpP+0Kaw8G4Yu31vPixwMwxp/487Jlu+iReK06JFT7wByL1eJm7/Zyfv16G4V5NtIytYy/qSX1GupPKqw/aHfi2Rx9vuFauQ7loD4IJDFJ918i9tuO8Z/H4TNT7o4uILPNq0jTtkIkDn/w2swlWMvyqSg8gMaUjC4+Ha0pGWt5UdTxnDYz2ZWrCQYEtDaNQKNVodHKqN/kwli5PxmCgQCVJYfYt2E+B3euITGrJU26XIQuLgWhWIIxpQHl+fsinhuX2ID8ZctJ7dH9uNcRGfQo+3ZD1r4V+H0I1YfD5o8+xmRAf+f1lD35Wtj5iv49ER4OpTwe6c27sm3ptIh9CfWaIZacuug43TitFdjNkRcuAgE/laV51QJeIKgyqAsGIL2+FnO8hF228MntFpuLnXYX3fU1Ikiu0yHX6TA0Pn7qw6kglMlQtmtFj7w8SpNTmGt3c2SvO14i5uEUE/GywwLeagtxpg4hEMBfbkacknhS9+Hx+CkrduCwecnPtbJzcwld+qaT2VBHVps+rJz+ccTzkrJaIZWGphCIxEJatEtg6nc7qtuO3emZ+8d+WndMRCK9MEwqXU4flRUucvdVggAy6uvQGeTIFWJU+nja9BtHvZZ9sFud7Nhs4efviklK1fDql4NITD094jp/70Z+eHosfl+V8dShXWvYtGAyF9/7EU06X3TaRWZ5iZPXn1jK/p3m6raZv+xmzHXNGTAyi3l/ZCMSCbjurnZodJGvLYo3IVQpESxbx/AvPmfO3ffgtVeZ3gklEnq9+hKSFEOIeD9CMBBg/nfPc9nEL1Go9Sd8/0qNkSZdhrFzxR8s/vZmbr7zaQLCeljMXhKSpCTXS65eRCrL389vr90QssBgLS/kp+fHc/Nb84lLO33PgcoKF8X5QZJaXc+4DqPZsewnPA4rrfqOJaVhWzSmZIJjUlEO7EUwGESoUiJUyPEHvGwtm8WnO8YRpOo+D9jWsqzwK+5p+RfNDYMQCgU0axvH6iV5Ea9dr4EOqSqeK578kS2LfmPDP98T8Plo3nM0HS+6Hn3CyZnynXWCQXyu6AZofrebYAT/jdAhAvgDkZ+nZe4cZlnv5eY7f+Oya5sDoNZII/o8nA4OHagkGMVOo7zEic3qPikBb4hTMO6GFvzy1baQdpVGwugJTTHGhb/j3S4fC/86wPcf10SB7NpSxrP3LOKOxzvTY0D6iUdWikQItRoClZErpAiNRhD++6M1Y4QSE/Ax/vOIBLV/DRQiLQJh+ES5suQQv712I4X7ax7U2rhUJjzzK027DmPb4t8jjpfRsivZnrXM2fkWz3TYQqqqxal9gAuAopztfPfkJXgOO7RvWzKFRT+9yhVP/kR60870veJhprx5S9h5cpWOpJTm2O3RF1giIdLWPumXtmpK3JvPYPnyRzy79iEy6lFfPgpFz86INHUTDA3a9UOm1OJ2hIvaflc9jlJ7cjnTZxKBIPwlH5fWmG4X34FUocbrdlJekI1KF4daK6djjxTWLMmn/YB0lvuj537OKqukrUaBUnT2BaUoLQXV9l2M3rmHYf16YhaJkAsEaAMB4oU15Q2PXcg5FoG87jsjQX8Af3kFeLx4EbJwRQU/frYNj9tPvQY6Lr6qGfNn7kcgFHDNrY2IS29M6cHdYeMMvP7ZiGaH8uNMdC+k7He71cOCWdn88MkWAocXIkQiAVff3obeQ+pVh8fr4xPRx4M+3kOHXo1QKiXITqCEZm1Yy4uY8d5d1eK9mmCQPz+6n6SslghEIpRqA/KTELvH4vcHmDNjb4h4P8Jvk7bz8Cs9MZe7uPyGlqRkRHecFsUZMT3/EKUPv4DemskV06Zhr6wkEAxgaN0SVVoqs78MLVuXlNWKxp2HIBRLydu1Dq/LcVICXqpQM+DqJyjYt5GKgn3M/vAaxFIFMqWaQdc9h85YFaHicdlZ9ts7EaMDAn4fa2dPYtB1z5yWnemKMiefvr6WDSsKAVCqJbTuOI4ufdJIbZ6EWlO1ECIQhefbmz35TNp9Q7V4r75H/Hy1+1qeaLcGgyyNjj1S+eWrbTjt4eZ8429pdbh0WArdRt9Bm37jCQYDKDVGvD4BRfk2vJ4ACqUYg0lRbVZ2vuFy+Wnz5CuYeg1m/7efU7hqVUh/47GXoYirPc1CLYmjc8IVzMx9PmJ/t8SrUclVqM7COrbkOD4MJ5uKqFRJGDS6Ps3bxjN7yh4qy900ammiS+9UTPEKDHHh/i2VFW5+/iJy6bxJ726geZv4E/Z9ERl0qMeOxPzWpxH7VSMGIvgPpFvGCCUm4GP859FKE2mi7cMuS3hem0QoJ0PXDuExAt5lt/DXpw+HiHeoyn3/6fnxXPn0LxiSs6goyA7pF4rEtB97PR+XXUmQICsKv6V/xgsUuH1ssjnQi0S01SoxikXIz4EYOhPYKoqZ+tZt1eL9CH6vm99fv4mb3viHei170P/KJ1gy5S28rqocdFNqQ0bf+jbSnYWk9O6D32qrs7g+HkKFAlnLJhiffZCg24NAJERo0J9QnpwuPp2rn5/K9Hdup+RwOKVCbWDQ9c+S3KDNabnP041CY0CXkF6dspCU1Ypelz/ArE8fxlZxOGpEIKD9oKvpMuo2xl+tYvCIZpj9atbXYs7kDQSpbcMmYHMQqLTgLy1DoFQiMuoQmU7PAodIo0I1bACKiko8u/ZhCoKkcX1EcQaEqpqJklCnRVwvDV/OofAx4k0I9bqw9kj4KypxzF2MbfIMAlYbQq2GdsOHorujCe+9vZ2cfZW899xK7n6qKz9+upmSkoaMf+JHFk9+nW2Lp+L3eYhLb8zgG14guX6riNfQ6KX0uSiT376OHDY5aHSDC2b3/eABC999GPqc9PuDTHp/Iw2aGmncMlQoqDRSTneQrdNajrkocslOr9tJwb7N/Pnxg6Q2bsdFt7yKKfXk0ieOUFnuYs7UyBFFALu2lnLXk11QKI6zqCQQIGmYScInr+LdvR9fXgHqxg0QZ6QgPiywtPFVYc5iqYLh/3sDS1ke25ZMw+tx0qBtv1pN146HPjGDa56fRv7eDexdPx9dfCrNuo9Ca0qu9srwuBwU5+6IOkbh/s143c5TFvBev5tKXy4DbvIz6KYUbEUKZn5RwsqF+axcmM8LH/enUfPoURRmTwEuf+QdzEpPIVZvKQZZGnGJSp55rx/vP7+KQweqFme1ehnX39OWzIb66nOEIhFqQ1WIdVmxg+8/2czKhYcI+INodFLG39SSLn3SokZXnAu8Hj8Fh6z8/MVWtm8sQaONZ8BNbzLg/lIWXj0Ov8eD3Gik00MTEctrV94ioZieSTeyrPArKjyhEQtpylY01vU+kx8lhJR0DVKZqNqv5WgyG+nRRKnXXhe0ejlavZyM+jqcTh9icVX1IIkk8vO3vMSJzxs5HMBu82KpdJ+QgLcXFWHNPYg8JR5Zz864l4Z6/GhvuRpx8vnltRPj7BAT8DH+86ikRq5q+BGvb+mPxVsT+i5ExPWZX6IRhedB2StL2b9hfsTxzEW5eBw2Jjz9C0t+e4eti37D73WT1rQTnSbcxiznm9h95QgR0SL+el45UMg+Z83OkKCgjLvTE+isUyH/F4RFOSxlUcPjndZyrBVFpDZqR+u+40jNaEtA4EMmUyLeX4D/1W9xFZXgApQjBqG9+jJEdRRax2KucOFx+RGJBOgMMsQSESJt9N2v4yEQCEjMbM6EZ37DYS3D7/Oi1BhQG5IQnqeLLxpjEqPv/oAfnqkKJe417n6mv3tn6OJKMMj6Od+i1Jo4uHMVebvX0/+aZ+meNZxsV+S6t30NGlRRdgD85WYqv/oJ59wl1bt0osQ4jM9ORJyRelp+VkKFAqFCUWs5HZFBh/Hxeyid+DyBypqoCYFKifGZBxDXwWU+4HRh/fWPkGoHAYsVfvqVBsOGMOiiJsz56xDBIMz4cScDR9Zn1pS93PVEF4be9DK9xz6AP+BHKlfVanAoEgnpd1EWS/7OoSg/tE54i/bxZDbSH/dezwdcTi/Tf9gZtX/GTzu588kux404OFWO5wweCPgRCAW4bJXM//5Fhtz0Yqix4AlfDxz26B4YVrP7uOL9CAKhEHFiPOLEyH8vLXpewtJf32bwDc+x7u9vOLSzZoK/tiCb7cumc90rf2JIPPFqKgDauBS0cSk07Tq8ui1wVMlOiUyBMbl+2GL1EeLSG1c5w58CNm85Kwq/ZUrOo3gDVXn4OmkS45/8jj/fM7BjQwV/T91L/caGWgzSjucOX9UvFArIbKjnyXf6YK10I6DqfaFSSyPuqJvLXbz++DKyd5sxxMkZMjSBzHQpvoCV7J3FtOyUdt5UjMjdX8mTd8zH76v6rE67j5++s9Omo4nBH3yOdfs62t5xO7oG9es0Xpy8Hg+3XcbSwi9YWfwDIoGEPsm30DH+cgyyupeiO1X0Rjl3PdGZt55eERJKr1CJuf3RTmhPQzUflUZaJzNN8XGM70QnEJVhyT3IjEsuo3j9BoRiMX1efJ6MR+9AXFiKSKVE1q4VIqMeofLC90OJceLEBHyMGECyujmPtlrGLvMidloXEieuR8eEcehEiajV4RMnr7t2p3JrRSGJWS3of91jNBw2FJfPSq53E1+ab6TSUxX+18Z0KcutOvY5Q8cKAu8dLOY9ZTopsjPnkny2OFLXPRo+T9WETGWKRyyWEsjJo/zB5zlWKjpm/oO8YxsU3Tqc0PUddi+7t5by7YebyMuxolCKGXxxA4Ze1ui0GIGp9HGo9HGnPM7ZIqVRW25+ez47V/6FpawgLDLiCOvnfEv/q58gZ+ty5nzxMBM+GsA/EjHF3tDfZ5JUTEetMmL0QtDrw/7HHJz/LA5p9xeVUv7UG4gmXoNbBsbkLJTa4ztjnyqSemnEf/Ai3j3ZePbsR5KZjrRZI0Txdbt2wFyJfdrsiH2+v+fS/8kezPmraoc/e4+Zi69qxr6dFQSDQSQyBbqEupthxiUqeeqdvqxdlsfiv3MQS4QMvbQRzdrEhZUiOl+xWjyUFkV/VpYUOfC6/WdcwCs1BjTGJKzlVc9egVBI+35X0bBFP/w2J1pNOtc+OoVDixZh3r2XwmWrEXbodNLGi3KlmJbtE9myNrIXSufep8/QUWNK5pL7P8XrdoSI9yM4LGWsmv4JA6975pTz/C05ORz4+x+y/5qFrn59Wt54Hdp69eh52b3sWx9e5x6BgM7Dbjplt//91pVMzr4vpK3SU8hX+SO57Y4V7Ly5AmulB78/EFXA66UpyIQq3AF7WJ9GEo9aEvqe1xvk+L0B9u4oZ/oPeRjjlfQclEFcojLEdLCk0E72bjPtOxkZ2V/CxucfZMHKlYgVChpNuJr0hIcwZJ3c4smJYC0vxG4uwWkzozUlo9TFhaROWC1uJr23oVq8H82mtWWMurIfKwvqseLTfK6+PY7UDG2dUgDi5PUYmfE0/VLuAARoJPEII6RqRcLi82Px+fEGg6hFQgxiMeKTWOyQSEW06ZzEG5OGsPCvbPJyrbRol0DnXqmnVKb0ZNDHKVBrpdgs4YvdCckqtHWMyHBbLBxcvpiubz6Lz+dBLJSSPWUmS59/kZQePRj6zZdIEk/OsyXGv4OYgI8R4zDx6gbEqxvQPXAtAgQIatn9liu1iKXyavF5LLr4qtVnpcKIzKjjzQ2Dw3LvOiTewfuFkY1kgsA6i4OU+AtfwCs0BmRKDW5HePiiUCRGG1ez0yVRKKn4Y07UsWyTpyNt2eSEQul3bCzhtceWVf/f6fAx/cdd7N1Rzj1Pd0VnOP/M5s4kIrEUU0oDuo7+H0smRy7fB1UTf+lR7tyznh/H489MZ0G5lRWBqu9GP4OGfgYNcdLIu4n+CjO2owSvQC5DeulQ/GlJ2EtKUDr8lOTuYufKv+h2yR2ozoKIFyfEIU6IQ9Gj0wmfG6i0gj9KGUK/H5mn5vssU4jw+wP0G37y5SDjEpUMuaQhPQfWQygUoFSf/w7XR3DYvRQetNGwuZHc/ZURj2naKu6kazSfCGpjEsNvf5PJL11NMBhk5I1vcuiXf/jjrtHVJf5MLVrQ583XWP/au2x672P0DRty2T+z0WWeuPhSqaVc9b/WPHbr3DDBlFFfS0YD/en4WADIFGoadRzMzA/ujXrM9mXT6X7Z3WhNtVfyqI2K3P2U7tqOOF6LUCZh4/sfsP6ddxn6zddkjhzK8NvfYs6Xj+N1V30HZEoNI+58B/1J7vwfweotZdqB8JKsAN6Aiz3eGTRp1YsufVKRyqL/LemkyVzV6GO+3HVNSLsAAdc2/hK9NDTioqTQzvP3LQqJgJn2w05ufqA9PQZmVIv4A3vN6AwyRg2UMXvEQIKHnw8+p5MdX3xG0fKlXDbnLzRnoArHEUoP7mbyy9dgLsqpbmvabSSDb3gejbFK5DntXnZvK486xoZVxRQWutizrZzdW0t55YtBJNXRQFIkFKOTRo9+ikSey8O7B4vZ56xK8ZALBYxPNNLXoEEjPvGoLJlcTFo9LRNua43fF0QsOTfRiwajnHuf7srLDy8J+e5LZSLufqpLnWq+A9gtpazdPJlDv64BQCAU0aLbaAZ8/RF/X34d5r37UMUE/H+aCz8+N0aM04xQKKpVvAOoDQl0GnZjxL767fqh0tWs5qcom3NPy78wymocauup25OuaoerlsThCm/tO9cXCmpDIgOufTpiX/dL70alO2r32uslUGaOOpa/0gK+uv9cKkqdTHp/Y8S+bRtKKCuO7sT7b8ZfVoF343YSE6Pn+mrjUnFaaiZ85QX7sa9biuLeu7izsoQ7EnS0UMlR1zLZCnq8BB1VP2OBTIr8vptYMOkLfhg4iGlXXMmP3Xqy+ZG3aNCwZ9Qc5fMJgbT2BbXAUXm+PQdmcGCvmaxGp1ZdQiAQoNZKLyjx7vX4Kcqz8ffUvXTrmxZxMi2RChlyScOouaSnE4FAQEbzbtz4+hz6XfkYpfPWsePrb6vFO0DZtm3MuelWerzwHADmvXtZ8tAjeGyRI1SOR2qGhpc+GUCL9lXvAplCxPBxjXjk1V6nvQSgWCJFWEuOuVAkQnCS1odet4tDu9Yx/aO7+eXTm5n56yMIe6Yy8u+pyHQ65txwE57ySlr2voRb3lnEtS/O4LqXZ3LzW/Np1GEQUvmp7YB6Ay6KHOEGkEcoZhMZDTW07li7mBELpbQzXczj7VbTznQJKcrmdI4fz1PtN9BM3z9k19jt8vHLV9vC0legqiyZ+ahyeqZ4JQMHJ7DppaeqxfvRlG/fTunmLXX5qCeFpayAH5+7PES8A+xc8QfLp76Pz1MlkAUCQa3h2zK5qDp32+nwMXfGPnzeKIuVp0ipx8vT+/OrxTuAKxBkUkEZ6621RzceD4FAcM7EO1RVEWnaOo43Jg3hsmua0aF7MuNvbsnrXw+mfuO6vQts5hJ+f+smDu1aU90WDPjZumwK2QVraXL1lTiKolc6ivHfILYDHyPGSSCWyuky8laEYgmr//gUr9uJUCSmZa9L6XvloyEO5DKRipbGoTzWdgV2XwUigQiVJI6gQEUDhSXkJXYEEdBVd/rrpZ4LRGIJzbqOQGtKZsEPL1N6cDf6xAx6jXuArNa9kMprPqdALkfeqQ2ebeE1XQFkbVogUNV9Quh0+iguCJ+EHWH3ttJ/dfm+SPjLKih/4R0823cT/+RtqPTxEUvLdR5xMxvn/Vj9f5FEhiY9g3avv8YisZw/D5UhAN5olEamInJYoEAmqSp/Y7EiGdybpW+/zcGFoWaRZdu2sfiW++j+yasEGgQQnse+D0K9FnF6Cr6D+WF94vQU9hRWLS5lNdYzeHQDtAZ5nXdc/k0cPGDhif/Nw+8L4rB7ufvJLkz+cit5OVVROGmZWm66vz0JyWcvvFUiU5CY2QJZUMl317aPeIzt0CEEAgFygwFXRQV7pkyl16svIVWfuHmmRCois5GB+5/rjsvhQyAArUFW5wULu82Dx+1HrhBHrRN/BIFQSLvBV7F18W8R+9sMuDJitYO6UHJwJ98+MZpgoErMSRVq1No4ghIBl837m/VvvkPxuvXo69dHn5COvo5pIl6PG3tRAc6iUko2bkKZEE9869aoU1MRHVUxQiKUk6RsQo4tvFQeQKqkI70nNI/oCH4scrGGLE0nbmz6HR6/HZlIjUwUfp7F7GbZvMgLisEgbFpTSHJ6lX9Ken0tAquCBUuXRr3uvhkzybpo6HHv72QoL9hfnRpyLBvn/kCXkbeiT0hHo5fRtV8ay+ZGrujSqLmJKUeVrty3s4LyUif5uTa2bSwmPklJ645JGOMVSE/RQDPb6aHCF3lx4MfCclqpFRhPoqZ5MBjEX1ZB0OlEIJEg1GkRKs5+hJ1EKiIlXcO4G1vi8wbqtKDg9/uwlRfhsJTh93kozolsDLll2e9cet176FOyTvdtx7jAiAn4GDFOEpU+np5j7qXtgCvxuOxIpApU+vioOw56WQp6WWiY3g0pJp7Ylx8SXH+xTEAXtx37N9+wWSQirW9vVElJyHRV5m12q4fKChc5+yqRK8SkZWrRG+XntSu1XK2jQbv+JDVog9/jRiiWRDTxEoiEKPr1wPr7nwStxwhvqQT1mOEIj7MLejRisRCRSBBWR/sI/7XweQDXuk14tlftaPm+mMIVd3/KHz8+RVF2VekbiVxJlxG3Hnbn3lR9XsvelxEwmHi81IfXXfXzDAJ/lJi5NS0eaQThLTIaUV9xMZZPv4NmDdl79x8R76ls+3YkAUmVSDiPBbzIqMf45H2UPvQCAXNNWLjQoEP54F2YN7l56p0+JCSriEuM7Avwb8dmcfPtBxurw0ePRLoMubQhpngFxngF+3dVoDfJEZ+F3fdj8bvceGvZVa/MzkaZmIirooKg34/fE92Mri6oNVLUdTC/OoLN4uHA3gp+/2Y7JYUOshobuOya5iSnqWstq2dKaUDznhezfem0kHZDUibtB199Ui7wTquZf75+ulq8Z7boQZfeN7D22ddYt/QpxHI5jcaNRd+48QmN67CUY849wPIHHyf377nV7WKlkotnTCW1V09Eh5/zGkkcF2e+yLtbLwobRypU0DV1LAbliS0EyUUq5KLoC+TBQDBirvgRbJU1i+6mOCXBTB0ynQ632RzxeFXSmQt1ri1yyedxVac0yOVixt/Ukl1bysJ8KcZc15yViw5VG8DJFCLG3dCCFx5YTFFezXtYJBIw8aUetGyfcErzjb3Ow0aEYhFD5Eqy/CIC/iAOqYDZPgfe49Sgj4Tfase9fjOVn31PoLQcRCLkvbqgu/EKxAnnzqOmLuLd5bCyZ+0c/v78MSRSOd0vuzvqsX6vG5Fajir5xFIWYvz7iAn4GDFOAbFEdtwdB7/Pg0Agiui2nSmX8nyDFL7KL2W/08MNcgHSDz9g5nvvhxzX7dmnaXfn7bgFSiZ/uZV5f9Q4/kplIu59piutOiQiPU49VLffgcVbRLkrB7FQhl6aik6aglh4dh4FdclxFiXGE//Ws1R+/A3u9VWhh9LmjdHdeT3ipPCKALWh1cvo0jeN5fPCdx3EEiENmp5/tdrPJH6LFfuMf2r+X1CM8PXvGH3JrfjGJBAQBVGkpLF8+kdsOmr3vWH7AXQYfjM/2sF7zNzqoNuLJxBEGmGeIhAJUfbvQdBmx+Z2RawVfQRvpf201Io+00jqpRH//gv4DhzEe+AQksw0xJnpiBPiGNH0XN/ducft9rNjU2lIW2GejW8Op7JcdFlD3E4f3frV3dDvdCJRKpGo1VFFvC4rC3th1Y6mrn59pKdQqeJEcTl9LJyVzXcf1ZTdKyl0sGZpHg+/0pO2nZOiLgqpdHEMuv452vS7nNV/foHX7aBV7zFkte2DLu7k8q89Lnu1MZ5cpaNr35v4Y+ilBLxVixo+l4sd335HwYqVjJ0/B01a3ZzHiw9sI/u76SHiHcDncDB12Eiu3bEFff0aJ/T6mi5c2eADfst+CE+gSnwapKnc1vwXTPLMk/pstSFXSWjQ1MC+nRUR+xtnibAVFKBOTkYoEmDMTKXN7f9j9UsvRz5+3Jg6X7ui1ElZiZOKUifxSUoMcYpaF5rj0hpF7ZMpNSEbCgnJap79oB87NpWwelEeepOc1p0S2bymiIV/Hag+rs+QTGb9vidEvENV+cc3n1zOW98OJSH55CME02RSUmQSbhSp+fWNDUw5nJtvjFcw5s7WiJL8IDuxd4Fnfw5mpwD/LbchstsQ//M3roXL8R3IJe6lxxCZzt9Iu5KcHcx4906gar6oNkRf8BGKJegzG6CMj5WO+68TE/AxYpwhKkvyOLB1KbtW/olKH0/7wdeiT8wIcYaVi0Q0Uyl4MisFTyBA5dy5TD9GvAOsePpZMgb0Z68tJUS8A3jcft54YjlvfjOElPTok02bt4zFBZ8zPecp/MGqCZhSrOfWppNprOuNRHR+7EYLBAIkGakYH7+HgNUGQRCqVQi1Jx7GKleIufLWVhzYYyY/t8ZETyQS8OCL3TGYzo/PfNYIBBGlJSMQi/DsOwAeL4HyCjxf/g6AtFkjdM8/TK9x99N2wJU4LGVo41LwyzV8aoMtznD/gSyFFFktzsEivQ71+NH4sw8gFItDSlAdjS4z83R8wrPCESM8eed25/pWzjl+rwdLWT5718+jLG8vaU278siLzfjqg9yI6SsKpYQR4xuj1Z+b+tiqlGQ63HcvK59/IaxPk5FBMBis3knt+86bqJNP3vjtRKmscPHjp+H50sEAfPb6Ol78eADG+OgpGWp9POq2fUlv1oVAwI9MceLPzKMRCIVIFWo8Thute45jw2vvVYv3ozHv2UPhmrV1EvC2imJcpRVs/3xSxH6/x8PB+QtDBLxKYqBX0k20No3A6ilCJJSikcSjl6ackSgXrU7G9fe046k7FxA4JnqrRWsDjq0ryd4bpNVNNwAglkpoe/tt5M6fT+HKVSHHD/z0ozpXMyg4aOXlh5eECOcGTQ3c/1w34hIjC2ZdQjqm1IaU5e0N6+sy6n9hYjAuQUmvQfXo0T8dl8vP1O+2M2daaJnX7v3TefbehRGv5/UE2L+74pQEfBOVnGtlGj64ezHWyhq39vISJ589vYpn3u9LXOvwd3PAHwCBIKwsn7nIyvLNXqb9XkxlhRu9Uc6ll11Cm56l+D77Al9e4Xkr4F32ShZPfr36/z6PC6e1AmNyfcoL9ocd36r3GLTxZ++ZFOP8JSbgY8Q4A5iLcvnuqUuxlOZVt22c+yN9r3yUDkOvQ67ShhyvFYtwVdqY/+rrxw5Vzdo33iL1ntcYckkD1izNp7ykxoAt4A+ycuEhLr26WdTz91tWMuXAoyFtDp+Z97YN57kO29HJGuAKBJAJhShO0jX7dCJUqxCqT90HID5RxZNv9+HQAQvbN5YQl6igZftEjPGK8zrt4HTjtJmxVhayp1EQf5aR+rdditwtxvfW5/gLiwFQDOqNSKNCp1GF7NplO91sLTkUNqYQGBGnR3KcsHehVIo6LZWWN97A5k8/C+tP7dUTdcrJ192OcW7w+3zk7ljF5Bevwu+rmoivmz0JtT6B2yd+x5vPZodM0AG69k1CIXMB58bjQySR0OaO23CUFLHl86+qjcfiWremzxuvMufGW4hr2ZI+b71OUpfOZ/XeivJsUdN9ykucWC3uWgX8ESSy0+O7oNLF0WHItayY9iHJ9VqxY9HbUY/d89sUGl1y8XHH9Ps8EAjgroxcmQCqDATXvPoGjtJSml91JZqMdOQGA3GiesTJz3xJNoDUdAVPvtiW3ycfZOfWctQaKQMGxdM8xcq88XdhaNKEBqNHVu+EqlNTGD31Nyp27yV71iwUJhP1Rw5HnZKCVHP8KA5zmZNXH10atuu9b2cFn725nnue6oJKHZ6KoTEkMv7xH5jxwT0c3L4SqPIr6Tz8JtoPuipqVJNQJESpEnLxhGb0HprJplWFBIPQtksSAgEh9dSPxWoO9+05EUwSMZu3VYQ9G47w4yebeejlnmgOl1yrKHWSvbuChbMPIJWKGDCqPqnpWrQGGS6nj5m/7+OPyXuqzzeXu/jq891cMjaLfv1649mxB1nr6HOjc4nX7aT0YKhJ45Jf3mTkne8y95tnKMndWd3epMsw+lzxEBLZ2S2NF+P8JCbgY8Q4zXjdDhZPfj1EvB9h4Y8v06TL0DABD+B3ubAXFUcd11FQSGl+JfkHrYy7oQUOm5fvPt5U/aLNy7FEPdfmLWV6zlMR+9rFX02pX8e3h4rIcXpJlooZk2gkTS5BFSHsv2q8MircB9lUNhOBQEgb00gM0lRUkvMzJN0Yp8AYpziuU/G/CWdZGbZDeeTOm0981w7s2j2f1X98Ut2/5JdXadZzHD2euhfRSx8gCAai7ignyyQ8VC+Jjw8VY/FX/cHpxCLuSksgoY5mQxKVim5PP0kwGGDbV5OqduIFAuoPu4gBH3+AMu7c5SnGODmsFQX89ur11eL9CDZzMaunPMVFlzzGL5Nq3LHHXFOPTXPeobJwB6Pu+QCbysTKSjtmn58OWiUZculJmVedKAKBAGV8AqOm/obf5UIkk+F1OHBXVtLnrdcRSaVkDOh/3GokpxvhcRZOj915PNOIxBI6DruRA1uX4XHZkRkM+AoKIh5b15xckViC02XF0KQJFbsim5WaWjRnyUOPYi8sZN0bb9L1madof/edyA1nbxdVjJ/dr0ykR4t2jLqtC15LJfu+fYI5i6pMOH0OR5jrvCopCVVSEmm9e57w9SrKXBQcjJzWsWlVIZYKd0QBD6BPzGDsQ19ht5ThdTlQqPWoDAlIpMePMFNppKg0UtIzqzx2Ai43ZrOH+CQlJYWRHeEbNj+197xIIGD7+uhznf27zXg8VT/b8lInbz21nD1HlcBb8k8uvQdncNXtbXA5ffz1W3j0AcAf03Lo9WxfRJ7o5fPOFjaLh9IiO0v+ycV1OIUotZ4GhUyOPjkLm7nm52GrKGLG+3fR7eI7Sa7fCoRCVFoTKl08crXuHH6KGOcTMQEfI8ZpxmEpZ9uy6VH7d6+ZQ1xauOmPTK8nY0C/qJOa+J59Wb2uki1rS9iytphu/dIYe10LfvlqGwCtO0UXp96Am1JXdlh7lqYbjeOe5Olsa7WRXoHHy3pbHv9LNtJdIUV5zC64xVPMlOxHWFr0dXXb1AOPMzDlXobXexyNJCbEzjX2oiIWP/AQO374EUV8PP1++SREvB9hx9JfSG/en6xHHkSvlyKOj+xRIBcK6aBV8nqjdCyHhbdOJMQgESM8gRBWVXISfd96g04PTcRdWYlUo0GREI9cF5uUXIhUFGTjcUWu8nBo5yquuVZLTnYqGo2Azj3V5G2ZyoZ5XwKw+Jc3OTDkHhbaqsT/X2WV1JNLeSwziTjpmfVC8Hu9bPzoE1xlZRH7s4YPo97QIYhPwDDzdJCYokIqE+Fxhzt0J6ep0WjPftqB1pTMuEcmYSsvptVtN7Hy6ecjHtf82qvrNJ7akIgyIZ6OT07kn6tuCuvXN2iAQCCs9iEAWPnMczQcPbJawAfcbgJmC0GvD6FCfkbCo6UaDQ1GjuSfm2+N2N943FjkppNz9o+EtbL2XW2Xq/byqQqNAYXm5H8OfosVX04ett9mIlDIufbmYbzx/Lqw41p3SsRUB8f/45GaET0qwRSvQCQSEgwGWbXwUIh4P8LiObn0HZaFXCGKGrXi8waw+UQkNo3uE3C6cdi9VFa4sFS4kSvF6PRyRGIBM37exYwfa+Z2c2fsp2nrOO59tit9Ln+QH54ZGzKO3VzC/O+e5+a3FxCXGr3ca4z/Luc+TjZGjH8ZwWCQgC+6c7HXFXlVWyyT0f6euxErwsMfpVotaaPGsXldTbmvFQsOkZapRa4Qo9XLaN4muqmJVKgkRdkirL1v2nN8VyQg0uvvq8IKCoqKsB9Tb3S/ZWWIeD/C3Px3yLNvDmuPcfbJnTuPHT9UmdA1uGw0m5ZHLi8FsH3Rl1j9/qji/QgigYA4qZj6Sjn1FTJMUskJifcjSFQq9A3qk9i+HYZGDc9b8e6vqMSzax+2mf/gXLkOX1FJxDrP/2Xc9uhRPwBCgYNM3XdovR8x+71RbPj74+q+bQt/pbswVLTkuDzMKK3EG6glfvc0INNqSenRPWp/1rCLzrp4B9Ab5fzvkY4c+7WSSIXc/lhn9OfIs0NtSCSpQSta33QTaX16h/X3eesNNBkZdR4vrWknxOlG+n/xQXV+uLFpU7o9+zTDJv/Aogcmhp2z/ZvvAfCVlFH5wdcU3XAfxTfeT8l9T+NctoaALXq50JMlc8hg9A3DxZMyIYFWN98QUu7uVDHGRxfFIrEAperMLWoF7A7sf/xD6YPP4lq5DueCZaRtWcQjT7QhPasqWlCplnDpNc24/ZFOaA2nvpDUrX8Gwih16S+5uhl6o5zKCjd/T4u8uw4we+peVJra70WeYEAUf3YiA83lLr55fyP3XTWbp+5cwEM3/MPLjywhP9caIt6PsHNzKcvnHiQxqyVDb3kFsbRm7qfQGLn88e/RJ9TNGDLGf4/YDnyMGKcZuVJDvZbdydm6PGJ/w44Do56rrZ/F+GWLmXfHXRSsqMpnS+3ThzZPvsxXkwrCTLy3rCui/4gsBo6sT3xS9JxSlcTAJVkv8tqmPiHtYlEqliiixB0MUu4LYv7xZ9rfcxcCoRCH18zsQ69Fvc7fB98iS9MFWS0lemKcWRwlJax59Y3q/0t1WpzWyLV/AZy2CuzWyItK/1X8JWWUvfAO3p01k0eBQo7pxUeQNm2IIEpqyX+NuIzotvsqXVUkzo7l0yL2+30eBD43ECpK55ZZGBWnIy5SWYPThFSjocdzz5D9519hizKK+HjqDw8vWXY2EAuhYys1n37bl/mLS9i0uogmLePoOyyz1uf72UKdksLwn3/AvG8/+//4E5lBT4NRI1GnpiDThqeFRUOlNdGg80CcLSrI6D8AtzGB3V4/m+wuCtwues+ZzZYHJ5I3d171OY6SEvzlZsqffgPvvgPV7f6iEsqfewvjMw+g6NbxdH5cNOlpjJ0/h82ffsHWr74m4PPR5PJxdLj/ntNuuqkzymjTOZFNq4vC+gaNboDeeOYWb/wVZqzf/hrSFpg9h5Qtm5k4YTyC5t0QScToTXJEp8kfJy5ByYMvdOedZ1aGRJwMHFmf9t2qTNqCgSAeV/RFU7fTh1whIiFZFdEwMyVDgy5BhUB88lLHF/Bi85YiFAjRSBKimiZ6vX5m/76HhbMOhLQnJqv4Z8a+iOcAzJ6ylx4DMmjTfzwN2vXHVlGMUCRCpU9AY0iMWL0oRgyICfgYMU47crWeQdc/x9ePDMfvDd1hathhEPqE6DsVIrGYhHZtufiPabgrzPj9QRYsKOPN9wqwRDCOEYmFjL2+OUrV8XeL0lVtubnpj/y4907svqqQtOOVjxMSZM2rr9N47Bg0aan4gp7qcyNh85XgC3hiAv4cEvB6cZbURGqUrNlA1k2DydsdHg4JkNq0FzLl+bkLfi4IuN1YvvstRLwjESMZ1p/KkmIcBw8gjzOhTkn5zxvvqXRxtO53OZsXTA7rG3T9c0ik0c3UlFoTTnG4KHEHg5zZ/fcqDE0aM27RfObddgelW7eCQEC9QQPp9947aOudHaO0o/EVFmP/cx7OpasQSCQMGjWY4U+0R2QynDbRdLK47BZc9koEAgEKnR5Ti+boMusR8PkQyWQnJN6PIJUrkMoVHFJ5eHJffshCslio5IGPP8Z3zTUUHV7Ibn71BHz5hSHi/WgqP/0OaZOGiIz6k/mIUdGkp9P1mSdpc/utBIOgiDMhlp3+VAaNVsZtD3Xih082sXzBIQL+IBKpkMEXN2DUFU2Ryc/cdN2zNXLani+vEN54h8Sv30accHrrjktlIlp3TOStb4eQl2PF6fBSr4EenUGGSlM1n1HrpHTpk8Zfv+2JOEafIZnojQomvtidZ+9dhM1S48Wh0Um5/7lu6E0nH+5f6sxmQcFHrC35FbFQRt/k/9ExfiwGWXhVAXOZi1m/h0cLSGUinPbo6Q9Oh5dAIIhYIkefkH7cssQxYhwhJuBjxDgDmFIa8r8PlrHwx1fYt34+CrWeLqNuo3GnwdU7U7WhMJlQmEy4XD7yS0ojineAngMz6iTeARRiLR3jx9JI2xObrxShQIxEnIJRXEK5L3yVWyEUICkpwVFURMBb9WJUivW0NAylwLEj4jXaGEeiEJ/4ZO5UcFdWYi8qonj9BsQyOXFtWqFKTkYSIRWhNvxWG7g9IJMi0pxa+aVziUynI61fH3b9VCWqDi5YSJdXn2ad2oDTFlrXWCpX0aT7BAzxJ1br2lpeiMNaDoEgCq0BjTH5jJRzOhcEKipxzF9a0yASobjnRpZ88B77bv2jullbrx6jZkzD2KwZYsl/c5dEodbT76rHScpqyfIp72MzFxOf0ZQB1zxFauMO+LwuErNaUpS9Nezc9mMfYI5PBoQ+25ooZSjOgnmcWC4ntUd3xsz7G7e5EoFIhNxkRK7Xn/FrH4uvsJiSe54iYK5xZq98/yskzZdieuJeOEclsAJ+P2X5+5j3zbPs27gAY3J9Rl7zOisfe5bcw7vjhiZNGPDxByR36YxEeWJiyeLz897B4rAoMF8Q3nfBxJdfoqhvf+JatSKhXTs8C1ZEHctfUEzQ6TrxD1kHRGLxWVmsM8YruPnBDoy9oSVulw+FUoLeKEcqO7PPl+CxoX1hB5yZ60qkIuKTVFGjSyQSEUMva8iSf3LCHOtT62lo2rpqLpVeX8crnw9k364KcveZyWxooH4TfdTSe3WhxJnNyxu7YvHWmMtN3n8fK4u/57bGU4lXhwptj9uPK0KZ1X07Kxg0ugFrl+VHvE67rkmoNGfW8yPGv5OYgI8R4zRSWXKI7M1L2LN2Dtq4VLqOuo2+VzyCSCJFrY+eox4NuVzM2OtbsGl1UcjqMkDXfmkkJp+Y0BQJxBjl6RipevkEg0Huzkjg+f0FHD2FEgA3yIXseOY5NOnpCA/n+omFUvql3MGSwi9w+a0hY6vERromTkAoODOTDUdxMX6PB7FCgeKweZCjpIS1r7/J2jfe4kh+gVAiYdDnn9Lo0ovrVL7Hb7Xj3bMf67e/4ssvRJyeivbacUgaZCBUXXiRBBKViq6PP8be36fi93ggGGTe1bcxfupPLJnyFvs2zCUYDJLZsjedRj+CJi6duIS6Tbz9Xg95e9Yz/b27sBwuK6c2JDL89reo16LbaStfdS4Jen3grZmISXt2YsNvk9k344+Q4yw5OUwdchEDZ8xFlVxVj1prkKFQ/jcmY/bCQir3Z5O3bDlxrVtx9bNTEUkliGWKoxYptYx9+GvmTnqWXav/IhgIIFfr6XX5RGytBrC1IlS8C4HrU+LQiM/egogyIQFlQsJZu96xBDxebFP+ChHvR/Bu3413f05EkzabxUNZiYM1S/LweQN06plKXLISnf70hVqbi3OZ9MiwaqPCgWMeZ+aIMTiKa0RNxa5d/D5wCFeuWk5ixw4nNL7V52efM/LitCMQwJWcQpcnH6f1zTehTIjHUVsus1QCpxAqfb4gV0hISj27zxBZy+ipMJKmDRFozt17MCFZxYsfD+CPybtYufAQYomQAcPr039EFqbD7y2BQFC9ENC1z6nnjPsCHublvxci3o+QY1vHrtIVyP0J1WXuAGQyEQqVOGy3PT/Xit4oJzlNTcGh0CoDMrmIS65qfkajK2L8exEEj7v09t/CYrGg0+morKxEexJhYTH+u5QXZPPdExeHlAMBGHbbG7TodTFS+cm9BIPBIMUFdhb8lc3apfkolBJGXN6YJq3iTktenDcQoNDp4s+CUnIQkkyAfn432a+8yt5J39Dnrdfxu9w0v+Zq1KkpBIIBCh07+WX/A2yr+BsQ0NY0msuyXiFR0ei078Q6S0vJnb+Qlc+9gOXAAeJataTnyy+S0KE9B+fNZ8YlYyKed/Wm9cS3blXr2AGPF8fs+VR+OCmsT//AbSj790RwFsXE6cLv8VCyeQvz77ybwlWrEQiFNBo7hp5vv44v4MXvC4BIhVylRWeo+99Qad5ePr+/f5hJo0Ao5MbX55CYGW6UeKHhKy2n5K4nCJRXRSvIHriZH0ePxueKvLvXf/J0luwyEZ+spDDPxtX/a1M9sfy3Yj10iD8uHUvhmrXVbSKplNHTp5Levy+iYwzg3E4bjspSfB43UqUajSGJikCQWaVm/i6z4ggEaKaUc12KiXSZFNk5Dhk/m/hLyym+47GIAh5A3rMzxsfuDvFdsFa6mfbDTmZODq0f3aVPKjfc2/60vBd8XjfzvnmWtbOqDEsTM1vQKm0Iy+5/LOLxGYMGMuKXn04oguGA080Dew5F7X8kPZ72akW1WZyvsJiimx4Eb7hJrHLkYHS3TEB4zN+e3eahrNjJigUHcdi8dO6dSkqGBoPpwl9sPF0EbHask2dg+2VGSLtAJiXurWeQNsw6R3dWg8fjx1bpQSAArUF2RtNKzO58XtjQCbMn8q55a+NwLtN/Q2pajfGr3xdg+o87mfzltrDjsxrrufPRNsyespel84rwuP20757M+JtakZymRiT+7zzvYhyfuurQ2LJPjBinAbfDyj+Tng4T7wCzPnuIeq16YDxJEyKBQEBiipqx17Vg2JjGiESC6hyx04FEKCRdpWS8QsSOGX9SumIFC377HYFQSJcnHsNrd7D8yac5uHARw374FkVcHCmq5tyUNglPshOhRopMpEEhDt/ttlnc2KxeCAZRaaQhK9Z1wWOzse7t91j90svVbQUrV/Frv4Fcvmwxq158Jeq5mz76hH4fvIuoll2ZQHkFli9+jNhX+fE3yNq2QJxw4ZXFE0mlJHXswCV/zqgKDxYKkJtMJ5WregS/z8u6WV9HrLAQDARYPuV9ht/+FlL5hS1eRSYD2puuwPzaRwD4/P6o4h3AlpPD3h0CegzMYNoPOyktcjDxxR4ntDByIeFzuVj14ish4h2qFo2mj76E63ZuRZdVNeEPBoP4yyoQud1oxGqESakID5eIM4lgfKKJi0x6AgSRC4Vndef9vEEggFo+t0Ai5lhb+kM5ljDxDrBqUR5deqfRY2DdHeGj4bJXsm/jwur/m1IbUrRkddTjC1euwmuzn5CAV4uEqEVCbP5w1wMBkKqUhzi9i0xGTM9NpOyp10NEvKRpQzSXjw4X71YPc6bv4+fPa1I4Zk/ZS+MWJu57rhvGuJiIBxCqVajHDEfWriXWydMJVFQibd0c9cVDESedeOTgmUAqFWGMP1u/LyFiYfQ5lhg5e7ZWhAh4kVhI/xH1sVo8/D11L35f1d5oViM9196WzLTXRpBYvx13TbwMkVhKSv0MEtJim4QxTp6YgI8R4zTgsJazd91cADTGZLpf8xTx9dviC/jJ3Tifgr0bMSZlntI1RGIhWv2ZqwOsT02l+dBBlKal0GTkcARCIdu//b46dDhnzj/YCwvxezwcWryETR99QjAQpOWN11Fv0EBIqxHwgUCQvAMWvnh7HTs3V9VabtDUwE0PdCCjvg5xHVecHUVFrHk1sut92Zat2PLyop5bmZ1NwOOpXcCbLQQDkYOQgg5n1a7YBSjgj3DES+F04HU7KNi3KWp/YfZWPC77BS/gBQIB8s7t0E/8H5Yvf0KMAKlWi8cSuWSatlFjzPMslJc6Uaml7NlWTnmJ84IW8F67HevBQ+z88Scq9u4lc+gQ0vv2QZuRgaOoiO2Tvol4nt/jIX/ZcnRZWfgtVlyr1mP5ejKBsgoEMhnKYf3RjB1ZHRIuFgowSf/b0xChXotySF9sP0yJ2K8aNhDBUZ4Abo+PAwcsJKerKThoCzv+j5930apTItoTXCw9FpFIikKt54hrhstuISEtOerxyqQkhJIT+10aJGJuS42juKiEFAJUBmGOSM4+p5vBRi26YxY2BBIxslbNSPziDTy79xMoq0DarBGixDhEBn3Y+CVFjhDxfoTd28pYNOsAo69sGrWU2X8NkU6LqH0rpE0aEvR6ESgV1YttFyJ+nxdbRRFOawUiiRSlxoRKX7d3udqropfxOqbmPxOxv63serJzwl3v9UY5429sydBLG1JZbsNlK6IybwN/f3g/9spSKksOsntVVZTD1S9MA2KGdTFOnv/2mzNGjNNEMOCHYJDmPS9l0NjHsK/fhvjraXhTkmjedwh+nQp/MIjoPDf68lhtTB91SdT+8p272PbNd2TP/LO6LX/ZMuJateKSv2agSavKPyspsPPkHfNxOmrywfbtrOCpO+bz2peDScmom2la5f5sgn4/Mr2ehlddiiwljsqNu8me+iclmzaT1LkT+6bPiHhuev9+iGsxsrN5y7AkO6l852KUQj3yvZUEv/w7NJRV+B/cEYyCWCrHmFI/qpu9ISnzX5EDDyDSqFEO6IWsbUv8Hg+dJj7IsiefCjvO2LQpZr8Bl7McpUqC21X1915w0EpW43NjPHaq+FwusmfN5s/LryR4uB77rp8mo0pKYtyi+SAU1B6RkF9A0B/AuWQ1le99Ud0edLuxT52F72A+hoduR6SL7T4BCEQiVBf1x7VweZXr91EoendFnFFlnlbp85Hr8jCr1IKnlZJBLTuhK/fy1TOrsFtrdqNtVk9VikwtuCoqqvwIjMaoKU8KjZ6uo/7HlDdvAeDA1qV0vu1zNn3wCWH1TIGOD02k3CHHXmA7bLx2/Omlt7KS5DVryXn2BXa7nEi0Wi65/HISh12EKdGAKkIJLYFEjDgpAXHS8X0LFs7Kjtr399S99LkoM7YLfwxClQK4sH8mTpuZ7cumM/+7F/A4qxa54tObcPH9HxOf3vS4aX7ebXvoqOrPasWv5DlDQ+I7mMZi3Z9Epx7hTvQAMoWYRIUambiCT1+8BJ/HGXaMQChCY0g8yU8XI0YVMQEfI8ZpQKbUktWmD0NGT6Ty3mcIWG1US9dfZyB+9G7MHdpgUp3fL0axQlEVrhnFGkOq03Fw/oKw9tItW9g3/Q/a3H4bgUCQRbMPhIj3I3g9Af78dTfX3tUWqbRmchYMBCAQQCAWY7d68Lj9yBRipBoNrR+9g/irerLE9x0VvrXUG96Oi56awrZHPqT7U0+zf+afYbWcpVotTcaNifqirnDn8c3um9haMbu6Lc3Uiltf+gTh498SqKhEGGdEqP/viYyg34+/pAzXmk14du9D2rQh8g6tEcXH0WXkrWxZ9FvEv48el92DTHHhuvcfi8vhI78Mpv+wn25dhtPhYTub3n23Wrym9u5N6+fe5q23DqAzyvD5Ang9VcLJcAGLAntBAbMmXFMt3qvbCwuZd+fdDP7iU/QNG2LeG14yCSClezf85Wask8JLywG4124iUG7+1wt4p8NLeYmTVYsOUVHmon33ZOo10EcUjOJ4E3GvPoFrw1Ycc5cglMtQjR6CpH49RHodlV4fXxWUstRcs+u3AUhXS/jfKz14446F1e3tuiZXp1g5bWYspXnsWD4Dr9tF405DkHglLL77IXwOB50enkhGvz4o4iOHSac370rLXpeydckUAj4vW9ZNp8+Hb7Hk7okEfDXP90ZXTqDC0JYXr5uDRCpkxLjGXDSmUa1RKMFAgJy//8FWUUTbZ++jMHcrcpWe+IQMzDOmkXrVVaA7tfKWlRWRDfIAHHYvTruXH37fg9PupWvfdFLrxXLj/w3kbl/J7M8eCWkrObiL7568lBtfn1NrqTa/1Ybt52n4D+bzv0fe4UDSfla4pyAVKOgtu4IkWwrrrSoSU2t/1ykUOq548DtKcnawc9PfHNi2jLTGHWnd9TLiUhsjDpy+NMgY/01iJnbHEDOxi3GyWPMP4Xz109D60UeQSpB+/CqGlCTEwvN3F95dWcmsCdew/8+/wvqUiYkM+vyTqDv0ca1bM3b+HJxueOuFjezZFrlefEqGhmfe64vOIMdfacF3qAD7zLkEnU4kvbpTqk7i44/3YEpUcuk1TbDIV/BJ3sUhY4gFMu5I/oUsTRcqtu/gn1v+Vy0okjp1YtCXnxLXokVI6OkRnD4r3+6+mTWl4QIjRdmc2ytfxv/2b8S98hjSFk3+NeXR6kIwGMS7ex+lD71A0FUz+RUoFcS99iSkJbBn3T/8+dH9eN1VOwsiiYzBNzxP8x6jkav+Hc9Mvy/AykWHeO+5VdVt/YemMnpUPOUHSwiIZOzc5+WvPwoJAnc+3pkfP93CoQMW9EY5L3064II1stv9+xRmjrk8av9NOfso2bQ54nMgoX07LvlzBlKXl+JbH4o6huHRu1D27X5a7vd8xOXwsXxBLp++Fhqtkpap4dHXehOXGP1vI+B0glCI8Kha41ttTp7en4+A8Ipeo1Vqsj/Ywfb1JcgVYl79YiBJaRqc1gpWTP+IFVM/CDk+o1k3ug+4FYQCDmWvw2zOp17LbmS27oUuPi3seeewlGMpzWP3mr8RisU06zwCoRuK123AZbEib9iazTu8/P7rIYJHrflcfXtrho1pHDVE3XrwEEU7N7Ns/scc2l3jpyAUS7jompfIbNkDfb1TM09bsySPN55YHrGvQ/cU4pOUzJ5S875u0tLEvc/GcuMvZGzmEn54egylh8I9IgBG3vUerfuOBcAbcOMLuJCK1IgOV8/xl5spufcp/EUlAIjTUxC0a4TA68e3aivi1GRUj96H0hQ5ijAYCGDet5/Vr7zGvmnTESsUNLn2SppNuIL9M/9k09sfYi8sxNisGb1fe5mUHt2RGy7MaK0YZ4aYiV2MGGcZqV+AJZJ4B/B4CR7Mx5WUgPo8DsuW6XT0//A9LLkHKd2ypbpdbjJx6ayZrH3zrVrPdZaWsvmr79HrB0U9TmeQIRYL8VdasXzzK44/51b3uVasQ5Geym23/o/HH13PhpWF3Pp0I+rru7HfVlMD2Bd084v5ESZmLCC9X18uX7IAV3kFApEIhcmIIi56rpvVW8za0l8j9uU7tuNqYSTt09cQJcb/p8Q7gL+sgrLn3g4R71DlB1D+/NuY3nyKzCbdufnN+VgrCgkGA+ji0lDpE5DILtyc72MpL3Py+Ruh4mv+7DzWry7j6jtao9XJkGrNXHV7Aiq1lMlfbuXQAQsGk5zHXu99wYp3AI/VWmu/1+4grXcvRk+fwsL7HqRy/35EMhnNr72ark8+jiopCV9+Ya2RPP/2yJbyMiefvR6eanLogJUp321n7HXN0RkUEcWt8Ji0H18gSL7bw2OZSbgDQSRCAcUeL9NKzJR7/Sz3uBg5tB5yuZgJt7Ym4XBp0fKC7DDxDpC7YwVNul7E9uUzyNtVJZy3LvkNhdrA1c9PJT6jScjxSq0RpdZIUv3Qih6Ghg3ZuaWUZ+9ZiN8f/nue9sNOuvVLj/pd8Hlc7Nu3JES8AwR8Xv6a9Ag3vjw74nknQv2mBlLracjLCf2blkiFDLq4Pu8+uzKkfdfWMpbNzWX4uMYIz+OFdqj6nvq9XuR6fcSF6v8qAZ8nqngHOLRrDY17XkShcydzDr1JuTuXJrp+9Ei6njh5Ji7ECJs0rBbwvoP5cLDGjV42YhAKfXRDYvP+/fzYqSvuyppUvKDTy7pX3mTHdz9Ut5Xv2MG0kRczZNJXNLvqSoQR0kVixKiNmICPEeN0EcFJNwSXG8lpmBRYygooyd1BzrYV6BMyyGrdG40xCbFUhtNXicNXiUAgQC2JRyo8cVGlrVePy+b8RWX2AUq3bkNbLwNj0yZo0tPJHDKEnT/8FPG8bs88yd/X3kjF7t30/eEy1iwrinjcxROaotJI8ezMDRHvRwgczEO1YSVtO6exYVUxP72by/VvPMF+2/CQ4wocO7B7y9FKE1ElJaFKSqrT53P5rQSJ/ruySCyI6zjWv41ApYVAaeTICX9RCY79OUy59lrUqSl0fvxREtq0Pm0meecTlgp3xBQQc7mL959fzatfDmLUlU2xmF2Ul7i4eEIzjHFy4pNUF7R4B0ju1Clqn65+fWR6HTKdjgajRpLUqSMeux2hWIIqMaHac0Ko0yLr2h73inARK1CrEKf8u79fG1YWRFu7YPHfObTplIRaK6VxCxMSae0Td4vPzw67i6VmW/VTK00m4Y60BD45VII3GKRD92T69UxFqaoKyw34faybPSnqmOv+/oa2A66oFvAATlsF09+9g8se+pKA34dUrkJtSKx1EXPfjvKI4h3AWlmVChWNgAQ2L/0tYl8w4CdnxwoSG7aMen5dMMUreez13vz5y27m/bkfj8tP606JjJ7QlF+/3BZWsxtg1u976Dko47wNpXeUllK4ag3r3nobr9VGozGX0uTysWjr1TvXt3bOCAaD2AsK8LvdBAXQtNsIdq6YGfHY+IxmbC7/k892jq9u22tZzty8d3i07QrseUloBw2B5avBF/r3K1AqkPftjiBKCTuv08nql14NEe8IBNQb2J+pw0dFPGfRAxPJ6N8XTXrM0C7GiRET8DFinCZEahWieCP+ksgCSN0wE9lRK+XBQABXeTkCkajOIVTm4lx+eGYc5qKc6jahWMIVT/+MKE3F7zmPsq1iNmKhjO4J13BRxmPEyU/8xX5EEKd06xrSnt6/L/Ft21KycWNIu75RIxTx8RSsqgo5tiz5g0vGDmLabwdDJrIjLm9M/aZGAOyz50e9fnDREvpNuIsNq4qxmN2IXZENY4JhAaXHRyHSIhJI8AfDy6EBGGVpJzzmvwZv+IQ2pLuykopdu6jYtYuD8xfQ9ekn6XD/vadUnu5C5EjlAq1ejlYvJ7OR/tze0GlEmZxEs6uvYsd334d2CAQM+PB91MnJ2AsLKd+1m92//IZEpaLpleORajU1Al6lRP+/ayk7VFC1g3VkCIWcuBceRmQyns2PdNaxWTxR+7yeAMEgvDRxCW9MGkxyWnRDT28gwF9llSw2h7rNH3J7+ehQMROSTOxzuDCppEiOercEAn6c1sjvIQC3w4JEFr7QVHRgG8U5O/jt1evRmFLof9XjNGjfH5FUQ2W5C68ngEwuwhCnQCgU1GpGqlCJa12cEIiF1QZjkbCZIy8AnyhxiUquvK0VI8Y3JhgEhULMpPc3smNzacTjnQ5fSCrA+YC1vJDygmyKD2xDIdcjDgqxFxZRvn07hWvWsP6d9xi/dBG6+ue+XvvZxllayr4//mT5U89gO3QIZWIiHR9+gMxrujH7uydCooDEUgVZbXvy2M5mYeO4A3bm5r2Dcs297N9WxnUPT0Tw/ff4cg5VndukIYErJ2ARqzk2vq/C68Pi8+P0Bsi4/34ERiPb3n6n2ijSkpMb9f5dZWW4yitiAj7GCRMT8DFinCZEJgPaO2+g4uk3wvpkIwchOMq0yZKby67Jv7Lzx58Ry2S0vfN20gf0Q50cvUyP22Hjn6+fDhHvACKRGLfaybubB+IJVOUlewMuFhV+xjbzHB5qvRijvO4vB19pOb4DB3Fv2YE4KQFZ2xaI4owIJBI0qalcMusPSvbmUZlXiCN3P0qNjAbDL8JRXFI9xsaXn6PZLaU8/fR17M/xEAgEadU9i0KdjD+dNgbIBYid0Z2sgx4PEknNzk+kCMEkRRNU4hMXAlppEj2TbmBRwadhffU1XdFK/927g0dT6SmkyLGbjWUzUIp1DNBeBxJxRCEvkEnxHmNstvK5F2g24Yp/nYDXGeSo1BLstvBFHo1OekbLOZ5rFEYjvV9/hZQe3VnzymvYCwpI7NSR3q+9QlyrltjyC5h11TUcXLCw+pxNH31M33ffpvHYy6r/FsSJ8cS9+gS+/EI8e7IRJ8YjaZiJKM4UdQfr30KbTolM+XZHxL4GTQ3k5VjweQMs/TuHSy+pWjAUGfVhx1b4/MwqqwxrByjz+hEAI+L0IeIdQCyR0azHKPaunxfx3Hote1Cwb2PEvoC/6rtvLctn+rt3MOLO9zhY0prJX2zD4/aj1csYd0MLuvRJIz1Lh1Yvw2ION4sbPrYxBlP0CDCZSktCvWYU51T9nBQaIy37XU9i/S74fS6S0qO/C08UiUSEKb5mwaJD9xQW/50T8di2XZJQqk9/+bRgMIgn4EQslFXnW9eGy2/D6TUTMLuZ/MLVVBTUOOor1AZGffI2S25+gIpdu7AXFLD6lVfp9947iOX/nlSm4+Fzudj82Rcse/zJ6jZHURGL73+INnf+j3b9JrBhftVCpFJrYszDX1EoPkCAyJEhObZ1dNZL2bC2hPwCB5defA31kiUgELBpr4O/3tnHM+9mhJxzyOXhtZxC8txV7wohUvrecjt9e/diwcWX4ne5kB7n/SiS/XvfJzHOHDEBHyPGaUIgECBv0xzTW89g/epnvHuyEcUbUV1xMdIOrZFqq3ITLTm5/NKnP5acmglEwapVpPfry0U/fBsi4h2lpRAIIDeZcFjK2LN2Tth1m/W9mAWVn1SL96MpdR1gd+UiusqvqtNn8BUWU/rIi/gLimsaJWJMzz2ErHUzbHY/e3OC/P6DmZLCAFmN2jJ2ZAskJi1yvx+xXF7t0r3js4/Y8fnHGBo1QiAW4+j0E6+XVL2oFlRYeblvD1i4ItJtIOrUnvWbqupuJySrsIr3hPYLJFzb+Et0shMX2zKRkpEZT+MPeFle9E31y7yFfjDXNv4crfT45YnOFV6PC0tpHtuXzaDs0G4yW/cms1XPWl11o2F25/PZzivYXbm4uq1Iv5NLrhyF85tpYcdLRw1m5RefhzYGgxStWYehUaMTvv75jMEk5/ZHO/HGE8tDIkgEQrjjsc7nbXjt6UKVmEibW2+mwagRBP1+JEolcqORYDDItknf1oh3gYAuzz1OXPf27NuxiIM/bKdFr0swpTVCrY9HZDIgMhmQtQrf8ToduP0O3H47cpEaqej8+Z0kpWlo3MLE7m1lIe0CYVUK0aT3NgJwYF8lFZ8vwbdzF9obrqhaLNXUuFt7AkFcgehRRs5AgARZZLGZ2bIHuoR0KosPhrRLZAra9BvPr69eF3aOQm0g4A9dtFr4/Qt0GDupOhzeYnbzxVvr8Xr8DLm0IU++04fXH11GcUGVQ75AAP2GZTFodANE4ugLNSpdHIOuf44fnhlLeotetBv+LNN+LmTb14XIFWIGjPAxTOs4IykpjVoYScnQkJ8bmhsvlYkYc21z5IrTNzUOBP2UuXJYXfIzuyoXkiBvSN+U/xEny0IuDncyDwaDFDv3MC3nKZIFjan8aWuIeIeqdIc/v3mYAc89wpzLrwdgxw8/0fWpJ6pLuV6o+H0+nCUlCAQCFPHxteaG2wsLWfX8ixH7Nn/8Gdds20jzPhcjkSnRGBIRSIzsKvo56niH7Ju5sVM8AiEU5dn5+MOdIf2DLq6P1lAjtks9Pp7dn0/5UaH2AWC+y4e2WSuyRo8me9o0RFIJMp0uNLT+MMldu6CIr1t9+hgxjiYm4GPEOI0IFQrkLZogeeYBcLtBJEZkqCmF4/d62fzZFyHi/QgHFyykZOMm1MnJ2PLyyf5rFhs/+gS/203TK6+g8fgxqA1JWMvyQ85La9sZibQpHRLuBwSUsZmFlpcpdR0AYE3JZDrGj0MsrL1sScDuwPzxN6HiHcDro/yZN4n79DXmzivj58+3VndtWFXIxtWFPPhiD9p2SKLjwxNZ+ezzNecGg1Ts3k3Dq65ivUwBrqod3DKvn+zkJNKbNMC3a1/I5QRKBf7BQ1n46CYkUiF3PN4JeUourT0jKHMfoIG2O4PS7iVe3qDWz1Mbelky4xu+y7CMx3D6zMhEajSSBFSS89cN1u/zcGDLUn595TqCgaoJw7al01BqTVz9/FTi0uouogPBACuKvw8R7wArzD+S1b4tnVNux/7tFHz5RYjTkhEP68eBHVvZ/fuUsLEE4n+f+Y5ILKRVx0Re/XIQf/26m4PZFuo11DNsTCMSUlRRnbX/bRwbEeQoKmLDu+9X/7/7q89TLD7Ewg9vqW7bMPd7str0YeRd756xWsdOn5Vi525mHXyFAsdO0lStGZr+EAmKhshE0Q2mzhZ6o5z7nuvGP9P38ffUvThsXpq0imPE5Y2Z/2c2ZSVVi631M+QE95bgzy+i4oV30N97M8rBfRAcFi0yoQC5UBBVxKfJoj/TtXGpXPXs76yY9gGbF/yC3+ehYfsB9B77IHvXz8fnCY+A6jHmXtbP+S6kzWYuRqkK37H8ddJ2OvdOIyNLx3Mf9MNc4cLl9GEwytEa5ChVx9/FTm7Yhiuf+QWnJ4kXH96Mz1v1fnA6fMz8ZQ+b1xbx6Ou9T7srvCleyeNv9Oav33Yz749sPB4/7bomccUtrY5bHuxEOWTfwmubeuPyVy0WbOcfFhV8wo1Nv6OD6TIkotAd81LXAV7a2A27r5z7UmYybevnkYbFVlGE2KRBJJPhd7shcJ7F/Z8E5eUVlOXkUjh/Prk//Uz9USNpcd01aDMyIh7vKi2r3jA4lqDfj7OkjMyePQj4/TgsZdgtpWQpuiFEFLILrxabaKUbTZy0ARqDkDsf78IHL6wKWbzNqK/lkgnNQsrf5rk9IeL9aOa4Azx41x1kT5vG2jfeZuh3k5g5dnzV7+owysREhkz66l/pIxPjzBMT8DFinAFEGjVowicCztLS8NzSo9j8yWfEtWrFzHHjKVhR45C7/Mmn2PzpZ4z65Wt+fGtCdY3m5AbtUSp6sujNnezfVZWrlVE/k3H3zGCB+F522+ajlsQjrEPIXqDSinvVhoh9Qbcbb/ZB5kzND+8LwhdvruOlTwfS9o7/ITcYWPXCSzhLS5FqNDS/8w4U19/Ad87QCcbbNjcvP3wXhuVrsM/8h6DLjaxrewRDh/DLjGJGjGtMn4syiU9SIRbHc0vzn/D4nSjEGiQnYc53LHKRGvkFVLfcWl7E1DdvqRbvR3BYyvjjg/u45O6P0KdEnugci8VTyPy89yL2/Vj8EK76LzPkzacJ+vz4fT7m3Hk3e6ZEEO9CIYkd2gPgD/iw+UoRIEQjufAd/GVyMfUa6Lnx/g54XH5kctFxDcdOlUpPIYWOXWwonYpCrKNj/FgM0jSUEv0ZvW5dCfgDeCxVkTFykwlNmwbM+/SdsOOyNy1i37p5tB145Wm/B1/Aw9byv/j0KBOqPMdWVpf8zB0tptLaOLxOz7sj2MwllOXtZcui3xCJxLTuNw5DUiZK7alNqo1xCsZc15xufdPJP2ghZ28ln7+5jsryqgm8VCaiS3stnmk1u3yWL39C1qE14oSqHTmDWMzIOD2/FleEjZ8gEZMUZff9CPqEdAZd9yw9Lr2HIEHkSi0ypQalPgG1MZFlU96hsuQQCRnN6DziFgqzt5C7LTQqSiAUIRCEX8dh8+KwV+3WG+IUGE5CZMsUGhLrd+XdZ1dWi/ejyd1vIWef+YyUdYtLVHLFLa0YMe5wbrxKUqdFhxPB6inh613XVYv3IwQJMmnXjTTs1J04UU3euj/gY2nhF9h9Vf4Ffm9kn5YjOO1mJEolfrebxuPHIb9AhaDd52e/3ckvZXYK5TrSRo1hxKhRFL3/Ab/1H8zYBf+E5YiXFjvwBmuXMFKVEktpPlsW/crGeT8RDAZo0mU0Twxcxft5F2N25zEq/h1M1l6smGynwO5H07+cVh0Tefu7oaxbkY+5zEWbTkmkZmrD/g7z3NG9LhyBANLUVJpfezWtb7sVQ4P6XLt9Mwdmz6F8xw5Se/ciuUvnqIsTMWIcj5iAjxHjPEKZnEThmrUh4v0ItkOHyJ3+Nw3a9GPvhnkIhEK6Xf4qLz64GrerRtTl7q/knQetPPzhO+y3d6Jv8m11mtAGfd6oZZ8AfBWVUQVMRZkLa6UbY8N42t55O40uvQSf00FQKuNXJMy1OMN8312BIJ84vTxxyVDiB/SEQACBRk1QLObau1OQSsWYK1zkHbAQDFblHhtMced9eZ8zRXne3ura68eSv2cdpbu24TfbMTU/frhygAAOnzlq/0HbBoTpOgQCAWKg8+OPkD17Nj6HI+S4Pm++jjIpiVLXAZYUfM7qkp8RC6T0Sb6NDvFjMMgimw/WhYDPh1Ac/ooKBIJUlDqx27yIxQI0Ohka3ZnLIZRKRSG7LmcKszufT3eMZ49lSXXbH7nPcXG95+mXcsd5ER0iNxrIHDaU7ZO+JWvkMHZs+CvqsatnfkajToNR6U5veGilp4Bv9twc1h4kwKTdN/JUu/V19vywVRTzx4f3UZ6/l6zWfQCY8d7dpDRuz8Brnz7lexeJhJgSFezdWc6s3/dUVzaIS1Ry151NkPz0A76jnrkBq42gzQGHs3jEQgFDTVpsfj9zyizVe4ZZcikP1EvCJDn+FE4slaONS6n+f1mxg+XzK9mzvR6dR39OWj0VWp2I31+7geLc8Lz9xp1HsG6VPaxdIACp9NS9DJx2L1vWRjesW7XwEO26nL58+KORSEQY489c1Qibr5SD9k0R+3xBN/n2HcTJawS8w1fBxrIZ1f8PyIJI5Eq8LkekIdDqk3CZzSji4uj6xGNIFOdPGkld8QYCLDXb+Cy/xlSw3OtnM3Db3feg2raNfTNm0ub226oXhUsK7Tx7z0JGjkrA1Lw5Zdu3h42rTktDatDx43OXU5ZXU9539R8fsHPFVO5/ag47nRvYOS2D7/+o6d+ytpj4JCVPv9uXEeOahI17NLVFwCiFQuIyM2j61RfVZf4U8fG0vf22uvxYYsQ4LjEBHyPGWUQRH0+L665hZZS8rda33szyJ5+Jev6un35hxPSfMZccRJeQyaqlthDxfgSfN8DiaWauu/p7EhR1C60WKJUITQYCZeG7PQDiBlmUlayPev6RsGKhSIQmvSoPzxsIIC0sJ0CV8EyQiOksAylBdgREdNKqkItECI8xcAqIguzcUsoHL6yqDjfV6KTcMrEjrTskIlf+9x5dniiTuCP4PG7+uGwcYxfORZVYe+iyQqSjuWEQG8qmRezvGD8uZAc9vnUrrt60nq1ffMWhRYvRZKTR4f77MDRpjEVUwssbu1PpKag+/uf997K86BvuavnHCYn4YDCI5UAO+/+YSe78BRibNqX5tVejzayHRKHAYfeyaXUhX7+3oXons0FTA7c/1pm0eheukd6RlIajxfsRpuU8SWvTiPNCwEsUCjo/8jB7fv0dsVKBxxXZyRvA7bKHRYucKmZ3AeXug2E7mkeweUuxekvqLOAPbF1Gs27DCfh87Fo9i2AgQMdhNyBX6SjO3UlWq56nfM8qtZQ+Q+rRqmMCFrMbod+H7MBeRN99gi83L/wEaeizTS8Rc1WSiRFxemx+PzKhAK1IhK4O4v1YyoodvPDA4urc71WLqtq79k7m0pteZvJLV4aIxYTM5nQaNZEXHtoGgEwuQquXYbN4aNo6Dq3h1COhBAIBCpUkqnP/hWwYGTiOnb03EBr+LRJKUIhrUu7+z95Zh0lZtXH4nu7c7l126W7pbpGQUAwQbOzGTgz0s7tFUVCQUEpAGhTpbha2c3Z2Or8/VnYZZ2ZZEAmd+7q+65Nzznvmnd3Zd87vnOf5PessX9Jm2AR+m/le0LX12/bHtPMAlz3xGE1vGI86NYlSx3E8PicSkRy9NPGMIlH+KfwuN96SMhxbd+ItKEbWojGSjFRE0VUGtOUeL1/kl4a8drrTzwNPPM6uh6fQ8OqxKIxGnA4Psz7bRXGBjXk/FnL325+yYuxQ7CU1zyKpVsuweXMor8xFLJUjEAqroxYBzCW57Fr+M/U6TuCDBb8GvW5xgY2fZh7g2tta1Bp1lSSXEiURUeoOfs4Ni9FhlIov+Ui0CBcv/71VcIQIFxCRWEyzGyey9+sZVBwNNKZJ698PTVJydf5jKIQiEdKjRYzscS/+Rs146+3QTroAh3dZuUraD5VEF3ZMwL1FGdDdeh3lLwSHVss6tgG9DpFIgCdEVF9SmibkKahEKGRQlI5VZZVcpwF17m4OLvkMl9NG745DadnlcoSCYGFSXGDj+ftX4fXUnE5VVrj43xPrmfphH+o1/HeXoQpFTGqjsH0aYzzuskrK9u3DVlR8egEv1jA8/Tl2li3E4w9cOMfKs6in7RjQJhSLMWRl0vn5Z3BXViKWyxErFHh9blZlvxwg3k9y3LqVw+YNtIsZVef3WLprNzO798JpMgFweN58/pj2KkNnzyJj8CCO7DfxxtOB0SmH95Xz9J2/8uJHfYmJv/D5z2dDbSkNAGvyP+Ga+u+cxzsKjz6zHuM2bWDnJ5+R3rALR3esDjmuftt+yNX6sPP4/D5MrjwqXPm4vDYMsmS0kljk4uDSZD6/jxzrDj7cM5qRGS/Ven91XTDbzGWIJVK2LPmSE3t/q24/umM1CZkt6TnuEdxOOxLZ3z/VFImFxMSpiIlT4SkqpejJz/BUBp9qS5s0QBjCsVouEhIvEgJ/L8R784b8IOM2gI2r87msV3tufn0leQe3U5p/Al1cEyosen6eU8rdT3bEUln1nCjKtxETrySjgf6chJzrjDIGDM9kdhjX/q79Lt365iqxgWh5erUfzakIEJKibhHQphTrGZD8AIf2rANgq2keDVp1p6vsfrbM/QKbuRSJTEGbAePpMPhGxEiRG41Uekv46fgLLMt9HbvXjEpsZEjqo1wWe90FNWX1u904t++h9Olp1TXVLbPmI0qMI/qlxxDHxVDu9uIKE/ln9frwxsYikkqqzezMJifrllcZM5YW23n/s2LGf7MYT/YeTLu3E9uqBak9OmKPdrAj7xfiJnWhs+x+Dq/8hR0/z6ie+8jmBRjSh4S995WLjnHFuIYB1Qv+SpREzNP1Enk1u5BsR9XfhwgYFK2jr1GLKCLeI/yDRAR8hAjnGW1qKqNXLufw/AXs/eprRHIZre+6g8QunVHGxtDy1ls4Mn9ByGubXDUW74atuLfvRtK/GGNU+HBpY7Qc5RmE1AkEAuRtWmB89kHMH3+D50QeApUS6RU98PRrillTzCOvdeTZO9cH1MmVyUVMfrQDemPo05hYqZhpiUp+/fQxNm78ubo97+BWdi36hOufnxvgou71+li24EiAeD+J3w+zv9rLHY91QKE896V+LmZU+hjaDBjPliVfBvV1u+Iedjz3PgBee+gw+78Sp2jAk222cbRyI06vlUL7AYQCCX2T7sIgC+1kLBKLERlqNlwq3SVsKg7v6rum4BOaGwcjE50+TNVWXMzi8ROrxftJ/D4fC8ddx3U7tzPjw8Mhr62scLFrcwHdB6QiEl96nwsfPmze0OXCACzuYnx+H0LBhS+/JhSLiWrcmM5PP4mlsoQtK6dTUZwTMEam1NDh8psQS0Kfnnr9HrIrN/PO7mF4/W7a6EagEhiI0WXRJmYkGmlMwPgy53Gmbe+B3WvGjw+FSIvdaw6aVyuJRS2JCWoPh9VUHCDeT5J/eDtFx/eR1LB9neeqK6IoA1HPPEjplKn4T8mhFUYZ0D9wKyLtP+PLYTG7WLXoWNj+ed8eJOu5zvz4o4aK8kyKC8qxWaoMTTeuyuGhqV15Zcra6oivqBgFj73WnaS/GfkiEgnpe0UmW3/P58g+U0Dftbe1ICr24gsLrzQ7MZU62Lu9GIlURKMW0eiN8qDvJL0skevqf8QbOwfi/0sS2eCUR9BIgsV1PW0nOsRcze/F3wIws+B+GqX1Yugzb2IUJiOVqVDpY6r/tuweM3OPPc6agk+q57B6yph15AGs7jKGpD5+wSo0eEtNlD77WrV4r27PK6Tiw+kYHrjttCJXALS66w5kuqqDCL/fH7A2yD9h4aUXDhATryU6bgDX9srge9tj/L51RsA8gzs/QnvlzWz6/iMARGIJtRR4wOPxQS39J0mUSXkyIwGz14vL50ctEqEXi5D/y0tlRrjwRAR8hAgXAG1qCq0m30ajcVchFImqv5wAYlu3JH3QAI4tWhJwjaFBAxr2H4D9xXcB8Kz/jUF39uC3NcGnnwDDrmmMUlW78/xfEapVKDq2wZmuwVJ5GIffynLbp+w8vBihQMSdTX7i9W/6s2T2EXKPVdKoRRRd+6URXUupH6FAgL3wGAdPEe8nqSzN4/efPqb3dY9VL0icDi9H9pWFnS/7kAmH3fOfE/BylZbuY+4nIaM56+e+g7kkj7iMpnTsdxMnZi2kcNMmhBIJiui65e3aPCZyrNv5JfcNzK4CMrVdGZb2FBpJ3Z3DBQIhohAGVyeRCGR1DuN0lJZRtDW0iaLHbsd08BDF+eHTCHZtKaJJEwdRSVmIpZdW2K1SpKOpfgBbSmeH7G8XM+aiEO+nItVoMGo0XPfsHDbO/4Adv87E63HT8LLBdB/7AIa48CenZc4TvLqjN/2j7qWesw37fp6PxbQPbXMVld3zUSVEITyltvk+06/Vgv3XvHe5MuNlvjl0O/5TVtgChNzQ8HP00sSg1wuFQChkz/r5Yft3r/mRFj3HgOLcRnUIREKkDTOJ/Wgazh178ZzIQ9qkPpKsDMQx/5wJmUAAolqqJ4hEQkxlDnZsKgrqc9q9LJ17iK79Ulm+oCpyrLTYzstT1vLMW73OysDuVIwxCh6a2pXcY2Z+X52LWiejU69kjDEKVOoz+w4713jdLirLi7BWFCEUipCpoli51MTMT2siBgQCmHBXK7r3T0P5l/vN0nbhiTZ/MO/YUxy3bqW9cRztdNcTrU5GIQ7c/PD4PHh8DgamPEiPhJvZXroAgUBE+5ixRMvTUEuCn+1mVyFrCz4Nee9Lcl6ja/yNxCiq8uytFSXYzGX4vG7kaj0aQ3ytZdr+DrbiYnx7D4ArtBGfY8Mf+CrM6KOMqEVCLN7gdAOjRISkrIikrl2q2xRKCfWbGDm4J3CNUFxgQ64QUyhZw+/FM/46FQuLXuLOjnORLlDhclhpO3ACuvg4YFfQWIAO3RJRaer22dNLxOjPIqUlQoS/Q+QTFyHCBUIgEKAwBoeCq+LjGfDZJxT8vomtb72D1+mkyYTxpHbpjO25mjBbv8VKjLuUa29rzjcf7qw+FRcIYOT1jclooD+r+zI585h6rA9lzsD6wV6/h4/3j+OpNtu4/vZWuN1eJFJRnUzlti//NmzfzlXf067/BHRxKYgkEqRSIUnpWvbuCJ1fG5+sRiK5cGLG7vJgKnfiB5QaCXrl+VtgqvQxNO0yAq0oBoepnPK9+1k74T4sOVUnoK3vvhNl/OkFuMVdxo/HHmdNQU2Joq2lc9hRNp+HWq4iU9u5TvejkcTQLf4mfjj6UMj+XomTkQjrJqZ9oXIzTsFtsxKToMZcETpXNjZOyNLPn2TIba9iTMgIOeZiw+/zUVlegNVUzGDhffRMmcR6yzdsLP+mekycon5QSsPFhC42hT7jn6TTiDvA70eu1iGV1y5695Yvp4thAoqtQhbMnlzdnrv/D3Yu+o7rnv+R2NSa6KKjlb9X//f+ilXoZUnc0XQeGwqnU+Q4RLKqJQOS7ydGnlnnjQ6BQIjP4wnb7/O68ddi6vl3EIjFiONjEceHD2+226r+Hs7VRqVKI6Xf8Ez27wqdb9x/eCa/rcoJ2QewY1MhE+5qVS3goapWtqnc8bcFPIAhSoEhSkGztv9M6cGzwWEzs3/jQpZ8+li1N4BCY6TX+Ndo2ymazRuqvqP8fvj8zW00aGakXoPATRi/S4q/IIOOlje4PFrLyp+P88mefKLjKrjiqoYkpGpQqaV4fC4Omdfxzu5hOLyVyERq6mk60lDXC4MsOaR4B6hwFwRsZJ2Kx+/E6ikj2p9O8fF9zHtzMkXZVRsPCrWBfjc8Q1b7/ihUdUuzqyuVObmseuBBOg66Ivwgnx+/x4tBIuaelDimHssPiFEQC+CuWD3pUSpUsTVRNRqdjAl3tebJySvwegPf99CJCfySH/q7COB320wy2/WhoiiXrLZ98aKkRbs4dvzFRFGhEjN6YjPkitASqaLcgc/nR62VIpFceJ+BCP9NIgI+QoSLEFV8PJlXDCWldy/8Xi8ynQ6/243qtafxHM/BZ7MjqZeG0KCjr0hG+27JHN5Xhs/nJ6uxEV0d6/CGotJdHCTeT2L1lFHhKsAoT0Emqvvjw1+LmY/f5yNv3XqO5pXS5PprUURFMXBEFst/OkKoy0ZdW5+SA0epFDiQa1UoY2KQ6fV1vpe/Q0GBlYWzD7J+STY+r5+2PZMYenUjYhNU5y1kTqJUEtesFXunf8P219/DUVqKIjqaDo8+QqNrrkaiPH24eoUrP0C8n8Tr9zD94K3c13xZnXInhQIhbfRj2aCcTq5tZ0BfS+NQktUt6/y+ZAYD6qQkLLkhjL0EAmKaN6OfTMThfX8EdYtEApq1VjB36Qb2/76YTsNuq/PrXgjspaU4ysvxuJ3s3fwzm375HIfFhFAsocOIm0lu/SLzi5+la/wkBiY/GDal4WJBLJGhjaq7U3iedSdtZFcwZ/aEoD6HtYJlnz/NyAc+Qv6nsEhTtw4Y81vRDLaVzKNN9EjaRY+hS/wN6KRnJvzkKi0teo8lZ/+mkP3Ne4xGqam7caC9pARbcTHuSgsygwFlXCyyEPnsteG0WbCYKzCVefn5hzzMJid9htajYfPoc1JKrWnrWBo0i+LAX0R8/aZGmreL48Du0OIeQK4U43IFP5CtlbVvvF3KFGfv46d37w1os1eWsfj9mxj+0AK2bSoNCOde8uMhJtwrQyGtSoPwenxs3ZjPjA93cv3kljxx2yrcf/4MD+8r57dVuUy8pzU9BqZTSQ5v7ByEx19lzun0WthrWs5e03JEQjEDkh8IGc0kF9WeciEVKqgozuGHVybRoOcgOlx3O05rJfuXLmD+23dx9ZPfUa9lj7/1czoVj93Ohmee4/Dc+Vx20y1hx4niohEqFYgEApqq5bzeIIVfyswcd7jIVMjobVATK5UiDnFAkJap48WP+vLtxzvZs60YtVbK5WMa0Li1gvm7i8O+ZqWvhEHD7kenT0JjjAfg9kfbs3VjPgu/r6oS0bZzAoNHNyA2IXgTsrzEzqa1uSyecwiH3UP7bkkMHlWf2ARVxKwuwnknIuAjRLiIkaprvpwFEgni+BjE8YE5ngqqTmnik85N7qTPH/5UCsDrP/MFW8teV7Fz5fch+xq2HcixeYs48O0sFNHRNLnuGmITVNz/bGfenfp7dekliVTIUy+0JG/WB2x/8w08f+Z6pw3oT+/338IaY6XIcQi9NBmjLAW97NyWHiostPL83SspLqgJ41790zG2rsnjqfd7k5IUbL71T6GKjaXNPXfRcMxoPE4HYrkcVUJCncMhD5nXhe3Lse7E5jHVScCXFNl46f49jLrzKyyxW9nlmoEIGa3lE0nTtj0jUaVOTKTPe28zb9jIoL6299+LMjaGNtEy+g2vxy9zj1T3yRQibr2vPtsXTwO/n9Kcg3V+zfON1+WiZMdOlt12B4V//AECAan9+jDy8XdYPOspyvIOs/H7dxmW/B4vtj+CUmKocwTDpURjQ38K1+8I2390x2rsFlO1gG9s6IdMqMLpqzF+c/qsbCiazqSGX52xeD9JZuveRCXVpzQ38DOjj0ulcZcrqss/nY6Ko8dYeM111eU/BUIhja4ZR7eXpqJOPP1zyOWwUZp7iNXfTaPg6E600Yl06nM7OflxvPH0Ruo3jeK+Zzv9bRFvjFZw7zOdOLi7lF/mHwY/ARsEvS/PYNn8IyGv7do3ld9XB5/Q/xM12i8GKssKWT3r1ZB9Pq+HQ7/PonXHQfyxrqC6vbzEidVZUS3gy0sdfDjtD0ZNaMp3n+yqFu+n8uXb22jVMZ59gmXV4v2vLDkxjctirw1Z0UMjiSNankGJ42hQX6q6NTppPDkH/6DbQ4+yuPw1DlU+i1oSQ/dxExlqv47V375CfEYzlNpzk75hLSxk71fT8bpc5G7fRnzLJni2B5d5091yPaKoqg0yqVBIslzK9QlRuH1+JEJBrbnxEqmItCw9dz3ZEbvVg1AkQG+U4/LZaazvw8air0Ne18p4BclJbQPaDFEKeg+pR9vOiVWn6hppSOf58lI7bz6zMSAycPHsQ6xZms3UD/ues/VXhAh1JSLgI0SIEIBGEoNSrA9ZJ1wilKOTxFOSc5Dda3/EXJxH/fb9SazfCm1U+NzTqOT6ZLbty+HNywLa1fpYmrUbxryHLgdg3eNPktqnN+rEBFp3SmDa5/3JO16JtdJFVIyUgtmfsvmlFwPmyF6ylPnDRpE54xa+KKs6eY2VZ3J3s4XEKRv8zZ9GDVs35geI95NUVrhYtuAIV97QBK3s/OXln1qu70yRCGsv/1TXvPVDe8vIPV7Jmw9WkpSWQON2z+DzwrfrylCq9vHkG3Go1SJclZWIFYpa6xQLBAJSevXkqg1rWffo4xRu3oI2NZWOTzxKSq+eyHQ6ZMCo67Po2lNN3nELcoUItcrGloVPkbtvAwBpzeoW/n+u8Pv9WMoL8ft8iKVylNrwFRJMhw7xXZfueF2ukxdzfOkyirdso9+cL5j5xgQA1s76H9c2m41Edmbi3ef14jSZEEmlSDXnb0PpTElTt6bcF7yoD+CU8PUoWSoPtFjBu3tGYHLlASASSBicMoVmhkFnfR/aqATGPfkdu9fNY/vyGfh9Ppr3HE3zHqPRRdet/KG1oIB5w0ZSsrMmAsXv87F3+teIlQp6/u/VWqNi/H4/x/dsZObUa6vfs6W8kLyDN9FuyN30HtSVFYvy2b2liG79/74juzFaQcceybTsEIcPsImqSnmV2xyoU9XccE9rPn8j0Isio4Gehs2j+WnmgYD29t0S0Rr+fRtMLqeNouN7Kc0NbZoJUFG4n6iYoQFt9dtIOepYS7RmLACmMgd2q4fYeCU5x4JNFwG8Xj8njlRQkVwQsh/A4ikNu3FukCVyZ9P5vLqjN5XumtNnoyyFWxp9h0pixB3n5e2Dw/FRZSZX4cpnQdELNNb0plPfUXhcoTcOzgavy139fFv56GOMmPUditQk3CvW4bfaEKclo7v5WqSNg8vbigSCWn0a/opSJQ3w+ZGJlAxJfZQ/ir8P2gzRSmJpGTX0r1NUoztNScQTRytCpvVZK938OH0vE+9tjUwWkVQRzh+RT1uECBEC0EkTuSbzXT7ef01Q35DUV9hpVZLgLmfX6tmYCo8j0soRJsjZ4/4VqUhJirolWml8gPO4Wh/D5be9xvG9G/l9wUe4nTYymnSnXv3OrLj+VnzuqsVJ5fHjeBxVtXHFYiEyuZhvPthBTraZe++rx/Y3/hfynkt37qJdWTRSoRKXz0aR4zDv7B7Gvc2WY1TUzdSqNuw2N7+tCJ8fum11HgNH1T+vAv50WHLzMB0+TNn+/Rjq10eflYUmuUqUZGk7I0AY5IwM0FjfF5W4bmX69m6vWTDmZleSm11Toio+QY63JIfsbUcQKMR4fG508Smo9DGojKGdwqUaDYmXdWTo7Fm4rTZEUgnK2MBIAH2UjvKc7eyYNxmP24nTVvOaSm0UqU0uq9O9nwusFSUc2rwcoUiEQCCk8NhuDPHppDXrgi46KcBMz22z8dsLL9WI91Owl5RQuGojSQ3akntgM2X5R2vNzw5FxdFj7P36Gw7PW4DMoKftffcQ17ZN0M/vYkAnTaBhq4Gs+yr033Nyow7Vp+9QtaGUrmnPY61/p9JVhNvnQCuLRyuJq1OFg9rQRidy2dBbaNFzNE5bJS67BbfThs1cVutmzEkseXkB4v1Udn/2BR0eeQhZtAGnw4JAIESliw6olGApK2DhBw8GbFicZPOitxn24BBWLMpnydxDtOmUUGdjrdMiE7Gz0sZ7OcXVBmIakZDbe8XzaoeB/PL9IWwWF137pZKYqmXdsmxkcjEOuweJVEjPQelceX0T1Ofqfi4irKZijmxbSXRSFpWleQF9UoWarHYDSWs5gL0HajYkNTop9TsIybcfC5rvdFYKPr+fepoOYfvjFQ2RCMNvfiapmvF46z/Is+2iwLafJFUz4pWNMcqSqXQVMyf/sWrxfip7K1fQv/69CM9h5Q6pSlWdCuV1Ovlh+EgyBg2k1fgJSJQKlPWzkDcMFu/nihh5Fo+23sCMQ3dyyLwOAUJaRQ1jVL2XiZKf3QaY3+9n1eLwJXt/W5XDmElNkcVEJFWE80fk0xYhQoQAREIxzaMu55GWa5l77EnybLuJUdSnR9ITHHJl8Xm+Fb1YwQMPfUnZ/mUcS97LtOy+1UY6IoGY6+t/TJvoK1H8WdPZ7/ejNsTSpPMVaMXRZC9dRv6s9cxdNjVgdaOIjkYkrVkQCoUglgjxevxIcOEyhz7FAKjYfxRdy3iKHVUhoPn2feSWHGfnHhfNWscSVYtT/ukQCgVI5eFPpWUKEaI6mPmdL8oPHWJ2v0GYjx0DQB4VRcePPkSn1WFCgFGcxIOt9vDurs5YPTVuvmpxFOOy3kYlqVvub2JK6FPeuCQV40ZqqCg+zi/zXqDkxJ8ndwIBTbuMoPd1j6ONDh9aLDcYkBvC30NCViv63fAsv3z+JE6qBHz99gPocfsbmCQKim0ONGIRBrEIaR3DoM8Up93C7/M/IKXJZfz6zdRqcygAoVjCmIe/IK1Zl2oR7zSZyFkVumY6QP6qDcQNb07ugc1EJ9dHJKm7MCo/dIjvOnXDXlJzQnR82XKaTbyBbi9PrXNlgvOFQCBAH5VC20E3sHnR5wF9YqmCgTdORfGX/HOBQIBBlhQyjPjvYreY2L78W9b+8DpuZ1VqTmKDNgy7653TGiJWZof2CwFI7tsTi7OMBdMeIHv3emQKNW0HTKDtoBuqPQMspuIgkXgSv8+HpewoGp0Un9d/WiEY8r3Z3DhsHiRSEWptzWcq3+lmWnZhgP1ZpdfHtOOFTKufzMR7An0HLr+qIV37peGwe5DJxeiNMqT/ohNHt9tLpanKnNTj8LJ3/QL6T3yWoztq/mbbDrmb2PpDWLPCzM55buo3FfHIK13ZuDKbzsMVzCobw62NZ1WP1xtlKNUSykvtJKSoyT9hCXpdoUhAWqYeiaoZBlky5c7gjeLR9aadNk0kSp5KlDyV5sbBAe0Or5njltDVPQCOebfQXH95rXOfCarEBLq9PJVF146vavD7ObpwEUcXLiKmdWtaPn8P6dHqM/LMOBPEQgmp6tbc0XQ+do+patNMHFW9FjkbBAIBUln473+xRMjF8+0f4b/Cv+fpGyFChHOGUqwlS9eFEVnfcdBaTqlHzOclQsrcVbv4Jo+XbUINGY31/HrsvYBrvX4Pnx+4gRR1K2SVWfy+OpdDe8rIamykfbcktInpbHn5ddyW4MVM+4cfRJUQX/1vtVbGoNH12b7uBPHJWkRSacgTTABVSkKAGAWosJfwyWt5GKLlPPl6T6Ljzk7Ey+RiBlxZn20bQ4c59h6ZiVFfewje+cJWXMzPY8bViHejkd4rlvORXEv2sRq33Qy5kofbZPN7/jSOWzbTxNCPVtHDiJan1/m1WnWMRywR4nEHnuQPvTwWn/UEP8+cGlgj3O9n99o5qA0x9LxmStg64adDrtLSpMswUhp3xGGtQCJTUqGN5YUTxeQ6q4y4JAIBI2P1DDBq0f0DJX5sFSVYKkrYtXpOgHiHKkf971++gVvfWoM+LhUAkVSKIiYmtEkfoIiLweGo2qDqOW4KKl3dRLfLamX9E08FiPeT7Prsc1pOvvWiE/AACrWebqPvJ7NVLzbMfRdrRQnpzbvQ4fKb0cf+/VDxuuL3+di38Wd+/WZqQHvegS188/Roxk9dUKvY0KSGTmNRxsbSaspdfPXEMHzeqmgKp62S9T++zZEdqxgz5SvEYik2c3jjOACRRIrX66Dn4IwAAX46nHYPeScq+f6L3Rw9YCI6VsmV4xuT2diIVC1hbpEppHe5D1hQbOKW5Bi8JhOOkhK8Ljcyg57oxMR/pVlXcYGVn78/wOrF2Xi8Pjp2T6LfLV9RcOBn+k96nlXfvkL9jiOp9Pfm88f3VV+3e2uVgdrDr7fm87LeZGk7BxhO6qMU3PpwO777aCdX3dict57bGGB6B3D1Tc3QGeTI5ck82GIlXx24mX0VK4CqsO8x9f5Hlq7bWb83gUCESCDGG8bbRi2Lxm2z1cn8tG6vJyC+a0d6vv8Gfzw/DUtuLkKJhPqjR9Lw1uuY+9GdjKvf+B8T8CdRS4yoJXWLJKsLvQZnsOKnYJ8BgF5DMtDq/32pJBEubiICPkKE/yj+P0N0BeLQjwGPz88PxR5+N4sAP/wlBE8uc7E8762Q10JVvWb173cw6+Oq09eNK3OY+ekunni9B1etX8PsfgOxFf4pKAUCmt84icbXXRNkxNa8gRj7jz+w/S0bDa8ay56vpge9lio+Hk+yAFuZKaBdI4zH5TxKYa6VxXMOctWNzRCfZdmX9CwdnfqmsGFZ4Ilb49YxtOmQENIt90JgKyoOqKfecurzfK7QkW0PzAk86nDxbo6J+6W3oNZ4EGkNiORndkoRFaNgystdmfbYehz2mgViZroYe6U3ULyfwuYlX9Fu8CT0sSln9HqnIhAK0UYnoo1OpNjl5umDOVSeUkvY7fczs7Acg1hEX6P2nAsPc2k+WW36MPfNySH7vR4XOQc2Vwt4RXQ07R9+kIVXB6emANS/djQLv3+c/hOfJ6VR+HDav+IsK+PgD3PC9h/4fg5xbdrUeb7ziUoXRf12/Uhu1B6fx41MqQ1IOzgfVJYXsnrmtJB95pJcSk7sr1VsqJOSiGrShNI9gTn9TW+bxPqlH1aL91MpOLyD0pyDaKISKMk5QExKQ4pP7A8aJ5bKESuS0BlyaNvpzATP3h0lvPTImupKHmXFdl58aC1jJzWlx+gsjjtDb4QCZDtdVJSWsmz4leSvr/KWUCUk0Out10nr3++M3fUvZkoKbTzzV3PSJcfZskHKg88MZvOCZxl82zRUMR14+KY1QddbzC5mvX+EiQ/9QJwhAbWkxgxOLBbSsn08cYlqtq7P49Fp3Vi1OJvD+8qIjlMy/JpGpNbTV5cri1VkcluTH7C4S/D4nSjFenTSxDqXRgyFRhxDu+gx/BaiNroAAanWJswdP4I2d91JfId2qOLjQ8xyZjh9drYf/YkO7z+HQqYFkYADu5bzw7u34HU7sVeW/+3XON/EJ6npOTidlQuPBbUPGJ551uuKCBHOloiAjxDhP4a3zIT7SDbWhcsBUA3qjSQzDZExOGRZXIvo0Yq8mJyhQz8BSh3H6D5AyoIZYuzWqkWs2+Xj9Sc38MKHfbjmj41YcvNwmc1o09NQxsYi0wXWo3VVVvLbU0+x+4svEUokDPnuG6wFBWQv/aV6jCYlhR4/vseX9rsCrm2iHcSBTTWbDisXHWPwqAYYY87ONdloVDB+civ6D8/i15+O4PX46TYwjeQMLTHR5+b04lzgsVkD/q3r1p1D9tAmRftsTswiKfZbH0bWsQ2Gu2+sdgauC2KJiEYtY3j1y/4UnLBgtbhJydAidxRSkB3+s+Fx2atDlc8Fh2zOAPF+KrOKyqmnkBEjlaAVn7tFlkRW9TmqrX691RRY0iilVw+a3jCe3Z9/WdMoEND1pakYGzTghhcXojbEnlH4vB9qr1deSwnHiwWFWn/BXtvjcgT9nk6l4Ogu6rXqGbZfFR/P8AVzWTD6Koq2bKlqFAhIHdCH1a8Gl2o8yYFNS2nd7xo2/fwJ/Sc9z9w3bq+uNX5yjt7jp2F3qHjifz3OKAWorMTOR9P+CPmr//6LPXS/PINkmYTjjtAiPkUqYdvUl6vFO4A1P5+fRl/FqBW/kNqrZ8B4j8dHebGdowdNmModZDY0EB2nPK0x2MXAtt8LQpqTWswu1q2sRCvXsWbmayS0nxY2hWHX5mJu8rZDKw12IpcrxKRn6UlMUeN0eLnhbiNulxepTIRCGZx7rpIY6pzCVBdkYhUjMl7gcOWGIKf6sVGvsGfqR5xYvoITy1eQOXwYfT94F1Xc2VV1OIlcpaP4xH5+/iJ0TXbd39i4PVt8Xi+V5QXYzWUgEKDURqExxtd5Y1erlzHu5ub06J/Goh8P4bB66No/9W+n50WIcLZEBHyECP8hvKXllL36Pq4tNaZLjnWbkLZuhvHB2wPEm1gooH+UlvUV1lBTIfdJSVe1o9wVOiQ4Vd2GzRVf02PoEBZ/VzOmvNSBudxJRoNkNMm1u6jbCgurT9x9bjcLx11HhykP0/rOyVjy8olq2gRlajwbfN9RdqLqZDxN3ZZuMTeT7OvLa7N3V8/ldvmq8/TPBqfDgwBIz9Bx+yPtAS7KcFK5MQqhWFxtguYUiaAWPzSHQIAEcP62BcuchWgnjEEgqbupkVgsJCZORUxcTd1ca4ENY3x62GskcmW1AD4XHAmzQQFQ5vZS5PKwscLKiFgDStG5yYlXG+Ioyt6LPi4NU2Fog6Pkhu2q/9vv9yMTiunyyBTa3H47x9esRqJQktyrB6rERGRn6Rrv0+qoN2I4h3+YHbK//qgrz2re/woiiRSZUovTFtpfIyox87Rz6OplMHLxT9iKinBVWlAYjQh0MqRyFS5H6OenQmNArtLhB9Z+/zpXPvAJx3auofDYHnQxKTTvORaVPhlDXBzCM4zusZhdlBaH3iDzef3kHDIxopmRDRXWoCeiABislrHoo9CbD2sefISRi3+qTsvwuL3s21HCy1PW4XLWbJg2aRXNnU9cdlGVmfN6fbicVeJZJBJit7lZv/x42PHbN5Vzz+P3krdnKRW+4LrgJ/H7we+r/btFKhPXeAaozq/ZabQ8nYdarOZo5W9sK52Pjliaenpy5J0fOPhlTXnXw3Pn0ebuO/+2gFcbYrls+GTWzKwpwxeVlEWrvtcQl9EMsVSO3+erc5nGv4vLbuXI9pUsfP9B7JbyP+8xjmF3v0tyw3Z1jvrRGeToDHLqN43C5/Mjk0ckVIQLR+TTFyHCfwjnzn0B4v0krq27cO7ch7Jnp4D2FJmUy7QqNpoDF6GJMgkxFiv9Yu5he/lPQQ63CpGWhvqefLB3DGNajITvAl/P66nbqaC9rAy/r2as1+lkw9PPIhAKkel0jFj8M4aUevT13UfH2Gtxee3sM61iS9n37BIu4boXb6J4VxzfvplNx55JZ+WY7HZ5Kci1MPebfRzcU0pUjIIR1zUho74eje7iy3tTxcfR4tZb2PbOuwDIXS4g9H0KAJW35ndn/ekXVMMGII6tyZn2+asiLayeUkQCCWpJzGlrxKvi44my1scQn055wbGg/vaDJ6Ex/L1F4qmky8P/XvViEVafj7nFJvoYtedMwGuM8SRktaLLlXfz83v3BfUnNWhXfdLk9/lwHz5G6eOv4DNVgERMckoismaNkZls+B3H8WWmI1SHFwnh2OUT0Ojpp8hd8SuOskAPiAbXjEObfv7yyf8JnHYLVlMxeYe24fd5SarfBqU+Brny3JTJUxvi6HD5zawJUfNbrtIRX695neZRxsSgjKmpruB1u2jV9xp+/+mjkOPTm3dFoTEy8KYXmTX1Or574RrSmnbGmJCBtaKEvWvn0GPcw2cs3oHTXiMUCkiQSbg/NY73coqx/fmMVYmETE6OoeKHWXhswafSAEVbt+K22zkpy8tKHLz0yNqgGud7tpWw4Lv9jLulOZILHF7scnopyrfyy7zDZB82kdHAQL8r6mGIViBXhl8GyxViopMyqdf0Hk4crYD3Qpc+zGxkOHfVAf4hjPJkjPJkmgh7seyW21gyb1TIShc7PvqEpO7dEP4NcS2WyGg7cAIyhZq1P7xBi15jiUlpyG/zP6Ak5wBqfSydr7yLJp2vQKUPXZHkXFKad4jZr95EZts+NB44DOQicHrZtORT1MY4opOyzmi+UHXiI0Q430QEfIQI/xF8FivW+UvC9lvnLUbWrgWiU0SEXiLmpqRo+kZpWVhSgcvnp6dBTXO1Arm1DMRR3N5kDt8ffYBC+0EA0tXtuCLtaeYcnYJKbMBpDlzYSaTCOodWStXBIYlQJYgc5eVIVFWha1KhHAR+3twzKCBMcBtzuCxzElffdTMdOzQ+qx3zQ3vLeO7eVXi9VScshblW9mxbzZiJTRg8ukHIMMgLiUSlouPjU5AbDWx5/U0K58yhw7jr+d0eHOrdWSlDsmZd9faL3+EEd804u6eCnWWLmHH4TizuKpO0RGUTbmz0DcmqFrXmZhqS6zHm4S+Z/85d5B/eDoBAKKJNv+toP+SmMwoTPx31VXJUIiHWEGH0g6N0/FpWiQ/IdbqIP0el/gQCATHJ9ZHKVYx84BNWfjOVsvwjiKUKWvcdx2XDJ6P+c3HqLS6j5KHn8dv+PBV1e/AcOY7nyHEQCvDmF+Ht3A5l3+4IziDM3+LxsrC0AotIyW3r1pA7fTp5C35GpteRec/duFu2wqc/d+G45xu7xcT2Fd+xYvpzNRt5AgFdr7yH9pffiFLz902qRCIxbfpfh6kom50ra04jNcZ4xkz5Cm0d68EHzSuR0nbgeI7tXENR9l5kSi2NOgxGrY4mNqsZe9b8iMYYT2qTyxg/dQG/fv0CeQe3YC7No8vIu8hq2zeglN6ZoNFJSUrTBJR1PIlEKiQ+SYNCJKSDTkWWUkaFp+oJoBeL0EvEbC0tC7ruJOrkZISn+Kbs31kSJN5PsnzBEYaMbnDW5qHnAq/Hx55tRbw8ZR2+P5/he7eXsOTHQzz5ek8GX1mfzevyQ147aFT9anMyQ5SCvkPrsWzBkYAxEqmQife2uSg3c0PhcTjIWb0mbJnKUO2VJ3Io338A09GjRDVuhK5ePdSJtXsyqLRRtB98I027DufQlhXVm5wCoYiE+u2pKLNxYMs6GnfsjVz1z3kquOwW1v7wJgPve4Vjxh18UjoJe6UZuUhDjyE3U+HIw+BJCyjtGCHCpUBEwEeI8B/B7/PhdwWKOIFSgaRLO4gyIBKJIUQYoF4iprVETBOVHJ8fFCdPMP8saxMnrE+fxLvQSuMRCkTk2Xbz1cGbMbnyGBz9Iuu+DgxNveqmZuiMdRPwipgY4tq2pXDz5qC+mFatqk+83F4nv+S+EZTjB7DR9ClTet1MnD70ZkBtmEodfPjKH9Xi/VS+/2IPXfqmXXQCHkAVF0fHxx+l2cQb8NjtNIuPQl5ayVqzFR8gAnqo5VxZYcL7/YLq64RRBpDVLESPW7bx0b6rA+bOs+1h2vYePNVmG9GK8CW2xDIZ0akNGDPlK2wVJbhdDpRaIyp9DFL5mZ8010aMRMwz9RJ5+VgBxe6qBagI6BulRSESss/mAKqc6c8lQpEYQ1wqhrhUUhq3x+2wIRSJUeljAhz23YeO1oj3v2Bbugr9nRMxvfM5vk4NMItN7DOtQC9OIsHfFafTjYMylHofekUsOmngwtnrh1yXhycR0WrCzTS8dgIOkYhPvQI0PhHNzuk7Pr+U5h5m+ZfPBDb6/az94XWSm7RHnhWNx+dGI4lBJ0tAJDi7JY3aEEv/ic/T5cp7MBfnIFNpURvizihHNhQlJw7S69onUGiSETmcbHllGjm71+Fqe5wG14/G63Qgi1WT3LAdox76HLfLhlAkqd74OVt0Bjl3PNaBp+9aidNRE2EjEMBtj7RHH1X1/BUJBMRIJcRIA59hWSOGs+6xJ0KKufYPP4D4FMfyksLQJ/UATocXTx2jrf4pysscvP3879Xi/SRej5/Xn9rAc+/3puegNFYuCkyDad4ullYdTq2IImXIhMaktY5m5ayDVJQ7qd8ymp5j6pOnF5Pk9dV8N17EKKKjaDTuKra++TYA9S4fQtMJ1+P3+xFKJGjT0wNO30t27+aHPgNqDGcBQ4MGjFz8E7qM2kssCkUivF4PK6Y/D0BCVls6jXmJ9asq2bzSgs4gRay1kN5Ahkb7z2yAuJw2DGn12K1bxeqiT6rbHd5KlhS/hjW6nDh3E9Tii69SR4QItRER8BEi/EcQqlUoenfGfbDqBEHStT3ey1qx8cMPKN29F0OD+nRslI6hcaOQ+biyMCF1OmkCTp+ND/aODmhvpOtNh9ir+c1+AKlMREKymrE3NqNhs6haa6oCOCoqcJsrEYhFDJn5DT8OvoLyAweq+/VZWQz94TuUsVWh3BZPCesKPg83HRtLp5NpqLuz90ksFhf5OcHl7qDKG+zYoXLik858Y+B8IJJI0KZVOaDbrG7qbamkc2MDmlgZ6opyJIuX4fn5F/DULPC148dU+yBY3GXMOTol5Nx2r5ltpfPpm3z3ae9DbYhFbag95P7vIhAIyFDIeLZeIjlOFw6fH6lQwG8VVj7Nq4ockAkE5+z0PRRqffj36MkLXX4QwG+zI5BIEE0axPyCF/i18H1Gxr7Pid0pfPj1RirKnKjUEnpfGU+zPkdIVMgpyXVz7HAF8UkqbkrV8o7PxAmnmy12F1sA/iz3ODRGj/oSEBWhcDvtbJz/ftj+DXPeRXpVKkuLX0clNnBt/Q9obhiMXHx2f48yuRqhxYXKkIVfLEGo0v8t8e7z+VHFtmXN0mz27DiBXi+h57X3IVvxI9tfeYE9X07nyl8WEZVSHwCFRo8C/Vm/3l9Jy9Qz7fP+rFmajd3qokkDGSn19OiSYpGeJgRYk5JcZcw3aixua036VONrxiEUS5h7+TD6ffg+xsaNaNgsKuw8cYkqZPJzE25scZdicRfj8FpQivVopfHIRaf/XVeUObCYQ5v1mcocWCtdXHNrS/pekcmqRcdwu310H5BGUpoW/SkbzZUeL++YyjAnCejyUAtUCDiOl5dtJtz5ft7WylGILu4weqjaWG1z910cmPUDTW8Yj0gqZfH4idW/Z0ODBgye+TXyxkkIS9zMHTIsQLwDlB84wOLxE7li7mwUxtqjYByWCuyVZagNcbQb8QovProvYFNp++9FXDm+MZePaYhSfe6fzxKpkvTO3Zh17OmQ/etLvmRw+hTURAR8hEuLiICPEOE/gkAoRNG1I9a5ixEY9JjSYvlpwEBOWuuW7dvH4fkLGPjVFzQcOxqRtG6LEaVET8+EW2gZNZRtJXOxeytoaRxKjCILnTSOx16Nw+3yIZacPnTe63JRtncfax55lOPLliPVamn/8IOMWPwT9qJiKo4eRZeRgSY1BXVCzUmkHz9ef/iySC5vaCOpcNg9ZuyeCnz+2k+KRZeIOFKqJGSkaXnu1l/RGWQ8/lhTBCfy4M+Qc4FahXb8aBSd2laLFrfPRo4t2C/hJAfMq+ntv/NvlTg618TKJFh9Pp48nIvtlGgSIXBvahzGMCUT/2kkWelh+4RGA36fj7wGTn7Nf582+jGYNrZm3ufHqsdYLW4WfHmCisJU4hPzmPVZTS6uWivlzmld+VRqIc9Vc2KaIJXQQau8KI0W64LH7aSyNHwlA0t5IamC1gBYPeV8uHcsj7X+jQzNmW/UecorsC1bg/X7+fgqKhEadEhHXoGnW0dUCeEFam3kZpt58o612Cw1UU+/rSngyrEDaTSphH2ffsjSiTczdu3KgGfZuUIkFhKXqKZ/Lw17v57BzrtnsFsiocUtN5E5bCiapPCpAWK5nOTevRi/ezslu3ZTcfgI+qxMjq/4leW3Tcbv8zGzaw+u2fI7CSlxpGRoOXE02ATwuttbYoj6+yZ2JY5sPt8/gf0VKwEQIqJz3PUMT38Bvaz2n93pzOX8Pj9avQytXkb9JuF/1xavl/22KrPMH1zB6UgHbE4SZBe/gAfQZaRzzR8bOb78VxZfPyGgr/zAAX7o1Z8+66ajsMgxZ4c26MxdsxZ7cfFpBfzJVKmW/W5l5he5AeL9JLO/3EvXfmn/iICXKdX41aIgn56T+PBi8ZQSy5nlwUeIcKGJCPgIEf5DiGOjiZ72FJUnTrBs4CBC1cVZfttkkrt3RZtWd/MrhViHQqwjMbVxUJ9WX/dSQqV79jKjQyd8f+ZhO8rKWPPwFA78MJth8+aQ0DH04lwlMtA6akTIWrcAneKur9Pre3xuCu37mX30YXaVLWFU3EekZSaTfbgiaKxIJCCt3tnlqF4I0hsYuPrmZsz8dDdPPrWD2+8cTtOJV+GtNOP1+3Aq5AgddpTaqugLkUBKtCydXNuukPMlq5pfVOL9JGlyKa/WT+E3s5XdVjvJMim9DBpiJGLEZ2EIdi6QpCYhzkjFk30iKE1FPWIQNlsxS60fANBBcQdvzghd2WHNkuM8/FLXgDaL2cUHj63nnrd68L7FhB/obdDQVa8hWnrpfsVLFWrSmnUh7+DWkP3xDVuQ6wk0Ffsp+zlubDQDhbjuBnc+m53Kb+dim7e4pq28Asen0/GWVeAZeTm62DMzzLNUuvj09S0B4v0kc2ad4KmnJrHvs4+oOHIER2lZrQLeWlGC3+dDodafsW+E+fhxZvXoHSDCVky+k92ffc4V835Ek5QY9lqxVIrMYGDv9G/IWb0GW2FhgKGoo7ycI/N/ovVdd/DIy1355oMdbFyVi8/rJypGwXWTW9Kk9d83KDO7Cnlv9wiOW2s+Bz68rC38HBFSOucOx1/pIrZVS9RJSQH5+QA6oxyZQoTTHizgVGpJnb+fTle/xFtbKceLEIFIxG8vvBiyz2kyUbFxP+742k+l3dbw6RMnUWqNJDZogyG5DYf2ht4MgCovhcSUc2NM+VcUktpz7KWiSBm4CJcel+63e4QIEc4KcVw0rtzjOEpLQ/a7rVasBYV1FvCekjI8R47j2LIDUVwM8g6tEUUbEZ7haYTTZGL1gw9Xi/dTKdz0B2V79oZd6MrEKq5If5odZT9j9waK7Ua6XiQoG9XpHgrtB3h+a3vcvqqc6eWmZxl77wLefMASdHIw8d7Wdc7lvxhQa6QMHFmfy3qmYCkux7xuIR8OuTNgEydtQH8GfP4J6oQEtNJYhqY9yQd7xwTNJRKI6RBzdVD7xYBQICBOJuGKGD2Do7SIBIILegptcuZRLs3F/GxXDE4D0s05+D5bDCIh6hGDEMQY8CdpGOqdgj/ej1oci0pjCijJdRK/H8wmJzK5KODzWF7iQGx280xWlSDTikUIL9GT95OIRGJa972GPxZ9FlgfHRBJZDQeOJLFeUMC2nNtu3D6LCiouxDwlldgW7A0ZJ/7p0VIenXFH6M+o8+Qxexi7/aSkH1+PxzJdmGoX//PtKDQwq+yrIBDm5exaeGnuBxWGnYcTLtBN6CPTa3Tvfi8XvZ8OT3kCWrh5i3krVtHwzGjQ1xZg6vSwvHlK7CXhH4vx5YspfktNxEdp+KWh9pz9U0t8Hi8yJWSc1Y+zuTKCxDvp7K26DPaKgeyuM9oJGo1Vy5dRHz7dgEiXh8l54a7WvPBy38EXT/pvjYYouv2DFeLhGTIpRx1hI70aqi8dL4LAHwuF+X794ftL9+4g9g7w298i+Vy5IbTG2QqNUauuONN8k6ELtN4En8dN0Cs+fn4PF7ECnl1KcPToZXFk6RsFnIzOkHZGI3kn03xihDhnyAi4CNE+A8iOEcnp57CYkoeeQFvXk2OnPmjrzE+cS+ydi0Q1jEMH8BVWcnx5SvC9h+aO4/UPr3D9sfIM3mizWaW5b7BttJ5yEUa+ibdTQvjUHTS+LDXncThqWTusSeqxTtAqTObxeJbeODd99i5ws/+HeXExKsYPLo+CUmaS64OrFwhJj5JTUl5NvPvuCOoP3vJUvbN+I62996NQCikoa4XQ1IeY9GJl6pDEBUiLbc2/p4o2cVfnkx8nuoMhyPfto83dw0OMFdsnNGHCV9/iLpIiL2smP3521j12as4bVWO4XHpTbl7yjQ+fL0gpP+CQinG7Q42BrOY3WRJLq3P4+nQxSRz/fPzWPj+A9WVDGLTmtB54r3Mr3gOj98ZMD5W3gCZ8MxO0zxlFeALY7TmduMuM2O3eVCqJDi9Nvz4Tpt7fTox4vX4EYhE6DIykBuDw7Yt5UXMffMOju9aV932+4IP2fnrLCa8vBBjfPpp35e9pIQ9X30dtn/nx5+SMWQwUlX4NCGRRIwqISGsgNfVq4dIUhX2LFeIkSvO/eevzHEibJ/X78Yjq0obcVsszBk4hOt3bAnYfJZIRHTsnkRCioYfvthNQY6FpDQtoyY0ITldW+c0KK1YzC3JMTx+OBfPX369V0Tr0F/gUnlnikAsRpeRQcXRYONXAE3zTI6It5AxcghH5/wc1N/mvntQJZz+exWqasAjLiOzcRmH95aHHNOoRe3RGtbCQg7PW8BvL7yIJSeHmFat6PHqy8S2axvSs+dUtNJYbmsym9d29qHcmVPdrpcmcnuTOeik566kaYQI54t/17d9hAj/IWxWN2VFVjavz8NmcdO6Qywx8UqiEk4f1q2IiUYZFxdkTgMg1Wrr9MXsszswfzMHkdGAUKXCfeho1fGSz0fZC28Q98n/ECacwc62QIBEpcJtCW0aJ4+qPRdVKBASq8hkdL1XGZzyKEKB6LT1yk/F5qlgd3lwmb2j1g28bW3L8P4v8vC4e5BKRZd8Hdg908Mv7Le8/iaNrr4KdWICGmk0g1IeoUv8DRTbDyMVKTHKUtFJExELI18ftVHuzOWNnQMpdQaegO6tWM4PuY9yWez1SIo8LP3qqYD+wmO7WfzudYy//TteenRvQJ8xRoHN6g5y1AaIipJgycs/bXmnSwmhSEx8RjOuevwb7JYK8Pvxyf387/AAih1HgsYPTXsShfjM0lqE8to3GQUyKW6BhZ1lG1mR+w5uv4MucRNoqO+FUZYc8hqVWkpGAz1HD5hC9mdmyjl65AgjF/8c8vdVfGJfgHg/id1SzvrZbzHgpqlIpLWf+AoEAgSi8M8poUh02pN8ZWwsHR59mIVXXxuyv/nNkxD8w5tkeln4MH8hIsTumrxpl9lM6Z69QdFjSrWURs2jue/ZTjgdXmRyMUrVmedbZ8hlvFY/hfnFJvZYHRgkIkbGGshUyFDV8rO+GFEnJHDZU4+zZMKkoD6xUomxV0veKBnBrS9MR5Ocyp6Pv8BjtyPT6Wj/8EM0nTQBsbzuUQdRcUZuvr8tT97xa1A028jrG6OvxR/HYTKx/smn2flRjYu8ragIe0kJR+b/RO7adegz61V5O6SkhLyveGUDHm21gQLbAQrse4lTNCRe2Sjs33CECBc7kRVYhAiXIFaLi5U/H+Gr92pMxubOOEDTlkZue7gNMUm1h7apExMZ+NXn/Dh4KH7vKV+mAgEDPvsYVR1MlUzlTvJa9mSn2IxGLaLldRrEy5fiW7UWPF5c+w4iPgMBr4yNpcUtN7H5tddD9jccPapO80iEstMaG4VCKBCiEGtxuYLz+vz4MHtyUKkvDZOi01F5Iidsn6O0FL+v5jMhF6uRi9XEKjLPx639ayhzHg8S7yfZXDybwcZHWDBjcsh+m7kUp2kvcUkqCnOrDBhVagl3PdGRD6cFhwJ36BzD4S8/5PA3X9D3w/dI7NIZifKfz+v0uH34fD6ksn92KaHURqHUVm3gubx2rsp8k0/3X4fNYwJAKlRydeZbJCnPvGie2KhHFB+Lt6AouC8tGaFeww/H7yHHuoPmijEIEPFr3vssPPEi9zZfGlIAaPUybrq/LU/d+WtQjfR+g5Pw5h/h2i2b0NULLsPl9/sD6tH/lX0bf6L7VQ8iiap6xplNTqwWFwIEqLVS1NqqZ5QiOpoWN01i1QMPhZyn5eTb6vQZSe3di5a338r29z6obhOKxfT75KPTlhE7FxikySQoGpFv3xfU104/ipzPAjddrfmha7oDKFVSlH+jgqVYKCBZLuXGpGhsXh8SgQCV+NIS7qeS0qcnre+/i+1vvlddMlAZF0ePmW/zo+N5vH437xddw9TnDtDh3vvx2O2IVUqEuhjycm2snL4Zt8tL9/5pJKdr0Z/GsDAlQ8fLn/bjl3mH2bWlCH2UnCuuakRapq7awM7i8WJ3u8HpRG61IlWrsBUWBoh3mV5P/08+ZOmNt2DJqfkuW/PIowybO5vUfn0Ry4LL0hlkyRhkyTQ2hI/kixDhUiEi4CNEuAQpzrcEiPeT7N5exppFhxk4MhOlMbyIFwiFJHfvxnXbt7Dt7Xco2rINY9PGtL33bnSZmdVhkeEoK7bz2rObOHRKONzXArj5tu40FgjxrVyNzxz6JD0cIqmUNnffRfbSZZTsDHxv3V99BXVyaNdkn8uFr8yEr6ISgVSCUKdFZNSf0WsDaKVx9EqYzNzsJ0L2d42feMZzXqzUHzmc/d9+F7IvqXs3JKcJSYxwesqdoY3ooMqES+QVUZ4fOnwVoKJgB3c9cQ87txSSmKImvb4OpVLOwJFZ/PDFHiorXMgUInr3S6BthpnlY5/D5/EwZ+AQxm3aSFyb1v/E2wKqRGPecTNLfjyE1eKmS59UmrWJJSr2n9s08DgcuC0WxEolTQ0DearNdsyuAnz40EsT0ErikYjOvJa0KMqA4cn7KHv4eXyVNc8soV6H9K7bsetcNDNdQ/SJyfy2yIzX66ddn+7U6+hmS/FseifdgVAQLOLSsvS8/Gk/Fny7nz3bi9Eb5Awb15CMTBVafZuwp5cCgQCRJPT7kCk1NO91PU6XDJvVRf4JCx+/upmjB00ANGhqZMLdrVEoJShVYhqMHc2uL76idFdg7m9q3z7EtWtbp5+PMjaWLi88R6s7JlO4aRNihYLYtm1Qxcefl00inSyeu5r9zLt7RpBj3VHd3kIzmG6mq1j6/jUB42NatvzH70kqFCK9wOk554IT2VuxJLsZunwuIq8Yp8xGhaaM7xxPVueLj8x4EaUyGqW2ygjOXO7k6w938OvPx6rnWbU4m6atY7jj8Y61eh+IxEISkjVcfXNz7DYPEokQhbJqrWH3+jjucPJNQSlH7W6iJGKGCNwkHTiI8C+Rgs0m3cDm114PEO8Afq+XBaPGMmHvzvOyuRQhwoVE4K+rc8R/BLPZjE6no6KiAq22dufKCBEuFF++tYWFsw+H7IuKVfDky+2Jr1e3vC6Py4XbYkGiVNYpJM7r8THz013MmxFsgCMQwCsvtUL0zFPEvP0C0qwz/xK15OVRvH0HB2f/iDI2hkbjrkKdkoJcFxwa660wY/15GZXfzoU/S/uIUxIxPn4P4rTkMzYvK3fm8v6eURyp3BjQPiztGXon3olKcnrTnkuBypwcZnXvHZT/KBCJuGbTRmJbt7owN/Yv4rhlK89uaROyTypU8mjWWmY/fgOW8uA0FoDBt71K677XBLX7vH7KS+2YThRgPX6Uo19/wpG5cwIcwjMGD2Lwt18j+we+wyornHz/+W6W/Bj4/IlLVPHE6z2IiT+7I06rqRhzaR6leYfRGhPQx6ejjUrA43BgOnyELf97g8ItWzFkZdLu4QcwNGx42tzXuuL3+3HlF+M6eBTnkeMI01KRZaXj0+s4VraTb6aWc3R/oDlmQrKaic9HkZqYil4WPuXI5fRgs3oQS4SoNXWL4MnZ/wdfPjo0oK1Rl7FkdbqZZT+XUZBnY9I9rXn+/tVBJ/wKpZh7n+nEl+9s475nO6MTmzm+/Fd2f/Y5QomEVnfcTnzHjqjrmL98sWB2FWF2F2L3mJDa5Rz7bB7bnn0Dr7PGByGpW1eG/jATZWzElOx0uJ0OfnhlIke2/QqAJiqBnpMfp0yZz17XStTCKLon3UyMoh5Kib76ut1bi3j2nlUh57z5wbb0ubzeGd+L3+/nD7ONl7ILgvoGKcT0KSvix8s6V7ddMed75l85JmQVHYChP8yiXu/eiAyXTpWYCBFOUlcdGjmBjxDhEsPv91Ne5gzbb6l04fWErnkaCrFUivg0tVxPxVTuYOnc0JsHfj9sPuigxwdTIEpf5zlPRZ2YiDoxkYxBA0871nr4KE78SDu3xbVuM7jdeE7kUfLgc8S8OxVxbN1cak9ikCUxuckc8mx7+KP4e1QSAx1irsYgT0UlPrv3czGiSU5m1IqlrH/yGfZ/NxOf2018+/b0evsNjI3r5tgfoXb00iQy1B04avk9qK9b/I1sss2m9bDrWfPFtKB+iUxBRvNuIecVigTodCJWT7ibo4sWhxxTsOkP3JWV/4iALyqwBol3gMI8Kwt/OMi4W5ojOUNDr4riHL5/ZSJFx3aT1aYPKU0uI//IThp2HELFjv3MGTi4OtWneNs2DvwwmwFffEbDq8aEDJU9UwQCAbLEWGSJsWh6dKxud/tcFKyXBYl3gPwcC/vWR5E+qvb3KpWJzzjFwBifQYteY9nx60wAkht2JKrBDbw4ZTd+P3Tpm8Lyn44GiXcAu83D1o35RMcpeerOX3np4740HX8dWSOGIRAKkaoDDfhsxcVY8wswZ2ejio9DnZSEOjF83vmFQiuNrfY0sXvLsCY3RG40Ys3PRyST0eT6a7nsyccj4r2OCIQCxNKav53K0nwWPDsZY0I94jKaIVOqibo6JUC8u11eSotsTLynNXnHK1mzNBvrKaUSF80+SLsuiehqyWcPRZnHy0e5xSH7Fts99MvKQqJWV/vj+DyesOIdwJ59grIX30J34zgkmem1ekFEiHCpcunHAEWI8B9DIBDQvkv405MmzY3IJP9cYI3fV7VIDEdxqZlvKh9jdsFTlDmO/yP3YC0sZO/0b5g36UbmTnmILXu3IX34FsQNqnb/feZKXPsOndXcOlkCjQ19uK7BB4zMeJFkdYt/lXg/iS49nb4fvMvEg3uZeHg/IxbOJ6FjhzMyJooQHq00llubfE8L4+UIqIoEkQjl9EqcTJKqGQtzpkJjFW0HTQgwAlNqoxj31Cy00eFFlEgiQZcV3pNAnZSEqA7C1uNyYreY8LpDl8b6K26vg9VLjoXt/3XhUSpNdZvrJA5bJUs+eRSHxcTYR79GF5PMliVfsW3ZN5hPHGPJhImBPh1/svy2ybXmO58LPA4BGxaXhe3//ZdyvNbwEQdenweTMx+TMx+vv+6bqkpdFL2ve5xrnv6eBh0G0H7EEyz7uYSJN6XzyJRMBg1N5uCe0GVAAQ7tKyMlXYfF7KoeJ9Nqg8R75YkTzB8xiukt2zDvihHM6NCZWd17U3bgQJ3v9UKgMBppfN21jPt9AxP27WLCvl30evN1NMkRQ7K6IpbIaDtwQlB7Wf4R9q6fT1RSfVTaGuNYs8nJlg35LPzhILO/2kNBroXJj3WgXdea55TD5sHnO/O1h8XjpSzMoYMfOFZhZsDnn1SXB3SUm2otcxvbojmu3fspefA5vEWhKyhEiHCpEzmBjxDhEqRBs2hiE1QU5VsD2kViAcOvTEKl/edEmEwuon5TIwd3h17YNmyvYL5lE9vLFrC7fCn3t1iGQZaMz2rDazKDy4VApUQUZTirnXFbSQm/3nkPB77/obpt9/Sv2T97DmMWzEf4/jf4zJW4Dx6F7ped9fv8LyBRKpHUshCK8PeIkqdyU6OvKXfmUuQ4jM/v5o/iH5h+8Bb8+LHKTHQdey8dhtxMRdEJpAo1GmM8GmN8re7eAqGQFrfcxLZ33gt5EtXx8Sm11kh22i2U5x/ltwUfUpZ3mISs1tU1xk+eyvmcTnzlFfhtdgRyOQ6Nj40VP2Czh8+d9rh9+MPUNQ+HraKEQ1t/ZeyUr1jwzt1YTTUncY6SUiy5ob0EPHY7lcdz0KWnn9Hr1YbbZsNWUIDTXIlErUZqjEIsqeWcQ1BlfhmKUkc2q/M/YkPRdEBAl7gb6BY/EaM8tU73otJFo2releSG7SnKLuPKLsfZ9tzd7Nq/n7ZPPIPe2In8E6F9RvRGORZL1UbKwd2FtGyrQqULrOLhNJtZcde95K1bH9BuOnyYuUOGMWb1CtR1MDO9UAgEAjRhfFEi1I3YtCY06jSEfRsCy8TF1WtOky5XVD+DbFYX82bs46eZNRs7234rYPumAu54rCMFORZyjplp3y2p2kTxTBCdJtVN7HJR+McfXL9rG4fnLaAyJ5du017i5zFXB43NGnYF4uP5eD1e/B4v9jW/oxkzNMSsESJc2kQEfIQIlyAxCRoem9aF2V/sZv2qfDxuHw2bGRl7TRqJqRoUpym59nfQ6GSMv6MVT0xegf8vEZzJGWpEsSeoyK/KZSuw7yfHsgONSYbp7c9w/rEd/H4EahXa60ah6NUFka4mj9Xt8lJhcuLz+pErxGj1waeI5mPZAeL9JB6bjbVTp9Jz1NU4f1yCpF7dFsoRIvwT2Mxl+LxuZEotiaomqMQG8u370EiiuTz1SdrFjMYgTUYp0YEGjAln5hehTUtj0PQvWTrxRryuP0+9BQLa3nsPyd1Dh98DeNxODmxawvw376huyzu0ja3LvmbckzNJa9oJb5mJyu/mYl24HNweEAqR9GhHvataoOipYPXC0HN36J50xpUa3E479dv25cDviwPEe5346wPob2DNL2Djs8+z89PP8LndCIRCGowexcSnnufB7SW4nMEnhH2G1Av5jCp1HOeV7T0CqhAsOP4MG4um82CLlRjlKXW+L5FESsWWdfx6XY1Y2f/5x/R/ZxB7t4c+XezaN5WPpm0GIC5BwOJPHmXApOdQ62vCy21FRRyeNz/k9aZDh7Dm5V3UAj7C30etj2HAjS/Spv/1bF7yJR6Xg5a9rya5QVs0UTW/e1OZM0C8n8Tvg1mf7eLyMQ347pNdDByZhUgopLTIhqnMgcfjwxClQGeUIasljUQjFpEhl3LUERy9IxMI0FWUI23fHmPDhhgfagiAs7KSUcuXsvrBRyjasgVlXBxtbr2FrI6dsL/9RfX1rj378XsGI7iEqwVEiBCKiICPEOESJT5Fx4Q7W3Dl9Y3webxIxT40ejmyEGZv55rUTB3Pvt2LL97exuF95UhlIjoPiKXTCDGfFQwKGKuxKCl97iU8J/Kq2/wWKxXvf4lAJkM5oAeCP7/053+7nxU/H8Xl9FKvoYEJd7Yivb4embzmUXXkp8DTglM5vnwFPPQwApUSaZMG5/6NR/jPYCpzUFZspyjfgjFGSXScslaH5ZNYK0o4vmcjG358B4upmLSmnehy5V0Y4tNppO9FI32vs7ofl8+B1V0CCNBIYpGq1dS/cgSJnS+jZNduPHYHMa1aooqLrfUZYCkvYuH7Dwa1+zxuFrx9F5Ne+BnHN/Ox/bz8lE4f7l9/R2WxkXTzABq3NrB3a3nA9QqVmFETmiJXnNmyQqbUkNGiG+t/fDeoz+6pRJ2YiCUvL6hPrFCgSTs3m3Qui4X1Tz3Nzo8/rW7z+3zsnzkLZ0UFE295gQ/eChQwSWkaOnRPCjLK9Pl9bCr+LmQJwWLHEbaVzqdX4u11Nti05uWz5v4HAtosOTn4D6xj4OWtWfxTzc9GIIBh1zTi0N4yrBY3CpWY5GQf876dT8fLbw4Q8G6LpdY8YmtBaHPFCP8u1PoY1PoYUhpfht/vQyINjt7LPmRCJBbg9QR/XgpzrSSkaJj6YV8M0Qr27ijm9ac2UFlRJcYlUiFX3diMnoPSUWtDp/XoxCLuTInliSN5WL01m3JC4BaFkMqFq2kx4brqdm+lFf/+IyhX/M6Aex5AmJqISKtBUlKO6dUPAuaW1EuLiPcI/0oiAj5ChEsYlV6NSn/+X1cmE9OgWTSPvNwNq81Grn0Hm20f8n7uTLz+GlMbpViPrkyG7UTwAhzA/NUsZO1aUClQ8vIja8k+XGMWdWR/OU/d+SvPvtOLBs1qwoFrM60SiEQIxGKiX3kCUcw/F4UQ4d+Dz+HAV16Bt7gUgUSCMNpIuVfOq4+v49jBms9jbIKKR6d1IyElvPO5vbKclTNeZtuyr6vbdq2ezd71Cxj/wnwSsupe4spmdVNpdiIQgF9TyNLcV9hUMguhQEznuPH0SbyTKHkauoyMMyqZVFGcg8dlD9snsDiwLV4Zst+zaRey8T0ZeKeblltTWT2vHIfdQ9vO8Vw+thGxCWfuQK82xGJMqIdIHLwc2bTic7q++RJLrrohKA++93tvo4o/N07qtsJCdn32Rci+Y4uX0O3Vadz1REeWzD2Ez+en1+AMWnWMJyomuIya1V3Gb0Uzwr7WhqKv6Bg7rs4VLZxmc8g0gt8eupfm9zzAS+/ewZFjTjxuH7EJKjauzGHlomNExSq49f56bJh1NwDH92wguWFN+oNMp0MkldZEb/wF7TnaHIlwaSCWBEfOuF1eTGUO1FopdzzaAYlUxPKfjrB1Y6BbvDFaQXySmvwTlbzwwOoAoe92+Zj+3g6S0rS0vix8REeqXMqr9ZPZVGFhl9VJggC6SAVo7TbUkyZUpwRVhcVvpOLNP+vCb9pWPYd61OUoenXB/uu6P9+UCGXvrmf5E4kQ4eImIuAjRIhw1mj1MtQ6MVtPrGdT+ddB/UZZKsKj4U1kfGUm/G4PuQWVAeL9JH4/fPXudh5+qSsaXZVwr3fF5ax99PGQ89W/ciTqZo2RxsbU6f49PjdiYe017yP8e/GaK7H+vJzKr3+AP02UxNddzcdbdAHiHaAo38rLU9by1Js9MUSFPom3lBcGiPfq1/G4WPTRw1z1+AyU2tNXfCjMs/DVu9vZsjGPKe835LOjfbC4a/6Olua8xpaS2TzUYlWdc6pP4j9N2LnA5oQQpnHVlFv5wjmC5HrN6fvoRBRCPW2SO6JXqcNfUwtiiYz4zBY0634la79/PaCv8NhudhkWctWmNWx5621Kt+xGl5VGg3uvJ7Zxi3NmuOg0VYQ0yjuJo6iQLn170vqyBPx+P6paysEJBSLEwvCbjGKhLGTd+HCIpFIEQmFAmcCT7HzjVTJ6dqLPsCuoKC0j5/Bx2neS07lHY5zmo6z7+ibK8o8AVSetp6KKj6fl5NvY8vqbQfMm9+yBMq5uZUgj/Dtx2D1sXp/Hh9P+wGmv+tuQykSMmdiUqFgly+ZXfa4yGxmq00jWLjse8pQeYNZnu0ltoEClkyAXBW/0CQQCYqUShsQYGBjtD5sX7y0rx/zxNyH7LHMXEfXkfdh/XYdAo8L46N2IzrASTYQIlwoRF/oIESL8LYQCEZ3jxlNfG7zT3T3hZiS1nJIJZDKECjnbfgvvJn1wTxkOe43rvTopiQ6PPhI0ThUfT9epzyE7jXj3+X0U24+w9MRrvL/nSmYfeYR82z5c3tCnkhH+vbh276fyi5nV4h3AmdmQnZuLQo7PP2HBVOYIO9+xXevC9uUf3o7DGrxJ9VdKCq08fddK/libR6vLYtnh/ipAvFePcxxjZ/mi0873V/SxqYgkoQVmVpt+CIS1h3YLNAo8fifHLH/wY/HtVMr2oVHU7TQ5HEqNkVZ9riE6JTjtxS8Er1ZIzBODMHw1mIqnonhPMIFV5k/wnYGze21I1LVvPsijqjZdlGpJreIdQCUx0CfxzrD9fRPvRiGue3k/RUw0GZcPAUCq0dBqyhP0X7KO3gvX0/+nX4lpX1X2TqlRcmzz1yx9ZyA/vdqPXz66uVq8C0ViUpoEGnqKFQraP/Qgbe67t7pigUAopP6okQya/gXKmLptgp5P/H4/9spy7BbThb6Vfz0FOZW89exv6I1yeg5Op/uANBQqMV+/v4MW7eJQqSUo1RJufbg9Gp0Mr8fHsUOm8PPlWjhUtolP9o3jsHkDNnfVs9Btt2MrLsZtr/n+rc3Uzldhxm8L813t8YJUQswbzxL73kvIWjZBII1s0Ef4dxI5gY8QIcLfRi9L5NbG31Ng38/W0h9Rigy0jRmFQZqEVORGoFSE/NJVDu6NUKNCbwx/kiZXiBGeIirkej1t77uHekMGs/Xtd7AVFZM1YjiZQy+vU9hnjnUHr2zvjsNbCcD2sgUsyXmVyU3n0tQwIHIi/x/BW2GmcvrswEapBIFcTv0mRo4eNOFxB596Wszhy6SJwwhjAAQCBGEcy09l385Syoqr/lYatpOxyRbaaAzgt6IZdIi5+owEoUoXQ/+Jz7How4cCb08oYtC4J3D+vhtpy6a4tu8Oulaclkye5ET1vxvqetIj4RZEwr+/lNDFJHHV4zM4sm0le9fNRySR0uiyIbgcVqZPGU7na+4mO2M720wLADhSuRGXz45cdHYn/6eijI0hrX8/spf+EtQX1bTpGZ9GN9L3poGuBwcqVgW0N9X3J1Pb+Yzmkmm19Hrjf1hz82j7yjv88FMlO1+oyq+XyUUMG2eg7xU6dAY5XUffS96hrRQd21N9vVAk5soHP0VtCH4Pqvg4uj7/DK3vuA1nhRmJWo0yNgaZtu6fp/OFuSSPA5sWs33FTIQiEW36j6deqx5ojOcmjSJCDS6nlyVzD3PHYx2wWd1s3ZiPWCLkmltaYK10sWZpNpMf60BKhpbouKrTdJFYSP3GRv5YGzpdLilNTZ57M9tK57OtdD7XZr5HM2cvfnviBUp37iaqWRPaP/QA+qysoHKHAYhqf9YIlUqkDcOX2IwQ4d9CRMBHiBDhnKCTxaOTxdNQ3yOg3R/jI/qlxyh9/GV85srqdlmHVmhGD0UgFtO2cyJfv78jpKdS3yvqoTUECiNFVBSKzp2IbdcWn8uFRKWqkymU2VXIJ/uuqRbvJ/Hh5aN9V/Ns211Eyf+7ZdVsRUU4yspBAHKj8aI8hTtX+N0ePPl/GnUJBIgmDsbeNp6dgi9ocSdcKR3A3vV+5n+WE/C5rM3ILq1p5yonsRAf5Hote6LQnP6kevP6mgWw2wlSYfi8cplQjUhwZhtOEpmcJl2HEZvWmPVz3qK8IJv4es3pNHwyComG4u/mYXz0LsxWG+5DR6uvEycnoL/vFsqijnGl7CUa6noRLU9DKz37UGuP20tZiYMTRyuwVLpIz9TgckkwJtbD63ax6tuXqSyryrf9beZ7DHz+jWoBn6BogkRwbkLo5QYD/T7+gJ/GXE3Bb79XtxsbNWLYvNmozlDA62WJ3NzoW45btrAq/0MEAiE9E24lRdUSnezMnd11Gen0n7eIlx/7nRNHzdXtToeXWZ/tQSIVMWRMA3TRSVz9+AxKcw+TvXsDGmM86c27oDbGhzQng6qT+DPxULgQmEvy+OaZMZTlHa5uyzu4lcT6rRn10GeXtIj3+/1UuPLx4UMqVKCWXHjvFpfTy2U9kpnx0U6OHTRVt29ak0fbzgm075ZEVmMjOkPgZ+qyXinM/mpvyIoNg66PZq75nep/f3fkHu5RzGb/jO8AKNm5k/3fzWLoDzOpd8XQkJ4YACK9FlFiPN68gqA+oU6L0Kg/i3ccIcKlR0TAR4gQ4R9FIBQiqZ9BzLtT8RYU460wI0lJRGjQV5eQM0QruP3RDrw39fcA7VOvkYEho+sjkYTOGRVLpSCte9kqi7uEPNuekH1Or4USx7H/pID3ulwUbd3GLzfeQsmuXQDEtGpF/48/wJCWjkgoRKjXIRD9e7KuBDIpkvQUXHsOIL5nFIsS57E296vq/qU8TZfWt3P13eOZ8UbViWerjvFBm0mnotLH0Of6J1n+5TMB7QqNkX4Tn0WuOv3JZmx8jWDf9IuZnq1u4YT1tpBj+ybdjVR0emf8vyJXaklu2I7h97yH2+VAKlchkSnwlpQhUMgpe+kdtNeMRHTNCLxlJkR6Hb4KM97Scho36ktjQ1/8fj8evwu/31+nzTO/30/liRzK9u3DfCyb2Ms6k2/V8b+nfgtY8LfrHEvf/r1Y+M74gOvdTjsCW9XDQYCQnom3nZOT/5NoU1MZPv9HrAUFVJ7IQRUfjzop8ayN8vSyBPSyITQ29EWAALHwzOtjn4rJ5AsQ76cy9+t9dO6dSnScErUhDrUhjrRmZ3bSf7Hi9/nYs35+gHg/Sd7BrRzfs5GmXYef/xs7B1S4CthcPJsc0z5ayK7FUaZCLXMQF6/FYFQiDvO9908jk4s4vK8sQLyfZPP6fLr1TwtZbSI6TskTr/fgzWc2UlJoA6qqU1x5SwrZ6u8oL8upHuvxu6iQFSPVanGZ//xc+/0snXQz123fgjY1dKlFkVGP8dG7KHnoucCoPokE4xP3IDL+vXSeCBEuFSICPkKECP84AqEQcWw04jCGMnKFmA7dkqj/9UC2bSzAVO6gVYd4EpI16KPOzSkbgNfvqbXf7ftv5sFXHDnKrO69Ahypi7dtY2b3Xoxbshj3+1+jvqI/yn49EEWd2QLJ5/fj8vkRCwSIT5NffT4RadRoJ4yl/LX3ycu0sjb/q6Ax68rf44ZmgzBGy2ncKobRE5qiCVMKCapKorXqfRWpTS5j08LPsJTlk9m6N406XY4uJrlO99WtfyrzZuzD74fsQxVoyztRX92Lg5ZfA8Z1iLmaFHWrM3rPf0WqUCNV1ISrCqMMaK4ZScVbn1Lx4XQQiRCqlfhsdgRSKbHvv4TH56bMmc3Goq85av6dZHVLusRNIEqWjkRU9bPxVlTiLSzGvuY3wI+iW0dcIiHfdOqCvbiq1vugFb8z7bkNeL2B0Qp/rC8mLTONtKZdyd69NqBPKBIhE6m5seF0ouXn/tRYGRuLMjaWmBYtztmckloM7c6EnOzQ4h3AanEH+IT8m6g0l7FjxXdh+7f+Mp2stn2RKf5+KsX5xOIuY9bh+9H56qPceR3TPjmBIUrOmIlqTMXlOGxFpGToiE1QYahD+cpzidXiZvWS4DKIJ1m77DjtuiYGtYvFQho0jeK593pTaXLidDsxifew1voEe8oWB08UYuPPaTJhKywMK+ChqjRc7Psv4fhtK67d+5HUz0DRpT2imOh/1SZzhAi1ERHwESJEuCiQK8QkJGtIGBW+TNffRSWJQiuJxewONikTIiJWUf+s5vVZrPicLoRyGUJVcGmpixmPw8Hm198IWU7KY7eza+ZMmmalY/58Jo4tOzFOuRORQX/aeX1+P8VuD+tMFnZa7ERLxAyK0hEnE6MSXRx1eSWZ6Sgev4mltjvCjtlkf4/7XvqEo7vtKNWnD1eXq/UkZrXi8ttfw+txIZEp61zzGyA6TsVtD7fl/Vc24/fBB08eYvzD0+iZVcAO2wyEAjHd4icRr2qEVhp7+gnPAIFAgKJLB7wFxVhm/wxeL76KSkRx0Rifuh9RbDSHzRt4bWcf3L4qM7+d5YtYmvMqdzdbSCN9b/wVFio+mYH9l9XV81pmLUDSuR09nnuWxbfeRlzbtuw74AwS7ydZOj+fW++8IUDAa4wJRBszeTZjF3ppIqL/mFdFdFz454pEKkQquzj+ps41Js/pNiYunk3BM6HClc++il8ZyYO89f5BomIU3HhfWz6a9gelxTUbyQkpah6d1o3YhPO3QeH3+3GH8P84idvlxecL/bcLVWlGxmgFJmc+n22fRInjaNAYsUCG1hZVc/p+Kqd5XgpEQsTxsaiHDYBhA2odGyHCv5XIVlWECBH+M+iliVyT9V7IvsGpj6KV1D3X1e/14skvwrn3IK4DR7CvXE/5Gx/j3HMAn9V2rm75H8dlNpO3dn3Y/rzfN0FClVB0bd+DJz+0Q/tfyXG6eeBADt8UlLHDYmdFeSUPHsphncmCI0RJrPOJz2bHk1+It7wcQYIRq7ss7FiLpxih2EOH7klBOZ+1IRJLkMrr5s1wKnKFmLQUE0++0oAJt2cwYlwSsSop3h1lRC1Wo18oQ35IhKg8/AL67yDSa9GMG0Hcp68R88azxLz/EjGvP4s0Mx2TM4+P9l1VLd5P4vV7+Gjv1ZicebgPHAkQ7ydxr/+D2Jh4tGlpyKOMlJSHd5CvrHAhUdSkG4ilCkY+8BGxsY2Ikqf958Q7QEKKBl2Y9I2eA9PRG8/NSf/FRLnbww8WP5k9x4Yd02bA9Zfc6TvAscpNdNTewi/fVj17hl/biC/e2hog3qGq8sU7z/9OZYXzvN2bRiulU6/wEUM9BqTi95z+O04vS+CGrE9D+nRclfY/Dr0bHFkhNxhQxp3bjckIEf6NRE7gI0SI8J9BKBDSxNCfR1qu48djj3HCuo0oWRpD056iga4bcnHdFoLeSgv2VRsxf/Yt/j/FuqRhJtrrR1P+0jtoJ4xB0aMTgovkpLk2RHI56qRESveE9gbQJCWCpWax5ti0DVmT4HJfp1Lp8fJhThG2EEL949wSWqiVxMsuzP6xp6CIio++xrHhD/D5kQ3qQbPB/cmzBbuuAzQ3Xk56WhKiM6jd/XcpObGdJZ88ijGhHmKpnMVLj+Bx1YjmZk0HU5BfRuYVQ/+R1xcq5CCXYjF68fk9iIVutIDJUUSZ80TIayyeUiqc+fjmLA07r3/9FhqPGc3eWd9Tf5KMZWHGpdbTodKoaHTZEBIbtKVRx8Foo5POeDPkUsHv9+M0mxGKRGEduKNjlTz2WndefGgN5SU1n4VWHeO5cnwTpLJ/33LO5vWxzmyjV9uBRK34ltLcgwH9iQ3akdKowwW6u7+HQqRFJ0ilKK/q2WqMUZKfYwk5dv+uUswmJxrd+dmkEUtE9B+exeql2VSUBW4cpNTToBQfYeaL9zBg0nNEpzRELA7t7+C227HO3MN9Peaz3v0dJ7w7iBal0UM5CXW5HvH97ag39HL2fz6DY4uWANDv049QJ5y50WOECP81/n1P/AgRIkSoBYVYQ5auM7c3mYPLZ0UslKORhM7NBzBnH+fEypUcX7YCfcP6NLx2LLLsIire/jRgnHv/Ycpf+wD9beMpf+NjpM0ahc35v5iQabW0n/Iw2b+EllOtbrgB95c/Vv9bqD59ioDF62WfLfSJkQ84ZHcQLzv/p6jekjJKHnkB7ylRBM4la+g2dDJrRJ9i9waGc6rERjoYx9RJvPu9XhAKz4nITMhsCVBdx/tUNMYEnKUV7PzsG6I69cRU4WXX5kKkMhHN2sZhjJZjt3moKHfitHvQGeXoDDIUyrr/vM2uQjYVz2LhialUuApIVDZldL1XkXr1tV7n9bnxWcL7SPisVmQJWszZ2UTJKoiKVVBaFDz+usktyWoZR2bzjxAIL2ygoLOigsoTOeyb8S3WwkKyRgwntnUrNElJ52R+8/ETHFnwE/tmfIdYqaD13XcS375dSOf7tEw9Uz/oS2mRDXOFk7hEFTqD/LwJu1A4KiqwFxVReSIHmU6LKj4BdVJwfvTZIBIIEABvmuHOB77EsmsVR1bPQiAUkdXnOtKbd7lkHehT1K3ZX/wRKfUyMJucOGzuWsc7HefX4yA2QcXz7/VhyY+H2LDiBGKJkC69o2jazMeS92/AYa3gi0cuZ9KrS4lJaRhyDltBAWvuqSpXWW/UUOo1H0xM0xY4BWUs+PlZKopOIFfpaH3lOK589H4UaiP6rEyEYRzoI0SIUMMl91fidDrp2LEj27dvZ+vWrbRq1aq6b8eOHUyePJlNmzYRExPDnXfeyUMPPRR+sggRIvxnUUkMqKjdkK1s335mdu9VbbwFoI+Nx7BhZ8jxvjIT3orKKuMvkxkuAQEPENOiOZ2ffYb1Tz1dXQJNIBTS7emnkJ8owGWxVo+Vd2hz2vlqSY8EwH26Af8Q7mPHA8Q7AD4fglfn8vCji5hd/gK7TIsAAc11gxigu4eS7XtI6NY47Jye4lJcew9iX7keoVqNakgfRAmxiLRn7+Wgi00hsX4b8g5uCerrPPh2dr70Ea0efZqv3tvNuhU1J+LxSWrueLwD777we/VpnkAIfS6vx+gbmqI3nj4FwOouZ/bRKawr/Ly6Lc+2mzd3DeKxln+gEOmweyuCrpMI5WilCcg7tcV9MHjjAUDYtAEnFs4FYMMdN3LX1z8yZ0EFO/4owu+HqBgF4+9qRVYj45/3foHFu9nMnq++5te77gFAolYjUOlxKWLRuRQotVLEQjsCoQCV7sz/1s3Z2czq0Qdzdo1h2PFly8kaMZw+778TUsQbYxQYY86vqVk4rAUFrHnkMfZ8Nb36uaFJSWH4T3OJbt4cU6kDh92DSCxEp5chC+FcfioetxNLeRFWUzFCkQi5LpohBjU/lVuYaoLM+n1p1agHPgTM9Yh4UHfphlrrpAk0ietM/XFRbP+tGJUmfJUCkVhQa/8/gc/jwWAUcNWNzejaU07O/k0c2/Ip8xbXpFt5PS7WzPofl0/+H1J5cLlLZ4W52lvl4IzZJPfuiTQjlhWzXqwe47BWsOHn9zGV5zDo5peRqsKXzYwQIUINl5yAf+ihh0hMTGT79u0B7Wazmf79+9O3b18++OADdu7cycSJE9Hr9dx8880X6G4jRIhwqWIvLWXpjTcHiHcAbVIS3rxwwb/gOXYCUXwc/EV8mJz5+PweJEI5GunFVV9dERVF63vupNHVYyn44w8EQFRyGr7Vv+GaX/NedXfcUCcXerVISIpMwgln6FOlBspzV1kgFB6HA9+f5le2gkLyNm7EY3eQ0KIF0hEDcM1dGlCr3XvkBMJ7P2PsK8/RW1j1fZGzZSMLfrmdyye/Hv51ikoonTIVT05+dZttya+orxqG+srLEWnPLjdXrY9h5AMfsXrGK+xeNxevx4XGmECnQbdh+f0QXoeDAl8y61bsCrhu7KSmvPrYekxlNSHWfh8sm38EY7SC4dc0QiSuXRSb3YUB4v1UFuVMZXTa63x1ZGJQ35C4F1AK4pD36Yp13mJ8FZUB/UKNGlm3Dhx/8B4APDYbzr2/c/tDY3C4xHg8PpQqCYZo+UUTKm/Jza0W75qUFLpPn8P8RRV89vwx/L5jpNfXM3ZCMvk7p5PSuDX1WvVEra/b37bX7Wbbu+8HiPeTHPpxLq3vvvOM68+fT7xuN9vf/5A9XwZWb6g8cYId079HO9TAV+/tpCjfikgsoEufVMZOahbWjM9hNbN3/QJ++fwJ3M6qqAyFxsgV97yLRZvJSouLw3Ynh6kyb7o3NRrDBSqzdi6QiuQ00vfBJC7jjmdacXhfGe27JbJpTV7Q2AEjsuq0+XYucFgrKC84xh+LPqeyNJ8OQ29hy5IvOPjHLyHHH9u5FofVHFLAS9VqBEIh/j9TqZpNvpHF854JGgewd/0Cuo99AIUmUgYuQoS6cEkJ+EWLFrF06VJmz57NokWLAvq++eYbXC4Xn332GVKplKZNm7Jt2zb+97//RQR8hAgRzhhHaSl564LN3RwmE0q9Dp8p+BQSQBQXjft4LiJ9lQlXpauYbWXz+fn485Q6sklSNWdUxsvU016GUqz/J9/CGSHTaJBpNOizMgHwlJTiFoiwSySIogwoe3dFFBOFUHn60z+dRMytyTE8eTiPv1qVXR6tQy/+ZxbetuJiSnftZstb75DY6TJEchmr7n2gegEJ0Gz89bSZNBbnJ38xUPKDNTuH+e/cWt0kliqIrxe6pJjf7cby46IA8X4Sy3fzUHS/7KwFPIAuOomeox+iUaN+eL1unCUmdr30MYWbNtHlvU+Z+WOg8NMZZDid3gDxfio/zTpAj4HptbqZA+RWho4uAdhSOoe+hqe4KWkxK81TyXfsIVaeRQ/NoyQrWqNWqkCpIub1ZzFP/x776t/A70ferSPa8WMgSs+EvTvxOhxIVCpUiYkIL2KfiEM/zqv+787vf8Ebb+dQdorJ2LGDJqY9WcGUqdfyy/tXktmqJ/0nvYBSazzt3PbiYvZ8OT1s/86PPiG5W9cLHoUQDmt+PlveeCuo3di4MbIuV/LqExur27weP6uXZHPsYDmPvtodQ1TwM6Qoew8LP3ggoM1eWcb3U69j0v9W0DY1ni2VNuKkEjrr1ERLxEgv0p9NXZGJlMTplMR081Pe2I7L5cUYrWTFz0dwu3zIFWKGjKnPgOFZyOT//HLdaatk67IZrPjq2eo2qUKNXK0Pe41CY0AkCn1vyrhYGowexf6ZswAQqeXYLeVh5yrJOUh0cu3+KhEiRKjikhHwhYWF3HTTTcydOxelMngBsmHDBrp3745UWhNmNGDAAF5++WXKy8sxGELv6jmdTpzOmlxNc6iSFhEiRLigWNylgB+VOOq8nc553aFzDjd//DG9r5uIc9ZPQX0CmRRJWjLShpkIowzYPWZ+Ov48y/NqFro51h28sWsQ4xt8QZJ2NGqRBKNUjPgiOXU8iTg6CnF0FIou7c/q+noKGdPqJzO7qJx9NgcGsZgrYw00VMpQ/QMC3l5SwrpHn2DnJ58iVihodsN45g0bGTRu15dfkdypE9FZ6fi9Pvw2G97CEqQDu7N67czqcWKpgqse+zpsjq2vohLb4l9D9gHYlq9Fmpn+t96TJiEJn8PFH6/+j30zvkMgENDqrjtIHzyYysWBm0t6o5yifGuYmcBmceNy1eL8npvL8WXL4TIJOmk8YoGMMucJ/AQaEYrFQqY/6qHz4JdpmyKi/KgXhzaW6B414czipHj0d9+EdtI48PsRatRVxniALj39LH4SFwZHWZVDeFSTJpwoV1BWXBg0xuf189PsItr0uI7NP79NpxF31EnA4ydgYymo2+vBz8VbJM3rcIYsAdb4zgf49vvgTS2A40fM5J+wBAl4h7WC1TNfDXmNz+thx/IZ9L72cTrr/7kSoxcSoVBAVGzVuvba21tw+dgGuJweZHIxhig54vMUaWAxFQWId4Aj235l2D3vsXPl9yGvueyKW1GFiTqRajR0f/Vl7KWlHF+2HGEYoX8ShTpy+h4hQl25JAS83+9nwoQJ3HrrrbRr145jx44FjSkoKCAjIyOgLe7P8LOCgoKwAv7FF1/kmWdCh/REiBDhwlLuzGVH2c+szv8Qn99H1/iJtI4ejlGWEvYan9dPSaGVrb8VcGhfGVmNjLS+LJ7oWBVCUd2XwzK9DnVSEpbc3ID2nFWrKb/xRoz9u+NYWlMuS6hRY3jkDkRJCYijDQgEAszuQlbkvR1y/tlH7mdUw058mC/h3tRYmqsVSC7xE6VTkQqFpClk3J4ci93nQywQoPmHTt4BTEeOsPOTKmPBjEEDOThnbshxYqUSmVaLZuLVuHfvR6jVIMlIRZAYQ9eyptRr3RNNVAIJmS3QGBMQiUObv/n9fvxOV9j78dvCm7mdCbqMDHq8No0OUx4BASiio/H4hDRpFcPGlTnV48pK7MQlhj/xV2kkyMLUCrfk5rFgxCjk8TF07j6VsZ4XcTusGNOz2OX6haUl/8OPnyxtF6K18Tz5RgaFuRZ8Pj/Ne6rRG+VBJ4RChbxatNcFS3kR5tI8KopOoItNQRuViNpwYXOc6w29nM2vvU50ixbs3h/eZOzArnL6D6jyhjiyfRVx6U1PO7c8JpqGV43lwKzvaTL5Xowdu+HzgSvvGLtff5FmkyYivIifB2KlAkV0NPaSkoB2ZUo98kNEpZxk345imrQKFHxup52yvMNhryk8thuP24FU9O/Pj5ZKRcQmXJj3eWzH2qA2t9NO9q71dB55F+vnBEZcNGg/gPrt+tc6pyY5mSHffo2tqBiPz0Vasy5k71oXNE6u0mFMysJcYcftdaHWSpCJT2+YGiHCf5ULKuAfeeQRXn755VrH7N27l6VLl1JZWcmUKVPO+T1MmTKF++67r/rfZrOZlJTw4iBChAjnh3JnLm/vupzj1m3VbQuOP8sfxd9zU+MZGGWh69QeOVDOs/euxGmvOm1cvTgbmULEU2/0JLNRHU7G/kSdmEjvd95k/ohRQX0HVi0m7ZHhpFz5BNIiJ0Kloiq83KhHKKkRfIW2g/gJbdhm8ZQiFZix+Qy8dKyANxqkkCg/v0ZF5wO5SIhc9M8LkT1ffV393zKDAUtOTtAYoVjMlbN/QLx4NWVf1DjrI5EQ9cz9JLVoQXLDtnV6PaFKibxjaxzr/wjZr+jZ6czeQC2I5XI0yTWu52Jg1IQmbFqbi9dT9fmqrHAhlgiJilEE1ZIGGD6uEfqo0II6b/16tPXrkTCmD188MgShSESzziOJFqTQXjmE9gkj+d72GNfV/wCtPBptAudUZJQXZjPzhesCyoRFJzdgzKPTMcSlnrPXOVOMDRuQ0OkyXBUm9Nrwm39avQynzQSARFo3gzmxVEq7h+4nYezNzJiRx4GpxwGIS9Rw7evfoW9xcflk/BVVQgIdnpjCqrvvD2j3ux3I5CKcjtDRHsaYYFEmkSmJTm5AZVlByGsS6rVALDk/OeD/dpwVFbhtNiQKBTK9PqDP4w5dOWTTzx/Tut913PT6Co5uW4XLYSOrTR+0McmodFGnfU1FdDSK6CqTxyHRr/LN06OpKK55Pqt00Vz1zAp+W2ti2fzt+Lx+2veJonOvFPQxElSSyMl8hAh/5YJu795///3s3bu31v/Vq1ePFStWsGHDBmQyGWKxmKysLADatWvH+PHjAYiPj6ewMDC87eS/4+PDlxmRyWRotdqA/0WIEOHCs7d8WbV4b6buz71R87i+4EV67RqF/VAe5uM5VBw7hvOUMM6yEjv/e3J9tXg/idPu5bUn1lNWUvdTUYFAQEqf3oxeuZz4Dh0QisVo09Pp9O4L6O7txBrnDEgwoujQGlmzRojjYgLEO4BcVHsOtJAqwe4Ffi2vrHVshNrxOmpyv0t37yG+Q3Dof/2RI5DtPoxn76HADreb0qdexVtSVufXEyoVaG+4CoEsuISXpEkDJKmhN5jOFfFJap57tzdZjasWtwIh7NlaxJRp3UjL0lWPE4kFDL2qAT0GpSMKsZHitlrZ9dkXNL51PIunP45MqWHU5I/wrD7BooFjmdO5P8sH3sDIE/dj8Jz7+szWilLmvHpzUI3vkpwD/PjazdgqSs/5a9YVVXw8Q3+YSUKXzlzWPvzfcp8hMexf+yUAGS2713l+h8TIqy8f5MDumrzgwjwr/3thJyXlF2vwfBVCkYi0UYNo/cS9iOU14tq0aTW9BqeFvEYkFtC4ZbBbv1ylpdvYB0JcAUKxhJZ9rr6ovRIudpweK2VlRzm+fjU/jR3HjPadmDf8So7/uhJHec1nL71517BzFGbvRm2Io+MVt9JtzH0kZLWsk3j/K4b4dK5/YT5XPf4N3cbcz7C732Hc86t56/ntfPzqNo4eMJF9uIIfPjrCS/f9QUFhOTZ3aL+ZCBH+y1zQE/iYmBhiYk6/y/zWW2/x/PPPV/87Ly+PAQMGMHPmTDp27AhAp06deOyxx3C73Uj+XET/8ssvNGzYMGz4fIQIES5O7J4KVhd8DEB79SjaH+jLsgnj8J7iV9Hi1psxNmrE8WXL6f7qKxga1Mdc7ghZVxqgtMiO2eTEGF33EkwyjYaUHt0Z9vOPOK1m7FRSqs4nSp5GK+lo1JLaFzBR8vSwZbdSVK044ao5xTzudOHx+RELL+6F+8VK42uvYdenVe7p+Rs30vHxKcijonCU1gjA5ldfjeuz2aEncHtw7tqHOKHuzt/ipHhi3p1K5Yw5ODZtQ6hUoBo6AGXvzoiM+r/zdk6LRCois5GRR17uhs3iRiAUoNZKUaokPPZqd8wmJ06HF41OGjLEvRqBgLg2bTi4awX4/fQd+wSrJt1D+f791UMqjhxh3tARXLl0EWn9+p7RfTodHkxlDvJPVAICElPU6KLkyGRV92OrKKHgyI6Q1+Yf3o7VXILyLITCuUKdmEiHRx7CXGjixnvlfPrGtlMLGNDmsmhSEkvZ9eM2+ox/CrW+7mH/W9bnYTEHp2H4/fDdxzu595nOKNWhUzguBn7zfE/OyGP0Gv0lgko/QrmU4+KdtNG6OLxfx8HdNc89sUTIg1O7hH3+xqQ24oq73mbJJ4/itFVtZqr1sQy/9330MZGoyLOl0l3C9qL5KFa6WHXtndXtltxcfujdj97vvk2ziROqonyiEmjec3RQvrtILGXgpKkoNXWPYDuJx+3D6ajK5xdLqjYQtVEJaKMSyGzdG4D1v2ZzeF+wuV1RvpWNywoZMEaMUqIL6o8Q4b/MJZEDn5oaGEKnVlfthGdmZpKcXHXKMW7cOJ555hkmTZrEww8/zK5du3jzzTd5/fXwJYAiRIhw8SIQCBEJxPQUTOTnccMCyn4B7PjgI3q+8T8qjhzlu05duWbzb3g8NV/yPQam0bFHMm6XF5FYiKnUETRHXVFodMgRoxPFkKhrWWdnaL0skclN5/DGzkF4/DULdbU4iuGZX/BOngj+9GlvpJRHxPvfwNioIWn9+pL9S1XZu9UPPMzgr7/k95deIWdVlVeBXKvF7QwdJgqc0Qk8gEAkQpKSiP7uG/FZbCAQIDLozqtzuEYnQ6MLjALQGeToDHULOZYolWSNGkGBuYiRHa5FUnAiQLyfysp7H2D0iqUoY+smUq2VLtYsy+ard7ZXh/qLxAIm3t2azr1TUKqluBzhjfeA0/afD4QiEfrEKLr119GsXTx7thZjszpp0ESDrXQneftXMGnaUvRxqciUdTNa83p87PyjKGz/4X3l2CzOi1rAA2wyf88m/hR8lqr/k5lVjLj7Xa73DuLQHhN6o4KsxkYMUXIk0tAn6V6fjOSmg7nx9Z44zPkgEKDURqExxl80ZQUvRfabVqIsV7DxrtApqKvvf5CMwQPRpaej1Bjofd0TZLbuzYYf38FmLiW1aWe6jrobQ3z6Gb2u0+mhKM/Koh8OcvxIBWlZegaOzCIuUY30FC8Oh93Dip+OhZ1n0zIT7fupUWPG7bAhkshQRkrNRYhwaQj4uqDT6Vi6dCmTJ0+mbdu2REdH8+STT0ZKyEWIcBb4/X6cNjMCoQiZ4uxLYZ0tCrGOHvG3IhUqOf7lorDCe+dHn9Dk+mtZ88ij7P3mOxrcchcSqZBrbm1BQY6F155YXy0c4hJV3PtsJ1wOGw6LCQC5Wheyfu1JnDYLblM5vk27sH43H4FUimrYAJQ9OtWpHrpIICZL25Vn2+1ma8kC8my7iFd1IUbVlU8L5ZR5qoyx5EIBnXTn/+f8b0IVH8+ALz7j6OLFbH3jbVyVleSuXceAzz/B7/Xh83lRabSYkuLx5IbOtZU1bXhWry2UyxHKL90cXV9UPWZ9WIZEWkwXwcqw40p378ZttdV53pxsM5+/sS2gzevx8/FrW0jP0pOWpUKm1DDy/o8QSaTk7P+DzYu/wGWvUoICofCicqaWK8UkKDUkJNeIdI8rhpbd+59xiLdILCQ2LvxnxhCtwO92ABfvc6Gl8XJ+PPZYULvTZ2WP+3u6NBpOgya1lwUryyvl2OFK5s86QlmJg6wmBoaPa0xCsias2I9QN8yuIuZnP8VY13PVFRX+isfhwJKbV10dQq2PoWnX4WS06IbX40Gm1CCVn5mZnNNuY+eWMl59fAP+PwstHNxTxoqfj/LQi11o2S6+xlBWAMJaNmiEQgEKl5Y5r91C0dFd6GKS6TbmAZIatInUjI/wn+aSFPDp6en4QyzoW7RowZo1ay7AHUWI8O+hojiX/b8vYs/auUhkCtoPuYnE+q3OKDT0XNBI35PCyv3YD+8OO6YyJ6faHOfozwtpcest3PxgW0oK7Sye85c8ZwG4LHks+vB99qyfD34/jTpdTo+rHsIQnx5wymO3mCg5sZ/1P75LZWkeiRktaPv/9u46TKrqf+D4ezp3YjvZpbtLQkBBEFHEbgwsxPrqV7G7vtgtdouFiYUgKIJ0d8d2zsb0zP39sT8Wxp0lF3YXPq/n4Xncc86991y8zM7nnnM+547L4bNfKZ/8Ed45C4i97xY0sfv/AqFV60k0tWJY+q2U+IOsd3t5JaeIilB18J5q0HFzRiIJ+ib5cdyoWFNT6HzVlbQ843SUUAhjbCwafWRiQNt1l1HywNO1jtVmZaBNq/813o1dSaGHx++YQ2Gem8yWdkxt0upsq7fZUOsO7Dn1egJ898m6qHUqFbgrivhzymss/OkdQv+fPKt5l0Gcfdtkvn1xAt7KMjqdeDYWe+NL5qYoCr5wFVqVHq2+dg6EAzVgcAI/f7Ml6vvJESPiMFA/OxkcKQ5DOielTOCP3Fcjyk0aO+e1eBqTdt/Tnkt25PHzt9v4/uudNWX5OVX880c29z8/iPZdG3YXgqaswuXD5dJyhvk9bKq6t4+E6uSe/2a2HdqylcrSAnJ3FPL6U5tqgvfdwiGFVx9fwP/ePqVm2zyjUcvQ0c1ZvrD2Fo0Ag4an8se7/2HnmnkAVLmK+PyJSxl80V30GXU1etOxvzOBENHIN0YhRA1X4S4+vP8sdHoTKW26EvR6+e7FCTTvMohTr3sK61H8Mu0wpHJysxtZd9LHbPr2u6htknp0p+T/p/uaEuPRmY2062zg/tdn1mp73X8y+emlC3CX71kTvWbOt2xdNpurJv2C4/+zXfs9VSyf8Rkz9toPN3/balbMmcrFt76DvrAE/+r1BHZkH1AAv5tGpSLBoEOnVvFYyzTcoTA6lQq7Vk2svnFPk21qzPvIrWLo2JbYB2/H9caHhPILQavBNLg/tivOP6BZFUdSmS+XikABgbCXGF0iNn0SBs2R3Uopd1cFhXnVo+rbN7uIvWwQaq2WcDBYq223CTdgTjqwHAF+X4iCvOjT3/sOTiZ3zVcs+vG1iPKtK/7EXVHCkIsmUrhzPQPOvRWDuXGNQBd5t7KkcCorSqfh0KdycurNJJtaYz6ETNlWnZdrxrfi3be2EAzsiXZOOiWZJFU2WkPDZeA/EFZdLKMzH6Rr3Bn8tusZKgJFdIk9jYHJ44g3Nt/nsV6Xi+JcFz9M3VmrLhRSmPz0Yh56aQiO2APPWSKqXy7t2lbO608trFlXftsdrYnJyKBiZ+2/a4PDgSWlfl5cBgM+Fkx7C2fW6Ki5HaB6twxXma8mgAdo0zGeDt3iWLMsMmFlWmYMbdv6mfrNvFrn+fPzp+k4cIwE8OK4JQG8EAKAUMDP8plTGDz+bopMu1gTmIFJZeOUsydRvHQtJdmbj2oADxCjT6DN6LNY+NCTEdlyAVCp6HbTBH6/fgIAPW/7DzqzGaWskrISb0TTTj3iyV7zc0TwvpunspTlM6cw8Pzb0Gi0VLkKmfnxY7XahQI+fp7yCGeP/g+hyZ/j+WMuxm6dDvqeHDotDonXG4zaasHUvxf6ti0Je7yotBrUDjtq46GPpB4uRVHIrlrJa2vOpsBbvR+2VqVnRPqdDEu7mRj9kft3t2NLZILF76eVcOr7nzL7qksJ+fd8Cc84+SS63Tgeje7AHl6jWUfr9nHs3FJeq27QUBsz3ngn6nH5W1eR3KILXU66AJ2hcQVvee71PLVsAJXBPZ8j/xR8wjnNn2JIynhM2oPbxSYm3o5myYfcO/EMCko1+HxhMtL15Pz8DbHJJx1wnBHZOgAAa3JJREFUroGGFKNPoFPsCFrZ+hNU/Jg0djTq/X+19JWWsn2zq860JLk7K6ksD0gAf5AK8908eNMfVFUEasq+/CqfK15+kxnnnxnxb1ql0XDaJx9iSa2fAL6ytIBFP73DKTeM3me7f8+gdcaZuOn+E1i7soDp32wlFFQ4YYSTHj3S+ez+IdHPEQ5RmrcVZ3L0HQ+EONZJAC+EAMBdXkxyrx68X3wdBa4908/n8TEntbuBWNc2MpS+RyWhUDCs4A2H0alV2DIzOf+vP/ht3LXkzV8AQEyzZvR76AE2fPk1nsJCet1xO/GdOgKg06lxxhkpLd4TxLduZ2LX6t/rvN6Ghb/Qe9Q4zLY48retRgmHo7Yr2rmBULIDAJWp6a55FqCJc9JYVtiW+Hbw9IohVAX3vKQKKn6m7XyMOGMzTky++oj9u9t7PTfA0oXFhEOxjP75b6rWr0DrLyd9YD9iMjIOKqDU6zWMOr8Ns3/ZRigU+YVdo/YT8NU9PbysYCdpbXoc3I0cYe6giymbb4kI3nf7eutddI8766ADeEtSEr1uvoGlL7/K1qnfEPL7KUpIYPBzT5PYvVs99fzoMGoPLHnfbiF/AI1m38+05K47eEvm5kQE7wA7t5YzdYaTS2fNY/t3H1G8aAWxnTrQ9dprsTXPQhNlCv2hCHjdBHwe1LgwWbR4qmrP4rFYddgdtX93xsabGXBSFl16JREI+zBbtFTkZOOpqDuxqEarr7NOiGOdBPBCCAAUrYp/fFMo8G6qVfdH8Wv0bHV2dTK5I/CtqjJQRIlvFzsrV5McM4LZZWHWuf0k6rWcmeAgrV07zpr2PZ6iYkI+Hxq9npy580jq2YO+992NNTUV4/9vF+mMN3HuFR1469klNef3ehUMlrq/XBvMNtSagxsWNw878dBuVoh/2VL+T0Twvrfvtz9El9hROAypR+Ta6Vk2bA4D5WV7svMvX1LC8iUlXDK+D6ed0xqt7tBedSSlWrjvucG88b+F5OdUT6dPTrOSnB6LWqMlHKr9BR/AFn9k7vVwVAVKWF36W53168pmkmzed8K2aKxpqfR/9CG63TgeJRRCa7Fgjq+9V/qxRm+xkJpchUajqvWCByCrtYMYW8PNimmKQqEwKxdH39lg9fJSHt1SwaT378ag82G1Jh504sX90RnNaPVGVvz2Epdeew9vPb+xVptr/tsTZ3zdL79jYkxA9ayLYIyTuLTWFGfXPo/OaMaeKNsLiuOXBPBCCAACBj/zSz6ts36F+2faqYfW+3XL/fl8seV2tpT/w5jWv3HvlnIC/z/FbrPHxzxXFdemxjPY6SA2bk9indh20TOGq1Qqeg9Ko7jQw/efrScYCLPgr2KuvuEKtq2InuSy7+jrMf5/gJ+U1RGVWh11FD4hoy2avDJM54xCk3jsf8kWh6/KVYSnohRFUTDFOKImg9xeubTO48v8OQSVure+O1zxSWbuf34wk+6eU7MWXqWCgac0Y9ApWYccvEP1XvUduiXw8MsnUVlRPXU3xmbAYg3TefC5LJ85pdYxtoR07Anph3zNI0UhjELd21DuvU3kwdLodMSkN+w9B30+3Hl5hPx+dGYLltSUIzrbShubgDqwhYvHZvLRe9si6gwmDdfc3h2bUwL4g6HRqEnNqHsmRGycGb3GjM1+8Pu5HwirI5Hup1zGwmlvYUv4jLufGMtvPxSRs9NNajMLZ1/WgbQsOxrNgW2zaXUmcuYtr/DRA2cR8O7Z/UKlVnPmLa8QE3tg+TiEOBZJAC+EAECFap+Bgk85Mvsxryv7g38KPuH8Vh/xcYGWgBKo1ebd3CK6xZgxHuCIgd1h5PTzmjNwaDJFeSVoNCFi41R0HnIeK2d9GdG248AxpLfpWfOzxZ7ASZfcy8yPHo1op9EZGDn2Uey2DDSJcWhsBzdlVDQsdyhEZSgMCpg1aqzaIzuBPhwKkr9tDT++eisF29cCEJvaklE3PEtqy65o9XtGoTKtdU8Xd+rT0KqObCDTrIWdR149GVepF09lAGe8CbvTWG97kDvjTTjjI9cyD75oIpVlBWxesifhpCMpkwvu/RhbXOPbDcCsddA8pg9bKxZErW/vOPko96j+VObksPB/T7PyrXcIejxY09IY+NQTND/tVEyxsfhCbsr9efjCVRjVVuyGFHTqw1tCVFkeYO4yGHCCjvse6cTMmcWUlARo087GwOFZbFrnolV7eUl6sAaPzOKHz9fXygAPcPbY9tidR27pl1ZvoP9ZE/BUFLNq9kdsWvgdHfufz6BBXWjTowexyQf//zMpqwPXPDuD1XO+Y+fa+cSnt6b7KZdgT8iQKfTiuKZSou3HdhwrLy/Hbrfjcrmw2Q5uPZsQTZknWM676y9nafG3Uevv6PIHbR1D6vWaFf4inl81nB2VS7mu0zKe2lV3xum7M5PpZT+wjLMBn5fVf33NtNf/W1OmUqsZdMEdZHbsz4aFv6IoCh0GjMaRkIHZvmdkv2LXLjb++D3GVimsnD+VitI8UrI60+eM63CmZB3WtlFHUlUwhDscxhsKY9JoiNVp9rm/7vFCURRyfAE+yC1iSYUHBehkMXJVajzpRj2aI/R3VJK7lbduG0rQH7nWW63RcvUzv5PQbM8MkmLvdh5Z0j3qNPrLWk9mUPI1RyX3xNHmqSilylWIqzAbsy0OqzORmNjkhu5WnbZXLObJZQNqvegcmHQV57V4GovuyIxsHknuwkJ+uuhSdsyovXPH8HfeIvOS05i67R7mFnxISAmgUxs5KeUGRmTcgV1/6P+vivLd3HjhNJLTrIw6PZHmbWIJhtUsWVLGD1M2MOaS9lww7uCThB7vfJ4gSxfk8cpj8wn4q6N4lQpGntOKsy7tcFRmNXjd5bjLiqgqL8ZgsmK2x2N1HF4iTkVRCAZ8aLV6VOoDG8EXoik60DhUAvh/kQBeHM+yS5by+JqB+MPuiPI21oFc2+ZTHNb6XXNW5svliWV9KfHt5NpOy/jfPgL4iZlJ9LEf2JZSZfk7eOOWQTX7S+8tNrUllz/+PWZb7S/b/qoqZoy/kbUffYwxNpbWF56LKSme0tXryZ07n4vm/kVMRuOa3hv2+vAXl1K+bDWhikrCndqx1RpD2Galh81yxEeaG7t8f4A7N+6qHn3fi0Gl4tk26aQY6n8UJxQKMvvTp5j37atR6zsPPpeR102qybK+Owv9q2vOotC7BQCNSsep6ROPeBb6wxEK+KkozcdbWYZGZ8Bsj8NyiPtHNwXBcIBC7yZ+2vEk612zidElMDJjIu1iTsJYDqG8AsJVbnQZaagdNtTWxr/FVcGy5XzcvVfUOlNCAkP+fJtnC0bVqhuSMp7zmj+NQXto9+hxB3j1iYUs/Cs7av1Tbw2jeZuG3daxqfL7Q5QVe8neXo7XGySzpQO704DFKiPWQjR2BxqHyhR6IQQAoeISdK9M596xv/Fz5SusqpiOUWvj5MQb6OQcwYrSn4gPtCDZ3I5YQ/0E8matgw7OU5iT9y4VvvWk6E8g1197Cr0aaHYQ23yVFe6MGrwDlORspqI0L2oA787PZ/1n1etyvSUlrHztzcjzbtnSqAL4sNeLZ95iyia9Bnut2W/VrRNFE65irUZD7wOctXAsCikKf5dV1greAXyKwo+FZVyRGo+unkd0Al43O9bOr7M+e8MSfJ7KmgBepVKRbu3CxK5zjvo+8IfKXV7CytlfMnvKpJr1qcnNOzPmP68Rl9aqgXt3ZGjVOlLM7bm09Rt4gi40Kh1WlQP/2g0UPPQsStWeF5/m04ZiG3seGqe9AXu8f0UrVtRZ5yksJFjpjlr3V95bjEi/nQRty0O6rsms4+LrOrNmaQFVlZGf+UNGZhGf1Dif+6ZAr9eQmGIhMeX4/ewX4lgnAbwQAgDF5ycwdymqxas5Y9hATu95GbTM4Nu8R/lixx017Wy6JG7vMoM0S8fDvqZeY+LU9DtZUPAZf+x6gEtb/cTzuyD4r3lBl6XE4TiIkWQV+9meqI76oMdLygl9saanU759B7nz5kXUV+zcBYA/HMYVDBFSwKhW4dA1zEdpqKiEsv+9yr83Uw4tW0X87LlMHTSA1mZDg/WvoXnDYRaV1527YUWVl1J3JYnW+p1tpdUZsCdkkL1+UdT6mLiUqEsxHIYUHIb6Wf8d8Hup9OahoGDVJ6A37nv2SmVZAUoohMFiQ2/c/xf/zUtn8Pv7D0WU5W1dyUcPnM2V//sZe3za4XS/UTNozDUvVoK5+RTd8xQEIoNQ908z0DVvhuWMUxr18gdzYt2JwFRqNSq9BqLE8CElSFWwjMOZG5KSbuWpt4cx86etLPk7F0uMnjMubEPLdrHE2BvnUiUhhGgMjs9vdUKI2rRaVEYDitdHcNoctKlO3jc/wcaKvyOalQfyeWnVKO7uNrdetraKN7bk7m7z+GzzLczddTP3NHuB2S4jmz1hEnRazk5y0sygx3iAmWsB7InpaPVGgn5vrTpbQjqmmNpTM0N+P2q1mtSBAyjbuIlmJw/hhPvu5p9HHid3fvVoanynDhT5A3yRX8rssgqCCqQZdIxLjaeN2YjpIPp4qMJeH+FSFygKnr/m1wred1O++4W+A/viP45XSWlVKuz7ePETo1GjeKugvgN4vYG+Z1zLmjnfRK0fcM4tGM1HbolWceVW/sx5k79Lqtct93KczSkZt5FgaR0RTAYDPipK8tm8ZAYLp72Np7KMrM4DOfH823CmNEdbR5KoipJ8Zn02KWpdVVkheZtXHNMB/N68C5fVCt53q5jyLaYBvdHENd6p4LHt26K32fCXl9eqazFmNCuZUeexRs2BLWmqi0qlIjHFynlXdOS0c9qg1arrLXGiEEIcyySAF0IAoHHasZxxCpVf/giAv0sKG/P+jtq22Lcdlz+3XgJ4rVpLhrUrEzp8gztYhkqlZlxqLEEM6FWqQwqKrc5ERl73P354+ZaIcrVGyxk3vlArUZYSDpP7z3y+Hj6SkG/P1Hut2cxpH3/AX3ffh8EWg6pVax7fmscO354to7J9AR7ZmsuDzVPoEnNkp32Giksp//BL3L//iaF7ZzROR51twxWVWNWqI5akrSkwqNWc5rCwoDz6NOBhWj+lm9aRFF//Wc9jU1oy8rpJ/Pr2PTX7nVcnUryT5OZHLjlXceVWnlt7KvmeDTVlfxROZlHZVO7pPJcEa/X0dk9lGYU71jHnqxfYunx2Tdu1c79nw4JfuOKpaXX2MxTwUV64q84+5G5eTtu+IyPKgh4PVXl5BNxudBYLlpQUtIamP8oa3JFTZ124uBQlFDqKvTl41rQ0zvrpB6aOOI1A1Z7ZKrEdO9J70pO847ow6nFt7IOw6uonN4NGo8bmaPrPghBCHC0SwAshAFDpdFjPOo3A1p34Fi3HT+3R6725g2X1en2LzolFV08jVUGFzNYncMUTP7Jg2tuU5G4hpVU3+owahyMxs1bzypwcfjzvwojgHSDodjPrP//lpJeeJ7FnD7ahigje9/Z+bjEPGvXY63m6ejikUFrsobTIg7+0HHvfE9FU+QiuW4+pX0/cv82Kepy2fWvURsNBLT04FiUqfkZZNUyrjAykBlk0GLcuwhB3ZPYSNlpi6DToHJp3HUTB9rUo4RCJWR2x2OMxmA5v5LIuiqKwrmRGRPC+W0WgkFnZrzGmxZPodAZyN6/A5y6PCN53CwX9TH/vAc69811MVketerVWh9kWh7u8OGo/4tPbRPxcmZvL4udeIGfO33iKS0jo2YuOt07EnJKG2WZq0sGbvlNbqn74LWqdNjMdlb7xjiiHQkH83kosXTtz8uJFVC1dgnvnTpw9e+JKS+d/QT3jWnzIZxvOqEmuCJBm7sRVbT/A2gSz7gshxLFAAnghRA1NnBPrf8eh5G9G7TCgKzASCEcP5J2GxpPMbW8Vu7KZ/9jjrH7/QzR6PR2vuZJe5z9AXJv2mJ3Rv3C68/NxFxRErSvfvp2YZhnEpKWxMi96wAKw3evHqyjUZ8qqgD/EhtXFPP/gPCpc1S8OtDo1510wgL4ZGajMJjSpSYRy8iMPVKnQjbuYxFjHcT0CD2BVQ8cdi+jdvBtrQhrCQAdNiMIlP7Ng2huMffS7I3ZtvdGM3piJM6n2S6Mjwecp5Z+yKXXWL3Z9wym+W9F7rKz9+zv0+3iRsGP1PHzu8qgBvMUWxwlnjmfmR4/VqtObrKS3613zs7esjPzFS0jq2QOd2Uyz869gxowiHnpwPT7PGjJb2bnipm60aBOL0dz0vpLoO7RB7bATLnPVqrONuxiNo+GT2O1+CVhR7kejVWF36Al581k+cwpbV/xFTFwKrUdeTfYJffm7Zz8K/EGqfGEgwMu5Zu7tOItwKIcS3w7ijc1xGtIPaws5IYQQh6fp/bYUQhxRLmMZDxYO5kTN1QxOuZ7fs1+o1aZ3/PnE6BKPfuf2o6qggGkXXULOnOqp/yGfj6XPvcTS515ixHvv0GHspVH3kA1FyXy/t3CwevQ2Vlv3R6ZRraK+x7qLCtw8ccdfBAN7sqgHA2E++3gzaXd2QvPNpzgmXIn711l4/l4IoRDarAxibrgcWjTDfJwmr9ubxR5P63a9mPrMtahUKtRqNT9vX4vBZOWCez7GkdSsobtYb1QaHXp13cs49GozKpWGYMBLSd420ttG3z4MqkfZVaroy1dK8ndiioml85DzWDn7q5o8DBZHAuf8921scXuW1niLipl7/0MULlvGiW99yGsvrWfzuj3B7vZNLh6+ZTb3Pz+ITj2OzGyII0mbGE/8Mw9Q+uwbBNZuBEBtj8F2zaXoO7TZz9FHnrsywPKFufz6zWYqy/1UVvj5z71pfP/s+fi9/z9lfiOs/2cafS+6i5bdRrN1r10bSgIhPCTR3pZBC/o20F0IIYTYm3y7E0JEUhSCip8/cl/j4lavMCrjHv7IfQ13sAy92sSJydcyMmNi/U13r0eV2dnk/D0XVCpM8fGEAwF8ZWUA/HXXPTQbNpSY9NrJtSzJSWgMhlpT6AF0VivmhHgAusWYUQO1NyWD4bG2fSZMOxRzZ+yICN739vU3u7hlSF9KHnkO00kDSHzjf6i0GlQmU6Pfuupoi03O4tw736G8KJuC7WsZmJBOXForHEmZqOt5C7mGZDDEcHLieJaX/BC1/qSE67Fb0nCHSwj6PWTsNVL+bx36j4661aKr1MuymVNZ8P3z9Dr1Si645yPcrmL0Jit+bxULf36HuLRWmG2xBDwe5j/+BIXLlmGw21GltWfzRxujXu/9l5Zx/3ODsccaD+3mG5AuI5W4h+8gXF4BgQCqGCuaWCeqo5DUcn9Kd+VjD+ZyUsIS1Kl6Wpw5gukf3r4neN/L/Cn/46xep6KKMdFTHUCFwhb0JOka7zIAIYQ4HkkAL4SIYNY6aWbtzo7KpXy66UY6OU/l4lavoFUZUAjT3Nqn3ra6qi/h8gpCZeWYq/xcPX8+it3M9k1LgOpMycuefoldM2bhr6iIerwlOZn+jzzEXxPvrlU36JlJWFKrRxSdWg13ZCbxzPZ89l5R3c5kYEyio173Ew+HwmzdWFZnfV52JUp8CorPTzA3H7XTjibmyKytPhY4EjNwJGbQrMMJDd2VIyrd3Jk+zvNZUPpFRHlLaz+6xZ8JgDkmlkEX3MGGBb/Q76wbmffNKxFtY+JSGXzhnegMtUfzt28upjR7FSgKi35+l0U/v4vOYCIUDBAOBTFa7AQD1S/C3Pn5rPu0ekp/bLt2bNwUPZkgwM6t5Xg9wXpdgnI0aewxaOwxDd0NoDopJyoVFTn5LHviIdZ/9EFNXVKPr8neEH17QxQF7+YlZO3cwO8/vU04HKJFt5MIjn2AcFpL1Br5yiiEEI2BfBoLISLE6BMY2/pN/rf8RAJhL6tKf2FV6S8AnN7sPjo5RzRwDyMFi0ooe3YyviUrasq0zdJIGX8un752PT53OcNvewSj04nWGD1ZltZkotO4K4lt146/738Q1+bNONu2ZeCTj5Hcpzea/x+BMmjUdIsx81LbZqyp8uAKhuhhBZ2Sz9zcZ3H58+gSexqp5o6HnaFfrVHTpmMcC/+KnuU6LdOGKj8PTfNmOG+/XoJ3AYDdms4FLZ5liOd6Zhe8RQAfA+MuJ8PSDadlz3KB1NbdKc3bhqtwF+fe+S4bFv6Kp6KUNr1H0LzrIOwJtXNcVFX4WfhXPhkpHYE9idsCPk/Nf8emtkCrq/53Fg4ECPmrczf4KyuJtdQ9Q0WnV6PRHN/5Gg5XqKSMwPaduH+dhTo+lnyLJiJ4h+pEh/viLS9m6+LpNTsnbFk6k51r5nH1M78Tm9riiPVdCCHEgZMAXghRS7qlKw/2WMbv2S+xwTUbhz6FUzMm0szaHZO28YyRhd0eXG9+HBG8AwR3ZKN99QtOOfdOvnn7dn567y4uvfsLTIl1r9s3xcXRcvTppPTrS8jnQ2s0YoqPr9VOr1aTbFCTbNDhDVayuOgr3t9wFQrVX4x/z36BdHNnbu40jVhjxmHdX59B6Xz1/hp83tpbUV1weVvikkJozhiCJtZxWNcRxxa7JR27JZ1WcYMBBbWqduBssjroNuxiKksL8FSUMPDcW9EbLZjt8RF7xe9NrVaxa3s5/QaOZMkvrxEK1F5yMujCiTVT73UWK7asLMq3baN49Wr6ZOlQa1SEQ7WDyEHDM7E5m970+cYiVFJKydOv41+yEgD9+aez8Jnna7UrW7WB5BZdyNuyolYdQGKzdpTmbY0oC/g8zPvuNYaPewydXv4fCSFEQ2v4BVpCiEZHq9aRbG7LBS2f444ufzC+w9d0cA7Dqotr6K5FCJW58P71T9S64K5cEu0ZqLU6lHCIzWtno7dY9ntOc0ICMenpUYP3fyvzZ0cE77vtcq/kl12TCIRrBzgHIyHJzAMvDCYhec9UZpNFy/UTe9GycyL6FpkSvIs6qVXqqMH7blqdAUdiBiktu+JMzsLiSKgzeAcwWXSMGNOKLz/K57QbP8Dq3JN0TmcwMeyKx0lt1bWmzJqawuBnJ9X8vPal/3HtDa34d268Zi1snH15B/SG43vLw8PhW7a6JngHwGSMurPGihdfZ9Dpt6DR1Z6N1Ou0cWxeOrN6Cv6/bFk2C19V7Uz7Qgghjj4ZgRdC1EmnNqDTJzR0N+qkeHwQrntKaLisAoPRiqeylOLsTYTD4XpNWraiZFqt4H23OXnvcGr6nYc1Cq/RqmnVPo5HXj2ZCpePUDCMzWHEGWdEo5X3r+Loa9s5jt9/0PHRO17OvOB97DYfSjiIzuQkJasZJmvkUo5mJ5/EGVO/5M/b72Tbd1PR6PU8/uxDbNwSorTUR+eeSaQ1i8EZb2qgO2r6QuUVVH77S2ThrjzSBw5g3ZTPI4ordu5k8f2TGPvGVJb9OYUda/7B4kik2/DrUClVfPfC+KjXMFrsqNT1+4LFW1pKOBTC6HSi1sjLGyGEOFASwAshmiy12Qg6LQSCUetVsTa87nIAsjoPrPeM45WBojrr/GEPYWpPfT8UsfEmYiXAEY2AM87EhHv6sG1jGb99u5lwWGHY6BakZ8biiKv9jBocDlqfNYaUE/oSqKpCrdNhSUqiZQ+Zil1vwgqKN3K2T2DOAnrdfj0bv/m21u4aJWvWYTHFcsI59xG0rqOiPMz7b5Zx2dV17yxywpnjsdj3PyvpQFTl5ZH9198seeEl/JWVtDn3bNpfdgn2rKx6Ob8QQhzrJIAXQjRZ6lgHltOGUvXdr7XqtG1bkp2/HiUcwmix06rnsHq/fkfnCH7a+WTUuuYxfTBqJLGcOPY440w440x06pGIoijoDbW/SgT9Pvw+N3qDGa3egDWlce1ccSxRx1gwDTqBio++qilTfH6U76dz3nffMuvBB8mbvwBUKpqfOoLBzz+LvXkWBblVfPLW9ppjNm+OpduI61n26xsR52/b9zSadxlUL32tys9n+rXj2fLDjzVlRStWsOzV17lo7l/YWzSvl+sIIcSxTAJ4IUST4isrw11QgKe4BH1MDLazT0MJhnD/8geEqke8dT06EzpnEL+/OI7kFl0448YXombVPlzJpjY0t/Zha+WCiHIVai5s+QJWXf2MWAnRGOn0tac9+71uSvO2Mf+HNyjcsZ6krA70OeM6nEmZ6Awyi+RIUGk0mE8ZRNW03wmXlNWUB1dtQBcIc+ZXX+D3elCrNRjjYjHYqxORWmJ0dO6VyMpF1Wvlv3h/O6MvGM6YO09n15oZ6HQh2p0wEntCBhZ7/eQ/KV2/ISJ4382dn8/CSc8w5IVn0RpldoYQQuyLStnfniLHmfLycux2Oy6XC5vN1tDdEULspTInlz9u/Q8bv/y6piyxRw/O+m4qBo2WcJUbldFI0KCm0lNW/cU2xonZduSS75X6spmR/TKzcl/DG6qgla0/57d4jnRLF/QaCVjE8SMUDLJp8XS+enoc7PXVQqVWc/5dH9Ki2xBZ63wEBfMKqJz6E54/5oJKhXnYiVjOHIE2qe48Jju3urhv/Ey8nj3LkLQ6Nedf2ZFTxrTAbNHXax9/veoaVr/3ftQ6vc3G5atXEJOeVq/XFEKIpuJA41AJ4P9FAnghGg8lECBUXIbidhMy6vnzgQdZ9c57tdrFtmvHuTN/a7BpusFwgIpAPmEljEFjwayJpbTIg98fQqfTSNI5cVxwFWXz1n9Oxvf/eSf2ZrbFMe7p37DFpzZAz44fYX+AcHkFKkBtj0Gl09XUBX0+vMXF1S82ExNRqaq39CvIq+KXqRtZsTAfq03P6Iva0rpDHPYjsK3fr+OuYfW770etM9jtjF21XAJ4IcRx60DjUJlCL4RolEJlLqq+/43Kr6ah+HwY7ryeNR98FLVtybp1VGbn1FsAHwr4qSwroMLlxaI1YdBqUGvUGFOi7yOvVetwGqqn6JeXeZn+x2Y2rcplUH8bSlkepRY9ca2zsGekodHX74iWEI1FVVlh1OAdwF1eTFV5kQTwR5har0MdHxtRpigKrq1bWfLiy2z+5ju0ZjPdJoyn9TlnYU1NJTnNyiXXd+GsSwJodWosMUfuM6rjlVfUGcC3v/RiTAmy7EgIIfZHAnghRIMKejx4XS40ej2m2OovnkogQNW0GVR8MrWmXcDnJRyMnm0eoHzndpJ79Tzs/oSCfnK3rkcVsGLIKcb/5Ve4t+9CkxCH5cIxmAf2RuOwRz024A/x+/dbqCgspZtxOTNH3VWTAVprNjP87TdpccYo9FZJbieOrMpAMYXu7Sws+Bx/yENX+zkkmloRa06Ouna9fijojGY6nHwWmSecCCpw7dzBsm8/wlWwkzp2XBRHmGvLFj7t0x9vSUlN2R8338q6z6ZwxldfYE1NQafTYI898ssbYtu0ptXZZ7Fp6jcR5dbUVHrefhtaQ+396YUQQkSSAF4I0SBCwSCuzZtZOOlZdv0xC1NCAr3vuoO0Af3RKyoqv/whor1Ob0Ct0xEOBKKeLyajfpLUVZYW4A9YsazdiP+tPdP1Q4XFlL/8DsFtO7FfeQFqi7nWsaUlXqZ/v4UbLjUx/az/RNQF3W5+uuQyLl2ykMRuXeulr0JEUxEo4vttD/NH7is1ZbMKXqZDzEjOT3+dZGcG2kNc0hEOhagszcNbVY5Gp8ccE4sppnr7MasziTGPvsnvVa/yffELhAmREd+VMyc+wNbvp9dbIjRx4IIeDwv/93RE8L5b7rx/KFy+HGvq0Vt6ZE5MZOhrL9Px8stY8vyL+CuraHvBebQ571xsmc2OWj+EEKIpkwBeCNEgileuYkr/Ewl6vQC4tm7lh7PPo/O1V9P/rrtQPN6I9sriVXQaexkr3nm31rniO3fGlBp9evvBchW7UFcaCU/5Mmq9+8fpWM86NWoAX1nu54R+sax//YnoJ1cUljz/IsPefF1Gmo5z3qpyqlyFFGxbg95kIS6tNTHOJDS6w5++nF25OiJ4321Nxc+sLvkNU/ACHHHGg17O4a1ysWHBL/z+wSN4KqoDwvR2fThjwvPEprYgaAnyzqZxFPv2bE22s2o5r1adx8RL/sTqSDq8GxMHzVNSwsavptZZv/r9D8kcMRy1+ujl6LAkJdFy9BmkDxmMEgxicDhQHcXrCyFEUyefmEKIo85TXMyMCTfVBO97W/nm23jKXfCvANc/ax49zruADpdcHPFlL2VAP4Z//S72lMx66VsorEfj9xKuqIzeQFEIZudFrTIYNMQ6NVRs21rn+Us3bCTo8dRHV0UTVeUqYtanT/LGTQOZ+uy1THnsEibfMpjNy/4g4Kv9b+JgBMI+Zua8VGf9vMpXKK3KZt7Dj1KZk3tQ596x5h9+eOXWmuAdYNe6BXz0wNmUF2WzpWJ+RPC+m0KY77Ifwhuu49+UOGJUKhXqfbyo0ZqMqFSqo9ijPQw2G8bYWAnehRDiIMmnphDiqPOVucid90+d9Tv+mIX1nJGRheEwnhffpdfgU7ho5T8M//MzTlv8Ld0+ugdLZnq9fQk1Wqyo9bp9tlGbo28PF+MwEFTpie3cpc5jk3r3QmexHFYfReNUUuhm3coi5s/exbZNZZSX+WrqQuEAVYFSAiEvm5bMYPEv70ccG/R7+GrSOMqLdh1WH0JKAHeorM56T9BFOBxmxeS3mH7tdXiKiw/ovJVlhcz86LHodaX55GxezibX33Uev9H1lwTwDcCUmEincVfVWd/5mqsP+LMz5ConsG0n3mWrCGzbSajMVV/dPK5UuYrI3rCEmR8/zp+fP0P+tjV4KssaultCiCZEptALIY46lUoFKlXEXtF7U+v1WM4YgX/1RvzLV9eUa2LtxJxyIiWJXiwZmcRo47EZUtCr62+7I0ecibxiL5ZWWQQ2bavdd6sFTWL0TMk2u4H+w1vhanYrW76ZihIOR96XTke3G65Ho9v3CwLR9OzY4uKJO/6ktGjPCHq7LvHcdH8fQtYcZue+wcbyOYyKvYu5X79Y63i90UKrXiPJ37kDZ3JL1JpDeyFl1FjpEXsu68pmRq1vZxmJIaDDlplJQrv2BMsrIG7/a9NDfi/F2ZvqrN+5dgFpIzrXWR+ji0ctYwZHnUarpcv117Dxq68p3bAhoq7DFWNxtml9QOcJFhRR+tTLBHfkoD2hB5iNaFERc86ofe4zLyJVlhbw85sT2bDgl5qyv754lhPOHE//s26qySchhBD7IgG8EOKoM8TFkjX8FLb9+lvU+mZDT0Ib6yD23psJl5QRzC1A47SjTohDGx/LkUy5ZLI6sGeoUa69mtDj/yPsqthTqdMR+8BtaOLq/pKVlBaDXtuGUd9M5Y/rx1OVWz1NOaZZM0Z++B72Fs2PYO9FQygucPPEf/+ktDhy+vu6FUV8MnkF8ed8zR9FzwHgj3FTURy5BKPrKdeR3uVc/vy9jOWfBui4djX9T0om7NuO1R6PxZGA3lg750Jdusafxs/Z6ZT6IkfzTRobJ8ZNQJ8X5NRbb0dZtgbf5E/Qnnkq+rYt0cQ66jynWqPDbIvDXR59xD4utSXpjv51Hj88/XZs+uQDvgdRf2wZGZw741d2zf6LtR99jM5qoduNE4jr0B5zwv6D71BFJaXPvgFtmhMY3JsF771HVW4e6f370XFrR+xGA1p73fsViz22LJ8dEbzv9s93r9Omz6lktOvTAL0SQjQ1KkWpYwjsOFVeXo7dbsflcmGzyS8kIY6UknXrmdL/RLylpRHl/R99mO43TcBgj75V29FSUeZBU+7Ct2odyvqNaDPTMfXtjjYpAZV2/9stKYpCZXYO3uJiUKswxcUf1WzP4uhZu6KQh26aFbVOrVFx+xspvJE3EIDRiQ9Q8uFistcvBqBtv3PRJV/Op29F5k0wmbXc/lBbZr07lp6nXkH3YZcc1OhcgXsLP+14kn8KPyKsBOniOJPTkh8kpiqe8GPPENwVuf7d0Kc7zv9cW2cQr4TDzP/xTWZ88HCtOo3OwHUvzMYYH8uiwi/4cOM1KHvtGdc59jSuaPMOdgngG1zA7Ual1aI9iASGgZ05lE75hi2uAuY+HpmgU2e1csHsmST26F7fXT3mVLmK+PThCyjYviZqffv+oxl980todZLgVIjj1YHGoTICL4RoEM62bbhk8QLWf/4F237+BXNKMj1uvQVnm9YNHrwDxDhM4DBhbpYMpw056ONVKhUx6WnEpKfVf+dEo1JaWHdSwnBIIezf88Lnz7K3ufyCV8l+pDqA7zB4HI9OrD013eMO8tm72Qw/+Vr++PhBklt0JqV1f8pdPkJBBZNZizMuei4GgERzCy5u/RKnpd1LwOOhamMBOz74mI4t2tcK3gF8C5YS2LqjzgBepVbTedA5FGxfy8pZX9SUG8wxnHfXB9jiU9FodfROuIA2jkGsL/sDd9BFO8fJxBmaEaOXadb7U1XpJ+gPY47RodMdmT3ZdeYDn8mxm+L2oOrTlbknnVyrLlBZyYwbb2bMD99iOoClGMczJRzC6y6vs95TUUI4FAQJ4IUQ+yEBvBCiQahUKuzNs+h953/pesP1aAwG2VpNNEkpGTF11pnMWtTGQM3PZf4cFlm+ZcRtk1jx/Wdk7wwRDkWfCLd5XRmxl/fAYo8npErm9acWsXBONuGQQlKahctv6kKLThacdYzM6zUmEmOycBVv47erbqDvLTcT+mdJnX2tmvY7hq4dUGmjfzWwOBI45cqHGXD2TRTt2ojRYseR1IyY2GTUmupjjForRm1rkkwHtrZagKvUy8bVxXw/ZT2V5X669E7i1LNbk5hsOeRcCPVJZY8h75ef6sxZkjvvH7wlpRLA74fRYqdlt5NYOv2jqPXtThiFznDwL1iEEMcfCeCFEA1KpVZjkOUqoglzOPW0au9k09rSWnWnnp2B3eSPKPu79AOyLWuYcOdXrFwYqHXM3pSwwuCxz/PyU5vJz66qKc/PrmLSXfP4z6SOONosJcPaDYsuNuo57FlZnPfHdBRXBVVzV9R9sVAY9rOozmR1YLI6iEtrte+G4oBUuHxMeWsVM6ftWUKRvb2CP6Zt47HXTyajecPPRtLYYkCzn6+Lshpzv7R6IyeMvp5Vf31NwOuOqIuJTaZVz2ENtqWfEKJpkZSwQgghxCGqys+ncN5vXDkhnd4Dk1D9/29Vg0nDmRc2Iyt1C0XrV5JsahtxXM/EczDZY2nVMbHOc6dlxuCr3EWFOy4ieN/b1Mm72JK/nr/z3ycQqnsPeWtqKtZWLTGdNLDONuaRJ6HSyXv9w6UoCoV5Vcz5fQcfvbacOdO3U5hbRThcO8gtKfREBO+7eT1BPnx1OVUV/lp1R1KFy0f29nK2rC+hILcSvy+I2mwi7eQhdR6T1LMnhtg9s0CqKv0U5VVSuT0fz6Yd+LPzCHvqXmZyPHEkZXLlUz/TqtcpoFKh1uroPOR8LnvsW+wJ6Q3dPSFEEyG/qYUQQohDEPT5WPriyzi6tmX6c+fQsf8FjDh9JMGgGjUe1s99mxnv/kj7/qMZeuFNzMh5mVhDJqOa3U26pStGrRWH08/oi9vy/afrI86t0ai4+OoMcpZPpjBwUZ192L7ZRYK2Pe9uG0mPuLOJN2XV2Val02I5fRjumXMIF5VE1Ok6tEHfusVh/X0cjypK83EV7KI0byuOxGY4EjNwVZh54MY/qKrYM7vCbNXx4ItDyGrliDh+6fza+Qh2W7kon6rKAJaYA084dzjycyp5+bH5bFxd/WxodWpOO681p5/fBktaKr3vupOFT02KOEZrNDL0jVcxx1dvrVlc4GbT4p1kubZQMfWb6l081GqMA3pjv/ZStHVswXm8UGs0JGS0Ycwtr+J1l6NChSnGic5Qdz4LIYT4NwnghRBCiENQlZvLkhdfZsDzT6I1mFnyy+ss+eX1Wu1i4lIYmHw1PRPOQ6c2YtLuWTJituo544K2dOiawNQP11Ja7KFVOxunnBHPsp8eR0WQpDZ1LzGxWHX4whUEwl5cgbx9BvAA2qQEEp57CPfvf+GZNReVTodl9HCMvbvuc3tEUVtp3jamPHYJJblbasrsiRkMv+5d9HoNVewJ4N2VAZ65928effVknPF7gjW1eh9TplVwtGZUlxR5ePz2P8nP2TPTIxgI8/2n6zFbdIy+qC29bv8PzU4+iQVPTareRm7wIHrcenPN1pjlZT6+/WQdo9KLCL734Z6Th8N4/5pPcFcu8U/chSZWnjODOQaDue7cGUIIsS8SwAshhBCHIFBZRdDtZsMHn9P1uvP467vno7bretIF6DQGdJro0+VtDgPdT0ihdYc4AoEQGpUPb1U+w8bejSkmFrfHyidvro2a7G7IWYks9DwOgFZ1YCO12qQEYi48E8uooaBSo7FLIHGw3OXFfPv8+IjgHcBVsJM/3p/A2Zc+xzsvRtYV5rlxlflqAnhvaSmdu0XPWwDQ44SUozf6nl0ZEbzv7fvP1nPiKZnEJ8WTecowkvv2IeTzobfZIhKPusq8dG9nRPnwm6jnCW7dQTCvUAJ4IYQ4TLIGXgghhDgEOosZjV5P7ty5xFszad55UGQDlYpTr3kS2wGubbXa9DjjTNhiHSRmtCUpqyO2uBRiE8zc/mh/tLrqX9karYouvZI496q2dB+hsMb1MzZdEjZ93evp/02l0aBx2CV4P0Tu8mJyNi2LWle0cwOpqdGTugV8IQC8LhfLXnmNLR+8zvDTUmq1s8TouHR8F8wWXb31eV+yt9e9vZm7MoDPG6z52WCzYU5IqLVriKvEh92sIlxSVue5Aptqr/cXQghxcGQEXgghhDgE5uRkOlx5OSsnv8X0i66i/zOP0XPCpezasgSDyUq7E88gJiEFg+nwgmS9QUOX3kk8/+EICsoK0MeXsqx8CiWBneSr+3N9+y9w6tOx61Pr6c7E/vj/lUX834L+KlRqUMJ7yjRaFXZnddDrLS5Go9eT1roVLRJC9O7biZ9+KqSyIkCnLjZOOqMtiWlH7+VKUpq1zjqDUYPesP996W12PZ5yNeh0EIi+u4LmOF8DL4QQ9UECeCGEEOIQ6Ewm+j1wH56CQjZ98y1/3XwnOquVrBEj6PXM/7A3y6y3a+n1GmyJsE41kw/XXVNTvrDwc2INzbijyx+oVTKp7mjRWuyo1BqUcKh2pUqFzuhECRdEFI++qC32WCOekhLy5i9gzUefULp+PY5Wreh+y42cdUIyuxYspWTqfIynvoZKVTv3QSgQwJ2XR8gfQGs2YU2pPXp/KFIzYnDEGikrqb2TwfAxrXDEGfd7DrvTyD+/uxk8eCDB3/+oVa8ym9A1b1Yv/RVCiOOZ/LYXQgghDpIvFCbfF2CT1U7X11/lsrWrOHfmdC6cM4uTX3kBe9aBB++BcJjiQIDiQJBAOFxnO1cgl482XlurvMS3g6+33Y03FH0Ns6hf7lCIRWEDHYdeHLW+48Cz0BgcxCVWr3WPSzBx7R09Oe3c1mhUIdZ+9Ak/XXwZxatXEw4GKVm3jhnjb6RgwXw8Wzew8/ffUaLsq16Zk8Pc+x/k/Q5deLdVWz4fOIQNX32NJyc3avuDEZdo5v7nB5GUaqkpU6lg4LAMTj+/DTrd/kfg7bFGhoxujeek4Wg7touoU1nMxD95D5r4uMPqpxBCCFAph/upf4wpLy/Hbrfjcrmw2erO/CuEEOL4VBUM8WdZJe/nFhH8/9+gWhVckxpPf4cVs2b/wc5uBf4APxaW8VdZJaBiiNPKyDg7Jm8YnV6N0bRnDfTsnMl8tOn6qOdRo+GJPpuIN2Ydxp2JA1HgD3Djuh3c7NRSMfMjVv76LgGfB63eSIehl9B59HiyEtMoLfLg8/pQhYMY1R6MsU68BUV80LErQXftKfgag4HTv/iMhZOe4czvv8EUuyfBnaeoiJ8vu4Jtv/xa67gRb7xOVvtOGDu3R+O0H9a9lRR5KC/14q4K4owzYnMYDjqRnqvUi6qyEk2li/COXWhiHegz09DExaLSyLiREELU5UDjUJlCL4QQQhyEXb4Ab+cURZQFFXg9u4hMk4HW5gML4Av8Ae7ZlE1pcM807O+LXMx1VXGDLobf3l3LmRe3Iy0rBotVjydUvW+0WeskEPbgD3tqjgsTIqwEo11GHAFh4IXSIP0GXs7Jgy9AE/AR1hmYEzYSxEyq241/53rmPfAQO2f8gcHhoOv4a2l28tCowTtAyOcj5PNx8ssvRgTvAJXZOVGDd4A5Dz1M0uQ38X/yNfYrL0RtMR/yfcXGm4iNP7w9ye1OIziNQDy0b3lY5xJCCFGbBPBCCCHEAfKGwkwtKK2z/rvCMm7KSMSg3vdIY0hRmF1aERG871YUCLLWEKTc5eP+CTMZ95/uDB6ZRZfY00mzdKLEtwOjxoYaNb/seprtlYtpZu2OSXN4o68R/QsECHo8aM1mNFr5qrA3i0ZNV6uJZZUe5lb5mYsaMIEHwM+LKWZK165mSv9BhHw+AIIeD3MfeJikXr32ee6YzEziOneqVZ6/dGmdx1Tl5RHUafD9+DsxZ512WAG8EEKIxk9+KwshhBAHyBcOUxCoe6Q7zx/AH1Yw7GemcGUwxDxX3WvWl4X8dOqTxIZVxXzwynK6n2xiet5z/JX3dk0bi9bJZa3fZGb2y5zd/Eli9AkHfT//FqiqwrV1G0tfeZWSNWtJ7t2Lztdeg715Fhr90dmTvLGzaDRckRrP3Zt24QlHrkIcHmvDVuni14l31wTve6vYuQtbVhbl27bVPm9KCjFpqVFfmJgT694iUKVWo9HpCCkKwZw8tGnJB39TQgghmgwJ4IUQQogDZNKoaW0ysMPrj1rfxmTEqFbt9zwalQr9PtoZ1KqaPcNbd3CypPSLiOAdoCpYyjvrL+P+7ouJNzY/iLuILhQIsH367/xwzvko/59ML/uvOSx75TXOmf4L6YNOPOxrHCvSDDqeaZ3Br8UullV6sGrUjElw0MpsILRjOztn1M7CDrD42ecZ/vZkvhtzDoHKyppyrcnE6KlfYkmNvhVgfMcO6CwWAlW1X/q0OG0kqjWbgepM70IIIY5tEsALIYQQB0ivVnNGgoNZpRX8e/K7VgWj4u3o9jN9HsCq1TAqzs4L7oKo9f1VBn6cuQuAwefa+SHnf1HbBcJeNpX/Taqlw0HdRzRVObn8MvbKmuAdlYrMU08lof8Qts9dgrNtGyxJSYd9nWOBWqUi2aDjkuQ4xoRC6NSqmuSFZWo1WpMp6lr30g0bKFm1moun/8r2P2aRv2YtSb160PKM04lploFKFf2ljiUtjTE/fsc3I08n6N2z1ZujdWsGTpyI75k3UcdY0SQceJb3UCBA0O1GazLJ7AohhGhCJIAXQgghDkKSXstDLVJ5ZVcB+f7q6fQpeh0TMhJI1B/4r9VOVhPd/n8t9d66G42ot7spyK0ebbXY1JSX59d5nhz3mkO4i9oqs7PxV1QA4GzThv6TP2LBEi/fr6zAbNahXummvc6LI3b/e4IfL7RqFXZ15P9za0oqHa8cy/JX34h6TEqnzngffYn0Fpl0ffE5NLHO/V5Ho9WSOqA/l69Zwa7Zf+Fat4GU7l2Jsdjwv/Q+hMPEPvxfNHGx+z1X0OvFtXUby197nYIly4jr2J7uN9+IvUULdGZZPy+EEI2dBPBCCCHEQdCr1XSwmni8ZRoVwRCoIEajwak7uF+pTp2WmzIS2eH1M6OkAhXQI6ylckM5701aXNPO4bCRFGhNvmdj1PO0tg08nNupEQ5VzynQmkz0e+tTnn5mGxWuPUsF1q8qpu/gNMbd1gO7Q4L4umgNBnr+9zZ2/P4HpevXR9QNuO9eVCvWQzCEvn1rVEbDAZ9Xo9Nhb94ce/PmBEvK8K/biH/5GoyXn4exRxc0Cfvfpk1RFHL+nsvUU0cRDla/fMqZO5dV77zHGVO/pPmo0yRpoRBCNHKyD/y/yD7wQgghjrawohAKhinKczPtqw1sWlNCUpqV0Re2JbVZDOvc03h1zVm1jrPrk7m323xijc0Ouw+urdv4oENnWl8ylg2pFzB3dvRR/0dfO5k2HQ98qvbxyrV9G3mLFrPp628wxcXT4awxqFdtJDRvKTEXj8F0Yl80jrp3DvC5XPgrKlHrtPW2dKEiO5tPe/ejKje3Vp3BbueyFUuxNcuol2sJIYQ4OAcah0oA/y8SwAshhGhIwUAYjzuA3qDBYKweDa0KlLCk6Bu+2noHVcHqbexaxpzAlW3fJ9nctn6u6/Wy+r0PUCU1Z9LrFQT84ajtTj27FVfe0r1ertnU+X0hyl0+lLCCyaLDGlP3WvJQSRlKIIBKo0Ed56xzvXvA46F03Xr+vvd+cub9gzkpid4T76D5aacediBfsGwZH3fvXWf9Rf/8TUrfPod1DSGEEIfmQONQmSclhBBCNCJanZoYe+TUaosulv7JV9DROZyqYAlatYEYXTxWXXz9XddopO1FF1BS6AHm1t1w/0n2jwuFeVV889Fa/vxtOwF/mI7dExg7oRvpWTFodZpa7TWxjpr/rsrLw51fgLekBEtKMqaEBExx1bMa8hct5suThqH8/5IGX1kZv111NR0uH8vgZyfVtAsGfKhVGtQHMeVdCe97zKYmgaEQQohGSwJ4IYQQognQqDTEGjOI5chNcTY6HMSZrAwYmsGsn7dHbXPiKZlH7PpNRXGBm0dvm01+9p5t3VYvLeTe8TN48s1hNGtR99T40k2b+P7Mcyhesyf5YNapIzjl7cmotVpmXD+hJnjf25oPPqTXHbcRUHzsWDuf1X9+jcFso+fIK4hLbYnZtv9lDQabDWNsLN6Sklp1OosFS2rKfs8hhBCiYe1/rxshhBBCHDcMBi1nj+2APbZ2grVBw5uRmGJpgF41LhtWF0cE77sFA2G+eGcVHncg6nFVubl8O+rMiOAdYNsvv/LnnXfhLSmtVbe3XX/+xbQ37uC7F25g05IZrJ7zDR/eeyZ/ffEc7vLaQXmt6xcUMPCJxyDK9P2BTz6OweHY7zmEEEI0LAnghRBCCBEhKdXK468P5ZLrO9OynZPOvRJ58MXBXHFLd2yOA8+cfiwKhxX+mbWrzvqViwtwV0YP4CuycyjdsCFq3YbPv4T9pSXSaSjJ2VyreNHP7+Iq3LnvY4EVb7zJrj//YvQ3X9Hi9FE4WrUia8RwTv9yCqUbNxL4/20EhRBCNF4yhV4IIYQQtSQkWzj9grYMPaMF7soAG9eU8Nuzi4lLMDP41Czik8yYLbqG7uZRUVVejK/KhUqlxuJIwBFX9zZ6lhgdak30RAHRsr/vpoRCqLQaMoefwvbfpteqV2k02Nq1oKyOpQ3LZ04hpWXXfd6H0eFg6cuvsHXaT7S98AJS+/ejMieX36+7AZVaTZ+Jd+7zeCGEEA1PAnghhBBCRKVWq6iqCPDIrbMozHPXlP/4+Qauvq0HA09phsl87AbxQb+P/G2r+HnyRPK3rQagZY+TGXzOC/zy9aaox5x2bmvszugBfkxG3fkLNHo9Wr2Bk19+gc9PPAl3QUFE/bA3X2fFP1/Vebzf666zbrcOV1zG0pdfwedysWLymxF1fe65G1NS4n7PIYQQomFJAC+EEEKIqLzeIJ+/syoieN/t7eeW0KlH4jEdwJfkbeXD+88iHNwzJX7zkpnEpX/IxdedxqeTV0W079g9gQFDm6FWRx+Bt6amkNSrJ/mLFteq63zt1ZiSEtGZTFy8YC7bfp3O1p9+xpaVSedxV2FOS2HzBwvr7GvnwefUKquq8FPu8qEOB9D6ylBCYc6fPZO8+QtY/PyLqFQq2t/xX+KGDCa2VUsCKjW18+cLIYRoTCSAF0IIIURUFWU+5s2se2318oX5pGTE7Pc8Abebqrw8vCWl6K1WTAnxNduhNVZ+bxV/f/VCRPC+24Lvn+Gcu/rw7AfDWfhXDu6qAL0GppKUasURW/f0enNiImd8/QXTr7m+Zpq8Wqul07ir6Hvv3ehMJgBsmZl0ufZqOl11RcQ2cSdecDublvyOzx25Vj2jfV8SMtpGlBXkVvHO80vo2tGIeeOvrHhuEkGPB4CEbt0Y/d1UytOb8X6ljw0eP5otefSzW7k4OZYkw7H7UkYIIZo6CeCFEEKIJs5TUYqnsgwAo8WO2RZbL+dVwhAK1Z1YzV3l3+85qvLymf/4k6x4YzLhYBCA1IEDGPnh+9ibZ9VLP48En7uCHWv+qbN+yc+vct7E9zjrsvYHdV5bs2aMmvIJ7oJCApWVGBwOzMlJ6C21s/v/e4/32OTmXP3M7+TkrUAxq1CpNBixkhjXBqtzz/T3kiIPT/z3T6w2Pc7i1cx7/OGI8xQuW8b3o8+i16yZbPBV5zMOAXNclaxze3m8ZSrxegnihRCiMZIAXgghhGiiwqEQhbvW8/Obd5G9rnp6dWqrboy8bhIJzdqj0R7er3mjWUvLdk42ryuNWt+1d/I+jw/6fCx54SWWvfJqRHnOnL/5ZtRozpvxK5aUxrn3uEarw+JIIODzEAoGCPo9EfW2uFQ02kMLco1OJ0an86CPCxHGZSni69CDbM9dAkCWtRdjk9/EpMSiUVX//87bVUHurkom3NyClTc/GfVcVXl5KGvWENeuK8WBPfvOFwWCrK7yMlgCeCGEaJRkGzkhhBCiiSor3MmH94yuCd4BcjYt44N7z8RVsOOwz29zGLjqlu5oomRV79Y3ifgk8z6Pd+fmsfTlV6LWlaxdS/mO/W991lCMWjMXjX+DsZe+wLjr3uay2z4gvXWvmvqeI69ArTk64yBVFX6qKvwU+7bwv2UD2V65pKZuW+Uinlo2kCLvtpqyrRvKAIgxQ2VOTp3nLVu6lHhd7XuY76oivL8t7YQQQjQIGYEXQgghmqBQMMCy6R/j91bVqgv6PSz86R2GnHkrwYUr8K3bhL5tS4w9u6BJiEOlOfBUZc1aOXjizWF89uZK1iwrJMau5/Tz29Dv5Iw6s63v5q+qIuiuOzt62abNpPTtc8B9OVpCZeVUfvk9lVN/gnB1IKu1mDn9pv8w3fwWLXsOxZmcdcT7UVLoYcWifGb8sIUOPZyETnwXf9iDTm1koO0KujICxRMkbIZNRX8Rl5aJVq0jMaV6Or7HD8a4OLzFxVHPH9O+PWXBUK3yWJ0GtSp6Ij4hhBANSwJ4IYQQognye6rYtnJOnfXbV/1NubUb/vemAuD5dRblRgPxk+5H16YFqgMM0PR6DVmtHNzywAl43AHUGhWOWOM+jy8vziV30zKMWNDo9YT80dfK27IyD6gPR5t38XIqv5oWUaZUufE+8y5nvT4JEpwYTNYj2oeSQjeT7vm7ZjS984km1lfOxqC2cEP8Z2x+6COmf3UhSiiE1mym081XU3VzHvaUDLJaOzCZtcycVcGJN97K4ofvr3V+g92OoXsP8v3BWnXDYm1H9N6EEEIcOplCL4QQQjRBGp0ei7PufbstjgQoLY8oU7w+ih95jlBx9DXt+2K26ohLNOOMM+0zeHcVZfPJQ+fx1aSrWD73C9pedlHUdrbMzEYXwIfDCqGSMio+nhq9QTCEf97SIx68AyxbkFcTvANUlSrYtKmMdtzHyvHPsenzb1BC1aPnQbebZU+9xIqX3yDo8xGXYOae//Vn22YXqi6n0u7Kq1Gp93zls6alcc7M6Sy21A7Ur0yJI0Ev4ztCCNFYySe0EEII0QTpjWb6nXkDmxZNj1rfd9AlBN/6rVZ5uKiEsKsc4mNx+fOoDBShoGDRxuHQpxzwyHw0Qb+PeVNfoSRnMwCr533HmOtexldSyuZvvq9pF9uuHaO/+5qYtLRDvlZ9Ksp3s3ZFIYvmZHPB2ekoBYV1tg1sPfzcAvtTWe5nxg9bI8rmTS/iyuG34iwNsnpW7RF1gKUvvEyXa65BYzSw6q7ruGncjXj1CvFX3kTPW2+kYvs2VCqI69ABe/Mszg2GGBxnZ1mFB5NaRTebGadWg/kgllgIIYQ4uiSAF0IIIZqohIy2DLrwDv6c8nREeb8zJ2DP8+EviT7SHtYobCmfzzvrLyPfsxEApyGdy1u/RWv7IAyafSenq4u7vIjlf0zZc51QkG/fvJkTTr+GM2+5DrwhbMlpmJMSsSTvO4P90ZKfU8nDt8yiuKA6y3z7lmZ6NM8ksHFL1PaGrh2OSr+UfyWRc5X4yF+eSoxqQ53HBD0e/OXlUK6w7eef2fbzz+isVvQxMXgKC1FpNIT8fi5btgiVSoVdp8Wu09LKvO9cBkIIIRoPCeCFEEKIJsoU46T3qGvoMGAMO9fOR1EUmrXvg0ljoezau6IfZNDjivPx9PIhBMLemuJS3y5eXDWK+3ssppm12yH1R1EUgn5vRFk4GGDutNeYy2ucMOYGho4YeUjnPhK8ngCfTl5ZE7wD/Dgtm27XngWTnq3VXhVjQdutLaW+bOz6ZNSqIzNSbTJrOWFIRq3t+756YzsP3ZVRq31ijx50HXsZRocDc2wcFfm5NXWBykoClZXVPwSr17sHvd5a5xBCCNE0yBp4IYQQogkzmmOIS21Bt6EX0X3YxcSltcIYG0fM2POitrfddjVzCt+PCN53UwgzbftjeEOVh9QXg8lKZqf+dda37nnKIZ33SKlw+VnwV3ZEWWGemxkrQmivvwa1PaamXNuiGfrHr+XxXSN4ZEl3ft31LC5f3hHpl1qjok3HODKaR65RD/jDlAXs2DKrcweotVrO/ORjTr31vySu2IL1h9l43/scpyOOrFNHRD23Rq/HnJBwRPothBDiyJMReCGEEOIYo9brsQwfjCYpgYoPviC4MwdNajK2y8+DXm3YtPneOo/dXrUEX6gSo+bgE7UZrQ6GXfEw7911GuFgIKIuo10fYlNaHPQ5j6RwSCEcqr3f+fff7mBjlzjGP3g/ccYwfrWXJf5f+KHsHEp9uwD4eutEtpbPZ2ybN7Hq4uq1XyqVCkuMjnOv6EjOjvKalwx9TkxD43By2tRvmDbmTHrffDMxq7fgW7Si5ljv3wvxzl/C8Mce49MVK6nMycFgt9Pq0suxZrUmrk1Wo1m+IIQQ4uBJAC+EEEIcg9S2GMwn9sXQqS1KMIRKo0ET6yAY9pNkbMNG119Rj4szZKJTmyLKgl4vVfn5hAMBdBYL1pSUOq/rTGvOlW/+hqskG1f2TjbN+JnMzgPoMuR8rPvImt8QTBYdzds4IrK977Z2RTFVuu4Y0r08sWwQJb7ayeuWFE9ltP+heg/gAeISzaxZVsiiv3Po3rf673vR3zls21TG1bf15MJ/5qAtKafk9odrHxwM4fn4G8Z89SUrvphKwqhL+PX3ErKXuEkrtnJ2azepzbSYLbp677cQQogjSwJ4IYQQ4himcToiftaq9QxNu4k5+e9EbX96s/sxa+01P1fsymb+40+y5v0PCHq92Fu0YPDzz5A+eBBGuz3i2FJfNr/tepbZuZPxh93EG5tz7vj/0c4xFKs+tt7v7XDZHAauurUHD970R62R+J4DUolPNFMVyosI3jvZhjPKejMWtwk0KvQuBSz13zezRcegEZl06Z3EqiUFeD1Brv1vT2ITzNgcBnCkUvHngjqP96/ZQHzLm7GcMZ4nHt7TLi+7ksVzc7nx3j70OykdrU4yzgshRFMiAbwQQghxnEkwtuSadp/ywYZx+MPVCdw0Ki1nZz1JM2v3mnZVeXn8cM555C1YWFPm2rKF7888m9Hffk2rM0fXlJf7C3ln/VjWlc2sKSvybuWNdedzTbtP6JNw0WFtUXekZLVy8OSbw5jy5krWrigixq7njAva0ndIOjaHAa9HjwoVCgojY2/lxB0nEnzrawB0QwdARy1FBSswJsRjTUut176ZzDpMZh0p6TFR61WmfWSPV6sp92t569mlUavfeX4J7bvGE590BN4+CCGEOGIkgBdCCCGOM0atle5xZ9GyVz8KPZsJKUGSTK2x6ZMwaPYEdK6t2yKC973Nvu0Okvv0rplOX+rfGRG87+3LLXfQxj4Yp6Fx7Pu+N71BQ1YrBzc90BevO4hao8IRa6x52RCji6eT81R2Vi1noGcUgRffQR3nRHf9Jcx56im23PwrKAq2zExOevkF0ocMxhBTHXAr4TDBgB+NVof6COytbujRpc4648A+lFUGqaoMRK33uIOUlfgkgBdCiCZGAnghhBDiOKTXGInXZBFvzKqzTc7ceXXWubZsIVBZVfPzjoolALS1DeZE7eWYwlbKVYXMCrzD9soleEPlQOML4HezWPVYrPpa5SatnYtavczqnd/C638CoL/8XKZeNpaKHXum1pdv3853o8/i3Bm/kTboRMqLdrHyz6/JXr+I+PQ2dD/lUuwJ6fj9GqoqA6hUYLXpMZkj16GHSl0E3G5CwQD62Fh0MXUnE9TEObBdexnlb34UUa6Oc2K/6kJcFfvebKgRTogQQgixHxLACyGEECIqS2rdyeo0BgMa/Z7g06ZP4sr4yYSn5bL06XvxFBZiy8zk1IduprDHGLQqw9Ho8hGRaGpJjP1CynY+hCYpnqLsnRHB+97+/O9ETv3iQ965/zSC/urlCVuWzWLXhiUMvvwtPpm8nlVLClBrVPQemMrF13UhOc1K2OPFvW07pZs3s/iNyVRk55DW7wS63TQBe+tWaLS1v7KpzWYspw7G0LUDVT/NIFxcirF/TwzdO6NNjMeucWNzGCgv89U61mrTY3fuYwq+EEKIRkkCeCGEEEJElXpCXzQGAyFf7QCww9hLMSfuySrfjC7Me+lb1k3+sKasfPt2/rrydvo98wjWNvFHpc9Hit5kQ5OahDrGyq5//qmzXcHSpZTlbqsJ3gE0OgMnnPs/HrplLj5vCKjewm7+7GzWrSjiyTeHYSrLZc2UL5jzyCM1xxUuW8aq997n/NkzSe7TO+r11BYL+lYWdDdeCeEwqr0CfWe8iRvv7cNTd82JSNKnUsMNd/fGGScBvBBCNDX7nlslhBBCiEatyB9gQ5WXr/NL+KmojF1ePxXBYL2c25qWxpgfvkWjj5xantijOyfcfx9a017bzZX4Wfevqdy7LXpoEoH80nrpU0PR2GKwjT2PcEUltvT0OtuZ4uOpKi+OKGtzwpn88WtpTfC+N1epj7KdxXiKi/n7scdq1Qe9XqZfOx53YeE++6dSqyOCdwC1WkX7rgk8/e5wThnTgjYd4xh6Rguefnc4nXomotHK10AhhGhqZAReCCGEaKIK/AHeyi5iSYW7pkxNMeNS4+lvt2DTHdqvea83iN8bwmjSkj54EFesW0XOvH+ozM4h5YQ+xGRlYk2PXM9esWMnKErU8wUqK/EUl2DLzDyk/jQWurYtMZ80gKzYGOZoNCih2gF5j9tuYeX8qRFlCc16MfPzsrrPqwmTv2wZSjgctb5w+XK8JSWYExIOus96g4b0LBuX39gNvzeEwahFq5PAXQghmir5BBdCCCGaoFA4zNyyyojgHSAMvJVTRHGwdnC5P1WVfjauKeaVx+bz6H9m887zi8nJ9mBMySB1+CDCba1Mm/oAnz19KYt/eZ+KkryaY3Ux0bc6201rbPrTtTUxVsyjhmJr05rRX32BxhC5rr/lmDNpd+nFFO5cH1Ee8JZjtdVOkLdbKMQRzyin02mwxOgleBdCiCZORuCFEEKIJqgoEOSX4vI662eXVtDcdOCJ4/y+EPNn72LypMU1ZTu2uPjrtx3cNWkAy769kZ1r96z9/vXte1j11zec89+3iIlNwpqagjkpCXd+fq1zJ3bvjimhaa+B302t12NISSJz5AiuWLeKgiVL8ZaWkty7N9bUFIyxsYy8fhJfTxpXc8yGf6Yw7LQXeWtDWdRzahw2kvr2qQ7io8xiiO/SBWNs7JG6JSGEEE2IvIYVQgghmiAFcO1jlL0kcHAj8GUlXt59YWmt8lBIYfKkJbQ78apaddnrF1KwfQ2wZ728zhq57Zk5KYnTPv3okKZ/N2ZagwF7Vhatzz6LzuOuIqFLZ0zx8ajUapp3GcQVT02jedfBWBwJGC12OnRLZMCwjFrnOevSdjgTzFhSU+j/wH21r2M0MvytN465vz8hhBCHRqUodSxYO06Vl5djt9txuVzYbLaG7o4QQggRVWkgyIs7C1hZ6YlaPyE9gZNjD/z32KolBTz6n9l11j/6YmdCRWtRB1SotVqCmhCbVs9EUak444bnUKnVhEMhKnbuJGfO3xStXkNKn94k9uyJrVntwPV44HWXE/C60eoMmGKclJf5KC50s3ReLlqdhh79knHGmbDEVE+vd+fmUbJuHQufeY6KXdmkDexP95tvxN6iBRqdbj9XE0II0ZQdaBwqU+iFEEKIJsip03JxUiz3Vmbz79Rn8Tot7S2mqMcditPPTMW9YA5z7piIv7x62r69RQuGTX4NtUmHu7AQS1ISPncFXpWbqrgAtlM6Y2/VDq3TctjXD7vdKG4vIcIEbVr0WjNadd1ryhsLo9mG0bznS5jNYcDmMNC8tTNqe3NKMuaUZBJ79yLo9aKPiUFrOPBlEEIIIY59EsALIYQQTVSaXscDLVJ4L6eY7V4/aqCXzcylyXGkGA5uxDYpxYJOrybgj3wdkJBspltGGdPPHx9R7tqyhe/HnMPpX3zGr1eOY9jk15j325ss+umdiHZdhpzPyWPvx2I/+DXwSiBAcFcurvc/x798DWqrBc3oAZT1b4Uq1kaCsXmTCOQPlt5qRf+vpQhCCCEESAAvhBBCNFkWnYbOOjN3Z+nwhMNoUBGj0WDTaQ76XPZYI1ff1oPXn1oUUX7KKfGsmHRr1GMCVVXkzPsHX0kZW378iV1bFtZqs2LWF7TpO5K2fU496D4Ftu+i8Jb74f/X+oc8XkLvfI9mcVu2j0ujKqmIVvYBB31eIYQQoqmSJHZCCCFEE5eg19HMaCDNqD+k4B2q9wvvMyidJyYP5YQh6TRv7WDoGc0ZMCSZkrVr6zyuZO06bFmZLHv5Ndp3Py1qm3++fQ1PZdlB9SdUUYnrjQ9rgveIumXraVbVnM8234zLnxflaCGEEOLYJCPwQgghhADAbNHRsl0sN9zdG78vhNGkJeAqwd6yJUUrVkQ9xtGyRfUofFkZekPtdfc6g4nMdv3wl7hQBRSMzujrv/9NcXvwr1xXZ7168VYMva1UBYqx65MP7AaFEEKIJk5G4IUQQggRwWDUEmM3oNNrMCck0P/hB6K20+j1pA8ZTPacv2k2Yhg5OyKD/BZdhnDutW9Q+dNKvhw8jG9PH8PWn37GU1S0/06oVKCre5xBMesJhL0oyGY6Qgghjh8SwAshhBBin9JOHMiAJx5DvddWZqb4eEZ88C6LnnkOndlM7zv/y9Y1f9fUO1Oa06PHeXx/ypls+PxLyrdtI2fuXL4ZNZpFz72Az+Xa5zXV9hjMJw+ssz7cqwWegAur7uCT4wkhhBBNlewD/y+yD7wQQghRW8Dtxp2fT/n27YT8frzFJSx+7gUsKSmc+NTjONu0obRgO7M+eYr1C37mlIseYPW9L1OyLso0eJWKK9etxtmm9T6vGcwvpOjOxwjlFUSUa686ndkd/qF7swtoba87yBdCCCGaigONQyWA/xcJ4IUQQoh9C3o8eIqKURQFg8OOYa/flz5PJd5KF4EiFx926FrnOU775CPaXXzh/q9VWIxvzXq8cxagOC0oJ3diu2kLaQk9SDC2PCa3kRNCCHH8OdA4VJLYCSGEEOKgaE0mYjLSa5WH/H68BcWUb9mKv6qSMT9+x4Yvv2bNBx/Warv3dPx9XishDu3g/pgH9SMQ9uELVdJV2wOd2nDY9yGEEEI0NRLACyGEEOKwBdxutk//nZ8vGUugqgoAlUZDj1tuov8jDzH3gYdq2qo0GhJ7dD+o86tUKvQaI3qNsR57LYQQQjQtksROCCGEEIetfPt2fjj7vJrgHUAJharXyScnY2/Roqb85JdfxJyc1BDdPCilhS7ydhaTsz0XV1EOfk9lQ3dJCCHEcU5G4IUQQghxWJRwmJVvv4cSDketXzH5LXrfdQc5f8+l+0034mjVEr3FcpR7eeC8niDbNxbzyZur2bSmBEeskVPOSKJztyKSM5OxOhIbuotCCCGOUxLACyGEEOKwhPx+SteurbO+fPt2Mk85hQ6Xj0Wrb/xJ59avLODJO/9md5rf4kIPU97dxqYBCZx+ViVtujnQ6Br/fQghhDj2yBR6IYQQQhyWiqowzp5966xP6N4Ng9PRJIL30mIP776wnGh79Cz6u5Bg2E5Faf7R75gQQgiBBPBCCCGEOAyhUJgZP2wlaegZ6KzWqG36P/IQRrv96HbsEHmqguRl173WfevGSpRQ6Cj2SAghhNhDAnghhBBCHDKvO8iy+Xl89GkBw76eRlyHDjV1luRkTv7wUyzNWzdgDw+ORqtCpaq73mTSyvR5IYQQDUbWwAshhBDikGl1auyxRhbNyeHVd/yccc/b9IgLo4RCVAaNLNgQprUl+sh8YxRjN9CtbzJL/8mrVafRqGjZIQ6zPb4BeiaEEEJIAC+EEEKIw2Awajn9/DYsmpNDYZ6bd9/cFlE/8ckBWKxNZ8TabNFxxc3d2b55FiWFnppylQqu+29H4hJtaGUEXgghRAORAF4IIYQQhyW9uY2zx7Zn6oeRmeiHj2lJqw6xDdSrQ5ecZuXRV09m3YoCli/IJyHFRL+T0nHG6bHams5sAiGEEMcelaJEy7N6/CovL8dut+NyubDZbA3dHSGEEKJJqKr0U1bsZeXiAkKhMF16JeGMM2G1yWi1EEIIsT8HGofKCLwQQgghDpvFqsdi1ZOWKS+/hRBCiCNFstALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsALIYQQQgghhBBNgATwQgghhBBCCCFEE9CkAvhp06bRt29fTCYTTqeTMWPGRNTv2LGDUaNGYTabSUxM5I477iAYDDZMZ4UQQgghhBBCiHqkbegOHKivv/6aa665hieeeIKTTz6ZYDDIqlWraupDoRCjRo0iOTmZuXPnkpuby9ixY9HpdDzxxBMN2HMhhBBCCCGEEOLwqRRFURq6E/sTDAbJysri4YcfZty4cVHb/Pzzz5x++unk5OSQlJQEwBtvvMHEiRMpLCxEr9cf0LXKy8ux2+24XC5sNlu93YMQQgghhBBCCBHNgcahTWIK/ZIlS8jOzkatVtO9e3dSUlIYOXJkxAj8vHnz6Ny5c03wDjBixAjKy8tZvXp1Q3RbCCGEEEIIIYSoN00igN+yZQsADz30EPfddx8//vgjTqeTIUOGUFJSAkBeXl5E8A7U/JyXl1fnuX0+H+Xl5RF/hBBCCCGEEEKIxqZBA/i77roLlUq1zz/r1q0jHA4DcO+993LOOefQs2dP3nvvPVQqFV9++eVh9eHJJ5/EbrfX/MnIyKiPWxNCCCGEEEIIIepVgyaxu/3227niiiv22aZFixbk5uYC0KFDh5pyg8FAixYt2LFjBwDJycksWLAg4tj8/Pyaurrcfffd3HbbbTU/l5eXSxAvhBBCCCGEEKLRadAAPiEhgYSEhP2269mzJwaDgfXr1zNw4EAAAoEA27ZtIzMzE4B+/frx+OOPU1BQQGJiIgDTp0/HZrNFBP7/ZjAYMBgM9XA3QgghhBBCCCHEkdMktpGz2Wxcf/31PPjgg2RkZJCZmcnTTz8NwHnnnQfA8OHD6dChA5dddhmTJk0iLy+P++67jwkTJkiALoQQQgghhBCiyWsSATzA008/jVar5bLLLsPj8dC3b19mzpyJ0+kEQKPR8OOPPzJ+/Hj69euHxWLh8ssv55FHHmngngshhBBCCCGEEIevSewDfzS5XC4cDgc7d+6UfeCFEEIIIYQQQhxxu3OxlZWVYbfb62zXZEbgj5aKigoASWQnhBBCCCGEEOKoqqio2GcALyPw/xIOh8nJySEmJgaVSlUv59z9NkVG9cXhkOdIHC55hsThkmdIHC55hkR9kOdIHK7G+AwpikJFRQWpqamo1XXv9i4j8P+iVqtJT08/Iue22WyN5gERTZc8R+JwyTMkDpc8Q+JwyTMk6oM8R+JwNbZnaF8j77vVHdoLIYQQQgghhBCi0ZAAXgghhBBCCCGEaAIkgD8KDAYDDz74oOxHLw6LPEficMkzJA6XPEPicMkzJOqDPEficDXlZ0iS2AkhhBBCCCGEEE2AjMALIYQQQgghhBBNgATwQgghhBBCCCFEEyABvBBCCCGEEEII0QRIAC+EEEIIIYQQQjQBEsAfBdOmTaNv376YTCacTidjxoyJqN+xYwejRo3CbDaTmJjIHXfcQTAYbJjOikbN5/PRrVs3VCoVy5Yti6hbsWIFJ554IkajkYyMDCZNmtQwnRSNzrZt2xg3bhzNmzfHZDLRsmVLHnzwQfx+f0Q7eYbE/rz66qtkZWVhNBrp27cvCxYsaOguiUbqySefpHfv3sTExJCYmMiYMWNYv359RBuv18uECROIi4vDarVyzjnnkJ+f30A9Fo3dU089hUql4tZbb60pk2dI7E92djaXXnopcXFxmEwmOnfuzKJFi2rqFUXhgQceICUlBZPJxLBhw9i4cWMD9nj/JIA/wr7++msuu+wyrrzySpYvX87ff//NxRdfXFMfCoUYNWoUfr+fuXPn8sEHH/D+++/zwAMPNGCvRWN15513kpqaWqu8vLyc4cOHk5mZyeLFi3n66ad56KGHePPNNxugl6KxWbduHeFwmMmTJ7N69Wqef/553njjDe65556aNvIMif35/PPPue2223jwwQdZsmQJXbt2ZcSIERQUFDR010QjNHv2bCZMmMA///zD9OnTCQQCDB8+nKqqqpo2//nPf/jhhx/48ssvmT17Njk5OZx99tkN2GvRWC1cuJDJkyfTpUuXiHJ5hsS+lJaWMmDAAHQ6HT///DNr1qzh2Wefxel01rSZNGkSL730Em+88Qbz58/HYrEwYsQIvF5vA/Z8PxRxxAQCASUtLU15++2362zz008/KWq1WsnLy6spe/311xWbzab4fL6j0U3RRPz0009Ku3btlNWrVyuAsnTp0pq61157TXE6nRHPzMSJE5W2bds2QE9FUzBp0iSlefPmNT/LMyT2p0+fPsqECRNqfg6FQkpqaqry5JNPNmCvRFNRUFCgAMrs2bMVRVGUsrIyRafTKV9++WVNm7Vr1yqAMm/evIbqpmiEKioqlNatWyvTp09XBg8erNxyyy2KosgzJPZv4sSJysCBA+usD4fDSnJysvL000/XlJWVlSkGg0H57LPPjkYXD4mMwB9BS5YsITs7G7VaTffu3UlJSWHkyJGsWrWqps28efPo3LkzSUlJNWUjRoygvLyc1atXN0S3RSOUn5/PNddcw0cffYTZbK5VP2/ePAYNGoRer68pGzFiBOvXr6e0tPRodlU0ES6Xi9jY2Jqf5RkS++L3+1m8eDHDhg2rKVOr1QwbNox58+Y1YM9EU+FyuQBqPncWL15MIBCIeKbatWtHs2bN5JkSESZMmMCoUaMinhWQZ0js3/fff0+vXr0477zzSExMpHv37rz11ls19Vu3biUvLy/iGbLb7fTt27dRP0MSwB9BW7ZsAeChhx7ivvvu48cff8TpdDJkyBBKSkoAyMvLiwjegZqf8/Lyjm6HRaOkKApXXHEF119/Pb169YraRp4jcTA2bdrEyy+/zHXXXVdTJs+Q2JeioiJCoVDUZ0SeD7E/4XCYW2+9lQEDBtCpUyeg+nNFr9fjcDgi2sozJfY2ZcoUlixZwpNPPlmrTp4hsT9btmzh9ddfp3Xr1vz666+MHz+em2++mQ8++ADY8/2mqf1ukwD+ENx1112oVKp9/tm95hTg3nvv5ZxzzqFnz5689957qFQqvvzyywa+C9HQDvQ5evnll6moqODuu+9u6C6LRuZAn6G9ZWdnc+qpp3LeeedxzTXXNFDPhRDHkwkTJrBq1SqmTJnS0F0RTcjOnTu55ZZb+OSTTzAajQ3dHdEEhcNhevTowRNPPEH37t259tprueaaa3jjjTcaumuHRdvQHWiKbr/9dq644op9tmnRogW5ubkAdOjQoabcYDDQokULduzYAUBycnKtLL67s2cmJyfXY69FY3Ogz9HMmTOZN28eBoMhoq5Xr15ccsklfPDBByQnJ9fKuirP0bHvQJ+h3XJycjjppJPo379/reR08gyJfYmPj0ej0UR9RuT5EPty44038uOPP/Lnn3+Snp5eU56cnIzf76esrCxiBFWeKbHb4sWLKSgooEePHjVloVCIP//8k1deeYVff/1VniGxTykpKRFxGED79u35+uuvgT3fb/Lz80lJSalpk5+fT7du3Y5aPw+WBPCHICEhgYSEhP2269mzJwaDgfXr1zNw4EAAAoEA27ZtIzMzE4B+/frx+OOPU1BQQGJiIgDTp0/HZrPVeuDEseVAn6OXXnqJxx57rObnnJwcRowYweeff07fvn2B6ufo3nvvJRAIoNPpgOrnqG3bthGZNsWx5UCfIageeT/ppJNqZgKp1ZETsOQZEvui1+vp2bMnM2bMqNkKNRwOM2PGDG688caG7ZxolBRF4aabbuKbb75h1qxZNG/ePKK+Z8+e6HQ6ZsyYwTnnnAPA+vXr2bFjB/369WuILotGZujQoaxcuTKi7Morr6Rdu3ZMnDiRjIwMeYbEPg0YMKDW9pUbNmyoicOaN29OcnIyM2bMqAnYy8vLmT9/PuPHjz/a3T1wDZ1F71h3yy23KGlpacqvv/6qrFu3Thk3bpySmJiolJSUKIqiKMFgUOnUqZMyfPhwZdmyZcovv/yiJCQkKHfffXcD91w0Vlu3bq2Vhb6srExJSkpSLrvsMmXVqlXKlClTFLPZrEyePLnhOioajV27dimtWrVShg4dquzatUvJzc2t+bObPENif6ZMmaIYDAbl/fffV9asWaNce+21isPhiNhFRYjdxo8fr9jtdmXWrFkRnzlut7umzfXXX680a9ZMmTlzprJo0SKlX79+Sr9+/Rqw16Kx2zsLvaLIMyT2bcGCBYpWq1Uef/xxZePGjconn3yimM1m5eOPP65p89RTTykOh0P57rvvlBUrVihnnnmm0rx5c8Xj8TRgz/dNAvgjzO/3K7fffruSmJioxMTEKMOGDVNWrVoV0Wbbtm3KyJEjFZPJpMTHxyu33367EggEGqjHorGLFsAriqIsX75cGThwoGIwGJS0tDTlqaeeapgOikbnvffeU4Cof/Ymz5DYn5dffllp1qyZotfrlT59+ij//PNPQ3dJNFJ1fea89957NW08Ho9yww03KE6nUzGbzcpZZ50V8WJRiH/7dwAvz5DYnx9++EHp1KmTYjAYlHbt2ilvvvlmRH04HFbuv/9+JSkpSTEYDMrQoUOV9evXN1BvD4xKURSlIUb+hRBCCCGEEEIIceAkC70QQgghhBBCCNEESAAvhBBCCCGEEEI0ARLACyGEEEIIIYQQTYAE8EIIIYQQQgghRBMgAbwQQgghhBBCCNEESAAvhBBCCCGEEEI0ARLACyGEEEIIIYQQTYAE8EIIIYTYpyFDhnDrrbcCkJWVxQsvvNCg/RFCCCGOVxLACyGEEOKALVy4kGuvvfawzpGbm8vFF19MmzZtUKvVNS8HhBBCCLFvEsALIYQQ4oAlJCRgNpsP6xw+n4+EhATuu+8+unbtWk89E0IIIY59EsALIYQQokZVVRVjx47FarWSkpLCs88+G1H/7yn0KpWKyZMnc/rpp2M2m2nfvj3z5s1j06ZNDBkyBIvFQv/+/dm8eXPEOV588UXGjh2L3W4/WrcmhBBCNHkSwAshhBCixh133MHs2bP57rvv+O2335g1axZLlizZ5zGPPvooY8eOZdmyZbRr146LL76Y6667jrvvvptFixahKAo33njjUboDIYQQ4tilbegOCCGEEKJxqKys5J133uHjjz9m6NChAHzwwQekp6fv87grr7yS888/H4CJEyfSr18/7r//fkaMGAHALbfcwpVXXnlkOy+EEEIcB2QEXgghhBAAbN68Gb/fT9++fWvKYmNjadu27T6P69KlS81/JyUlAdC5c+eIMq/XS3l5eT33WAghhDi+SAAvhBBCiMOi0+lq/lulUtVZFg6Hj27HhBBCiGOMBPBCCCGEAKBly5bodDrmz59fU1ZaWsqGDRsasFdCCCGE2E3WwAshhBACAKvVyrhx47jjjjuIi4sjMTGRe++9F7W6/t/3L1u2DKhed19YWMiyZcvQ6/V06NCh3q8lhBBCHCskgBdCCCFEjaeffprKykrOOOMMYmJiuP3223G5XPV+ne7du9f89+LFi/n000/JzMxk27Zt9X4tIYQQ4lihUhRFaehOCCGEEEIIIYQQYt9kDbwQQgghhBBCCNEESAAvhBBCCCGEEEI0ARLACyGEEEIIIYQQTYAE8EIIIYQQQgghRBMgAbwQQgghhBBCCNEESAAvhBBCCCGEEEI0ARLACyGEEEIIIYQQTYAE8EIIIYQQQgghRBMgAbwQQgghhBBCCNEESAAvhBBCCCGEEEI0ARLACyGEEEIIIYQQTYAE8EIIIYQQQgghRBPwf0XW/bdTzreUAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model.eval()\\n\",\n    \"out = model(data.x)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"17dc17be\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Train MLP model on graph dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"185e523a\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001, Loss: 1.8052, Val: 0.1020\\n\",\n      \"Epoch: 002, Loss: 1.7371, Val: 0.1540\\n\",\n      \"Epoch: 003, Loss: 1.6468, Val: 0.1940\\n\",\n      \"Epoch: 004, Loss: 1.5291, Val: 0.2820\\n\",\n      \"Epoch: 005, Loss: 1.3691, Val: 0.3720\\n\",\n      \"Epoch: 006, Loss: 1.2793, Val: 0.4420\\n\",\n      \"Epoch: 007, Loss: 1.0911, Val: 0.4760\\n\",\n      \"Epoch: 008, Loss: 0.9573, Val: 0.5120\\n\",\n      \"Epoch: 009, Loss: 0.8932, Val: 0.5180\\n\",\n      \"Epoch: 010, Loss: 0.7698, Val: 0.5220\\n\",\n      \"Epoch: 011, Loss: 0.6757, Val: 0.5300\\n\",\n      \"Epoch: 012, Loss: 0.5749, Val: 0.5240\\n\",\n      \"Epoch: 013, Loss: 0.5312, Val: 0.5260\\n\",\n      \"Epoch: 014, Loss: 0.4824, Val: 0.5340\\n\",\n      \"Epoch: 015, Loss: 0.4760, Val: 0.5300\\n\",\n      \"Epoch: 016, Loss: 0.4488, Val: 0.5320\\n\",\n      \"Epoch: 017, Loss: 0.3238, Val: 0.5260\\n\",\n      \"Epoch: 018, Loss: 0.3813, Val: 0.5260\\n\",\n      \"Epoch: 019, Loss: 0.3356, Val: 0.5260\\n\",\n      \"Epoch: 020, Loss: 0.2751, Val: 0.5260\\n\",\n      \"Epoch: 021, Loss: 0.3348, Val: 0.5360\\n\",\n      \"Epoch: 022, Loss: 0.2972, Val: 0.5340\\n\",\n      \"Epoch: 023, Loss: 0.2597, Val: 0.5300\\n\",\n      \"Epoch: 024, Loss: 0.3105, Val: 0.5300\\n\",\n      \"Epoch: 025, Loss: 0.2979, Val: 0.5300\\n\",\n      \"Epoch: 026, Loss: 0.2541, Val: 0.5260\\n\",\n      \"Epoch: 027, Loss: 0.2523, Val: 0.5320\\n\",\n      \"Epoch: 028, Loss: 0.1908, Val: 0.5420\\n\",\n      \"Epoch: 029, Loss: 0.2535, Val: 0.5440\\n\",\n      \"Epoch: 030, Loss: 0.3117, Val: 0.5540\\n\",\n      \"Epoch: 031, Loss: 0.1908, Val: 0.5600\\n\",\n      \"Epoch: 032, Loss: 0.2364, Val: 0.5580\\n\",\n      \"Epoch: 033, Loss: 0.2312, Val: 0.5500\\n\",\n      \"Epoch: 034, Loss: 0.2628, Val: 0.5540\\n\",\n      \"Epoch: 035, Loss: 0.2432, Val: 0.5460\\n\",\n      \"Epoch: 036, Loss: 0.2519, Val: 0.5460\\n\",\n      \"Epoch: 037, Loss: 0.2516, Val: 0.5500\\n\",\n      \"Epoch: 038, Loss: 0.2714, Val: 0.5520\\n\",\n      \"Epoch: 039, Loss: 0.2009, Val: 0.5560\\n\",\n      \"Epoch: 040, Loss: 0.2315, Val: 0.5560\\n\",\n      \"Epoch: 041, Loss: 0.2384, Val: 0.5540\\n\",\n      \"Epoch: 042, Loss: 0.2353, Val: 0.5540\\n\",\n      \"Epoch: 043, Loss: 0.3189, Val: 0.5460\\n\",\n      \"Epoch: 044, Loss: 0.2611, Val: 0.5460\\n\",\n      \"Epoch: 045, Loss: 0.2651, Val: 0.5440\\n\",\n      \"Epoch: 046, Loss: 0.2256, Val: 0.5400\\n\",\n      \"Epoch: 047, Loss: 0.2284, Val: 0.5380\\n\",\n      \"Epoch: 048, Loss: 0.2443, Val: 0.5380\\n\",\n      \"Epoch: 049, Loss: 0.2451, Val: 0.5440\\n\",\n      \"Epoch: 050, Loss: 0.2260, Val: 0.5440\\n\",\n      \"Epoch: 051, Loss: 0.2794, Val: 0.5540\\n\",\n      \"Epoch: 052, Loss: 0.2077, Val: 0.5540\\n\",\n      \"Epoch: 053, Loss: 0.2638, Val: 0.5500\\n\",\n      \"Epoch: 054, Loss: 0.2315, Val: 0.5480\\n\",\n      \"Epoch: 055, Loss: 0.2369, Val: 0.5500\\n\",\n      \"Epoch: 056, Loss: 0.2459, Val: 0.5460\\n\",\n      \"Epoch: 057, Loss: 0.2470, Val: 0.5500\\n\",\n      \"Epoch: 058, Loss: 0.2508, Val: 0.5500\\n\",\n      \"Epoch: 059, Loss: 0.2682, Val: 0.5480\\n\",\n      \"Epoch: 060, Loss: 0.2693, Val: 0.5520\\n\",\n      \"Epoch: 061, Loss: 0.3073, Val: 0.5560\\n\",\n      \"Epoch: 062, Loss: 0.2693, Val: 0.5560\\n\",\n      \"Epoch: 063, Loss: 0.2439, Val: 0.5560\\n\",\n      \"Epoch: 064, Loss: 0.2296, Val: 0.5540\\n\",\n      \"Epoch: 065, Loss: 0.2546, Val: 0.5560\\n\",\n      \"Epoch: 066, Loss: 0.2780, Val: 0.5500\\n\",\n      \"Epoch: 067, Loss: 0.2248, Val: 0.5500\\n\",\n      \"Epoch: 068, Loss: 0.2089, Val: 0.5480\\n\",\n      \"Epoch: 069, Loss: 0.2087, Val: 0.5520\\n\",\n      \"Epoch: 070, Loss: 0.3002, Val: 0.5500\\n\",\n      \"Epoch: 071, Loss: 0.2100, Val: 0.5500\\n\",\n      \"Epoch: 072, Loss: 0.2168, Val: 0.5520\\n\",\n      \"Epoch: 073, Loss: 0.2183, Val: 0.5540\\n\",\n      \"Epoch: 074, Loss: 0.2243, Val: 0.5560\\n\",\n      \"Epoch: 075, Loss: 0.2122, Val: 0.5520\\n\",\n      \"Epoch: 076, Loss: 0.2276, Val: 0.5520\\n\",\n      \"Epoch: 077, Loss: 0.2418, Val: 0.5460\\n\",\n      \"Epoch: 078, Loss: 0.2519, Val: 0.5480\\n\",\n      \"Epoch: 079, Loss: 0.1970, Val: 0.5500\\n\",\n      \"Epoch: 080, Loss: 0.1741, Val: 0.5520\\n\",\n      \"Epoch: 081, Loss: 0.1612, Val: 0.5480\\n\",\n      \"Epoch: 082, Loss: 0.1788, Val: 0.5480\\n\",\n      \"Epoch: 083, Loss: 0.2389, Val: 0.5520\\n\",\n      \"Epoch: 084, Loss: 0.2468, Val: 0.5620\\n\",\n      \"Epoch: 085, Loss: 0.2417, Val: 0.5680\\n\",\n      \"Epoch: 086, Loss: 0.2522, Val: 0.5680\\n\",\n      \"Epoch: 087, Loss: 0.1438, Val: 0.5640\\n\",\n      \"Epoch: 088, Loss: 0.2136, Val: 0.5600\\n\",\n      \"Epoch: 089, Loss: 0.2170, Val: 0.5620\\n\",\n      \"Epoch: 090, Loss: 0.1844, Val: 0.5640\\n\",\n      \"Epoch: 091, Loss: 0.2008, Val: 0.5700\\n\",\n      \"Epoch: 092, Loss: 0.2188, Val: 0.5680\\n\",\n      \"Epoch: 093, Loss: 0.1833, Val: 0.5720\\n\",\n      \"Epoch: 094, Loss: 0.2039, Val: 0.5740\\n\",\n      \"Epoch: 095, Loss: 0.2079, Val: 0.5720\\n\",\n      \"Epoch: 096, Loss: 0.2214, Val: 0.5720\\n\",\n      \"Epoch: 097, Loss: 0.1520, Val: 0.5720\\n\",\n      \"Epoch: 098, Loss: 0.2303, Val: 0.5740\\n\",\n      \"Epoch: 099, Loss: 0.2675, Val: 0.5700\\n\",\n      \"Epoch: 100, Loss: 0.2040, Val: 0.5620\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = MLP(hidden_channels=16)\\n\",\n    \"criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-3)  # Define optimizer.\\n\",\n    \"\\n\",\n    \"def train():\\n\",\n    \"      model.train()\\n\",\n    \"      optimizer.zero_grad()  # Clear gradients.\\n\",\n    \"      out = model(data.x)  # Perform a single forward pass.\\n\",\n    \"      loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\\n\",\n    \"      loss.backward()  # Derive gradients.\\n\",\n    \"      optimizer.step()  # Update parameters based on gradients.\\n\",\n    \"      return loss\\n\",\n    \"\\n\",\n    \"def test(mask):\\n\",\n    \"      model.eval()\\n\",\n    \"      out = model(data.x)\\n\",\n    \"      pred = out.argmax(dim=1)  # Use the class with highest probability.\\n\",\n    \"      correct = pred[mask] == data.y[mask]  # Check against ground-truth labels.\\n\",\n    \"      acc = int(correct.sum()) / int(mask.sum())  # Derive ratio of correct predictions.\\n\",\n    \"      return acc\\n\",\n    \"\\n\",\n    \"for epoch in range(1, 101):\\n\",\n    \"    loss = train()\\n\",\n    \"    val_acc = test(data.val_mask)\\n\",\n    \"    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val: {val_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1942975a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluate model performance on test set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"7e25ea30\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test Accuracy: 0.5710\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = test(data.test_mask)\\n\",\n    \"print(f'Test Accuracy: {test_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a8dd9fec\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize trained model's embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"12079feb\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"out = model(data.x)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"fcc738a9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"512c31f2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Graph model 2: GCN\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4e831745\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Define and instantiate GCN model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"c51c9e4b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GCN(\\n\",\n      \"  (conv1): GCNConv(3703, 16)\\n\",\n      \"  (conv2): GCNConv(16, 6)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class GCN(torch.nn.Module):\\n\",\n    \"    def __init__(self, hidden_channels):\\n\",\n    \"        super().__init__()\\n\",\n    \"        torch.manual_seed(1234567)\\n\",\n    \"        self.conv1 = GCNConv(dataset.num_features, hidden_channels)\\n\",\n    \"        self.conv2 = GCNConv(hidden_channels, dataset.num_classes)\\n\",\n    \"\\n\",\n    \"    def forward(self, x, edge_index):\\n\",\n    \"        x = self.conv1(x, edge_index)\\n\",\n    \"        x = x.relu()\\n\",\n    \"        x = F.dropout(x, p=0.5, training=self.training)\\n\",\n    \"        x = self.conv2(x, edge_index)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"model = GCN(hidden_channels=16)\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cc35736a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize initial GCN embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"13e193f3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model = GCN(hidden_channels=32)\\n\",\n    \"model.eval()\\n\",\n    \"\\n\",\n    \"out = model(data.x, data.edge_index)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"01d5d817\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Train GCN model on graph dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"692f6484\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001, Loss: 1.7871, Val: 0.3620\\n\",\n      \"Epoch: 002, Loss: 1.6260, Val: 0.4100\\n\",\n      \"Epoch: 003, Loss: 1.4544, Val: 0.5100\\n\",\n      \"Epoch: 004, Loss: 1.2277, Val: 0.5740\\n\",\n      \"Epoch: 005, Loss: 1.0790, Val: 0.6200\\n\",\n      \"Epoch: 006, Loss: 0.9047, Val: 0.6580\\n\",\n      \"Epoch: 007, Loss: 0.7185, Val: 0.6840\\n\",\n      \"Epoch: 008, Loss: 0.6246, Val: 0.6880\\n\",\n      \"Epoch: 009, Loss: 0.5116, Val: 0.7020\\n\",\n      \"Epoch: 010, Loss: 0.4641, Val: 0.7040\\n\",\n      \"Epoch: 011, Loss: 0.4482, Val: 0.7060\\n\",\n      \"Epoch: 012, Loss: 0.3567, Val: 0.7060\\n\",\n      \"Epoch: 013, Loss: 0.2940, Val: 0.6960\\n\",\n      \"Epoch: 014, Loss: 0.2789, Val: 0.6900\\n\",\n      \"Epoch: 015, Loss: 0.2486, Val: 0.6880\\n\",\n      \"Epoch: 016, Loss: 0.2356, Val: 0.6920\\n\",\n      \"Epoch: 017, Loss: 0.2410, Val: 0.6980\\n\",\n      \"Epoch: 018, Loss: 0.1904, Val: 0.6880\\n\",\n      \"Epoch: 019, Loss: 0.2034, Val: 0.6880\\n\",\n      \"Epoch: 020, Loss: 0.1713, Val: 0.6860\\n\",\n      \"Epoch: 021, Loss: 0.1485, Val: 0.6840\\n\",\n      \"Epoch: 022, Loss: 0.1872, Val: 0.6840\\n\",\n      \"Epoch: 023, Loss: 0.1672, Val: 0.6880\\n\",\n      \"Epoch: 024, Loss: 0.1606, Val: 0.6920\\n\",\n      \"Epoch: 025, Loss: 0.1417, Val: 0.6880\\n\",\n      \"Epoch: 026, Loss: 0.1766, Val: 0.6920\\n\",\n      \"Epoch: 027, Loss: 0.1684, Val: 0.6920\\n\",\n      \"Epoch: 028, Loss: 0.1731, Val: 0.7020\\n\",\n      \"Epoch: 029, Loss: 0.2027, Val: 0.7040\\n\",\n      \"Epoch: 030, Loss: 0.1470, Val: 0.7060\\n\",\n      \"Epoch: 031, Loss: 0.1502, Val: 0.7020\\n\",\n      \"Epoch: 032, Loss: 0.1560, Val: 0.7040\\n\",\n      \"Epoch: 033, Loss: 0.1821, Val: 0.7100\\n\",\n      \"Epoch: 034, Loss: 0.1667, Val: 0.7020\\n\",\n      \"Epoch: 035, Loss: 0.1544, Val: 0.7040\\n\",\n      \"Epoch: 036, Loss: 0.1599, Val: 0.7020\\n\",\n      \"Epoch: 037, Loss: 0.1603, Val: 0.6920\\n\",\n      \"Epoch: 038, Loss: 0.1678, Val: 0.6940\\n\",\n      \"Epoch: 039, Loss: 0.1699, Val: 0.6900\\n\",\n      \"Epoch: 040, Loss: 0.1621, Val: 0.6900\\n\",\n      \"Epoch: 041, Loss: 0.1596, Val: 0.6920\\n\",\n      \"Epoch: 042, Loss: 0.1753, Val: 0.6900\\n\",\n      \"Epoch: 043, Loss: 0.1401, Val: 0.6900\\n\",\n      \"Epoch: 044, Loss: 0.1689, Val: 0.6940\\n\",\n      \"Epoch: 045, Loss: 0.1560, Val: 0.6960\\n\",\n      \"Epoch: 046, Loss: 0.2215, Val: 0.7020\\n\",\n      \"Epoch: 047, Loss: 0.1407, Val: 0.6960\\n\",\n      \"Epoch: 048, Loss: 0.1517, Val: 0.6960\\n\",\n      \"Epoch: 049, Loss: 0.1666, Val: 0.6920\\n\",\n      \"Epoch: 050, Loss: 0.1250, Val: 0.6880\\n\",\n      \"Epoch: 051, Loss: 0.1649, Val: 0.6800\\n\",\n      \"Epoch: 052, Loss: 0.1247, Val: 0.6840\\n\",\n      \"Epoch: 053, Loss: 0.1681, Val: 0.6820\\n\",\n      \"Epoch: 054, Loss: 0.1440, Val: 0.6820\\n\",\n      \"Epoch: 055, Loss: 0.1310, Val: 0.6740\\n\",\n      \"Epoch: 056, Loss: 0.1256, Val: 0.6840\\n\",\n      \"Epoch: 057, Loss: 0.1651, Val: 0.6800\\n\",\n      \"Epoch: 058, Loss: 0.1326, Val: 0.6840\\n\",\n      \"Epoch: 059, Loss: 0.1465, Val: 0.6900\\n\",\n      \"Epoch: 060, Loss: 0.1065, Val: 0.6880\\n\",\n      \"Epoch: 061, Loss: 0.1218, Val: 0.6920\\n\",\n      \"Epoch: 062, Loss: 0.1336, Val: 0.6980\\n\",\n      \"Epoch: 063, Loss: 0.1299, Val: 0.6980\\n\",\n      \"Epoch: 064, Loss: 0.1261, Val: 0.6920\\n\",\n      \"Epoch: 065, Loss: 0.1253, Val: 0.6900\\n\",\n      \"Epoch: 066, Loss: 0.1188, Val: 0.6900\\n\",\n      \"Epoch: 067, Loss: 0.1197, Val: 0.6880\\n\",\n      \"Epoch: 068, Loss: 0.0942, Val: 0.6860\\n\",\n      \"Epoch: 069, Loss: 0.1252, Val: 0.6860\\n\",\n      \"Epoch: 070, Loss: 0.1196, Val: 0.6840\\n\",\n      \"Epoch: 071, Loss: 0.1269, Val: 0.6840\\n\",\n      \"Epoch: 072, Loss: 0.1127, Val: 0.6880\\n\",\n      \"Epoch: 073, Loss: 0.1181, Val: 0.6840\\n\",\n      \"Epoch: 074, Loss: 0.1150, Val: 0.6800\\n\",\n      \"Epoch: 075, Loss: 0.1114, Val: 0.6860\\n\",\n      \"Epoch: 076, Loss: 0.1310, Val: 0.6840\\n\",\n      \"Epoch: 077, Loss: 0.1232, Val: 0.6860\\n\",\n      \"Epoch: 078, Loss: 0.1206, Val: 0.6820\\n\",\n      \"Epoch: 079, Loss: 0.1090, Val: 0.6860\\n\",\n      \"Epoch: 080, Loss: 0.1314, Val: 0.6940\\n\",\n      \"Epoch: 081, Loss: 0.0915, Val: 0.6920\\n\",\n      \"Epoch: 082, Loss: 0.1083, Val: 0.6880\\n\",\n      \"Epoch: 083, Loss: 0.1212, Val: 0.6840\\n\",\n      \"Epoch: 084, Loss: 0.1231, Val: 0.6920\\n\",\n      \"Epoch: 085, Loss: 0.0890, Val: 0.6920\\n\",\n      \"Epoch: 086, Loss: 0.1562, Val: 0.6900\\n\",\n      \"Epoch: 087, Loss: 0.0993, Val: 0.6880\\n\",\n      \"Epoch: 088, Loss: 0.1170, Val: 0.6860\\n\",\n      \"Epoch: 089, Loss: 0.1569, Val: 0.6820\\n\",\n      \"Epoch: 090, Loss: 0.1001, Val: 0.6780\\n\",\n      \"Epoch: 091, Loss: 0.1378, Val: 0.6840\\n\",\n      \"Epoch: 092, Loss: 0.0961, Val: 0.6860\\n\",\n      \"Epoch: 093, Loss: 0.1207, Val: 0.6880\\n\",\n      \"Epoch: 094, Loss: 0.0920, Val: 0.6920\\n\",\n      \"Epoch: 095, Loss: 0.0932, Val: 0.6880\\n\",\n      \"Epoch: 096, Loss: 0.1098, Val: 0.6880\\n\",\n      \"Epoch: 097, Loss: 0.1258, Val: 0.6940\\n\",\n      \"Epoch: 098, Loss: 0.0871, Val: 0.6960\\n\",\n      \"Epoch: 099, Loss: 0.1123, Val: 0.7000\\n\",\n      \"Epoch: 100, Loss: 0.1076, Val: 0.7000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = GCN(hidden_channels=16)\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-3)\\n\",\n    \"criterion = torch.nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"def train():\\n\",\n    \"      model.train()\\n\",\n    \"      optimizer.zero_grad()  # Clear gradients.\\n\",\n    \"      out = model(data.x, data.edge_index)  # Perform a single forward pass.\\n\",\n    \"      loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\\n\",\n    \"      loss.backward()  # Derive gradients.\\n\",\n    \"      optimizer.step()  # Update parameters based on gradients.\\n\",\n    \"      return loss\\n\",\n    \"\\n\",\n    \"def test(mask):\\n\",\n    \"      model.eval()\\n\",\n    \"      out = model(data.x, data.edge_index)\\n\",\n    \"      pred = out.argmax(dim=1)  # Use the class with highest probability.\\n\",\n    \"      correct = pred[mask] == data.y[mask]  # Check against ground-truth labels.\\n\",\n    \"      acc = int(correct.sum()) / int(mask.sum())  # Derive ratio of correct predictions.\\n\",\n    \"      return acc\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for epoch in range(1, 101):\\n\",\n    \"    loss = train()\\n\",\n    \"    val_acc = test(data.val_mask)\\n\",\n    \"    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val: {val_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d620e7e2\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluate model performance on test set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"c9e3b8cd\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test Accuracy: 0.6960\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = test(data.test_mask)\\n\",\n    \"print(f'Test Accuracy: {test_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e53d1d86\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize trained model's embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"693459c1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"out = model(data.x, data.edge_index)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98518fe0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Graph model 3: GAT\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ae96e27\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Define and instantiate GAT model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"7d3cbc13\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GAT(\\n\",\n      \"  (conv1): GATConv(3703, 16, heads=8)\\n\",\n      \"  (conv2): GATConv(128, 6, heads=1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class GAT(torch.nn.Module):\\n\",\n    \"    def __init__(self, hidden_channels, heads):\\n\",\n    \"        super().__init__()\\n\",\n    \"        torch.manual_seed(1234567)\\n\",\n    \"        self.conv1 = GATConv(dataset.num_features, hidden_channels, heads)\\n\",\n    \"        self.conv2 = GATConv(hidden_channels * heads, dataset.num_classes, heads=1)\\n\",\n    \"\\n\",\n    \"    def forward(self, x, edge_index):\\n\",\n    \"        x = F.dropout(x, p=0.6, training=self.training)\\n\",\n    \"        x = self.conv1(x, edge_index)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.dropout(x, p=0.6, training=self.training)\\n\",\n    \"        x = self.conv2(x, edge_index)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"model = GAT(hidden_channels=16, heads=8)\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"54c93751\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize initial GAT embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"8162a0a9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model.eval()\\n\",\n    \"\\n\",\n    \"out = model(data.x, data.edge_index)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f7d869b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Train GAT model on graph dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"39de3eaa\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001, Loss: 1.7793, Val: 0.2180\\n\",\n      \"Epoch: 002, Loss: 1.7874, Val: 0.2320\\n\",\n      \"Epoch: 003, Loss: 1.7888, Val: 0.2480\\n\",\n      \"Epoch: 004, Loss: 1.7795, Val: 0.2760\\n\",\n      \"Epoch: 005, Loss: 1.7772, Val: 0.2980\\n\",\n      \"Epoch: 006, Loss: 1.7481, Val: 0.3220\\n\",\n      \"Epoch: 007, Loss: 1.7355, Val: 0.3560\\n\",\n      \"Epoch: 008, Loss: 1.7179, Val: 0.3980\\n\",\n      \"Epoch: 009, Loss: 1.7231, Val: 0.4520\\n\",\n      \"Epoch: 010, Loss: 1.7194, Val: 0.4820\\n\",\n      \"Epoch: 011, Loss: 1.7055, Val: 0.5000\\n\",\n      \"Epoch: 012, Loss: 1.7018, Val: 0.5200\\n\",\n      \"Epoch: 013, Loss: 1.6984, Val: 0.5360\\n\",\n      \"Epoch: 014, Loss: 1.6669, Val: 0.5640\\n\",\n      \"Epoch: 015, Loss: 1.6698, Val: 0.5740\\n\",\n      \"Epoch: 016, Loss: 1.6604, Val: 0.5880\\n\",\n      \"Epoch: 017, Loss: 1.6674, Val: 0.5920\\n\",\n      \"Epoch: 018, Loss: 1.6656, Val: 0.6060\\n\",\n      \"Epoch: 019, Loss: 1.6460, Val: 0.6160\\n\",\n      \"Epoch: 020, Loss: 1.6564, Val: 0.6280\\n\",\n      \"Epoch: 021, Loss: 1.6414, Val: 0.6380\\n\",\n      \"Epoch: 022, Loss: 1.6111, Val: 0.6440\\n\",\n      \"Epoch: 023, Loss: 1.6215, Val: 0.6500\\n\",\n      \"Epoch: 024, Loss: 1.6152, Val: 0.6560\\n\",\n      \"Epoch: 025, Loss: 1.6079, Val: 0.6620\\n\",\n      \"Epoch: 026, Loss: 1.5766, Val: 0.6660\\n\",\n      \"Epoch: 027, Loss: 1.5875, Val: 0.6700\\n\",\n      \"Epoch: 028, Loss: 1.5831, Val: 0.6800\\n\",\n      \"Epoch: 029, Loss: 1.5571, Val: 0.6800\\n\",\n      \"Epoch: 030, Loss: 1.5565, Val: 0.6860\\n\",\n      \"Epoch: 031, Loss: 1.5513, Val: 0.6880\\n\",\n      \"Epoch: 032, Loss: 1.5325, Val: 0.6940\\n\",\n      \"Epoch: 033, Loss: 1.5323, Val: 0.7040\\n\",\n      \"Epoch: 034, Loss: 1.5012, Val: 0.7040\\n\",\n      \"Epoch: 035, Loss: 1.4896, Val: 0.7020\\n\",\n      \"Epoch: 036, Loss: 1.5015, Val: 0.7040\\n\",\n      \"Epoch: 037, Loss: 1.4811, Val: 0.7060\\n\",\n      \"Epoch: 038, Loss: 1.4601, Val: 0.7060\\n\",\n      \"Epoch: 039, Loss: 1.4565, Val: 0.7080\\n\",\n      \"Epoch: 040, Loss: 1.4541, Val: 0.7140\\n\",\n      \"Epoch: 041, Loss: 1.4642, Val: 0.7200\\n\",\n      \"Epoch: 042, Loss: 1.4246, Val: 0.7180\\n\",\n      \"Epoch: 043, Loss: 1.4276, Val: 0.7200\\n\",\n      \"Epoch: 044, Loss: 1.4162, Val: 0.7280\\n\",\n      \"Epoch: 045, Loss: 1.3871, Val: 0.7320\\n\",\n      \"Epoch: 046, Loss: 1.4007, Val: 0.7320\\n\",\n      \"Epoch: 047, Loss: 1.4157, Val: 0.7340\\n\",\n      \"Epoch: 048, Loss: 1.3692, Val: 0.7340\\n\",\n      \"Epoch: 049, Loss: 1.3775, Val: 0.7380\\n\",\n      \"Epoch: 050, Loss: 1.3547, Val: 0.7320\\n\",\n      \"Epoch: 051, Loss: 1.3594, Val: 0.7300\\n\",\n      \"Epoch: 052, Loss: 1.3468, Val: 0.7300\\n\",\n      \"Epoch: 053, Loss: 1.3180, Val: 0.7260\\n\",\n      \"Epoch: 054, Loss: 1.3181, Val: 0.7260\\n\",\n      \"Epoch: 055, Loss: 1.3017, Val: 0.7260\\n\",\n      \"Epoch: 056, Loss: 1.3061, Val: 0.7280\\n\",\n      \"Epoch: 057, Loss: 1.2813, Val: 0.7240\\n\",\n      \"Epoch: 058, Loss: 1.2732, Val: 0.7220\\n\",\n      \"Epoch: 059, Loss: 1.2873, Val: 0.7220\\n\",\n      \"Epoch: 060, Loss: 1.2936, Val: 0.7220\\n\",\n      \"Epoch: 061, Loss: 1.2980, Val: 0.7220\\n\",\n      \"Epoch: 062, Loss: 1.2601, Val: 0.7220\\n\",\n      \"Epoch: 063, Loss: 1.2690, Val: 0.7220\\n\",\n      \"Epoch: 064, Loss: 1.2657, Val: 0.7200\\n\",\n      \"Epoch: 065, Loss: 1.2469, Val: 0.7200\\n\",\n      \"Epoch: 066, Loss: 1.2372, Val: 0.7220\\n\",\n      \"Epoch: 067, Loss: 1.2154, Val: 0.7180\\n\",\n      \"Epoch: 068, Loss: 1.2307, Val: 0.7160\\n\",\n      \"Epoch: 069, Loss: 1.2295, Val: 0.7180\\n\",\n      \"Epoch: 070, Loss: 1.2264, Val: 0.7180\\n\",\n      \"Epoch: 071, Loss: 1.1955, Val: 0.7160\\n\",\n      \"Epoch: 072, Loss: 1.2046, Val: 0.7140\\n\",\n      \"Epoch: 073, Loss: 1.1936, Val: 0.7140\\n\",\n      \"Epoch: 074, Loss: 1.2025, Val: 0.7140\\n\",\n      \"Epoch: 075, Loss: 1.2105, Val: 0.7140\\n\",\n      \"Epoch: 076, Loss: 1.2059, Val: 0.7160\\n\",\n      \"Epoch: 077, Loss: 1.1888, Val: 0.7160\\n\",\n      \"Epoch: 078, Loss: 1.1948, Val: 0.7120\\n\",\n      \"Epoch: 079, Loss: 1.1608, Val: 0.7120\\n\",\n      \"Epoch: 080, Loss: 1.1629, Val: 0.7120\\n\",\n      \"Epoch: 081, Loss: 1.1727, Val: 0.7140\\n\",\n      \"Epoch: 082, Loss: 1.1502, Val: 0.7140\\n\",\n      \"Epoch: 083, Loss: 1.1375, Val: 0.7160\\n\",\n      \"Epoch: 084, Loss: 1.1527, Val: 0.7160\\n\",\n      \"Epoch: 085, Loss: 1.1563, Val: 0.7180\\n\",\n      \"Epoch: 086, Loss: 1.1801, Val: 0.7180\\n\",\n      \"Epoch: 087, Loss: 1.1628, Val: 0.7200\\n\",\n      \"Epoch: 088, Loss: 1.1555, Val: 0.7180\\n\",\n      \"Epoch: 089, Loss: 1.1310, Val: 0.7180\\n\",\n      \"Epoch: 090, Loss: 1.1341, Val: 0.7200\\n\",\n      \"Epoch: 091, Loss: 1.0956, Val: 0.7180\\n\",\n      \"Epoch: 092, Loss: 1.1343, Val: 0.7180\\n\",\n      \"Epoch: 093, Loss: 1.1428, Val: 0.7180\\n\",\n      \"Epoch: 094, Loss: 1.1560, Val: 0.7180\\n\",\n      \"Epoch: 095, Loss: 1.1357, Val: 0.7180\\n\",\n      \"Epoch: 096, Loss: 1.1057, Val: 0.7180\\n\",\n      \"Epoch: 097, Loss: 1.1514, Val: 0.7200\\n\",\n      \"Epoch: 098, Loss: 1.1342, Val: 0.7220\\n\",\n      \"Epoch: 099, Loss: 1.1354, Val: 0.7220\\n\",\n      \"Epoch: 100, Loss: 1.1131, Val: 0.7220\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=0.0005, weight_decay=1e-1)\\n\",\n    \"criterion = torch.nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"def train():\\n\",\n    \"      model.train()\\n\",\n    \"      optimizer.zero_grad()  # Clear gradients.\\n\",\n    \"      out = model(data.x, data.edge_index)  # Perform a single forward pass.\\n\",\n    \"      loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\\n\",\n    \"      loss.backward()  # Derive gradients.\\n\",\n    \"      optimizer.step()  # Update parameters based on gradients.\\n\",\n    \"      return loss\\n\",\n    \"\\n\",\n    \"def test(mask):\\n\",\n    \"      model.eval()\\n\",\n    \"      out = model(data.x, data.edge_index)\\n\",\n    \"      pred = out.argmax(dim=1)  # Use the class with highest probability.\\n\",\n    \"      correct = pred[mask] == data.y[mask]  # Check against ground-truth labels.\\n\",\n    \"      acc = int(correct.sum()) / int(mask.sum())  # Derive ratio of correct predictions.\\n\",\n    \"      return acc\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for epoch in range(1, 101):\\n\",\n    \"    loss = train()\\n\",\n    \"    val_acc = test(data.val_mask)\\n\",\n    \"    test_acc = test(data.test_mask)\\n\",\n    \"    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val: {val_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d4a43eb6\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluate model performance on test set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"2a5f5c80\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test Accuracy: 0.7210\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = test(data.test_mask)\\n\",\n    \"print(f'Test Accuracy: {test_acc:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"34744b8c\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize trained model's embeddings for different graph nodes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"1cb98870\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2835311/2296163890.py:12: UserWarning: The palette list has more values (7) than needed (6), which may not be intended.\\n\",\n      \"  sns.scatterplot(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1200x1200 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"out = model(data.x, data.edge_index)\\n\",\n    \"visualize(out.detach().cpu().numpy(), data.y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"4deb9bb5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter07/music_generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (10.2.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:20:43.948987Z\",\n     \"start_time\": \"2018-11-27T12:20:30.225783Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"import skimage.io as io\\n\",\n    \"from struct import pack, unpack\\n\",\n    \"from io import StringIO, BytesIO\\n\",\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining MIDI constants\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"NOTE_MIDI_OFF = 0x80\\n\",\n    \"NOTE_MIDI_ON = 0x90\\n\",\n    \"A_TOUCH = 0xA0\\n\",\n    \"CONT_CONTROL = 0xB0 \\n\",\n    \"PATCH_CHG_MIDI = 0xC0\\n\",\n    \"CHNL_PRESS = 0xD0\\n\",\n    \"MIDI_PITCH_BND = 0xE0                                                                                  \\n\",\n    \"SYS_EXCL_MIDI = 0xF0\\n\",\n    \"MTC_MIDI = 0xF1\\n\",\n    \"SNG_POS_MIDI_POINTER = 0xF2\\n\",\n    \"SNG_SEL_MIDI = 0xF3\\n\",\n    \"TUNE_REQ_MIDI = 0xF6\\n\",\n    \"END_OFF_EXCL_MIDI = 0xF7 \\n\",\n    \"SEQ_NUM = 0x00  \\n\",\n    \"MIDI_TXT            = 0x01  \\n\",\n    \"MIDI_CR       = 0x02  \\n\",\n    \"SEQ_NAME_MIDI   = 0x03  \\n\",\n    \"INSTR_NAME_MIDI = 0x04  \\n\",\n    \"MIDI_LRC           = 0x05  \\n\",\n    \"MIDI_MRKR          = 0x06  \\n\",\n    \"CUEPNT_MIDI        = 0x07  \\n\",\n    \"PROG_NAME_MIDI    = 0x08  \\n\",\n    \"DVC_NAME     = 0x09  \\n\",\n    \"CH_PREF  = 0x20  \\n\",\n    \"PRT       = 0x21  \\n\",\n    \"E_O_T    = 0x2F  \\n\",\n    \"TMP           = 0x51  \\n\",\n    \"SMTP_MIDI_OFF     = 0x54  \\n\",\n    \"TIME_SIGN_MIDI  = 0x58  \\n\",\n    \"KEY_SIGN_MIDI   = 0x59  \\n\",\n    \"MIDI_SPEC        = 0x7F  \\n\",\n    \"MIDI_TRK_HEAD    = 'MTrk'\\n\",\n    \"MIDI_MET_EVNT     = 0xFF\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## MIDI handling code\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MOStrm:\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self._abs_t = 0\\n\",\n    \"        self._rel_t = 0\\n\",\n    \"        self._cur_trk = 0\\n\",\n    \"        self._run_stat = None\\n\",\n    \"    def time_update(self, t_new=0, rel_flag=1):\\n\",\n    \"        if rel_flag:\\n\",\n    \"            self._rel_t = t_new\\n\",\n    \"            self._abs_t += t_new\\n\",\n    \"        else:\\n\",\n    \"            self._rel_t = t_new - self._abs_t\\n\",\n    \"            self._abs_t = t_new\\n\",\n    \"    def t_reset(self):\\n\",\n    \"        self._rel_t = 0\\n\",\n    \"        self._abs_t = 0        \\n\",\n    \"    def t_rel(self):\\n\",\n    \"        return self._rel_t\\n\",\n    \"    def t_abs(self):\\n\",\n    \"        return self._abs_t    \\n\",\n    \"    def set_cur_trk(self, trk_new):\\n\",\n    \"        self._cur_trk = trk_new\\n\",\n    \"    def midi_nt_on(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        pass\\n\",\n    \"    def midi_nt_off(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        pass\\n\",\n    \"    def aft_tch(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        pass\\n\",\n    \"    def cont_ctrl(self, chnl, crtler, v):\\n\",\n    \"        pass\\n\",\n    \"    def pch_chg(self, chnl, pch):\\n\",\n    \"        pass\\n\",\n    \"    def chnl_press(self, chnl, press):\\n\",\n    \"        pass\\n\",\n    \"    def pch_bnd(self, chnl, v):\\n\",\n    \"        pass\\n\",\n    \"    def sng_pos_ptr(self, v):\\n\",\n    \"        pass\\n\",\n    \"    def sng_sel(self, sng_num):\\n\",\n    \"        pass\\n\",\n    \"    def tun_req(self):\\n\",\n    \"        pass        \\n\",\n    \"    def t_cod(self, typ_m, vals):\\n\",\n    \"        pass\\n\",\n    \"    def hdr(self, fmt=0, num_trs=1, divs=96):\\n\",\n    \"        pass\\n\",\n    \"    def end_of_file(self):\\n\",\n    \"        pass\\n\",\n    \"    def mt_evt(self, mt_typ, d):\\n\",\n    \"        pass\\n\",\n    \"    def s_o_trk(self, num_trk=0):\\n\",\n    \"        pass\\n\",\n    \"    def e_o_trk(self):\\n\",\n    \"        pass\\n\",\n    \"    def seq_num(self, v):\\n\",\n    \"        pass\\n\",\n    \"    def txt(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def cr(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def seq_nam(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def inst_nam(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def lrc(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def mrkr(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def cuepnt(self, txt):\\n\",\n    \"        pass\\n\",\n    \"    def ch_prf(self, chnl):\\n\",\n    \"        pass\\n\",\n    \"    def m_prt(self, v):\\n\",\n    \"        pass\\n\",\n    \"    def tmp(self, v):\\n\",\n    \"        pass\\n\",\n    \"    def smtp_midi_off(self, hr, mn, sec, fm, fmprt):\\n\",\n    \"        pass\\n\",\n    \"    def t_sign_midi(self, n, d, c, b):\\n\",\n    \"        pass\\n\",\n    \"    def k_sign_midi(self, s, m):\\n\",\n    \"        pass\\n\",\n    \"    def seq_spc(self, d):\\n\",\n    \"        pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MIFl:\\n\",\n    \"    def __init__(self, ostr, ifl):\\n\",\n    \"        self.r_input = RIStrFl(ifl)\\n\",\n    \"        self.prs = MFlPrsr(self.r_input, ostr)\\n\",\n    \"    def op_rd(self):\\n\",\n    \"        prs = self.prs\\n\",\n    \"        prs.pMdChk()\\n\",\n    \"        prs.pMTChks()\\n\",\n    \"    def d_set(self, d=''):\\n\",\n    \"        self.r_input.d_set(d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MOFl(MOStrm):\\n\",\n    \"    def __init__(self, r_output=''):\\n\",\n    \"        self.r_output = ROStrFl(r_output)\\n\",\n    \"        MOStrm.__init__(self)    \\n\",\n    \"    def op_wrt(self):\\n\",\n    \"        self.r_output.op_wrt()\\n\",\n    \"    def midi_evt_slc(self, e_slice):\\n\",\n    \"        curr_track = self._cur_trk_bfr\\n\",\n    \"        curr_track.op_wrt_var_len(self.t_rel())\\n\",\n    \"        curr_track.op_wrt_slc(e_slice)\\n\",\n    \"    def midi_nt_on(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        curr_slice = strm_to_data([NOTE_MIDI_ON + chnl, nt, vel])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def midi_nt_off(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        curr_slice = strm_to_data([NOTE_MIDI_OFF + chnl, nt, vel])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def aft_tch(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"        curr_slice = strm_to_data([A_TOUCH + chnl, nt, vel])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def cont_ctrl(self, chnl, ctrler, v):\\n\",\n    \"        curr_slice = strm_to_data([CONT_CONTROL + chnl, ctrler, v])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def pch_chg(self, chnl, pch):\\n\",\n    \"        curr_slice = strm_to_data([PATCH_CHG_MIDI + chnl, pch])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def chnl_press(self, chnl, press):\\n\",\n    \"        curr_slice = strm_to_data([CHNL_PRESS + chnl, press])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def pch_bnd(self, chnl, v):\\n\",\n    \"        m = (v>>7) & 0xFF\\n\",\n    \"        l = v & 0xFF\\n\",\n    \"        curr_slice = strm_to_data([MIDI_PITCH_BND + chnl, m, l])\\n\",\n    \"        self.midi_evt_slc(curr_slice)\\n\",\n    \"    def t_cod(self, typ_m, vals):\\n\",\n    \"        v = (typ_m<<4) + vals\\n\",\n    \"        self.midi_evt_slc(strm_to_data([MIDI_TIME_CODE, v]))\\n\",\n    \"    def sng_pos_ptr(self, v):\\n\",\n    \"        l = (v & 0x7F)\\n\",\n    \"        m = (v >> 7) & 0x7F\\n\",\n    \"        self.midi_evt_slc(strm_to_data([SNG_POS_MIDI_POINTER, l, m]))\\n\",\n    \"    def sng_sel(self, sng_num):\\n\",\n    \"        self.midi_evt_slc(strm_to_data([SNG_SEL_MIDI, sng_num]))\\n\",\n    \"    def tun_req(self):\\n\",\n    \"        self.midi_evt_slc(chr(TUNE_REQ_MIDI))\\n\",\n    \"    def hdr(self, fmt=0, num_trs=1, divs=96):       \\n\",\n    \"        r_data = self.r_output\\n\",\n    \"        r_data.op_wrt_slc('MThd')\\n\",\n    \"        b = r_data.op_bew_wrt\\n\",\n    \"        b(6, 4)\\n\",\n    \"        b(fmt, 2)\\n\",\n    \"        b(num_trs, 2)\\n\",\n    \"        b(divs, 2)\\n\",\n    \"    def end_of_file(self):\\n\",\n    \"        self.op_wrt()\\n\",\n    \"    def m_slc(self, mt_typ, data_slice):\\n\",\n    \"        slc = strm_to_data([MIDI_MET_EVNT, mt_typ]) + \\\\\\n\",\n    \"                         op_wrt_var(len(data_slice)) +  data_slice\\n\",\n    \"        self.midi_evt_slc(slc)\\n\",\n    \"    def mt_evt(self, mt_typ, d):\\n\",\n    \"        self.m_slc(mt_typ, strm_to_data(d))\\n\",\n    \"    def s_o_trk(self, num_trk=0):\\n\",\n    \"        self._cur_trk_bfr = ROStrFl()\\n\",\n    \"        self.t_reset()\\n\",\n    \"        self._cur_trk += 1\\n\",\n    \"    def e_o_trk(self):\\n\",\n    \"        r_output = self.r_output\\n\",\n    \"        r_output.op_wrt_slc(MIDI_TRK_HEAD)\\n\",\n    \"        d_trk = self._cur_trk_bfr.fetch_val()\\n\",\n    \"        e_o_t_slc = op_wrt_var(self.t_rel()) + strm_to_data([MIDI_MET_EVNT, E_O_T, 0])\\n\",\n    \"        r_output.op_bew_wrt(len(d_trk)+len(e_o_t_slc), 4)\\n\",\n    \"        r_output.op_wrt_slc(d_trk)\\n\",\n    \"        r_output.op_wrt_slc(e_o_t_slc)\\n\",\n    \"    def seq_num(self, v):\\n\",\n    \"        self.m_slc(mt_typ, op_bew_wrt(v, 2))\\n\",\n    \"    def midi_txt(self, txt):\\n\",\n    \"        self.m_slc(MIDI_TXT, txt)\\n\",\n    \"    def cr(self, txt):\\n\",\n    \"        self.m_slc(MIDI_CR, txt)\\n\",\n    \"    def seq_nam(self, txt):\\n\",\n    \"        self.m_slc(SEQ_NAME_MIDI, txt)\\n\",\n    \"    def inst_nam(self, txt):\\n\",\n    \"        self.m_slc(INSTR_NAME_MIDI, txt)\\n\",\n    \"    def lrc(self, txt):\\n\",\n    \"        self.m_slc(MIDI_LRC, txt)\\n\",\n    \"    def mrkr(self, txt):\\n\",\n    \"        self.m_slc(MIDI_MRKR, txt)\\n\",\n    \"    def cuepnt(self, txt):\\n\",\n    \"        self.m_slc(CUEPNT_MIDI, txt)\\n\",\n    \"    def ch_prf(self, chnl):\\n\",\n    \"        self.m_slc(CH_PREF, chr(chnl))\\n\",\n    \"    def m_prt(self, v):\\n\",\n    \"        self.m_slc(CH_PREF, chr(v))\\n\",\n    \"    def tmp(self, v):\\n\",\n    \"        h, m, l = (v>>16 & 0xff), (v>>8 & 0xff), (v & 0xff)\\n\",\n    \"        self.m_slc(TMP, strm_to_data([h, m, l]))\\n\",\n    \"    def smtp_midi_off(self, hr, mn, sec, fm, fmprt):\\n\",\n    \"        self.m_slc(SMTP_MIDI_OFF, strm_to_data([hr, mn, sec, fm, fmprt]))\\n\",\n    \"    def t_sign_midi(self, n, d, c, b):\\n\",\n    \"        self.m_slc(TIME_SIGN_MIDI, strm_to_data([n, d, c, b]))\\n\",\n    \"    def k_sign_midi(self, s, m):\\n\",\n    \"        self.m_slc(KEY_SIGN_MIDI, strm_to_data([s, m]))\\n\",\n    \"    def seq_spc(self, d):\\n\",\n    \"        self.m_slc(SEQ_SPC, d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RIStrFl:    \\n\",\n    \"    def __init__(self, ifl=''):\\n\",\n    \"        if ifl:\\n\",\n    \"            if isinstance(ifl, str):\\n\",\n    \"                ifl = open(ifl, 'rb')\\n\",\n    \"                self.d = ifl.read()\\n\",\n    \"                ifl.close()\\n\",\n    \"            else:\\n\",\n    \"                self.d = ifl.op_rd()\\n\",\n    \"        else:\\n\",\n    \"            self.d = ''\\n\",\n    \"        self.csr = 0\\n\",\n    \"    def d_set(self, d=''):\\n\",\n    \"        self.d = d\\n\",\n    \"    def get_csr(self):\\n\",\n    \"        return self.csr\\n\",\n    \"    def mv_csr(self, rl_ps=0):\\n\",\n    \"        self.csr += rl_ps\\n\",\n    \"    def nxt_slc(self, ln, mv_csr=1):\\n\",\n    \"        c = self.csr\\n\",\n    \"        slc = self.d[int(c):int(c+ln)]\\n\",\n    \"        if mv_csr:\\n\",\n    \"            self.mv_csr(ln)\\n\",\n    \"        return slc\\n\",\n    \"    def b_read(self, num_bts=1, mv_csr=1):\\n\",\n    \"        return b_read(self.nxt_slc(num_bts, mv_csr))\\n\",\n    \"    def op_rd_var_len(self):\\n\",\n    \"        MX_VLN = 4\\n\",\n    \"        v = op_rd_var(self.nxt_slc(MX_VLN, 0))\\n\",\n    \"        self.mv_csr(get_len_var(v))\\n\",\n    \"        return v\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ROStrFl:\\n\",\n    \"    def __init__(self, ofl=''):\\n\",\n    \"        self.bfr = BytesIO()\\n\",\n    \"        self.ofl = ofl\\n\",\n    \"    def op_wrt_slc(self, s_slc):\\n\",\n    \"        if isinstance(s_slc, str):\\n\",\n    \"            self.bfr.write(s_slc.encode())\\n\",\n    \"        else:\\n\",\n    \"            self.bfr.write(s_slc)\\n\",\n    \"    def op_bew_wrt(self, v, ln=1):\\n\",\n    \"        self.op_wrt_slc(op_bew_wrt(v, ln))\\n\",\n    \"    def op_wrt_var_len(self, v):\\n\",\n    \"        var = self.op_wrt_slc(op_wrt_var(v))\\n\",\n    \"    def op_wrt(self):\\n\",\n    \"        if self.ofl:\\n\",\n    \"            if isinstance(self.ofl, str):\\n\",\n    \"                ofl = open(self.ofl, 'wb')\\n\",\n    \"                ofl.write(self.fetch_val())\\n\",\n    \"                ofl.close()\\n\",\n    \"            else:\\n\",\n    \"                self.ofl.op_wrt(self.fetch_val())\\n\",\n    \"        else:\\n\",\n    \"            sys.stdout.op_wrt(self.fetch_val())                \\n\",\n    \"    def fetch_val(self):\\n\",\n    \"        return self.bfr.getvalue()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MFlPrsr:\\n\",\n    \"    def __init__(self, r_input, ostr):\\n\",\n    \"        self.r_input = r_input\\n\",\n    \"        self.dpch = EvtDspch(ostr)\\n\",\n    \"        self._run_stat = None\\n\",\n    \"    def pMdChk(self):\\n\",\n    \"        r_input = self.r_input\\n\",\n    \"        hd_chk_typ = r_input.nxt_slc(4)\\n\",\n    \"        hd_chk_sz = r_input.b_read(4)\\n\",\n    \"        if hd_chk_typ != b'MThd':\\n\",\n    \"            raise TypeError(\\\"Invalid file type - non MIDI !\\\")\\n\",\n    \"        self.fmt = r_input.b_read(2)\\n\",\n    \"        self.num_tr = r_input.b_read(2)\\n\",\n    \"        self.divs = r_input.b_read(2)\\n\",\n    \"        if hd_chk_sz > 6:\\n\",\n    \"            r_input.mv_csr(hd_chk_sz-6)\\n\",\n    \"        self.dpch.hdr(self.fmt, self.num_tr, self.divs)\\n\",\n    \"    def pMTChk(self):\\n\",\n    \"        self.dpch.t_reset()        \\n\",\n    \"        dpch = self.dpch\\n\",\n    \"        r_input = self.r_input\\n\",\n    \"        dpch.s_o_trk(self._cur_trk)\\n\",\n    \"        r_input.mv_csr(4)\\n\",\n    \"        tr_len = r_input.b_read(4)\\n\",\n    \"        tr_end = r_input.get_csr() + tr_len\\n\",\n    \"        while r_input.get_csr() < tr_end:\\n\",\n    \"            t = r_input.op_rd_var_len()\\n\",\n    \"            dpch.time_update(t)\\n\",\n    \"            pk_fwd = r_input.b_read(mv_csr=0)\\n\",\n    \"            if (pk_fwd & 0x80): \\n\",\n    \"                st = self._run_stat = r_input.b_read()\\n\",\n    \"            else:\\n\",\n    \"                st = self._run_stat\\n\",\n    \"            h_n, l_n = st & 0xF0, st & 0x0F\\n\",\n    \"            if st == MIDI_MET_EVNT:\\n\",\n    \"                mt_typ = r_input.b_read()\\n\",\n    \"                mt_ln = r_input.op_rd_var_len()\\n\",\n    \"                mt_d = r_input.nxt_slc(mt_ln)\\n\",\n    \"                dpch.mt_evt(mt_typ, mt_d)\\n\",\n    \"            elif st == SYS_EXCL_MIDI:\\n\",\n    \"                ssx_ln = r_input.op_rd_var_len()\\n\",\n    \"                ssx_d = r_input.nxt_slc(ssx_ln-1)\\n\",\n    \"                if r_input.b_read(mv_csr=0) == END_OFF_EXCL_MIDI:\\n\",\n    \"                    e_o_ssx = r_input.b_read()\\n\",\n    \"                dpch.ssx_ev(ssx_d)\\n\",\n    \"            elif h_n == 0xF0:\\n\",\n    \"                d_sz = {\\n\",\n    \"                    MTC_MIDI:1,\\n\",\n    \"                    SNG_POS_MIDI_POINTER:2,\\n\",\n    \"                    SNG_SEL_MIDI:1,\\n\",\n    \"                }\\n\",\n    \"                d_s = d_sz.get(h_n, 0)\\n\",\n    \"                cmn_d = r_input.nxt_slc(d_s)\\n\",\n    \"                cmn_t = l_n\\n\",\n    \"                dpch.sys_cmn(cmn_t, cmn_d)\\n\",\n    \"            else:\\n\",\n    \"                d_sz = {\\n\",\n    \"                    PATCH_CHG_MIDI:1,\\n\",\n    \"                    CHNL_PRESS:1,\\n\",\n    \"                    NOTE_MIDI_OFF:2,\\n\",\n    \"                    NOTE_MIDI_ON:2,\\n\",\n    \"                    A_TOUCH:2,\\n\",\n    \"                    CONT_CONTROL:2,\\n\",\n    \"                    MIDI_PITCH_BND:2,\\n\",\n    \"                }\\n\",\n    \"                d_s = d_sz.get(h_n, 0)\\n\",\n    \"                ch_d = r_input.nxt_slc(d_s)\\n\",\n    \"                ev_ty, chnl = h_n, l_n\\n\",\n    \"                dpch.chnl_msg(ev_ty, chnl, ch_d)\\n\",\n    \"    def pMTChks(self):\\n\",\n    \"        for t in range(self.num_tr):\\n\",\n    \"            self._cur_trk = t\\n\",\n    \"            self.pMTChk()\\n\",\n    \"        self.dpch.end_of_file()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class EvtDspch:\\n\",\n    \"    def __init__(self, ostr):\\n\",\n    \"        self.ostr = ostr\\n\",\n    \"        self.conv_vel_zero = 1\\n\",\n    \"        self.cont_ctrl_dpch = 1\\n\",\n    \"        self.dpch = 1\\n\",\n    \"    def hdr(self, fmt, num_trs, divs):\\n\",\n    \"        self.ostr.hdr(fmt, num_trs, divs)\\n\",\n    \"    def s_o_trk(self, cur_trk):\\n\",\n    \"        self.ostr.set_cur_trk(cur_trk)\\n\",\n    \"        self.ostr.s_o_trk(cur_trk)\\n\",\n    \"    def ssx_ev(self, d):\\n\",\n    \"        self.ostr.ssx_ev(d)\\n\",\n    \"    def end_of_file(self):\\n\",\n    \"        self.ostr.end_of_file()\\n\",\n    \"    def time_update(self, t_new=0, rel_flag=1):\\n\",\n    \"        self.ostr.time_update(t_new, rel_flag)\\n\",\n    \"    def t_reset(self):\\n\",\n    \"        self.ostr.t_reset()\\n\",\n    \"    def chnl_msg(self, h_n, chnl, d):\\n\",\n    \"        strm = self.ostr\\n\",\n    \"        d = data_to_strm(d)\\n\",\n    \"        if (NOTE_MIDI_ON & 0xF0) == h_n:\\n\",\n    \"            nt, vel = d\\n\",\n    \"            if vel==0 and self.conv_vel_zero:\\n\",\n    \"                strm.midi_nt_off(chnl, nt, 0x40)\\n\",\n    \"            else:\\n\",\n    \"                strm.midi_nt_on(chnl, nt, vel)        \\n\",\n    \"        elif (NOTE_MIDI_OFF & 0xF0) == h_n:\\n\",\n    \"            nt, vel = d\\n\",\n    \"            strm.midi_nt_off(chnl, nt, vel)        \\n\",\n    \"        elif (A_TOUCH & 0xF0) == h_n:\\n\",\n    \"            nt, vel = d\\n\",\n    \"            strm.aft_tch(chnl, nt, vel)        \\n\",\n    \"        elif (CONT_CONTROL & 0xF0) == h_n:\\n\",\n    \"            ctrlr, v = d\\n\",\n    \"            if self.cont_ctrl_dpch:\\n\",\n    \"                self.cnt_ctrls(chnl, ctrlr, v)\\n\",\n    \"            else:\\n\",\n    \"                strm.cont_ctrl(chnl, ctrlr, v)\\n\",\n    \"        elif (PATCH_CHG_MIDI & 0xF0) == h_n:\\n\",\n    \"            prg = d[0]\\n\",\n    \"            strm.pch_chg(chnl, prg)    \\n\",\n    \"        elif (CHNL_PRESS & 0xF0) == h_n:\\n\",\n    \"            press = d[0]\\n\",\n    \"#             strm.ch_press(chnl, press)            \\n\",\n    \"        elif (MIDI_PITCH_BND & 0xF0) == h_n:\\n\",\n    \"            hb, lb = d\\n\",\n    \"            v = (hb<<7) + lb\\n\",\n    \"            strm.pch_bnd(chnl, v)\\n\",\n    \"        else:\\n\",\n    \"            raise ValueError('Channel message error - illegal message !')            \\n\",\n    \"    def cnt_ctrls(self, chnl, ctrlr, v):\\n\",\n    \"        strm = self.ostr\\n\",\n    \"        strm.cont_ctrl(chnl, ctrlr, v)\\n\",\n    \"    def mt_evt(self, mt_typ, d):\\n\",\n    \"        strm = self.ostr\\n\",\n    \"        if mt_typ == SEQ_NUM:\\n\",\n    \"            n = b_read(d)\\n\",\n    \"            strm.seq_num(n)\\n\",\n    \"        elif mt_typ == MIDI_TXT:\\n\",\n    \"            strm.txt(d)\\n\",\n    \"        elif mt_typ == MIDI_CR:\\n\",\n    \"            strm.cr(d)\\n\",\n    \"        elif mt_typ == SEQ_NAME_MIDI:\\n\",\n    \"            strm.seq_nam(d)\\n\",\n    \"        elif mt_typ == INSTR_NAME_MIDI:\\n\",\n    \"            strm.inst_nam(d)\\n\",\n    \"        elif mt_typ == MIDI_LRC:\\n\",\n    \"            strm.lrc(d)\\n\",\n    \"        elif mt_typ == MIDI_MRKR:\\n\",\n    \"            strm.mrkr(d)\\n\",\n    \"        elif mt_typ == CUEPNT_MIDI:\\n\",\n    \"            strm.cuepnt(d)\\n\",\n    \"        elif mt_typ == DVC_NAME:\\n\",\n    \"            strm.dvc_nam(d)\\n\",\n    \"        elif mt_typ == CH_PREF:\\n\",\n    \"            chnl = b_read(d)\\n\",\n    \"            strm.ch_prf(chnl)\\n\",\n    \"        elif mt_typ == PRT:\\n\",\n    \"            port = b_read(d)\\n\",\n    \"            strm.m_prt(port)\\n\",\n    \"        elif mt_typ == E_O_T:\\n\",\n    \"            strm.e_o_trk()\\n\",\n    \"        elif mt_typ == TMP:\\n\",\n    \"            b1, b2, b3 = data_to_strm(d)\\n\",\n    \"            strm.tmp((b1<<16) + (b2<<8) + b3)\\n\",\n    \"        elif mt_typ == SMTP_MIDI_OFF:\\n\",\n    \"            hr, mn, sec, fm, fmprt = data_to_strm(d)\\n\",\n    \"            strm.smtp_midi_off(\\n\",\n    \"                    hr, mn, sec, fm, fmprt)\\n\",\n    \"        elif mt_typ == TIME_SIGN_MIDI:\\n\",\n    \"            n, d, c, b = data_to_strm(d)\\n\",\n    \"            strm.t_sign_midi(n, d, c, b)\\n\",\n    \"        elif mt_typ == KEY_SIGN_MIDI:\\n\",\n    \"            sf, mi = data_to_strm(d)\\n\",\n    \"            strm.k_sign_midi(sf, mi)\\n\",\n    \"        elif mt_typ == MIDI_SPEC:\\n\",\n    \"            mt_d = data_to_strm(d)\\n\",\n    \"            strm.seq_spc(mt_d)\\n\",\n    \"        else:\\n\",\n    \"            mt_d = data_to_strm(d)\\n\",\n    \"            strm.meta_event(mt_typ, mt_d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def b_read(v):\\n\",\n    \"    return unpack('>%s' % {1:'B', 2:'H', 4:'L'}[len(v)], v)[0]\\n\",\n    \"def op_bew_wrt(v, l):\\n\",\n    \"    return pack('>%s' % {1:'B', 2:'H', 4:'L'}[l], v)\\n\",\n    \"def op_rd_var(v):\\n\",\n    \"    count = 0\\n\",\n    \"    for b in unpack('%sB' % len(v), v):\\n\",\n    \"        count = (count << 7) + (b & 0x7F)\\n\",\n    \"        if not 0x80 & b: \\n\",\n    \"            break\\n\",\n    \"    return count\\n\",\n    \"def get_len_var(v):\\n\",\n    \"    if v <= 127:\\n\",\n    \"        return 1\\n\",\n    \"    elif v <= 16383:\\n\",\n    \"        return 2\\n\",\n    \"    elif v <= 2097151:\\n\",\n    \"        return 3\\n\",\n    \"    else:\\n\",\n    \"        return 4\\n\",\n    \"def op_wrt_var(v):\\n\",\n    \"    s = data_to_bits(v, get_len_var(v))\\n\",\n    \"    for i in range(len(s)-1):\\n\",\n    \"        s[i] = s[i] | 0x80\\n\",\n    \"    return strm_to_data(s)\\n\",\n    \"def data_to_bits(v, l=1, nbs=7):\\n\",\n    \"    b = [(v >> (j*nbs)) & 0x7F for j in range(l)]\\n\",\n    \"    b.reverse()\\n\",\n    \"    return b\\n\",\n    \"def data_to_strm(v):\\n\",\n    \"    return unpack('%sB' % len(v), v)\\n\",\n    \"def strm_to_data(v):\\n\",\n    \"    if not v: return ''\\n\",\n    \"    return pack('%sB' % len(v), *v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MidiDataRead(MOStrm):\\n\",\n    \"  def __init__(self, fname, rng=(21, 109), dtm=0.2):\\n\",\n    \"    self.nts = []\\n\",\n    \"    self._tmp = 500000\\n\",\n    \"    self.bt = 0\\n\",\n    \"    self.t = 0.0\\n\",\n    \"    m_in = MIFl(self, fname)\\n\",\n    \"    m_in.op_rd()\\n\",\n    \"    self.nts = [n for n in self.nts if n[2] is not None]  \\n\",\n    \"    ln = int(np.ceil(max(list(zip(*self.nts))[2]) / dtm))  \\n\",\n    \"    self.pio_rl = np.zeros((ln, rng[1]-rng[0]))\\n\",\n    \"    for n in self.nts:\\n\",\n    \"      self.pio_rl[int(np.ceil(n[1]/dtm)) : int(np.ceil(n[2]/dtm)), n[0]-rng[0]] = 1\\n\",\n    \"  def t_in_sec(self):\\n\",\n    \"    return self.t + self._tmp * (self.t_abs() - self.bt) * 1e-6 / self.div\\n\",\n    \"  def tmp(self, v):\\n\",\n    \"    self.t = self.t_in_sec()\\n\",\n    \"    self.bt = self.t_abs()\\n\",\n    \"    self._tmp = v  \\n\",\n    \"  def hdr(self, fmt=0, num_tr=1, divs=96):\\n\",\n    \"    self.div = divs\\n\",\n    \"  def midi_nt_on(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"    self.nts.append([nt, self.t_in_sec(), None])\\n\",\n    \"  def midi_nt_off(self, chnl=0, nt=0x40, vel=0x40):\\n\",\n    \"    i = len(self.nts) - 1\\n\",\n    \"    while i >= 0 and self.nts[i][0] != nt:\\n\",\n    \"      i -= 1\\n\",\n    \"    if i >= 0 and self.nts[i][2] is None:\\n\",\n    \"      self.nts[i][2] = self.t_in_sec()\\n\",\n    \"  def ssx_ev(*args):\\n\",\n    \"    pass\\n\",\n    \"  def dvc_nam(*args):\\n\",\n    \"    pass\\n\",\n    \"def midiwrite(fname, pio_rl, rng=(21, 109), dtm=0.2, patch=0):\\n\",\n    \"  md = MOFl(fname)\\n\",\n    \"  md.hdr(divs=100)\\n\",\n    \"  md.s_o_trk() \\n\",\n    \"  md.pch_chg(chnl=0, pch=patch)\\n\",\n    \"  tm = 0\\n\",\n    \"  smp = [i.nonzero()[0] + rng[0] for i in pio_rl]\\n\",\n    \"  for i in range(len(smp)):\\n\",\n    \"    for f in smp[i]:\\n\",\n    \"      if i==0 or f not in smp[i-1]:\\n\",\n    \"        md.time_update(tm)\\n\",\n    \"        md.midi_nt_on(chnl=0, nt=f, vel=90)\\n\",\n    \"        tm = 0\\n\",\n    \"    tm += int(dtm*200)\\n\",\n    \"    for f in smp[i]:\\n\",\n    \"      if i==len(smp)-1 or f not in smp[i+1]:\\n\",\n    \"        md.time_update(tm)\\n\",\n    \"        md.midi_nt_off(chnl=0, nt=f, vel=0)\\n\",\n    \"        tm = 0\\n\",\n    \"  md.time_update(0)\\n\",\n    \"  md.e_o_trk()\\n\",\n    \"  md.end_of_file()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# PyTorch CODE STARTS HERE !\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset Instructions\\n\",\n    \"- For this exercise, we will be using a set of Mozart’s compositions. \\n\",\n    \"- We download the dataset from here: http://www.piano-midi.de/zip/mozart.zip . \\n\",\n    \"- The downloaded folder consists of 21 MIDI files which we shall split into 18 training and 3 validation set files (alphabetically).  \\n\",\n    \"- We create two subfolders within the mozart folder - train and valid where we move the 18 training and 3 validation files respectively\\n\",\n    \"- The downloaded data should be stored under ./mozart/train and ./mozart/valid\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DataLoader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:20:52.867674Z\",\n     \"start_time\": \"2018-11-27T12:20:52.819795Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def md_fl_to_pio_rl(md_fl):    \\n\",\n    \"    md_d = MidiDataRead(md_fl, dtm=0.3)\\n\",\n    \"    pio_rl = md_d.pio_rl.transpose()\\n\",\n    \"    pio_rl[pio_rl > 0] = 1    \\n\",\n    \"    return pio_rl\\n\",\n    \"def pd_pio_rl(pio_rl, mx_l=132333, pd_v=0):        \\n\",\n    \"    orig_rol_len = pio_rl.shape[1]    \\n\",\n    \"    pdd_rol = np.zeros((88, mx_l))\\n\",\n    \"    pdd_rol[:] = pd_v    \\n\",\n    \"    pdd_rol[:, - orig_rol_len:] = pio_rl\\n\",\n    \"    return pdd_rol\\n\",\n    \"class NtGenDataset(data.Dataset):    \\n\",\n    \"    def __init__(self, md_pth, mx_seq_ln=1491):        \\n\",\n    \"        self.md_pth = md_pth        \\n\",\n    \"        md_fnames = os.listdir(md_pth)        \\n\",\n    \"        self.mx_seq_ln = mx_seq_ln        \\n\",\n    \"        md_fnames_ful = map(lambda fname: os.path.join(md_pth, fname),md_fnames)        \\n\",\n    \"        self.md_fnames_ful = list(md_fnames_ful)        \\n\",\n    \"        if mx_seq_ln is None:            \\n\",\n    \"            self.mx_len_upd()    \\n\",\n    \"    def mx_len_upd(self):        \\n\",\n    \"        seq_lens = map(lambda fname: md_fl_to_pio_rl(fname).shape[1],self.md_fnames_ful)        \\n\",\n    \"        mx_l = max(seq_lens)\\n\",\n    \"        self.mx_seq_ln = mx_l    \\n\",\n    \"    def __len__(self):        \\n\",\n    \"        return len(self.md_fnames_ful)    \\n\",\n    \"    def __getitem__(self, index):        \\n\",\n    \"        md_fname_ful = self.md_fnames_ful[index]        \\n\",\n    \"        pio_rl = md_fl_to_pio_rl(md_fname_ful)\\n\",\n    \"        seq_len = pio_rl.shape[1] - 1\\n\",\n    \"        ip_seq = pio_rl[:, :-1]\\n\",\n    \"        gt_seq = pio_rl[:, 1:]\\n\",\n    \"        ip_seq_pad = pd_pio_rl(ip_seq, mx_l=self.mx_seq_ln)\\n\",\n    \"        gt_seq_pad = pd_pio_rl(gt_seq,mx_l=self.mx_seq_ln,pd_v=-100)       \\n\",\n    \"        ip_seq_pad = ip_seq_pad.transpose()\\n\",\n    \"        gt_seq_pad = gt_seq_pad.transpose()\\n\",\n    \"        return (torch.FloatTensor(ip_seq_pad),\\n\",\n    \"                torch.LongTensor(gt_seq_pad), torch.LongTensor([seq_len]))\\n\",\n    \"def pos_proc_seq(btch):\\n\",\n    \"    ip_seqs, op_seqs, lens = btch    \\n\",\n    \"    ip_seq_splt_btch = ip_seqs.split(split_size=1)\\n\",\n    \"    op_seq_splt_btch = op_seqs.split(split_size=1)\\n\",\n    \"    btch_splt_lens = lens.split(split_size=1)\\n\",\n    \"    tr_data_tups = zip(ip_seq_splt_btch,\\n\",\n    \"                               op_seq_splt_btch,\\n\",\n    \"                               btch_splt_lens)\\n\",\n    \"    ord_tr_data_tups = sorted(tr_data_tups,\\n\",\n    \"                                         key=lambda c: int(c[2]),\\n\",\n    \"                                         reverse=True)\\n\",\n    \"    ip_seq_splt_btch, op_seq_splt_btch, btch_splt_lens = zip(*ord_tr_data_tups)\\n\",\n    \"    ord_ip_seq_btch = torch.cat(ip_seq_splt_btch)\\n\",\n    \"    ord_op_seq_btch = torch.cat(op_seq_splt_btch)\\n\",\n    \"    ord_btch_lens = torch.cat(btch_splt_lens)    \\n\",\n    \"    ord_ip_seq_btch = ord_ip_seq_btch[:, -ord_btch_lens[0, 0]:, :]\\n\",\n    \"    ord_op_seq_btch = ord_op_seq_btch[:, -ord_btch_lens[0, 0]:, :]    \\n\",\n    \"    tps_ip_seq_btch = ord_ip_seq_btch.transpose(0, 1)    \\n\",\n    \"    ord_btch_lens_l = list(ord_btch_lens)\\n\",\n    \"    ord_btch_lens_l = map(lambda k: int(k), ord_btch_lens_l)    \\n\",\n    \"    return tps_ip_seq_btch, ord_op_seq_btch, list(ord_btch_lens_l)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:21:07.678124Z\",\n     \"start_time\": \"2018-11-27T12:20:56.931002Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"training_dataset = NtGenDataset('./mozart/train', mx_seq_ln=None)\\n\",\n    \"training_datasetloader = data.DataLoader(training_dataset, batch_size=5,shuffle=True, drop_last=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:21:08.232332Z\",\n     \"start_time\": \"2018-11-27T12:21:07.693745Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"torch.Size([5, 4812, 88])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_train = next(iter(training_datasetloader))\\n\",\n    \"X_train[0].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:21:11.258476Z\",\n     \"start_time\": \"2018-11-27T12:21:08.259258Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"validation_dataset = NtGenDataset('./mozart/valid/', mx_seq_ln=None)\\n\",\n    \"validation_datasetloader = data.DataLoader(validation_dataset, batch_size=3, shuffle=False, drop_last=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:21:11.406325Z\",\n     \"start_time\": \"2018-11-27T12:21:11.278687Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"torch.Size([3, 1587, 88])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"X_validation = next(iter(validation_datasetloader))\\n\",\n    \"X_validation[0].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x700 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10,7))\\n\",\n    \"plt.title(\\\"Matrix representation of a Mozart composition\\\")\\n\",\n    \"plt.imshow(X_validation[0][0][:300].numpy().T);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Music LSTM\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### model definition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:22:33.314323Z\",\n     \"start_time\": \"2018-11-27T12:22:33.291386Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class MusicLSTM(nn.Module):    \\n\",\n    \"    def __init__(self, ip_sz, hd_sz, n_cls, lyrs=2):        \\n\",\n    \"        super(MusicLSTM, self).__init__()        \\n\",\n    \"        self.ip_sz = ip_sz\\n\",\n    \"        self.hd_sz = hd_sz\\n\",\n    \"        self.n_cls = n_cls\\n\",\n    \"        self.lyrs = lyrs        \\n\",\n    \"        self.nts_enc = nn.Linear(in_features=ip_sz, out_features=hd_sz)        \\n\",\n    \"        self.bn_layer = nn.BatchNorm1d(hd_sz)        \\n\",\n    \"        self.lstm_layer = nn.LSTM(hd_sz, hd_sz, lyrs)        \\n\",\n    \"        self.fc_layer = nn.Linear(hd_sz, n_cls)\\n\",\n    \"        \\n\",\n    \"    def forward(self, ip_seqs, ip_seqs_len, hd=None):\\n\",\n    \"        nts_enc = self.nts_enc(ip_seqs)       \\n\",\n    \"        nts_enc_rol = nts_enc.permute(1,2,0).contiguous()\\n\",\n    \"        nts_enc_nrm = self.bn_layer(nts_enc_rol)        \\n\",\n    \"        nts_enc_nrm_drp = nn.Dropout(0.25)(nts_enc_nrm)\\n\",\n    \"        nts_enc_ful = nts_enc_nrm_drp.permute(2,0,1)\\n\",\n    \"        \\n\",\n    \"        pkd = torch.nn.utils.rnn.pack_padded_sequence(nts_enc_ful, ip_seqs_len)\\n\",\n    \"        op, hd = self.lstm_layer(pkd, hd)\\n\",\n    \"        \\n\",\n    \"        op, op_l = torch.nn.utils.rnn.pad_packed_sequence(op)\\n\",\n    \"        \\n\",\n    \"        op_nrm = self.bn_layer(op.permute(1,2,0).contiguous())\\n\",\n    \"        op_nrm_drp = nn.Dropout(0.1)(op_nrm)\\n\",\n    \"        lgts = self.fc_layer(op_nrm_drp.permute(2,0,1))\\n\",\n    \"        lgts = lgts.transpose(0, 1).contiguous()\\n\",\n    \"        \\n\",\n    \"        rev_lgts = (1 - lgts)\\n\",\n    \"        \\n\",\n    \"        zero_one_lgts = torch.stack((lgts, rev_lgts), dim=3).contiguous()\\n\",\n    \"        flt_lgts = zero_one_lgts.view(-1, 2)\\n\",\n    \"        return flt_lgts, hd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### training and validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-26T16:16:18.949227Z\",\n     \"start_time\": \"2018-11-26T16:16:18.930281Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def lstm_model_training(lstm_model, lr, ep=10, val_loss_best=float(\\\"inf\\\")):\\n\",\n    \"    list_of_losses = []\\n\",\n    \"    list_of_val_losses =[]\\n\",\n    \"    model_params = lstm_model.parameters()\\n\",\n    \"    opt = torch.optim.Adam(model_params, lr=lr)\\n\",\n    \"    grad_clip = 1.0\\n\",\n    \"    for curr_ep in range(ep):\\n\",\n    \"        lstm_model.train()\\n\",\n    \"        loss_ep = []\\n\",\n    \"        for batch in training_datasetloader:\\n\",\n    \"            post_proc_b = pos_proc_seq(batch)\\n\",\n    \"            ip_seq_b, op_seq_b, seq_l = post_proc_b\\n\",\n    \"            op_seq_b_v =  Variable(op_seq_b.contiguous().view(-1).cpu())\\n\",\n    \"            ip_seq_b_v = Variable(ip_seq_b.cpu())\\n\",\n    \"            opt.zero_grad()\\n\",\n    \"            lgts, _ = lstm_model(ip_seq_b_v, seq_l)\\n\",\n    \"            loss = loss_func(lgts, op_seq_b_v)\\n\",\n    \"            list_of_losses.append(loss.item())\\n\",\n    \"            loss_ep.append(loss.item())\\n\",\n    \"            loss.backward()\\n\",\n    \"            torch.nn.utils.clip_grad_norm_(lstm_model.parameters(), grad_clip)\\n\",\n    \"            opt.step()\\n\",\n    \"\\n\",\n    \"        tr_ep_cur = sum(loss_ep)/len(training_datasetloader)\\n\",\n    \"        print(f'ep {curr_ep} , train loss = {tr_ep_cur}')\\n\",\n    \"\\n\",\n    \"        vl_ep_cur = evaluate_model(lstm_model)\\n\",\n    \"        print(f'ep {curr_ep} , val loss = {vl_ep_cur}\\\\n')\\n\",\n    \"\\n\",\n    \"        list_of_val_losses.append(vl_ep_cur)\\n\",\n    \"\\n\",\n    \"        if vl_ep_cur < val_loss_best:\\n\",\n    \"            torch.save(lstm_model.state_dict(), 'best_model.pth')\\n\",\n    \"            val_loss_best = vl_ep_cur\\n\",\n    \"    return val_loss_best, lstm_model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-27T12:22:58.002722Z\",\n     \"start_time\": \"2018-11-27T12:22:57.987764Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def evaluate_model(lstm_model):\\n\",\n    \"    lstm_model.eval()\\n\",\n    \"    vl_loss_full = 0.0\\n\",\n    \"    seq_len = 0.0\\n\",\n    \"\\n\",\n    \"    for batch in validation_datasetloader:\\n\",\n    \"        post_proc_b = pos_proc_seq(batch)\\n\",\n    \"        ip_seq_b, op_seq_b, seq_l = post_proc_b\\n\",\n    \"        op_seq_b_v =  Variable( op_seq_b.contiguous().view(-1).cpu() )\\n\",\n    \"        ip_seq_b_v = Variable( ip_seq_b.cpu() )\\n\",\n    \"        lgts, _ = lstm_model(ip_seq_b_v, seq_l)\\n\",\n    \"        loss = loss_func(lgts, op_seq_b_v)\\n\",\n    \"        vl_loss_full += loss.item()\\n\",\n    \"        seq_len += sum(seq_l)\\n\",\n    \"\\n\",\n    \"    return vl_loss_full/(seq_len*88)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-26T16:19:05.465667Z\",\n     \"start_time\": \"2018-11-26T16:16:20.312589Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ep 0 , train loss = 1.4977599183718364\\n\",\n      \"ep 0 , val loss = 3.359648520086258e-06\\n\",\n      \"\\n\",\n      \"ep 1 , train loss = 2.2600611249605813\\n\",\n      \"ep 1 , val loss = 9.399909746101137e-07\\n\",\n      \"\\n\",\n      \"ep 2 , train loss = 1.3531951705614726\\n\",\n      \"ep 2 , val loss = 1.6474151244813573e-06\\n\",\n      \"\\n\",\n      \"ep 3 , train loss = 0.8641716837882996\\n\",\n      \"ep 3 , val loss = 5.708250628016667e-07\\n\",\n      \"\\n\",\n      \"ep 4 , train loss = 0.6801068683465322\\n\",\n      \"ep 4 , val loss = 2.1908977324171416e-06\\n\",\n      \"\\n\",\n      \"ep 5 , train loss = 0.6633423070112864\\n\",\n      \"ep 5 , val loss = 5.530907588653284e-07\\n\",\n      \"\\n\",\n      \"ep 6 , train loss = 0.15917229652404785\\n\",\n      \"ep 6 , val loss = 5.34580371513167e-07\\n\",\n      \"\\n\",\n      \"ep 7 , train loss = 0.18867839376131693\\n\",\n      \"ep 7 , val loss = 3.671834715649115e-07\\n\",\n      \"\\n\",\n      \"ep 8 , train loss = 0.1214339683453242\\n\",\n      \"ep 8 , val loss = 4.971649697420895e-07\\n\",\n      \"\\n\",\n      \"ep 9 , train loss = 0.13249559700489044\\n\",\n      \"ep 9 , val loss = 4.5515213342500874e-07\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"loss_func = nn.CrossEntropyLoss().cpu()\\n\",\n    \"lstm_model = MusicLSTM(ip_sz=88, hd_sz=512, n_cls=88).cpu()\\n\",\n    \"val_loss_best, lstm_model = lstm_model_training(lstm_model, lr=0.01, ep=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Generate samples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-26T16:25:11.202962Z\",\n     \"start_time\": \"2018-11-26T16:25:11.189993Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_music(lstm_model, ln=100, tmp=1, seq_st=None):\\n\",\n    \"    if seq_st is None:\\n\",\n    \"        seq_ip_cur = torch.zeros(1, 1, 88)\\n\",\n    \"        seq_ip_cur[0, 0, 40] = 1\\n\",\n    \"        seq_ip_cur[0, 0, 50] = 0\\n\",\n    \"        seq_ip_cur[0, 0, 56] = 0\\n\",\n    \"        seq_ip_cur = Variable(seq_ip_cur.cpu())\\n\",\n    \"    else:\\n\",\n    \"        seq_ip_cur = seq_st\\n\",\n    \"        \\n\",\n    \"    op_seq = [seq_ip_cur.data.squeeze(1)]\\n\",\n    \"    hd = None\\n\",\n    \"\\n\",\n    \"    for i in range(ln):\\n\",\n    \"        op, hd = lstm_model(seq_ip_cur, [1], hd)\\n\",\n    \"        probs = nn.functional.softmax(op.div(tmp), dim=1)\\n\",\n    \"        seq_ip_cur = torch.multinomial(probs.data, 1).squeeze().unsqueeze(0).unsqueeze(1)\\n\",\n    \"        seq_ip_cur = Variable(seq_ip_cur.float())\\n\",\n    \"        op_seq.append(seq_ip_cur.data.squeeze(1))\\n\",\n    \"\\n\",\n    \"    gen_seq = torch.cat(op_seq, dim=0).cpu().numpy()\\n\",\n    \"    \\n\",\n    \"    return gen_seq\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2018-11-26T16:25:36.632928Z\",\n     \"start_time\": \"2018-11-26T16:25:34.966547Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\\n\",\n      \"  lo, hi, cmap = _get_display_range(image)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"seq = generate_music(lstm_model, ln=100, tmp=0.8, seq_st=None).transpose()\\n\",\n    \"io.imshow(seq)\\n\",\n    \"midiwrite('generated_music.mid', seq.transpose(), dtm=0.25)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "Chapter07/text_generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mRequirement already satisfied: torchtext==0.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: tqdm in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (4.66.2)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (2.31.0)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (1.26.4)\\n\",\n      \"Requirement already satisfied: torchdata==0.7.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchtext==0.17) (0.7.1)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchtext==0.17) (2024.2.0)\\n\",\n      \"Requirement already satisfied: urllib3>=1.25 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchdata==0.7.1->torchtext==0.17) (2.2.1)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (3.6)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchtext==0.17) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2.0->torchtext==0.17) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2.0->torchtext==0.17) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0m\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchtext==0.17\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"from torch import nn, Tensor\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.nn import TransformerEncoder, TransformerEncoderLayer\\n\",\n    \"from torch.utils.data import dataset\\n\",\n    \"\\n\",\n    \"from torchtext.datasets import PennTreebank\\n\",\n    \"from torchtext.data.utils import get_tokenizer\\n\",\n    \"from torchtext.vocab import build_vocab_from_iterator\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Transformer(nn.Module):\\n\",\n    \"    def __init__(self, num_token, num_inputs, num_heads, num_hidden, num_layers, dropout=0.3):\\n\",\n    \"        super(Transformer, self).__init__()\\n\",\n    \"        self.model_name = 'transformer'\\n\",\n    \"        self.position_enc = PosEnc(num_inputs, dropout)\\n\",\n    \"        layers_enc = TransformerEncoderLayer(num_inputs, num_heads, num_hidden, dropout)\\n\",\n    \"        self.enc_transformer = TransformerEncoder(layers_enc, num_layers)\\n\",\n    \"        self.enc = nn.Embedding(num_token, num_inputs)\\n\",\n    \"        self.num_inputs = num_inputs\\n\",\n    \"        self.dec = nn.Linear(num_inputs, num_token)\\n\",\n    \"        self.init_params()\\n\",\n    \"\\n\",\n    \"    def init_params(self):\\n\",\n    \"        initial_rng = 0.12\\n\",\n    \"        self.enc.weight.data.uniform_(-initial_rng, initial_rng)\\n\",\n    \"        self.dec.bias.data.zero_()\\n\",\n    \"        self.dec.weight.data.uniform_(-initial_rng, initial_rng)\\n\",\n    \"\\n\",\n    \"    def forward(self, source, mask_source):\\n\",\n    \"        source = self.enc(source) * math.sqrt(self.num_inputs)\\n\",\n    \"        source = self.position_enc(source)\\n\",\n    \"        op = self.enc_transformer(source, mask_source)\\n\",\n    \"        op = self.dec(op)\\n\",\n    \"        return op\\n\",\n    \"\\n\",\n    \"def gen_sqr_nxt_mask(size):\\n\",\n    \"    msk = torch.triu(torch.ones(size, size) * float('-inf'), diagonal=1)\\n\",\n    \"    return msk\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class PosEnc(nn.Module):\\n\",\n    \"    def __init__(self, d_m, dropout=0.2, size_limit=5000):\\n\",\n    \"        super(PosEnc, self).__init__()\\n\",\n    \"        self.dropout = nn.Dropout(dropout)\\n\",\n    \"        p_enc = torch.zeros(size_limit, 1, d_m)\\n\",\n    \"        pos = torch.arange(size_limit, dtype=torch.float).unsqueeze(1)\\n\",\n    \"        divider = torch.exp(torch.arange(0, d_m, 2).float() * (-math.log(10000.0) / d_m))\\n\",\n    \"        p_enc[:, 0, 0::2] = torch.sin(pos * divider)\\n\",\n    \"        p_enc[:, 0, 1::2] = torch.cos(pos * divider)\\n\",\n    \"        self.register_buffer('p_enc', p_enc)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.dropout(x + self.p_enc[:x.size(0)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tr_iter = PennTreebank(split='train')\\n\",\n    \"tkzer = get_tokenizer('basic_english')\\n\",\n    \"vocabulary = build_vocab_from_iterator(map(tkzer, tr_iter), specials=['<unk>'])\\n\",\n    \"vocabulary.set_default_index(vocabulary['<unk>'])\\n\",\n    \"\\n\",\n    \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"def process_data(raw_text):\\n\",\n    \"    numericalised_text = [torch.tensor(vocabulary(tkzer(text)), dtype=torch.long) for text in raw_text]\\n\",\n    \"    return torch.cat(tuple(filter(lambda t: t.numel() > 0, numericalised_text)))\\n\",\n    \"\\n\",\n    \"tr_iter, val_iter, te_iter = PennTreebank()\\n\",\n    \"training_text = process_data(tr_iter)\\n\",\n    \"validation_text = process_data(val_iter)\\n\",\n    \"testing_text = process_data(te_iter)\\n\",\n    \"\\n\",\n    \"def gen_batches(text_dataset, batch_size):\\n\",\n    \"    num_batches = text_dataset.size(0) // batch_size\\n\",\n    \"    text_dataset = text_dataset[:num_batches * batch_size]\\n\",\n    \"    text_dataset = text_dataset.view(batch_size, num_batches).t().contiguous()\\n\",\n    \"    return text_dataset.to(device)\\n\",\n    \"\\n\",\n    \"training_batch_size = 32\\n\",\n    \"evaluation_batch_size = 16\\n\",\n    \"\\n\",\n    \"training_data = gen_batches(training_text, training_batch_size)\\n\",\n    \"validation_data = gen_batches(validation_text, evaluation_batch_size)\\n\",\n    \"testing_data = gen_batches(testing_text, evaluation_batch_size)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_seq_len = 64\\n\",\n    \"def return_batch(src, k):\\n\",\n    \"    sequence_length = min(max_seq_len, len(src) - 1 - k)\\n\",\n    \"    sequence_data = src[k:k+sequence_length]\\n\",\n    \"    sequence_label = src[k+1:k+1+sequence_length].reshape(-1)\\n\",\n    \"    return sequence_data, sequence_label\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\\n\",\n      \"  warnings.warn(f\\\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_tokens = len(vocabulary) # vocabulary size\\n\",\n    \"embedding_size = 256 # dimension of embedding layer\\n\",\n    \"num_hidden_params = 256 # transformer encoder's hidden (feed forward) layer dimension\\n\",\n    \"num_layers = 2 # num of transformer encoder layers within transformer encoder\\n\",\n    \"num_heads = 2 # num of heads in (multi head) attention models\\n\",\n    \"dropout = 0.25 # value (fraction) of dropout\\n\",\n    \"loss_func = nn.CrossEntropyLoss()\\n\",\n    \"lrate = 10.0 # learning rate\\n\",\n    \"transformer_model = Transformer(num_tokens, embedding_size, num_heads, num_hidden_params, num_layers, \\n\",\n    \"                                     dropout).to(device)\\n\",\n    \"optim_module = torch.optim.SGD(transformer_model.parameters(), lr=lrate)\\n\",\n    \"sched_module = torch.optim.lr_scheduler.StepLR(optim_module, 1.0, gamma=0.88)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train_model():\\n\",\n    \"    transformer_model.train()\\n\",\n    \"    loss_total = 0.\\n\",\n    \"    time_start = time.time()\\n\",\n    \"    mask_source = gen_sqr_nxt_mask(max_seq_len).to(device)\\n\",\n    \"    num_batches = len(training_data) // max_seq_len\\n\",\n    \"    for b, i in enumerate(range(0, training_data.size(0) - 1, max_seq_len)):\\n\",\n    \"        train_data_batch, train_label_batch = return_batch(training_data, i)\\n\",\n    \"        sequence_length = train_data_batch.size(0)\\n\",\n    \"        if sequence_length != max_seq_len:  # only on last batch\\n\",\n    \"            mask_source = mask_source[:sequence_length, :sequence_length]\\n\",\n    \"        op = transformer_model(train_data_batch, mask_source)\\n\",\n    \"        loss_curr = loss_func(op.view(-1, num_tokens), train_label_batch)\\n\",\n    \"        optim_module.zero_grad()\\n\",\n    \"        loss_curr.backward()\\n\",\n    \"        torch.nn.utils.clip_grad_norm_(transformer_model.parameters(), 0.6)\\n\",\n    \"        optim_module.step()\\n\",\n    \"\\n\",\n    \"        loss_total += loss_curr.item()\\n\",\n    \"        interval = 100\\n\",\n    \"        if b % interval == 0 and b > 0:\\n\",\n    \"            loss_interval = loss_total / interval\\n\",\n    \"            time_delta = time.time() - time_start\\n\",\n    \"            print(f\\\"epoch {ep}, {b}/{len(training_data)//max_seq_len} batches, training loss {loss_interval:.2f}, training perplexity {math.exp(loss_interval):.2f}\\\")\\n\",\n    \"            loss_total = 0\\n\",\n    \"            time_start = time.time()\\n\",\n    \"\\n\",\n    \"def eval_model(eval_model_obj, eval_data_source):\\n\",\n    \"    eval_model_obj.eval() \\n\",\n    \"    loss_total = 0.\\n\",\n    \"    mask_source = gen_sqr_nxt_mask(max_seq_len).to(device)\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for j in range(0, eval_data_source.size(0) - 1, max_seq_len):\\n\",\n    \"            eval_data, eval_label = return_batch(eval_data_source, j)\\n\",\n    \"            sequence_length = eval_data.size(0)\\n\",\n    \"            if sequence_length != max_seq_len:\\n\",\n    \"                mask_source = mask_source[:sequence_length, :sequence_length]\\n\",\n    \"            op = eval_model_obj(eval_data, mask_source)\\n\",\n    \"            op_flat = op.view(-1, num_tokens)\\n\",\n    \"            loss_total += sequence_length * loss_func(op_flat, eval_label).item()\\n\",\n    \"    return loss_total / (len(eval_data_source) - 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch 1, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 1, 200/451 batches, training loss 6.59, training perplexity 724.72\\n\",\n      \"epoch 1, 300/451 batches, training loss 6.55, training perplexity 700.21\\n\",\n      \"epoch 1, 400/451 batches, training loss 6.55, training perplexity 701.72\\n\",\n      \"\\n\",\n      \"epoch 1, validation loss 6.56, validation perplexity 706.02\\n\",\n      \"\\n\",\n      \"epoch 2, 100/451 batches, training loss 6.64, training perplexity 761.70\\n\",\n      \"epoch 2, 200/451 batches, training loss 6.59, training perplexity 724.71\\n\",\n      \"epoch 2, 300/451 batches, training loss 6.55, training perplexity 700.20\\n\",\n      \"epoch 2, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 2, validation loss 6.56, validation perplexity 706.02\\n\",\n      \"\\n\",\n      \"epoch 3, 100/451 batches, training loss 6.64, training perplexity 761.70\\n\",\n      \"epoch 3, 200/451 batches, training loss 6.59, training perplexity 724.71\\n\",\n      \"epoch 3, 300/451 batches, training loss 6.55, training perplexity 700.21\\n\",\n      \"epoch 3, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 3, validation loss 6.56, validation perplexity 706.02\\n\",\n      \"\\n\",\n      \"epoch 4, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 4, 200/451 batches, training loss 6.59, training perplexity 724.69\\n\",\n      \"epoch 4, 300/451 batches, training loss 6.55, training perplexity 700.19\\n\",\n      \"epoch 4, 400/451 batches, training loss 6.55, training perplexity 701.72\\n\",\n      \"\\n\",\n      \"epoch 4, validation loss 6.56, validation perplexity 706.02\\n\",\n      \"\\n\",\n      \"epoch 5, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 5, 200/451 batches, training loss 6.59, training perplexity 724.70\\n\",\n      \"epoch 5, 300/451 batches, training loss 6.55, training perplexity 700.19\\n\",\n      \"epoch 5, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 5, validation loss 6.56, validation perplexity 706.02\\n\",\n      \"\\n\",\n      \"epoch 6, 100/451 batches, training loss 6.64, training perplexity 761.70\\n\",\n      \"epoch 6, 200/451 batches, training loss 6.59, training perplexity 724.70\\n\",\n      \"epoch 6, 300/451 batches, training loss 6.55, training perplexity 700.18\\n\",\n      \"epoch 6, 400/451 batches, training loss 6.55, training perplexity 701.72\\n\",\n      \"\\n\",\n      \"epoch 6, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 7, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 7, 200/451 batches, training loss 6.59, training perplexity 724.70\\n\",\n      \"epoch 7, 300/451 batches, training loss 6.55, training perplexity 700.18\\n\",\n      \"epoch 7, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 7, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 8, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 8, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 8, 300/451 batches, training loss 6.55, training perplexity 700.17\\n\",\n      \"epoch 8, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 8, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 9, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 9, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 9, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 9, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 9, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 10, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 10, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 10, 300/451 batches, training loss 6.55, training perplexity 700.17\\n\",\n      \"epoch 10, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 10, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 11, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 11, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 11, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 11, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 11, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 12, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 12, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 12, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 12, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 12, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 13, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 13, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 13, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 13, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 13, validation loss 6.56, validation perplexity 706.03\\n\",\n      \"\\n\",\n      \"epoch 14, 100/451 batches, training loss 6.64, training perplexity 761.70\\n\",\n      \"epoch 14, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 14, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 14, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 14, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 15, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 15, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 15, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 15, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 15, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 16, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 16, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 16, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 16, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 16, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 17, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 17, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 17, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 17, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 17, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 18, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 18, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 18, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 18, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 18, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 19, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 19, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 19, 300/451 batches, training loss 6.55, training perplexity 700.14\\n\",\n      \"epoch 19, 400/451 batches, training loss 6.55, training perplexity 701.68\\n\",\n      \"\\n\",\n      \"epoch 19, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 20, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 20, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 20, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 20, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 20, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 21, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 21, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 21, 300/451 batches, training loss 6.55, training perplexity 700.14\\n\",\n      \"epoch 21, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 21, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 22, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 22, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 22, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 22, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 22, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 23, 100/451 batches, training loss 6.64, training perplexity 761.70\\n\",\n      \"epoch 23, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 23, 300/451 batches, training loss 6.55, training perplexity 700.18\\n\",\n      \"epoch 23, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 23, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 24, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 24, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 24, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 24, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 24, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 25, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 25, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 25, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 25, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 25, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 26, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 26, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 26, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 26, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 26, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 27, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 27, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 27, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 27, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 27, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 28, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 28, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 28, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 28, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 28, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 29, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 29, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 29, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 29, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 29, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 30, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 30, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 30, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 30, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 30, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 31, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 31, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 31, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 31, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 31, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 32, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 32, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 32, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 32, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 32, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 33, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 33, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 33, 300/451 batches, training loss 6.55, training perplexity 700.14\\n\",\n      \"epoch 33, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 33, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 34, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 34, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 34, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 34, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 34, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 35, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 35, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 35, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 35, 400/451 batches, training loss 6.55, training perplexity 701.68\\n\",\n      \"\\n\",\n      \"epoch 35, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 36, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 36, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 36, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 36, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 36, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 37, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 37, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 37, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 37, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 37, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 38, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 38, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 38, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 38, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 38, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 39, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 39, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 39, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 39, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 39, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 40, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 40, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 40, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 40, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 40, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 41, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 41, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 41, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 41, 400/451 batches, training loss 6.55, training perplexity 701.71\\n\",\n      \"\\n\",\n      \"epoch 41, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 42, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 42, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 42, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 42, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 42, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 43, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 43, 200/451 batches, training loss 6.59, training perplexity 724.66\\n\",\n      \"epoch 43, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 43, 400/451 batches, training loss 6.55, training perplexity 701.68\\n\",\n      \"\\n\",\n      \"epoch 43, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 44, 100/451 batches, training loss 6.64, training perplexity 761.67\\n\",\n      \"epoch 44, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 44, 300/451 batches, training loss 6.55, training perplexity 700.14\\n\",\n      \"epoch 44, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 44, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 45, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 45, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 45, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 45, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 45, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 46, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 46, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 46, 300/451 batches, training loss 6.55, training perplexity 700.16\\n\",\n      \"epoch 46, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 46, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 47, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 47, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 47, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 47, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 47, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 48, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 48, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 48, 300/451 batches, training loss 6.55, training perplexity 700.15\\n\",\n      \"epoch 48, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 48, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 49, 100/451 batches, training loss 6.64, training perplexity 761.69\\n\",\n      \"epoch 49, 200/451 batches, training loss 6.59, training perplexity 724.67\\n\",\n      \"epoch 49, 300/451 batches, training loss 6.55, training perplexity 700.17\\n\",\n      \"epoch 49, 400/451 batches, training loss 6.55, training perplexity 701.69\\n\",\n      \"\\n\",\n      \"epoch 49, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\",\n      \"epoch 50, 100/451 batches, training loss 6.64, training perplexity 761.68\\n\",\n      \"epoch 50, 200/451 batches, training loss 6.59, training perplexity 724.68\\n\",\n      \"epoch 50, 300/451 batches, training loss 6.55, training perplexity 700.14\\n\",\n      \"epoch 50, 400/451 batches, training loss 6.55, training perplexity 701.70\\n\",\n      \"\\n\",\n      \"epoch 50, validation loss 6.56, validation perplexity 706.04\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"min_validation_loss = float(\\\"inf\\\")\\n\",\n    \"eps = 50\\n\",\n    \"best_model_so_far = None\\n\",\n    \"\\n\",\n    \"for ep in range(1, eps + 1):\\n\",\n    \"    ep_time_start = time.time()\\n\",\n    \"    train_model()\\n\",\n    \"    validation_loss = eval_model(transformer_model, validation_data)\\n\",\n    \"    print()\\n\",\n    \"    print(f\\\"epoch {ep:}, validation loss {validation_loss:.2f}, validation perplexity {math.exp(validation_loss):.2f}\\\")\\n\",\n    \"    print()\\n\",\n    \"\\n\",\n    \"    if validation_loss < min_validation_loss:\\n\",\n    \"        min_validation_loss = validation_loss\\n\",\n    \"        best_model_so_far = transformer_model\\n\",\n    \"\\n\",\n    \"    sched_module.step()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"testing loss 6.49, testing perplexity 659.74\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"testing_loss = eval_model(best_model_so_far, testing_data)\\n\",\n    \"print(f\\\"testing loss {testing_loss:.2f}, testing perplexity {math.exp(testing_loss):.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mdl_pth = './transformer.pth'\\n\",\n    \"torch.save(best_model_so_far.state_dict(), mdl_pth)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<All keys matched successfully>\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# load the best trained model\\n\",\n    \"transformer_cached = Transformer(num_tokens, embedding_size, num_heads, num_hidden_params, num_layers, \\n\",\n    \"                                     dropout).to(device)\\n\",\n    \"transformer_cached.load_state_dict(torch.load(mdl_pth))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They are the the the the\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ln = 5\\n\",\n    \"sntc = 'They are _'\\n\",\n    \"sntc_split = sntc.split()\\n\",\n    \"torch.manual_seed(34)\\n\",\n    \"mask_source = gen_sqr_nxt_mask(max_seq_len).to(device)\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for i in range(ln):\\n\",\n    \"        sntc = ' '.join(sntc_split)\\n\",\n    \"        txt_ds = Tensor(vocabulary(sntc_split)).unsqueeze(0).to(torch.long)\\n\",\n    \"        num_b = txt_ds.size(0)\\n\",\n    \"        txt_ds = txt_ds.narrow(0, 0, num_b)\\n\",\n    \"        txt_ds = txt_ds.view(1, -1).t().contiguous().to(device)\\n\",\n    \"        ev_X, _ = return_batch(txt_ds, i+1)\\n\",\n    \"        sequence_length = ev_X.size(0)\\n\",\n    \"        if sequence_length != max_seq_len:\\n\",\n    \"            mask_source = mask_source[:sequence_length, :sequence_length]\\n\",\n    \"        op = transformer_cached(ev_X, mask_source)\\n\",\n    \"        op_flat = op.view(-1, num_tokens)\\n\",\n    \"        res = vocabulary.get_itos()[op_flat.argmax(1)[0]]\\n\",\n    \"        sntc_split.insert(-1, res)\\n\",\n    \"print(sntc[:-2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter07/text_generation_out_of_the_box.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Collecting transformers==4.36.0\\n\",\n      \"  Downloading transformers-4.36.0-py3-none-any.whl.metadata (126 kB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m126.8/126.8 kB\\u001b[0m \\u001b[31m6.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n      \"\\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2022.6.15)\\n\",\n      \"Downloading transformers-4.36.0-py3-none-any.whl (8.2 MB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m8.2/8.2 MB\\u001b[0m \\u001b[31m47.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m00:01\\u001b[0mm\\n\",\n      \"\\u001b[?25hInstalling collected packages: transformers\\n\",\n      \"  Attempting uninstall: transformers\\n\",\n      \"    Found existing installation: transformers 4.36.2\\n\",\n      \"    Uninstalling transformers-4.36.2:\\n\",\n      \"      Successfully uninstalled transformers-4.36.2\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"minimagen 0.0.9 requires attrs==21.4.0, but you have attrs 23.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires filelock==3.7.1, but you have filelock 3.13.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires fsspec==2022.5.0, but you have fsspec 2023.12.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires huggingface-hub==0.8.1, but you have huggingface-hub 0.20.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires numpy==1.23.1, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires Pillow==9.2.0, but you have pillow 10.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tokenizers==0.12.1, but you have tokenizers 0.15.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torch==1.12.0, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torchvision==0.13.0, but you have torchvision 0.17.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tqdm==4.64.0, but you have tqdm 4.66.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires transformers==4.20.1, but you have transformers 4.36.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires typing_extensions==4.3.0, but you have typing-extensions 4.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed transformers-4.36.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install transformers==4.36.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import GPT2LMHeadModel, GPT2Tokenizer\\n\",\n    \"import torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cc87b3a5a41b49ea8473376e34f6cea9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"473f61fb367e4fd7934081acbe4842dc\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fc9bee52b6cd476bb90d8d45c4848e1e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d3fe8d7e999c4970ba8671e9d1885be6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"10855abc8d97444d81efbe8371a55d01\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1ff1bfb41ed0427595228277c15b295b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(799)\\n\",\n    \"tkz = GPT2Tokenizer.from_pretrained(\\\"gpt2\\\")\\n\",\n    \"mdl = GPT2LMHeadModel.from_pretrained('gpt2')\\n\",\n    \"ln = 10\\n\",\n    \"cue = \\\"They\\\"\\n\",\n    \"gen = tkz(cue, return_tensors=\\\"pt\\\")\\n\",\n    \"to_ret = gen[\\\"input_ids\\\"][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They are not the only ones who are being targeted.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prv=None\\n\",\n    \"for i in range(ln):\\n\",\n    \"    outputs = mdl(**gen)\\n\",\n    \"    next_token_logits = torch.argmax(outputs.logits[-1, :])\\n\",\n    \"    to_ret = torch.cat([to_ret, next_token_logits.unsqueeze(0)])\\n\",\n    \"    gen = {\\\"input_ids\\\": to_ret}\\n\",\n    \"\\n\",\n    \"seq = tkz.decode(to_ret)\\n\",\n    \"\\n\",\n    \"print(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They are not the only ones who are being targeted\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ip_ids = tkz.encode(cue, return_tensors='pt')\\n\",\n    \"op_greedy = mdl.generate(ip_ids, max_length=ln, pad_token_id=tkz.eos_token_id)\\n\",\n    \"seq = tkz.decode(op_greedy[0], skip_special_tokens=True)\\n\",\n    \"print(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They have a lot of\\n\",\n      \"They have a lot to\\n\",\n      \"They are not the only\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"op_beam = mdl.generate(\\n\",\n    \"    ip_ids, \\n\",\n    \"    max_length=5, \\n\",\n    \"    num_beams=3, \\n\",\n    \"    num_return_sequences=3,\\n\",\n    \"    pad_token_id=tkz.eos_token_id\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for op_beam_cur in op_beam:\\n\",\n    \"    print(tkz.decode(op_beam_cur, skip_special_tokens=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They are the only ones\\n\",\n      \"They have been in the\\n\",\n      \"They have a lot of\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(3):\\n\",\n    \"    torch.manual_seed(i+10)\\n\",\n    \"    op = mdl.generate(\\n\",\n    \"        ip_ids, \\n\",\n    \"        do_sample=True, \\n\",\n    \"        max_length=5, \\n\",\n    \"        top_k=2,\\n\",\n    \"        pad_token_id=tkz.eos_token_id\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    seq = tkz.decode(op[0], skip_special_tokens=True)\\n\",\n    \"    print(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They are not the only\\n\",\n      \"They are not the only\\n\",\n      \"They are not the only\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(3):\\n\",\n    \"    torch.manual_seed(i+10)\\n\",\n    \"    op_greedy = mdl.generate(ip_ids, max_length=5, pad_token_id=tkz.eos_token_id)\\n\",\n    \"    seq = tkz.decode(op_greedy[0], skip_special_tokens=True)\\n\",\n    \"    print(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"They in the jail know\\n\",\n      \"They live on a budget\\n\",\n      \"They\\n\",\n      \"\\n\",\n      \"Says\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(3):\\n\",\n    \"    torch.manual_seed(i+10)\\n\",\n    \"    op = mdl.generate(\\n\",\n    \"        ip_ids, \\n\",\n    \"        do_sample=True, \\n\",\n    \"        max_length=5, \\n\",\n    \"        top_p=0.75, \\n\",\n    \"        top_k=0,\\n\",\n    \"        pad_token_id=tkz.eos_token_id\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    seq = tkz.decode(op[0], skip_special_tokens=True)\\n\",\n    \"    print(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter07/text_generation_out_of_the_box_gpt3.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f74df50b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install openai\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"0d75715c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from openai import OpenAI\\n\",\n    \"\\n\",\n    \"client = OpenAI(api_key = \\\"<your-open-ai-api-key-here>\\\") \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"10a1cb27\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" are an important part of\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prompt = \\\"They\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"response = client.chat.completions.create(\\n\",\n    \"  model=\\\"gpt-3.5-turbo-instruct\\\",\\n\",\n    \"  response_format={ \\\"type\\\": \\\"json_object\\\" },\\n\",\n    \"  messages=[\\n\",\n    \"    {\\\"role\\\": \\\"user\\\", \\\"content\\\": prompt}\\n\",\n    \"  ],\\n\",\n    \"  temperature=0.5,\\n\",\n    \"  max_tokens=5,\\n\",\n    \"  top_p=1.0,\\n\",\n    \"  frequency_penalty=0.0,\\n\",\n    \"  presence_penalty=0.0\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(response.choices[0].message.content)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"3c10c706\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"\\n\",\n      \"They always say\\n\",\n      \"\\n\",\n      \"That life is a mystery\\n\",\n      \"\\n\",\n      \"And we will never really know\\n\",\n      \"\\n\",\n      \"What happens after we die\\n\",\n      \"\\n\",\n      \"But I think\\n\",\n      \"\\n\",\n      \"That we can be pretty sure\\n\",\n      \"\\n\",\n      \"That there is something\\n\",\n      \"\\n\",\n      \"After this life\\n\",\n      \"\\n\",\n      \"Something better\\n\",\n      \"\\n\",\n      \"And we will finally be able\\n\",\n      \"\\n\",\n      \"To rest in peace\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prompt = \\\"Write a poem starting with they\\\"\\n\",\n    \"\\n\",\n    \"response = client.chat.completions.create(\\n\",\n    \"  model=\\\"gpt-3.5-turbo-instruct\\\",\\n\",\n    \"  response_format={ \\\"type\\\": \\\"json_object\\\" },\\n\",\n    \"  messages=[\\n\",\n    \"    {\\\"role\\\": \\\"user\\\", \\\"content\\\": prompt}\\n\",\n    \"  ],\\n\",\n    \"  temperature=0.5,\\n\",\n    \"  max_tokens=100,\\n\",\n    \"  top_p=1.0,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(response.choices[0].message.content)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"1991c344\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter08/neural_style_transfer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: nltk==3.7 in /opt/conda/lib/python3.10/site-packages (3.7)\\n\",\n      \"Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (8.1.7)\\n\",\n      \"Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (1.3.2)\\n\",\n      \"Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (2022.7.9)\\n\",\n      \"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from nltk==3.7) (4.66.1)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (10.2.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install nltk==3.7\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"dvc = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def image_to_tensor(image_filepath, image_dimension=128):\\n\",\n    \"    img = Image.open(image_filepath).convert('RGB')\\n\",\n    \"    \\n\",\n    \"    # display image to check \\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.title(image_filepath)\\n\",\n    \"    plt.imshow(img)\\n\",\n    \"    \\n\",\n    \"    if max(img.size) <= image_dimension:\\n\",\n    \"        img_size = max(img.size)\\n\",\n    \"    else:\\n\",\n    \"        img_size = image_dimension\\n\",\n    \"  \\n\",\n    \"    torch_transformation = torchvision.transforms.Compose([\\n\",\n    \"        torchvision.transforms.Resize(img_size),\\n\",\n    \"        torchvision.transforms.ToTensor()\\n\",\n    \"    ])\\n\",\n    \"  \\n\",\n    \"    img = torch_transformation(img).unsqueeze(0)\\n\",\n    \"  \\n\",\n    \"    return img.to(dvc, torch.float)\\n\",\n    \"\\n\",\n    \"style_image = image_to_tensor(\\\"./images/style.jpg\\\")\\n\",\n    \"content_image = image_to_tensor(\\\"./images/content.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define gram matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def gram_matrix(ip):\\n\",\n    \"    num_batch, num_channels, height, width = ip.size()\\n\",\n    \"    feats = ip.view(num_batch * num_channels, width * height)\\n\",\n    \"    gram_mat = torch.mm(feats, feats.t()) \\n\",\n    \"    return gram_mat.div(num_batch * num_channels * width * height)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\\n\",\n      \"  warnings.warn(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\\n\",\n      \"  warnings.warn(msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"VGG(\\n\",\n      \"  (features): Sequential(\\n\",\n      \"    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (1): ReLU(inplace=True)\\n\",\n      \"    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (3): ReLU(inplace=True)\\n\",\n      \"    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (6): ReLU(inplace=True)\\n\",\n      \"    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (8): ReLU(inplace=True)\\n\",\n      \"    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (11): ReLU(inplace=True)\\n\",\n      \"    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (13): ReLU(inplace=True)\\n\",\n      \"    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (15): ReLU(inplace=True)\\n\",\n      \"    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (17): ReLU(inplace=True)\\n\",\n      \"    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (20): ReLU(inplace=True)\\n\",\n      \"    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (22): ReLU(inplace=True)\\n\",\n      \"    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (24): ReLU(inplace=True)\\n\",\n      \"    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (26): ReLU(inplace=True)\\n\",\n      \"    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (29): ReLU(inplace=True)\\n\",\n      \"    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (31): ReLU(inplace=True)\\n\",\n      \"    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (33): ReLU(inplace=True)\\n\",\n      \"    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"    (35): ReLU(inplace=True)\\n\",\n      \"    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\\n\",\n      \"  (classifier): Sequential(\\n\",\n      \"    (0): Linear(in_features=25088, out_features=4096, bias=True)\\n\",\n      \"    (1): ReLU(inplace=True)\\n\",\n      \"    (2): Dropout(p=0.5, inplace=False)\\n\",\n      \"    (3): Linear(in_features=4096, out_features=4096, bias=True)\\n\",\n      \"    (4): ReLU(inplace=True)\\n\",\n      \"    (5): Dropout(p=0.5, inplace=False)\\n\",\n      \"    (6): Linear(in_features=4096, out_features=1000, bias=True)\\n\",\n      \"  )\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"vgg19_model = torchvision.models.vgg19(pretrained=True).to(dvc)\\n\",\n    \"print(vgg19_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"vgg19_model = vgg19_model.features\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for param in vgg19_model.parameters():\\n\",\n    \"    param.requires_grad_(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## refine model for this task\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### change maxpool layers to avgpool\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential(\\n\",\n      \"  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (1): ReLU(inplace=True)\\n\",\n      \"  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (3): ReLU(inplace=True)\\n\",\n      \"  (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (6): ReLU(inplace=True)\\n\",\n      \"  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (8): ReLU(inplace=True)\\n\",\n      \"  (9): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (11): ReLU(inplace=True)\\n\",\n      \"  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (13): ReLU(inplace=True)\\n\",\n      \"  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (15): ReLU(inplace=True)\\n\",\n      \"  (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (17): ReLU(inplace=True)\\n\",\n      \"  (18): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (20): ReLU(inplace=True)\\n\",\n      \"  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (22): ReLU(inplace=True)\\n\",\n      \"  (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (24): ReLU(inplace=True)\\n\",\n      \"  (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (26): ReLU(inplace=True)\\n\",\n      \"  (27): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (29): ReLU(inplace=True)\\n\",\n      \"  (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (31): ReLU(inplace=True)\\n\",\n      \"  (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (33): ReLU(inplace=True)\\n\",\n      \"  (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (35): ReLU(inplace=True)\\n\",\n      \"  (36): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"conv_indices = []\\n\",\n    \"\\n\",\n    \"for i in range(len(vgg19_model)):\\n\",\n    \"    if vgg19_model[i]._get_name() == 'MaxPool2d':\\n\",\n    \"        vgg19_model[i] = nn.AvgPool2d(kernel_size=vgg19_model[i].kernel_size, \\n\",\n    \"                                      stride=vgg19_model[i].stride, \\n\",\n    \"                                      padding=vgg19_model[i].padding)\\n\",\n    \"    if vgg19_model[i]._get_name() == 'Conv2d':\\n\",\n    \"        conv_indices.append(i)\\n\",\n    \"        \\n\",\n    \"conv_indices = dict(enumerate(conv_indices, 1))\\n\",\n    \"print(vgg19_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### clip until the last relevant layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"layers = {1: 's', 2: 's', 3: 's', 4: 'sc', 5: 's'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential(\\n\",\n      \"  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (1): ReLU(inplace=True)\\n\",\n      \"  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (3): ReLU(inplace=True)\\n\",\n      \"  (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (6): ReLU(inplace=True)\\n\",\n      \"  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (8): ReLU(inplace=True)\\n\",\n      \"  (9): AvgPool2d(kernel_size=2, stride=2, padding=0)\\n\",\n      \"  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"vgg_layers = nn.ModuleList(vgg19_model)\\n\",\n    \"\\n\",\n    \"last_layer_idx = conv_indices[max(layers.keys())]\\n\",\n    \"vgg_layers_trimmed = vgg_layers[:last_layer_idx+1]\\n\",\n    \"\\n\",\n    \"neural_style_transfer_model = nn.Sequential(*vgg_layers_trimmed)\\n\",\n    \"print(neural_style_transfer_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# initialize as the content image\\n\",\n    \"# ip_image = content_image.clone()\\n\",\n    \"# initialize as random noise:\\n\",\n    \"ip_image = torch.randn(content_image.data.size(), device=dvc)\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"plt.imshow(ip_image.squeeze(0).cpu().detach().numpy().transpose(1,2,0).clip(0,1));\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 50\\n\",\n      \"style loss = 2.6422502994537354, content loss = 4.994408130645752\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 100\\n\",\n      \"style loss = 1.13825523853302, content loss = 3.2329275608062744\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 150\\n\",\n      \"style loss = 0.7191422581672668, content loss = 2.76252818107605\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 200\\n\",\n      \"style loss = 0.5332462787628174, content loss = 2.53411865234375\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 250\\n\",\n      \"style loss = 0.4476768374443054, content loss = 2.403201103210449\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 300\\n\",\n      \"style loss = 0.406703919172287, content loss = 2.3228225708007812\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"num_epochs=300\\n\",\n    \"wt_style=1e6\\n\",\n    \"wt_content=1\\n\",\n    \"style_losses = []\\n\",\n    \"content_losses = []\\n\",\n    \"opt = optim.Adam([ip_image.requires_grad_()], lr=0.1)\\n\",\n    \"\\n\",\n    \"for curr_epoch in range(1, num_epochs+1):\\n\",\n    \"    \\n\",\n    \"    ip_image.data.clamp_(0, 1)\\n\",\n    \"    opt.zero_grad()\\n\",\n    \"    epoch_style_loss = 0\\n\",\n    \"    epoch_content_loss = 0\\n\",\n    \"\\n\",\n    \"    for k in layers.keys():\\n\",\n    \"        if 'c' in layers[k]:\\n\",\n    \"            target = neural_style_transfer_model[:conv_indices[k]+1](content_image).detach()\\n\",\n    \"            ip = neural_style_transfer_model[:conv_indices[k]+1](ip_image)\\n\",\n    \"            epoch_content_loss += torch.nn.functional.mse_loss(ip, target)\\n\",\n    \"        if 's' in layers[k]:\\n\",\n    \"            target = gram_matrix(neural_style_transfer_model[:conv_indices[k]+1](style_image)).detach()\\n\",\n    \"            ip = gram_matrix(neural_style_transfer_model[:conv_indices[k]+1](ip_image))\\n\",\n    \"            epoch_style_loss += torch.nn.functional.mse_loss(ip, target)\\n\",\n    \"\\n\",\n    \"    epoch_style_loss *= wt_style\\n\",\n    \"    epoch_content_loss *= wt_content\\n\",\n    \"    total_loss = epoch_style_loss + epoch_content_loss\\n\",\n    \"    total_loss.backward()\\n\",\n    \"    \\n\",\n    \"    if curr_epoch % 50 == 0:\\n\",\n    \"        print(f\\\"epoch number {curr_epoch}\\\")\\n\",\n    \"        print(f\\\"style loss = {epoch_style_loss}, content loss = {epoch_content_loss}\\\")\\n\",\n    \"        plt.figure()\\n\",\n    \"        plt.title(f\\\"epoch number {curr_epoch}\\\")\\n\",\n    \"        plt.imshow(ip_image.data.clamp_(0, 1).squeeze(0).cpu().detach().numpy().transpose(1,2,0))\\n\",\n    \"        plt.show()\\n\",\n    \"        style_losses += [epoch_style_loss.detach().numpy()]\\n\",\n    \"        content_losses += [epoch_content_loss.detach().numpy()]\\n\",\n    \"\\n\",\n    \"    opt.step()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(range(50, 300+1, 50), style_losses, label='style_loss');\\n\",\n    \"plt.plot(range(50, 300+1, 50), content_losses, label='content_loss');\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter09/dcgan.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Install required libraries\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (10.2.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Import required librarires\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import torchvision.transforms as transforms\\n\",\n    \"from torchvision.utils import save_image\\n\",\n    \"from torchvision import datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Define constants / model hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"num_eps=10\\n\",\n    \"bsize=32\\n\",\n    \"lrate=0.001\\n\",\n    \"lat_dimension=64\\n\",\n    \"image_sz=64\\n\",\n    \"chnls=1\\n\",\n    \"logging_intv=200\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining the Generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class GANGenerator(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(GANGenerator, self).__init__()\\n\",\n    \"        self.inp_sz = image_sz // 4\\n\",\n    \"        self.lin = nn.Linear(lat_dimension, 128 * self.inp_sz ** 2)\\n\",\n    \"        self.bn1 = nn.BatchNorm2d(128)\\n\",\n    \"        self.up1 = nn.Upsample(scale_factor=2)\\n\",\n    \"        self.cn1 = nn.Conv2d(128, 128, 3, stride=1, padding=1)\\n\",\n    \"        self.bn2 = nn.BatchNorm2d(128, 0.8)\\n\",\n    \"        self.rl1 = nn.LeakyReLU(0.2, inplace=True)\\n\",\n    \"        self.up2 = nn.Upsample(scale_factor=2)\\n\",\n    \"        self.cn2 = nn.Conv2d(128, 64, 3, stride=1, padding=1)\\n\",\n    \"        self.bn3 = nn.BatchNorm2d(64, 0.8)\\n\",\n    \"        self.rl2 = nn.LeakyReLU(0.2, inplace=True)\\n\",\n    \"        self.cn3 = nn.Conv2d(64, chnls, 3, stride=1, padding=1)\\n\",\n    \"        self.act = nn.Tanh()\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.lin(x)\\n\",\n    \"        x = x.view(x.shape[0], 128, self.inp_sz, self.inp_sz)\\n\",\n    \"        x = self.bn1(x)\\n\",\n    \"        x = self.up1(x)\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = self.bn2(x)\\n\",\n    \"        x = self.rl1(x)\\n\",\n    \"        x = self.up2(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = self.bn3(x)\\n\",\n    \"        x = self.rl2(x)\\n\",\n    \"        x = self.cn3(x)\\n\",\n    \"        out = self.act(x)\\n\",\n    \"        return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining the Discriminator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class GANDiscriminator(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(GANDiscriminator, self).__init__()\\n\",\n    \"\\n\",\n    \"        def disc_module(ip_chnls, op_chnls, bnorm=True):\\n\",\n    \"            mod = [nn.Conv2d(ip_chnls, op_chnls, 3, 2, 1), \\n\",\n    \"                   nn.LeakyReLU(0.2, inplace=True), \\n\",\n    \"                   nn.Dropout2d(0.25)]\\n\",\n    \"            if bnorm:\\n\",\n    \"                mod += [nn.BatchNorm2d(op_chnls, 0.8)]\\n\",\n    \"            return mod\\n\",\n    \"\\n\",\n    \"        self.disc_model = nn.Sequential(\\n\",\n    \"            *disc_module(chnls, 16, bnorm=False),\\n\",\n    \"            *disc_module(16, 32),\\n\",\n    \"            *disc_module(32, 64),\\n\",\n    \"            *disc_module(64, 128),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        # width and height of the down-sized image\\n\",\n    \"        ds_size = image_sz // 2 ** 4\\n\",\n    \"        self.adverse_lyr = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.disc_model(x)\\n\",\n    \"        x = x.view(x.shape[0], -1)\\n\",\n    \"        out = self.adverse_lyr(x)\\n\",\n    \"        return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# instantiate the discriminator and generator models\\n\",\n    \"gen = GANGenerator()\\n\",\n    \"disc = GANDiscriminator()\\n\",\n    \"\\n\",\n    \"# define the loss metric\\n\",\n    \"adv_loss_func = torch.nn.BCELoss()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Loading the image dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw/train-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:00<00:00, 205995755.02it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./data/mnist/MNIST/raw/train-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28881/28881 [00:00<00:00, 124241737.26it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw\\n\",\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1648877/1648877 [00:00<00:00, 66561036.70it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/mnist/MNIST/raw\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4542/4542 [00:00<00:00, 24644927.25it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/mnist/MNIST/raw\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# define the dataset and corresponding dataloader\\n\",\n    \"dloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST(\\n\",\n    \"        \\\"./data/mnist/\\\",\\n\",\n    \"        download=True,\\n\",\n    \"        transform=transforms.Compose(\\n\",\n    \"            [transforms.Resize((image_sz, image_sz)), \\n\",\n    \"             transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\\n\",\n    \"        ),\\n\",\n    \"    ),\\n\",\n    \"    batch_size=bsize,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# define the optimization schedule for both G and D\\n\",\n    \"opt_gen = torch.optim.Adam(gen.parameters(), lr=lrate)\\n\",\n    \"opt_disc = torch.optim.Adam(disc.parameters(), lr=lrate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training loop for DCGAN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch number 0 | batch number 0 | generator loss = 0.6827291250228882 | discriminator loss = 0.6916588544845581\\n\",\n      \"epoch number 0 | batch number 200 | generator loss = 1.3753488063812256 | discriminator loss = 0.5124980211257935\\n\",\n      \"epoch number 0 | batch number 400 | generator loss = 0.8567953705787659 | discriminator loss = 0.4100569188594818\\n\",\n      \"epoch number 0 | batch number 600 | generator loss = 1.2081809043884277 | discriminator loss = 0.3683425486087799\\n\",\n      \"epoch number 0 | batch number 800 | generator loss = 1.4627505540847778 | discriminator loss = 0.5539621114730835\\n\",\n      \"epoch number 0 | batch number 1000 | generator loss = 2.522969961166382 | discriminator loss = 0.22689306735992432\\n\",\n      \"epoch number 0 | batch number 1200 | generator loss = 0.7817821502685547 | discriminator loss = 0.47197943925857544\\n\",\n      \"epoch number 0 | batch number 1400 | generator loss = 1.5259451866149902 | discriminator loss = 0.7356035709381104\\n\",\n      \"epoch number 0 | batch number 1600 | generator loss = 2.405836820602417 | discriminator loss = 0.3994973301887512\\n\",\n      \"epoch number 0 | batch number 1800 | generator loss = 1.8351649045944214 | discriminator loss = 0.48325562477111816\\n\",\n      \"epoch number 1 | batch number 125 | generator loss = 3.6158790588378906 | discriminator loss = 0.532764732837677\\n\",\n      \"epoch number 1 | batch number 325 | generator loss = 5.781711101531982 | discriminator loss = 0.4379121959209442\\n\",\n      \"epoch number 1 | batch number 525 | generator loss = 1.9844849109649658 | discriminator loss = 0.44709718227386475\\n\",\n      \"epoch number 1 | batch number 725 | generator loss = 4.3647541999816895 | discriminator loss = 0.8382872343063354\\n\",\n      \"epoch number 1 | batch number 925 | generator loss = 6.970343112945557 | discriminator loss = 0.27220046520233154\\n\",\n      \"epoch number 1 | batch number 1125 | generator loss = 2.351853370666504 | discriminator loss = 0.13503685593605042\\n\",\n      \"epoch number 1 | batch number 1325 | generator loss = 0.5296928286552429 | discriminator loss = 0.4074157178401947\\n\",\n      \"epoch number 1 | batch number 1525 | generator loss = 2.7166566848754883 | discriminator loss = 0.1553489714860916\\n\",\n      \"epoch number 1 | batch number 1725 | generator loss = 4.88264274597168 | discriminator loss = 0.06121131032705307\\n\",\n      \"epoch number 2 | batch number 50 | generator loss = 2.3263041973114014 | discriminator loss = 0.121161088347435\\n\",\n      \"epoch number 2 | batch number 250 | generator loss = 1.4021943807601929 | discriminator loss = 0.1081303060054779\\n\",\n      \"epoch number 2 | batch number 450 | generator loss = 4.399090766906738 | discriminator loss = 0.2777940332889557\\n\",\n      \"epoch number 2 | batch number 650 | generator loss = 4.517523765563965 | discriminator loss = 0.16361413896083832\\n\",\n      \"epoch number 2 | batch number 850 | generator loss = 3.468811511993408 | discriminator loss = 0.6588649749755859\\n\",\n      \"epoch number 2 | batch number 1050 | generator loss = 4.116889953613281 | discriminator loss = 0.3950495421886444\\n\",\n      \"epoch number 2 | batch number 1250 | generator loss = 3.0756163597106934 | discriminator loss = 0.3150275647640228\\n\",\n      \"epoch number 2 | batch number 1450 | generator loss = 1.878197193145752 | discriminator loss = 0.1765824407339096\\n\",\n      \"epoch number 2 | batch number 1650 | generator loss = 4.071030139923096 | discriminator loss = 0.3719501197338104\\n\",\n      \"epoch number 2 | batch number 1850 | generator loss = 4.845463275909424 | discriminator loss = 0.5089448690414429\\n\",\n      \"epoch number 3 | batch number 175 | generator loss = 1.359919786453247 | discriminator loss = 0.6091972589492798\\n\",\n      \"epoch number 3 | batch number 375 | generator loss = 5.107836723327637 | discriminator loss = 0.03625940531492233\\n\",\n      \"epoch number 3 | batch number 575 | generator loss = 1.296502947807312 | discriminator loss = 0.23591016232967377\\n\",\n      \"epoch number 3 | batch number 775 | generator loss = 2.5184953212738037 | discriminator loss = 0.2834915220737457\\n\",\n      \"epoch number 3 | batch number 975 | generator loss = 3.4772181510925293 | discriminator loss = 0.119707852602005\\n\",\n      \"epoch number 3 | batch number 1175 | generator loss = 3.1785566806793213 | discriminator loss = 0.09721270948648453\\n\",\n      \"epoch number 3 | batch number 1375 | generator loss = 0.8171387910842896 | discriminator loss = 0.5593546628952026\\n\",\n      \"epoch number 3 | batch number 1575 | generator loss = 4.523009300231934 | discriminator loss = 0.20698320865631104\\n\",\n      \"epoch number 3 | batch number 1775 | generator loss = 5.275208950042725 | discriminator loss = 0.029274985194206238\\n\",\n      \"epoch number 4 | batch number 100 | generator loss = 4.304698944091797 | discriminator loss = 0.13636040687561035\\n\",\n      \"epoch number 4 | batch number 300 | generator loss = 2.9817628860473633 | discriminator loss = 0.43162867426872253\\n\",\n      \"epoch number 4 | batch number 500 | generator loss = 7.312787055969238 | discriminator loss = 0.03579274192452431\\n\",\n      \"epoch number 4 | batch number 700 | generator loss = 2.0278713703155518 | discriminator loss = 0.10602187365293503\\n\",\n      \"epoch number 4 | batch number 900 | generator loss = 3.584226131439209 | discriminator loss = 0.6118831038475037\\n\",\n      \"epoch number 4 | batch number 1100 | generator loss = 6.313017845153809 | discriminator loss = 0.00797022320330143\\n\",\n      \"epoch number 4 | batch number 1300 | generator loss = 6.951356887817383 | discriminator loss = 0.04062850773334503\\n\",\n      \"epoch number 4 | batch number 1500 | generator loss = 7.487911224365234 | discriminator loss = 0.10682754963636398\\n\",\n      \"epoch number 4 | batch number 1700 | generator loss = 3.9839158058166504 | discriminator loss = 0.1216537281870842\\n\",\n      \"epoch number 5 | batch number 25 | generator loss = 6.013751029968262 | discriminator loss = 0.14520937204360962\\n\",\n      \"epoch number 5 | batch number 225 | generator loss = 3.639707088470459 | discriminator loss = 0.26481893658638\\n\",\n      \"epoch number 5 | batch number 425 | generator loss = 2.0560028553009033 | discriminator loss = 0.47513139247894287\\n\",\n      \"epoch number 5 | batch number 625 | generator loss = 2.8454513549804688 | discriminator loss = 0.03515440225601196\\n\",\n      \"epoch number 5 | batch number 825 | generator loss = 1.5685524940490723 | discriminator loss = 0.06366463750600815\\n\",\n      \"epoch number 5 | batch number 1025 | generator loss = 6.179746627807617 | discriminator loss = 0.005660687107592821\\n\",\n      \"epoch number 5 | batch number 1225 | generator loss = 4.440589904785156 | discriminator loss = 0.20085813105106354\\n\",\n      \"epoch number 5 | batch number 1425 | generator loss = 5.666544437408447 | discriminator loss = 0.11899282783269882\\n\",\n      \"epoch number 5 | batch number 1625 | generator loss = 2.9011645317077637 | discriminator loss = 0.02219812013208866\\n\",\n      \"epoch number 5 | batch number 1825 | generator loss = 3.602222442626953 | discriminator loss = 0.17869994044303894\\n\",\n      \"epoch number 6 | batch number 150 | generator loss = 2.7667925357818604 | discriminator loss = 0.09315657615661621\\n\",\n      \"epoch number 6 | batch number 350 | generator loss = 3.399181604385376 | discriminator loss = 0.05052967369556427\\n\",\n      \"epoch number 6 | batch number 550 | generator loss = 3.652853488922119 | discriminator loss = 0.060660507529973984\\n\",\n      \"epoch number 6 | batch number 750 | generator loss = 8.414639472961426 | discriminator loss = 0.018418243154883385\\n\",\n      \"epoch number 6 | batch number 950 | generator loss = 5.858670711517334 | discriminator loss = 0.1595914363861084\\n\",\n      \"epoch number 6 | batch number 1150 | generator loss = 3.801039218902588 | discriminator loss = 0.24717527627944946\\n\",\n      \"epoch number 6 | batch number 1350 | generator loss = 1.436686396598816 | discriminator loss = 0.27819910645484924\\n\",\n      \"epoch number 6 | batch number 1550 | generator loss = 5.327937602996826 | discriminator loss = 0.038817644119262695\\n\",\n      \"epoch number 6 | batch number 1750 | generator loss = 3.9502553939819336 | discriminator loss = 0.21309791505336761\\n\",\n      \"epoch number 7 | batch number 75 | generator loss = 6.7195539474487305 | discriminator loss = 0.1338636875152588\\n\",\n      \"epoch number 7 | batch number 275 | generator loss = 2.8770134449005127 | discriminator loss = 0.08344466984272003\\n\",\n      \"epoch number 7 | batch number 475 | generator loss = 5.707181930541992 | discriminator loss = 0.24805203080177307\\n\",\n      \"epoch number 7 | batch number 675 | generator loss = 4.36092472076416 | discriminator loss = 0.013633709400892258\\n\",\n      \"epoch number 7 | batch number 875 | generator loss = 7.694408416748047 | discriminator loss = 0.061054777354002\\n\",\n      \"epoch number 7 | batch number 1075 | generator loss = 2.6442079544067383 | discriminator loss = 0.03422663360834122\\n\",\n      \"epoch number 7 | batch number 1275 | generator loss = 6.279775619506836 | discriminator loss = 0.0639989823102951\\n\",\n      \"epoch number 7 | batch number 1475 | generator loss = 2.6854538917541504 | discriminator loss = 0.015679141506552696\\n\",\n      \"epoch number 7 | batch number 1675 | generator loss = 6.811121940612793 | discriminator loss = 0.16857674717903137\\n\",\n      \"epoch number 8 | batch number 0 | generator loss = 2.719939708709717 | discriminator loss = 0.13963723182678223\\n\",\n      \"epoch number 8 | batch number 200 | generator loss = 3.630988597869873 | discriminator loss = 0.08019018918275833\\n\",\n      \"epoch number 8 | batch number 400 | generator loss = 2.3935306072235107 | discriminator loss = 0.07276156544685364\\n\",\n      \"epoch number 8 | batch number 600 | generator loss = 7.66723108291626 | discriminator loss = 0.026261799037456512\\n\",\n      \"epoch number 8 | batch number 800 | generator loss = 5.463866233825684 | discriminator loss = 0.0509343259036541\\n\",\n      \"epoch number 8 | batch number 1000 | generator loss = 7.421459197998047 | discriminator loss = 0.3396588861942291\\n\",\n      \"epoch number 8 | batch number 1200 | generator loss = 4.4102091789245605 | discriminator loss = 0.35801374912261963\\n\",\n      \"epoch number 8 | batch number 1400 | generator loss = 7.551414489746094 | discriminator loss = 0.5030229091644287\\n\",\n      \"epoch number 8 | batch number 1600 | generator loss = 7.160701274871826 | discriminator loss = 0.25210389494895935\\n\",\n      \"epoch number 8 | batch number 1800 | generator loss = 8.119586944580078 | discriminator loss = 0.07027419656515121\\n\",\n      \"epoch number 9 | batch number 125 | generator loss = 3.109663963317871 | discriminator loss = 0.012378710322082043\\n\",\n      \"epoch number 9 | batch number 325 | generator loss = 3.354196310043335 | discriminator loss = 0.2538221478462219\\n\",\n      \"epoch number 9 | batch number 525 | generator loss = 2.339954137802124 | discriminator loss = 0.2265104055404663\\n\",\n      \"epoch number 9 | batch number 725 | generator loss = 5.504094123840332 | discriminator loss = 0.09025933593511581\\n\",\n      \"epoch number 9 | batch number 925 | generator loss = 2.890489101409912 | discriminator loss = 0.8024916648864746\\n\",\n      \"epoch number 9 | batch number 1125 | generator loss = 6.0101423263549805 | discriminator loss = 0.01851765438914299\\n\",\n      \"epoch number 9 | batch number 1325 | generator loss = 8.085104942321777 | discriminator loss = 0.2512941062450409\\n\",\n      \"epoch number 9 | batch number 1525 | generator loss = 2.770570755004883 | discriminator loss = 0.11811449378728867\\n\",\n      \"epoch number 9 | batch number 1725 | generator loss = 7.662922382354736 | discriminator loss = 0.0023379907943308353\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"os.makedirs(\\\"./images_mnist\\\", exist_ok=True)\\n\",\n    \"\\n\",\n    \"for ep in range(num_eps):\\n\",\n    \"    for idx, (images, _) in enumerate(dloader):\\n\",\n    \"\\n\",\n    \"        # generate grounnd truths for real and fake images\\n\",\n    \"        good_img = Variable(torch.FloatTensor(images.shape[0], 1).fill_(1.0), requires_grad=False)\\n\",\n    \"        bad_img = Variable(torch.FloatTensor(images.shape[0], 1).fill_(0.0), requires_grad=False)\\n\",\n    \"\\n\",\n    \"        # get a real image\\n\",\n    \"        actual_images = Variable(images.type(torch.FloatTensor))\\n\",\n    \"\\n\",\n    \"        # train the generator model\\n\",\n    \"        opt_gen.zero_grad()\\n\",\n    \"\\n\",\n    \"        # generate a batch of images based on random noise as input\\n\",\n    \"        noise = Variable(torch.FloatTensor(np.random.normal(0, 1, (images.shape[0], lat_dimension))))\\n\",\n    \"        gen_images = gen(noise)\\n\",\n    \"\\n\",\n    \"        # generator model optimization - how well can it fool the discriminator\\n\",\n    \"        generator_loss = adv_loss_func(disc(gen_images), good_img)\\n\",\n    \"        generator_loss.backward()\\n\",\n    \"        opt_gen.step()\\n\",\n    \"\\n\",\n    \"        # train the discriminator model\\n\",\n    \"        opt_disc.zero_grad()\\n\",\n    \"\\n\",\n    \"        # calculate discriminator loss as average of mistakes(losses) in confusing real images as fake and vice versa\\n\",\n    \"        actual_image_loss = adv_loss_func(disc(actual_images), good_img)\\n\",\n    \"        fake_image_loss = adv_loss_func(disc(gen_images.detach()), bad_img)\\n\",\n    \"        discriminator_loss = (actual_image_loss + fake_image_loss) / 2\\n\",\n    \"\\n\",\n    \"        # discriminator model optimization\\n\",\n    \"        discriminator_loss.backward()\\n\",\n    \"        opt_disc.step()\\n\",\n    \"\\n\",\n    \"        batches_completed = ep * len(dloader) + idx\\n\",\n    \"        if batches_completed % logging_intv == 0:\\n\",\n    \"            print(f\\\"epoch number {ep} | batch number {idx} | generator loss = {generator_loss.item()} | discriminator loss = {discriminator_loss.item()}\\\")\\n\",\n    \"            save_image(gen_images.data[:25], f\\\"images_mnist/{batches_completed}.png\\\", nrow=5, normalize=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter09/pix2pix_architecture.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (10.2.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (10.2.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining the U-Net based Generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class UNetGenerator(nn.Module):\\n\",\n    \"    def __init__(self, chnls_in=3, chnls_op=3):\\n\",\n    \"        super(UNetGenerator, self).__init__()\\n\",\n    \"        self.down_conv_layer_1 = DownConvBlock(chnls_in, 64, norm=False)\\n\",\n    \"        self.down_conv_layer_2 = DownConvBlock(64, 128)\\n\",\n    \"        self.down_conv_layer_3 = DownConvBlock(128, 256)\\n\",\n    \"        self.down_conv_layer_4 = DownConvBlock(256, 512, dropout=0.5)\\n\",\n    \"        self.down_conv_layer_5 = DownConvBlock(512, 512, dropout=0.5)\\n\",\n    \"        self.down_conv_layer_6 = DownConvBlock(512, 512, dropout=0.5)\\n\",\n    \"        self.down_conv_layer_7 = DownConvBlock(512, 512, dropout=0.5)\\n\",\n    \"        self.down_conv_layer_8 = DownConvBlock(512, 512, norm=False, dropout=0.5)\\n\",\n    \"        self.up_conv_layer_1 = UpConvBlock(512, 512, dropout=0.5)\\n\",\n    \"        self.up_conv_layer_2 = UpConvBlock(1024, 512, dropout=0.5)\\n\",\n    \"        self.up_conv_layer_3 = UpConvBlock(1024, 512, dropout=0.5)\\n\",\n    \"        self.up_conv_layer_4 = UpConvBlock(1024, 512, dropout=0.5)\\n\",\n    \"        self.up_conv_layer_5 = UpConvBlock(1024, 256)\\n\",\n    \"        self.up_conv_layer_6 = UpConvBlock(512, 128)\\n\",\n    \"        self.up_conv_layer_7 = UpConvBlock(256, 64)\\n\",\n    \"        self.upsample_layer = nn.Upsample(scale_factor=2)\\n\",\n    \"        self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0))\\n\",\n    \"        self.conv_layer_1 = nn.Conv2d(128, chnls_op, 4, padding=1)\\n\",\n    \"        self.activation = nn.Tanh()\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        enc1 = self.down_conv_layer_1(x)\\n\",\n    \"        enc2 = self.down_conv_layer_2(enc1)\\n\",\n    \"        enc3 = self.down_conv_layer_3(enc2)\\n\",\n    \"        enc4 = self.down_conv_layer_4(enc3)\\n\",\n    \"        enc5 = self.down_conv_layer_5(enc4)\\n\",\n    \"        enc6 = self.down_conv_layer_6(enc5)\\n\",\n    \"        enc7 = self.down_conv_layer_7(enc6)\\n\",\n    \"        enc8 = self.down_conv_layer_8(enc7)\\n\",\n    \"        dec1 = self.up_conv_layer_1(enc8, enc7)\\n\",\n    \"        dec2 = self.up_conv_layer_2(dec1, enc6)\\n\",\n    \"        dec3 = self.up_conv_layer_3(dec2, enc5)\\n\",\n    \"        dec4 = self.up_conv_layer_4(dec3, enc4)\\n\",\n    \"        dec5 = self.up_conv_layer_5(dec4, enc3)\\n\",\n    \"        dec6 = self.up_conv_layer_6(dec5, enc2)\\n\",\n    \"        dec7 = self.up_conv_layer_7(dec6, enc1)\\n\",\n    \"        final = self.upsample_layer(dec7)\\n\",\n    \"        final = self.zero_pad(final)\\n\",\n    \"        final = self.conv_layer_1(final)\\n\",\n    \"        return self.activation(final)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class UpConvBlock(nn.Module):\\n\",\n    \"    def __init__(self, ip_sz, op_sz, dropout=0.0):\\n\",\n    \"        super(UpConvBlock, self).__init__()\\n\",\n    \"        self.layers = [\\n\",\n    \"            nn.ConvTranspose2d(ip_sz, op_sz, 4, 2, 1),\\n\",\n    \"            nn.InstanceNorm2d(op_sz),\\n\",\n    \"            nn.ReLU(),\\n\",\n    \"        ]\\n\",\n    \"        if dropout:\\n\",\n    \"            self.layers += [nn.Dropout(dropout)]\\n\",\n    \"    def forward(self, x, enc_ip):\\n\",\n    \"        x = nn.Sequential(*(self.layers))(x)\\n\",\n    \"        op = torch.cat((x, enc_ip), 1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class DownConvBlock(nn.Module):\\n\",\n    \"    def __init__(self, ip_sz, op_sz, norm=True, dropout=0.0):\\n\",\n    \"        super(DownConvBlock, self).__init__()\\n\",\n    \"        self.layers = [nn.Conv2d(ip_sz, op_sz, 4, 2, 1)]\\n\",\n    \"        if norm:\\n\",\n    \"            self.layers.append(nn.InstanceNorm2d(op_sz))\\n\",\n    \"        self.layers += [nn.LeakyReLU(0.2)]\\n\",\n    \"        if dropout:\\n\",\n    \"            self.layers += [nn.Dropout(dropout)]\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        op = nn.Sequential(*(self.layers))(x)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining the Discriminator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Pix2PixDiscriminator(nn.Module):\\n\",\n    \"    def __init__(self, chnls_in=3):\\n\",\n    \"        super(Pix2PixDiscriminator, self).__init__()\\n\",\n    \"        def disc_conv_block(chnls_in, chnls_op, norm=1):\\n\",\n    \"            layers = [nn.Conv2d(chnls_in, chnls_op, 4, stride=2, padding=1)]\\n\",\n    \"            if normalization:\\n\",\n    \"                layers.append(nn.InstanceNorm2d(chnls_op))\\n\",\n    \"            layers.append(nn.LeakyReLU(0.2, inplace=True))\\n\",\n    \"            return layers\\n\",\n    \"        self.lyr1 = disc_conv_block(chnls_in * 2, 64, norm=0)\\n\",\n    \"        self.lyr2 = disc_conv_block(64, 128)\\n\",\n    \"        self.lyr3 = disc_conv_block(128, 256)\\n\",\n    \"        self.lyr4 = disc_conv_block(256, 512)\\n\",\n    \"    def forward(self, real_image, translated_image):\\n\",\n    \"        ip = torch.cat((real_image, translated_image), 1)\\n\",\n    \"        op = self.lyr1(ip)\\n\",\n    \"        op = self.lyr2(op)\\n\",\n    \"        op = self.lyr3(op)\\n\",\n    \"        op = self.lyr4(op)\\n\",\n    \"        op = nn.ZeroPad2d((1, 0, 1, 0))(op)\\n\",\n    \"        op = nn.Conv2d(512, 1, 4, padding=1)(op)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter10/image_generation_using_diffusion.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"28a6c8ec\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: numpy==1.26.2 in /opt/conda/lib/python3.10/site-packages (1.26.2)\\n\",\n      \"Requirement already satisfied: pillow==10.2.0 in /opt/conda/lib/python3.10/site-packages (10.2.0)\\n\",\n      \"Requirement already satisfied: diffusers==0.25.0.dev0 in /opt/conda/lib/python3.10/site-packages (0.25.0.dev0)\\n\",\n      \"Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (6.11.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub>=0.19.4 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (1.26.2)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (2.28.1)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (0.4.1)\\n\",\n      \"Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (10.2.0)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (4.66.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (4.9.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (21.3)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata->diffusers==0.25.0.dev0) (3.17.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (3.0.9)\\n\",\n      \"Requirement already satisfied: datasets==2.3.2 in /opt/conda/lib/python3.10/site-packages (2.3.2)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: pyarrow>=6.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (8.0.0)\\n\",\n      \"Requirement already satisfied: dill<0.3.6 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.3.5.1)\\n\",\n      \"Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (1.4.3)\\n\",\n      \"Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (2.28.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (4.66.1)\\n\",\n      \"Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (3.0.0)\\n\",\n      \"Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.70.13)\\n\",\n      \"Requirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]>=2021.05.0->datasets==2.3.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (3.8.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.20.1)\\n\",\n      \"Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (21.3)\\n\",\n      \"Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.18.0)\\n\",\n      \"Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (23.2.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (2.1.0)\\n\",\n      \"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (6.0.2)\\n\",\n      \"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (4.0.2)\\n\",\n      \"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.7.2)\\n\",\n      \"Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->datasets==2.3.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (2022.6.15)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.3.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.3.2) (2022.1)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas->datasets==2.3.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: accelerate==0.25.0 in /opt/conda/lib/python3.10/site-packages (0.25.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (21.3)\\n\",\n      \"Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (5.9.3)\\n\",\n      \"Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (6.0)\\n\",\n      \"Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->accelerate==0.25.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate==0.25.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate==0.25.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate==0.25.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.10.0->accelerate==0.25.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.10.0->accelerate==0.25.0) (1.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install numpy==1.26.2\\n\",\n    \"!pip install pillow==10.2.0\\n\",\n    \"!pip install diffusers==0.25.0.dev0\\n\",\n    \"!pip install datasets==2.3.2\\n\",\n    \"!pip install accelerate==0.25.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1615d302\",\n   \"metadata\": {},\n   \"source\": [\n    \"## load dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"3acd667d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"dbae0ac2\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration huggan--selfie2anime-2601eb6cdec450c3\\n\",\n      \"Reusing dataset parquet (/home/ashish.jha/.cache/huggingface/datasets/huggan___parquet/huggan--selfie2anime-2601eb6cdec450c3/0.0.0/7328ef7ee03eaf3f86ae40594d46a1cec86161704e02dd19f232d81eee72ade8)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from datasets import load_dataset\\n\",\n    \"\\n\",\n    \"dataset = load_dataset(\\\"huggan/selfie2anime\\\", split=\\\"train\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"ca1f872e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset({\\n\",\n      \"    features: ['imageA', 'imageB'],\\n\",\n      \"    num_rows: 3400\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"4ebd2217\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB 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<PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,\\n\",\n       \" ...]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset[\\\"imageB\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"08df8c21\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n 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\",\n      \"text/plain\": [\n       \"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"img = dataset[\\\"imageB\\\"][0]\\n\",\n    \"img\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"0951c6e7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=1024x1024>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from diffusers.utils import make_image_grid\\n\",\n    \"make_image_grid(dataset[\\\"imageB\\\"][:16], rows=4, cols=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6895351f\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### demonstration of adding noise to images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"6b16d715\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": \"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\",\n 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=256x256>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"noise = 256*np.random.rand(*img.size, 3)\\n\",\n    \"noisy_img = ((img + noise)/2).astype(np.uint8)\\n\",\n    \"Image.fromarray(noisy_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"592ee215\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=256x256>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Image.fromarray(noise.astype(np.uint8))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"3a0c081e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=256x256>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"more_noisy_img = ((img + 4*noise)/5).astype(np.uint8)\\n\",\n    \"Image.fromarray(more_noisy_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"ec88db6f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=256x256>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"less_noisy_img = ((img + 0.5*noise)/1.5).astype(np.uint8)\\n\",\n    \"Image.fromarray(less_noisy_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"97571d0c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## process dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"daa109ea\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"Parameter 'transform'=<function transform at 0x7f546f7b4b80> of the transform datasets.arrow_dataset.Dataset.set_format couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import transforms\\n\",\n    \"IMAGE_SIZE = 128\\n\",\n    \"preprocess = transforms.Compose(\\n\",\n    \"    [\\n\",\n    \"        transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),\\n\",\n    \"        transforms.RandomHorizontalFlip(),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        transforms.Normalize([0.5], [0.5]),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"def transform(examples):\\n\",\n    \"    images = [preprocess(image) for image in examples[\\\"imageB\\\"]]\\n\",\n    \"    return {\\\"images\\\": images}\\n\",\n    \"\\n\",\n    \"dataset.set_transform(transform)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"e7602555\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"BSIZE = 16\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=BSIZE, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b26f1e44\",\n   \"metadata\": {},\n   \"source\": [\n    \"## add noise to dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"a647f82d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from diffusers import DDPMScheduler\\n\",\n    \"noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bb67579a\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### demonstrated incremental amount of noise\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"7f9a31c1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/tmp/ipykernel_2974259/545426060.py:7: UserWarning: torch.range is deprecated and will be removed in a future release because its behavior is inconsistent with Python's range builtin. Instead, use torch.arange, which produces values in [start, end).\\n\",\n      \"  timesteps = torch.range(10, 161, 10, dtype=torch.int)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"clean_images = next(iter(train_dataloader))[\\\"images\\\"]\\n\",\n    \"# Sample noise to add to the images\\n\",\n    \"noise = torch.randn(clean_images.shape, device=clean_images.device)\\n\",\n    \"bs = clean_images.shape[0]\\n\",\n    \"\\n\",\n    \"# Sample a random timestep for each image\\n\",\n    \"timesteps = torch.range(10, 161, 10, dtype=torch.int)\\n\",\n    \"\\n\",\n    \"# Add noise to the clean images according to the noise magnitude at each timestep\\n\",\n    \"# (this is the forward diffusion process)\\n\",\n    \"noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"324336cb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=512x512>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"make_image_grid([transforms.ToPILImage()(clean_image) for clean_image in clean_images], rows=4, cols=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"ddec3827\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=512x512>\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"make_image_grid([transforms.ToPILImage()(noisy_image) for noisy_image in noisy_images], rows=4, cols=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3ae72b17\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### actual noise addition code\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"edf686b6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"clean_images = next(iter(train_dataloader))[\\\"images\\\"]\\n\",\n    \"# Sample noise to add to the images\\n\",\n    \"noise = torch.randn(clean_images.shape, device=clean_images.device)\\n\",\n    \"bs = clean_images.shape[0]\\n\",\n    \"\\n\",\n    \"# Sample a random timestep for each image\\n\",\n    \"timesteps = torch.randint(\\n\",\n    \"    0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\\n\",\n    \"    dtype=torch.int64\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Add noise to the clean images according to the noise magnitude at each timestep\\n\",\n    \"# (this is the forward diffusion process)\\n\",\n    \"noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"ff334b44\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([929, 367, 245, 606, 695, 274, 421, 991, 333,  21,   1, 280, 649, 661,\\n\",\n       \"         79,  16])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"timesteps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f96c577\",\n   \"metadata\": {},\n   \"source\": [\n    \"## UNet model definition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"07e10597\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from diffusers import UNet2DModel\\n\",\n    \"model = UNet2DModel(\\n\",\n    \"    sample_size=IMAGE_SIZE,  # the target image resolution\\n\",\n    \"    in_channels=3,  # the number of input channels, 3 for RGB images\\n\",\n    \"    out_channels=3,  # the number of output channels\\n\",\n    \"    layers_per_block=2,  # how many ResNet layers to use per UNet block\\n\",\n    \"    block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block\\n\",\n    \"    down_block_types=(\\n\",\n    \"        \\\"DownBlock2D\\\",  # a regular ResNet downsampling block\\n\",\n    \"        \\\"DownBlock2D\\\",\\n\",\n    \"        \\\"DownBlock2D\\\",\\n\",\n    \"        \\\"DownBlock2D\\\",\\n\",\n    \"        \\\"AttnDownBlock2D\\\",  # a ResNet downsampling block with spatial self-attention\\n\",\n    \"        \\\"DownBlock2D\\\",\\n\",\n    \"    ),\\n\",\n    \"    up_block_types=(\\n\",\n    \"        \\\"UpBlock2D\\\",  # a regular ResNet upsampling block\\n\",\n    \"        \\\"AttnUpBlock2D\\\",  # a ResNet upsampling block with spatial self-attention\\n\",\n    \"        \\\"UpBlock2D\\\",\\n\",\n    \"        \\\"UpBlock2D\\\",\\n\",\n    \"        \\\"UpBlock2D\\\",\\n\",\n    \"        \\\"UpBlock2D\\\",\\n\",\n    \"    ),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"6f95d1a5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"UNet2DModel(\\n\",\n       \"  (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"  (time_proj): Timesteps()\\n\",\n       \"  (time_embedding): TimestepEmbedding(\\n\",\n       \"    (linear_1): Linear(in_features=128, out_features=512, bias=True)\\n\",\n       \"    (act): SiLU()\\n\",\n       \"    (linear_2): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"  )\\n\",\n       \"  (down_blocks): ModuleList(\\n\",\n       \"    (0-1): 2 x DownBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (2): DownBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (3): DownBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (4): AttnDownBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-1): 2 x Attention(\\n\",\n       \"          (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (to_q): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_k): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_v): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_out): ModuleList(\\n\",\n       \"            (0): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"            (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"          )\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (5): DownBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (up_blocks): ModuleList(\\n\",\n       \"    (0): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-2): 3 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (1): AttnUpBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-2): 3 x Attention(\\n\",\n       \"          (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (to_q): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_k): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_v): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (to_out): ModuleList(\\n\",\n       \"            (0): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"            (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"          )\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1024, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (2): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(768, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (2): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 768, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1-2): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (3): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (2): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=256, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (4): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 384, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1-2): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (5): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-2): 3 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 256, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=512, out_features=128, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (mid_block): UNetMidBlock2D(\\n\",\n       \"    (attentions): ModuleList(\\n\",\n       \"      (0): Attention(\\n\",\n       \"        (group_norm): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"        (to_q): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"        (to_k): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"        (to_v): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"        (to_out): ModuleList(\\n\",\n       \"          (0): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"          (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (resnets): ModuleList(\\n\",\n       \"      (0-1): 2 x ResnetBlock2D(\\n\",\n       \"        (norm1): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        (time_emb_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n       \"        (norm2): GroupNorm(32, 512, eps=1e-05, affine=True)\\n\",\n       \"        (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        (nonlinearity): SiLU()\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (conv_norm_out): GroupNorm(32, 128, eps=1e-05, affine=True)\\n\",\n       \"  (conv_act): SiLU()\\n\",\n       \"  (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"46e8c250\",\n   \"metadata\": {},\n   \"source\": [\n    \"## UNet model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8bbad9b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### training setup\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"551625ce\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from diffusers.optimization import get_cosine_schedule_with_warmup\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LR = 1e-4\\n\",\n    \"LR_WARMUP_STEPS = 500\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.AdamW(model.parameters(), lr=LR)\\n\",\n    \"lr_scheduler = get_cosine_schedule_with_warmup(\\n\",\n    \"    optimizer=optimizer,\\n\",\n    \"    num_warmup_steps=LR_WARMUP_STEPS,\\n\",\n    \"    num_training_steps=(len(train_dataloader) * NUM_EPOCHS),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"47bb4a4d\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### model training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"ec0169be\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from accelerate import Accelerator\\n\",\n    \"MODEL_SAVE_DIR = \\\"anime-128\\\"\\n\",\n    \"\\n\",\n    \"# Initialize accelerator and tensorboard logging\\n\",\n    \"accelerator = Accelerator(\\n\",\n    \"    mixed_precision=\\\"fp16\\\",\\n\",\n    \"    log_with=\\\"tensorboard\\\",\\n\",\n    \"    project_dir=os.path.join(MODEL_SAVE_DIR, \\\"logs\\\"),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"697caeb2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2024-02-16 00:36:23.629014: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\",\n      \"2024-02-16 00:36:23.629070: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\",\n      \"2024-02-16 00:36:23.630697: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if accelerator.is_main_process:\\n\",\n    \"    if MODEL_SAVE_DIR is not None:\\n\",\n    \"        os.makedirs(MODEL_SAVE_DIR, exist_ok=True)\\n\",\n    \"    accelerator.init_trackers(\\\"train_example\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"6446b612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\\n\",\n    \"        model, optimizer, train_dataloader, lr_scheduler\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"0655e6d4\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\",\n      \"Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:43<00:00,  1.39it/s, loss=0.0592, lr=4.26e-5, step=212]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2024-02-16 00:39:09,948] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"56b35a61ef584bc8849e2eec4124366b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:28<00:00,  2.11s/it, loss=0.0592, lr=4.26e-5, step=212]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 1:   0%|                                                                                                                                                                                                                                                              | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 1:   0%|█▏                                                                                                                                                            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    | 2/213 [00:01<02:55,  1.20it/s, loss=0.0388, lr=4.3e-5, step=214]\\u001b[A\\n\",\n      \"Epoch 1:   1%|██▉                                                                                                                                                                                                                 | 3/213 [00:02<02:53,  1.21it/s, loss=0.0388, lr=4.3e-5, step=214]\\u001b[A\\n\",\n      \"Epoch 1:   1%|██▉                                                                                                                                                                                                                | 3/213 [00:02<02:53,  1.21it/s, loss=0.0389, lr=4.32e-5, step=215]\\u001b[A\\n\",\n      \"Epoch 1:   2%|███▉                                                                                                                                                                                                               | 4/213 [00:03<02:51,  1.22it/s, loss=0.0389, lr=4.32e-5, step=215]\\u001b[A\\n\",\n      \"Epoch 1:   2%|███▉                                                                                                                                                                                                               | 4/213 [00:03<02:51,  1.22it/s, loss=0.0482, lr=4.34e-5, step=216]\\u001b[A\\n\",\n      \"Epoch 1:   2%|████▉                                                                                                                                                                                                              | 5/213 [00:04<02:50,  1.22it/s, loss=0.0482, lr=4.34e-5, step=216]\\u001b[A\\n\",\n      \"Epoch 1:   2%|████▉                                                                                                                                                                                                               | 5/213 [00:04<02:50,  1.22it/s, loss=0.134, lr=4.36e-5, step=217]\\u001b[A\\n\",\n      \"Epoch 1:   3%|█████▉                                                                                                                                                                                                              | 6/213 [00:04<02:49,  1.22it/s, loss=0.134, lr=4.36e-5, step=217]\\u001b[A\\n\",\n      \"Epoch 1:   3%|█████▉                                                                                                                                                                                                             | 6/213 [00:04<02:49,  1.22it/s, loss=0.0275, lr=4.38e-5, step=218]\\u001b[A\\n\",\n      \"Epoch 1:   3%|██████▉                                                                                                                                                                                                            | 7/213 [00:05<02:48,  1.22it/s, loss=0.0275, lr=4.38e-5, step=218]\\u001b[A\\n\",\n      \"Epoch 1:   3%|██████▉                                                                                                                                                                                                             | 7/213 [00:05<02:48,  1.22it/s, loss=0.0472, lr=4.4e-5, step=219]\\u001b[A\\n\",\n      \"Epoch 1:   4%|███████▉                                                                                                                                                                                                            | 8/213 [00:06<02:47,  1.22it/s, loss=0.0472, lr=4.4e-5, step=219]\\u001b[A\\n\",\n      \"Epoch 1:   4%|███████▉                                                                                                                                                                                                           | 8/213 [00:06<02:47,  1.22it/s, loss=0.0495, lr=4.42e-5, step=220]\\u001b[A\\n\",\n      \"Epoch 1:   4%|████████▉                                                                                                                                                                                                          | 9/213 [00:07<02:47,  1.21it/s, loss=0.0495, lr=4.42e-5, step=220]\\u001b[A\\n\",\n      \"Epoch 1:   4%|████████▉                                                                                                                                                                                                          | 9/213 [00:07<02:47,  1.21it/s, loss=0.0329, lr=4.44e-5, step=221]\\u001b[A\\n\",\n      \"Epoch 1:   5%|█████████▊                                                                                                                                                                                                        | 10/213 [00:08<02:47,  1.21it/s, loss=0.0329, lr=4.44e-5, step=221]\\u001b[A\\n\",\n      \"Epoch 1:   5%|█████████▊                                                                                                                                                                                                        | 10/213 [00:08<02:47,  1.21it/s, loss=0.0402, lr=4.46e-5, step=222]\\u001b[A\\n\",\n      \"Epoch 1:   5%|██████████▊                                                                                                                                                                                                       | 11/213 [00:09<02:50,  1.19it/s, loss=0.0402, lr=4.46e-5, step=222]\\u001b[A\\n\",\n      \"Epoch 1:   5%|██████████▊                                                                                                                                                                                                       | 11/213 [00:09<02:50,  1.19it/s, loss=0.0535, lr=4.48e-5, step=223]\\u001b[A\\n\",\n      \"Epoch 1:   6%|███████████▊                                                                                                                                                                                                      | 12/213 [00:09<02:46,  1.21it/s, loss=0.0535, lr=4.48e-5, step=223]\\u001b[A\\n\",\n      \"Epoch 1:   6%|███████████▉                                                                                                                                                                                                       | 12/213 [00:09<02:46,  1.21it/s, loss=0.0727, lr=4.5e-5, step=224]\\u001b[A\\n\",\n      \"Epoch 1:   6%|████████████▉                                                                                                                                                                                                      | 13/213 [00:10<02:45,  1.21it/s, loss=0.0727, lr=4.5e-5, step=224]\\u001b[A\\n\",\n      \"Epoch 1:   6%|████████████▊                                                                                                                                                                                                     | 13/213 [00:10<02:45,  1.21it/s, loss=0.0731, lr=4.52e-5, step=225]\\u001b[A\\n\",\n      \"Epoch 1:   7%|█████████████▊                                                                                                                                                                                                    | 14/213 [00:11<02:44,  1.21it/s, loss=0.0731, lr=4.52e-5, step=225]\\u001b[A\\n\",\n      \"Epoch 1:   7%|█████████████▊                                                                                                                                                                                                     | 14/213 [00:11<02:44,  1.21it/s, loss=0.102, lr=4.54e-5, step=226]\\u001b[A\\n\",\n      \"Epoch 1:   7%|██████████████▊                                                                                                                                                                                                    | 15/213 [00:12<02:44,  1.21it/s, loss=0.102, lr=4.54e-5, step=226]\\u001b[A\\n\",\n      \"Epoch 1:   7%|██████████████▊                                                                                                                                                                                                   | 15/213 [00:12<02:44,  1.21it/s, loss=0.0373, lr=4.56e-5, step=227]\\u001b[A\\n\",\n      \"Epoch 1:   8%|███████████████▊                                                                                                                                                                                                  | 16/213 [00:13<02:42,  1.21it/s, loss=0.0373, lr=4.56e-5, step=227]\\u001b[A\\n\",\n      \"Epoch 1:   8%|███████████████▊                                                                                                                                                                                                  | 16/213 [00:13<02:42,  1.21it/s, loss=0.0412, lr=4.58e-5, step=228]\\u001b[A\\n\",\n      \"Epoch 1:   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                                                                                                                                                                   | 18/213 [00:14<02:39,  1.22it/s, loss=0.0865, lr=4.62e-5, step=230]\\u001b[A\\n\",\n      \"Epoch 1:   9%|██████████████████▋                                                                                                                                                                                               | 19/213 [00:15<02:38,  1.23it/s, loss=0.0865, lr=4.62e-5, step=230]\\u001b[A\\n\",\n      \"Epoch 1:   9%|██████████████████▋                                                                                                                                                                                               | 19/213 [00:15<02:38,  1.23it/s, loss=0.0526, lr=4.64e-5, step=231]\\u001b[A\\n\",\n      \"Epoch 1:   9%|███████████████████▋                                                                                                                                                                                              | 20/213 [00:16<02:37,  1.23it/s, loss=0.0526, lr=4.64e-5, step=231]\\u001b[A\\n\",\n      \"Epoch 1:   9%|███████████████████▋                                                                                                                                                                                              | 20/213 [00:16<02:37,  1.23it/s, loss=0.0464, lr=4.66e-5, step=232]\\u001b[A\\n\",\n      \"Epoch 1:  10%|████████████████████▋                                                                                                                                                                                             | 21/213 [00:17<02:36,  1.23it/s, loss=0.0464, lr=4.66e-5, step=232]\\u001b[A\\n\",\n      \"Epoch 1:  10%|████████████████████▋                                                                                                                                                                                             | 21/213 [00:17<02:36,  1.23it/s, loss=0.0329, lr=4.68e-5, step=233]\\u001b[A\\n\",\n      \"Epoch 1:  10%|█████████████████████▋                                                                                                                                                                                            | 22/213 [00:18<02:35,  1.23it/s, loss=0.0329, lr=4.68e-5, step=233]\\u001b[A\\n\",\n      \"Epoch 1:  10%|█████████████████████▊                                                                                                                                                                                             | 22/213 [00:18<02:35,  1.23it/s, loss=0.0367, lr=4.7e-5, step=234]\\u001b[A\\n\",\n      \"Epoch 1:  11%|██████████████████████▊                                                                                                                                                                                            | 23/213 [00:18<02:34,  1.23it/s, loss=0.0367, lr=4.7e-5, step=234]\\u001b[A\\n\",\n      \"Epoch 1:  11%|██████████████████████▋                                                                                                                                                                                           | 23/213 [00:18<02:34,  1.23it/s, loss=0.0915, lr=4.72e-5, step=235]\\u001b[A\\n\",\n      \"Epoch 1:  11%|███████████████████████▋                                                                                                                                                                                          | 24/213 [00:19<02:33,  1.23it/s, loss=0.0915, lr=4.72e-5, step=235]\\u001b[A\\n\",\n      \"Epoch 1:  11%|███████████████████████▋                                                                                                                                                                                          | 24/213 [00:19<02:33,  1.23it/s, loss=0.0514, lr=4.74e-5, step=236]\\u001b[A\\n\",\n      \"Epoch 1:  12%|████████████████████████▋                                                                                                                                                                                         | 25/213 [00:20<02:32,  1.23it/s, loss=0.0514, lr=4.74e-5, step=236]\\u001b[A\\n\",\n      \"Epoch 1:  12%|████████████████████████▋                                                                                                                                                                                         | 25/213 [00:20<02:32,  1.23it/s, loss=0.0504, lr=4.76e-5, step=237]\\u001b[A\\n\",\n      \"Epoch 1:  12%|█████████████████████████▋                                                                                                                                                                                        | 26/213 [00:21<02:31,  1.23it/s, loss=0.0504, lr=4.76e-5, step=237]\\u001b[A\\n\",\n      \"Epoch 1:  12%|█████████████████████████▋                                                                                                                                                                                        | 26/213 [00:21<02:31,  1.23it/s, loss=0.0285, lr=4.78e-5, step=238]\\u001b[A\\n\",\n      \"Epoch 1:  13%|██████████████████████████▌                                                                                                                                                                                       | 27/213 [00:22<02:30,  1.23it/s, loss=0.0285, lr=4.78e-5, step=238]\\u001b[A\\n\",\n      \"Epoch 1:  13%|██████████████████████████▋                                                                                                                                                                                        | 27/213 [00:22<02:30,  1.23it/s, loss=0.0302, lr=4.8e-5, step=239]\\u001b[A\\n\",\n      \"Epoch 1:  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                                                                                                                                                                    | 29/213 [00:23<02:29,  1.23it/s, loss=0.0443, lr=4.84e-5, step=241]\\u001b[A\\n\",\n      \"Epoch 1:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:29,  1.23it/s, loss=0.0443, lr=4.84e-5, step=241]\\u001b[A\\n\",\n      \"Epoch 1:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:29,  1.23it/s, loss=0.0833, lr=4.86e-5, step=242]\\u001b[A\\n\",\n      \"Epoch 1:  15%|██████████████████████████████▌                                                                                                                                                                                   | 31/213 [00:25<02:29,  1.22it/s, loss=0.0833, lr=4.86e-5, step=242]\\u001b[A\\n\",\n      \"Epoch 1:  15%|██████████████████████████████▌                                                                                                                                                                                   | 31/213 [00:25<02:29,  1.22it/s, loss=0.0486, lr=4.88e-5, step=243]\\u001b[A\\n\",\n      \"Epoch 1:  15%|███████████████████████████████▌                                                                                                                                                                                  | 32/213 [00:26<02:28,  1.22it/s, loss=0.0486, lr=4.88e-5, step=243]\\u001b[A\\n\",\n      \"Epoch 1:  15%|███████████████████████████████▋                                                                                                                                                                                   | 32/213 [00:26<02:28,  1.22it/s, loss=0.0466, lr=4.9e-5, step=244]\\u001b[A\\n\",\n      \"Epoch 1:  15%|████████████████████████████████▋                                                                                                                                                                                  | 33/213 [00:27<02:26,  1.23it/s, loss=0.0466, lr=4.9e-5, step=244]\\u001b[A\\n\",\n      \"Epoch 1:  15%|████████████████████████████████▌                                                                                                                                                                                 | 33/213 [00:27<02:26,  1.23it/s, loss=0.0384, lr=4.92e-5, step=245]\\u001b[A\\n\",\n      \"Epoch 1:  16%|█████████████████████████████████▌                                                                                                                                                                                | 34/213 [00:27<02:25,  1.23it/s, loss=0.0384, lr=4.92e-5, step=245]\\u001b[A\\n\",\n      \"Epoch 1:  16%|█████████████████████████████████▌                                                                                                                                                                                | 34/213 [00:27<02:25,  1.23it/s, loss=0.0351, lr=4.94e-5, step=246]\\u001b[A\\n\",\n      \"Epoch 1:  16%|██████████████████████████████████▌                                                                                                                                                                               | 35/213 [00:28<02:24,  1.23it/s, loss=0.0351, lr=4.94e-5, step=246]\\u001b[A\\n\",\n      \"Epoch 1:  16%|██████████████████████████████████▌                                                                                                                                                                               | 35/213 [00:28<02:24,  1.23it/s, loss=0.0569, lr=4.96e-5, step=247]\\u001b[A\\n\",\n      \"Epoch 1:  17%|███████████████████████████████████▍                                                                                                                                                                              | 36/213 [00:29<02:24,  1.23it/s, loss=0.0569, lr=4.96e-5, step=247]\\u001b[A\\n\",\n      \"Epoch 1:  17%|███████████████████████████████████▍                                                                                                                                                                              | 36/213 [00:29<02:24,  1.23it/s, loss=0.0308, lr=4.98e-5, step=248]\\u001b[A\\n\",\n      \"Epoch 1:  17%|████████████████████████████████████▍                                                                                                                                                                             | 37/213 [00:30<02:23,  1.23it/s, loss=0.0308, lr=4.98e-5, step=248]\\u001b[A\\n\",\n      \"Epoch 1:  17%|█████████████████████████████████████                                                                                                                                                                                | 37/213 [00:30<02:23,  1.23it/s, loss=0.0887, lr=5e-5, step=249]\\u001b[A\\n\",\n      \"Epoch 1:  18%|██████████████████████████████████████                                                                                                                                                                               | 38/213 [00:31<02:22,  1.23it/s, loss=0.0887, lr=5e-5, step=249]\\u001b[A\\n\",\n      \"Epoch 1:  18%|█████████████████████████████████████▍                                                                                                                                                                            | 38/213 [00:31<02:22,  1.23it/s, loss=0.0353, lr=5.02e-5, step=250]\\u001b[A\\n\",\n      \"Epoch 1:  18%|██████████████████████████████████████▍                                                                                                                                                                           | 39/213 [00:31<02:22,  1.22it/s, loss=0.0353, lr=5.02e-5, step=250]\\u001b[A\\n\",\n      \"Epoch 1:  18%|██████████████████████████████████████▊                                                                                                                                                                             | 39/213 [00:31<02:22,  1.22it/s, loss=0.13, lr=5.04e-5, step=251]\\u001b[A\\n\",\n      \"Epoch 1:  19%|███████████████████████████████████████▊                                                                                                                                                                            | 40/213 [00:32<02:21,  1.22it/s, loss=0.13, lr=5.04e-5, step=251]\\u001b[A\\n\",\n      \"Epoch 1:  19%|███████████████████████████████████████▍                                                                                                                                                                          | 40/213 [00:32<02:21,  1.22it/s, loss=0.0502, lr=5.06e-5, step=252]\\u001b[A\\n\",\n      \"Epoch 1:  19%|████████████████████████████████████████▍                                                                                                                                                                         | 41/213 [00:33<02:20,  1.22it/s, loss=0.0502, lr=5.06e-5, step=252]\\u001b[A\\n\",\n      \"Epoch 1:  19%|████████████████████████████████████████▍                                                                                                                                                                         | 41/213 [00:33<02:20,  1.22it/s, loss=0.0439, lr=5.08e-5, step=253]\\u001b[A\\n\",\n      \"Epoch 1:  20%|█████████████████████████████████████████▍                                                                                                                                                                        | 42/213 [00:34<02:20,  1.22it/s, loss=0.0439, lr=5.08e-5, step=253]\\u001b[A\\n\",\n      \"Epoch 1:  20%|█████████████████████████████████████████▊                                                                                                                                                                          | 42/213 [00:34<02:20,  1.22it/s, loss=0.025, lr=5.1e-5, step=254]\\u001b[A\\n\",\n      \"Epoch 1:  20%|██████████████████████████████████████████▊                                                                                                                                                                         | 43/213 [00:35<02:19,  1.22it/s, loss=0.025, lr=5.1e-5, step=254]\\u001b[A\\n\",\n      \"Epoch 1:  20%|██████████████████████████████████████████▍                                                                                                                                                                       | 43/213 [00:35<02:19,  1.22it/s, loss=0.0824, lr=5.12e-5, step=255]\\u001b[A\\n\",\n      \"Epoch 1:  21%|███████████████████████████████████████████▍                                                                                                                                                                      | 44/213 [00:36<02:17,  1.23it/s, loss=0.0824, lr=5.12e-5, step=255]\\u001b[A\\n\",\n      \"Epoch 1:  21%|███████████████████████████████████████████▍                                                                                                                                                                      | 44/213 [00:36<02:17,  1.23it/s, loss=0.0632, lr=5.14e-5, step=256]\\u001b[A\\n\",\n      \"Epoch 1:  21%|████████████████████████████████████████████▎                                                                                                                                                                     | 45/213 [00:36<02:16,  1.23it/s, loss=0.0632, lr=5.14e-5, step=256]\\u001b[A\\n\",\n      \"Epoch 1:  21%|████████████████████████████████████████████▎                                                                                                                                                                     | 45/213 [00:36<02:16,  1.23it/s, loss=0.0755, lr=5.16e-5, step=257]\\u001b[A\\n\",\n      \"Epoch 1:  22%|█████████████████████████████████████████████▎                                                                                                                                                                    | 46/213 [00:37<02:16,  1.22it/s, loss=0.0755, lr=5.16e-5, step=257]\\u001b[A\\n\",\n      \"Epoch 1:  22%|█████████████████████████████████████████████▎                                                                                                                                                                    | 46/213 [00:37<02:16,  1.22it/s, loss=0.0763, lr=5.18e-5, step=258]\\u001b[A\\n\",\n      \"Epoch 1:  22%|██████████████████████████████████████████████▎                                                                                                                                                                   | 47/213 [00:38<02:15,  1.23it/s, loss=0.0763, lr=5.18e-5, step=258]\\u001b[A\\n\",\n      \"Epoch 1:  22%|██████████████████████████████████████████████▌                                                                                                                                                                    | 47/213 [00:38<02:15,  1.23it/s, loss=0.0443, lr=5.2e-5, step=259]\\u001b[A\\n\",\n      \"Epoch 1:  23%|███████████████████████████████████████████████▌                                                                                                                                                                   | 48/213 [00:39<02:15,  1.22it/s, loss=0.0443, lr=5.2e-5, step=259]\\u001b[A\\n\",\n      \"Epoch 1:  23%|███████████████████████████████████████████████▌                                                                                                                                                                   | 48/213 [00:39<02:15,  1.22it/s, loss=0.042, lr=5.22e-5, step=260]\\u001b[A\\n\",\n      \"Epoch 1:  23%|████████████████████████████████████████████████▌                                                                                                                                                                  | 49/213 [00:40<02:13,  1.23it/s, loss=0.042, lr=5.22e-5, step=260]\\u001b[A\\n\",\n      \"Epoch 1:  23%|████████████████████████████████████████████████▌                                                                                                                                                                  | 49/213 [00:40<02:13,  1.23it/s, loss=0.085, lr=5.24e-5, step=261]\\u001b[A\\n\",\n      \"Epoch 1:  23%|█████████████████████████████████████████████████▌                                                                                                                                                                 | 50/213 [00:40<02:14,  1.22it/s, loss=0.085, lr=5.24e-5, step=261]\\u001b[A\\n\",\n      \"Epoch 1:  23%|█████████████████████████████████████████████████▌                                                                                                                                                                 | 50/213 [00:40<02:14,  1.22it/s, loss=0.056, lr=5.26e-5, step=262]\\u001b[A\\n\",\n      \"Epoch 1:  24%|██████████████████████████████████████████████████▌                                                                                                                                                                | 51/213 [00:41<02:12,  1.23it/s, loss=0.056, lr=5.26e-5, step=262]\\u001b[A\\n\",\n      \"Epoch 1:  24%|██████████████████████████████████████████████████▌                                                                                                                                                                | 51/213 [00:41<02:12,  1.23it/s, loss=0.106, lr=5.28e-5, step=263]\\u001b[A\\n\",\n      \"Epoch 1:  24%|███████████████████████████████████████████████████▌                                                                                                                                                               | 52/213 [00:42<02:12,  1.22it/s, loss=0.106, lr=5.28e-5, step=263]\\u001b[A\\n\",\n      \"Epoch 1:  24%|███████████████████████████████████████████████████▌                                                                                                                                                               | 52/213 [00:42<02:12,  1.22it/s, loss=0.0462, lr=5.3e-5, step=264]\\u001b[A\\n\",\n      \"Epoch 1:  25%|████████████████████████████████████████████████████▌                                                                                                                                                              | 53/213 [00:43<02:11,  1.22it/s, loss=0.0462, lr=5.3e-5, step=264]\\u001b[A\\n\",\n      \"Epoch 1:  25%|████████████████████████████████████████████████████▎                                                                                                                                                             | 53/213 [00:43<02:11,  1.22it/s, loss=0.0719, lr=5.32e-5, step=265]\\u001b[A\\n\",\n      \"Epoch 1:  25%|█████████████████████████████████████████████████████▏                                                                                                                                                            | 54/213 [00:44<02:09,  1.23it/s, loss=0.0719, lr=5.32e-5, step=265]\\u001b[A\\n\",\n      \"Epoch 1:  25%|█████████████████████████████████████████████████████▏                                                                                                                                                            | 54/213 [00:44<02:09,  1.23it/s, loss=0.0599, lr=5.34e-5, step=266]\\u001b[A\\n\",\n      \"Epoch 1:  26%|██████████████████████████████████████████████████████▏                                                                                                                                                           | 55/213 [00:45<02:09,  1.22it/s, loss=0.0599, lr=5.34e-5, step=266]\\u001b[A\\n\",\n      \"Epoch 1:  26%|██████████████████████████████████████████████████████▏                                                                                                                                                           | 55/213 [00:45<02:09,  1.22it/s, loss=0.0281, lr=5.36e-5, step=267]\\u001b[A\\n\",\n      \"Epoch 1:  26%|███████████████████████████████████████████████████████▏                                                                                                                                                          | 56/213 [00:45<02:08,  1.22it/s, loss=0.0281, lr=5.36e-5, step=267]\\u001b[A\\n\",\n      \"Epoch 1:  26%|███████████████████████████████████████████████████████▏                                                                                                                                                          | 56/213 [00:45<02:08,  1.22it/s, loss=0.0477, lr=5.38e-5, step=268]\\u001b[A\\n\",\n      \"Epoch 1:  27%|████████████████████████████████████████████████████████▏                                                                                                                                                         | 57/213 [00:46<02:07,  1.22it/s, loss=0.0477, lr=5.38e-5, step=268]\\u001b[A\\n\",\n      \"Epoch 1:  27%|████████████████████████████████████████████████████████▍                                                                                                                                                          | 57/213 [00:46<02:07,  1.22it/s, loss=0.0684, lr=5.4e-5, step=269]\\u001b[A\\n\",\n      \"Epoch 1:  27%|█████████████████████████████████████████████████████████▍                                                                                                                                                         | 58/213 [00:47<02:07,  1.22it/s, loss=0.0684, lr=5.4e-5, step=269]\\u001b[A\\n\",\n      \"Epoch 1:  27%|█████████████████████████████████████████████████████████▏                                                                                                                                                        | 58/213 [00:47<02:07,  1.22it/s, loss=0.0509, lr=5.42e-5, step=270]\\u001b[A\\n\",\n      \"Epoch 1:  28%|██████████████████████████████████████████████████████████▏                                                                                                                                                       | 59/213 [00:48<02:05,  1.22it/s, loss=0.0509, lr=5.42e-5, step=270]\\u001b[A\\n\",\n      \"Epoch 1:  28%|██████████████████████████████████████████████████████████▏                                                                                                                                                       | 59/213 [00:48<02:05,  1.22it/s, loss=0.0891, lr=5.44e-5, step=271]\\u001b[A\\n\",\n      \"Epoch 1:  28%|███████████████████████████████████████████████████████████▏                                                                                                                                                      | 60/213 [00:49<02:04,  1.23it/s, loss=0.0891, lr=5.44e-5, step=271]\\u001b[A\\n\",\n      \"Epoch 1:  28%|███████████████████████████████████████████████████████████▏                                                                                                                                                      | 60/213 [00:49<02:04,  1.23it/s, loss=0.0371, lr=5.46e-5, step=272]\\u001b[A\\n\",\n      \"Epoch 1:  29%|████████████████████████████████████████████████████████████▏                                                                                                                                                     | 61/213 [00:49<02:03,  1.23it/s, loss=0.0371, lr=5.46e-5, step=272]\\u001b[A\\n\",\n      \"Epoch 1:  29%|████████████████████████████████████████████████████████████▏                                                                                                                                                     | 61/213 [00:49<02:03,  1.23it/s, loss=0.0391, lr=5.48e-5, step=273]\\u001b[A\\n\",\n      \"Epoch 1:  29%|█████████████████████████████████████████████████████████████▏                                                                                                                                                    | 62/213 [00:50<02:02,  1.23it/s, loss=0.0391, lr=5.48e-5, step=273]\\u001b[A\\n\",\n      \"Epoch 1:  29%|█████████████████████████████████████████████████████████████▍                                                                                                                                                     | 62/213 [00:50<02:02,  1.23it/s, loss=0.0276, lr=5.5e-5, step=274]\\u001b[A\\n\",\n      \"Epoch 1:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                    | 63/213 [00:51<02:02,  1.23it/s, loss=0.0276, lr=5.5e-5, step=274]\\u001b[A\\n\",\n      \"Epoch 1:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                    | 63/213 [00:51<02:02,  1.23it/s, loss=0.057, lr=5.52e-5, step=275]\\u001b[A\\n\",\n      \"Epoch 1:  30%|███████████████████████████████████████████████████████████████▍                                                                                                                                                   | 64/213 [00:52<02:01,  1.23it/s, loss=0.057, lr=5.52e-5, step=275]\\u001b[A\\n\",\n      \"Epoch 1:  30%|███████████████████████████████████████████████████████████████                                                                                                                                                   | 64/213 [00:52<02:01,  1.23it/s, loss=0.0435, lr=5.54e-5, step=276]\\u001b[A\\n\",\n      \"Epoch 1:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:53<02:00,  1.23it/s, loss=0.0435, lr=5.54e-5, step=276]\\u001b[A\\n\",\n      \"Epoch 1:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:53<02:00,  1.23it/s, loss=0.0324, lr=5.56e-5, step=277]\\u001b[A\\n\",\n      \"Epoch 1:  31%|█████████████████████████████████████████████████████████████████                                                                                                                                                 | 66/213 [00:53<01:59,  1.23it/s, loss=0.0324, lr=5.56e-5, step=277]\\u001b[A\\n\",\n      \"Epoch 1:  31%|█████████████████████████████████████████████████████████████████                                                                                                                                                 | 66/213 [00:54<01:59,  1.23it/s, loss=0.0625, lr=5.58e-5, step=278]\\u001b[A\\n\",\n      \"Epoch 1:  31%|██████████████████████████████████████████████████████████████████                                                                                                                                                | 67/213 [00:54<01:58,  1.23it/s, loss=0.0625, lr=5.58e-5, step=278]\\u001b[A\\n\",\n      \"Epoch 1:  31%|██████████████████████████████████████████████████████████████████▎                                                                                                                                                | 67/213 [00:54<01:58,  1.23it/s, loss=0.0494, lr=5.6e-5, step=279]\\u001b[A\\n\",\n      \"Epoch 1:  32%|███████████████████████████████████████████████████████████████████▎                                                                                                                                               | 68/213 [00:55<01:57,  1.23it/s, loss=0.0494, lr=5.6e-5, step=279]\\u001b[A\\n\",\n      \"Epoch 1:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                               | 68/213 [00:55<01:57,  1.23it/s, loss=0.0384, lr=5.62e-5, step=280]\\u001b[A\\n\",\n      \"Epoch 1:  32%|████████████████████████████████████████████████████████████████████                                                                                                                                              | 69/213 [00:56<01:57,  1.23it/s, loss=0.0384, lr=5.62e-5, step=280]\\u001b[A\\n\",\n      \"Epoch 1:  32%|████████████████████████████████████████████████████████████████████                                                                                                                                              | 69/213 [00:56<01:57,  1.23it/s, loss=0.0344, lr=5.64e-5, step=281]\\u001b[A\\n\",\n      \"Epoch 1:  33%|█████████████████████████████████████████████████████████████████████                                                                                                                                             | 70/213 [00:57<01:55,  1.24it/s, loss=0.0344, lr=5.64e-5, step=281]\\u001b[A\\n\",\n      \"Epoch 1:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                             | 70/213 [00:57<01:55,  1.24it/s, loss=0.061, lr=5.66e-5, step=282]\\u001b[A\\n\",\n      \"Epoch 1:  33%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                            | 71/213 [00:58<01:55,  1.23it/s, loss=0.061, lr=5.66e-5, step=282]\\u001b[A\\n\",\n      \"Epoch 1:  33%|██████████████████████████████████████████████████████████████████████                                                                                                                                            | 71/213 [00:58<01:55,  1.23it/s, loss=0.0345, lr=5.68e-5, step=283]\\u001b[A\\n\",\n      \"Epoch 1:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                           | 72/213 [00:58<01:55,  1.22it/s, loss=0.0345, lr=5.68e-5, step=283]\\u001b[A\\n\",\n      \"Epoch 1:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 72/213 [00:58<01:55,  1.22it/s, loss=0.0242, lr=5.7e-5, step=284]\\u001b[A\\n\",\n      \"Epoch 1:  34%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 73/213 [00:59<01:54,  1.23it/s, loss=0.0242, lr=5.7e-5, step=284]\\u001b[A\\n\",\n      \"Epoch 1:  34%|███████████████████████████████████████████████████████████████████████▉                                                                                                                                          | 73/213 [00:59<01:54,  1.23it/s, loss=0.0324, lr=5.72e-5, step=285]\\u001b[A\\n\",\n      \"Epoch 1:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [01:00<01:53,  1.23it/s, loss=0.0324, lr=5.72e-5, step=285]\\u001b[A\\n\",\n      \"Epoch 1:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [01:00<01:53,  1.23it/s, loss=0.0596, lr=5.74e-5, step=286]\\u001b[A\\n\",\n      \"Epoch 1:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:01<01:52,  1.23it/s, loss=0.0596, lr=5.74e-5, step=286]\\u001b[A\\n\",\n      \"Epoch 1:  35%|██████████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 75/213 [01:01<01:52,  1.23it/s, loss=0.087, lr=5.76e-5, step=287]\\u001b[A\\n\",\n      \"Epoch 1:  36%|███████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 76/213 [01:02<01:51,  1.22it/s, loss=0.087, lr=5.76e-5, step=287]\\u001b[A\\n\",\n      \"Epoch 1:  36%|███████████████████████████████████████████████████████████████████████████▋                                                                                                                                        | 76/213 [01:02<01:51,  1.22it/s, loss=0.03, lr=5.78e-5, step=288]\\u001b[A\\n\",\n      \"Epoch 1:  36%|████████████████████████████████████████████████████████████████████████████▋                                                                                                                                       | 77/213 [01:02<01:51,  1.22it/s, loss=0.03, lr=5.78e-5, step=288]\\u001b[A\\n\",\n      \"Epoch 1:  36%|████████████████████████████████████████████████████████████████████████████▎                                                                                                                                      | 77/213 [01:02<01:51,  1.22it/s, loss=0.0513, lr=5.8e-5, step=289]\\u001b[A\\n\",\n      \"Epoch 1:  37%|█████████████████████████████████████████████████████████████████████████████▎                                                                                                                                     | 78/213 [01:03<01:50,  1.23it/s, loss=0.0513, lr=5.8e-5, step=289]\\u001b[A\\n\",\n      \"Epoch 1:  37%|████████████████████████████████████████████████████████████████████████████▉                                                                                                                                     | 78/213 [01:03<01:50,  1.23it/s, loss=0.0289, lr=5.82e-5, step=290]\\u001b[A\\n\",\n      \"Epoch 1:  37%|█████████████████████████████████████████████████████████████████████████████▉                                                                                                                                    | 79/213 [01:04<01:48,  1.24it/s, loss=0.0289, lr=5.82e-5, step=290]\\u001b[A\\n\",\n      \"Epoch 1:  37%|█████████████████████████████████████████████████████████████████████████████▉                                                                                                                                    | 79/213 [01:04<01:48,  1.24it/s, loss=0.0291, lr=5.84e-5, step=291]\\u001b[A\\n\",\n      \"Epoch 1:  38%|██████████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 80/213 [01:05<01:48,  1.22it/s, loss=0.0291, lr=5.84e-5, step=291]\\u001b[A\\n\",\n      \"Epoch 1:  38%|██████████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 80/213 [01:05<01:48,  1.22it/s, loss=0.0257, lr=5.86e-5, step=292]\\u001b[A\\n\",\n      \"Epoch 1:  38%|███████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 81/213 [01:06<01:47,  1.23it/s, loss=0.0257, lr=5.86e-5, step=292]\\u001b[A\\n\",\n      \"Epoch 1:  38%|███████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 81/213 [01:06<01:47,  1.23it/s, loss=0.0398, lr=5.88e-5, step=293]\\u001b[A\\n\",\n      \"Epoch 1:  38%|████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                 | 82/213 [01:06<01:45,  1.24it/s, loss=0.0398, lr=5.88e-5, step=293]\\u001b[A\\n\",\n      \"Epoch 1:  38%|█████████████████████████████████████████████████████████████████████████████████▏                                                                                                                                 | 82/213 [01:07<01:45,  1.24it/s, loss=0.0889, lr=5.9e-5, step=294]\\u001b[A\\n\",\n      \"Epoch 1:  39%|██████████████████████████████████████████████████████████████████████████████████▏                                                                                                                                | 83/213 [01:07<01:45,  1.24it/s, loss=0.0889, lr=5.9e-5, step=294]\\u001b[A\\n\",\n      \"Epoch 1:  39%|█████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                | 83/213 [01:07<01:45,  1.24it/s, loss=0.0866, lr=5.92e-5, step=295]\\u001b[A\\n\",\n      \"Epoch 1:  39%|██████████████████████████████████████████████████████████████████████████████████▊                                                                                                                               | 84/213 [01:08<01:44,  1.23it/s, loss=0.0866, lr=5.92e-5, step=295]\\u001b[A\\n\",\n      \"Epoch 1:  39%|███████████████████████████████████████████████████████████████████████████████████▏                                                                                                                               | 84/213 [01:08<01:44,  1.23it/s, loss=0.132, lr=5.94e-5, step=296]\\u001b[A\\n\",\n      \"Epoch 1:  40%|████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                              | 85/213 [01:09<01:44,  1.23it/s, loss=0.132, lr=5.94e-5, step=296]\\u001b[A\\n\",\n      \"Epoch 1:  40%|████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                               | 85/213 [01:09<01:44,  1.23it/s, loss=0.03, lr=5.96e-5, step=297]\\u001b[A\\n\",\n      \"Epoch 1:  40%|█████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                              | 86/213 [01:10<01:42,  1.23it/s, loss=0.03, lr=5.96e-5, step=297]\\u001b[A\\n\",\n      \"Epoch 1:  40%|████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                             | 86/213 [01:10<01:42,  1.23it/s, loss=0.0561, lr=5.98e-5, step=298]\\u001b[A\\n\",\n      \"Epoch 1:  41%|█████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                            | 87/213 [01:11<01:42,  1.23it/s, loss=0.0561, lr=5.98e-5, step=298]\\u001b[A\\n\",\n      \"Epoch 1:  41%|███████████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 87/213 [01:11<01:42,  1.23it/s, loss=0.0704, lr=6e-5, step=299]\\u001b[A\\n\",\n      \"Epoch 1:  41%|████████████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 88/213 [01:11<01:42,  1.22it/s, loss=0.0704, lr=6e-5, step=299]\\u001b[A\\n\",\n      \"Epoch 1:  41%|██████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                           | 88/213 [01:11<01:42,  1.22it/s, loss=0.0329, lr=6.02e-5, step=300]\\u001b[A\\n\",\n      \"Epoch 1:  42%|███████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                          | 89/213 [01:12<01:41,  1.22it/s, loss=0.0329, lr=6.02e-5, step=300]\\u001b[A\\n\",\n      \"Epoch 1:  42%|███████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                          | 89/213 [01:12<01:41,  1.22it/s, loss=0.0261, lr=6.04e-5, step=301]\\u001b[A\\n\",\n      \"Epoch 1:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:13<01:39,  1.23it/s, loss=0.0261, lr=6.04e-5, step=301]\\u001b[A\\n\",\n      \"Epoch 1:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:13<01:39,  1.23it/s, loss=0.0348, lr=6.06e-5, step=302]\\u001b[A\\n\",\n      \"Epoch 1:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:14<01:38,  1.23it/s, loss=0.0348, lr=6.06e-5, step=302]\\u001b[A\\n\",\n      \"Epoch 1:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:14<01:38,  1.23it/s, loss=0.0554, lr=6.08e-5, step=303]\\u001b[A\\n\",\n      \"Epoch 1:  43%|██████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                       | 92/213 [01:15<01:37,  1.24it/s, loss=0.0554, lr=6.08e-5, step=303]\\u001b[A\\n\",\n      \"Epoch 1:  43%|███████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                       | 92/213 [01:15<01:37,  1.24it/s, loss=0.0592, lr=6.1e-5, step=304]\\u001b[A\\n\",\n      \"Epoch 1:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                      | 93/213 [01:15<01:36,  1.24it/s, loss=0.0592, lr=6.1e-5, step=304]\\u001b[A\\n\",\n      \"Epoch 1:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                      | 93/213 [01:15<01:36,  1.24it/s, loss=0.053, lr=6.12e-5, step=305]\\u001b[A\\n\",\n      \"Epoch 1:  44%|█████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                      | 94/213 [01:16<01:36,  1.24it/s, loss=0.053, lr=6.12e-5, step=305]\\u001b[A\\n\",\n      \"Epoch 1:  44%|█████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                      | 94/213 [01:16<01:36,  1.24it/s, loss=0.105, lr=6.14e-5, step=306]\\u001b[A\\n\",\n      \"Epoch 1:  45%|██████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                     | 95/213 [01:17<01:35,  1.23it/s, loss=0.105, lr=6.14e-5, step=306]\\u001b[A\\n\",\n      \"Epoch 1:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                    | 95/213 [01:17<01:35,  1.23it/s, loss=0.0294, lr=6.16e-5, step=307]\\u001b[A\\n\",\n      \"Epoch 1:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                   | 96/213 [01:18<01:35,  1.23it/s, loss=0.0294, lr=6.16e-5, step=307]\\u001b[A\\n\",\n      \"Epoch 1:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                   | 96/213 [01:18<01:35,  1.23it/s, loss=0.0489, lr=6.18e-5, step=308]\\u001b[A\\n\",\n      \"Epoch 1:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 97/213 [01:19<01:33,  1.23it/s, loss=0.0489, lr=6.18e-5, step=308]\\u001b[A\\n\",\n      \"Epoch 1:  46%|████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                   | 97/213 [01:19<01:33,  1.23it/s, loss=0.0429, lr=6.2e-5, step=309]\\u001b[A\\n\",\n      \"Epoch 1:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                  | 98/213 [01:19<01:33,  1.23it/s, loss=0.0429, lr=6.2e-5, step=309]\\u001b[A\\n\",\n      \"Epoch 1:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                 | 98/213 [01:20<01:33,  1.23it/s, loss=0.0745, lr=6.22e-5, step=310]\\u001b[A\\n\",\n      \"Epoch 1:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                | 99/213 [01:20<01:33,  1.22it/s, loss=0.0745, lr=6.22e-5, step=310]\\u001b[A\\n\",\n      \"Epoch 1:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                | 99/213 [01:20<01:33,  1.22it/s, loss=0.0501, lr=6.24e-5, step=311]\\u001b[A\\n\",\n      \"Epoch 1:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 100/213 [01:21<01:32,  1.23it/s, loss=0.0501, lr=6.24e-5, step=311]\\u001b[A\\n\",\n      \"Epoch 1:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                               | 100/213 [01:21<01:32,  1.23it/s, loss=0.035, lr=6.26e-5, step=312]\\u001b[A\\n\",\n      \"Epoch 1:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                              | 101/213 [01:22<01:30,  1.23it/s, loss=0.035, lr=6.26e-5, step=312]\\u001b[A\\n\",\n      \"Epoch 1:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                              | 101/213 [01:22<01:30,  1.23it/s, loss=0.0404, lr=6.28e-5, step=313]\\u001b[A\\n\",\n      \"Epoch 1:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                             | 102/213 [01:23<01:29,  1.24it/s, loss=0.0404, lr=6.28e-5, step=313]\\u001b[A\\n\",\n      \"Epoch 1:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                             | 102/213 [01:23<01:29,  1.24it/s, loss=0.0267, lr=6.3e-5, step=314]\\u001b[A\\n\",\n      \"Epoch 1:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 103/213 [01:24<01:29,  1.23it/s, loss=0.0267, lr=6.3e-5, step=314]\\u001b[A\\n\",\n      \"Epoch 1:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 103/213 [01:24<01:29,  1.23it/s, loss=0.028, lr=6.32e-5, step=315]\\u001b[A\\n\",\n      \"Epoch 1:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 104/213 [01:24<01:28,  1.23it/s, loss=0.028, lr=6.32e-5, step=315]\\u001b[A\\n\",\n      \"Epoch 1:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 104/213 [01:24<01:28,  1.23it/s, loss=0.0516, lr=6.34e-5, step=316]\\u001b[A\\n\",\n      \"Epoch 1:  49%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 105/213 [01:25<01:28,  1.23it/s, loss=0.0516, lr=6.34e-5, step=316]\\u001b[A\\n\",\n      \"Epoch 1:  49%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 105/213 [01:25<01:28,  1.23it/s, loss=0.0706, lr=6.36e-5, step=317]\\u001b[A\\n\",\n      \"Epoch 1:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 106/213 [01:26<01:27,  1.23it/s, loss=0.0706, lr=6.36e-5, step=317]\\u001b[A\\n\",\n      \"Epoch 1:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 106/213 [01:26<01:27,  1.23it/s, loss=0.0408, lr=6.38e-5, step=318]\\u001b[A\\n\",\n      \"Epoch 1:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                        | 107/213 [01:27<01:26,  1.23it/s, loss=0.0408, lr=6.38e-5, step=318]\\u001b[A\\n\",\n      \"Epoch 1:  50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                         | 107/213 [01:27<01:26,  1.23it/s, loss=0.032, lr=6.4e-5, step=319]\\u001b[A\\n\",\n      \"Epoch 1:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                        | 108/213 [01:28<01:25,  1.23it/s, loss=0.032, lr=6.4e-5, step=319]\\u001b[A\\n\",\n      \"Epoch 1:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 108/213 [01:28<01:25,  1.23it/s, loss=0.0984, lr=6.42e-5, step=320]\\u001b[A\\n\",\n      \"Epoch 1:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 109/213 [01:28<01:24,  1.23it/s, loss=0.0984, lr=6.42e-5, step=320]\\u001b[A\\n\",\n      \"Epoch 1:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 109/213 [01:28<01:24,  1.23it/s, loss=0.0755, lr=6.44e-5, step=321]\\u001b[A\\n\",\n      \"Epoch 1:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 110/213 [01:29<01:23,  1.23it/s, loss=0.0755, lr=6.44e-5, step=321]\\u001b[A\\n\",\n      \"Epoch 1:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 110/213 [01:29<01:23,  1.23it/s, loss=0.0711, lr=6.46e-5, step=322]\\u001b[A\\n\",\n      \"Epoch 1:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 111/213 [01:30<01:22,  1.23it/s, loss=0.0711, lr=6.46e-5, step=322]\\u001b[A\\n\",\n      \"Epoch 1:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 111/213 [01:30<01:22,  1.23it/s, loss=0.0317, lr=6.48e-5, step=323]\\u001b[A\\n\",\n      \"Epoch 1:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                   | 112/213 [01:31<01:22,  1.23it/s, loss=0.0317, lr=6.48e-5, step=323]\\u001b[A\\n\",\n      \"Epoch 1:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 112/213 [01:31<01:22,  1.23it/s, loss=0.0876, lr=6.5e-5, step=324]\\u001b[A\\n\",\n      \"Epoch 1:  53%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                  | 113/213 [01:32<01:21,  1.22it/s, loss=0.0876, lr=6.5e-5, step=324]\\u001b[A\\n\",\n      \"Epoch 1:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                  | 113/213 [01:32<01:21,  1.22it/s, loss=0.0188, lr=6.52e-5, step=325]\\u001b[A\\n\",\n      \"Epoch 1:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 114/213 [01:33<01:20,  1.23it/s, loss=0.0188, lr=6.52e-5, step=325]\\u001b[A\\n\",\n      \"Epoch 1:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 114/213 [01:33<01:20,  1.23it/s, loss=0.0304, lr=6.54e-5, step=326]\\u001b[A\\n\",\n      \"Epoch 1:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 115/213 [01:33<01:19,  1.23it/s, loss=0.0304, lr=6.54e-5, step=326]\\u001b[A\\n\",\n      \"Epoch 1:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 115/213 [01:33<01:19,  1.23it/s, loss=0.0378, lr=6.56e-5, step=327]\\u001b[A\\n\",\n      \"Epoch 1:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 116/213 [01:34<01:19,  1.23it/s, loss=0.0378, lr=6.56e-5, step=327]\\u001b[A\\n\",\n      \"Epoch 1:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 116/213 [01:34<01:19,  1.23it/s, loss=0.0528, lr=6.58e-5, step=328]\\u001b[A\\n\",\n      \"Epoch 1:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                              | 117/213 [01:35<01:18,  1.23it/s, loss=0.0528, lr=6.58e-5, step=328]\\u001b[A\\n\",\n      \"Epoch 1:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                               | 117/213 [01:35<01:18,  1.23it/s, loss=0.037, lr=6.6e-5, step=329]\\u001b[A\\n\",\n      \"Epoch 1:  55%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                              | 118/213 [01:36<01:17,  1.23it/s, loss=0.037, lr=6.6e-5, step=329]\\u001b[A\\n\",\n      \"Epoch 1:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                             | 118/213 [01:36<01:17,  1.23it/s, loss=0.0231, lr=6.62e-5, step=330]\\u001b[A\\n\",\n      \"Epoch 1:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 119/213 [01:37<01:16,  1.23it/s, loss=0.0231, lr=6.62e-5, step=330]\\u001b[A\\n\",\n      \"Epoch 1:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 119/213 [01:37<01:16,  1.23it/s, loss=0.0519, lr=6.64e-5, step=331]\\u001b[A\\n\",\n      \"Epoch 1:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 120/213 [01:37<01:15,  1.23it/s, loss=0.0519, lr=6.64e-5, step=331]\\u001b[A\\n\",\n      \"Epoch 1:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 120/213 [01:37<01:15,  1.23it/s, loss=0.0525, lr=6.66e-5, step=332]\\u001b[A\\n\",\n      \"Epoch 1:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:38<01:15,  1.22it/s, loss=0.0525, lr=6.66e-5, step=332]\\u001b[A\\n\",\n      \"Epoch 1:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:38<01:15,  1.22it/s, loss=0.0471, lr=6.68e-5, step=333]\\u001b[A\\n\",\n      \"Epoch 1:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                         | 122/213 [01:39<01:14,  1.22it/s, loss=0.0471, lr=6.68e-5, step=333]\\u001b[A\\n\",\n      \"Epoch 1:  57%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                         | 122/213 [01:39<01:14,  1.22it/s, loss=0.0384, lr=6.7e-5, step=334]\\u001b[A\\n\",\n      \"Epoch 1:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                        | 123/213 [01:40<01:13,  1.22it/s, loss=0.0384, lr=6.7e-5, step=334]\\u001b[A\\n\",\n      \"Epoch 1:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:40<01:13,  1.22it/s, loss=0.0437, lr=6.72e-5, step=335]\\u001b[A\\n\",\n      \"Epoch 1:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:41<01:13,  1.21it/s, loss=0.0437, lr=6.72e-5, step=335]\\u001b[A\\n\",\n      \"Epoch 1:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:41<01:13,  1.21it/s, loss=0.0219, lr=6.74e-5, step=336]\\u001b[A\\n\",\n      \"Epoch 1:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                      | 125/213 [01:42<01:12,  1.22it/s, loss=0.0219, lr=6.74e-5, step=336]\\u001b[A\\n\",\n      \"Epoch 1:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                      | 125/213 [01:42<01:12,  1.22it/s, loss=0.0798, lr=6.76e-5, step=337]\\u001b[A\\n\",\n      \"Epoch 1:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                     | 126/213 [01:42<01:10,  1.23it/s, loss=0.0798, lr=6.76e-5, step=337]\\u001b[A\\n\",\n      \"Epoch 1:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                     | 126/213 [01:42<01:10,  1.23it/s, loss=0.027, lr=6.78e-5, step=338]\\u001b[A\\n\",\n      \"Epoch 1:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                    | 127/213 [01:43<01:10,  1.22it/s, loss=0.027, lr=6.78e-5, step=338]\\u001b[A\\n\",\n      \"Epoch 1:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                    | 127/213 [01:43<01:10,  1.22it/s, loss=0.0247, lr=6.8e-5, step=339]\\u001b[A\\n\",\n      \"Epoch 1:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                   | 128/213 [01:44<01:09,  1.23it/s, loss=0.0247, lr=6.8e-5, step=339]\\u001b[A\\n\",\n      \"Epoch 1:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                   | 128/213 [01:44<01:09,  1.23it/s, loss=0.0704, lr=6.82e-5, step=340]\\u001b[A\\n\",\n      \"Epoch 1:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                  | 129/213 [01:45<01:08,  1.23it/s, loss=0.0704, lr=6.82e-5, step=340]\\u001b[A\\n\",\n      \"Epoch 1:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                  | 129/213 [01:45<01:08,  1.23it/s, loss=0.0371, lr=6.84e-5, step=341]\\u001b[A\\n\",\n      \"Epoch 1:  61%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 130/213 [01:46<01:07,  1.23it/s, loss=0.0371, lr=6.84e-5, step=341]\\u001b[A\\n\",\n      \"Epoch 1:  61%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                 | 130/213 [01:46<01:07,  1.23it/s, loss=0.0219, lr=6.86e-5, step=342]\\u001b[A\\n\",\n      \"Epoch 1:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 131/213 [01:46<01:07,  1.22it/s, loss=0.0219, lr=6.86e-5, step=342]\\u001b[A\\n\",\n      \"Epoch 1:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 131/213 [01:46<01:07,  1.22it/s, loss=0.0409, lr=6.88e-5, step=343]\\u001b[A\\n\",\n      \"Epoch 1:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                               | 132/213 [01:47<01:08,  1.19it/s, loss=0.0409, lr=6.88e-5, step=343]\\u001b[A\\n\",\n      \"Epoch 1:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                               | 132/213 [01:47<01:08,  1.19it/s, loss=0.0369, lr=6.9e-5, step=344]\\u001b[A\\n\",\n      \"Epoch 1:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                              | 133/213 [01:48<01:06,  1.20it/s, loss=0.0369, lr=6.9e-5, step=344]\\u001b[A\\n\",\n      \"Epoch 1:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                              | 133/213 [01:48<01:06,  1.20it/s, loss=0.0419, lr=6.92e-5, step=345]\\u001b[A\\n\",\n      \"Epoch 1:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                             | 134/213 [01:49<01:05,  1.21it/s, loss=0.0419, lr=6.92e-5, step=345]\\u001b[A\\n\",\n      \"Epoch 1:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                             | 134/213 [01:49<01:05,  1.21it/s, loss=0.0362, lr=6.94e-5, step=346]\\u001b[A\\n\",\n      \"Epoch 1:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                            | 135/213 [01:50<01:04,  1.21it/s, loss=0.0362, lr=6.94e-5, step=346]\\u001b[A\\n\",\n      \"Epoch 1:  63%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                             | 135/213 [01:50<01:04,  1.21it/s, loss=0.121, lr=6.96e-5, step=347]\\u001b[A\\n\",\n      \"Epoch 1:  64%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                            | 136/213 [01:51<01:04,  1.19it/s, loss=0.121, lr=6.96e-5, step=347]\\u001b[A\\n\",\n      \"Epoch 1:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                           | 136/213 [01:51<01:04,  1.19it/s, loss=0.0251, lr=6.98e-5, step=348]\\u001b[A\\n\",\n      \"Epoch 1:  64%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                          | 137/213 [01:51<01:03,  1.20it/s, loss=0.0251, lr=6.98e-5, step=348]\\u001b[A\\n\",\n      \"Epoch 1:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                           | 137/213 [01:51<01:03,  1.20it/s, loss=0.0469, lr=7e-5, step=349]\\u001b[A\\n\",\n      \"Epoch 1:  65%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                          | 138/213 [01:52<01:02,  1.21it/s, loss=0.0469, lr=7e-5, step=349]\\u001b[A\\n\",\n      \"Epoch 1:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 138/213 [01:52<01:02,  1.21it/s, loss=0.0176, lr=7.02e-5, step=350]\\u001b[A\\n\",\n      \"Epoch 1:  65%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 139/213 [01:53<01:00,  1.21it/s, loss=0.0176, lr=7.02e-5, step=350]\\u001b[A\\n\",\n      \"Epoch 1:  65%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 139/213 [01:53<01:00,  1.21it/s, loss=0.0602, lr=7.04e-5, step=351]\\u001b[A\\n\",\n      \"Epoch 1:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:54<01:00,  1.21it/s, loss=0.0602, lr=7.04e-5, step=351]\\u001b[A\\n\",\n      \"Epoch 1:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:54<01:00,  1.21it/s, loss=0.0728, lr=7.06e-5, step=352]\\u001b[A\\n\",\n      \"Epoch 1:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:55<00:59,  1.22it/s, loss=0.0728, lr=7.06e-5, step=352]\\u001b[A\\n\",\n      \"Epoch 1:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:55<00:59,  1.22it/s, loss=0.0832, lr=7.08e-5, step=353]\\u001b[A\\n\",\n      \"Epoch 1:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                     | 142/213 [01:56<00:58,  1.21it/s, loss=0.0832, lr=7.08e-5, step=353]\\u001b[A\\n\",\n      \"Epoch 1:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 142/213 [01:56<00:58,  1.21it/s, loss=0.0496, lr=7.1e-5, step=354]\\u001b[A\\n\",\n      \"Epoch 1:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                     | 143/213 [01:56<00:57,  1.22it/s, loss=0.0496, lr=7.1e-5, step=354]\\u001b[A\\n\",\n      \"Epoch 1:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                    | 143/213 [01:56<00:57,  1.22it/s, loss=0.0439, lr=7.12e-5, step=355]\\u001b[A\\n\",\n      \"Epoch 1:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                   | 144/213 [01:57<00:56,  1.22it/s, loss=0.0439, lr=7.12e-5, step=355]\\u001b[A\\n\",\n      \"Epoch 1:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                   | 144/213 [01:57<00:56,  1.22it/s, loss=0.0362, lr=7.14e-5, step=356]\\u001b[A\\n\",\n      \"Epoch 1:  68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                  | 145/213 [01:58<00:55,  1.22it/s, loss=0.0362, lr=7.14e-5, step=356]\\u001b[A\\n\",\n      \"Epoch 1:  68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                  | 145/213 [01:58<00:55,  1.22it/s, loss=0.0716, lr=7.16e-5, step=357]\\u001b[A\\n\",\n      \"Epoch 1:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:59<00:54,  1.23it/s, loss=0.0716, lr=7.16e-5, step=357]\\u001b[A\\n\",\n      \"Epoch 1:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:59<00:54,  1.23it/s, loss=0.0326, lr=7.18e-5, step=358]\\u001b[A\\n\",\n      \"Epoch 1:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [02:00<00:54,  1.22it/s, loss=0.0326, lr=7.18e-5, step=358]\\u001b[A\\n\",\n      \"Epoch 1:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 147/213 [02:00<00:54,  1.22it/s, loss=0.0457, lr=7.2e-5, step=359]\\u001b[A\\n\",\n      \"Epoch 1:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                | 148/213 [02:00<00:53,  1.22it/s, loss=0.0457, lr=7.2e-5, step=359]\\u001b[A\\n\",\n      \"Epoch 1:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                               | 148/213 [02:00<00:53,  1.22it/s, loss=0.0512, lr=7.22e-5, step=360]\\u001b[A\\n\",\n      \"Epoch 1:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                              | 149/213 [02:01<00:51,  1.23it/s, loss=0.0512, lr=7.22e-5, step=360]\\u001b[A\\n\",\n      \"Epoch 1:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                              | 149/213 [02:01<00:51,  1.23it/s, loss=0.0265, lr=7.24e-5, step=361]\\u001b[A\\n\",\n      \"Epoch 1:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                             | 150/213 [02:02<00:51,  1.21it/s, loss=0.0265, lr=7.24e-5, step=361]\\u001b[A\\n\",\n      \"Epoch 1:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                             | 150/213 [02:02<00:51,  1.21it/s, loss=0.0341, lr=7.26e-5, step=362]\\u001b[A\\n\",\n      \"Epoch 1:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                            | 151/213 [02:03<00:51,  1.21it/s, loss=0.0341, lr=7.26e-5, step=362]\\u001b[A\\n\",\n      \"Epoch 1:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 151/213 [02:03<00:51,  1.21it/s, loss=0.036, lr=7.28e-5, step=363]\\u001b[A\\n\",\n      \"Epoch 1:  71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                            | 152/213 [02:04<00:50,  1.21it/s, loss=0.036, lr=7.28e-5, step=363]\\u001b[A\\n\",\n      \"Epoch 1:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                            | 152/213 [02:04<00:50,  1.21it/s, loss=0.025, lr=7.3e-5, step=364]\\u001b[A\\n\",\n      \"Epoch 1:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                           | 153/213 [02:05<00:49,  1.21it/s, loss=0.025, lr=7.3e-5, step=364]\\u001b[A\\n\",\n      \"Epoch 1:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                           | 153/213 [02:05<00:49,  1.21it/s, loss=0.04, lr=7.32e-5, step=365]\\u001b[A\\n\",\n      \"Epoch 1:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                          | 154/213 [02:05<00:48,  1.22it/s, loss=0.04, lr=7.32e-5, step=365]\\u001b[A\\n\",\n      \"Epoch 1:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                          | 154/213 [02:05<00:48,  1.22it/s, loss=0.0265, lr=7.34e-5, step=366]\\u001b[A\\n\",\n      \"Epoch 1:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                         | 155/213 [02:06<00:47,  1.22it/s, loss=0.0265, lr=7.34e-5, step=366]\\u001b[A\\n\",\n      \"Epoch 1:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                         | 155/213 [02:06<00:47,  1.22it/s, loss=0.0253, lr=7.36e-5, step=367]\\u001b[A\\n\",\n      \"Epoch 1:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                        | 156/213 [02:07<00:48,  1.18it/s, loss=0.0253, lr=7.36e-5, step=367]\\u001b[A\\n\",\n      \"Epoch 1:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                        | 156/213 [02:07<00:48,  1.18it/s, loss=0.0334, lr=7.38e-5, step=368]\\u001b[A\\n\",\n      \"Epoch 1:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                       | 157/213 [02:08<00:48,  1.16it/s, loss=0.0334, lr=7.38e-5, step=368]\\u001b[A\\n\",\n      \"Epoch 1:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                       | 157/213 [02:08<00:48,  1.16it/s, loss=0.0534, lr=7.4e-5, step=369]\\u001b[A\\n\",\n      \"Epoch 1:  74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                      | 158/213 [02:09<00:46,  1.17it/s, loss=0.0534, lr=7.4e-5, step=369]\\u001b[A\\n\",\n      \"Epoch 1:  74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                      | 158/213 [02:09<00:46,  1.17it/s, loss=0.0197, lr=7.42e-5, step=370]\\u001b[A\\n\",\n      \"Epoch 1:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                     | 159/213 [02:10<00:45,  1.19it/s, loss=0.0197, lr=7.42e-5, step=370]\\u001b[A\\n\",\n      \"Epoch 1:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                     | 159/213 [02:10<00:45,  1.19it/s, loss=0.0211, lr=7.44e-5, step=371]\\u001b[A\\n\",\n      \"Epoch 1:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                    | 160/213 [02:10<00:44,  1.19it/s, loss=0.0211, lr=7.44e-5, step=371]\\u001b[A\\n\",\n      \"Epoch 1:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                    | 160/213 [02:11<00:44,  1.19it/s, loss=0.0205, lr=7.46e-5, step=372]\\u001b[A\\n\",\n      \"Epoch 1:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                   | 161/213 [02:11<00:43,  1.18it/s, loss=0.0205, lr=7.46e-5, step=372]\\u001b[A\\n\",\n      \"Epoch 1:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                   | 161/213 [02:11<00:43,  1.18it/s, loss=0.0653, lr=7.48e-5, step=373]\\u001b[A\\n\",\n      \"Epoch 1:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                  | 162/213 [02:12<00:42,  1.20it/s, loss=0.0653, lr=7.48e-5, step=373]\\u001b[A\\n\",\n      \"Epoch 1:  76%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                  | 162/213 [02:12<00:42,  1.20it/s, loss=0.0595, lr=7.5e-5, step=374]\\u001b[A\\n\",\n      \"Epoch 1:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                 | 163/213 [02:13<00:41,  1.20it/s, loss=0.0595, lr=7.5e-5, step=374]\\u001b[A\\n\",\n      \"Epoch 1:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                 | 163/213 [02:13<00:41,  1.20it/s, loss=0.0532, lr=7.52e-5, step=375]\\u001b[A\\n\",\n      \"Epoch 1:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                | 164/213 [02:14<00:40,  1.20it/s, loss=0.0532, lr=7.52e-5, step=375]\\u001b[A\\n\",\n      \"Epoch 1:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                | 164/213 [02:14<00:40,  1.20it/s, loss=0.0468, lr=7.54e-5, step=376]\\u001b[A\\n\",\n      \"Epoch 1:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                               | 165/213 [02:15<00:40,  1.20it/s, loss=0.0468, lr=7.54e-5, step=376]\\u001b[A\\n\",\n      \"Epoch 1:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                               | 165/213 [02:15<00:40,  1.20it/s, loss=0.0299, lr=7.56e-5, step=377]\\u001b[A\\n\",\n      \"Epoch 1:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:15<00:38,  1.21it/s, loss=0.0299, lr=7.56e-5, step=377]\\u001b[A\\n\",\n      \"Epoch 1:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:15<00:38,  1.21it/s, loss=0.0379, lr=7.58e-5, step=378]\\u001b[A\\n\",\n      \"Epoch 1:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                             | 167/213 [02:16<00:37,  1.22it/s, loss=0.0379, lr=7.58e-5, step=378]\\u001b[A\\n\",\n      \"Epoch 1:  78%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                             | 167/213 [02:16<00:37,  1.22it/s, loss=0.0294, lr=7.6e-5, step=379]\\u001b[A\\n\",\n      \"Epoch 1:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                            | 168/213 [02:17<00:36,  1.23it/s, loss=0.0294, lr=7.6e-5, step=379]\\u001b[A\\n\",\n      \"Epoch 1:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                            | 168/213 [02:17<00:36,  1.23it/s, loss=0.0302, lr=7.62e-5, step=380]\\u001b[A\\n\",\n      \"Epoch 1:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:18<00:36,  1.20it/s, loss=0.0302, lr=7.62e-5, step=380]\\u001b[A\\n\",\n      \"Epoch 1:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:18<00:36,  1.20it/s, loss=0.0204, lr=7.64e-5, step=381]\\u001b[A\\n\",\n      \"Epoch 1:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                          | 170/213 [02:19<00:35,  1.20it/s, loss=0.0204, lr=7.64e-5, step=381]\\u001b[A\\n\",\n      \"Epoch 1:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                          | 170/213 [02:19<00:35,  1.20it/s, loss=0.0819, lr=7.66e-5, step=382]\\u001b[A\\n\",\n      \"Epoch 1:  80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                         | 171/213 [02:20<00:34,  1.21it/s, loss=0.0819, lr=7.66e-5, step=382]\\u001b[A\\n\",\n      \"Epoch 1:  80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                         | 171/213 [02:20<00:34,  1.21it/s, loss=0.0404, lr=7.68e-5, step=383]\\u001b[A\\n\",\n      \"Epoch 1:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                        | 172/213 [02:20<00:34,  1.21it/s, loss=0.0404, lr=7.68e-5, step=383]\\u001b[A\\n\",\n      \"Epoch 1:  81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                        | 172/213 [02:20<00:34,  1.21it/s, loss=0.0562, lr=7.7e-5, step=384]\\u001b[A\\n\",\n      \"Epoch 1:  81%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                       | 173/213 [02:21<00:33,  1.20it/s, loss=0.0562, lr=7.7e-5, step=384]\\u001b[A\\n\",\n      \"Epoch 1:  81%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                       | 173/213 [02:21<00:33,  1.20it/s, loss=0.075, lr=7.72e-5, step=385]\\u001b[A\\n\",\n      \"Epoch 1:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                      | 174/213 [02:22<00:32,  1.19it/s, loss=0.075, lr=7.72e-5, step=385]\\u001b[A\\n\",\n      \"Epoch 1:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                      | 174/213 [02:22<00:32,  1.19it/s, loss=0.0243, lr=7.74e-5, step=386]\\u001b[A\\n\",\n      \"Epoch 1:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                     | 175/213 [02:23<00:31,  1.20it/s, loss=0.0243, lr=7.74e-5, step=386]\\u001b[A\\n\",\n      \"Epoch 1:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                     | 175/213 [02:23<00:31,  1.20it/s, loss=0.0265, lr=7.76e-5, step=387]\\u001b[A\\n\",\n      \"Epoch 1:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                    | 176/213 [02:24<00:30,  1.20it/s, loss=0.0265, lr=7.76e-5, step=387]\\u001b[A\\n\",\n      \"Epoch 1:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                    | 176/213 [02:24<00:30,  1.20it/s, loss=0.0248, lr=7.78e-5, step=388]\\u001b[A\\n\",\n      \"Epoch 1:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 177/213 [02:25<00:29,  1.21it/s, loss=0.0248, lr=7.78e-5, step=388]\\u001b[A\\n\",\n      \"Epoch 1:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                   | 177/213 [02:25<00:29,  1.21it/s, loss=0.0332, lr=7.8e-5, step=389]\\u001b[A\\n\",\n      \"Epoch 1:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                  | 178/213 [02:25<00:29,  1.20it/s, loss=0.0332, lr=7.8e-5, step=389]\\u001b[A\\n\",\n      \"Epoch 1:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                  | 178/213 [02:25<00:29,  1.20it/s, loss=0.0566, lr=7.82e-5, step=390]\\u001b[A\\n\",\n      \"Epoch 1:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:26<00:28,  1.21it/s, loss=0.0566, lr=7.82e-5, step=390]\\u001b[A\\n\",\n      \"Epoch 1:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:26<00:28,  1.21it/s, loss=0.0257, lr=7.84e-5, step=391]\\u001b[A\\n\",\n      \"Epoch 1:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                | 180/213 [02:27<00:26,  1.22it/s, loss=0.0257, lr=7.84e-5, step=391]\\u001b[A\\n\",\n      \"Epoch 1:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                | 180/213 [02:27<00:26,  1.22it/s, loss=0.0229, lr=7.86e-5, step=392]\\u001b[A\\n\",\n      \"Epoch 1:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                               | 181/213 [02:28<00:26,  1.23it/s, loss=0.0229, lr=7.86e-5, step=392]\\u001b[A\\n\",\n      \"Epoch 1:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                               | 181/213 [02:28<00:26,  1.23it/s, loss=0.0551, lr=7.88e-5, step=393]\\u001b[A\\n\",\n      \"Epoch 1:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                              | 182/213 [02:29<00:25,  1.22it/s, loss=0.0551, lr=7.88e-5, step=393]\\u001b[A\\n\",\n      \"Epoch 1:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                              | 182/213 [02:29<00:25,  1.22it/s, loss=0.0167, lr=7.9e-5, step=394]\\u001b[A\\n\",\n      \"Epoch 1:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 183/213 [02:30<00:24,  1.22it/s, loss=0.0167, lr=7.9e-5, step=394]\\u001b[A\\n\",\n      \"Epoch 1:  86%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                             | 183/213 [02:30<00:24,  1.22it/s, loss=0.0196, lr=7.92e-5, step=395]\\u001b[A\\n\",\n      \"Epoch 1:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                            | 184/213 [02:30<00:23,  1.22it/s, loss=0.0196, lr=7.92e-5, step=395]\\u001b[A\\n\",\n      \"Epoch 1:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                            | 184/213 [02:30<00:23,  1.22it/s, loss=0.0181, lr=7.94e-5, step=396]\\u001b[A\\n\",\n      \"Epoch 1:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                           | 185/213 [02:31<00:23,  1.20it/s, loss=0.0181, lr=7.94e-5, step=396]\\u001b[A\\n\",\n      \"Epoch 1:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                           | 185/213 [02:31<00:23,  1.20it/s, loss=0.0403, lr=7.96e-5, step=397]\\u001b[A\\n\",\n      \"Epoch 1:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                          | 186/213 [02:32<00:22,  1.18it/s, loss=0.0403, lr=7.96e-5, step=397]\\u001b[A\\n\",\n      \"Epoch 1:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                          | 186/213 [02:32<00:22,  1.18it/s, loss=0.0329, lr=7.98e-5, step=398]\\u001b[A\\n\",\n      \"Epoch 1:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                         | 187/213 [02:33<00:22,  1.16it/s, loss=0.0329, lr=7.98e-5, step=398]\\u001b[A\\n\",\n      \"Epoch 1:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                          | 187/213 [02:33<00:22,  1.16it/s, loss=0.0562, lr=8e-5, step=399]\\u001b[A\\n\",\n      \"Epoch 1:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                         | 188/213 [02:34<00:21,  1.18it/s, loss=0.0562, lr=8e-5, step=399]\\u001b[A\\n\",\n      \"Epoch 1:  88%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                        | 188/213 [02:34<00:21,  1.18it/s, loss=0.0372, lr=8.02e-5, step=400]\\u001b[A\\n\",\n      \"Epoch 1:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                       | 189/213 [02:35<00:20,  1.20it/s, loss=0.0372, lr=8.02e-5, step=400]\\u001b[A\\n\",\n      \"Epoch 1:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                       | 189/213 [02:35<00:20,  1.20it/s, loss=0.0423, lr=8.04e-5, step=401]\\u001b[A\\n\",\n      \"Epoch 1:  89%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                      | 190/213 [02:35<00:19,  1.20it/s, loss=0.0423, lr=8.04e-5, step=401]\\u001b[A\\n\",\n      \"Epoch 1:  89%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                      | 190/213 [02:35<00:19,  1.20it/s, loss=0.0331, lr=8.06e-5, step=402]\\u001b[A\\n\",\n      \"Epoch 1:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                     | 191/213 [02:36<00:18,  1.21it/s, loss=0.0331, lr=8.06e-5, step=402]\\u001b[A\\n\",\n      \"Epoch 1:  90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                     | 191/213 [02:36<00:18,  1.21it/s, loss=0.032, lr=8.08e-5, step=403]\\u001b[A\\n\",\n      \"Epoch 1:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                    | 192/213 [02:37<00:17,  1.21it/s, loss=0.032, lr=8.08e-5, step=403]\\u001b[A\\n\",\n      \"Epoch 1:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                    | 192/213 [02:37<00:17,  1.21it/s, loss=0.025, lr=8.1e-5, step=404]\\u001b[A\\n\",\n      \"Epoch 1:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                   | 193/213 [02:38<00:16,  1.21it/s, loss=0.025, lr=8.1e-5, step=404]\\u001b[A\\n\",\n      \"Epoch 1:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:38<00:16,  1.21it/s, loss=0.0453, lr=8.12e-5, step=405]\\u001b[A\\n\",\n      \"Epoch 1:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                  | 194/213 [02:39<00:15,  1.21it/s, loss=0.0453, lr=8.12e-5, step=405]\\u001b[A\\n\",\n      \"Epoch 1:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                  | 194/213 [02:39<00:15,  1.21it/s, loss=0.135, lr=8.14e-5, step=406]\\u001b[A\\n\",\n      \"Epoch 1:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 195/213 [02:40<00:14,  1.22it/s, loss=0.135, lr=8.14e-5, step=406]\\u001b[A\\n\",\n      \"Epoch 1:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 195/213 [02:40<00:14,  1.22it/s, loss=0.0257, lr=8.16e-5, step=407]\\u001b[A\\n\",\n      \"Epoch 1:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                | 196/213 [02:40<00:13,  1.23it/s, loss=0.0257, lr=8.16e-5, step=407]\\u001b[A\\n\",\n      \"Epoch 1:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                | 196/213 [02:40<00:13,  1.23it/s, loss=0.0225, lr=8.18e-5, step=408]\\u001b[A\\n\",\n      \"Epoch 1:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎               | 197/213 [02:41<00:12,  1.23it/s, loss=0.0225, lr=8.18e-5, step=408]\\u001b[A\\n\",\n      \"Epoch 1:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏               | 197/213 [02:41<00:12,  1.23it/s, loss=0.0313, lr=8.2e-5, step=409]\\u001b[A\\n\",\n      \"Epoch 1:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 198/213 [02:42<00:12,  1.23it/s, loss=0.0313, lr=8.2e-5, step=409]\\u001b[A\\n\",\n      \"Epoch 1:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:42<00:12,  1.23it/s, loss=0.0268, lr=8.22e-5, step=410]\\u001b[A\\n\",\n      \"Epoch 1:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎             | 199/213 [02:43<00:11,  1.23it/s, loss=0.0268, lr=8.22e-5, step=410]\\u001b[A\\n\",\n      \"Epoch 1:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎             | 199/213 [02:43<00:11,  1.23it/s, loss=0.0175, lr=8.24e-5, step=411]\\u001b[A\\n\",\n      \"Epoch 1:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏            | 200/213 [02:44<00:10,  1.21it/s, loss=0.0175, lr=8.24e-5, step=411]\\u001b[A\\n\",\n      \"Epoch 1:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏            | 200/213 [02:44<00:10,  1.21it/s, loss=0.0268, lr=8.26e-5, step=412]\\u001b[A\\n\",\n      \"Epoch 1:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏           | 201/213 [02:44<00:09,  1.22it/s, loss=0.0268, lr=8.26e-5, step=412]\\u001b[A\\n\",\n      \"Epoch 1:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏           | 201/213 [02:44<00:09,  1.22it/s, loss=0.0457, lr=8.28e-5, step=413]\\u001b[A\\n\",\n      \"Epoch 1:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏          | 202/213 [02:45<00:09,  1.22it/s, loss=0.0457, lr=8.28e-5, step=413]\\u001b[A\\n\",\n      \"Epoch 1:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏          | 202/213 [02:45<00:09,  1.22it/s, loss=0.0617, lr=8.3e-5, step=414]\\u001b[A\\n\",\n      \"Epoch 1:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:46<00:08,  1.22it/s, loss=0.0617, lr=8.3e-5, step=414]\\u001b[A\\n\",\n      \"Epoch 1:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:46<00:08,  1.22it/s, loss=0.0479, lr=8.32e-5, step=415]\\u001b[A\\n\",\n      \"Epoch 1:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:47<00:07,  1.22it/s, loss=0.0479, lr=8.32e-5, step=415]\\u001b[A\\n\",\n      \"Epoch 1:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:47<00:07,  1.22it/s, loss=0.0193, lr=8.34e-5, step=416]\\u001b[A\\n\",\n      \"Epoch 1:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:48<00:06,  1.22it/s, loss=0.0193, lr=8.34e-5, step=416]\\u001b[A\\n\",\n      \"Epoch 1:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:48<00:06,  1.22it/s, loss=0.0313, lr=8.36e-5, step=417]\\u001b[A\\n\",\n      \"Epoch 1:  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100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:53<00:00,  1.23it/s, loss=0.0469, lr=8.48e-5, step=423]\\u001b[A\\n\",\n      \"Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:53<00:00,  1.23it/s, loss=0.0205, lr=8.5e-5, step=424]\\u001b[A\\n\",\n      \"Epoch 1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:54<00:00,  1.42it/s, loss=0.0205, lr=8.5e-5, step=424]\\u001b[A\\n\",\n      \"Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:54<00:00,  1.42it/s, loss=0.0182, lr=8.52e-5, step=425]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"720b36e9ad744a278903833a39e8c78d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:39<00:00,  2.16s/it, loss=0.0182, lr=8.52e-5, step=425]\\n\",\n      \"Epoch 2: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:53<00:00,  1.44it/s, loss=0.0144, lr=9.97e-5, step=638]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6f69e6515059499abe2960da208b6fb1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 2: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:38<00:00,  2.15s/it, loss=0.0144, lr=9.97e-5, step=638]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 3:   0%|                                                                                                                                                                                                                                                              | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 3:   0%|█▏                                                                                                                                                                                                                                           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step=640]\\u001b[A\\n\",\n      \"Epoch 3:   1%|██▉                                                                                                                                                                                                                 | 3/213 [00:02<02:53,  1.21it/s, loss=0.018, lr=9.97e-5, step=640]\\u001b[A\\n\",\n      \"Epoch 3:   1%|██▉                                                                                                                                                                                                                | 3/213 [00:02<02:53,  1.21it/s, loss=0.0186, lr=9.96e-5, step=641]\\u001b[A\\n\",\n      \"Epoch 3:   2%|███▉                                                                                                                                                                                                               | 4/213 [00:03<02:51,  1.22it/s, loss=0.0186, lr=9.96e-5, step=641]\\u001b[A\\n\",\n      \"Epoch 3:   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loss=0.027, lr=9.96e-5, step=649]\\u001b[A\\n\",\n      \"Epoch 3:   6%|███████████▊                                                                                                                                                                                                      | 12/213 [00:09<02:41,  1.24it/s, loss=0.0347, lr=9.96e-5, step=650]\\u001b[A\\n\",\n      \"Epoch 3:   6%|████████████▊                                                                                                                                                                                                     | 13/213 [00:10<02:40,  1.25it/s, loss=0.0347, lr=9.96e-5, step=650]\\u001b[A\\n\",\n      \"Epoch 3:   6%|████████████▊                                                                                                                                                                                                     | 13/213 [00:10<02:40,  1.25it/s, loss=0.0371, lr=9.96e-5, step=651]\\u001b[A\\n\",\n      \"Epoch 3:   7%|█████████████▊                                                                                                                                                                                                    | 14/213 [00:11<02:39,  1.25it/s, loss=0.0371, lr=9.96e-5, step=651]\\u001b[A\\n\",\n      \"Epoch 3:   7%|█████████████▊                                                                                                                                                                                                    | 14/213 [00:11<02:39,  1.25it/s, loss=0.0588, lr=9.96e-5, step=652]\\u001b[A\\n\",\n      \"Epoch 3:   7%|██████████████▊                                                                                                                                                                                                   | 15/213 [00:12<02:38,  1.25it/s, loss=0.0588, lr=9.96e-5, step=652]\\u001b[A\\n\",\n      \"Epoch 3:   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                                                               | 20/213 [00:16<02:34,  1.25it/s, loss=0.0258, lr=9.96e-5, step=657]\\u001b[A\\n\",\n      \"Epoch 3:   9%|███████████████████▋                                                                                                                                                                                              | 20/213 [00:16<02:34,  1.25it/s, loss=0.0232, lr=9.96e-5, step=658]\\u001b[A\\n\",\n      \"Epoch 3:  10%|████████████████████▋                                                                                                                                                                                             | 21/213 [00:16<02:34,  1.25it/s, loss=0.0232, lr=9.96e-5, step=658]\\u001b[A\\n\",\n      \"Epoch 3:  10%|████████████████████▋                                                                                                                                                                                             | 21/213 [00:16<02:34,  1.25it/s, loss=0.0143, lr=9.96e-5, step=659]\\u001b[A\\n\",\n      \"Epoch 3:  10%|█████████████████████▋                                                                                                                                                                                            | 22/213 [00:17<02:33,  1.25it/s, loss=0.0143, lr=9.96e-5, step=659]\\u001b[A\\n\",\n      \"Epoch 3:  10%|█████████████████████▋                                                                                                                                                                                            | 22/213 [00:17<02:33,  1.25it/s, loss=0.0127, lr=9.95e-5, step=660]\\u001b[A\\n\",\n      \"Epoch 3:  11%|██████████████████████▋                                                                                                                                                                                           | 23/213 [00:18<02:32,  1.25it/s, loss=0.0127, lr=9.95e-5, step=660]\\u001b[A\\n\",\n      \"Epoch 3:  11%|██████████████████████▉                                                                                                                                                                                             | 23/213 [00:18<02:32,  1.25it/s, loss=0.05, lr=9.95e-5, step=661]\\u001b[A\\n\",\n      \"Epoch 3:  11%|███████████████████████▉                                                                                                                                                                                            | 24/213 [00:19<02:31,  1.25it/s, loss=0.05, lr=9.95e-5, step=661]\\u001b[A\\n\",\n      \"Epoch 3:  11%|███████████████████████▋                                                                                                                                                                                          | 24/213 [00:19<02:31,  1.25it/s, loss=0.0222, lr=9.95e-5, step=662]\\u001b[A\\n\",\n      \"Epoch 3:  12%|████████████████████████▋                                                                                                                                                                                         | 25/213 [00:20<02:30,  1.25it/s, loss=0.0222, lr=9.95e-5, step=662]\\u001b[A\\n\",\n      \"Epoch 3:  12%|████████████████████████▊                                                                                                                                                                                          | 25/213 [00:20<02:30,  1.25it/s, loss=0.026, lr=9.95e-5, step=663]\\u001b[A\\n\",\n      \"Epoch 3:  12%|█████████████████████████▊                                                                                                                                                                                         | 26/213 [00:20<02:30,  1.25it/s, loss=0.026, lr=9.95e-5, step=663]\\u001b[A\\n\",\n      \"Epoch 3:  12%|█████████████████████████▋                                                                                                                                                                                        | 26/213 [00:20<02:30,  1.25it/s, loss=0.0106, lr=9.95e-5, step=664]\\u001b[A\\n\",\n      \"Epoch 3:  13%|██████████████████████████▌                                                                                                                                                                                       | 27/213 [00:21<02:29,  1.24it/s, loss=0.0106, lr=9.95e-5, step=664]\\u001b[A\\n\",\n      \"Epoch 3:  13%|██████████████████████████▌                                                                                                                                                                                       | 27/213 [00:21<02:29,  1.24it/s, loss=0.0123, lr=9.95e-5, step=665]\\u001b[A\\n\",\n      \"Epoch 3:  13%|███████████████████████████▌                                                                                                                                                                                      | 28/213 [00:22<02:28,  1.25it/s, loss=0.0123, lr=9.95e-5, step=665]\\u001b[A\\n\",\n      \"Epoch 3:  13%|███████████████████████████▌                                                                                                                                                                                      | 28/213 [00:22<02:28,  1.25it/s, loss=0.0448, lr=9.95e-5, step=666]\\u001b[A\\n\",\n      \"Epoch 3:  14%|████████████████████████████▌                                                                                                                                                                                     | 29/213 [00:23<02:27,  1.25it/s, loss=0.0448, lr=9.95e-5, step=666]\\u001b[A\\n\",\n      \"Epoch 3:  14%|████████████████████████████▌                                                                                                                                                                                     | 29/213 [00:23<02:27,  1.25it/s, loss=0.0184, lr=9.95e-5, step=667]\\u001b[A\\n\",\n      \"Epoch 3:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:26,  1.25it/s, loss=0.0184, lr=9.95e-5, step=667]\\u001b[A\\n\",\n      \"Epoch 3:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:26,  1.25it/s, loss=0.0434, lr=9.95e-5, step=668]\\u001b[A\\n\",\n      \"Epoch 3:  15%|██████████████████████████████▌                                                                                                                                                                                   | 31/213 [00:24<02:26,  1.25it/s, loss=0.0434, lr=9.95e-5, step=668]\\u001b[A\\n\",\n      \"Epoch 3:  15%|██████████████████████████████▌                                                                                                                                                                                   | 31/213 [00:25<02:26,  1.25it/s, loss=0.0241, lr=9.95e-5, step=669]\\u001b[A\\n\",\n      \"Epoch 3:  15%|███████████████████████████████▌                                                                                                                                                                                  | 32/213 [00:25<02:25,  1.25it/s, loss=0.0241, lr=9.95e-5, step=669]\\u001b[A\\n\",\n      \"Epoch 3:  15%|███████████████████████████████▊                                                                                                                                                                                    | 32/213 [00:25<02:25,  1.25it/s, loss=0.02, lr=9.95e-5, step=670]\\u001b[A\\n\",\n      \"Epoch 3:  15%|████████████████████████████████▊                                                                                                                                                                                   | 33/213 [00:26<02:24,  1.25it/s, loss=0.02, lr=9.95e-5, step=670]\\u001b[A\\n\",\n      \"Epoch 3:  15%|████████████████████████████████▌                                                                                                                                                                                 | 33/213 [00:26<02:24,  1.25it/s, loss=0.0191, lr=9.95e-5, step=671]\\u001b[A\\n\",\n      \"Epoch 3:  16%|█████████████████████████████████▌                                                                                                                                                                                | 34/213 [00:27<02:23,  1.25it/s, loss=0.0191, lr=9.95e-5, step=671]\\u001b[A\\n\",\n      \"Epoch 3:  16%|█████████████████████████████████▌                                                                                                                                                                                | 34/213 [00:27<02:23,  1.25it/s, loss=0.0182, lr=9.95e-5, step=672]\\u001b[A\\n\",\n      \"Epoch 3:  16%|██████████████████████████████████▌                                                                                                                                                                               | 35/213 [00:28<02:22,  1.25it/s, loss=0.0182, lr=9.95e-5, step=672]\\u001b[A\\n\",\n      \"Epoch 3:  16%|██████████████████████████████████▌                                                                                                                                                                               | 35/213 [00:28<02:22,  1.25it/s, loss=0.0316, lr=9.95e-5, step=673]\\u001b[A\\n\",\n      \"Epoch 3:  17%|███████████████████████████████████▍                                                                                                                                                                              | 36/213 [00:28<02:22,  1.24it/s, loss=0.0316, lr=9.95e-5, step=673]\\u001b[A\\n\",\n      \"Epoch 3:  17%|███████████████████████████████████▋                                                                                                                                                                               | 36/213 [00:29<02:22,  1.24it/s, loss=0.013, lr=9.95e-5, step=674]\\u001b[A\\n\",\n      \"Epoch 3:  17%|████████████████████████████████████▋                                                                                                                                                                              | 37/213 [00:29<02:21,  1.25it/s, loss=0.013, lr=9.95e-5, step=674]\\u001b[A\\n\",\n      \"Epoch 3:  17%|████████████████████████████████████▍                                                                                                                                                                             | 37/213 [00:29<02:21,  1.25it/s, loss=0.0479, lr=9.95e-5, step=675]\\u001b[A\\n\",\n      \"Epoch 3:  18%|█████████████████████████████████████▍                                                                                                                                                                            | 38/213 [00:30<02:20,  1.25it/s, loss=0.0479, lr=9.95e-5, step=675]\\u001b[A\\n\",\n      \"Epoch 3:  18%|█████████████████████████████████████▍                                                                                                                                                                            | 38/213 [00:30<02:20,  1.25it/s, loss=0.0137, lr=9.95e-5, step=676]\\u001b[A\\n\",\n      \"Epoch 3:  18%|██████████████████████████████████████▍                                                                                                                                                                           | 39/213 [00:31<02:19,  1.25it/s, loss=0.0137, lr=9.95e-5, step=676]\\u001b[A\\n\",\n      \"Epoch 3:  18%|██████████████████████████████████████▋                                                                                                                                                                            | 39/213 [00:31<02:19,  1.25it/s, loss=0.064, lr=9.94e-5, step=677]\\u001b[A\\n\",\n      \"Epoch 3:  19%|███████████████████████████████████████▌                                                                                                                                                                           | 40/213 [00:32<02:18,  1.25it/s, loss=0.064, lr=9.94e-5, step=677]\\u001b[A\\n\",\n      \"Epoch 3:  19%|███████████████████████████████████████▍                                                                                                                                                                          | 40/213 [00:32<02:18,  1.25it/s, loss=0.0248, lr=9.94e-5, step=678]\\u001b[A\\n\",\n      \"Epoch 3:  19%|████████████████████████████████████████▍                                                                                                                                                                         | 41/213 [00:32<02:18,  1.25it/s, loss=0.0248, lr=9.94e-5, step=678]\\u001b[A\\n\",\n      \"Epoch 3:  19%|████████████████████████████████████████▍                                                                                                                                                                         | 41/213 [00:33<02:18,  1.25it/s, loss=0.0151, lr=9.94e-5, step=679]\\u001b[A\\n\",\n      \"Epoch 3:  20%|█████████████████████████████████████████▍                                                                                                                                                                        | 42/213 [00:33<02:17,  1.25it/s, loss=0.0151, lr=9.94e-5, step=679]\\u001b[A\\n\",\n      \"Epoch 3:  20%|█████████████████████████████████████████▏                                                                                                                                                                       | 42/213 [00:33<02:17,  1.25it/s, loss=0.00915, lr=9.94e-5, step=680]\\u001b[A\\n\",\n      \"Epoch 3:  20%|██████████████████████████████████████████▏                                                                                                                                                                      | 43/213 [00:34<02:16,  1.25it/s, loss=0.00915, lr=9.94e-5, step=680]\\u001b[A\\n\",\n      \"Epoch 3:  20%|██████████████████████████████████████████▍                                                                                                                                                                       | 43/213 [00:34<02:16,  1.25it/s, loss=0.0449, lr=9.94e-5, step=681]\\u001b[A\\n\",\n      \"Epoch 3:  21%|███████████████████████████████████████████▍                                                                                                                                                                      | 44/213 [00:35<02:15,  1.25it/s, loss=0.0449, lr=9.94e-5, step=681]\\u001b[A\\n\",\n      \"Epoch 3:  21%|███████████████████████████████████████████▍                                                                                                                                                                      | 44/213 [00:35<02:15,  1.25it/s, loss=0.0234, lr=9.94e-5, step=682]\\u001b[A\\n\",\n      \"Epoch 3:  21%|████████████████████████████████████████████▎                                                                                                                                                                     | 45/213 [00:36<02:14,  1.25it/s, loss=0.0234, lr=9.94e-5, step=682]\\u001b[A\\n\",\n      \"Epoch 3:  21%|████████████████████████████████████████████▎                                                                                                                                                                     | 45/213 [00:36<02:14,  1.25it/s, loss=0.0474, lr=9.94e-5, step=683]\\u001b[A\\n\",\n      \"Epoch 3:  22%|█████████████████████████████████████████████▎                                                                                                                                                                    | 46/213 [00:37<02:14,  1.25it/s, loss=0.0474, lr=9.94e-5, step=683]\\u001b[A\\n\",\n      \"Epoch 3:  22%|█████████████████████████████████████████████▎                                                                                                                                                                    | 46/213 [00:37<02:14,  1.25it/s, loss=0.0359, lr=9.94e-5, step=684]\\u001b[A\\n\",\n      \"Epoch 3:  22%|██████████████████████████████████████████████▎                                                                                                                                                                   | 47/213 [00:37<02:13,  1.25it/s, loss=0.0359, lr=9.94e-5, step=684]\\u001b[A\\n\",\n      \"Epoch 3:  22%|██████████████████████████████████████████████▎                                                                                                                                                                   | 47/213 [00:37<02:13,  1.25it/s, loss=0.0198, lr=9.94e-5, step=685]\\u001b[A\\n\",\n      \"Epoch 3:  23%|███████████████████████████████████████████████▎                                                                                                                                                                  | 48/213 [00:38<02:12,  1.24it/s, loss=0.0198, lr=9.94e-5, step=685]\\u001b[A\\n\",\n      \"Epoch 3:  23%|███████████████████████████████████████████████▎                                                                                                                                                                  | 48/213 [00:38<02:12,  1.24it/s, loss=0.0211, lr=9.94e-5, step=686]\\u001b[A\\n\",\n      \"Epoch 3:  23%|████████████████████████████████████████████████▎                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0211, lr=9.94e-5, step=686]\\u001b[A\\n\",\n      \"Epoch 3:  23%|████████████████████████████████████████████████▎                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0367, lr=9.94e-5, step=687]\\u001b[A\\n\",\n      \"Epoch 3:  23%|█████████████████████████████████████████████████▎                                                                                                                                                                | 50/213 [00:40<02:11,  1.24it/s, loss=0.0367, lr=9.94e-5, step=687]\\u001b[A\\n\",\n      \"Epoch 3:  23%|█████████████████████████████████████████████████▎                                                                                                                                                                | 50/213 [00:40<02:11,  1.24it/s, loss=0.0295, lr=9.94e-5, step=688]\\u001b[A\\n\",\n      \"Epoch 3:  24%|██████████████████████████████████████████████████▎                                                                                                                                                               | 51/213 [00:41<02:10,  1.24it/s, loss=0.0295, lr=9.94e-5, step=688]\\u001b[A\\n\",\n      \"Epoch 3:  24%|██████████████████████████████████████████████████▎                                                                                                                                                               | 51/213 [00:41<02:10,  1.24it/s, loss=0.0506, lr=9.94e-5, step=689]\\u001b[A\\n\",\n      \"Epoch 3:  24%|███████████████████████████████████████████████████▎                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0506, lr=9.94e-5, step=689]\\u001b[A\\n\",\n      \"Epoch 3:  24%|███████████████████████████████████████████████████▎                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0214, lr=9.94e-5, step=690]\\u001b[A\\n\",\n      \"Epoch 3:  25%|████████████████████████████████████████████████████▎                                                                                                                                                             | 53/213 [00:42<02:08,  1.24it/s, loss=0.0214, lr=9.94e-5, step=690]\\u001b[A\\n\",\n      \"Epoch 3:  25%|████████████████████████████████████████████████████▎                                                                                                                                                             | 53/213 [00:42<02:08,  1.24it/s, loss=0.0341, lr=9.94e-5, step=691]\\u001b[A\\n\",\n      \"Epoch 3:  25%|█████████████████████████████████████████████████████▏                                                                                                                                                            | 54/213 [00:43<02:07,  1.24it/s, loss=0.0341, lr=9.94e-5, step=691]\\u001b[A\\n\",\n      \"Epoch 3:  25%|█████████████████████████████████████████████████████▏                                                                                                                                                            | 54/213 [00:43<02:07,  1.24it/s, loss=0.0332, lr=9.94e-5, step=692]\\u001b[A\\n\",\n      \"Epoch 3:  26%|██████████████████████████████████████████████████████▏                                                                                                                                                           | 55/213 [00:44<02:07,  1.24it/s, loss=0.0332, lr=9.94e-5, step=692]\\u001b[A\\n\",\n      \"Epoch 3:  26%|██████████████████████████████████████████████████████▏                                                                                                                                                           | 55/213 [00:44<02:07,  1.24it/s, loss=0.0114, lr=9.93e-5, step=693]\\u001b[A\\n\",\n      \"Epoch 3:  26%|███████████████████████████████████████████████████████▏                                                                                                                                                          | 56/213 [00:45<02:05,  1.25it/s, loss=0.0114, lr=9.93e-5, step=693]\\u001b[A\\n\",\n      \"Epoch 3:  26%|███████████████████████████████████████████████████████▏                                                                                                                                                          | 56/213 [00:45<02:05,  1.25it/s, loss=0.0264, lr=9.93e-5, step=694]\\u001b[A\\n\",\n      \"Epoch 3:  27%|████████████████████████████████████████████████████████▏                                                                                                                                                         | 57/213 [00:45<02:05,  1.24it/s, loss=0.0264, lr=9.93e-5, step=694]\\u001b[A\\n\",\n      \"Epoch 3:  27%|████████████████████████████████████████████████████████▏                                                                                                                                                         | 57/213 [00:45<02:05,  1.24it/s, loss=0.0463, lr=9.93e-5, step=695]\\u001b[A\\n\",\n      \"Epoch 3:  27%|█████████████████████████████████████████████████████████▏                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0463, lr=9.93e-5, step=695]\\u001b[A\\n\",\n      \"Epoch 3:  27%|█████████████████████████████████████████████████████████▏                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0225, lr=9.93e-5, step=696]\\u001b[A\\n\",\n      \"Epoch 3:  28%|██████████████████████████████████████████████████████████▏                                                                                                                                                       | 59/213 [00:47<02:03,  1.24it/s, loss=0.0225, lr=9.93e-5, step=696]\\u001b[A\\n\",\n      \"Epoch 3:  28%|██████████████████████████████████████████████████████████▏                                                                                                                                                       | 59/213 [00:47<02:03,  1.24it/s, loss=0.0671, lr=9.93e-5, step=697]\\u001b[A\\n\",\n      \"Epoch 3:  28%|███████████████████████████████████████████████████████████▏                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0671, lr=9.93e-5, step=697]\\u001b[A\\n\",\n      \"Epoch 3:  28%|███████████████████████████████████████████████████████████▏                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0192, lr=9.93e-5, step=698]\\u001b[A\\n\",\n      \"Epoch 3:  29%|████████████████████████████████████████████████████████████▏                                                                                                                                                     | 61/213 [00:49<02:02,  1.24it/s, loss=0.0192, lr=9.93e-5, step=698]\\u001b[A\\n\",\n      \"Epoch 3:  29%|████████████████████████████████████████████████████████████▏                                                                                                                                                     | 61/213 [00:49<02:02,  1.24it/s, loss=0.0235, lr=9.93e-5, step=699]\\u001b[A\\n\",\n      \"Epoch 3:  29%|█████████████████████████████████████████████████████████████▏                                                                                                                                                    | 62/213 [00:49<02:01,  1.24it/s, loss=0.0235, lr=9.93e-5, step=699]\\u001b[A\\n\",\n      \"Epoch 3:  29%|█████████████████████████████████████████████████████████████▏                                                                                                                                                    | 62/213 [00:49<02:01,  1.24it/s, loss=0.0139, lr=9.93e-5, step=700]\\u001b[A\\n\",\n      \"Epoch 3:  30%|██████████████████████████████████████████████████████████████                                                                                                                                                    | 63/213 [00:50<02:00,  1.24it/s, loss=0.0139, lr=9.93e-5, step=700]\\u001b[A\\n\",\n      \"Epoch 3:  30%|██████████████████████████████████████████████████████████████                                                                                                                                                    | 63/213 [00:50<02:00,  1.24it/s, loss=0.0318, lr=9.93e-5, step=701]\\u001b[A\\n\",\n      \"Epoch 3:  30%|███████████████████████████████████████████████████████████████                                                                                                                                                   | 64/213 [00:51<01:59,  1.25it/s, loss=0.0318, lr=9.93e-5, step=701]\\u001b[A\\n\",\n      \"Epoch 3:  30%|███████████████████████████████████████████████████████████████                                                                                                                                                   | 64/213 [00:51<01:59,  1.25it/s, loss=0.0174, lr=9.93e-5, step=702]\\u001b[A\\n\",\n      \"Epoch 3:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:52<01:58,  1.25it/s, loss=0.0174, lr=9.93e-5, step=702]\\u001b[A\\n\",\n      \"Epoch 3:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:52<01:58,  1.25it/s, loss=0.0156, lr=9.93e-5, step=703]\\u001b[A\\n\",\n      \"Epoch 3:  31%|█████████████████████████████████████████████████████████████████                                                                                                                                                 | 66/213 [00:53<01:57,  1.25it/s, loss=0.0156, lr=9.93e-5, step=703]\\u001b[A\\n\",\n      \"Epoch 3:  31%|█████████████████████████████████████████████████████████████████                                                                                                                                                 | 66/213 [00:53<01:57,  1.25it/s, loss=0.0276, lr=9.93e-5, step=704]\\u001b[A\\n\",\n      \"Epoch 3:  31%|██████████████████████████████████████████████████████████████████                                                                                                                                                | 67/213 [00:53<01:57,  1.24it/s, loss=0.0276, lr=9.93e-5, step=704]\\u001b[A\\n\",\n      \"Epoch 3:  31%|██████████████████████████████████████████████████████████████████                                                                                                                                                | 67/213 [00:53<01:57,  1.24it/s, loss=0.0332, lr=9.93e-5, step=705]\\u001b[A\\n\",\n      \"Epoch 3:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                               | 68/213 [00:54<01:56,  1.24it/s, loss=0.0332, lr=9.93e-5, step=705]\\u001b[A\\n\",\n      \"Epoch 3:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                               | 68/213 [00:54<01:56,  1.24it/s, loss=0.0183, lr=9.93e-5, step=706]\\u001b[A\\n\",\n      \"Epoch 3:  32%|████████████████████████████████████████████████████████████████████                                                                                                                                              | 69/213 [00:55<01:55,  1.24it/s, loss=0.0183, lr=9.93e-5, step=706]\\u001b[A\\n\",\n      \"Epoch 3:  32%|████████████████████████████████████████████████████████████████████                                                                                                                                              | 69/213 [00:55<01:55,  1.24it/s, loss=0.0191, lr=9.92e-5, step=707]\\u001b[A\\n\",\n      \"Epoch 3:  33%|█████████████████████████████████████████████████████████████████████                                                                                                                                             | 70/213 [00:56<01:55,  1.24it/s, loss=0.0191, lr=9.92e-5, step=707]\\u001b[A\\n\",\n      \"Epoch 3:  33%|█████████████████████████████████████████████████████████████████████                                                                                                                                             | 70/213 [00:56<01:55,  1.24it/s, loss=0.0355, lr=9.92e-5, step=708]\\u001b[A\\n\",\n      \"Epoch 3:  33%|██████████████████████████████████████████████████████████████████████                                                                                                                                            | 71/213 [00:57<01:53,  1.25it/s, loss=0.0355, lr=9.92e-5, step=708]\\u001b[A\\n\",\n      \"Epoch 3:  33%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                            | 71/213 [00:57<01:53,  1.25it/s, loss=0.018, lr=9.92e-5, step=709]\\u001b[A\\n\",\n      \"Epoch 3:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 72/213 [00:57<01:53,  1.24it/s, loss=0.018, lr=9.92e-5, step=709]\\u001b[A\\n\",\n      \"Epoch 3:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                           | 72/213 [00:57<01:53,  1.24it/s, loss=0.0099, lr=9.92e-5, step=710]\\u001b[A\\n\",\n      \"Epoch 3:  34%|███████████████████████████████████████████████████████████████████████▉                                                                                                                                          | 73/213 [00:58<01:52,  1.24it/s, loss=0.0099, lr=9.92e-5, step=710]\\u001b[A\\n\",\n      \"Epoch 3:  34%|███████████████████████████████████████████████████████████████████████▉                                                                                                                                          | 73/213 [00:58<01:52,  1.24it/s, loss=0.0234, lr=9.92e-5, step=711]\\u001b[A\\n\",\n      \"Epoch 3:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.24it/s, loss=0.0234, lr=9.92e-5, step=711]\\u001b[A\\n\",\n      \"Epoch 3:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.24it/s, loss=0.0299, lr=9.92e-5, step=712]\\u001b[A\\n\",\n      \"Epoch 3:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.24it/s, loss=0.0299, lr=9.92e-5, step=712]\\u001b[A\\n\",\n      \"Epoch 3:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.24it/s, loss=0.0528, lr=9.92e-5, step=713]\\u001b[A\\n\",\n      \"Epoch 3:  36%|██████████████████████████████████████████████████████████████████████████▉                                                                                                                                       | 76/213 [01:01<01:49,  1.25it/s, loss=0.0528, lr=9.92e-5, step=713]\\u001b[A\\n\",\n      \"Epoch 3:  36%|██████████████████████████████████████████████████████████████████████████▉                                                                                                                                       | 76/213 [01:01<01:49,  1.25it/s, loss=0.0157, lr=9.92e-5, step=714]\\u001b[A\\n\",\n      \"Epoch 3:  36%|███████████████████████████████████████████████████████████████████████████▉                                                                                                                                      | 77/213 [01:01<01:48,  1.25it/s, loss=0.0157, lr=9.92e-5, step=714]\\u001b[A\\n\",\n      \"Epoch 3:  36%|███████████████████████████████████████████████████████████████████████████▉                                                                                                                                      | 77/213 [01:01<01:48,  1.25it/s, loss=0.0353, lr=9.92e-5, step=715]\\u001b[A\\n\",\n      \"Epoch 3:  37%|████████████████████████████████████████████████████████████████████████████▉                                                                                                                                     | 78/213 [01:02<01:48,  1.25it/s, loss=0.0353, lr=9.92e-5, step=715]\\u001b[A\\n\",\n      \"Epoch 3:  37%|████████████████████████████████████████████████████████████████████████████▉                                                                                                                                     | 78/213 [01:02<01:48,  1.25it/s, loss=0.0173, lr=9.92e-5, step=716]\\u001b[A\\n\",\n      \"Epoch 3:  37%|█████████████████████████████████████████████████████████████████████████████▉                                                                                                                                    | 79/213 [01:03<01:47,  1.25it/s, loss=0.0173, lr=9.92e-5, step=716]\\u001b[A\\n\",\n      \"Epoch 3:  37%|█████████████████████████████████████████████████████████████████████████████▉                                                                                                                                    | 79/213 [01:03<01:47,  1.25it/s, loss=0.0132, lr=9.92e-5, step=717]\\u001b[A\\n\",\n      \"Epoch 3:  38%|██████████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 80/213 [01:04<01:46,  1.25it/s, loss=0.0132, lr=9.92e-5, step=717]\\u001b[A\\n\",\n      \"Epoch 3:  38%|██████████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 80/213 [01:04<01:46,  1.25it/s, loss=0.0129, lr=9.92e-5, step=718]\\u001b[A\\n\",\n      \"Epoch 3:  38%|███████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 81/213 [01:05<01:46,  1.24it/s, loss=0.0129, lr=9.92e-5, step=718]\\u001b[A\\n\",\n      \"Epoch 3:  38%|███████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 81/213 [01:05<01:46,  1.24it/s, loss=0.0188, lr=9.92e-5, step=719]\\u001b[A\\n\",\n      \"Epoch 3:  38%|████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                 | 82/213 [01:05<01:45,  1.24it/s, loss=0.0188, lr=9.92e-5, step=719]\\u001b[A\\n\",\n      \"Epoch 3:  38%|████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                 | 82/213 [01:05<01:45,  1.24it/s, loss=0.0553, lr=9.92e-5, step=720]\\u001b[A\\n\",\n      \"Epoch 3:  39%|█████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                | 83/213 [01:06<01:44,  1.24it/s, loss=0.0553, lr=9.92e-5, step=720]\\u001b[A\\n\",\n      \"Epoch 3:  39%|██████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                 | 83/213 [01:06<01:44,  1.24it/s, loss=0.06, lr=9.91e-5, step=721]\\u001b[A\\n\",\n      \"Epoch 3:  39%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                                                                | 84/213 [01:07<01:43,  1.25it/s, loss=0.06, lr=9.91e-5, step=721]\\u001b[A\\n\",\n      \"Epoch 3:  39%|██████████████████████████████████████████████████████████████████████████████████▊                                                                                                                               | 84/213 [01:07<01:43,  1.25it/s, loss=0.0761, lr=9.91e-5, step=722]\\u001b[A\\n\",\n      \"Epoch 3:  40%|███████████████████████████████████████████████████████████████████████████████████▊                                                                                                                              | 85/213 [01:08<01:42,  1.25it/s, loss=0.0761, lr=9.91e-5, step=722]\\u001b[A\\n\",\n      \"Epoch 3:  40%|███████████████████████████████████████████████████████████████████████████████████▊                                                                                                                              | 85/213 [01:08<01:42,  1.25it/s, loss=0.0188, lr=9.91e-5, step=723]\\u001b[A\\n\",\n      \"Epoch 3:  40%|████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                             | 86/213 [01:09<01:41,  1.25it/s, loss=0.0188, lr=9.91e-5, step=723]\\u001b[A\\n\",\n      \"Epoch 3:  40%|████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                             | 86/213 [01:09<01:41,  1.25it/s, loss=0.0269, lr=9.91e-5, step=724]\\u001b[A\\n\",\n      \"Epoch 3:  41%|█████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                            | 87/213 [01:09<01:41,  1.25it/s, loss=0.0269, lr=9.91e-5, step=724]\\u001b[A\\n\",\n      \"Epoch 3:  41%|█████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                            | 87/213 [01:09<01:41,  1.25it/s, loss=0.0352, lr=9.91e-5, step=725]\\u001b[A\\n\",\n      \"Epoch 3:  41%|██████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                           | 88/213 [01:10<01:40,  1.24it/s, loss=0.0352, lr=9.91e-5, step=725]\\u001b[A\\n\",\n      \"Epoch 3:  41%|██████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                           | 88/213 [01:10<01:40,  1.24it/s, loss=0.0165, lr=9.91e-5, step=726]\\u001b[A\\n\",\n      \"Epoch 3:  42%|███████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                          | 89/213 [01:11<01:39,  1.25it/s, loss=0.0165, lr=9.91e-5, step=726]\\u001b[A\\n\",\n      \"Epoch 3:  42%|███████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                          | 89/213 [01:11<01:39,  1.25it/s, loss=0.0151, lr=9.91e-5, step=727]\\u001b[A\\n\",\n      \"Epoch 3:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:12<01:38,  1.25it/s, loss=0.0151, lr=9.91e-5, step=727]\\u001b[A\\n\",\n      \"Epoch 3:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:12<01:38,  1.25it/s, loss=0.0186, lr=9.91e-5, step=728]\\u001b[A\\n\",\n      \"Epoch 3:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:13<01:37,  1.25it/s, loss=0.0186, lr=9.91e-5, step=728]\\u001b[A\\n\",\n      \"Epoch 3:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:13<01:37,  1.25it/s, loss=0.0383, lr=9.91e-5, step=729]\\u001b[A\\n\",\n      \"Epoch 3:  43%|██████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                       | 92/213 [01:13<01:36,  1.25it/s, loss=0.0383, lr=9.91e-5, step=729]\\u001b[A\\n\",\n      \"Epoch 3:  43%|███████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                       | 92/213 [01:13<01:36,  1.25it/s, loss=0.034, lr=9.91e-5, step=730]\\u001b[A\\n\",\n      \"Epoch 3:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                      | 93/213 [01:14<01:36,  1.25it/s, loss=0.034, lr=9.91e-5, step=730]\\u001b[A\\n\",\n      \"Epoch 3:  44%|███████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                      | 93/213 [01:14<01:36,  1.25it/s, loss=0.0284, lr=9.91e-5, step=731]\\u001b[A\\n\",\n      \"Epoch 3:  44%|████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                     | 94/213 [01:15<01:35,  1.25it/s, loss=0.0284, lr=9.91e-5, step=731]\\u001b[A\\n\",\n      \"Epoch 3:  44%|████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                     | 94/213 [01:15<01:35,  1.25it/s, loss=0.0681, lr=9.91e-5, step=732]\\u001b[A\\n\",\n      \"Epoch 3:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                    | 95/213 [01:16<01:34,  1.25it/s, loss=0.0681, lr=9.91e-5, step=732]\\u001b[A\\n\",\n      \"Epoch 3:  45%|██████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                     | 95/213 [01:16<01:34,  1.25it/s, loss=0.0147, lr=9.9e-5, step=733]\\u001b[A\\n\",\n      \"Epoch 3:  45%|███████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                    | 96/213 [01:17<01:33,  1.25it/s, loss=0.0147, lr=9.9e-5, step=733]\\u001b[A\\n\",\n      \"Epoch 3:  45%|███████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                    | 96/213 [01:17<01:33,  1.25it/s, loss=0.0306, lr=9.9e-5, step=734]\\u001b[A\\n\",\n      \"Epoch 3:  46%|████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                   | 97/213 [01:17<01:32,  1.25it/s, loss=0.0306, lr=9.9e-5, step=734]\\u001b[A\\n\",\n      \"Epoch 3:  46%|████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                   | 97/213 [01:17<01:32,  1.25it/s, loss=0.0193, lr=9.9e-5, step=735]\\u001b[A\\n\",\n      \"Epoch 3:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                  | 98/213 [01:18<01:32,  1.24it/s, loss=0.0193, lr=9.9e-5, step=735]\\u001b[A\\n\",\n      \"Epoch 3:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                  | 98/213 [01:18<01:32,  1.24it/s, loss=0.0413, lr=9.9e-5, step=736]\\u001b[A\\n\",\n      \"Epoch 3:  46%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                 | 99/213 [01:19<01:31,  1.25it/s, loss=0.0413, lr=9.9e-5, step=736]\\u001b[A\\n\",\n      \"Epoch 3:  46%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                 | 99/213 [01:19<01:31,  1.25it/s, loss=0.0289, lr=9.9e-5, step=737]\\u001b[A\\n\",\n      \"Epoch 3:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                               | 100/213 [01:20<01:30,  1.24it/s, loss=0.0289, lr=9.9e-5, step=737]\\u001b[A\\n\",\n      \"Epoch 3:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                               | 100/213 [01:20<01:30,  1.24it/s, loss=0.0195, lr=9.9e-5, step=738]\\u001b[A\\n\",\n      \"Epoch 3:  47%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                              | 101/213 [01:21<01:30,  1.24it/s, loss=0.0195, lr=9.9e-5, step=738]\\u001b[A\\n\",\n      \"Epoch 3:  47%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 101/213 [01:21<01:30,  1.24it/s, loss=0.023, lr=9.9e-5, step=739]\\u001b[A\\n\",\n      \"Epoch 3:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                              | 102/213 [01:21<01:29,  1.25it/s, loss=0.023, lr=9.9e-5, step=739]\\u001b[A\\n\",\n      \"Epoch 3:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                             | 102/213 [01:21<01:29,  1.25it/s, loss=0.0108, lr=9.9e-5, step=740]\\u001b[A\\n\",\n      \"Epoch 3:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 103/213 [01:22<01:28,  1.25it/s, loss=0.0108, lr=9.9e-5, step=740]\\u001b[A\\n\",\n      \"Epoch 3:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 103/213 [01:22<01:28,  1.25it/s, loss=0.0135, lr=9.9e-5, step=741]\\u001b[A\\n\",\n      \"Epoch 3:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 104/213 [01:23<01:27,  1.25it/s, loss=0.0135, lr=9.9e-5, step=741]\\u001b[A\\n\",\n      \"Epoch 3:  49%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 104/213 [01:23<01:27,  1.25it/s, loss=0.02, lr=9.9e-5, step=742]\\u001b[A\\n\",\n      \"Epoch 3:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 105/213 [01:24<01:26,  1.24it/s, loss=0.02, lr=9.9e-5, step=742]\\u001b[A\\n\",\n      \"Epoch 3:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 105/213 [01:24<01:26,  1.24it/s, loss=0.041, lr=9.9e-5, step=743]\\u001b[A\\n\",\n      \"Epoch 3:  50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 106/213 [01:25<01:25,  1.25it/s, loss=0.041, lr=9.9e-5, step=743]\\u001b[A\\n\",\n      \"Epoch 3:  50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 106/213 [01:25<01:25,  1.25it/s, loss=0.022, lr=9.9e-5, step=744]\\u001b[A\\n\",\n      \"Epoch 3:  50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                         | 107/213 [01:25<01:25,  1.24it/s, loss=0.022, lr=9.9e-5, step=744]\\u001b[A\\n\",\n      \"Epoch 3:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                        | 107/213 [01:26<01:25,  1.24it/s, loss=0.0169, lr=9.89e-5, step=745]\\u001b[A\\n\",\n      \"Epoch 3:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 108/213 [01:26<01:24,  1.25it/s, loss=0.0169, lr=9.89e-5, step=745]\\u001b[A\\n\",\n      \"Epoch 3:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 108/213 [01:26<01:24,  1.25it/s, loss=0.0643, lr=9.89e-5, step=746]\\u001b[A\\n\",\n      \"Epoch 3:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 109/213 [01:27<01:23,  1.24it/s, loss=0.0643, lr=9.89e-5, step=746]\\u001b[A\\n\",\n      \"Epoch 3:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 109/213 [01:27<01:23,  1.24it/s, loss=0.0344, lr=9.89e-5, step=747]\\u001b[A\\n\",\n      \"Epoch 3:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 110/213 [01:28<01:22,  1.25it/s, loss=0.0344, lr=9.89e-5, step=747]\\u001b[A\\n\",\n      \"Epoch 3:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 110/213 [01:28<01:22,  1.25it/s, loss=0.0423, lr=9.89e-5, step=748]\\u001b[A\\n\",\n      \"Epoch 3:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 111/213 [01:29<01:21,  1.25it/s, loss=0.0423, lr=9.89e-5, step=748]\\u001b[A\\n\",\n      \"Epoch 3:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 111/213 [01:29<01:21,  1.25it/s, loss=0.0173, lr=9.89e-5, step=749]\\u001b[A\\n\",\n      \"Epoch 3:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                   | 112/213 [01:29<01:21,  1.25it/s, loss=0.0173, lr=9.89e-5, step=749]\\u001b[A\\n\",\n      \"Epoch 3:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                   | 112/213 [01:30<01:21,  1.25it/s, loss=0.0708, lr=9.89e-5, step=750]\\u001b[A\\n\",\n      \"Epoch 3:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                  | 113/213 [01:30<01:20,  1.25it/s, loss=0.0708, lr=9.89e-5, step=750]\\u001b[A\\n\",\n      \"Epoch 3:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:20,  1.25it/s, loss=0.00923, lr=9.89e-5, step=751]\\u001b[A\\n\",\n      \"Epoch 3:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 114/213 [01:31<01:19,  1.25it/s, loss=0.00923, lr=9.89e-5, step=751]\\u001b[A\\n\",\n      \"Epoch 3:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 114/213 [01:31<01:19,  1.25it/s, loss=0.0184, lr=9.89e-5, step=752]\\u001b[A\\n\",\n      \"Epoch 3:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 115/213 [01:32<01:18,  1.25it/s, loss=0.0184, lr=9.89e-5, step=752]\\u001b[A\\n\",\n      \"Epoch 3:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 115/213 [01:32<01:18,  1.25it/s, loss=0.0198, lr=9.89e-5, step=753]\\u001b[A\\n\",\n      \"Epoch 3:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 116/213 [01:33<01:17,  1.25it/s, loss=0.0198, lr=9.89e-5, step=753]\\u001b[A\\n\",\n      \"Epoch 3:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 116/213 [01:33<01:17,  1.25it/s, loss=0.0304, lr=9.89e-5, step=754]\\u001b[A\\n\",\n      \"Epoch 3:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                              | 117/213 [01:34<01:16,  1.25it/s, loss=0.0304, lr=9.89e-5, step=754]\\u001b[A\\n\",\n      \"Epoch 3:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                              | 117/213 [01:34<01:16,  1.25it/s, loss=0.0162, lr=9.89e-5, step=755]\\u001b[A\\n\",\n      \"Epoch 3:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                             | 118/213 [01:34<01:15,  1.25it/s, loss=0.0162, lr=9.89e-5, step=755]\\u001b[A\\n\",\n      \"Epoch 3:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                             | 118/213 [01:34<01:15,  1.25it/s, loss=0.0111, lr=9.89e-5, step=756]\\u001b[A\\n\",\n      \"Epoch 3:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 119/213 [01:35<01:15,  1.25it/s, loss=0.0111, lr=9.89e-5, step=756]\\u001b[A\\n\",\n      \"Epoch 3:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 119/213 [01:35<01:15,  1.25it/s, loss=0.0398, lr=9.88e-5, step=757]\\u001b[A\\n\",\n      \"Epoch 3:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 120/213 [01:36<01:14,  1.25it/s, loss=0.0398, lr=9.88e-5, step=757]\\u001b[A\\n\",\n      \"Epoch 3:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 120/213 [01:36<01:14,  1.25it/s, loss=0.0362, lr=9.88e-5, step=758]\\u001b[A\\n\",\n      \"Epoch 3:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:37<01:13,  1.25it/s, loss=0.0362, lr=9.88e-5, step=758]\\u001b[A\\n\",\n      \"Epoch 3:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:37<01:13,  1.25it/s, loss=0.0275, lr=9.88e-5, step=759]\\u001b[A\\n\",\n      \"Epoch 3:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                         | 122/213 [01:37<01:12,  1.25it/s, loss=0.0275, lr=9.88e-5, step=759]\\u001b[A\\n\",\n      \"Epoch 3:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                         | 122/213 [01:38<01:12,  1.25it/s, loss=0.0219, lr=9.88e-5, step=760]\\u001b[A\\n\",\n      \"Epoch 3:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:38<01:12,  1.25it/s, loss=0.0219, lr=9.88e-5, step=760]\\u001b[A\\n\",\n      \"Epoch 3:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:38<01:12,  1.25it/s, loss=0.0238, lr=9.88e-5, step=761]\\u001b[A\\n\",\n      \"Epoch 3:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:39<01:11,  1.25it/s, loss=0.0238, lr=9.88e-5, step=761]\\u001b[A\\n\",\n      \"Epoch 3:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:39<01:11,  1.25it/s, loss=0.0111, lr=9.88e-5, step=762]\\u001b[A\\n\",\n      \"Epoch 3:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                      | 125/213 [01:40<01:10,  1.25it/s, loss=0.0111, lr=9.88e-5, step=762]\\u001b[A\\n\",\n      \"Epoch 3:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                      | 125/213 [01:40<01:10,  1.25it/s, loss=0.0476, lr=9.88e-5, step=763]\\u001b[A\\n\",\n      \"Epoch 3:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                     | 126/213 [01:41<01:09,  1.25it/s, loss=0.0476, lr=9.88e-5, step=763]\\u001b[A\\n\",\n      \"Epoch 3:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                     | 126/213 [01:41<01:09,  1.25it/s, loss=0.0161, lr=9.88e-5, step=764]\\u001b[A\\n\",\n      \"Epoch 3:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                    | 127/213 [01:41<01:08,  1.25it/s, loss=0.0161, lr=9.88e-5, step=764]\\u001b[A\\n\",\n      \"Epoch 3:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                    | 127/213 [01:42<01:08,  1.25it/s, loss=0.0108, lr=9.88e-5, step=765]\\u001b[A\\n\",\n      \"Epoch 3:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                   | 128/213 [01:42<01:07,  1.25it/s, loss=0.0108, lr=9.88e-5, step=765]\\u001b[A\\n\",\n      \"Epoch 3:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                   | 128/213 [01:42<01:07,  1.25it/s, loss=0.0376, lr=9.88e-5, step=766]\\u001b[A\\n\",\n      \"Epoch 3:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.0376, lr=9.88e-5, step=766]\\u001b[A\\n\",\n      \"Epoch 3:  61%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.019, lr=9.88e-5, step=767]\\u001b[A\\n\",\n      \"Epoch 3:  61%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                 | 130/213 [01:44<01:06,  1.25it/s, loss=0.019, lr=9.88e-5, step=767]\\u001b[A\\n\",\n      \"Epoch 3:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                 | 130/213 [01:44<01:06,  1.25it/s, loss=0.00996, lr=9.87e-5, step=768]\\u001b[A\\n\",\n      \"Epoch 3:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [01:45<01:05,  1.25it/s, loss=0.00996, lr=9.87e-5, step=768]\\u001b[A\\n\",\n      \"Epoch 3:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                | 131/213 [01:45<01:05,  1.25it/s, loss=0.0232, lr=9.87e-5, step=769]\\u001b[A\\n\",\n      \"Epoch 3:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                               | 132/213 [01:45<01:04,  1.25it/s, loss=0.0232, lr=9.87e-5, step=769]\\u001b[A\\n\",\n      \"Epoch 3:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                               | 132/213 [01:46<01:04,  1.25it/s, loss=0.0198, lr=9.87e-5, step=770]\\u001b[A\\n\",\n      \"Epoch 3:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0198, lr=9.87e-5, step=770]\\u001b[A\\n\",\n      \"Epoch 3:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0245, lr=9.87e-5, step=771]\\u001b[A\\n\",\n      \"Epoch 3:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0245, lr=9.87e-5, step=771]\\u001b[A\\n\",\n      \"Epoch 3:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0208, lr=9.87e-5, step=772]\\u001b[A\\n\",\n      \"Epoch 3:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                            | 135/213 [01:48<01:02,  1.25it/s, loss=0.0208, lr=9.87e-5, step=772]\\u001b[A\\n\",\n      \"Epoch 3:  63%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                            | 135/213 [01:48<01:02,  1.25it/s, loss=0.0792, lr=9.87e-5, step=773]\\u001b[A\\n\",\n      \"Epoch 3:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                           | 136/213 [01:49<01:01,  1.24it/s, loss=0.0792, lr=9.87e-5, step=773]\\u001b[A\\n\",\n      \"Epoch 3:  64%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                            | 136/213 [01:49<01:01,  1.24it/s, loss=0.013, lr=9.87e-5, step=774]\\u001b[A\\n\",\n      \"Epoch 3:  64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                           | 137/213 [01:50<01:01,  1.24it/s, loss=0.013, lr=9.87e-5, step=774]\\u001b[A\\n\",\n      \"Epoch 3:  64%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                          | 137/213 [01:50<01:01,  1.24it/s, loss=0.0238, lr=9.87e-5, step=775]\\u001b[A\\n\",\n      \"Epoch 3:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 138/213 [01:50<01:00,  1.24it/s, loss=0.0238, lr=9.87e-5, step=775]\\u001b[A\\n\",\n      \"Epoch 3:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<01:00,  1.24it/s, loss=0.00928, lr=9.87e-5, step=776]\\u001b[A\\n\",\n      \"Epoch 3:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:59,  1.24it/s, loss=0.00928, lr=9.87e-5, step=776]\\u001b[A\\n\",\n      \"Epoch 3:  65%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 139/213 [01:51<00:59,  1.24it/s, loss=0.0409, lr=9.87e-5, step=777]\\u001b[A\\n\",\n      \"Epoch 3:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:52<00:58,  1.24it/s, loss=0.0409, lr=9.87e-5, step=777]\\u001b[A\\n\",\n      \"Epoch 3:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:52<00:58,  1.24it/s, loss=0.0449, lr=9.86e-5, step=778]\\u001b[A\\n\",\n      \"Epoch 3:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:53<00:57,  1.24it/s, loss=0.0449, lr=9.86e-5, step=778]\\u001b[A\\n\",\n      \"Epoch 3:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:53<00:57,  1.24it/s, loss=0.0583, lr=9.86e-5, step=779]\\u001b[A\\n\",\n      \"Epoch 3:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                     | 142/213 [01:54<00:57,  1.24it/s, loss=0.0583, lr=9.86e-5, step=779]\\u001b[A\\n\",\n      \"Epoch 3:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                     | 142/213 [01:54<00:57,  1.24it/s, loss=0.0324, lr=9.86e-5, step=780]\\u001b[A\\n\",\n      \"Epoch 3:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                    | 143/213 [01:54<00:56,  1.24it/s, loss=0.0324, lr=9.86e-5, step=780]\\u001b[A\\n\",\n      \"Epoch 3:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                    | 143/213 [01:54<00:56,  1.24it/s, loss=0.0237, lr=9.86e-5, step=781]\\u001b[A\\n\",\n      \"Epoch 3:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                   | 144/213 [01:55<00:55,  1.25it/s, loss=0.0237, lr=9.86e-5, step=781]\\u001b[A\\n\",\n      \"Epoch 3:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                   | 144/213 [01:55<00:55,  1.25it/s, loss=0.0214, lr=9.86e-5, step=782]\\u001b[A\\n\",\n      \"Epoch 3:  68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                  | 145/213 [01:56<00:54,  1.25it/s, loss=0.0214, lr=9.86e-5, step=782]\\u001b[A\\n\",\n      \"Epoch 3:  68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                  | 145/213 [01:56<00:54,  1.25it/s, loss=0.0474, lr=9.86e-5, step=783]\\u001b[A\\n\",\n      \"Epoch 3:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:57<00:53,  1.25it/s, loss=0.0474, lr=9.86e-5, step=783]\\u001b[A\\n\",\n      \"Epoch 3:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:57<00:53,  1.25it/s, loss=0.0199, lr=9.86e-5, step=784]\\u001b[A\\n\",\n      \"Epoch 3:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:58<00:52,  1.25it/s, loss=0.0199, lr=9.86e-5, step=784]\\u001b[A\\n\",\n      \"Epoch 3:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:58<00:52,  1.25it/s, loss=0.0279, lr=9.86e-5, step=785]\\u001b[A\\n\",\n      \"Epoch 3:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                               | 148/213 [01:58<00:52,  1.25it/s, loss=0.0279, lr=9.86e-5, step=785]\\u001b[A\\n\",\n      \"Epoch 3:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                               | 148/213 [01:58<00:52,  1.25it/s, loss=0.0259, lr=9.86e-5, step=786]\\u001b[A\\n\",\n      \"Epoch 3:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                              | 149/213 [01:59<00:51,  1.25it/s, loss=0.0259, lr=9.86e-5, step=786]\\u001b[A\\n\",\n      \"Epoch 3:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                               | 149/213 [01:59<00:51,  1.25it/s, loss=0.017, lr=9.86e-5, step=787]\\u001b[A\\n\",\n      \"Epoch 3:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                              | 150/213 [02:00<00:50,  1.25it/s, loss=0.017, lr=9.86e-5, step=787]\\u001b[A\\n\",\n      \"Epoch 3:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.0212, lr=9.85e-5, step=788]\\u001b[A\\n\",\n      \"Epoch 3:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                            | 151/213 [02:01<00:49,  1.25it/s, loss=0.0212, lr=9.85e-5, step=788]\\u001b[A\\n\",\n      \"Epoch 3:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                            | 151/213 [02:01<00:49,  1.25it/s, loss=0.0203, lr=9.85e-5, step=789]\\u001b[A\\n\",\n      \"Epoch 3:  71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                           | 152/213 [02:02<00:48,  1.25it/s, loss=0.0203, lr=9.85e-5, step=789]\\u001b[A\\n\",\n      \"Epoch 3:  71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                           | 152/213 [02:02<00:48,  1.25it/s, loss=0.0169, lr=9.85e-5, step=790]\\u001b[A\\n\",\n      \"Epoch 3:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                          | 153/213 [02:02<00:48,  1.24it/s, loss=0.0169, lr=9.85e-5, step=790]\\u001b[A\\n\",\n      \"Epoch 3:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                          | 153/213 [02:02<00:48,  1.24it/s, loss=0.0196, lr=9.85e-5, step=791]\\u001b[A\\n\",\n      \"Epoch 3:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                          | 154/213 [02:03<00:47,  1.24it/s, loss=0.0196, lr=9.85e-5, step=791]\\u001b[A\\n\",\n      \"Epoch 3:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                          | 154/213 [02:03<00:47,  1.24it/s, loss=0.0145, lr=9.85e-5, step=792]\\u001b[A\\n\",\n      \"Epoch 3:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                         | 155/213 [02:04<00:46,  1.25it/s, loss=0.0145, lr=9.85e-5, step=792]\\u001b[A\\n\",\n      \"Epoch 3:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                         | 155/213 [02:04<00:46,  1.25it/s, loss=0.0136, lr=9.85e-5, step=793]\\u001b[A\\n\",\n      \"Epoch 3:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                        | 156/213 [02:05<00:45,  1.25it/s, loss=0.0136, lr=9.85e-5, step=793]\\u001b[A\\n\",\n      \"Epoch 3:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                        | 156/213 [02:05<00:45,  1.25it/s, loss=0.0181, lr=9.85e-5, step=794]\\u001b[A\\n\",\n      \"Epoch 3:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                       | 157/213 [02:06<00:44,  1.25it/s, loss=0.0181, lr=9.85e-5, step=794]\\u001b[A\\n\",\n      \"Epoch 3:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                       | 157/213 [02:06<00:44,  1.25it/s, loss=0.0379, lr=9.85e-5, step=795]\\u001b[A\\n\",\n      \"Epoch 3:  74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                      | 158/213 [02:06<00:44,  1.25it/s, loss=0.0379, lr=9.85e-5, step=795]\\u001b[A\\n\",\n      \"Epoch 3:  74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                      | 158/213 [02:06<00:44,  1.25it/s, loss=0.0112, lr=9.85e-5, step=796]\\u001b[A\\n\",\n      \"Epoch 3:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                     | 159/213 [02:07<00:43,  1.25it/s, loss=0.0112, lr=9.85e-5, step=796]\\u001b[A\\n\",\n      \"Epoch 3:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                     | 159/213 [02:07<00:43,  1.25it/s, loss=0.0101, lr=9.85e-5, step=797]\\u001b[A\\n\",\n      \"Epoch 3:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                    | 160/213 [02:08<00:42,  1.25it/s, loss=0.0101, lr=9.85e-5, step=797]\\u001b[A\\n\",\n      \"Epoch 3:  75%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                    | 160/213 [02:08<00:42,  1.25it/s, loss=0.0119, lr=9.84e-5, step=798]\\u001b[A\\n\",\n      \"Epoch 3:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                   | 161/213 [02:09<00:41,  1.25it/s, loss=0.0119, lr=9.84e-5, step=798]\\u001b[A\\n\",\n      \"Epoch 3:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                   | 161/213 [02:09<00:41,  1.25it/s, loss=0.0356, lr=9.84e-5, step=799]\\u001b[A\\n\",\n      \"Epoch 3:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                  | 162/213 [02:10<00:40,  1.25it/s, loss=0.0356, lr=9.84e-5, step=799]\\u001b[A\\n\",\n      \"Epoch 3:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                  | 162/213 [02:10<00:40,  1.25it/s, loss=0.0353, lr=9.84e-5, step=800]\\u001b[A\\n\",\n      \"Epoch 3:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                 | 163/213 [02:10<00:40,  1.25it/s, loss=0.0353, lr=9.84e-5, step=800]\\u001b[A\\n\",\n      \"Epoch 3:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                 | 163/213 [02:10<00:40,  1.25it/s, loss=0.0279, lr=9.84e-5, step=801]\\u001b[A\\n\",\n      \"Epoch 3:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                | 164/213 [02:11<00:39,  1.25it/s, loss=0.0279, lr=9.84e-5, step=801]\\u001b[A\\n\",\n      \"Epoch 3:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                | 164/213 [02:11<00:39,  1.25it/s, loss=0.0247, lr=9.84e-5, step=802]\\u001b[A\\n\",\n      \"Epoch 3:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                               | 165/213 [02:12<00:38,  1.25it/s, loss=0.0247, lr=9.84e-5, step=802]\\u001b[A\\n\",\n      \"Epoch 3:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                               | 165/213 [02:12<00:38,  1.25it/s, loss=0.0162, lr=9.84e-5, step=803]\\u001b[A\\n\",\n      \"Epoch 3:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:13<00:37,  1.25it/s, loss=0.0162, lr=9.84e-5, step=803]\\u001b[A\\n\",\n      \"Epoch 3:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:13<00:37,  1.25it/s, loss=0.0232, lr=9.84e-5, step=804]\\u001b[A\\n\",\n      \"Epoch 3:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                             | 167/213 [02:14<00:36,  1.25it/s, loss=0.0232, lr=9.84e-5, step=804]\\u001b[A\\n\",\n      \"Epoch 3:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                             | 167/213 [02:14<00:36,  1.25it/s, loss=0.0172, lr=9.84e-5, step=805]\\u001b[A\\n\",\n      \"Epoch 3:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.0172, lr=9.84e-5, step=805]\\u001b[A\\n\",\n      \"Epoch 3:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.0174, lr=9.84e-5, step=806]\\u001b[A\\n\",\n      \"Epoch 3:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.0174, lr=9.84e-5, step=806]\\u001b[A\\n\",\n      \"Epoch 3:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.0114, lr=9.84e-5, step=807]\\u001b[A\\n\",\n      \"Epoch 3:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                          | 170/213 [02:16<00:34,  1.25it/s, loss=0.0114, lr=9.84e-5, step=807]\\u001b[A\\n\",\n      \"Epoch 3:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                          | 170/213 [02:16<00:34,  1.25it/s, loss=0.0623, lr=9.83e-5, step=808]\\u001b[A\\n\",\n      \"Epoch 3:  80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                         | 171/213 [02:17<00:33,  1.25it/s, loss=0.0623, lr=9.83e-5, step=808]\\u001b[A\\n\",\n      \"Epoch 3:  80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                         | 171/213 [02:17<00:33,  1.25it/s, loss=0.0288, lr=9.83e-5, step=809]\\u001b[A\\n\",\n      \"Epoch 3:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                        | 172/213 [02:18<00:32,  1.25it/s, loss=0.0288, lr=9.83e-5, step=809]\\u001b[A\\n\",\n      \"Epoch 3:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                        | 172/213 [02:18<00:32,  1.25it/s, loss=0.0321, lr=9.83e-5, step=810]\\u001b[A\\n\",\n      \"Epoch 3:  81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0321, lr=9.83e-5, step=810]\\u001b[A\\n\",\n      \"Epoch 3:  81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0469, lr=9.83e-5, step=811]\\u001b[A\\n\",\n      \"Epoch 3:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0469, lr=9.83e-5, step=811]\\u001b[A\\n\",\n      \"Epoch 3:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0129, lr=9.83e-5, step=812]\\u001b[A\\n\",\n      \"Epoch 3:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.0129, lr=9.83e-5, step=812]\\u001b[A\\n\",\n      \"Epoch 3:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.0134, lr=9.83e-5, step=813]\\u001b[A\\n\",\n      \"Epoch 3:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                    | 176/213 [02:21<00:29,  1.25it/s, loss=0.0134, lr=9.83e-5, step=813]\\u001b[A\\n\",\n      \"Epoch 3:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                    | 176/213 [02:21<00:29,  1.25it/s, loss=0.0156, lr=9.83e-5, step=814]\\u001b[A\\n\",\n      \"Epoch 3:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 177/213 [02:22<00:28,  1.25it/s, loss=0.0156, lr=9.83e-5, step=814]\\u001b[A\\n\",\n      \"Epoch 3:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 177/213 [02:22<00:28,  1.25it/s, loss=0.0179, lr=9.83e-5, step=815]\\u001b[A\\n\",\n      \"Epoch 3:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                  | 178/213 [02:22<00:28,  1.25it/s, loss=0.0179, lr=9.83e-5, step=815]\\u001b[A\\n\",\n      \"Epoch 3:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                  | 178/213 [02:22<00:28,  1.25it/s, loss=0.0346, lr=9.83e-5, step=816]\\u001b[A\\n\",\n      \"Epoch 3:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0346, lr=9.83e-5, step=816]\\u001b[A\\n\",\n      \"Epoch 3:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0175, lr=9.82e-5, step=817]\\u001b[A\\n\",\n      \"Epoch 3:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.0175, lr=9.82e-5, step=817]\\u001b[A\\n\",\n      \"Epoch 3:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.017, lr=9.82e-5, step=818]\\u001b[A\\n\",\n      \"Epoch 3:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                               | 181/213 [02:25<00:25,  1.25it/s, loss=0.017, lr=9.82e-5, step=818]\\u001b[A\\n\",\n      \"Epoch 3:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                               | 181/213 [02:25<00:25,  1.25it/s, loss=0.0316, lr=9.82e-5, step=819]\\u001b[A\\n\",\n      \"Epoch 3:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                              | 182/213 [02:26<00:24,  1.25it/s, loss=0.0316, lr=9.82e-5, step=819]\\u001b[A\\n\",\n      \"Epoch 3:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:26<00:24,  1.25it/s, loss=0.00855, lr=9.82e-5, step=820]\\u001b[A\\n\",\n      \"Epoch 3:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00855, lr=9.82e-5, step=820]\\u001b[A\\n\",\n      \"Epoch 3:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00914, lr=9.82e-5, step=821]\\u001b[A\\n\",\n      \"Epoch 3:  86%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.00914, lr=9.82e-5, step=821]\\u001b[A\\n\",\n      \"Epoch 3:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.0117, lr=9.82e-5, step=822]\\u001b[A\\n\",\n      \"Epoch 3:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                           | 185/213 [02:28<00:22,  1.25it/s, loss=0.0117, lr=9.82e-5, step=822]\\u001b[A\\n\",\n      \"Epoch 3:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                           | 185/213 [02:28<00:22,  1.25it/s, loss=0.0316, lr=9.82e-5, step=823]\\u001b[A\\n\",\n      \"Epoch 3:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                          | 186/213 [02:29<00:21,  1.25it/s, loss=0.0316, lr=9.82e-5, step=823]\\u001b[A\\n\",\n      \"Epoch 3:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                          | 186/213 [02:29<00:21,  1.25it/s, loss=0.0196, lr=9.82e-5, step=824]\\u001b[A\\n\",\n      \"Epoch 3:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                         | 187/213 [02:30<00:20,  1.25it/s, loss=0.0196, lr=9.82e-5, step=824]\\u001b[A\\n\",\n      \"Epoch 3:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                         | 187/213 [02:30<00:20,  1.25it/s, loss=0.0327, lr=9.82e-5, step=825]\\u001b[A\\n\",\n      \"Epoch 3:  88%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0327, lr=9.82e-5, step=825]\\u001b[A\\n\",\n      \"Epoch 3:  88%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0203, lr=9.81e-5, step=826]\\u001b[A\\n\",\n      \"Epoch 3:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0203, lr=9.81e-5, step=826]\\u001b[A\\n\",\n      \"Epoch 3:  89%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.025, lr=9.81e-5, step=827]\\u001b[A\\n\",\n      \"Epoch 3:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.025, lr=9.81e-5, step=827]\\u001b[A\\n\",\n      \"Epoch 3:  89%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.0214, lr=9.81e-5, step=828]\\u001b[A\\n\",\n      \"Epoch 3:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                     | 191/213 [02:33<00:17,  1.25it/s, loss=0.0214, lr=9.81e-5, step=828]\\u001b[A\\n\",\n      \"Epoch 3:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                     | 191/213 [02:33<00:17,  1.25it/s, loss=0.0224, lr=9.81e-5, step=829]\\u001b[A\\n\",\n      \"Epoch 3:  90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:34<00:16,  1.25it/s, loss=0.0224, lr=9.81e-5, step=829]\\u001b[A\\n\",\n      \"Epoch 3:  90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:34<00:16,  1.25it/s, loss=0.0162, lr=9.81e-5, step=830]\\u001b[A\\n\",\n      \"Epoch 3:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.0162, lr=9.81e-5, step=830]\\u001b[A\\n\",\n      \"Epoch 3:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.0271, lr=9.81e-5, step=831]\\u001b[A\\n\",\n      \"Epoch 3:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0271, lr=9.81e-5, step=831]\\u001b[A\\n\",\n      \"Epoch 3:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0739, lr=9.81e-5, step=832]\\u001b[A\\n\",\n      \"Epoch 3:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0739, lr=9.81e-5, step=832]\\u001b[A\\n\",\n      \"Epoch 3:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0136, lr=9.81e-5, step=833]\\u001b[A\\n\",\n      \"Epoch 3:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                | 196/213 [02:37<00:13,  1.25it/s, loss=0.0136, lr=9.81e-5, step=833]\\u001b[A\\n\",\n      \"Epoch 3:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                | 196/213 [02:37<00:13,  1.25it/s, loss=0.0155, lr=9.81e-5, step=834]\\u001b[A\\n\",\n      \"Epoch 3:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎               | 197/213 [02:38<00:12,  1.25it/s, loss=0.0155, lr=9.81e-5, step=834]\\u001b[A\\n\",\n      \"Epoch 3:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏               | 197/213 [02:38<00:12,  1.25it/s, loss=0.0156, lr=9.8e-5, step=835]\\u001b[A\\n\",\n      \"Epoch 3:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0156, lr=9.8e-5, step=835]\\u001b[A\\n\",\n      \"Epoch 3:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0174, lr=9.8e-5, step=836]\\u001b[A\\n\",\n      \"Epoch 3:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0174, lr=9.8e-5, step=836]\\u001b[A\\n\",\n      \"Epoch 3:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0104, lr=9.8e-5, step=837]\\u001b[A\\n\",\n      \"Epoch 3:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏            | 200/213 [02:40<00:10,  1.25it/s, loss=0.0104, lr=9.8e-5, step=837]\\u001b[A\\n\",\n      \"Epoch 3:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏            | 200/213 [02:40<00:10,  1.25it/s, loss=0.0158, lr=9.8e-5, step=838]\\u001b[A\\n\",\n      \"Epoch 3:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏           | 201/213 [02:41<00:09,  1.25it/s, loss=0.0158, lr=9.8e-5, step=838]\\u001b[A\\n\",\n      \"Epoch 3:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏           | 201/213 [02:41<00:09,  1.25it/s, loss=0.0304, lr=9.8e-5, step=839]\\u001b[A\\n\",\n      \"Epoch 3:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏          | 202/213 [02:42<00:08,  1.25it/s, loss=0.0304, lr=9.8e-5, step=839]\\u001b[A\\n\",\n      \"Epoch 3:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏          | 202/213 [02:42<00:08,  1.25it/s, loss=0.0351, lr=9.8e-5, step=840]\\u001b[A\\n\",\n      \"Epoch 3:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0351, lr=9.8e-5, step=840]\\u001b[A\\n\",\n      \"Epoch 3:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0325, lr=9.8e-5, step=841]\\u001b[A\\n\",\n      \"Epoch 3:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:43<00:07,  1.25it/s, loss=0.0325, lr=9.8e-5, step=841]\\u001b[A\\n\",\n      \"Epoch 3:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████         | 204/213 [02:43<00:07,  1.25it/s, loss=0.01, lr=9.8e-5, step=842]\\u001b[A\\n\",\n      \"Epoch 3:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████        | 205/213 [02:44<00:06,  1.25it/s, loss=0.01, lr=9.8e-5, step=842]\\u001b[A\\n\",\n      \"Epoch 3:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:44<00:06,  1.25it/s, loss=0.0205, lr=9.79e-5, step=843]\\u001b[A\\n\",\n      \"Epoch 3:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:45<00:05,  1.25it/s, loss=0.0205, lr=9.79e-5, step=843]\\u001b[A\\n\",\n      \"Epoch 3:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:45<00:05,  1.25it/s, loss=0.0198, lr=9.79e-5, step=844]\\u001b[A\\n\",\n      \"Epoch 3:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:46<00:04,  1.25it/s, loss=0.0198, lr=9.79e-5, step=844]\\u001b[A\\n\",\n      \"Epoch 3:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:46<00:04,  1.25it/s, loss=0.0263, lr=9.79e-5, step=845]\\u001b[A\\n\",\n      \"Epoch 3:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.0263, lr=9.79e-5, step=845]\\u001b[A\\n\",\n      \"Epoch 3:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.019, lr=9.79e-5, step=846]\\u001b[A\\n\",\n      \"Epoch 3:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.019, lr=9.79e-5, step=846]\\u001b[A\\n\",\n      \"Epoch 3:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0269, lr=9.79e-5, step=847]\\u001b[A\\n\",\n      \"Epoch 3:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.0269, lr=9.79e-5, step=847]\\u001b[A\\n\",\n      \"Epoch 3:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.00754, lr=9.79e-5, step=848]\\u001b[A\\n\",\n      \"Epoch 3:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:49<00:01,  1.25it/s, loss=0.00754, lr=9.79e-5, step=848]\\u001b[A\\n\",\n      \"Epoch 3:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:49<00:01,  1.25it/s, loss=0.0232, lr=9.79e-5, step=849]\\u001b[A\\n\",\n      \"Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:50<00:00,  1.25it/s, loss=0.0232, lr=9.79e-5, step=849]\\u001b[A\\n\",\n      \"Epoch 3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:50<00:00,  1.25it/s, loss=0.0124, lr=9.79e-5, step=850]\\u001b[A\\n\",\n      \"Epoch 3: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.0124, lr=9.79e-5, step=850]\\u001b[A\\n\",\n      \"Epoch 3: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.0124, lr=9.79e-5, step=851]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d75e3d88d2a4461e8725ff51284b9480\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 3: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:36<00:00,  2.14s/it, loss=0.0124, lr=9.79e-5, step=851]\\n\",\n      \"Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.0127, lr=9.45e-5, step=1064]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4fbfdd905c634a70bfa6a5255027c998\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.0127, lr=9.45e-5, step=1064]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 5:   0%|                                                                                                                                                                                                                                                              | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 5:   0%|█▏                                                                                                                                                                                                                                           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step=1066]\\u001b[A\\n\",\n      \"Epoch 5:   1%|██▉                                                                                                                                                                                                               | 3/213 [00:02<02:53,  1.21it/s, loss=0.0148, lr=9.45e-5, step=1066]\\u001b[A\\n\",\n      \"Epoch 5:   1%|██▉                                                                                                                                                                                                               | 3/213 [00:02<02:53,  1.21it/s, loss=0.0127, lr=9.45e-5, step=1067]\\u001b[A\\n\",\n      \"Epoch 5:   2%|███▉                                                                                                                                                                                                              | 4/213 [00:03<02:50,  1.23it/s, loss=0.0127, lr=9.45e-5, step=1067]\\u001b[A\\n\",\n      \"Epoch 5:   2%|███▉                                                                                                                                                                                                              | 4/213 [00:03<02:50,  1.23it/s, loss=0.0134, lr=9.45e-5, step=1068]\\u001b[A\\n\",\n      \"Epoch 5:   2%|████▉                                                                                                                                                                                                             | 5/213 [00:04<02:48,  1.23it/s, loss=0.0134, lr=9.45e-5, step=1068]\\u001b[A\\n\",\n      \"Epoch 5:   2%|████▉                                                                                                                                                                                                             | 5/213 [00:04<02:48,  1.23it/s, loss=0.0654, lr=9.44e-5, step=1069]\\u001b[A\\n\",\n      \"Epoch 5:   3%|█████▉                                         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                                                                                                                 | 7/213 [00:05<02:47,  1.23it/s, loss=0.0177, lr=9.44e-5, step=1071]\\u001b[A\\n\",\n      \"Epoch 5:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:46,  1.23it/s, loss=0.0177, lr=9.44e-5, step=1071]\\u001b[A\\n\",\n      \"Epoch 5:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:46,  1.23it/s, loss=0.0204, lr=9.44e-5, step=1072]\\u001b[A\\n\",\n      \"Epoch 5:   4%|████████▊                                                                                                                                                                                                         | 9/213 [00:07<02:44,  1.24it/s, loss=0.0204, lr=9.44e-5, step=1072]\\u001b[A\\n\",\n      \"Epoch 5:   4%|████████▊                                                                                                                                                                                                         | 9/213 [00:07<02:44,  1.24it/s, loss=0.0101, lr=9.44e-5, step=1073]\\u001b[A\\n\",\n      \"Epoch 5:   5%|█████████▊                                                                                                                                                                                                       | 10/213 [00:08<02:43,  1.24it/s, loss=0.0101, lr=9.44e-5, step=1073]\\u001b[A\\n\",\n      \"Epoch 5:   5%|█████████▊                                                                                                                                                                                                       | 10/213 [00:08<02:43,  1.24it/s, loss=0.0119, lr=9.43e-5, step=1074]\\u001b[A\\n\",\n      \"Epoch 5:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:42,  1.24it/s, loss=0.0119, lr=9.43e-5, step=1074]\\u001b[A\\n\",\n      \"Epoch 5:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:42,  1.24it/s, loss=0.0178, lr=9.43e-5, step=1075]\\u001b[A\\n\",\n      \"Epoch 5:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:41,  1.24it/s, loss=0.0178, lr=9.43e-5, step=1075]\\u001b[A\\n\",\n      \"Epoch 5:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:41,  1.24it/s, loss=0.0349, lr=9.43e-5, step=1076]\\u001b[A\\n\",\n      \"Epoch 5:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:40,  1.25it/s, loss=0.0349, lr=9.43e-5, step=1076]\\u001b[A\\n\",\n      \"Epoch 5:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:40,  1.25it/s, loss=0.0225, lr=9.43e-5, step=1077]\\u001b[A\\n\",\n      \"Epoch 5:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:39,  1.25it/s, loss=0.0225, lr=9.43e-5, step=1077]\\u001b[A\\n\",\n      \"Epoch 5:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:39,  1.25it/s, loss=0.0404, lr=9.43e-5, step=1078]\\u001b[A\\n\",\n      \"Epoch 5:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:38,  1.25it/s, loss=0.0404, lr=9.43e-5, step=1078]\\u001b[A\\n\",\n      \"Epoch 5:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:38,  1.25it/s, loss=0.0127, lr=9.42e-5, step=1079]\\u001b[A\\n\",\n      \"Epoch 5:   8%|███████████████▋                                                                                                                                                                                                 | 16/213 [00:12<02:38,  1.25it/s, loss=0.0127, lr=9.42e-5, step=1079]\\u001b[A\\n\",\n      \"Epoch 5:   8%|███████████████▋                                                                                                                                                                                                 | 16/213 [00:12<02:38,  1.25it/s, loss=0.0139, lr=9.42e-5, step=1080]\\u001b[A\\n\",\n      \"Epoch 5:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:37,  1.25it/s, loss=0.0139, lr=9.42e-5, step=1080]\\u001b[A\\n\",\n      \"Epoch 5:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:37,  1.25it/s, loss=0.0554, lr=9.42e-5, step=1081]\\u001b[A\\n\",\n      \"Epoch 5:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:36,  1.25it/s, loss=0.0554, lr=9.42e-5, step=1081]\\u001b[A\\n\",\n      \"Epoch 5:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:36,  1.25it/s, loss=0.0315, lr=9.42e-5, step=1082]\\u001b[A\\n\",\n      \"Epoch 5:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0315, lr=9.42e-5, step=1082]\\u001b[A\\n\",\n      \"Epoch 5:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0182, lr=9.42e-5, step=1083]\\u001b[A\\n\",\n      \"Epoch 5:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0182, lr=9.42e-5, step=1083]\\u001b[A\\n\",\n      \"Epoch 5:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0166, lr=9.41e-5, step=1084]\\u001b[A\\n\",\n      \"Epoch 5:  10%|████████████████████▌                                                                                                                                                                                            | 21/213 [00:16<02:33,  1.25it/s, loss=0.0166, lr=9.41e-5, step=1084]\\u001b[A\\n\",\n      \"Epoch 5:  10%|████████████████████▌                                                                                                                                                                                            | 21/213 [00:16<02:33,  1.25it/s, loss=0.0121, lr=9.41e-5, step=1085]\\u001b[A\\n\",\n      \"Epoch 5:  10%|█████████████████████▌                                                                                                                                                                                           | 22/213 [00:17<02:32,  1.25it/s, loss=0.0121, lr=9.41e-5, step=1085]\\u001b[A\\n\",\n      \"Epoch 5:  10%|█████████████████████▌                                                                                                                                                                                           | 22/213 [00:17<02:32,  1.25it/s, loss=0.0101, lr=9.41e-5, step=1086]\\u001b[A\\n\",\n      \"Epoch 5:  11%|██████████████████████▌                                                                                                                                                                                          | 23/213 [00:18<02:31,  1.25it/s, loss=0.0101, lr=9.41e-5, step=1086]\\u001b[A\\n\",\n      \"Epoch 5:  11%|██████████████████████▌                                                                                                                                                                                          | 23/213 [00:18<02:31,  1.25it/s, loss=0.0446, lr=9.41e-5, step=1087]\\u001b[A\\n\",\n      \"Epoch 5:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:31,  1.25it/s, loss=0.0446, lr=9.41e-5, step=1087]\\u001b[A\\n\",\n      \"Epoch 5:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:31,  1.25it/s, loss=0.0173, lr=9.41e-5, step=1088]\\u001b[A\\n\",\n      \"Epoch 5:  12%|████████████████████████▌                                                                                                                                                                                        | 25/213 [00:20<02:30,  1.25it/s, loss=0.0173, lr=9.41e-5, step=1088]\\u001b[A\\n\",\n      \"Epoch 5:  12%|████████████████████████▋                                                                                                                                                                                         | 25/213 [00:20<02:30,  1.25it/s, loss=0.0231, lr=9.4e-5, step=1089]\\u001b[A\\n\",\n      \"Epoch 5:  12%|█████████████████████████▋                                                                                                                                                                                        | 26/213 [00:20<02:30,  1.24it/s, loss=0.0231, lr=9.4e-5, step=1089]\\u001b[A\\n\",\n      \"Epoch 5:  12%|█████████████████████████▌                                                                                                                                                                                       | 26/213 [00:20<02:30,  1.24it/s, loss=0.00815, lr=9.4e-5, step=1090]\\u001b[A\\n\",\n      \"Epoch 5:  13%|██████████████████████████▍                                                                                                                                                                                      | 27/213 [00:21<02:29,  1.25it/s, loss=0.00815, lr=9.4e-5, step=1090]\\u001b[A\\n\",\n      \"Epoch 5:  13%|██████████████████████████▌                                                                                                                                                                                       | 27/213 [00:21<02:29,  1.25it/s, loss=0.0101, lr=9.4e-5, step=1091]\\u001b[A\\n\",\n      \"Epoch 5:  13%|███████████████████████████▌                                                                                                                                                                                      | 28/213 [00:22<02:28,  1.25it/s, loss=0.0101, lr=9.4e-5, step=1091]\\u001b[A\\n\",\n      \"Epoch 5:  13%|███████████████████████████▌                                                                                                                                                                                      | 28/213 [00:22<02:28,  1.25it/s, loss=0.0322, lr=9.4e-5, step=1092]\\u001b[A\\n\",\n      \"Epoch 5:  14%|████████████████████████████▌                                                                                                                                                                                     | 29/213 [00:23<02:27,  1.25it/s, loss=0.0322, lr=9.4e-5, step=1092]\\u001b[A\\n\",\n      \"Epoch 5:  14%|████████████████████████████▌                                                                                                                                                                                     | 29/213 [00:23<02:27,  1.25it/s, loss=0.0174, lr=9.4e-5, step=1093]\\u001b[A\\n\",\n      \"Epoch 5:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:26,  1.25it/s, loss=0.0174, lr=9.4e-5, step=1093]\\u001b[A\\n\",\n      \"Epoch 5:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:26,  1.25it/s, loss=0.0331, lr=9.39e-5, step=1094]\\u001b[A\\n\",\n      \"Epoch 5:  15%|██████████████████████████████▍                                                                                                                                                                                  | 31/213 [00:24<02:26,  1.24it/s, loss=0.0331, lr=9.39e-5, step=1094]\\u001b[A\\n\",\n      \"Epoch 5:  15%|██████████████████████████████▍                                                                                                                                                                                  | 31/213 [00:25<02:26,  1.24it/s, loss=0.0188, lr=9.39e-5, step=1095]\\u001b[A\\n\",\n      \"Epoch 5:  15%|███████████████████████████████▍                                                                                                                                                                                 | 32/213 [00:25<02:25,  1.25it/s, loss=0.0188, lr=9.39e-5, step=1095]\\u001b[A\\n\",\n      \"Epoch 5:  15%|███████████████████████████████▍                                                                                                                                                                                 | 32/213 [00:25<02:25,  1.25it/s, loss=0.0152, lr=9.39e-5, step=1096]\\u001b[A\\n\",\n      \"Epoch 5:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:24,  1.25it/s, loss=0.0152, lr=9.39e-5, step=1096]\\u001b[A\\n\",\n      \"Epoch 5:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:24,  1.25it/s, loss=0.0127, lr=9.39e-5, step=1097]\\u001b[A\\n\",\n      \"Epoch 5:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [00:27<02:23,  1.25it/s, loss=0.0127, lr=9.39e-5, step=1097]\\u001b[A\\n\",\n      \"Epoch 5:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [00:27<02:23,  1.25it/s, loss=0.0121, lr=9.39e-5, step=1098]\\u001b[A\\n\",\n      \"Epoch 5:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:22,  1.25it/s, loss=0.0121, lr=9.39e-5, step=1098]\\u001b[A\\n\",\n      \"Epoch 5:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:22,  1.25it/s, loss=0.0235, lr=9.38e-5, step=1099]\\u001b[A\\n\",\n      \"Epoch 5:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:28<02:21,  1.25it/s, loss=0.0235, lr=9.38e-5, step=1099]\\u001b[A\\n\",\n      \"Epoch 5:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:29<02:21,  1.25it/s, loss=0.0107, lr=9.38e-5, step=1100]\\u001b[A\\n\",\n      \"Epoch 5:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:20,  1.25it/s, loss=0.0107, lr=9.38e-5, step=1100]\\u001b[A\\n\",\n      \"Epoch 5:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:20,  1.25it/s, loss=0.0382, lr=9.38e-5, step=1101]\\u001b[A\\n\",\n      \"Epoch 5:  18%|█████████████████████████████████████▎                                                                                                                                                                           | 38/213 [00:30<02:20,  1.25it/s, loss=0.0382, lr=9.38e-5, step=1101]\\u001b[A\\n\",\n      \"Epoch 5:  18%|█████████████████████████████████████▎                                                                                                                                                                           | 38/213 [00:30<02:20,  1.25it/s, loss=0.0106, lr=9.38e-5, step=1102]\\u001b[A\\n\",\n      \"Epoch 5:  18%|██████████████████████████████████████▎                                                                                                                                                                          | 39/213 [00:31<02:19,  1.24it/s, loss=0.0106, lr=9.38e-5, step=1102]\\u001b[A\\n\",\n      \"Epoch 5:  18%|██████████████████████████████████████▎                                                                                                                                                                          | 39/213 [00:31<02:19,  1.24it/s, loss=0.0548, lr=9.38e-5, step=1103]\\u001b[A\\n\",\n      \"Epoch 5:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0548, lr=9.38e-5, step=1103]\\u001b[A\\n\",\n      \"Epoch 5:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0191, lr=9.37e-5, step=1104]\\u001b[A\\n\",\n      \"Epoch 5:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:32<02:17,  1.25it/s, loss=0.0191, lr=9.37e-5, step=1104]\\u001b[A\\n\",\n      \"Epoch 5:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:33<02:17,  1.25it/s, loss=0.0149, lr=9.37e-5, step=1105]\\u001b[A\\n\",\n      \"Epoch 5:  20%|█████████████████████████████████████████▏                                                                                                                                                                       | 42/213 [00:33<02:16,  1.25it/s, loss=0.0149, lr=9.37e-5, step=1105]\\u001b[A\\n\",\n      \"Epoch 5:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:16,  1.25it/s, loss=0.00753, lr=9.37e-5, step=1106]\\u001b[A\\n\",\n      \"Epoch 5:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:15,  1.25it/s, loss=0.00753, lr=9.37e-5, step=1106]\\u001b[A\\n\",\n      \"Epoch 5:  20%|██████████████████████████████████████████▏                                                                                                                                                                      | 43/213 [00:34<02:15,  1.25it/s, loss=0.0367, lr=9.37e-5, step=1107]\\u001b[A\\n\",\n      \"Epoch 5:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0367, lr=9.37e-5, step=1107]\\u001b[A\\n\",\n      \"Epoch 5:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0174, lr=9.37e-5, step=1108]\\u001b[A\\n\",\n      \"Epoch 5:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0174, lr=9.37e-5, step=1108]\\u001b[A\\n\",\n      \"Epoch 5:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0295, lr=9.36e-5, step=1109]\\u001b[A\\n\",\n      \"Epoch 5:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:36<02:13,  1.25it/s, loss=0.0295, lr=9.36e-5, step=1109]\\u001b[A\\n\",\n      \"Epoch 5:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:37<02:13,  1.25it/s, loss=0.0314, lr=9.36e-5, step=1110]\\u001b[A\\n\",\n      \"Epoch 5:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:12,  1.25it/s, loss=0.0314, lr=9.36e-5, step=1110]\\u001b[A\\n\",\n      \"Epoch 5:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:12,  1.25it/s, loss=0.0169, lr=9.36e-5, step=1111]\\u001b[A\\n\",\n      \"Epoch 5:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0169, lr=9.36e-5, step=1111]\\u001b[A\\n\",\n      \"Epoch 5:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0153, lr=9.36e-5, step=1112]\\u001b[A\\n\",\n      \"Epoch 5:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0153, lr=9.36e-5, step=1112]\\u001b[A\\n\",\n      \"Epoch 5:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0302, lr=9.36e-5, step=1113]\\u001b[A\\n\",\n      \"Epoch 5:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0302, lr=9.36e-5, step=1113]\\u001b[A\\n\",\n      \"Epoch 5:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0281, lr=9.35e-5, step=1114]\\u001b[A\\n\",\n      \"Epoch 5:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:40<02:09,  1.25it/s, loss=0.0281, lr=9.35e-5, step=1114]\\u001b[A\\n\",\n      \"Epoch 5:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:41<02:09,  1.25it/s, loss=0.0405, lr=9.35e-5, step=1115]\\u001b[A\\n\",\n      \"Epoch 5:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0405, lr=9.35e-5, step=1115]\\u001b[A\\n\",\n      \"Epoch 5:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0237, lr=9.35e-5, step=1116]\\u001b[A\\n\",\n      \"Epoch 5:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0237, lr=9.35e-5, step=1116]\\u001b[A\\n\",\n      \"Epoch 5:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0313, lr=9.35e-5, step=1117]\\u001b[A\\n\",\n      \"Epoch 5:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.0313, lr=9.35e-5, step=1117]\\u001b[A\\n\",\n      \"Epoch 5:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.0246, lr=9.35e-5, step=1118]\\u001b[A\\n\",\n      \"Epoch 5:  26%|█████████████████████████████████████████████████████▉                                                                                                                                                           | 55/213 [00:44<02:06,  1.25it/s, loss=0.0246, lr=9.35e-5, step=1118]\\u001b[A\\n\",\n      \"Epoch 5:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.25it/s, loss=0.00968, lr=9.34e-5, step=1119]\\u001b[A\\n\",\n      \"Epoch 5:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [00:44<02:05,  1.25it/s, loss=0.00968, lr=9.34e-5, step=1119]\\u001b[A\\n\",\n      \"Epoch 5:  26%|██████████████████████████████████████████████████████▉                                                                                                                                                          | 56/213 [00:45<02:05,  1.25it/s, loss=0.0163, lr=9.34e-5, step=1120]\\u001b[A\\n\",\n      \"Epoch 5:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.25it/s, loss=0.0163, lr=9.34e-5, step=1120]\\u001b[A\\n\",\n      \"Epoch 5:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.25it/s, loss=0.0317, lr=9.34e-5, step=1121]\\u001b[A\\n\",\n      \"Epoch 5:  27%|████████████████████████████████████████████████████████▉                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0317, lr=9.34e-5, step=1121]\\u001b[A\\n\",\n      \"Epoch 5:  27%|████████████████████████████████████████████████████████▉                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0181, lr=9.34e-5, step=1122]\\u001b[A\\n\",\n      \"Epoch 5:  28%|█████████████████████████████████████████████████████████▉                                                                                                                                                       | 59/213 [00:47<02:03,  1.24it/s, loss=0.0181, lr=9.34e-5, step=1122]\\u001b[A\\n\",\n      \"Epoch 5:  28%|█████████████████████████████████████████████████████████▉                                                                                                                                                       | 59/213 [00:47<02:03,  1.24it/s, loss=0.0406, lr=9.34e-5, step=1123]\\u001b[A\\n\",\n      \"Epoch 5:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0406, lr=9.34e-5, step=1123]\\u001b[A\\n\",\n      \"Epoch 5:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0124, lr=9.33e-5, step=1124]\\u001b[A\\n\",\n      \"Epoch 5:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:49<02:01,  1.25it/s, loss=0.0124, lr=9.33e-5, step=1124]\\u001b[A\\n\",\n      \"Epoch 5:  29%|████████████████████████████████████████████████████████████▏                                                                                                                                                     | 61/213 [00:49<02:01,  1.25it/s, loss=0.018, lr=9.33e-5, step=1125]\\u001b[A\\n\",\n      \"Epoch 5:  29%|█████████████████████████████████████████████████████████████▏                                                                                                                                                    | 62/213 [00:49<02:00,  1.25it/s, loss=0.018, lr=9.33e-5, step=1125]\\u001b[A\\n\",\n      \"Epoch 5:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:49<02:00,  1.25it/s, loss=0.00857, lr=9.33e-5, step=1126]\\u001b[A\\n\",\n      \"Epoch 5:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<02:00,  1.25it/s, loss=0.00857, lr=9.33e-5, step=1126]\\u001b[A\\n\",\n      \"Epoch 5:  30%|█████████████████████████████████████████████████████████████▊                                                                                                                                                   | 63/213 [00:50<02:00,  1.25it/s, loss=0.0266, lr=9.33e-5, step=1127]\\u001b[A\\n\",\n      \"Epoch 5:  30%|██████████████████████████████████████████████████████████████▊                                                                                                                                                  | 64/213 [00:51<01:59,  1.24it/s, loss=0.0266, lr=9.33e-5, step=1127]\\u001b[A\\n\",\n      \"Epoch 5:  30%|██████████████████████████████████████████████████████████████▊                                                                                                                                                  | 64/213 [00:51<01:59,  1.24it/s, loss=0.0172, lr=9.33e-5, step=1128]\\u001b[A\\n\",\n      \"Epoch 5:  31%|███████████████████████████████████████████████████████████████▊                                                                                                                                                 | 65/213 [00:52<01:58,  1.24it/s, loss=0.0172, lr=9.33e-5, step=1128]\\u001b[A\\n\",\n      \"Epoch 5:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:52<01:58,  1.24it/s, loss=0.013, lr=9.32e-5, step=1129]\\u001b[A\\n\",\n      \"Epoch 5:  31%|█████████████████████████████████████████████████████████████████                                                                                                                                                 | 66/213 [00:53<01:57,  1.25it/s, loss=0.013, lr=9.32e-5, step=1129]\\u001b[A\\n\",\n      \"Epoch 5:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:53<01:57,  1.25it/s, loss=0.0264, lr=9.32e-5, step=1130]\\u001b[A\\n\",\n      \"Epoch 5:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:56,  1.25it/s, loss=0.0264, lr=9.32e-5, step=1130]\\u001b[A\\n\",\n      \"Epoch 5:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:56,  1.25it/s, loss=0.0203, lr=9.32e-5, step=1131]\\u001b[A\\n\",\n      \"Epoch 5:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:56,  1.25it/s, loss=0.0203, lr=9.32e-5, step=1131]\\u001b[A\\n\",\n      \"Epoch 5:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:56,  1.25it/s, loss=0.0249, lr=9.32e-5, step=1132]\\u001b[A\\n\",\n      \"Epoch 5:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.0249, lr=9.32e-5, step=1132]\\u001b[A\\n\",\n      \"Epoch 5:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.0148, lr=9.31e-5, step=1133]\\u001b[A\\n\",\n      \"Epoch 5:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.0148, lr=9.31e-5, step=1133]\\u001b[A\\n\",\n      \"Epoch 5:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.0245, lr=9.31e-5, step=1134]\\u001b[A\\n\",\n      \"Epoch 5:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:57<01:53,  1.25it/s, loss=0.0245, lr=9.31e-5, step=1134]\\u001b[A\\n\",\n      \"Epoch 5:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:57<01:53,  1.25it/s, loss=0.0144, lr=9.31e-5, step=1135]\\u001b[A\\n\",\n      \"Epoch 5:  34%|██████████████████████████████████████████████████████████████████████▋                                                                                                                                          | 72/213 [00:57<01:53,  1.25it/s, loss=0.0144, lr=9.31e-5, step=1135]\\u001b[A\\n\",\n      \"Epoch 5:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [00:57<01:53,  1.25it/s, loss=0.00903, lr=9.31e-5, step=1136]\\u001b[A\\n\",\n      \"Epoch 5:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 73/213 [00:58<01:52,  1.25it/s, loss=0.00903, lr=9.31e-5, step=1136]\\u001b[A\\n\",\n      \"Epoch 5:  34%|███████████████████████████████████████████████████████████████████████▋                                                                                                                                         | 73/213 [00:58<01:52,  1.25it/s, loss=0.0162, lr=9.31e-5, step=1137]\\u001b[A\\n\",\n      \"Epoch 5:  35%|████████████████████████████████████████████████████████████████████████▌                                                                                                                                        | 74/213 [00:59<01:51,  1.25it/s, loss=0.0162, lr=9.31e-5, step=1137]\\u001b[A\\n\",\n      \"Epoch 5:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.25it/s, loss=0.0242, lr=9.3e-5, step=1138]\\u001b[A\\n\",\n      \"Epoch 5:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.25it/s, loss=0.0242, lr=9.3e-5, step=1138]\\u001b[A\\n\",\n      \"Epoch 5:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.25it/s, loss=0.0552, lr=9.3e-5, step=1139]\\u001b[A\\n\",\n      \"Epoch 5:  36%|██████████████████████████████████████████████████████████████████████████▉                                                                                                                                       | 76/213 [01:01<01:49,  1.25it/s, loss=0.0552, lr=9.3e-5, step=1139]\\u001b[A\\n\",\n      \"Epoch 5:  36%|██████████████████████████████████████████████████████████████████████████▉                                                                                                                                       | 76/213 [01:01<01:49,  1.25it/s, loss=0.0138, lr=9.3e-5, step=1140]\\u001b[A\\n\",\n      \"Epoch 5:  36%|███████████████████████████████████████████████████████████████████████████▉                                                                                                                                      | 77/213 [01:01<01:48,  1.25it/s, loss=0.0138, lr=9.3e-5, step=1140]\\u001b[A\\n\",\n      \"Epoch 5:  36%|███████████████████████████████████████████████████████████████████████████▉                                                                                                                                      | 77/213 [01:01<01:48,  1.25it/s, loss=0.0221, lr=9.3e-5, step=1141]\\u001b[A\\n\",\n      \"Epoch 5:  37%|████████████████████████████████████████████████████████████████████████████▉                                                                                                                                     | 78/213 [01:02<01:48,  1.25it/s, loss=0.0221, lr=9.3e-5, step=1141]\\u001b[A\\n\",\n      \"Epoch 5:  37%|████████████████████████████████████████████████████████████████████████████▉                                                                                                                                     | 78/213 [01:02<01:48,  1.25it/s, loss=0.0122, lr=9.3e-5, step=1142]\\u001b[A\\n\",\n      \"Epoch 5:  37%|█████████████████████████████████████████████████████████████████████████████▉                                                                                                                                    | 79/213 [01:03<01:47,  1.25it/s, loss=0.0122, lr=9.3e-5, step=1142]\\u001b[A\\n\",\n      \"Epoch 5:  37%|█████████████████████████████████████████████████████████████████████████████▌                                                                                                                                   | 79/213 [01:03<01:47,  1.25it/s, loss=0.0094, lr=9.29e-5, step=1143]\\u001b[A\\n\",\n      \"Epoch 5:  38%|██████████████████████████████████████████████████████████████████████████████▍                                                                                                                                  | 80/213 [01:04<01:46,  1.25it/s, loss=0.0094, lr=9.29e-5, step=1143]\\u001b[A\\n\",\n      \"Epoch 5:  38%|██████████████████████████████████████████████████████████████████████████████▍                                                                                                                                  | 80/213 [01:04<01:46,  1.25it/s, loss=0.0119, lr=9.29e-5, step=1144]\\u001b[A\\n\",\n      \"Epoch 5:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [01:05<01:45,  1.25it/s, loss=0.0119, lr=9.29e-5, step=1144]\\u001b[A\\n\",\n      \"Epoch 5:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [01:05<01:45,  1.25it/s, loss=0.0184, lr=9.29e-5, step=1145]\\u001b[A\\n\",\n      \"Epoch 5:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.24it/s, loss=0.0184, lr=9.29e-5, step=1145]\\u001b[A\\n\",\n      \"Epoch 5:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.24it/s, loss=0.0464, lr=9.29e-5, step=1146]\\u001b[A\\n\",\n      \"Epoch 5:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:44,  1.25it/s, loss=0.0464, lr=9.29e-5, step=1146]\\u001b[A\\n\",\n      \"Epoch 5:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:44,  1.25it/s, loss=0.0464, lr=9.28e-5, step=1147]\\u001b[A\\n\",\n      \"Epoch 5:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:43,  1.25it/s, loss=0.0464, lr=9.28e-5, step=1147]\\u001b[A\\n\",\n      \"Epoch 5:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:43,  1.25it/s, loss=0.0553, lr=9.28e-5, step=1148]\\u001b[A\\n\",\n      \"Epoch 5:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0553, lr=9.28e-5, step=1148]\\u001b[A\\n\",\n      \"Epoch 5:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0101, lr=9.28e-5, step=1149]\\u001b[A\\n\",\n      \"Epoch 5:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:09<01:41,  1.25it/s, loss=0.0101, lr=9.28e-5, step=1149]\\u001b[A\\n\",\n      \"Epoch 5:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:09<01:41,  1.25it/s, loss=0.0278, lr=9.28e-5, step=1150]\\u001b[A\\n\",\n      \"Epoch 5:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0278, lr=9.28e-5, step=1150]\\u001b[A\\n\",\n      \"Epoch 5:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0284, lr=9.28e-5, step=1151]\\u001b[A\\n\",\n      \"Epoch 5:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0284, lr=9.28e-5, step=1151]\\u001b[A\\n\",\n      \"Epoch 5:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0139, lr=9.27e-5, step=1152]\\u001b[A\\n\",\n      \"Epoch 5:  42%|███████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                         | 89/213 [01:11<01:39,  1.25it/s, loss=0.0139, lr=9.27e-5, step=1152]\\u001b[A\\n\",\n      \"Epoch 5:  42%|███████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                         | 89/213 [01:11<01:39,  1.25it/s, loss=0.0119, lr=9.27e-5, step=1153]\\u001b[A\\n\",\n      \"Epoch 5:  42%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                        | 90/213 [01:12<01:38,  1.25it/s, loss=0.0119, lr=9.27e-5, step=1153]\\u001b[A\\n\",\n      \"Epoch 5:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:12<01:38,  1.25it/s, loss=0.012, lr=9.27e-5, step=1154]\\u001b[A\\n\",\n      \"Epoch 5:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:13<01:37,  1.25it/s, loss=0.012, lr=9.27e-5, step=1154]\\u001b[A\\n\",\n      \"Epoch 5:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:13<01:37,  1.25it/s, loss=0.0225, lr=9.27e-5, step=1155]\\u001b[A\\n\",\n      \"Epoch 5:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0225, lr=9.27e-5, step=1155]\\u001b[A\\n\",\n      \"Epoch 5:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0262, lr=9.27e-5, step=1156]\\u001b[A\\n\",\n      \"Epoch 5:  44%|███████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                     | 93/213 [01:14<01:35,  1.25it/s, loss=0.0262, lr=9.27e-5, step=1156]\\u001b[A\\n\",\n      \"Epoch 5:  44%|███████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                     | 93/213 [01:14<01:35,  1.25it/s, loss=0.0242, lr=9.26e-5, step=1157]\\u001b[A\\n\",\n      \"Epoch 5:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0242, lr=9.26e-5, step=1157]\\u001b[A\\n\",\n      \"Epoch 5:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0423, lr=9.26e-5, step=1158]\\u001b[A\\n\",\n      \"Epoch 5:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0423, lr=9.26e-5, step=1158]\\u001b[A\\n\",\n      \"Epoch 5:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0136, lr=9.26e-5, step=1159]\\u001b[A\\n\",\n      \"Epoch 5:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                  | 96/213 [01:17<01:33,  1.25it/s, loss=0.0136, lr=9.26e-5, step=1159]\\u001b[A\\n\",\n      \"Epoch 5:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                  | 96/213 [01:17<01:33,  1.25it/s, loss=0.0251, lr=9.26e-5, step=1160]\\u001b[A\\n\",\n      \"Epoch 5:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0251, lr=9.26e-5, step=1160]\\u001b[A\\n\",\n      \"Epoch 5:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0163, lr=9.25e-5, step=1161]\\u001b[A\\n\",\n      \"Epoch 5:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.0163, lr=9.25e-5, step=1161]\\u001b[A\\n\",\n      \"Epoch 5:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.0355, lr=9.25e-5, step=1162]\\u001b[A\\n\",\n      \"Epoch 5:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                               | 99/213 [01:19<01:30,  1.25it/s, loss=0.0355, lr=9.25e-5, step=1162]\\u001b[A\\n\",\n      \"Epoch 5:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                               | 99/213 [01:19<01:30,  1.25it/s, loss=0.0296, lr=9.25e-5, step=1163]\\u001b[A\\n\",\n      \"Epoch 5:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                              | 100/213 [01:20<01:30,  1.25it/s, loss=0.0296, lr=9.25e-5, step=1163]\\u001b[A\\n\",\n      \"Epoch 5:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                              | 100/213 [01:20<01:30,  1.25it/s, loss=0.0154, lr=9.25e-5, step=1164]\\u001b[A\\n\",\n      \"Epoch 5:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:21<01:29,  1.25it/s, loss=0.0154, lr=9.25e-5, step=1164]\\u001b[A\\n\",\n      \"Epoch 5:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:21<01:29,  1.25it/s, loss=0.0197, lr=9.25e-5, step=1165]\\u001b[A\\n\",\n      \"Epoch 5:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 102/213 [01:21<01:28,  1.25it/s, loss=0.0197, lr=9.25e-5, step=1165]\\u001b[A\\n\",\n      \"Epoch 5:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 102/213 [01:21<01:28,  1.25it/s, loss=0.0118, lr=9.24e-5, step=1166]\\u001b[A\\n\",\n      \"Epoch 5:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.0118, lr=9.24e-5, step=1166]\\u001b[A\\n\",\n      \"Epoch 5:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.0128, lr=9.24e-5, step=1167]\\u001b[A\\n\",\n      \"Epoch 5:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 104/213 [01:23<01:27,  1.25it/s, loss=0.0128, lr=9.24e-5, step=1167]\\u001b[A\\n\",\n      \"Epoch 5:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 104/213 [01:23<01:27,  1.25it/s, loss=0.0194, lr=9.24e-5, step=1168]\\u001b[A\\n\",\n      \"Epoch 5:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0194, lr=9.24e-5, step=1168]\\u001b[A\\n\",\n      \"Epoch 5:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0307, lr=9.24e-5, step=1169]\\u001b[A\\n\",\n      \"Epoch 5:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:25<01:25,  1.25it/s, loss=0.0307, lr=9.24e-5, step=1169]\\u001b[A\\n\",\n      \"Epoch 5:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:25<01:25,  1.25it/s, loss=0.0203, lr=9.23e-5, step=1170]\\u001b[A\\n\",\n      \"Epoch 5:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0203, lr=9.23e-5, step=1170]\\u001b[A\\n\",\n      \"Epoch 5:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0107, lr=9.23e-5, step=1171]\\u001b[A\\n\",\n      \"Epoch 5:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.26it/s, loss=0.0107, lr=9.23e-5, step=1171]\\u001b[A\\n\",\n      \"Epoch 5:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.26it/s, loss=0.0498, lr=9.23e-5, step=1172]\\u001b[A\\n\",\n      \"Epoch 5:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0498, lr=9.23e-5, step=1172]\\u001b[A\\n\",\n      \"Epoch 5:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0308, lr=9.23e-5, step=1173]\\u001b[A\\n\",\n      \"Epoch 5:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0308, lr=9.23e-5, step=1173]\\u001b[A\\n\",\n      \"Epoch 5:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0311, lr=9.23e-5, step=1174]\\u001b[A\\n\",\n      \"Epoch 5:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:29<01:21,  1.26it/s, loss=0.0311, lr=9.23e-5, step=1174]\\u001b[A\\n\",\n      \"Epoch 5:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:29<01:21,  1.26it/s, loss=0.0133, lr=9.22e-5, step=1175]\\u001b[A\\n\",\n      \"Epoch 5:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0133, lr=9.22e-5, step=1175]\\u001b[A\\n\",\n      \"Epoch 5:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0415, lr=9.22e-5, step=1176]\\u001b[A\\n\",\n      \"Epoch 5:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:19,  1.26it/s, loss=0.0415, lr=9.22e-5, step=1176]\\u001b[A\\n\",\n      \"Epoch 5:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:30<01:19,  1.26it/s, loss=0.00724, lr=9.22e-5, step=1177]\\u001b[A\\n\",\n      \"Epoch 5:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:31<01:19,  1.25it/s, loss=0.00724, lr=9.22e-5, step=1177]\\u001b[A\\n\",\n      \"Epoch 5:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 114/213 [01:31<01:19,  1.25it/s, loss=0.0121, lr=9.22e-5, step=1178]\\u001b[A\\n\",\n      \"Epoch 5:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.0121, lr=9.22e-5, step=1178]\\u001b[A\\n\",\n      \"Epoch 5:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 115/213 [01:32<01:18,  1.25it/s, loss=0.021, lr=9.21e-5, step=1179]\\u001b[A\\n\",\n      \"Epoch 5:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 116/213 [01:32<01:17,  1.26it/s, loss=0.021, lr=9.21e-5, step=1179]\\u001b[A\\n\",\n      \"Epoch 5:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                              | 116/213 [01:33<01:17,  1.26it/s, loss=0.0202, lr=9.21e-5, step=1180]\\u001b[A\\n\",\n      \"Epoch 5:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0202, lr=9.21e-5, step=1180]\\u001b[A\\n\",\n      \"Epoch 5:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0166, lr=9.21e-5, step=1181]\\u001b[A\\n\",\n      \"Epoch 5:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                            | 118/213 [01:34<01:16,  1.25it/s, loss=0.0166, lr=9.21e-5, step=1181]\\u001b[A\\n\",\n      \"Epoch 5:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                            | 118/213 [01:34<01:16,  1.25it/s, loss=0.0103, lr=9.21e-5, step=1182]\\u001b[A\\n\",\n      \"Epoch 5:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                           | 119/213 [01:35<01:14,  1.25it/s, loss=0.0103, lr=9.21e-5, step=1182]\\u001b[A\\n\",\n      \"Epoch 5:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                           | 119/213 [01:35<01:14,  1.25it/s, loss=0.0353, lr=9.21e-5, step=1183]\\u001b[A\\n\",\n      \"Epoch 5:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                          | 120/213 [01:36<01:14,  1.25it/s, loss=0.0353, lr=9.21e-5, step=1183]\\u001b[A\\n\",\n      \"Epoch 5:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 120/213 [01:36<01:14,  1.25it/s, loss=0.0236, lr=9.2e-5, step=1184]\\u001b[A\\n\",\n      \"Epoch 5:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:37<01:13,  1.25it/s, loss=0.0236, lr=9.2e-5, step=1184]\\u001b[A\\n\",\n      \"Epoch 5:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                          | 121/213 [01:37<01:13,  1.25it/s, loss=0.0213, lr=9.2e-5, step=1185]\\u001b[A\\n\",\n      \"Epoch 5:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                         | 122/213 [01:37<01:12,  1.25it/s, loss=0.0213, lr=9.2e-5, step=1185]\\u001b[A\\n\",\n      \"Epoch 5:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                         | 122/213 [01:37<01:12,  1.25it/s, loss=0.0164, lr=9.2e-5, step=1186]\\u001b[A\\n\",\n      \"Epoch 5:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:38<01:11,  1.25it/s, loss=0.0164, lr=9.2e-5, step=1186]\\u001b[A\\n\",\n      \"Epoch 5:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:38<01:11,  1.25it/s, loss=0.0181, lr=9.2e-5, step=1187]\\u001b[A\\n\",\n      \"Epoch 5:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:39<01:11,  1.25it/s, loss=0.0181, lr=9.2e-5, step=1187]\\u001b[A\\n\",\n      \"Epoch 5:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                       | 124/213 [01:39<01:11,  1.25it/s, loss=0.0115, lr=9.19e-5, step=1188]\\u001b[A\\n\",\n      \"Epoch 5:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                      | 125/213 [01:40<01:10,  1.25it/s, loss=0.0115, lr=9.19e-5, step=1188]\\u001b[A\\n\",\n      \"Epoch 5:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                      | 125/213 [01:40<01:10,  1.25it/s, loss=0.043, lr=9.19e-5, step=1189]\\u001b[A\\n\",\n      \"Epoch 5:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                     | 126/213 [01:40<01:09,  1.25it/s, loss=0.043, lr=9.19e-5, step=1189]\\u001b[A\\n\",\n      \"Epoch 5:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     | 126/213 [01:41<01:09,  1.25it/s, loss=0.0113, lr=9.19e-5, step=1190]\\u001b[A\\n\",\n      \"Epoch 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                    | 127/213 [01:41<01:09,  1.25it/s, loss=0.0113, lr=9.19e-5, step=1190]\\u001b[A\\n\",\n      \"Epoch 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                    | 127/213 [01:41<01:09,  1.25it/s, loss=0.0105, lr=9.19e-5, step=1191]\\u001b[A\\n\",\n      \"Epoch 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                   | 128/213 [01:42<01:08,  1.25it/s, loss=0.0105, lr=9.19e-5, step=1191]\\u001b[A\\n\",\n      \"Epoch 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                   | 128/213 [01:42<01:08,  1.25it/s, loss=0.0372, lr=9.18e-5, step=1192]\\u001b[A\\n\",\n      \"Epoch 5:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.0372, lr=9.18e-5, step=1192]\\u001b[A\\n\",\n      \"Epoch 5:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.0162, lr=9.18e-5, step=1193]\\u001b[A\\n\",\n      \"Epoch 5:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                 | 130/213 [01:44<01:06,  1.25it/s, loss=0.0162, lr=9.18e-5, step=1193]\\u001b[A\\n\",\n      \"Epoch 5:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:44<01:06,  1.25it/s, loss=0.00759, lr=9.18e-5, step=1194]\\u001b[A\\n\",\n      \"Epoch 5:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.00759, lr=9.18e-5, step=1194]\\u001b[A\\n\",\n      \"Epoch 5:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [01:45<01:05,  1.25it/s, loss=0.0178, lr=9.18e-5, step=1195]\\u001b[A\\n\",\n      \"Epoch 5:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.25it/s, loss=0.0178, lr=9.18e-5, step=1195]\\u001b[A\\n\",\n      \"Epoch 5:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.25it/s, loss=0.0195, lr=9.18e-5, step=1196]\\u001b[A\\n\",\n      \"Epoch 5:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0195, lr=9.18e-5, step=1196]\\u001b[A\\n\",\n      \"Epoch 5:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0216, lr=9.17e-5, step=1197]\\u001b[A\\n\",\n      \"Epoch 5:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0216, lr=9.17e-5, step=1197]\\u001b[A\\n\",\n      \"Epoch 5:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0138, lr=9.17e-5, step=1198]\\u001b[A\\n\",\n      \"Epoch 5:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.26it/s, loss=0.0138, lr=9.17e-5, step=1198]\\u001b[A\\n\",\n      \"Epoch 5:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.26it/s, loss=0.0618, lr=9.17e-5, step=1199]\\u001b[A\\n\",\n      \"Epoch 5:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:48<01:01,  1.26it/s, loss=0.0618, lr=9.17e-5, step=1199]\\u001b[A\\n\",\n      \"Epoch 5:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:49<01:01,  1.26it/s, loss=0.0128, lr=9.17e-5, step=1200]\\u001b[A\\n\",\n      \"Epoch 5:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.0128, lr=9.17e-5, step=1200]\\u001b[A\\n\",\n      \"Epoch 5:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.0179, lr=9.16e-5, step=1201]\\u001b[A\\n\",\n      \"Epoch 5:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<00:59,  1.26it/s, loss=0.0179, lr=9.16e-5, step=1201]\\u001b[A\\n\",\n      \"Epoch 5:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:50<00:59,  1.26it/s, loss=0.00633, lr=9.16e-5, step=1202]\\u001b[A\\n\",\n      \"Epoch 5:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.00633, lr=9.16e-5, step=1202]\\u001b[A\\n\",\n      \"Epoch 5:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0368, lr=9.16e-5, step=1203]\\u001b[A\\n\",\n      \"Epoch 5:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0368, lr=9.16e-5, step=1203]\\u001b[A\\n\",\n      \"Epoch 5:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.038, lr=9.16e-5, step=1204]\\u001b[A\\n\",\n      \"Epoch 5:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.038, lr=9.16e-5, step=1204]\\u001b[A\\n\",\n      \"Epoch 5:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0378, lr=9.16e-5, step=1205]\\u001b[A\\n\",\n      \"Epoch 5:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0378, lr=9.16e-5, step=1205]\\u001b[A\\n\",\n      \"Epoch 5:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0252, lr=9.15e-5, step=1206]\\u001b[A\\n\",\n      \"Epoch 5:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0252, lr=9.15e-5, step=1206]\\u001b[A\\n\",\n      \"Epoch 5:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0191, lr=9.15e-5, step=1207]\\u001b[A\\n\",\n      \"Epoch 5:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0191, lr=9.15e-5, step=1207]\\u001b[A\\n\",\n      \"Epoch 5:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0171, lr=9.15e-5, step=1208]\\u001b[A\\n\",\n      \"Epoch 5:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:56<00:53,  1.26it/s, loss=0.0171, lr=9.15e-5, step=1208]\\u001b[A\\n\",\n      \"Epoch 5:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:56<00:53,  1.26it/s, loss=0.0482, lr=9.15e-5, step=1209]\\u001b[A\\n\",\n      \"Epoch 5:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.0482, lr=9.15e-5, step=1209]\\u001b[A\\n\",\n      \"Epoch 5:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.019, lr=9.14e-5, step=1210]\\u001b[A\\n\",\n      \"Epoch 5:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.019, lr=9.14e-5, step=1210]\\u001b[A\\n\",\n      \"Epoch 5:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.022, lr=9.14e-5, step=1211]\\u001b[A\\n\",\n      \"Epoch 5:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                               | 148/213 [01:58<00:51,  1.27it/s, loss=0.022, lr=9.14e-5, step=1211]\\u001b[A\\n\",\n      \"Epoch 5:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 148/213 [01:58<00:51,  1.27it/s, loss=0.0255, lr=9.14e-5, step=1212]\\u001b[A\\n\",\n      \"Epoch 5:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0255, lr=9.14e-5, step=1212]\\u001b[A\\n\",\n      \"Epoch 5:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0142, lr=9.14e-5, step=1213]\\u001b[A\\n\",\n      \"Epoch 5:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                             | 150/213 [02:00<00:50,  1.26it/s, loss=0.0142, lr=9.14e-5, step=1213]\\u001b[A\\n\",\n      \"Epoch 5:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                             | 150/213 [02:00<00:50,  1.26it/s, loss=0.0201, lr=9.13e-5, step=1214]\\u001b[A\\n\",\n      \"Epoch 5:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0201, lr=9.13e-5, step=1214]\\u001b[A\\n\",\n      \"Epoch 5:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0144, lr=9.13e-5, step=1215]\\u001b[A\\n\",\n      \"Epoch 5:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.0144, lr=9.13e-5, step=1215]\\u001b[A\\n\",\n      \"Epoch 5:  71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.017, lr=9.13e-5, step=1216]\\u001b[A\\n\",\n      \"Epoch 5:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.017, lr=9.13e-5, step=1216]\\u001b[A\\n\",\n      \"Epoch 5:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0198, lr=9.13e-5, step=1217]\\u001b[A\\n\",\n      \"Epoch 5:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0198, lr=9.13e-5, step=1217]\\u001b[A\\n\",\n      \"Epoch 5:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0156, lr=9.12e-5, step=1218]\\u001b[A\\n\",\n      \"Epoch 5:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [02:04<00:46,  1.26it/s, loss=0.0156, lr=9.12e-5, step=1218]\\u001b[A\\n\",\n      \"Epoch 5:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [02:04<00:46,  1.26it/s, loss=0.0123, lr=9.12e-5, step=1219]\\u001b[A\\n\",\n      \"Epoch 5:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0123, lr=9.12e-5, step=1219]\\u001b[A\\n\",\n      \"Epoch 5:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0169, lr=9.12e-5, step=1220]\\u001b[A\\n\",\n      \"Epoch 5:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0169, lr=9.12e-5, step=1220]\\u001b[A\\n\",\n      \"Epoch 5:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0305, lr=9.12e-5, step=1221]\\u001b[A\\n\",\n      \"Epoch 5:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:06<00:43,  1.26it/s, loss=0.0305, lr=9.12e-5, step=1221]\\u001b[A\\n\",\n      \"Epoch 5:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:06<00:43,  1.26it/s, loss=0.0105, lr=9.12e-5, step=1222]\\u001b[A\\n\",\n      \"Epoch 5:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                    | 159/213 [02:07<00:42,  1.26it/s, loss=0.0105, lr=9.12e-5, step=1222]\\u001b[A\\n\",\n      \"Epoch 5:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:07<00:42,  1.26it/s, loss=0.00832, lr=9.11e-5, step=1223]\\u001b[A\\n\",\n      \"Epoch 5:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:08<00:42,  1.26it/s, loss=0.00832, lr=9.11e-5, step=1223]\\u001b[A\\n\",\n      \"Epoch 5:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:08<00:42,  1.26it/s, loss=0.00923, lr=9.11e-5, step=1224]\\u001b[A\\n\",\n      \"Epoch 5:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.00923, lr=9.11e-5, step=1224]\\u001b[A\\n\",\n      \"Epoch 5:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                   | 161/213 [02:08<00:41,  1.25it/s, loss=0.032, lr=9.11e-5, step=1225]\\u001b[A\\n\",\n      \"Epoch 5:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                  | 162/213 [02:09<00:40,  1.25it/s, loss=0.032, lr=9.11e-5, step=1225]\\u001b[A\\n\",\n      \"Epoch 5:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0221, lr=9.11e-5, step=1226]\\u001b[A\\n\",\n      \"Epoch 5:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:40,  1.25it/s, loss=0.0221, lr=9.11e-5, step=1226]\\u001b[A\\n\",\n      \"Epoch 5:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                 | 163/213 [02:10<00:40,  1.25it/s, loss=0.0258, lr=9.1e-5, step=1227]\\u001b[A\\n\",\n      \"Epoch 5:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                | 164/213 [02:11<00:39,  1.25it/s, loss=0.0258, lr=9.1e-5, step=1227]\\u001b[A\\n\",\n      \"Epoch 5:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                | 164/213 [02:11<00:39,  1.25it/s, loss=0.017, lr=9.1e-5, step=1228]\\u001b[A\\n\",\n      \"Epoch 5:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                               | 165/213 [02:12<00:38,  1.25it/s, loss=0.017, lr=9.1e-5, step=1228]\\u001b[A\\n\",\n      \"Epoch 5:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                               | 165/213 [02:12<00:38,  1.25it/s, loss=0.0175, lr=9.1e-5, step=1229]\\u001b[A\\n\",\n      \"Epoch 5:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:12<00:37,  1.25it/s, loss=0.0175, lr=9.1e-5, step=1229]\\u001b[A\\n\",\n      \"Epoch 5:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                              | 166/213 [02:12<00:37,  1.25it/s, loss=0.0201, lr=9.1e-5, step=1230]\\u001b[A\\n\",\n      \"Epoch 5:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0201, lr=9.1e-5, step=1230]\\u001b[A\\n\",\n      \"Epoch 5:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0142, lr=9.09e-5, step=1231]\\u001b[A\\n\",\n      \"Epoch 5:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.0142, lr=9.09e-5, step=1231]\\u001b[A\\n\",\n      \"Epoch 5:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.018, lr=9.09e-5, step=1232]\\u001b[A\\n\",\n      \"Epoch 5:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:15<00:34,  1.26it/s, loss=0.018, lr=9.09e-5, step=1232]\\u001b[A\\n\",\n      \"Epoch 5:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 169/213 [02:15<00:34,  1.26it/s, loss=0.0104, lr=9.09e-5, step=1233]\\u001b[A\\n\",\n      \"Epoch 5:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                          | 170/213 [02:16<00:34,  1.26it/s, loss=0.0104, lr=9.09e-5, step=1233]\\u001b[A\\n\",\n      \"Epoch 5:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                          | 170/213 [02:16<00:34,  1.26it/s, loss=0.0445, lr=9.09e-5, step=1234]\\u001b[A\\n\",\n      \"Epoch 5:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.25it/s, loss=0.0445, lr=9.09e-5, step=1234]\\u001b[A\\n\",\n      \"Epoch 5:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.25it/s, loss=0.0229, lr=9.08e-5, step=1235]\\u001b[A\\n\",\n      \"Epoch 5:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [02:17<00:32,  1.26it/s, loss=0.0229, lr=9.08e-5, step=1235]\\u001b[A\\n\",\n      \"Epoch 5:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [02:17<00:32,  1.26it/s, loss=0.0201, lr=9.08e-5, step=1236]\\u001b[A\\n\",\n      \"Epoch 5:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0201, lr=9.08e-5, step=1236]\\u001b[A\\n\",\n      \"Epoch 5:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0386, lr=9.08e-5, step=1237]\\u001b[A\\n\",\n      \"Epoch 5:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0386, lr=9.08e-5, step=1237]\\u001b[A\\n\",\n      \"Epoch 5:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.00986, lr=9.08e-5, step=1238]\\u001b[A\\n\",\n      \"Epoch 5:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.00986, lr=9.08e-5, step=1238]\\u001b[A\\n\",\n      \"Epoch 5:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.0135, lr=9.07e-5, step=1239]\\u001b[A\\n\",\n      \"Epoch 5:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:20<00:29,  1.25it/s, loss=0.0135, lr=9.07e-5, step=1239]\\u001b[A\\n\",\n      \"Epoch 5:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:20<00:29,  1.25it/s, loss=0.0137, lr=9.07e-5, step=1240]\\u001b[A\\n\",\n      \"Epoch 5:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.0137, lr=9.07e-5, step=1240]\\u001b[A\\n\",\n      \"Epoch 5:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.0179, lr=9.07e-5, step=1241]\\u001b[A\\n\",\n      \"Epoch 5:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:22<00:28,  1.25it/s, loss=0.0179, lr=9.07e-5, step=1241]\\u001b[A\\n\",\n      \"Epoch 5:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:22<00:28,  1.25it/s, loss=0.0375, lr=9.07e-5, step=1242]\\u001b[A\\n\",\n      \"Epoch 5:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0375, lr=9.07e-5, step=1242]\\u001b[A\\n\",\n      \"Epoch 5:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0117, lr=9.06e-5, step=1243]\\u001b[A\\n\",\n      \"Epoch 5:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.0117, lr=9.06e-5, step=1243]\\u001b[A\\n\",\n      \"Epoch 5:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.0114, lr=9.06e-5, step=1244]\\u001b[A\\n\",\n      \"Epoch 5:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0114, lr=9.06e-5, step=1244]\\u001b[A\\n\",\n      \"Epoch 5:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0243, lr=9.06e-5, step=1245]\\u001b[A\\n\",\n      \"Epoch 5:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.0243, lr=9.06e-5, step=1245]\\u001b[A\\n\",\n      \"Epoch 5:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.00786, lr=9.06e-5, step=1246]\\u001b[A\\n\",\n      \"Epoch 5:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00786, lr=9.06e-5, step=1246]\\u001b[A\\n\",\n      \"Epoch 5:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00616, lr=9.05e-5, step=1247]\\u001b[A\\n\",\n      \"Epoch 5:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.00616, lr=9.05e-5, step=1247]\\u001b[A\\n\",\n      \"Epoch 5:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.00899, lr=9.05e-5, step=1248]\\u001b[A\\n\",\n      \"Epoch 5:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:28<00:22,  1.26it/s, loss=0.00899, lr=9.05e-5, step=1248]\\u001b[A\\n\",\n      \"Epoch 5:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                           | 185/213 [02:28<00:22,  1.26it/s, loss=0.0177, lr=9.05e-5, step=1249]\\u001b[A\\n\",\n      \"Epoch 5:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0177, lr=9.05e-5, step=1249]\\u001b[A\\n\",\n      \"Epoch 5:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0144, lr=9.05e-5, step=1250]\\u001b[A\\n\",\n      \"Epoch 5:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.26it/s, loss=0.0144, lr=9.05e-5, step=1250]\\u001b[A\\n\",\n      \"Epoch 5:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.26it/s, loss=0.0306, lr=9.05e-5, step=1251]\\u001b[A\\n\",\n      \"Epoch 5:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:19,  1.25it/s, loss=0.0306, lr=9.05e-5, step=1251]\\u001b[A\\n\",\n      \"Epoch 5:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:19,  1.25it/s, loss=0.0213, lr=9.04e-5, step=1252]\\u001b[A\\n\",\n      \"Epoch 5:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0213, lr=9.04e-5, step=1252]\\u001b[A\\n\",\n      \"Epoch 5:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0214, lr=9.04e-5, step=1253]\\u001b[A\\n\",\n      \"Epoch 5:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.0214, lr=9.04e-5, step=1253]\\u001b[A\\n\",\n      \"Epoch 5:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.0158, lr=9.04e-5, step=1254]\\u001b[A\\n\",\n      \"Epoch 5:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0158, lr=9.04e-5, step=1254]\\u001b[A\\n\",\n      \"Epoch 5:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0159, lr=9.04e-5, step=1255]\\u001b[A\\n\",\n      \"Epoch 5:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0159, lr=9.04e-5, step=1255]\\u001b[A\\n\",\n      \"Epoch 5:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0111, lr=9.03e-5, step=1256]\\u001b[A\\n\",\n      \"Epoch 5:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.0111, lr=9.03e-5, step=1256]\\u001b[A\\n\",\n      \"Epoch 5:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.0213, lr=9.03e-5, step=1257]\\u001b[A\\n\",\n      \"Epoch 5:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0213, lr=9.03e-5, step=1257]\\u001b[A\\n\",\n      \"Epoch 5:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0571, lr=9.03e-5, step=1258]\\u001b[A\\n\",\n      \"Epoch 5:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0571, lr=9.03e-5, step=1258]\\u001b[A\\n\",\n      \"Epoch 5:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.011, lr=9.03e-5, step=1259]\\u001b[A\\n\",\n      \"Epoch 5:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                | 196/213 [02:36<00:13,  1.25it/s, loss=0.011, lr=9.03e-5, step=1259]\\u001b[A\\n\",\n      \"Epoch 5:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0133, lr=9.02e-5, step=1260]\\u001b[A\\n\",\n      \"Epoch 5:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0133, lr=9.02e-5, step=1260]\\u001b[A\\n\",\n      \"Epoch 5:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎               | 197/213 [02:37<00:12,  1.25it/s, loss=0.013, lr=9.02e-5, step=1261]\\u001b[A\\n\",\n      \"Epoch 5:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:38<00:12,  1.25it/s, loss=0.013, lr=9.02e-5, step=1261]\\u001b[A\\n\",\n      \"Epoch 5:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:38<00:12,  1.25it/s, loss=0.0158, lr=9.02e-5, step=1262]\\u001b[A\\n\",\n      \"Epoch 5:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0158, lr=9.02e-5, step=1262]\\u001b[A\\n\",\n      \"Epoch 5:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00817, lr=9.02e-5, step=1263]\\u001b[A\\n\",\n      \"Epoch 5:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:40<00:10,  1.25it/s, loss=0.00817, lr=9.02e-5, step=1263]\\u001b[A\\n\",\n      \"Epoch 5:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:40<00:10,  1.25it/s, loss=0.0138, lr=9.01e-5, step=1264]\\u001b[A\\n\",\n      \"Epoch 5:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0138, lr=9.01e-5, step=1264]\\u001b[A\\n\",\n      \"Epoch 5:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0209, lr=9.01e-5, step=1265]\\u001b[A\\n\",\n      \"Epoch 5:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0209, lr=9.01e-5, step=1265]\\u001b[A\\n\",\n      \"Epoch 5:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0366, lr=9.01e-5, step=1266]\\u001b[A\\n\",\n      \"Epoch 5:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0366, lr=9.01e-5, step=1266]\\u001b[A\\n\",\n      \"Epoch 5:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0292, lr=9.01e-5, step=1267]\\u001b[A\\n\",\n      \"Epoch 5:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:43<00:07,  1.25it/s, loss=0.0292, lr=9.01e-5, step=1267]\\u001b[A\\n\",\n      \"Epoch 5:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:43<00:07,  1.25it/s, loss=0.00801, lr=9e-5, step=1268]\\u001b[A\\n\",\n      \"Epoch 5:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████        | 205/213 [02:44<00:06,  1.25it/s, loss=0.00801, lr=9e-5, step=1268]\\u001b[A\\n\",\n      \"Epoch 5:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████        | 205/213 [02:44<00:06,  1.25it/s, loss=0.017, lr=9e-5, step=1269]\\u001b[A\\n\",\n      \"Epoch 5:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████       | 206/213 [02:44<00:05,  1.25it/s, loss=0.017, lr=9e-5, step=1269]\\u001b[A\\n\",\n      \"Epoch 5:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████       | 206/213 [02:44<00:05,  1.25it/s, loss=0.021, lr=9e-5, step=1270]\\u001b[A\\n\",\n      \"Epoch 5:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:45<00:04,  1.25it/s, loss=0.021, lr=9e-5, step=1270]\\u001b[A\\n\",\n      \"Epoch 5:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:45<00:04,  1.25it/s, loss=0.0279, lr=9e-5, step=1271]\\u001b[A\\n\",\n      \"Epoch 5:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.0279, lr=9e-5, step=1271]\\u001b[A\\n\",\n      \"Epoch 5:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.0186, lr=8.99e-5, step=1272]\\u001b[A\\n\",\n      \"Epoch 5:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0186, lr=8.99e-5, step=1272]\\u001b[A\\n\",\n      \"Epoch 5:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.023, lr=8.99e-5, step=1273]\\u001b[A\\n\",\n      \"Epoch 5:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.023, lr=8.99e-5, step=1273]\\u001b[A\\n\",\n      \"Epoch 5:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.00574, lr=8.99e-5, step=1274]\\u001b[A\\n\",\n      \"Epoch 5:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.00574, lr=8.99e-5, step=1274]\\u001b[A\\n\",\n      \"Epoch 5:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.022, lr=8.99e-5, step=1275]\\u001b[A\\n\",\n      \"Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.022, lr=8.99e-5, step=1275]\\u001b[A\\n\",\n      \"Epoch 5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.0118, lr=8.98e-5, step=1276]\\u001b[A\\n\",\n      \"Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.0118, lr=8.98e-5, step=1276]\\u001b[A\\n\",\n      \"Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.0133, lr=8.98e-5, step=1277]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"32a06b66ccd04d73bdacb946bb5c3385\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:37<00:00,  2.15s/it, loss=0.0133, lr=8.98e-5, step=1277]\\n\",\n      \"Epoch 6: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [12:34<00:00,  1.51it/s, loss=0.011, lr=8.38e-5, step=1490]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c0bbb0ef049247de94b53f776265a423\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 6: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [17:18<00:00,  4.88s/it, loss=0.011, lr=8.38e-5, step=1490]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 7:   0%|                                                                                                                                                                                                                                                              | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 7:   0%|█▏                                                                                                                                                                                                                                           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step=1492]\\u001b[A\\n\",\n      \"Epoch 7:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:51,  1.22it/s, loss=0.00991, lr=8.38e-5, step=1492]\\u001b[A\\n\",\n      \"Epoch 7:   1%|██▉                                                                                                                                                                                                               | 3/213 [00:02<02:51,  1.22it/s, loss=0.0118, lr=8.37e-5, step=1493]\\u001b[A\\n\",\n      \"Epoch 7:   2%|███▉                                                                                                                                                                                                              | 4/213 [00:03<02:49,  1.24it/s, loss=0.0118, lr=8.37e-5, step=1493]\\u001b[A\\n\",\n      \"Epoch 7:   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                                                                                                                                                                    | 6/213 [00:04<02:46,  1.25it/s, loss=0.062, lr=8.37e-5, step=1495]\\u001b[A\\n\",\n      \"Epoch 7:   3%|█████▉                                                                                                                                                                                                           | 6/213 [00:04<02:46,  1.25it/s, loss=0.00407, lr=8.36e-5, step=1496]\\u001b[A\\n\",\n      \"Epoch 7:   3%|██████▊                                                                                                                                                                                                          | 7/213 [00:05<02:45,  1.25it/s, loss=0.00407, lr=8.36e-5, step=1496]\\u001b[A\\n\",\n      \"Epoch 7:   3%|██████▉                                                                                                                                                                                                           | 7/213 [00:05<02:45,  1.25it/s, loss=0.0162, lr=8.36e-5, step=1497]\\u001b[A\\n\",\n      \"Epoch 7:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:44,  1.25it/s, loss=0.0162, lr=8.36e-5, step=1497]\\u001b[A\\n\",\n      \"Epoch 7:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:44,  1.25it/s, loss=0.0171, lr=8.36e-5, step=1498]\\u001b[A\\n\",\n      \"Epoch 7:   4%|████████▊                                                                                                                                                                                                         | 9/213 [00:07<02:43,  1.25it/s, loss=0.0171, lr=8.36e-5, step=1498]\\u001b[A\\n\",\n      \"Epoch 7:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:43,  1.25it/s, loss=0.00765, lr=8.35e-5, step=1499]\\u001b[A\\n\",\n      \"Epoch 7:   5%|█████████▊                                                                                                                                                                                                      | 10/213 [00:08<02:42,  1.25it/s, loss=0.00765, lr=8.35e-5, step=1499]\\u001b[A\\n\",\n      \"Epoch 7:   5%|█████████▊                                                                                                                                                                                                       | 10/213 [00:08<02:42,  1.25it/s, loss=0.0102, lr=8.35e-5, step=1500]\\u001b[A\\n\",\n      \"Epoch 7:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:41,  1.25it/s, loss=0.0102, lr=8.35e-5, step=1500]\\u001b[A\\n\",\n      \"Epoch 7:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:41,  1.25it/s, loss=0.0201, lr=8.35e-5, step=1501]\\u001b[A\\n\",\n      \"Epoch 7:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:40,  1.25it/s, loss=0.0201, lr=8.35e-5, step=1501]\\u001b[A\\n\",\n      \"Epoch 7:   6%|███████████▊                                                                                                                                                                                                      | 12/213 [00:09<02:40,  1.25it/s, loss=0.028, lr=8.34e-5, step=1502]\\u001b[A\\n\",\n      \"Epoch 7:   6%|████████████▊                                                                                                                                                                                                     | 13/213 [00:10<02:39,  1.25it/s, loss=0.028, lr=8.34e-5, step=1502]\\u001b[A\\n\",\n      \"Epoch 7:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:39,  1.25it/s, loss=0.0225, lr=8.34e-5, step=1503]\\u001b[A\\n\",\n      \"Epoch 7:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:38,  1.25it/s, loss=0.0225, lr=8.34e-5, step=1503]\\u001b[A\\n\",\n      \"Epoch 7:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:38,  1.25it/s, loss=0.0421, lr=8.34e-5, step=1504]\\u001b[A\\n\",\n      \"Epoch 7:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:38,  1.25it/s, loss=0.0421, lr=8.34e-5, step=1504]\\u001b[A\\n\",\n      \"Epoch 7:   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                                                                                                                                                                  | 17/213 [00:13<02:36,  1.25it/s, loss=0.0122, lr=8.33e-5, step=1506]\\u001b[A\\n\",\n      \"Epoch 7:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:36,  1.25it/s, loss=0.0414, lr=8.33e-5, step=1507]\\u001b[A\\n\",\n      \"Epoch 7:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:35,  1.25it/s, loss=0.0414, lr=8.33e-5, step=1507]\\u001b[A\\n\",\n      \"Epoch 7:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:35,  1.25it/s, loss=0.0297, lr=8.33e-5, step=1508]\\u001b[A\\n\",\n      \"Epoch 7:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0297, lr=8.33e-5, step=1508]\\u001b[A\\n\",\n      \"Epoch 7:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0196, lr=8.32e-5, step=1509]\\u001b[A\\n\",\n      \"Epoch 7:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0196, lr=8.32e-5, step=1509]\\u001b[A\\n\",\n      \"Epoch 7:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0146, lr=8.32e-5, step=1510]\\u001b[A\\n\",\n      \"Epoch 7:  10%|████████████████████▌                                                                                                                                                                                            | 21/213 [00:16<02:33,  1.25it/s, loss=0.0146, lr=8.32e-5, step=1510]\\u001b[A\\n\",\n      \"Epoch 7:  10%|████████████████████▌                                                                                                                                                                                            | 21/213 [00:16<02:33,  1.25it/s, loss=0.0103, lr=8.32e-5, step=1511]\\u001b[A\\n\",\n      \"Epoch 7:  10%|█████████████████████▌                                                                                                                                                                                           | 22/213 [00:17<02:33,  1.25it/s, loss=0.0103, lr=8.32e-5, step=1511]\\u001b[A\\n\",\n      \"Epoch 7:  10%|█████████████████████▍                                                                                                                                                                                          | 22/213 [00:17<02:33,  1.25it/s, loss=0.00893, lr=8.31e-5, step=1512]\\u001b[A\\n\",\n      \"Epoch 7:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [00:18<02:32,  1.25it/s, loss=0.00893, lr=8.31e-5, step=1512]\\u001b[A\\n\",\n      \"Epoch 7:  11%|██████████████████████▌                                                                                                                                                                                          | 23/213 [00:18<02:32,  1.25it/s, loss=0.0385, lr=8.31e-5, step=1513]\\u001b[A\\n\",\n      \"Epoch 7:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:31,  1.25it/s, loss=0.0385, lr=8.31e-5, step=1513]\\u001b[A\\n\",\n      \"Epoch 7:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:31,  1.25it/s, loss=0.0121, lr=8.31e-5, step=1514]\\u001b[A\\n\",\n      \"Epoch 7:  12%|████████████████████████▌                                                                                                                                                                                        | 25/213 [00:20<02:30,  1.25it/s, loss=0.0121, lr=8.31e-5, step=1514]\\u001b[A\\n\",\n      \"Epoch 7:  12%|████████████████████████▋                                                                                                                                                                                         | 25/213 [00:20<02:30,  1.25it/s, loss=0.0247, lr=8.3e-5, step=1515]\\u001b[A\\n\",\n      \"Epoch 7:  12%|█████████████████████████▋                                                                                                                                                                                        | 26/213 [00:20<02:29,  1.25it/s, loss=0.0247, lr=8.3e-5, step=1515]\\u001b[A\\n\",\n      \"Epoch 7:  12%|█████████████████████████▌                                                                                                                                                                                       | 26/213 [00:20<02:29,  1.25it/s, loss=0.00755, lr=8.3e-5, step=1516]\\u001b[A\\n\",\n      \"Epoch 7:  13%|██████████████████████████▍                                                                                                                                                                                      | 27/213 [00:21<02:28,  1.25it/s, loss=0.00755, lr=8.3e-5, step=1516]\\u001b[A\\n\",\n      \"Epoch 7:  13%|██████████████████████████▌                                                                                                                                                                                       | 27/213 [00:21<02:28,  1.25it/s, loss=0.0083, lr=8.3e-5, step=1517]\\u001b[A\\n\",\n      \"Epoch 7:  13%|███████████████████████████▌                                                                                                                                                                                      | 28/213 [00:22<02:27,  1.25it/s, loss=0.0083, lr=8.3e-5, step=1517]\\u001b[A\\n\",\n      \"Epoch 7:  13%|███████████████████████████▍                                                                                                                                                                                     | 28/213 [00:22<02:27,  1.25it/s, loss=0.0322, lr=8.29e-5, step=1518]\\u001b[A\\n\",\n      \"Epoch 7:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:27,  1.25it/s, loss=0.0322, lr=8.29e-5, step=1518]\\u001b[A\\n\",\n      \"Epoch 7:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:27,  1.25it/s, loss=0.0143, lr=8.29e-5, step=1519]\\u001b[A\\n\",\n      \"Epoch 7:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:26,  1.25it/s, loss=0.0143, lr=8.29e-5, step=1519]\\u001b[A\\n\",\n      \"Epoch 7:  14%|█████████████████████████████▌                                                                                                                                                                                    | 30/213 [00:24<02:26,  1.25it/s, loss=0.028, lr=8.29e-5, step=1520]\\u001b[A\\n\",\n      \"Epoch 7:  15%|██████████████████████████████▌                                                                                                                   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             | 32/213 [00:25<02:24,  1.25it/s, loss=0.0185, lr=8.28e-5, step=1522]\\u001b[A\\n\",\n      \"Epoch 7:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:24,  1.25it/s, loss=0.0185, lr=8.28e-5, step=1522]\\u001b[A\\n\",\n      \"Epoch 7:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:24,  1.25it/s, loss=0.0125, lr=8.28e-5, step=1523]\\u001b[A\\n\",\n      \"Epoch 7:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [00:27<02:23,  1.24it/s, loss=0.0125, lr=8.28e-5, step=1523]\\u001b[A\\n\",\n      \"Epoch 7:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:23,  1.24it/s, loss=0.00944, lr=8.28e-5, step=1524]\\u001b[A\\n\",\n      \"Epoch 7:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:23,  1.24it/s, loss=0.00944, lr=8.28e-5, step=1524]\\u001b[A\\n\",\n      \"Epoch 7:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:23,  1.24it/s, loss=0.0182, lr=8.27e-5, step=1525]\\u001b[A\\n\",\n      \"Epoch 7:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:28<02:22,  1.24it/s, loss=0.0182, lr=8.27e-5, step=1525]\\u001b[A\\n\",\n      \"Epoch 7:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:28<02:22,  1.24it/s, loss=0.0107, lr=8.27e-5, step=1526]\\u001b[A\\n\",\n      \"Epoch 7:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:21,  1.24it/s, loss=0.0107, lr=8.27e-5, step=1526]\\u001b[A\\n\",\n      \"Epoch 7:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:21,  1.24it/s, loss=0.0329, lr=8.27e-5, step=1527]\\u001b[A\\n\",\n      \"Epoch 7:  18%|█████████████████████████████████████▎                                                                                                                                                                           | 38/213 [00:30<02:21,  1.24it/s, loss=0.0329, lr=8.27e-5, step=1527]\\u001b[A\\n\",\n      \"Epoch 7:  18%|█████████████████████████████████████                                                                                                                                                                           | 38/213 [00:30<02:21,  1.24it/s, loss=0.00964, lr=8.26e-5, step=1528]\\u001b[A\\n\",\n      \"Epoch 7:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [00:31<02:20,  1.24it/s, loss=0.00964, lr=8.26e-5, step=1528]\\u001b[A\\n\",\n      \"Epoch 7:  18%|██████████████████████████████████████▎                                                                                                                                                                          | 39/213 [00:31<02:20,  1.24it/s, loss=0.0435, lr=8.26e-5, step=1529]\\u001b[A\\n\",\n      \"Epoch 7:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.0435, lr=8.26e-5, step=1529]\\u001b[A\\n\",\n      \"Epoch 7:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.0201, lr=8.26e-5, step=1530]\\u001b[A\\n\",\n      \"Epoch 7:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.0201, lr=8.26e-5, step=1530]\\u001b[A\\n\",\n      \"Epoch 7:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.0122, lr=8.25e-5, step=1531]\\u001b[A\\n\",\n      \"Epoch 7:  20%|█████████████████████████████████████████▏                                                                                                                                                                       | 42/213 [00:33<02:17,  1.25it/s, loss=0.0122, lr=8.25e-5, step=1531]\\u001b[A\\n\",\n      \"Epoch 7:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.25it/s, loss=0.00601, lr=8.25e-5, step=1532]\\u001b[A\\n\",\n      \"Epoch 7:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.25it/s, loss=0.00601, lr=8.25e-5, step=1532]\\u001b[A\\n\",\n      \"Epoch 7:  20%|██████████████████████████████████████████▏                                                                                                                                                                      | 43/213 [00:34<02:16,  1.25it/s, loss=0.0361, lr=8.25e-5, step=1533]\\u001b[A\\n\",\n      \"Epoch 7:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0361, lr=8.25e-5, step=1533]\\u001b[A\\n\",\n      \"Epoch 7:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0179, lr=8.24e-5, step=1534]\\u001b[A\\n\",\n      \"Epoch 7:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0179, lr=8.24e-5, step=1534]\\u001b[A\\n\",\n      \"Epoch 7:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0322, lr=8.24e-5, step=1535]\\u001b[A\\n\",\n      \"Epoch 7:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:36<02:13,  1.25it/s, loss=0.0322, lr=8.24e-5, step=1535]\\u001b[A\\n\",\n      \"Epoch 7:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:36<02:13,  1.25it/s, loss=0.0247, lr=8.24e-5, step=1536]\\u001b[A\\n\",\n      \"Epoch 7:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:12,  1.25it/s, loss=0.0247, lr=8.24e-5, step=1536]\\u001b[A\\n\",\n      \"Epoch 7:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:12,  1.25it/s, loss=0.0162, lr=8.23e-5, step=1537]\\u001b[A\\n\",\n      \"Epoch 7:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:11,  1.25it/s, loss=0.0162, lr=8.23e-5, step=1537]\\u001b[A\\n\",\n      \"Epoch 7:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:11,  1.25it/s, loss=0.0125, lr=8.23e-5, step=1538]\\u001b[A\\n\",\n      \"Epoch 7:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.25it/s, loss=0.0125, lr=8.23e-5, step=1538]\\u001b[A\\n\",\n      \"Epoch 7:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.25it/s, loss=0.0259, lr=8.23e-5, step=1539]\\u001b[A\\n\",\n      \"Epoch 7:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0259, lr=8.23e-5, step=1539]\\u001b[A\\n\",\n      \"Epoch 7:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0198, lr=8.22e-5, step=1540]\\u001b[A\\n\",\n      \"Epoch 7:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:40<02:09,  1.25it/s, loss=0.0198, lr=8.22e-5, step=1540]\\u001b[A\\n\",\n      \"Epoch 7:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:40<02:09,  1.25it/s, loss=0.0511, lr=8.22e-5, step=1541]\\u001b[A\\n\",\n      \"Epoch 7:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0511, lr=8.22e-5, step=1541]\\u001b[A\\n\",\n      \"Epoch 7:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0172, lr=8.22e-5, step=1542]\\u001b[A\\n\",\n      \"Epoch 7:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0172, lr=8.22e-5, step=1542]\\u001b[A\\n\",\n      \"Epoch 7:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0276, lr=8.22e-5, step=1543]\\u001b[A\\n\",\n      \"Epoch 7:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.0276, lr=8.22e-5, step=1543]\\u001b[A\\n\",\n      \"Epoch 7:  25%|█████████████████████████████████████████████████████▏                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.019, lr=8.21e-5, step=1544]\\u001b[A\\n\",\n      \"Epoch 7:  26%|██████████████████████████████████████████████████████▏                                                                                                                                                           | 55/213 [00:44<02:06,  1.25it/s, loss=0.019, lr=8.21e-5, step=1544]\\u001b[A\\n\",\n      \"Epoch 7:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.25it/s, loss=0.00715, lr=8.21e-5, step=1545]\\u001b[A\\n\",\n      \"Epoch 7:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [00:44<02:05,  1.25it/s, loss=0.00715, lr=8.21e-5, step=1545]\\u001b[A\\n\",\n      \"Epoch 7:  26%|███████████████████████████████████████████████████████▏                                                                                                                                                          | 56/213 [00:44<02:05,  1.25it/s, loss=0.016, lr=8.21e-5, step=1546]\\u001b[A\\n\",\n      \"Epoch 7:  27%|████████████████████████████████████████████████████████▏                                                                                                                                                         | 57/213 [00:45<02:04,  1.25it/s, loss=0.016, lr=8.21e-5, step=1546]\\u001b[A\\n\",\n      \"Epoch 7:  27%|████████████████████████████████████████████████████████▏                                                                                                                                                         | 57/213 [00:45<02:04,  1.25it/s, loss=0.0241, lr=8.2e-5, step=1547]\\u001b[A\\n\",\n      \"Epoch 7:  27%|█████████████████████████████████████████████████████████▏                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0241, lr=8.2e-5, step=1547]\\u001b[A\\n\",\n      \"Epoch 7:  27%|█████████████████████████████████████████████████████████▍                                                                                                                                                         | 58/213 [00:46<02:04,  1.25it/s, loss=0.017, lr=8.2e-5, step=1548]\\u001b[A\\n\",\n      \"Epoch 7:  28%|██████████████████████████████████████████████████████████▍                                                                                                                                                        | 59/213 [00:47<02:03,  1.25it/s, loss=0.017, lr=8.2e-5, step=1548]\\u001b[A\\n\",\n      \"Epoch 7:  28%|██████████████████████████████████████████████████████████▏                                                                                                                                                       | 59/213 [00:47<02:03,  1.25it/s, loss=0.0341, lr=8.2e-5, step=1549]\\u001b[A\\n\",\n      \"Epoch 7:  28%|███████████████████████████████████████████████████████████▏                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0341, lr=8.2e-5, step=1549]\\u001b[A\\n\",\n      \"Epoch 7:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0128, lr=8.19e-5, step=1550]\\u001b[A\\n\",\n      \"Epoch 7:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:48<02:02,  1.24it/s, loss=0.0128, lr=8.19e-5, step=1550]\\u001b[A\\n\",\n      \"Epoch 7:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:48<02:02,  1.24it/s, loss=0.0175, lr=8.19e-5, step=1551]\\u001b[A\\n\",\n      \"Epoch 7:  29%|████████████████████████████████████████████████████████████▊                                                                                                                                                    | 62/213 [00:49<02:00,  1.25it/s, loss=0.0175, lr=8.19e-5, step=1551]\\u001b[A\\n\",\n      \"Epoch 7:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:49<02:00,  1.25it/s, loss=0.00794, lr=8.19e-5, step=1552]\\u001b[A\\n\",\n      \"Epoch 7:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<02:00,  1.25it/s, loss=0.00794, lr=8.19e-5, step=1552]\\u001b[A\\n\",\n      \"Epoch 7:  30%|█████████████████████████████████████████████████████████████▊                                                                                                                                                   | 63/213 [00:50<02:00,  1.25it/s, loss=0.0195, lr=8.18e-5, step=1553]\\u001b[A\\n\",\n      \"Epoch 7:  30%|██████████████████████████████████████████████████████████████▊                                                                                                                                                  | 64/213 [00:51<01:59,  1.25it/s, loss=0.0195, lr=8.18e-5, step=1553]\\u001b[A\\n\",\n      \"Epoch 7:  30%|██████████████████████████████████████████████████████████████▊                                                                                                                                                  | 64/213 [00:51<01:59,  1.25it/s, loss=0.0104, lr=8.18e-5, step=1554]\\u001b[A\\n\",\n      \"Epoch 7:  31%|███████████████████████████████████████████████████████████████▊                                                                                                                                                 | 65/213 [00:52<01:58,  1.25it/s, loss=0.0104, lr=8.18e-5, step=1554]\\u001b[A\\n\",\n      \"Epoch 7:  31%|███████████████████████████████████████████████████████████████▊                                                                                                                                                 | 65/213 [00:52<01:58,  1.25it/s, loss=0.0124, lr=8.18e-5, step=1555]\\u001b[A\\n\",\n      \"Epoch 7:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:52<01:57,  1.25it/s, loss=0.0124, lr=8.18e-5, step=1555]\\u001b[A\\n\",\n      \"Epoch 7:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:52<01:57,  1.25it/s, loss=0.0274, lr=8.17e-5, step=1556]\\u001b[A\\n\",\n      \"Epoch 7:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:57,  1.25it/s, loss=0.0274, lr=8.17e-5, step=1556]\\u001b[A\\n\",\n      \"Epoch 7:  31%|██████████████████████████████████████████████████████████████████                                                                                                                                                | 67/213 [00:53<01:57,  1.25it/s, loss=0.019, lr=8.17e-5, step=1557]\\u001b[A\\n\",\n      \"Epoch 7:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                               | 68/213 [00:54<01:56,  1.25it/s, loss=0.019, lr=8.17e-5, step=1557]\\u001b[A\\n\",\n      \"Epoch 7:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:56,  1.25it/s, loss=0.0105, lr=8.17e-5, step=1558]\\u001b[A\\n\",\n      \"Epoch 7:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:55,  1.25it/s, loss=0.0105, lr=8.17e-5, step=1558]\\u001b[A\\n\",\n      \"Epoch 7:  32%|████████████████████████████████████████████████████████████████████                                                                                                                                              | 69/213 [00:55<01:55,  1.25it/s, loss=0.012, lr=8.16e-5, step=1559]\\u001b[A\\n\",\n      \"Epoch 7:  33%|█████████████████████████████████████████████████████████████████████                                                                                                                                             | 70/213 [00:56<01:54,  1.25it/s, loss=0.012, lr=8.16e-5, step=1559]\\u001b[A\\n\",\n      \"Epoch 7:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.0201, lr=8.16e-5, step=1560]\\u001b[A\\n\",\n      \"Epoch 7:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:56<01:53,  1.25it/s, loss=0.0201, lr=8.16e-5, step=1560]\\u001b[A\\n\",\n      \"Epoch 7:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:56<01:53,  1.25it/s, loss=0.0156, lr=8.16e-5, step=1561]\\u001b[A\\n\",\n      \"Epoch 7:  34%|██████████████████████████████████████████████████████████████████████▋                                                                                                                                          | 72/213 [00:57<01:53,  1.25it/s, loss=0.0156, lr=8.16e-5, step=1561]\\u001b[A\\n\",\n      \"Epoch 7:  34%|██████████████████████████████████████████████████████████████████████▋                                                                                                                                          | 72/213 [00:57<01:53,  1.25it/s, loss=0.0068, lr=8.15e-5, step=1562]\\u001b[A\\n\",\n      \"Epoch 7:  34%|███████████████████████████████████████████████████████████████████████▋                                                                                                                                         | 73/213 [00:58<01:52,  1.25it/s, loss=0.0068, lr=8.15e-5, step=1562]\\u001b[A\\n\",\n      \"Epoch 7:  34%|███████████████████████████████████████████████████████████████████████▋                                                                                                                                         | 73/213 [00:58<01:52,  1.25it/s, loss=0.0155, lr=8.15e-5, step=1563]\\u001b[A\\n\",\n      \"Epoch 7:  35%|████████████████████████████████████████████████████████████████████████▌                                                                                                                                        | 74/213 [00:59<01:51,  1.25it/s, loss=0.0155, lr=8.15e-5, step=1563]\\u001b[A\\n\",\n      \"Epoch 7:  35%|████████████████████████████████████████████████████████████████████████▌                                                                                                                                        | 74/213 [00:59<01:51,  1.25it/s, loss=0.0193, lr=8.15e-5, step=1564]\\u001b[A\\n\",\n      \"Epoch 7:  35%|█████████████████████████████████████████████████████████████████████████▌                                                                                                                                       | 75/213 [01:00<01:50,  1.25it/s, loss=0.0193, lr=8.15e-5, step=1564]\\u001b[A\\n\",\n      \"Epoch 7:  35%|█████████████████████████████████████████████████████████████████████████▌                                                                                                                                       | 75/213 [01:00<01:50,  1.25it/s, loss=0.0463, lr=8.14e-5, step=1565]\\u001b[A\\n\",\n      \"Epoch 7:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:00<01:49,  1.25it/s, loss=0.0463, lr=8.14e-5, step=1565]\\u001b[A\\n\",\n      \"Epoch 7:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:01<01:49,  1.25it/s, loss=0.0127, lr=8.14e-5, step=1566]\\u001b[A\\n\",\n      \"Epoch 7:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:01<01:49,  1.25it/s, loss=0.0127, lr=8.14e-5, step=1566]\\u001b[A\\n\",\n      \"Epoch 7:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:01<01:49,  1.25it/s, loss=0.0213, lr=8.14e-5, step=1567]\\u001b[A\\n\",\n      \"Epoch 7:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [01:02<01:48,  1.24it/s, loss=0.0213, lr=8.14e-5, step=1567]\\u001b[A\\n\",\n      \"Epoch 7:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [01:02<01:48,  1.24it/s, loss=0.0112, lr=8.13e-5, step=1568]\\u001b[A\\n\",\n      \"Epoch 7:  37%|█████████████████████████████████████████████████████████████████████████████▌                                                                                                                                   | 79/213 [01:03<01:47,  1.25it/s, loss=0.0112, lr=8.13e-5, step=1568]\\u001b[A\\n\",\n      \"Epoch 7:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [01:03<01:47,  1.25it/s, loss=0.00958, lr=8.13e-5, step=1569]\\u001b[A\\n\",\n      \"Epoch 7:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:46,  1.24it/s, loss=0.00958, lr=8.13e-5, step=1569]\\u001b[A\\n\",\n      \"Epoch 7:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:46,  1.24it/s, loss=0.00981, lr=8.13e-5, step=1570]\\u001b[A\\n\",\n      \"Epoch 7:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:04<01:46,  1.24it/s, loss=0.00981, lr=8.13e-5, step=1570]\\u001b[A\\n\",\n      \"Epoch 7:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [01:05<01:46,  1.24it/s, loss=0.0174, lr=8.12e-5, step=1571]\\u001b[A\\n\",\n      \"Epoch 7:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.25it/s, loss=0.0174, lr=8.12e-5, step=1571]\\u001b[A\\n\",\n      \"Epoch 7:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.25it/s, loss=0.0426, lr=8.12e-5, step=1572]\\u001b[A\\n\",\n      \"Epoch 7:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:43,  1.25it/s, loss=0.0426, lr=8.12e-5, step=1572]\\u001b[A\\n\",\n      \"Epoch 7:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:43,  1.25it/s, loss=0.0458, lr=8.12e-5, step=1573]\\u001b[A\\n\",\n      \"Epoch 7:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:42,  1.26it/s, loss=0.0458, lr=8.12e-5, step=1573]\\u001b[A\\n\",\n      \"Epoch 7:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:42,  1.26it/s, loss=0.0467, lr=8.12e-5, step=1574]\\u001b[A\\n\",\n      \"Epoch 7:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0467, lr=8.12e-5, step=1574]\\u001b[A\\n\",\n      \"Epoch 7:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0091, lr=8.11e-5, step=1575]\\u001b[A\\n\",\n      \"Epoch 7:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:08<01:41,  1.25it/s, loss=0.0091, lr=8.11e-5, step=1575]\\u001b[A\\n\",\n      \"Epoch 7:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:09<01:41,  1.25it/s, loss=0.0297, lr=8.11e-5, step=1576]\\u001b[A\\n\",\n      \"Epoch 7:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0297, lr=8.11e-5, step=1576]\\u001b[A\\n\",\n      \"Epoch 7:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0266, lr=8.11e-5, step=1577]\\u001b[A\\n\",\n      \"Epoch 7:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [01:10<01:40,  1.25it/s, loss=0.0266, lr=8.11e-5, step=1577]\\u001b[A\\n\",\n      \"Epoch 7:  41%|██████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                           | 88/213 [01:10<01:40,  1.25it/s, loss=0.0139, lr=8.1e-5, step=1578]\\u001b[A\\n\",\n      \"Epoch 7:  42%|███████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                          | 89/213 [01:11<01:39,  1.25it/s, loss=0.0139, lr=8.1e-5, step=1578]\\u001b[A\\n\",\n      \"Epoch 7:  42%|███████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                         | 89/213 [01:11<01:39,  1.25it/s, loss=0.00997, lr=8.1e-5, step=1579]\\u001b[A\\n\",\n      \"Epoch 7:  42%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                        | 90/213 [01:12<01:38,  1.25it/s, loss=0.00997, lr=8.1e-5, step=1579]\\u001b[A\\n\",\n      \"Epoch 7:  42%|████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                         | 90/213 [01:12<01:38,  1.25it/s, loss=0.0123, lr=8.1e-5, step=1580]\\u001b[A\\n\",\n      \"Epoch 7:  43%|█████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                        | 91/213 [01:12<01:37,  1.25it/s, loss=0.0123, lr=8.1e-5, step=1580]\\u001b[A\\n\",\n      \"Epoch 7:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:13<01:37,  1.25it/s, loss=0.0282, lr=8.09e-5, step=1581]\\u001b[A\\n\",\n      \"Epoch 7:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0282, lr=8.09e-5, step=1581]\\u001b[A\\n\",\n      \"Epoch 7:  43%|██████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                       | 92/213 [01:13<01:36,  1.25it/s, loss=0.023, lr=8.09e-5, step=1582]\\u001b[A\\n\",\n      \"Epoch 7:  44%|███████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                      | 93/213 [01:14<01:35,  1.25it/s, loss=0.023, lr=8.09e-5, step=1582]\\u001b[A\\n\",\n      \"Epoch 7:  44%|███████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                     | 93/213 [01:14<01:35,  1.25it/s, loss=0.0234, lr=8.09e-5, step=1583]\\u001b[A\\n\",\n      \"Epoch 7:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0234, lr=8.09e-5, step=1583]\\u001b[A\\n\",\n      \"Epoch 7:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0458, lr=8.08e-5, step=1584]\\u001b[A\\n\",\n      \"Epoch 7:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0458, lr=8.08e-5, step=1584]\\u001b[A\\n\",\n      \"Epoch 7:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0137, lr=8.08e-5, step=1585]\\u001b[A\\n\",\n      \"Epoch 7:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                  | 96/213 [01:16<01:33,  1.25it/s, loss=0.0137, lr=8.08e-5, step=1585]\\u001b[A\\n\",\n      \"Epoch 7:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                  | 96/213 [01:17<01:33,  1.25it/s, loss=0.0198, lr=8.08e-5, step=1586]\\u001b[A\\n\",\n      \"Epoch 7:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0198, lr=8.08e-5, step=1586]\\u001b[A\\n\",\n      \"Epoch 7:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0136, lr=8.07e-5, step=1587]\\u001b[A\\n\",\n      \"Epoch 7:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:32,  1.25it/s, loss=0.0136, lr=8.07e-5, step=1587]\\u001b[A\\n\",\n      \"Epoch 7:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:32,  1.25it/s, loss=0.0273, lr=8.07e-5, step=1588]\\u001b[A\\n\",\n      \"Epoch 7:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                               | 99/213 [01:19<01:31,  1.25it/s, loss=0.0273, lr=8.07e-5, step=1588]\\u001b[A\\n\",\n      \"Epoch 7:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                | 99/213 [01:19<01:31,  1.25it/s, loss=0.034, lr=8.07e-5, step=1589]\\u001b[A\\n\",\n      \"Epoch 7:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 100/213 [01:20<01:30,  1.25it/s, loss=0.034, lr=8.07e-5, step=1589]\\u001b[A\\n\",\n      \"Epoch 7:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                              | 100/213 [01:20<01:30,  1.25it/s, loss=0.0158, lr=8.06e-5, step=1590]\\u001b[A\\n\",\n      \"Epoch 7:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:20<01:29,  1.25it/s, loss=0.0158, lr=8.06e-5, step=1590]\\u001b[A\\n\",\n      \"Epoch 7:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:21<01:29,  1.25it/s, loss=0.0167, lr=8.06e-5, step=1591]\\u001b[A\\n\",\n      \"Epoch 7:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 102/213 [01:21<01:28,  1.25it/s, loss=0.0167, lr=8.06e-5, step=1591]\\u001b[A\\n\",\n      \"Epoch 7:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.00944, lr=8.06e-5, step=1592]\\u001b[A\\n\",\n      \"Epoch 7:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.00944, lr=8.06e-5, step=1592]\\u001b[A\\n\",\n      \"Epoch 7:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.00984, lr=8.05e-5, step=1593]\\u001b[A\\n\",\n      \"Epoch 7:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 104/213 [01:23<01:26,  1.25it/s, loss=0.00984, lr=8.05e-5, step=1593]\\u001b[A\\n\",\n      \"Epoch 7:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 104/213 [01:23<01:26,  1.25it/s, loss=0.0169, lr=8.05e-5, step=1594]\\u001b[A\\n\",\n      \"Epoch 7:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0169, lr=8.05e-5, step=1594]\\u001b[A\\n\",\n      \"Epoch 7:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0298, lr=8.05e-5, step=1595]\\u001b[A\\n\",\n      \"Epoch 7:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:24<01:25,  1.26it/s, loss=0.0298, lr=8.05e-5, step=1595]\\u001b[A\\n\",\n      \"Epoch 7:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:24<01:25,  1.26it/s, loss=0.0203, lr=8.04e-5, step=1596]\\u001b[A\\n\",\n      \"Epoch 7:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0203, lr=8.04e-5, step=1596]\\u001b[A\\n\",\n      \"Epoch 7:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0133, lr=8.04e-5, step=1597]\\u001b[A\\n\",\n      \"Epoch 7:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.25it/s, loss=0.0133, lr=8.04e-5, step=1597]\\u001b[A\\n\",\n      \"Epoch 7:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.25it/s, loss=0.0536, lr=8.04e-5, step=1598]\\u001b[A\\n\",\n      \"Epoch 7:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.0536, lr=8.04e-5, step=1598]\\u001b[A\\n\",\n      \"Epoch 7:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.0258, lr=8.03e-5, step=1599]\\u001b[A\\n\",\n      \"Epoch 7:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0258, lr=8.03e-5, step=1599]\\u001b[A\\n\",\n      \"Epoch 7:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0291, lr=8.03e-5, step=1600]\\u001b[A\\n\",\n      \"Epoch 7:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:28<01:21,  1.25it/s, loss=0.0291, lr=8.03e-5, step=1600]\\u001b[A\\n\",\n      \"Epoch 7:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:28<01:21,  1.25it/s, loss=0.0128, lr=8.03e-5, step=1601]\\u001b[A\\n\",\n      \"Epoch 7:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0128, lr=8.03e-5, step=1601]\\u001b[A\\n\",\n      \"Epoch 7:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0394, lr=8.02e-5, step=1602]\\u001b[A\\n\",\n      \"Epoch 7:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:19,  1.25it/s, loss=0.0394, lr=8.02e-5, step=1602]\\u001b[A\\n\",\n      \"Epoch 7:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:30<01:19,  1.25it/s, loss=0.00733, lr=8.02e-5, step=1603]\\u001b[A\\n\",\n      \"Epoch 7:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:31<01:18,  1.26it/s, loss=0.00733, lr=8.02e-5, step=1603]\\u001b[A\\n\",\n      \"Epoch 7:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 114/213 [01:31<01:18,  1.26it/s, loss=0.0108, lr=8.02e-5, step=1604]\\u001b[A\\n\",\n      \"Epoch 7:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 115/213 [01:32<01:17,  1.26it/s, loss=0.0108, lr=8.02e-5, step=1604]\\u001b[A\\n\",\n      \"Epoch 7:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                | 115/213 [01:32<01:17,  1.26it/s, loss=0.02, lr=8.01e-5, step=1605]\\u001b[A\\n\",\n      \"Epoch 7:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 116/213 [01:32<01:17,  1.25it/s, loss=0.02, lr=8.01e-5, step=1605]\\u001b[A\\n\",\n      \"Epoch 7:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                              | 116/213 [01:32<01:17,  1.25it/s, loss=0.0283, lr=8.01e-5, step=1606]\\u001b[A\\n\",\n      \"Epoch 7:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0283, lr=8.01e-5, step=1606]\\u001b[A\\n\",\n      \"Epoch 7:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0153, lr=8.01e-5, step=1607]\\u001b[A\\n\",\n      \"Epoch 7:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                            | 118/213 [01:34<01:15,  1.25it/s, loss=0.0153, lr=8.01e-5, step=1607]\\u001b[A\\n\",\n      \"Epoch 7:  55%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 118/213 [01:34<01:15,  1.25it/s, loss=0.00954, lr=8e-5, step=1608]\\u001b[A\\n\",\n      \"Epoch 7:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                            | 119/213 [01:35<01:14,  1.26it/s, loss=0.00954, lr=8e-5, step=1608]\\u001b[A\\n\",\n      \"Epoch 7:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                             | 119/213 [01:35<01:14,  1.26it/s, loss=0.0309, lr=8e-5, step=1609]\\u001b[A\\n\",\n      \"Epoch 7:  56%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 120/213 [01:36<01:13,  1.26it/s, loss=0.0309, lr=8e-5, step=1609]\\u001b[A\\n\",\n      \"Epoch 7:  56%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                            | 120/213 [01:36<01:13,  1.26it/s, loss=0.0292, lr=8e-5, step=1610]\\u001b[A\\n\",\n      \"Epoch 7:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                           | 121/213 [01:36<01:13,  1.25it/s, loss=0.0292, lr=8e-5, step=1610]\\u001b[A\\n\",\n      \"Epoch 7:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0231, lr=7.99e-5, step=1611]\\u001b[A\\n\",\n      \"Epoch 7:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0231, lr=7.99e-5, step=1611]\\u001b[A\\n\",\n      \"Epoch 7:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0173, lr=7.99e-5, step=1612]\\u001b[A\\n\",\n      \"Epoch 7:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                        | 123/213 [01:38<01:11,  1.25it/s, loss=0.0173, lr=7.99e-5, step=1612]\\u001b[A\\n\",\n      \"Epoch 7:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                        | 123/213 [01:38<01:11,  1.25it/s, loss=0.0172, lr=7.99e-5, step=1613]\\u001b[A\\n\",\n      \"Epoch 7:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                       | 124/213 [01:39<01:10,  1.26it/s, loss=0.0172, lr=7.99e-5, step=1613]\\u001b[A\\n\",\n      \"Epoch 7:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                      | 124/213 [01:39<01:10,  1.26it/s, loss=0.00816, lr=7.98e-5, step=1614]\\u001b[A\\n\",\n      \"Epoch 7:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                     | 125/213 [01:40<01:10,  1.26it/s, loss=0.00816, lr=7.98e-5, step=1614]\\u001b[A\\n\",\n      \"Epoch 7:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                      | 125/213 [01:40<01:10,  1.26it/s, loss=0.0498, lr=7.98e-5, step=1615]\\u001b[A\\n\",\n      \"Epoch 7:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     | 126/213 [01:40<01:09,  1.26it/s, loss=0.0498, lr=7.98e-5, step=1615]\\u001b[A\\n\",\n      \"Epoch 7:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     | 126/213 [01:40<01:09,  1.26it/s, loss=0.0108, lr=7.98e-5, step=1616]\\u001b[A\\n\",\n      \"Epoch 7:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                    | 127/213 [01:41<01:08,  1.26it/s, loss=0.0108, lr=7.98e-5, step=1616]\\u001b[A\\n\",\n      \"Epoch 7:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                   | 127/213 [01:41<01:08,  1.26it/s, loss=0.00992, lr=7.97e-5, step=1617]\\u001b[A\\n\",\n      \"Epoch 7:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                  | 128/213 [01:42<01:07,  1.26it/s, loss=0.00992, lr=7.97e-5, step=1617]\\u001b[A\\n\",\n      \"Epoch 7:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                   | 128/213 [01:42<01:07,  1.26it/s, loss=0.039, lr=7.97e-5, step=1618]\\u001b[A\\n\",\n      \"Epoch 7:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                  | 129/213 [01:43<01:06,  1.26it/s, loss=0.039, lr=7.97e-5, step=1618]\\u001b[A\\n\",\n      \"Epoch 7:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:06,  1.26it/s, loss=0.0173, lr=7.97e-5, step=1619]\\u001b[A\\n\",\n      \"Epoch 7:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                 | 130/213 [01:44<01:06,  1.26it/s, loss=0.0173, lr=7.97e-5, step=1619]\\u001b[A\\n\",\n      \"Epoch 7:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:44<01:06,  1.26it/s, loss=0.00814, lr=7.96e-5, step=1620]\\u001b[A\\n\",\n      \"Epoch 7:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.26it/s, loss=0.00814, lr=7.96e-5, step=1620]\\u001b[A\\n\",\n      \"Epoch 7:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [01:44<01:05,  1.26it/s, loss=0.0198, lr=7.96e-5, step=1621]\\u001b[A\\n\",\n      \"Epoch 7:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.26it/s, loss=0.0198, lr=7.96e-5, step=1621]\\u001b[A\\n\",\n      \"Epoch 7:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.26it/s, loss=0.0151, lr=7.96e-5, step=1622]\\u001b[A\\n\",\n      \"Epoch 7:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.26it/s, loss=0.0151, lr=7.96e-5, step=1622]\\u001b[A\\n\",\n      \"Epoch 7:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.26it/s, loss=0.0218, lr=7.95e-5, step=1623]\\u001b[A\\n\",\n      \"Epoch 7:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:02,  1.26it/s, loss=0.0218, lr=7.95e-5, step=1623]\\u001b[A\\n\",\n      \"Epoch 7:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:02,  1.26it/s, loss=0.0133, lr=7.95e-5, step=1624]\\u001b[A\\n\",\n      \"Epoch 7:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.26it/s, loss=0.0133, lr=7.95e-5, step=1624]\\u001b[A\\n\",\n      \"Epoch 7:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.26it/s, loss=0.0565, lr=7.95e-5, step=1625]\\u001b[A\\n\",\n      \"Epoch 7:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:48<01:01,  1.25it/s, loss=0.0565, lr=7.95e-5, step=1625]\\u001b[A\\n\",\n      \"Epoch 7:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:48<01:01,  1.25it/s, loss=0.0108, lr=7.94e-5, step=1626]\\u001b[A\\n\",\n      \"Epoch 7:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.25it/s, loss=0.0108, lr=7.94e-5, step=1626]\\u001b[A\\n\",\n      \"Epoch 7:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.25it/s, loss=0.0198, lr=7.94e-5, step=1627]\\u001b[A\\n\",\n      \"Epoch 7:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<01:00,  1.25it/s, loss=0.0198, lr=7.94e-5, step=1627]\\u001b[A\\n\",\n      \"Epoch 7:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:50<01:00,  1.25it/s, loss=0.00736, lr=7.94e-5, step=1628]\\u001b[A\\n\",\n      \"Epoch 7:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.00736, lr=7.94e-5, step=1628]\\u001b[A\\n\",\n      \"Epoch 7:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0352, lr=7.93e-5, step=1629]\\u001b[A\\n\",\n      \"Epoch 7:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0352, lr=7.93e-5, step=1629]\\u001b[A\\n\",\n      \"Epoch 7:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0361, lr=7.93e-5, step=1630]\\u001b[A\\n\",\n      \"Epoch 7:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0361, lr=7.93e-5, step=1630]\\u001b[A\\n\",\n      \"Epoch 7:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0483, lr=7.93e-5, step=1631]\\u001b[A\\n\",\n      \"Epoch 7:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0483, lr=7.93e-5, step=1631]\\u001b[A\\n\",\n      \"Epoch 7:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0214, lr=7.92e-5, step=1632]\\u001b[A\\n\",\n      \"Epoch 7:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0214, lr=7.92e-5, step=1632]\\u001b[A\\n\",\n      \"Epoch 7:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0179, lr=7.92e-5, step=1633]\\u001b[A\\n\",\n      \"Epoch 7:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                   | 144/213 [01:55<00:55,  1.25it/s, loss=0.0179, lr=7.92e-5, step=1633]\\u001b[A\\n\",\n      \"Epoch 7:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                   | 144/213 [01:55<00:55,  1.25it/s, loss=0.0133, lr=7.92e-5, step=1634]\\u001b[A\\n\",\n      \"Epoch 7:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:56<00:54,  1.26it/s, loss=0.0133, lr=7.92e-5, step=1634]\\u001b[A\\n\",\n      \"Epoch 7:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:56<00:54,  1.26it/s, loss=0.0392, lr=7.91e-5, step=1635]\\u001b[A\\n\",\n      \"Epoch 7:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.0392, lr=7.91e-5, step=1635]\\u001b[A\\n\",\n      \"Epoch 7:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.016, lr=7.91e-5, step=1636]\\u001b[A\\n\",\n      \"Epoch 7:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:57<00:52,  1.25it/s, loss=0.016, lr=7.91e-5, step=1636]\\u001b[A\\n\",\n      \"Epoch 7:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 147/213 [01:57<00:52,  1.25it/s, loss=0.0166, lr=7.91e-5, step=1637]\\u001b[A\\n\",\n      \"Epoch 7:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0166, lr=7.91e-5, step=1637]\\u001b[A\\n\",\n      \"Epoch 7:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                | 148/213 [01:58<00:51,  1.26it/s, loss=0.022, lr=7.9e-5, step=1638]\\u001b[A\\n\",\n      \"Epoch 7:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                               | 149/213 [01:59<00:50,  1.26it/s, loss=0.022, lr=7.9e-5, step=1638]\\u001b[A\\n\",\n      \"Epoch 7:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0136, lr=7.9e-5, step=1639]\\u001b[A\\n\",\n      \"Epoch 7:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.0136, lr=7.9e-5, step=1639]\\u001b[A\\n\",\n      \"Epoch 7:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.0174, lr=7.89e-5, step=1640]\\u001b[A\\n\",\n      \"Epoch 7:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0174, lr=7.89e-5, step=1640]\\u001b[A\\n\",\n      \"Epoch 7:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0138, lr=7.89e-5, step=1641]\\u001b[A\\n\",\n      \"Epoch 7:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.0138, lr=7.89e-5, step=1641]\\u001b[A\\n\",\n      \"Epoch 7:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.0102, lr=7.89e-5, step=1642]\\u001b[A\\n\",\n      \"Epoch 7:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                          | 153/213 [02:02<00:47,  1.26it/s, loss=0.0102, lr=7.89e-5, step=1642]\\u001b[A\\n\",\n      \"Epoch 7:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                          | 153/213 [02:02<00:47,  1.26it/s, loss=0.0166, lr=7.88e-5, step=1643]\\u001b[A\\n\",\n      \"Epoch 7:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:46,  1.26it/s, loss=0.0166, lr=7.88e-5, step=1643]\\u001b[A\\n\",\n      \"Epoch 7:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:46,  1.26it/s, loss=0.0121, lr=7.88e-5, step=1644]\\u001b[A\\n\",\n      \"Epoch 7:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [02:04<00:46,  1.26it/s, loss=0.0121, lr=7.88e-5, step=1644]\\u001b[A\\n\",\n      \"Epoch 7:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [02:04<00:46,  1.26it/s, loss=0.0113, lr=7.88e-5, step=1645]\\u001b[A\\n\",\n      \"Epoch 7:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0113, lr=7.88e-5, step=1645]\\u001b[A\\n\",\n      \"Epoch 7:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0131, lr=7.87e-5, step=1646]\\u001b[A\\n\",\n      \"Epoch 7:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0131, lr=7.87e-5, step=1646]\\u001b[A\\n\",\n      \"Epoch 7:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0341, lr=7.87e-5, step=1647]\\u001b[A\\n\",\n      \"Epoch 7:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.0341, lr=7.87e-5, step=1647]\\u001b[A\\n\",\n      \"Epoch 7:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.00721, lr=7.87e-5, step=1648]\\u001b[A\\n\",\n      \"Epoch 7:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00721, lr=7.87e-5, step=1648]\\u001b[A\\n\",\n      \"Epoch 7:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00789, lr=7.86e-5, step=1649]\\u001b[A\\n\",\n      \"Epoch 7:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:07<00:42,  1.25it/s, loss=0.00789, lr=7.86e-5, step=1649]\\u001b[A\\n\",\n      \"Epoch 7:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:08<00:42,  1.25it/s, loss=0.00782, lr=7.86e-5, step=1650]\\u001b[A\\n\",\n      \"Epoch 7:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.00782, lr=7.86e-5, step=1650]\\u001b[A\\n\",\n      \"Epoch 7:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.0285, lr=7.86e-5, step=1651]\\u001b[A\\n\",\n      \"Epoch 7:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0285, lr=7.86e-5, step=1651]\\u001b[A\\n\",\n      \"Epoch 7:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0229, lr=7.85e-5, step=1652]\\u001b[A\\n\",\n      \"Epoch 7:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:40,  1.25it/s, loss=0.0229, lr=7.85e-5, step=1652]\\u001b[A\\n\",\n      \"Epoch 7:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:40,  1.25it/s, loss=0.0269, lr=7.85e-5, step=1653]\\u001b[A\\n\",\n      \"Epoch 7:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0269, lr=7.85e-5, step=1653]\\u001b[A\\n\",\n      \"Epoch 7:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0214, lr=7.85e-5, step=1654]\\u001b[A\\n\",\n      \"Epoch 7:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [02:12<00:38,  1.25it/s, loss=0.0214, lr=7.85e-5, step=1654]\\u001b[A\\n\",\n      \"Epoch 7:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [02:12<00:38,  1.25it/s, loss=0.0128, lr=7.84e-5, step=1655]\\u001b[A\\n\",\n      \"Epoch 7:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                              | 166/213 [02:12<00:37,  1.25it/s, loss=0.0128, lr=7.84e-5, step=1655]\\u001b[A\\n\",\n      \"Epoch 7:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                              | 166/213 [02:12<00:37,  1.25it/s, loss=0.0178, lr=7.84e-5, step=1656]\\u001b[A\\n\",\n      \"Epoch 7:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0178, lr=7.84e-5, step=1656]\\u001b[A\\n\",\n      \"Epoch 7:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0153, lr=7.84e-5, step=1657]\\u001b[A\\n\",\n      \"Epoch 7:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:14<00:36,  1.24it/s, loss=0.0153, lr=7.84e-5, step=1657]\\u001b[A\\n\",\n      \"Epoch 7:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:14<00:36,  1.24it/s, loss=0.0136, lr=7.83e-5, step=1658]\\u001b[A\\n\",\n      \"Epoch 7:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.0136, lr=7.83e-5, step=1658]\\u001b[A\\n\",\n      \"Epoch 7:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.0105, lr=7.83e-5, step=1659]\\u001b[A\\n\",\n      \"Epoch 7:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                          | 170/213 [02:16<00:34,  1.24it/s, loss=0.0105, lr=7.83e-5, step=1659]\\u001b[A\\n\",\n      \"Epoch 7:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                          | 170/213 [02:16<00:34,  1.24it/s, loss=0.0404, lr=7.83e-5, step=1660]\\u001b[A\\n\",\n      \"Epoch 7:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.24it/s, loss=0.0404, lr=7.83e-5, step=1660]\\u001b[A\\n\",\n      \"Epoch 7:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.24it/s, loss=0.0232, lr=7.82e-5, step=1661]\\u001b[A\\n\",\n      \"Epoch 7:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [02:17<00:33,  1.24it/s, loss=0.0232, lr=7.82e-5, step=1661]\\u001b[A\\n\",\n      \"Epoch 7:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [02:17<00:33,  1.24it/s, loss=0.0224, lr=7.82e-5, step=1662]\\u001b[A\\n\",\n      \"Epoch 7:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                       | 173/213 [02:18<00:32,  1.24it/s, loss=0.0224, lr=7.82e-5, step=1662]\\u001b[A\\n\",\n      \"Epoch 7:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                       | 173/213 [02:18<00:32,  1.24it/s, loss=0.0347, lr=7.82e-5, step=1663]\\u001b[A\\n\",\n      \"Epoch 7:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                      | 174/213 [02:19<00:31,  1.24it/s, loss=0.0347, lr=7.82e-5, step=1663]\\u001b[A\\n\",\n      \"Epoch 7:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                      | 174/213 [02:19<00:31,  1.24it/s, loss=0.0106, lr=7.81e-5, step=1664]\\u001b[A\\n\",\n      \"Epoch 7:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:20<00:30,  1.24it/s, loss=0.0106, lr=7.81e-5, step=1664]\\u001b[A\\n\",\n      \"Epoch 7:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:20<00:30,  1.24it/s, loss=0.0108, lr=7.81e-5, step=1665]\\u001b[A\\n\",\n      \"Epoch 7:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:20<00:29,  1.24it/s, loss=0.0108, lr=7.81e-5, step=1665]\\u001b[A\\n\",\n      \"Epoch 7:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                    | 176/213 [02:20<00:29,  1.24it/s, loss=0.012, lr=7.81e-5, step=1666]\\u001b[A\\n\",\n      \"Epoch 7:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 177/213 [02:21<00:28,  1.24it/s, loss=0.012, lr=7.81e-5, step=1666]\\u001b[A\\n\",\n      \"Epoch 7:  83%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                   | 177/213 [02:21<00:28,  1.24it/s, loss=0.0146, lr=7.8e-5, step=1667]\\u001b[A\\n\",\n      \"Epoch 7:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                  | 178/213 [02:22<00:28,  1.24it/s, loss=0.0146, lr=7.8e-5, step=1667]\\u001b[A\\n\",\n      \"Epoch 7:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                  | 178/213 [02:22<00:28,  1.24it/s, loss=0.0311, lr=7.8e-5, step=1668]\\u001b[A\\n\",\n      \"Epoch 7:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:23<00:27,  1.24it/s, loss=0.0311, lr=7.8e-5, step=1668]\\u001b[A\\n\",\n      \"Epoch 7:  84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                 | 179/213 [02:23<00:27,  1.24it/s, loss=0.0112, lr=7.8e-5, step=1669]\\u001b[A\\n\",\n      \"Epoch 7:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                | 180/213 [02:24<00:26,  1.24it/s, loss=0.0112, lr=7.8e-5, step=1669]\\u001b[A\\n\",\n      \"Epoch 7:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:24<00:26,  1.24it/s, loss=0.00879, lr=7.79e-5, step=1670]\\u001b[A\\n\",\n      \"Epoch 7:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.24it/s, loss=0.00879, lr=7.79e-5, step=1670]\\u001b[A\\n\",\n      \"Epoch 7:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.24it/s, loss=0.0307, lr=7.79e-5, step=1671]\\u001b[A\\n\",\n      \"Epoch 7:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:25<00:25,  1.24it/s, loss=0.0307, lr=7.79e-5, step=1671]\\u001b[A\\n\",\n      \"Epoch 7:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:25<00:25,  1.24it/s, loss=0.0068, lr=7.78e-5, step=1672]\\u001b[A\\n\",\n      \"Epoch 7:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                             | 183/213 [02:26<00:24,  1.24it/s, loss=0.0068, lr=7.78e-5, step=1672]\\u001b[A\\n\",\n      \"Epoch 7:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:24,  1.24it/s, loss=0.00666, lr=7.78e-5, step=1673]\\u001b[A\\n\",\n      \"Epoch 7:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.24it/s, loss=0.00666, lr=7.78e-5, step=1673]\\u001b[A\\n\",\n      \"Epoch 7:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.24it/s, loss=0.00783, lr=7.78e-5, step=1674]\\u001b[A\\n\",\n      \"Epoch 7:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:28<00:22,  1.24it/s, loss=0.00783, lr=7.78e-5, step=1674]\\u001b[A\\n\",\n      \"Epoch 7:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                           | 185/213 [02:28<00:22,  1.24it/s, loss=0.0182, lr=7.77e-5, step=1675]\\u001b[A\\n\",\n      \"Epoch 7:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.24it/s, loss=0.0182, lr=7.77e-5, step=1675]\\u001b[A\\n\",\n      \"Epoch 7:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.24it/s, loss=0.0162, lr=7.77e-5, step=1676]\\u001b[A\\n\",\n      \"Epoch 7:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.24it/s, loss=0.0162, lr=7.77e-5, step=1676]\\u001b[A\\n\",\n      \"Epoch 7:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.24it/s, loss=0.0277, lr=7.77e-5, step=1677]\\u001b[A\\n\",\n      \"Epoch 7:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0277, lr=7.77e-5, step=1677]\\u001b[A\\n\",\n      \"Epoch 7:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0192, lr=7.76e-5, step=1678]\\u001b[A\\n\",\n      \"Epoch 7:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.24it/s, loss=0.0192, lr=7.76e-5, step=1678]\\u001b[A\\n\",\n      \"Epoch 7:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.24it/s, loss=0.0205, lr=7.76e-5, step=1679]\\u001b[A\\n\",\n      \"Epoch 7:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:32<00:18,  1.24it/s, loss=0.0205, lr=7.76e-5, step=1679]\\u001b[A\\n\",\n      \"Epoch 7:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:32<00:18,  1.24it/s, loss=0.0147, lr=7.76e-5, step=1680]\\u001b[A\\n\",\n      \"Epoch 7:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0147, lr=7.76e-5, step=1680]\\u001b[A\\n\",\n      \"Epoch 7:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0133, lr=7.75e-5, step=1681]\\u001b[A\\n\",\n      \"Epoch 7:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.24it/s, loss=0.0133, lr=7.75e-5, step=1681]\\u001b[A\\n\",\n      \"Epoch 7:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.24it/s, loss=0.0107, lr=7.75e-5, step=1682]\\u001b[A\\n\",\n      \"Epoch 7:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:16,  1.24it/s, loss=0.0107, lr=7.75e-5, step=1682]\\u001b[A\\n\",\n      \"Epoch 7:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:16,  1.24it/s, loss=0.019, lr=7.75e-5, step=1683]\\u001b[A\\n\",\n      \"Epoch 7:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.019, lr=7.75e-5, step=1683]\\u001b[A\\n\",\n      \"Epoch 7:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0493, lr=7.74e-5, step=1684]\\u001b[A\\n\",\n      \"Epoch 7:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0493, lr=7.74e-5, step=1684]\\u001b[A\\n\",\n      \"Epoch 7:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0107, lr=7.74e-5, step=1685]\\u001b[A\\n\",\n      \"Epoch 7:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0107, lr=7.74e-5, step=1685]\\u001b[A\\n\",\n      \"Epoch 7:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0123, lr=7.74e-5, step=1686]\\u001b[A\\n\",\n      \"Epoch 7:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0123, lr=7.74e-5, step=1686]\\u001b[A\\n\",\n      \"Epoch 7:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0122, lr=7.73e-5, step=1687]\\u001b[A\\n\",\n      \"Epoch 7:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0122, lr=7.73e-5, step=1687]\\u001b[A\\n\",\n      \"Epoch 7:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0122, lr=7.73e-5, step=1688]\\u001b[A\\n\",\n      \"Epoch 7:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0122, lr=7.73e-5, step=1688]\\u001b[A\\n\",\n      \"Epoch 7:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00656, lr=7.73e-5, step=1689]\\u001b[A\\n\",\n      \"Epoch 7:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:40<00:10,  1.25it/s, loss=0.00656, lr=7.73e-5, step=1689]\\u001b[A\\n\",\n      \"Epoch 7:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:40<00:10,  1.25it/s, loss=0.0123, lr=7.72e-5, step=1690]\\u001b[A\\n\",\n      \"Epoch 7:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0123, lr=7.72e-5, step=1690]\\u001b[A\\n\",\n      \"Epoch 7:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0212, lr=7.72e-5, step=1691]\\u001b[A\\n\",\n      \"Epoch 7:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0212, lr=7.72e-5, step=1691]\\u001b[A\\n\",\n      \"Epoch 7:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0283, lr=7.72e-5, step=1692]\\u001b[A\\n\",\n      \"Epoch 7:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0283, lr=7.72e-5, step=1692]\\u001b[A\\n\",\n      \"Epoch 7:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.026, lr=7.71e-5, step=1693]\\u001b[A\\n\",\n      \"Epoch 7:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:43<00:07,  1.25it/s, loss=0.026, lr=7.71e-5, step=1693]\\u001b[A\\n\",\n      \"Epoch 7:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.00873, lr=7.71e-5, step=1694]\\u001b[A\\n\",\n      \"Epoch 7:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:44<00:06,  1.25it/s, loss=0.00873, lr=7.71e-5, step=1694]\\u001b[A\\n\",\n      \"Epoch 7:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████        | 205/213 [02:44<00:06,  1.25it/s, loss=0.017, lr=7.7e-5, step=1695]\\u001b[A\\n\",\n      \"Epoch 7:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████       | 206/213 [02:44<00:05,  1.25it/s, loss=0.017, lr=7.7e-5, step=1695]\\u001b[A\\n\",\n      \"Epoch 7:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0197, lr=7.7e-5, step=1696]\\u001b[A\\n\",\n      \"Epoch 7:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:45<00:04,  1.25it/s, loss=0.0197, lr=7.7e-5, step=1696]\\u001b[A\\n\",\n      \"Epoch 7:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████      | 207/213 [02:45<00:04,  1.25it/s, loss=0.0195, lr=7.7e-5, step=1697]\\u001b[A\\n\",\n      \"Epoch 7:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.0195, lr=7.7e-5, step=1697]\\u001b[A\\n\",\n      \"Epoch 7:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:04,  1.25it/s, loss=0.016, lr=7.69e-5, step=1698]\\u001b[A\\n\",\n      \"Epoch 7:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.016, lr=7.69e-5, step=1698]\\u001b[A\\n\",\n      \"Epoch 7:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0193, lr=7.69e-5, step=1699]\\u001b[A\\n\",\n      \"Epoch 7:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.0193, lr=7.69e-5, step=1699]\\u001b[A\\n\",\n      \"Epoch 7:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.25it/s, loss=0.00609, lr=7.69e-5, step=1700]\\u001b[A\\n\",\n      \"Epoch 7:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.00609, lr=7.69e-5, step=1700]\\u001b[A\\n\",\n      \"Epoch 7:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.0307, lr=7.68e-5, step=1701]\\u001b[A\\n\",\n      \"Epoch 7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.0307, lr=7.68e-5, step=1701]\\u001b[A\\n\",\n      \"Epoch 7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.0113, lr=7.68e-5, step=1702]\\u001b[A\\n\",\n      \"Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.46it/s, loss=0.0113, lr=7.68e-5, step=1702]\\u001b[A\\n\",\n      \"Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.46it/s, loss=0.0103, lr=7.68e-5, step=1703]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"300f0714de4b4bcc96660a0d8bc9b71d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 7: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.0103, lr=7.68e-5, step=1703]\\n\",\n      \"Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.45it/s, loss=0.0117, lr=6.89e-5, step=1916]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e5a65582554f41a5815a4efa4af4c6e6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 8: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:36<00:00,  2.15s/it, loss=0.0117, lr=6.89e-5, step=1916]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 9:   0%|                                                                                                                                                                                                                                                              | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 9:   0%|█▏                                                                                                                                                                                                                                                    | 1/213 [00:00<03:09,  1.12it/s]\\u001b[A\\n\",\n      \"Epoch 9:   0%|▉                                                                                                                                                                                                                  | 1/213 [00:00<03:09,  1.12it/s, loss=0.017, lr=6.88e-5, step=1917]\\u001b[A\\n\",\n      \"Epoch 9:   1%|█▉                                                                                                                                                                                                                 | 2/213 [00:01<02:55,  1.20it/s, loss=0.017, lr=6.88e-5, step=1917]\\u001b[A\\n\",\n      \"Epoch 9:   1%|█▉                                                                                                                                                                                                               | 2/213 [00:01<02:55,  1.20it/s, loss=0.00915, lr=6.88e-5, step=1918]\\u001b[A\\n\",\n      \"Epoch 9:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:50,  1.23it/s, loss=0.00915, lr=6.88e-5, step=1918]\\u001b[A\\n\",\n      \"Epoch 9:   1%|██▉                                                                                                                                                                                                                | 3/213 [00:02<02:50,  1.23it/s, loss=0.011, lr=6.87e-5, step=1919]\\u001b[A\\n\",\n      \"Epoch 9:   2%|███▉                                                                                                                                                                                                               | 4/213 [00:03<02:47,  1.25it/s, loss=0.011, lr=6.87e-5, step=1919]\\u001b[A\\n\",\n      \"Epoch 9:   2%|███▉                                                                                                                                                                                                              | 4/213 [00:03<02:47,  1.25it/s, loss=0.0109, lr=6.87e-5, step=1920]\\u001b[A\\n\",\n      \"Epoch 9:   2%|████▉                                                                                                                                                                                                             | 5/213 [00:04<02:46,  1.25it/s, loss=0.0109, lr=6.87e-5, step=1920]\\u001b[A\\n\",\n      \"Epoch 9:   2%|████▉                                                                                                                                                                                                             | 5/213 [00:04<02:46,  1.25it/s, loss=0.0436, lr=6.87e-5, step=1921]\\u001b[A\\n\",\n      \"Epoch 9:   3%|█████▉                                                                                                                                                                                                            | 6/213 [00:04<02:45,  1.25it/s, loss=0.0436, lr=6.87e-5, step=1921]\\u001b[A\\n\",\n      \"Epoch 9:   3%|█████▉                                                                                                                                                                                                           | 6/213 [00:04<02:45,  1.25it/s, loss=0.00434, lr=6.86e-5, step=1922]\\u001b[A\\n\",\n      \"Epoch 9:   3%|██████▊                                                                                                                                                                                                          | 7/213 [00:05<02:44,  1.25it/s, loss=0.00434, lr=6.86e-5, step=1922]\\u001b[A\\n\",\n      \"Epoch 9:   3%|██████▉                                                                                                                                                                                                           | 7/213 [00:05<02:44,  1.25it/s, loss=0.0142, lr=6.86e-5, step=1923]\\u001b[A\\n\",\n      \"Epoch 9:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:43,  1.26it/s, loss=0.0142, lr=6.86e-5, step=1923]\\u001b[A\\n\",\n      \"Epoch 9:   4%|███████▉                                                                                                                                                                                                           | 8/213 [00:06<02:43,  1.26it/s, loss=0.017, lr=6.86e-5, step=1924]\\u001b[A\\n\",\n      \"Epoch 9:   4%|████████▉                                                                                                                                                                                                          | 9/213 [00:07<02:42,  1.26it/s, loss=0.017, lr=6.86e-5, step=1924]\\u001b[A\\n\",\n      \"Epoch 9:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:42,  1.26it/s, loss=0.00785, lr=6.85e-5, step=1925]\\u001b[A\\n\",\n      \"Epoch 9:   5%|█████████▊                                                                                                                                                                                                      | 10/213 [00:08<02:41,  1.26it/s, loss=0.00785, lr=6.85e-5, step=1925]\\u001b[A\\n\",\n      \"Epoch 9:   5%|█████████▊                                                                                                                                                                                                       | 10/213 [00:08<02:41,  1.26it/s, loss=0.0085, lr=6.85e-5, step=1926]\\u001b[A\\n\",\n      \"Epoch 9:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:40,  1.26it/s, loss=0.0085, lr=6.85e-5, step=1926]\\u001b[A\\n\",\n      \"Epoch 9:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:40,  1.26it/s, loss=0.0143, lr=6.84e-5, step=1927]\\u001b[A\\n\",\n      \"Epoch 9:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:39,  1.26it/s, loss=0.0143, lr=6.84e-5, step=1927]\\u001b[A\\n\",\n      \"Epoch 9:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:39,  1.26it/s, loss=0.0222, lr=6.84e-5, step=1928]\\u001b[A\\n\",\n      \"Epoch 9:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:39,  1.26it/s, loss=0.0222, lr=6.84e-5, step=1928]\\u001b[A\\n\",\n      \"Epoch 9:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:39,  1.26it/s, loss=0.0252, lr=6.84e-5, step=1929]\\u001b[A\\n\",\n      \"Epoch 9:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:38,  1.25it/s, loss=0.0252, lr=6.84e-5, step=1929]\\u001b[A\\n\",\n      \"Epoch 9:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:38,  1.25it/s, loss=0.0373, lr=6.83e-5, step=1930]\\u001b[A\\n\",\n      \"Epoch 9:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:38,  1.25it/s, loss=0.0373, lr=6.83e-5, step=1930]\\u001b[A\\n\",\n      \"Epoch 9:   7%|██████████████▋                                                                                                                                                                                                 | 15/213 [00:12<02:38,  1.25it/s, loss=0.00933, lr=6.83e-5, step=1931]\\u001b[A\\n\",\n      \"Epoch 9:   8%|███████████████▌                                                                                                                                                                                                | 16/213 [00:12<02:37,  1.25it/s, loss=0.00933, lr=6.83e-5, step=1931]\\u001b[A\\n\",\n      \"Epoch 9:   8%|███████████████▋                                                                                                                                                                                                 | 16/213 [00:12<02:37,  1.25it/s, loss=0.0129, lr=6.82e-5, step=1932]\\u001b[A\\n\",\n      \"Epoch 9:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:36,  1.25it/s, loss=0.0129, lr=6.82e-5, step=1932]\\u001b[A\\n\",\n      \"Epoch 9:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:36,  1.25it/s, loss=0.0419, lr=6.82e-5, step=1933]\\u001b[A\\n\",\n      \"Epoch 9:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:36,  1.25it/s, loss=0.0419, lr=6.82e-5, step=1933]\\u001b[A\\n\",\n      \"Epoch 9:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:36,  1.25it/s, loss=0.0211, lr=6.82e-5, step=1934]\\u001b[A\\n\",\n      \"Epoch 9:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0211, lr=6.82e-5, step=1934]\\u001b[A\\n\",\n      \"Epoch 9:   9%|██████████████████▋                                                                                                                                                                                              | 19/213 [00:15<02:35,  1.25it/s, loss=0.0189, lr=6.81e-5, step=1935]\\u001b[A\\n\",\n      \"Epoch 9:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0189, lr=6.81e-5, step=1935]\\u001b[A\\n\",\n      \"Epoch 9:   9%|███████████████████▌                                                                                                                                                                                             | 20/213 [00:16<02:34,  1.25it/s, loss=0.0164, lr=6.81e-5, step=1936]\\u001b[A\\n\",\n      \"Epoch 9:  10%|████████████████████▌                                                                                                                                                                                            | 21/213 [00:16<02:33,  1.25it/s, loss=0.0164, lr=6.81e-5, step=1936]\\u001b[A\\n\",\n      \"Epoch 9:  10%|████████████████████▋                                                                                                                                                                                             | 21/213 [00:16<02:33,  1.25it/s, loss=0.0103, lr=6.8e-5, step=1937]\\u001b[A\\n\",\n      \"Epoch 9:  10%|█████████████████████▋                                                                                                                                                                                            | 22/213 [00:17<02:32,  1.25it/s, loss=0.0103, lr=6.8e-5, step=1937]\\u001b[A\\n\",\n      \"Epoch 9:  10%|█████████████████████▋                                                                                                                                                                                            | 22/213 [00:17<02:32,  1.25it/s, loss=0.0109, lr=6.8e-5, step=1938]\\u001b[A\\n\",\n      \"Epoch 9:  11%|██████████████████████▋                                                                                                                                                                                           | 23/213 [00:18<02:31,  1.26it/s, loss=0.0109, lr=6.8e-5, step=1938]\\u001b[A\\n\",\n      \"Epoch 9:  11%|██████████████████████▋                                                                                                                                                                                           | 23/213 [00:18<02:31,  1.26it/s, loss=0.0329, lr=6.8e-5, step=1939]\\u001b[A\\n\",\n      \"Epoch 9:  11%|███████████████████████▋                                                                                                                                                                                          | 24/213 [00:19<02:30,  1.25it/s, loss=0.0329, lr=6.8e-5, step=1939]\\u001b[A\\n\",\n      \"Epoch 9:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:30,  1.25it/s, loss=0.0118, lr=6.79e-5, step=1940]\\u001b[A\\n\",\n      \"Epoch 9:  12%|████████████████████████▌                                                                                                                                                                                        | 25/213 [00:20<02:29,  1.25it/s, loss=0.0118, lr=6.79e-5, step=1940]\\u001b[A\\n\",\n      \"Epoch 9:  12%|████████████████████████▌                                                                                                                                                                                        | 25/213 [00:20<02:29,  1.25it/s, loss=0.0166, lr=6.79e-5, step=1941]\\u001b[A\\n\",\n      \"Epoch 9:  12%|█████████████████████████▌                                                                                                                                                                                       | 26/213 [00:20<02:28,  1.26it/s, loss=0.0166, lr=6.79e-5, step=1941]\\u001b[A\\n\",\n      \"Epoch 9:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [00:20<02:28,  1.26it/s, loss=0.00685, lr=6.79e-5, step=1942]\\u001b[A\\n\",\n      \"Epoch 9:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [00:21<02:28,  1.25it/s, loss=0.00685, lr=6.79e-5, step=1942]\\u001b[A\\n\",\n      \"Epoch 9:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [00:21<02:28,  1.25it/s, loss=0.00722, lr=6.78e-5, step=1943]\\u001b[A\\n\",\n      \"Epoch 9:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:27,  1.25it/s, loss=0.00722, lr=6.78e-5, step=1943]\\u001b[A\\n\",\n      \"Epoch 9:  13%|███████████████████████████▍                                                                                                                                                                                     | 28/213 [00:22<02:27,  1.25it/s, loss=0.0307, lr=6.78e-5, step=1944]\\u001b[A\\n\",\n      \"Epoch 9:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:27,  1.25it/s, loss=0.0307, lr=6.78e-5, step=1944]\\u001b[A\\n\",\n      \"Epoch 9:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:27,  1.25it/s, loss=0.0113, lr=6.77e-5, step=1945]\\u001b[A\\n\",\n      \"Epoch 9:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:26,  1.25it/s, loss=0.0113, lr=6.77e-5, step=1945]\\u001b[A\\n\",\n      \"Epoch 9:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:26,  1.25it/s, loss=0.0282, lr=6.77e-5, step=1946]\\u001b[A\\n\",\n      \"Epoch 9:  15%|██████████████████████████████▍                                                                                                                                                                                  | 31/213 [00:24<02:25,  1.25it/s, loss=0.0282, lr=6.77e-5, step=1946]\\u001b[A\\n\",\n      \"Epoch 9:  15%|██████████████████████████████▍                                                                                                                                                                                  | 31/213 [00:24<02:25,  1.25it/s, loss=0.0204, lr=6.77e-5, step=1947]\\u001b[A\\n\",\n      \"Epoch 9:  15%|███████████████████████████████▍                                                                                                                                                                                 | 32/213 [00:25<02:35,  1.17it/s, loss=0.0204, lr=6.77e-5, step=1947]\\u001b[A\\n\",\n      \"Epoch 9:  15%|███████████████████████████████▍                                                                                                                                                                                 | 32/213 [00:25<02:35,  1.17it/s, loss=0.0137, lr=6.76e-5, step=1948]\\u001b[A\\n\",\n      \"Epoch 9:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:30,  1.19it/s, loss=0.0137, lr=6.76e-5, step=1948]\\u001b[A\\n\",\n      \"Epoch 9:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [00:26<02:30,  1.19it/s, loss=0.0114, lr=6.76e-5, step=1949]\\u001b[A\\n\",\n      \"Epoch 9:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [00:27<02:28,  1.21it/s, loss=0.0114, lr=6.76e-5, step=1949]\\u001b[A\\n\",\n      \"Epoch 9:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [00:27<02:28,  1.21it/s, loss=0.0114, lr=6.75e-5, step=1950]\\u001b[A\\n\",\n      \"Epoch 9:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:26,  1.22it/s, loss=0.0114, lr=6.75e-5, step=1950]\\u001b[A\\n\",\n      \"Epoch 9:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:26,  1.22it/s, loss=0.0146, lr=6.75e-5, step=1951]\\u001b[A\\n\",\n      \"Epoch 9:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:29<02:24,  1.23it/s, loss=0.0146, lr=6.75e-5, step=1951]\\u001b[A\\n\",\n      \"Epoch 9:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:29<02:24,  1.23it/s, loss=0.0087, lr=6.75e-5, step=1952]\\u001b[A\\n\",\n      \"Epoch 9:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:23,  1.23it/s, loss=0.0087, lr=6.75e-5, step=1952]\\u001b[A\\n\",\n      \"Epoch 9:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:23,  1.23it/s, loss=0.0346, lr=6.74e-5, step=1953]\\u001b[A\\n\",\n      \"Epoch 9:  18%|█████████████████████████████████████▎                                                                                                                                                                           | 38/213 [00:30<02:21,  1.23it/s, loss=0.0346, lr=6.74e-5, step=1953]\\u001b[A\\n\",\n      \"Epoch 9:  18%|█████████████████████████████████████                                                                                                                                                                           | 38/213 [00:30<02:21,  1.23it/s, loss=0.00872, lr=6.74e-5, step=1954]\\u001b[A\\n\",\n      \"Epoch 9:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [00:31<02:20,  1.24it/s, loss=0.00872, lr=6.74e-5, step=1954]\\u001b[A\\n\",\n      \"Epoch 9:  18%|██████████████████████████████████████▎                                                                                                                                                                          | 39/213 [00:31<02:20,  1.24it/s, loss=0.0466, lr=6.73e-5, step=1955]\\u001b[A\\n\",\n      \"Epoch 9:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.0466, lr=6.73e-5, step=1955]\\u001b[A\\n\",\n      \"Epoch 9:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.0212, lr=6.73e-5, step=1956]\\u001b[A\\n\",\n      \"Epoch 9:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:33<02:18,  1.24it/s, loss=0.0212, lr=6.73e-5, step=1956]\\u001b[A\\n\",\n      \"Epoch 9:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:33<02:18,  1.24it/s, loss=0.0145, lr=6.73e-5, step=1957]\\u001b[A\\n\",\n      \"Epoch 9:  20%|█████████████████████████████████████████▏                                                                                                                                                                       | 42/213 [00:33<02:17,  1.24it/s, loss=0.0145, lr=6.73e-5, step=1957]\\u001b[A\\n\",\n      \"Epoch 9:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.24it/s, loss=0.00599, lr=6.72e-5, step=1958]\\u001b[A\\n\",\n      \"Epoch 9:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.24it/s, loss=0.00599, lr=6.72e-5, step=1958]\\u001b[A\\n\",\n      \"Epoch 9:  20%|██████████████████████████████████████████▏                                                                                                                                                                      | 43/213 [00:34<02:16,  1.24it/s, loss=0.0244, lr=6.72e-5, step=1959]\\u001b[A\\n\",\n      \"Epoch 9:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:35<02:15,  1.24it/s, loss=0.0244, lr=6.72e-5, step=1959]\\u001b[A\\n\",\n      \"Epoch 9:  21%|███████████████████████████████████████████▍                                                                                                                                                                      | 44/213 [00:35<02:15,  1.24it/s, loss=0.013, lr=6.71e-5, step=1960]\\u001b[A\\n\",\n      \"Epoch 9:  21%|████████████████████████████████████████████▎                                                                                                                                                                     | 45/213 [00:36<02:15,  1.24it/s, loss=0.013, lr=6.71e-5, step=1960]\\u001b[A\\n\",\n      \"Epoch 9:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:15,  1.24it/s, loss=0.0265, lr=6.71e-5, step=1961]\\u001b[A\\n\",\n      \"Epoch 9:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:37<02:14,  1.24it/s, loss=0.0265, lr=6.71e-5, step=1961]\\u001b[A\\n\",\n      \"Epoch 9:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:37<02:14,  1.24it/s, loss=0.0323, lr=6.71e-5, step=1962]\\u001b[A\\n\",\n      \"Epoch 9:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:13,  1.25it/s, loss=0.0323, lr=6.71e-5, step=1962]\\u001b[A\\n\",\n      \"Epoch 9:  22%|██████████████████████████████████████████████▎                                                                                                                                                                   | 47/213 [00:37<02:13,  1.25it/s, loss=0.0127, lr=6.7e-5, step=1963]\\u001b[A\\n\",\n      \"Epoch 9:  23%|███████████████████████████████████████████████▎                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0127, lr=6.7e-5, step=1963]\\u001b[A\\n\",\n      \"Epoch 9:  23%|███████████████████████████████████████████████▎                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0122, lr=6.7e-5, step=1964]\\u001b[A\\n\",\n      \"Epoch 9:  23%|████████████████████████████████████████████████▎                                                                                                                                                                 | 49/213 [00:39<02:11,  1.25it/s, loss=0.0122, lr=6.7e-5, step=1964]\\u001b[A\\n\",\n      \"Epoch 9:  23%|████████████████████████████████████████████████▎                                                                                                                                                                 | 49/213 [00:39<02:11,  1.25it/s, loss=0.0297, lr=6.7e-5, step=1965]\\u001b[A\\n\",\n      \"Epoch 9:  23%|█████████████████████████████████████████████████▎                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0297, lr=6.7e-5, step=1965]\\u001b[A\\n\",\n      \"Epoch 9:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:10,  1.25it/s, loss=0.0185, lr=6.69e-5, step=1966]\\u001b[A\\n\",\n      \"Epoch 9:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:41<02:10,  1.24it/s, loss=0.0185, lr=6.69e-5, step=1966]\\u001b[A\\n\",\n      \"Epoch 9:  24%|██████████████████████████████████████████████████                                                                                                                                                               | 51/213 [00:41<02:10,  1.24it/s, loss=0.0364, lr=6.69e-5, step=1967]\\u001b[A\\n\",\n      \"Epoch 9:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0364, lr=6.69e-5, step=1967]\\u001b[A\\n\",\n      \"Epoch 9:  24%|███████████████████████████████████████████████████                                                                                                                                                              | 52/213 [00:41<02:09,  1.25it/s, loss=0.0175, lr=6.68e-5, step=1968]\\u001b[A\\n\",\n      \"Epoch 9:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0175, lr=6.68e-5, step=1968]\\u001b[A\\n\",\n      \"Epoch 9:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [00:42<02:08,  1.25it/s, loss=0.0202, lr=6.68e-5, step=1969]\\u001b[A\\n\",\n      \"Epoch 9:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.0202, lr=6.68e-5, step=1969]\\u001b[A\\n\",\n      \"Epoch 9:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [00:43<02:07,  1.25it/s, loss=0.0212, lr=6.68e-5, step=1970]\\u001b[A\\n\",\n      \"Epoch 9:  26%|█████████████████████████████████████████████████████▉                                                                                                                                                           | 55/213 [00:44<02:06,  1.25it/s, loss=0.0212, lr=6.68e-5, step=1970]\\u001b[A\\n\",\n      \"Epoch 9:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.25it/s, loss=0.00784, lr=6.67e-5, step=1971]\\u001b[A\\n\",\n      \"Epoch 9:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [00:45<02:05,  1.25it/s, loss=0.00784, lr=6.67e-5, step=1971]\\u001b[A\\n\",\n      \"Epoch 9:  26%|██████████████████████████████████████████████████████▉                                                                                                                                                          | 56/213 [00:45<02:05,  1.25it/s, loss=0.0209, lr=6.67e-5, step=1972]\\u001b[A\\n\",\n      \"Epoch 9:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.25it/s, loss=0.0209, lr=6.67e-5, step=1972]\\u001b[A\\n\",\n      \"Epoch 9:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.25it/s, loss=0.0283, lr=6.66e-5, step=1973]\\u001b[A\\n\",\n      \"Epoch 9:  27%|████████████████████████████████████████████████████████▉                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0283, lr=6.66e-5, step=1973]\\u001b[A\\n\",\n      \"Epoch 9:  27%|████████████████████████████████████████████████████████▉                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0164, lr=6.66e-5, step=1974]\\u001b[A\\n\",\n      \"Epoch 9:  28%|█████████████████████████████████████████████████████████▉                                                                                                                                                       | 59/213 [00:47<02:03,  1.25it/s, loss=0.0164, lr=6.66e-5, step=1974]\\u001b[A\\n\",\n      \"Epoch 9:  28%|█████████████████████████████████████████████████████████▉                                                                                                                                                       | 59/213 [00:47<02:03,  1.25it/s, loss=0.0323, lr=6.66e-5, step=1975]\\u001b[A\\n\",\n      \"Epoch 9:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0323, lr=6.66e-5, step=1975]\\u001b[A\\n\",\n      \"Epoch 9:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:48<02:02,  1.25it/s, loss=0.0142, lr=6.65e-5, step=1976]\\u001b[A\\n\",\n      \"Epoch 9:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:49<02:01,  1.25it/s, loss=0.0142, lr=6.65e-5, step=1976]\\u001b[A\\n\",\n      \"Epoch 9:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:49<02:01,  1.25it/s, loss=0.0159, lr=6.65e-5, step=1977]\\u001b[A\\n\",\n      \"Epoch 9:  29%|████████████████████████████████████████████████████████████▊                                                                                                                                                    | 62/213 [00:49<02:00,  1.25it/s, loss=0.0159, lr=6.65e-5, step=1977]\\u001b[A\\n\",\n      \"Epoch 9:  29%|████████████████████████████████████████████████████████████▊                                                                                                                                                    | 62/213 [00:49<02:00,  1.25it/s, loss=0.0107, lr=6.64e-5, step=1978]\\u001b[A\\n\",\n      \"Epoch 9:  30%|█████████████████████████████████████████████████████████████▊                                                                                                                                                   | 63/213 [00:50<01:59,  1.25it/s, loss=0.0107, lr=6.64e-5, step=1978]\\u001b[A\\n\",\n      \"Epoch 9:  30%|█████████████████████████████████████████████████████████████▊                                                                                                                                                   | 63/213 [00:50<01:59,  1.25it/s, loss=0.0249, lr=6.64e-5, step=1979]\\u001b[A\\n\",\n      \"Epoch 9:  30%|██████████████████████████████████████████████████████████████▊                                                                                                                                                  | 64/213 [00:51<01:58,  1.25it/s, loss=0.0249, lr=6.64e-5, step=1979]\\u001b[A\\n\",\n      \"Epoch 9:  30%|███████████████████████████████████████████████████████████████                                                                                                                                                   | 64/213 [00:51<01:58,  1.25it/s, loss=0.012, lr=6.64e-5, step=1980]\\u001b[A\\n\",\n      \"Epoch 9:  31%|████████████████████████████████████████████████████████████████                                                                                                                                                  | 65/213 [00:52<01:58,  1.25it/s, loss=0.012, lr=6.64e-5, step=1980]\\u001b[A\\n\",\n      \"Epoch 9:  31%|███████████████████████████████████████████████████████████████▊                                                                                                                                                 | 65/213 [00:52<01:58,  1.25it/s, loss=0.0112, lr=6.63e-5, step=1981]\\u001b[A\\n\",\n      \"Epoch 9:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:53<01:57,  1.25it/s, loss=0.0112, lr=6.63e-5, step=1981]\\u001b[A\\n\",\n      \"Epoch 9:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:53<01:57,  1.25it/s, loss=0.0229, lr=6.63e-5, step=1982]\\u001b[A\\n\",\n      \"Epoch 9:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:56,  1.25it/s, loss=0.0229, lr=6.63e-5, step=1982]\\u001b[A\\n\",\n      \"Epoch 9:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:56,  1.25it/s, loss=0.0197, lr=6.62e-5, step=1983]\\u001b[A\\n\",\n      \"Epoch 9:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:55,  1.25it/s, loss=0.0197, lr=6.62e-5, step=1983]\\u001b[A\\n\",\n      \"Epoch 9:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:55,  1.25it/s, loss=0.0106, lr=6.62e-5, step=1984]\\u001b[A\\n\",\n      \"Epoch 9:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.0106, lr=6.62e-5, step=1984]\\u001b[A\\n\",\n      \"Epoch 9:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.0111, lr=6.62e-5, step=1985]\\u001b[A\\n\",\n      \"Epoch 9:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.0111, lr=6.62e-5, step=1985]\\u001b[A\\n\",\n      \"Epoch 9:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.0188, lr=6.61e-5, step=1986]\\u001b[A\\n\",\n      \"Epoch 9:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:57<01:53,  1.25it/s, loss=0.0188, lr=6.61e-5, step=1986]\\u001b[A\\n\",\n      \"Epoch 9:  33%|█████████████████████████████████████████████████████████████████████▋                                                                                                                                           | 71/213 [00:57<01:53,  1.25it/s, loss=0.0141, lr=6.61e-5, step=1987]\\u001b[A\\n\",\n      \"Epoch 9:  34%|██████████████████████████████████████████████████████████████████████▋                                                                                                                                          | 72/213 [00:57<01:52,  1.25it/s, loss=0.0141, lr=6.61e-5, step=1987]\\u001b[A\\n\",\n      \"Epoch 9:  34%|██████████████████████████████████████████████████████████████████████▋                                                                                                                                          | 72/213 [00:57<01:52,  1.25it/s, loss=0.00743, lr=6.6e-5, step=1988]\\u001b[A\\n\",\n      \"Epoch 9:  34%|███████████████████████████████████████████████████████████████████████▋                                                                                                                                         | 73/213 [00:58<01:51,  1.25it/s, loss=0.00743, lr=6.6e-5, step=1988]\\u001b[A\\n\",\n      \"Epoch 9:  34%|███████████████████████████████████████████████████████████████████████▉                                                                                                                                          | 73/213 [00:58<01:51,  1.25it/s, loss=0.0138, lr=6.6e-5, step=1989]\\u001b[A\\n\",\n      \"Epoch 9:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.25it/s, loss=0.0138, lr=6.6e-5, step=1989]\\u001b[A\\n\",\n      \"Epoch 9:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.25it/s, loss=0.0239, lr=6.6e-5, step=1990]\\u001b[A\\n\",\n      \"Epoch 9:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.25it/s, loss=0.0239, lr=6.6e-5, step=1990]\\u001b[A\\n\",\n      \"Epoch 9:  35%|█████████████████████████████████████████████████████████████████████████▌                                                                                                                                       | 75/213 [01:00<01:50,  1.25it/s, loss=0.0364, lr=6.59e-5, step=1991]\\u001b[A\\n\",\n      \"Epoch 9:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:01<01:49,  1.25it/s, loss=0.0364, lr=6.59e-5, step=1991]\\u001b[A\\n\",\n      \"Epoch 9:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:01<01:49,  1.25it/s, loss=0.0119, lr=6.59e-5, step=1992]\\u001b[A\\n\",\n      \"Epoch 9:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:01<01:48,  1.26it/s, loss=0.0119, lr=6.59e-5, step=1992]\\u001b[A\\n\",\n      \"Epoch 9:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:01<01:48,  1.26it/s, loss=0.0191, lr=6.58e-5, step=1993]\\u001b[A\\n\",\n      \"Epoch 9:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [01:02<01:47,  1.26it/s, loss=0.0191, lr=6.58e-5, step=1993]\\u001b[A\\n\",\n      \"Epoch 9:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [01:02<01:47,  1.26it/s, loss=0.0115, lr=6.58e-5, step=1994]\\u001b[A\\n\",\n      \"Epoch 9:  37%|█████████████████████████████████████████████████████████████████████████████▌                                                                                                                                   | 79/213 [01:03<01:46,  1.26it/s, loss=0.0115, lr=6.58e-5, step=1994]\\u001b[A\\n\",\n      \"Epoch 9:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [01:03<01:46,  1.26it/s, loss=0.00975, lr=6.58e-5, step=1995]\\u001b[A\\n\",\n      \"Epoch 9:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:45,  1.26it/s, loss=0.00975, lr=6.58e-5, step=1995]\\u001b[A\\n\",\n      \"Epoch 9:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:45,  1.26it/s, loss=0.00924, lr=6.57e-5, step=1996]\\u001b[A\\n\",\n      \"Epoch 9:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:05<01:45,  1.25it/s, loss=0.00924, lr=6.57e-5, step=1996]\\u001b[A\\n\",\n      \"Epoch 9:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [01:05<01:45,  1.25it/s, loss=0.0122, lr=6.57e-5, step=1997]\\u001b[A\\n\",\n      \"Epoch 9:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0122, lr=6.57e-5, step=1997]\\u001b[A\\n\",\n      \"Epoch 9:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0376, lr=6.56e-5, step=1998]\\u001b[A\\n\",\n      \"Epoch 9:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:43,  1.26it/s, loss=0.0376, lr=6.56e-5, step=1998]\\u001b[A\\n\",\n      \"Epoch 9:  39%|█████████████████████████████████████████████████████████████████████████████████▊                                                                                                                                | 83/213 [01:06<01:43,  1.26it/s, loss=0.042, lr=6.56e-5, step=1999]\\u001b[A\\n\",\n      \"Epoch 9:  39%|██████████████████████████████████████████████████████████████████████████████████▊                                                                                                                               | 84/213 [01:07<01:42,  1.26it/s, loss=0.042, lr=6.56e-5, step=1999]\\u001b[A\\n\",\n      \"Epoch 9:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:42,  1.26it/s, loss=0.0438, lr=6.56e-5, step=2000]\\u001b[A\\n\",\n      \"Epoch 9:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0438, lr=6.56e-5, step=2000]\\u001b[A\\n\",\n      \"Epoch 9:  40%|███████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.00869, lr=6.55e-5, step=2001]\\u001b[A\\n\",\n      \"Epoch 9:  40%|███████████████████████████████████████████████████████████████████████████████████▉                                                                                                                            | 86/213 [01:08<01:41,  1.26it/s, loss=0.00869, lr=6.55e-5, step=2001]\\u001b[A\\n\",\n      \"Epoch 9:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:09<01:41,  1.26it/s, loss=0.0216, lr=6.55e-5, step=2002]\\u001b[A\\n\",\n      \"Epoch 9:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0216, lr=6.55e-5, step=2002]\\u001b[A\\n\",\n      \"Epoch 9:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0215, lr=6.55e-5, step=2003]\\u001b[A\\n\",\n      \"Epoch 9:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0215, lr=6.55e-5, step=2003]\\u001b[A\\n\",\n      \"Epoch 9:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0164, lr=6.54e-5, step=2004]\\u001b[A\\n\",\n      \"Epoch 9:  42%|███████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                         | 89/213 [01:11<01:39,  1.25it/s, loss=0.0164, lr=6.54e-5, step=2004]\\u001b[A\\n\",\n      \"Epoch 9:  42%|██████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                         | 89/213 [01:11<01:39,  1.25it/s, loss=0.00993, lr=6.54e-5, step=2005]\\u001b[A\\n\",\n      \"Epoch 9:  42%|███████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                        | 90/213 [01:12<01:37,  1.26it/s, loss=0.00993, lr=6.54e-5, step=2005]\\u001b[A\\n\",\n      \"Epoch 9:  42%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                        | 90/213 [01:12<01:37,  1.26it/s, loss=0.0102, lr=6.53e-5, step=2006]\\u001b[A\\n\",\n      \"Epoch 9:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:12<01:37,  1.25it/s, loss=0.0102, lr=6.53e-5, step=2006]\\u001b[A\\n\",\n      \"Epoch 9:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:13<01:37,  1.25it/s, loss=0.0173, lr=6.53e-5, step=2007]\\u001b[A\\n\",\n      \"Epoch 9:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0173, lr=6.53e-5, step=2007]\\u001b[A\\n\",\n      \"Epoch 9:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0192, lr=6.53e-5, step=2008]\\u001b[A\\n\",\n      \"Epoch 9:  44%|███████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                     | 93/213 [01:14<01:35,  1.26it/s, loss=0.0192, lr=6.53e-5, step=2008]\\u001b[A\\n\",\n      \"Epoch 9:  44%|███████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                      | 93/213 [01:14<01:35,  1.26it/s, loss=0.022, lr=6.52e-5, step=2009]\\u001b[A\\n\",\n      \"Epoch 9:  44%|████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                     | 94/213 [01:15<01:34,  1.26it/s, loss=0.022, lr=6.52e-5, step=2009]\\u001b[A\\n\",\n      \"Epoch 9:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:34,  1.26it/s, loss=0.0449, lr=6.52e-5, step=2010]\\u001b[A\\n\",\n      \"Epoch 9:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:33,  1.26it/s, loss=0.0449, lr=6.52e-5, step=2010]\\u001b[A\\n\",\n      \"Epoch 9:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:33,  1.26it/s, loss=0.0112, lr=6.51e-5, step=2011]\\u001b[A\\n\",\n      \"Epoch 9:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                  | 96/213 [01:16<01:33,  1.26it/s, loss=0.0112, lr=6.51e-5, step=2011]\\u001b[A\\n\",\n      \"Epoch 9:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                   | 96/213 [01:16<01:33,  1.26it/s, loss=0.019, lr=6.51e-5, step=2012]\\u001b[A\\n\",\n      \"Epoch 9:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 97/213 [01:17<01:32,  1.26it/s, loss=0.019, lr=6.51e-5, step=2012]\\u001b[A\\n\",\n      \"Epoch 9:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.26it/s, loss=0.0145, lr=6.51e-5, step=2013]\\u001b[A\\n\",\n      \"Epoch 9:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:31,  1.26it/s, loss=0.0145, lr=6.51e-5, step=2013]\\u001b[A\\n\",\n      \"Epoch 9:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                 | 98/213 [01:18<01:31,  1.26it/s, loss=0.0243, lr=6.5e-5, step=2014]\\u001b[A\\n\",\n      \"Epoch 9:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                | 99/213 [01:19<01:30,  1.25it/s, loss=0.0243, lr=6.5e-5, step=2014]\\u001b[A\\n\",\n      \"Epoch 9:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                | 99/213 [01:19<01:30,  1.25it/s, loss=0.0194, lr=6.5e-5, step=2015]\\u001b[A\\n\",\n      \"Epoch 9:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                               | 100/213 [01:20<01:30,  1.25it/s, loss=0.0194, lr=6.5e-5, step=2015]\\u001b[A\\n\",\n      \"Epoch 9:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                              | 100/213 [01:20<01:30,  1.25it/s, loss=0.0142, lr=6.49e-5, step=2016]\\u001b[A\\n\",\n      \"Epoch 9:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:20<01:29,  1.25it/s, loss=0.0142, lr=6.49e-5, step=2016]\\u001b[A\\n\",\n      \"Epoch 9:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:20<01:29,  1.25it/s, loss=0.0244, lr=6.49e-5, step=2017]\\u001b[A\\n\",\n      \"Epoch 9:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 102/213 [01:21<01:28,  1.25it/s, loss=0.0244, lr=6.49e-5, step=2017]\\u001b[A\\n\",\n      \"Epoch 9:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.00882, lr=6.49e-5, step=2018]\\u001b[A\\n\",\n      \"Epoch 9:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.00882, lr=6.49e-5, step=2018]\\u001b[A\\n\",\n      \"Epoch 9:  48%|████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 103/213 [01:22<01:27,  1.25it/s, loss=0.00944, lr=6.48e-5, step=2019]\\u001b[A\\n\",\n      \"Epoch 9:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 104/213 [01:23<01:26,  1.25it/s, loss=0.00944, lr=6.48e-5, step=2019]\\u001b[A\\n\",\n      \"Epoch 9:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 104/213 [01:23<01:26,  1.25it/s, loss=0.0159, lr=6.48e-5, step=2020]\\u001b[A\\n\",\n      \"Epoch 9:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:25,  1.26it/s, loss=0.0159, lr=6.48e-5, step=2020]\\u001b[A\\n\",\n      \"Epoch 9:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:24<01:25,  1.26it/s, loss=0.0296, lr=6.47e-5, step=2021]\\u001b[A\\n\",\n      \"Epoch 9:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:24<01:24,  1.26it/s, loss=0.0296, lr=6.47e-5, step=2021]\\u001b[A\\n\",\n      \"Epoch 9:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                        | 106/213 [01:24<01:24,  1.26it/s, loss=0.0184, lr=6.47e-5, step=2022]\\u001b[A\\n\",\n      \"Epoch 9:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.26it/s, loss=0.0184, lr=6.47e-5, step=2022]\\u001b[A\\n\",\n      \"Epoch 9:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                       | 107/213 [01:25<01:24,  1.26it/s, loss=0.0109, lr=6.47e-5, step=2023]\\u001b[A\\n\",\n      \"Epoch 9:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.26it/s, loss=0.0109, lr=6.47e-5, step=2023]\\u001b[A\\n\",\n      \"Epoch 9:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.26it/s, loss=0.0457, lr=6.46e-5, step=2024]\\u001b[A\\n\",\n      \"Epoch 9:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0457, lr=6.46e-5, step=2024]\\u001b[A\\n\",\n      \"Epoch 9:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0304, lr=6.46e-5, step=2025]\\u001b[A\\n\",\n      \"Epoch 9:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0304, lr=6.46e-5, step=2025]\\u001b[A\\n\",\n      \"Epoch 9:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0283, lr=6.45e-5, step=2026]\\u001b[A\\n\",\n      \"Epoch 9:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:28<01:21,  1.26it/s, loss=0.0283, lr=6.45e-5, step=2026]\\u001b[A\\n\",\n      \"Epoch 9:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:28<01:21,  1.26it/s, loss=0.0114, lr=6.45e-5, step=2027]\\u001b[A\\n\",\n      \"Epoch 9:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0114, lr=6.45e-5, step=2027]\\u001b[A\\n\",\n      \"Epoch 9:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0429, lr=6.45e-5, step=2028]\\u001b[A\\n\",\n      \"Epoch 9:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:19,  1.26it/s, loss=0.0429, lr=6.45e-5, step=2028]\\u001b[A\\n\",\n      \"Epoch 9:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:30<01:19,  1.26it/s, loss=0.00554, lr=6.44e-5, step=2029]\\u001b[A\\n\",\n      \"Epoch 9:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:31<01:18,  1.26it/s, loss=0.00554, lr=6.44e-5, step=2029]\\u001b[A\\n\",\n      \"Epoch 9:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 114/213 [01:31<01:18,  1.26it/s, loss=0.0108, lr=6.44e-5, step=2030]\\u001b[A\\n\",\n      \"Epoch 9:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 115/213 [01:32<01:17,  1.26it/s, loss=0.0108, lr=6.44e-5, step=2030]\\u001b[A\\n\",\n      \"Epoch 9:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 115/213 [01:32<01:17,  1.26it/s, loss=0.0145, lr=6.43e-5, step=2031]\\u001b[A\\n\",\n      \"Epoch 9:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                              | 116/213 [01:32<01:17,  1.26it/s, loss=0.0145, lr=6.43e-5, step=2031]\\u001b[A\\n\",\n      \"Epoch 9:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                              | 116/213 [01:32<01:17,  1.26it/s, loss=0.0218, lr=6.43e-5, step=2032]\\u001b[A\\n\",\n      \"Epoch 9:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0218, lr=6.43e-5, step=2032]\\u001b[A\\n\",\n      \"Epoch 9:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0134, lr=6.43e-5, step=2033]\\u001b[A\\n\",\n      \"Epoch 9:  55%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                            | 118/213 [01:34<01:15,  1.26it/s, loss=0.0134, lr=6.43e-5, step=2033]\\u001b[A\\n\",\n      \"Epoch 9:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [01:34<01:15,  1.26it/s, loss=0.00896, lr=6.42e-5, step=2034]\\u001b[A\\n\",\n      \"Epoch 9:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [01:35<01:14,  1.26it/s, loss=0.00896, lr=6.42e-5, step=2034]\\u001b[A\\n\",\n      \"Epoch 9:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                           | 119/213 [01:35<01:14,  1.26it/s, loss=0.0226, lr=6.42e-5, step=2035]\\u001b[A\\n\",\n      \"Epoch 9:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                          | 120/213 [01:36<01:14,  1.26it/s, loss=0.0226, lr=6.42e-5, step=2035]\\u001b[A\\n\",\n      \"Epoch 9:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                          | 120/213 [01:36<01:14,  1.26it/s, loss=0.0235, lr=6.41e-5, step=2036]\\u001b[A\\n\",\n      \"Epoch 9:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                         | 121/213 [01:36<01:13,  1.26it/s, loss=0.0235, lr=6.41e-5, step=2036]\\u001b[A\\n\",\n      \"Epoch 9:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                         | 121/213 [01:36<01:13,  1.26it/s, loss=0.0189, lr=6.41e-5, step=2037]\\u001b[A\\n\",\n      \"Epoch 9:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 122/213 [01:37<01:12,  1.26it/s, loss=0.0189, lr=6.41e-5, step=2037]\\u001b[A\\n\",\n      \"Epoch 9:  57%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                        | 122/213 [01:37<01:12,  1.26it/s, loss=0.0179, lr=6.41e-5, step=2038]\\u001b[A\\n\",\n      \"Epoch 9:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                        | 123/213 [01:38<01:11,  1.26it/s, loss=0.0179, lr=6.41e-5, step=2038]\\u001b[A\\n\",\n      \"Epoch 9:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                        | 123/213 [01:38<01:11,  1.26it/s, loss=0.0152, lr=6.4e-5, step=2039]\\u001b[A\\n\",\n      \"Epoch 9:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                       | 124/213 [01:39<01:10,  1.26it/s, loss=0.0152, lr=6.4e-5, step=2039]\\u001b[A\\n\",\n      \"Epoch 9:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                       | 124/213 [01:39<01:10,  1.26it/s, loss=0.00812, lr=6.4e-5, step=2040]\\u001b[A\\n\",\n      \"Epoch 9:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                      | 125/213 [01:40<01:10,  1.26it/s, loss=0.00812, lr=6.4e-5, step=2040]\\u001b[A\\n\",\n      \"Epoch 9:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                      | 125/213 [01:40<01:10,  1.26it/s, loss=0.0355, lr=6.39e-5, step=2041]\\u001b[A\\n\",\n      \"Epoch 9:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     | 126/213 [01:40<01:09,  1.25it/s, loss=0.0355, lr=6.39e-5, step=2041]\\u001b[A\\n\",\n      \"Epoch 9:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [01:40<01:09,  1.25it/s, loss=0.00929, lr=6.39e-5, step=2042]\\u001b[A\\n\",\n      \"Epoch 9:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                   | 127/213 [01:41<01:08,  1.26it/s, loss=0.00929, lr=6.39e-5, step=2042]\\u001b[A\\n\",\n      \"Epoch 9:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                   | 127/213 [01:41<01:08,  1.26it/s, loss=0.00854, lr=6.39e-5, step=2043]\\u001b[A\\n\",\n      \"Epoch 9:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                  | 128/213 [01:42<01:07,  1.25it/s, loss=0.00854, lr=6.39e-5, step=2043]\\u001b[A\\n\",\n      \"Epoch 9:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                   | 128/213 [01:42<01:07,  1.25it/s, loss=0.0306, lr=6.38e-5, step=2044]\\u001b[A\\n\",\n      \"Epoch 9:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.0306, lr=6.38e-5, step=2044]\\u001b[A\\n\",\n      \"Epoch 9:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.0157, lr=6.38e-5, step=2045]\\u001b[A\\n\",\n      \"Epoch 9:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                 | 130/213 [01:44<01:06,  1.25it/s, loss=0.0157, lr=6.38e-5, step=2045]\\u001b[A\\n\",\n      \"Epoch 9:  61%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                 | 130/213 [01:44<01:06,  1.25it/s, loss=0.01, lr=6.37e-5, step=2046]\\u001b[A\\n\",\n      \"Epoch 9:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                | 131/213 [01:44<01:05,  1.26it/s, loss=0.01, lr=6.37e-5, step=2046]\\u001b[A\\n\",\n      \"Epoch 9:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [01:44<01:05,  1.26it/s, loss=0.0176, lr=6.37e-5, step=2047]\\u001b[A\\n\",\n      \"Epoch 9:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.26it/s, loss=0.0176, lr=6.37e-5, step=2047]\\u001b[A\\n\",\n      \"Epoch 9:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:45<01:04,  1.26it/s, loss=0.0139, lr=6.37e-5, step=2048]\\u001b[A\\n\",\n      \"Epoch 9:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0139, lr=6.37e-5, step=2048]\\u001b[A\\n\",\n      \"Epoch 9:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0196, lr=6.36e-5, step=2049]\\u001b[A\\n\",\n      \"Epoch 9:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0196, lr=6.36e-5, step=2049]\\u001b[A\\n\",\n      \"Epoch 9:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0144, lr=6.36e-5, step=2050]\\u001b[A\\n\",\n      \"Epoch 9:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.25it/s, loss=0.0144, lr=6.36e-5, step=2050]\\u001b[A\\n\",\n      \"Epoch 9:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:48<01:02,  1.25it/s, loss=0.0516, lr=6.35e-5, step=2051]\\u001b[A\\n\",\n      \"Epoch 9:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:48<01:01,  1.26it/s, loss=0.0516, lr=6.35e-5, step=2051]\\u001b[A\\n\",\n      \"Epoch 9:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:48<01:01,  1.26it/s, loss=0.0104, lr=6.35e-5, step=2052]\\u001b[A\\n\",\n      \"Epoch 9:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.0104, lr=6.35e-5, step=2052]\\u001b[A\\n\",\n      \"Epoch 9:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.0173, lr=6.35e-5, step=2053]\\u001b[A\\n\",\n      \"Epoch 9:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<00:59,  1.26it/s, loss=0.0173, lr=6.35e-5, step=2053]\\u001b[A\\n\",\n      \"Epoch 9:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<00:59,  1.26it/s, loss=0.0057, lr=6.34e-5, step=2054]\\u001b[A\\n\",\n      \"Epoch 9:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:58,  1.26it/s, loss=0.0057, lr=6.34e-5, step=2054]\\u001b[A\\n\",\n      \"Epoch 9:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:58,  1.26it/s, loss=0.0273, lr=6.34e-5, step=2055]\\u001b[A\\n\",\n      \"Epoch 9:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:51<00:58,  1.26it/s, loss=0.0273, lr=6.34e-5, step=2055]\\u001b[A\\n\",\n      \"Epoch 9:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:52<00:58,  1.26it/s, loss=0.0373, lr=6.33e-5, step=2056]\\u001b[A\\n\",\n      \"Epoch 9:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                      | 141/213 [01:52<00:57,  1.26it/s, loss=0.0373, lr=6.33e-5, step=2056]\\u001b[A\\n\",\n      \"Epoch 9:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                      | 141/213 [01:52<00:57,  1.26it/s, loss=0.0361, lr=6.33e-5, step=2057]\\u001b[A\\n\",\n      \"Epoch 9:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0361, lr=6.33e-5, step=2057]\\u001b[A\\n\",\n      \"Epoch 9:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0214, lr=6.33e-5, step=2058]\\u001b[A\\n\",\n      \"Epoch 9:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0214, lr=6.33e-5, step=2058]\\u001b[A\\n\",\n      \"Epoch 9:  67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                     | 143/213 [01:54<00:55,  1.25it/s, loss=0.02, lr=6.32e-5, step=2059]\\u001b[A\\n\",\n      \"Epoch 9:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 144/213 [01:55<00:55,  1.25it/s, loss=0.02, lr=6.32e-5, step=2059]\\u001b[A\\n\",\n      \"Epoch 9:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                   | 144/213 [01:55<00:55,  1.25it/s, loss=0.014, lr=6.32e-5, step=2060]\\u001b[A\\n\",\n      \"Epoch 9:  68%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                  | 145/213 [01:55<00:54,  1.26it/s, loss=0.014, lr=6.32e-5, step=2060]\\u001b[A\\n\",\n      \"Epoch 9:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:56<00:54,  1.26it/s, loss=0.0285, lr=6.31e-5, step=2061]\\u001b[A\\n\",\n      \"Epoch 9:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.0285, lr=6.31e-5, step=2061]\\u001b[A\\n\",\n      \"Epoch 9:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.0158, lr=6.31e-5, step=2062]\\u001b[A\\n\",\n      \"Epoch 9:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0158, lr=6.31e-5, step=2062]\\u001b[A\\n\",\n      \"Epoch 9:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0189, lr=6.3e-5, step=2063]\\u001b[A\\n\",\n      \"Epoch 9:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0189, lr=6.3e-5, step=2063]\\u001b[A\\n\",\n      \"Epoch 9:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 148/213 [01:58<00:51,  1.26it/s, loss=0.02, lr=6.3e-5, step=2064]\\u001b[A\\n\",\n      \"Epoch 9:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 149/213 [01:59<00:50,  1.26it/s, loss=0.02, lr=6.3e-5, step=2064]\\u001b[A\\n\",\n      \"Epoch 9:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0129, lr=6.3e-5, step=2065]\\u001b[A\\n\",\n      \"Epoch 9:  70%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                             | 150/213 [01:59<00:50,  1.26it/s, loss=0.0129, lr=6.3e-5, step=2065]\\u001b[A\\n\",\n      \"Epoch 9:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                             | 150/213 [01:59<00:50,  1.26it/s, loss=0.0164, lr=6.29e-5, step=2066]\\u001b[A\\n\",\n      \"Epoch 9:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0164, lr=6.29e-5, step=2066]\\u001b[A\\n\",\n      \"Epoch 9:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.013, lr=6.29e-5, step=2067]\\u001b[A\\n\",\n      \"Epoch 9:  71%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.013, lr=6.29e-5, step=2067]\\u001b[A\\n\",\n      \"Epoch 9:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.0101, lr=6.28e-5, step=2068]\\u001b[A\\n\",\n      \"Epoch 9:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0101, lr=6.28e-5, step=2068]\\u001b[A\\n\",\n      \"Epoch 9:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0184, lr=6.28e-5, step=2069]\\u001b[A\\n\",\n      \"Epoch 9:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0184, lr=6.28e-5, step=2069]\\u001b[A\\n\",\n      \"Epoch 9:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0127, lr=6.28e-5, step=2070]\\u001b[A\\n\",\n      \"Epoch 9:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [02:03<00:46,  1.25it/s, loss=0.0127, lr=6.28e-5, step=2070]\\u001b[A\\n\",\n      \"Epoch 9:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [02:03<00:46,  1.25it/s, loss=0.00959, lr=6.27e-5, step=2071]\\u001b[A\\n\",\n      \"Epoch 9:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.00959, lr=6.27e-5, step=2071]\\u001b[A\\n\",\n      \"Epoch 9:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0126, lr=6.27e-5, step=2072]\\u001b[A\\n\",\n      \"Epoch 9:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.25it/s, loss=0.0126, lr=6.27e-5, step=2072]\\u001b[A\\n\",\n      \"Epoch 9:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.25it/s, loss=0.0539, lr=6.26e-5, step=2073]\\u001b[A\\n\",\n      \"Epoch 9:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:06<00:43,  1.26it/s, loss=0.0539, lr=6.26e-5, step=2073]\\u001b[A\\n\",\n      \"Epoch 9:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                     | 158/213 [02:06<00:43,  1.26it/s, loss=0.00762, lr=6.26e-5, step=2074]\\u001b[A\\n\",\n      \"Epoch 9:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:07<00:43,  1.26it/s, loss=0.00762, lr=6.26e-5, step=2074]\\u001b[A\\n\",\n      \"Epoch 9:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:07<00:43,  1.26it/s, loss=0.00809, lr=6.26e-5, step=2075]\\u001b[A\\n\",\n      \"Epoch 9:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:07<00:42,  1.25it/s, loss=0.00809, lr=6.26e-5, step=2075]\\u001b[A\\n\",\n      \"Epoch 9:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:07<00:42,  1.25it/s, loss=0.00833, lr=6.25e-5, step=2076]\\u001b[A\\n\",\n      \"Epoch 9:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.26it/s, loss=0.00833, lr=6.25e-5, step=2076]\\u001b[A\\n\",\n      \"Epoch 9:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                  | 161/213 [02:08<00:41,  1.26it/s, loss=0.0334, lr=6.25e-5, step=2077]\\u001b[A\\n\",\n      \"Epoch 9:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                 | 162/213 [02:09<00:40,  1.26it/s, loss=0.0334, lr=6.25e-5, step=2077]\\u001b[A\\n\",\n      \"Epoch 9:  76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                 | 162/213 [02:09<00:40,  1.26it/s, loss=0.0265, lr=6.24e-5, step=2078]\\u001b[A\\n\",\n      \"Epoch 9:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:39,  1.26it/s, loss=0.0265, lr=6.24e-5, step=2078]\\u001b[A\\n\",\n      \"Epoch 9:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:39,  1.26it/s, loss=0.0188, lr=6.24e-5, step=2079]\\u001b[A\\n\",\n      \"Epoch 9:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0188, lr=6.24e-5, step=2079]\\u001b[A\\n\",\n      \"Epoch 9:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0166, lr=6.24e-5, step=2080]\\u001b[A\\n\",\n      \"Epoch 9:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [02:11<00:38,  1.25it/s, loss=0.0166, lr=6.24e-5, step=2080]\\u001b[A\\n\",\n      \"Epoch 9:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [02:11<00:38,  1.25it/s, loss=0.0126, lr=6.23e-5, step=2081]\\u001b[A\\n\",\n      \"Epoch 9:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                              | 166/213 [02:12<00:37,  1.26it/s, loss=0.0126, lr=6.23e-5, step=2081]\\u001b[A\\n\",\n      \"Epoch 9:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                              | 166/213 [02:12<00:37,  1.26it/s, loss=0.0181, lr=6.23e-5, step=2082]\\u001b[A\\n\",\n      \"Epoch 9:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0181, lr=6.23e-5, step=2082]\\u001b[A\\n\",\n      \"Epoch 9:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:13<00:36,  1.25it/s, loss=0.0137, lr=6.22e-5, step=2083]\\u001b[A\\n\",\n      \"Epoch 9:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.0137, lr=6.22e-5, step=2083]\\u001b[A\\n\",\n      \"Epoch 9:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:14<00:35,  1.25it/s, loss=0.0146, lr=6.22e-5, step=2084]\\u001b[A\\n\",\n      \"Epoch 9:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.0146, lr=6.22e-5, step=2084]\\u001b[A\\n\",\n      \"Epoch 9:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                           | 169/213 [02:15<00:35,  1.25it/s, loss=0.011, lr=6.22e-5, step=2085]\\u001b[A\\n\",\n      \"Epoch 9:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                          | 170/213 [02:15<00:34,  1.25it/s, loss=0.011, lr=6.22e-5, step=2085]\\u001b[A\\n\",\n      \"Epoch 9:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                          | 170/213 [02:15<00:34,  1.25it/s, loss=0.0373, lr=6.21e-5, step=2086]\\u001b[A\\n\",\n      \"Epoch 9:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.25it/s, loss=0.0373, lr=6.21e-5, step=2086]\\u001b[A\\n\",\n      \"Epoch 9:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [02:16<00:33,  1.25it/s, loss=0.0234, lr=6.21e-5, step=2087]\\u001b[A\\n\",\n      \"Epoch 9:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [02:17<00:32,  1.25it/s, loss=0.0234, lr=6.21e-5, step=2087]\\u001b[A\\n\",\n      \"Epoch 9:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                        | 172/213 [02:17<00:32,  1.25it/s, loss=0.0223, lr=6.2e-5, step=2088]\\u001b[A\\n\",\n      \"Epoch 9:  81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0223, lr=6.2e-5, step=2088]\\u001b[A\\n\",\n      \"Epoch 9:  81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                       | 173/213 [02:18<00:31,  1.25it/s, loss=0.0337, lr=6.2e-5, step=2089]\\u001b[A\\n\",\n      \"Epoch 9:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0337, lr=6.2e-5, step=2089]\\u001b[A\\n\",\n      \"Epoch 9:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.00909, lr=6.2e-5, step=2090]\\u001b[A\\n\",\n      \"Epoch 9:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:19<00:30,  1.25it/s, loss=0.00909, lr=6.2e-5, step=2090]\\u001b[A\\n\",\n      \"Epoch 9:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:19<00:30,  1.25it/s, loss=0.0133, lr=6.19e-5, step=2091]\\u001b[A\\n\",\n      \"Epoch 9:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:20<00:29,  1.25it/s, loss=0.0133, lr=6.19e-5, step=2091]\\u001b[A\\n\",\n      \"Epoch 9:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:20<00:29,  1.25it/s, loss=0.0104, lr=6.19e-5, step=2092]\\u001b[A\\n\",\n      \"Epoch 9:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.0104, lr=6.19e-5, step=2092]\\u001b[A\\n\",\n      \"Epoch 9:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.0108, lr=6.18e-5, step=2093]\\u001b[A\\n\",\n      \"Epoch 9:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.0108, lr=6.18e-5, step=2093]\\u001b[A\\n\",\n      \"Epoch 9:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.0298, lr=6.18e-5, step=2094]\\u001b[A\\n\",\n      \"Epoch 9:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0298, lr=6.18e-5, step=2094]\\u001b[A\\n\",\n      \"Epoch 9:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.00957, lr=6.18e-5, step=2095]\\u001b[A\\n\",\n      \"Epoch 9:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:23<00:26,  1.25it/s, loss=0.00957, lr=6.18e-5, step=2095]\\u001b[A\\n\",\n      \"Epoch 9:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 180/213 [02:23<00:26,  1.25it/s, loss=0.0106, lr=6.17e-5, step=2096]\\u001b[A\\n\",\n      \"Epoch 9:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0106, lr=6.17e-5, step=2096]\\u001b[A\\n\",\n      \"Epoch 9:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0223, lr=6.17e-5, step=2097]\\u001b[A\\n\",\n      \"Epoch 9:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.0223, lr=6.17e-5, step=2097]\\u001b[A\\n\",\n      \"Epoch 9:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.00565, lr=6.16e-5, step=2098]\\u001b[A\\n\",\n      \"Epoch 9:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00565, lr=6.16e-5, step=2098]\\u001b[A\\n\",\n      \"Epoch 9:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00689, lr=6.16e-5, step=2099]\\u001b[A\\n\",\n      \"Epoch 9:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.00689, lr=6.16e-5, step=2099]\\u001b[A\\n\",\n      \"Epoch 9:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.00854, lr=6.16e-5, step=2100]\\u001b[A\\n\",\n      \"Epoch 9:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:27<00:22,  1.25it/s, loss=0.00854, lr=6.16e-5, step=2100]\\u001b[A\\n\",\n      \"Epoch 9:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                           | 185/213 [02:27<00:22,  1.25it/s, loss=0.0168, lr=6.15e-5, step=2101]\\u001b[A\\n\",\n      \"Epoch 9:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0168, lr=6.15e-5, step=2101]\\u001b[A\\n\",\n      \"Epoch 9:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0146, lr=6.15e-5, step=2102]\\u001b[A\\n\",\n      \"Epoch 9:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0146, lr=6.15e-5, step=2102]\\u001b[A\\n\",\n      \"Epoch 9:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0278, lr=6.14e-5, step=2103]\\u001b[A\\n\",\n      \"Epoch 9:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0278, lr=6.14e-5, step=2103]\\u001b[A\\n\",\n      \"Epoch 9:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0186, lr=6.14e-5, step=2104]\\u001b[A\\n\",\n      \"Epoch 9:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0186, lr=6.14e-5, step=2104]\\u001b[A\\n\",\n      \"Epoch 9:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0159, lr=6.13e-5, step=2105]\\u001b[A\\n\",\n      \"Epoch 9:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0159, lr=6.13e-5, step=2105]\\u001b[A\\n\",\n      \"Epoch 9:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0135, lr=6.13e-5, step=2106]\\u001b[A\\n\",\n      \"Epoch 9:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.26it/s, loss=0.0135, lr=6.13e-5, step=2106]\\u001b[A\\n\",\n      \"Epoch 9:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.26it/s, loss=0.0131, lr=6.13e-5, step=2107]\\u001b[A\\n\",\n      \"Epoch 9:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.26it/s, loss=0.0131, lr=6.13e-5, step=2107]\\u001b[A\\n\",\n      \"Epoch 9:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.26it/s, loss=0.0114, lr=6.12e-5, step=2108]\\u001b[A\\n\",\n      \"Epoch 9:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.26it/s, loss=0.0114, lr=6.12e-5, step=2108]\\u001b[A\\n\",\n      \"Epoch 9:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.26it/s, loss=0.0226, lr=6.12e-5, step=2109]\\u001b[A\\n\",\n      \"Epoch 9:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.26it/s, loss=0.0226, lr=6.12e-5, step=2109]\\u001b[A\\n\",\n      \"Epoch 9:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.26it/s, loss=0.0521, lr=6.11e-5, step=2110]\\u001b[A\\n\",\n      \"Epoch 9:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.0521, lr=6.11e-5, step=2110]\\u001b[A\\n\",\n      \"Epoch 9:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.0115, lr=6.11e-5, step=2111]\\u001b[A\\n\",\n      \"Epoch 9:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0115, lr=6.11e-5, step=2111]\\u001b[A\\n\",\n      \"Epoch 9:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0115, lr=6.11e-5, step=2112]\\u001b[A\\n\",\n      \"Epoch 9:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0115, lr=6.11e-5, step=2112]\\u001b[A\\n\",\n      \"Epoch 9:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏               | 197/213 [02:37<00:12,  1.25it/s, loss=0.012, lr=6.1e-5, step=2113]\\u001b[A\\n\",\n      \"Epoch 9:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏              | 198/213 [02:38<00:11,  1.25it/s, loss=0.012, lr=6.1e-5, step=2113]\\u001b[A\\n\",\n      \"Epoch 9:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0131, lr=6.1e-5, step=2114]\\u001b[A\\n\",\n      \"Epoch 9:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0131, lr=6.1e-5, step=2114]\\u001b[A\\n\",\n      \"Epoch 9:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00662, lr=6.09e-5, step=2115]\\u001b[A\\n\",\n      \"Epoch 9:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:39<00:10,  1.25it/s, loss=0.00662, lr=6.09e-5, step=2115]\\u001b[A\\n\",\n      \"Epoch 9:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:39<00:10,  1.25it/s, loss=0.0118, lr=6.09e-5, step=2116]\\u001b[A\\n\",\n      \"Epoch 9:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0118, lr=6.09e-5, step=2116]\\u001b[A\\n\",\n      \"Epoch 9:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0212, lr=6.09e-5, step=2117]\\u001b[A\\n\",\n      \"Epoch 9:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0212, lr=6.09e-5, step=2117]\\u001b[A\\n\",\n      \"Epoch 9:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0267, lr=6.08e-5, step=2118]\\u001b[A\\n\",\n      \"Epoch 9:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:07,  1.25it/s, loss=0.0267, lr=6.08e-5, step=2118]\\u001b[A\\n\",\n      \"Epoch 9:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:07,  1.25it/s, loss=0.0195, lr=6.08e-5, step=2119]\\u001b[A\\n\",\n      \"Epoch 9:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:43<00:07,  1.25it/s, loss=0.0195, lr=6.08e-5, step=2119]\\u001b[A\\n\",\n      \"Epoch 9:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.00611, lr=6.07e-5, step=2120]\\u001b[A\\n\",\n      \"Epoch 9:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:43<00:06,  1.25it/s, loss=0.00611, lr=6.07e-5, step=2120]\\u001b[A\\n\",\n      \"Epoch 9:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:43<00:06,  1.25it/s, loss=0.0162, lr=6.07e-5, step=2121]\\u001b[A\\n\",\n      \"Epoch 9:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0162, lr=6.07e-5, step=2121]\\u001b[A\\n\",\n      \"Epoch 9:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0199, lr=6.07e-5, step=2122]\\u001b[A\\n\",\n      \"Epoch 9:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.0199, lr=6.07e-5, step=2122]\\u001b[A\\n\",\n      \"Epoch 9:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.0205, lr=6.06e-5, step=2123]\\u001b[A\\n\",\n      \"Epoch 9:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:03,  1.25it/s, loss=0.0205, lr=6.06e-5, step=2123]\\u001b[A\\n\",\n      \"Epoch 9:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:03,  1.25it/s, loss=0.0147, lr=6.06e-5, step=2124]\\u001b[A\\n\",\n      \"Epoch 9:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0147, lr=6.06e-5, step=2124]\\u001b[A\\n\",\n      \"Epoch 9:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0166, lr=6.05e-5, step=2125]\\u001b[A\\n\",\n      \"Epoch 9:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.0166, lr=6.05e-5, step=2125]\\u001b[A\\n\",\n      \"Epoch 9:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.00531, lr=6.05e-5, step=2126]\\u001b[A\\n\",\n      \"Epoch 9:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.26it/s, loss=0.00531, lr=6.05e-5, step=2126]\\u001b[A\\n\",\n      \"Epoch 9:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.26it/s, loss=0.0158, lr=6.05e-5, step=2127]\\u001b[A\\n\",\n      \"Epoch 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.26it/s, loss=0.0158, lr=6.05e-5, step=2127]\\u001b[A\\n\",\n      \"Epoch 9: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.26it/s, loss=0.00936, lr=6.04e-5, step=2128]\\u001b[A\\n\",\n      \"Epoch 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.00936, lr=6.04e-5, step=2128]\\u001b[A\\n\",\n      \"Epoch 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.00967, lr=6.04e-5, step=2129]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"08631cf6fc5346c7bc2d84d191cfc03e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.00967, lr=6.04e-5, step=2129]\\n\",\n      \"Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.41it/s, loss=0.0106, lr=5.15e-5, step=2342]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"59796d967dfb49b5912f803f9d9ee52e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 10: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:38<00:00,  2.15s/it, loss=0.0106, lr=5.15e-5, step=2342]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 11:   0%|                                                                                                                                                                                                                                                             | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 11:   0%|█▏                                                                                                                                                                                                                                          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step=2344]\\u001b[A\\n\",\n      \"Epoch 11:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:51,  1.23it/s, loss=0.0115, lr=5.15e-5, step=2344]\\u001b[A\\n\",\n      \"Epoch 11:   1%|██▉                                                                                                                                                                                                             | 3/213 [00:02<02:51,  1.23it/s, loss=0.00958, lr=5.14e-5, step=2345]\\u001b[A\\n\",\n      \"Epoch 11:   2%|███▉                                                                                                                                                                                                            | 4/213 [00:03<02:48,  1.24it/s, loss=0.00958, lr=5.14e-5, step=2345]\\u001b[A\\n\",\n      \"Epoch 11:   2%|███▉                                                                                                                                                                                                             | 4/213 [00:03<02:48,  1.24it/s, loss=0.0162, lr=5.14e-5, step=2346]\\u001b[A\\n\",\n      \"Epoch 11:   2%|████▉                                                                                                                                                                                                            | 5/213 [00:04<02:46,  1.25it/s, loss=0.0162, lr=5.14e-5, step=2346]\\u001b[A\\n\",\n      \"Epoch 11:   2%|████▉                                                                                                                                                                                                            | 5/213 [00:04<02:46,  1.25it/s, loss=0.0521, lr=5.13e-5, step=2347]\\u001b[A\\n\",\n      \"Epoch 11:   3%|█████▉                                                                                                                                                                                                           | 6/213 [00:04<02:45,  1.25it/s, loss=0.0521, lr=5.13e-5, step=2347]\\u001b[A\\n\",\n      \"Epoch 11:   3%|█████▊                                                                                                                                                                                                          | 6/213 [00:04<02:45,  1.25it/s, loss=0.00423, lr=5.13e-5, step=2348]\\u001b[A\\n\",\n      \"Epoch 11:   3%|██████▊                                                                                                                                                                                                         | 7/213 [00:05<02:45,  1.25it/s, loss=0.00423, lr=5.13e-5, step=2348]\\u001b[A\\n\",\n      \"Epoch 11:   3%|██████▉                                                                                                                                                                                                           | 7/213 [00:05<02:45,  1.25it/s, loss=0.015, lr=5.13e-5, step=2349]\\u001b[A\\n\",\n      \"Epoch 11:   4%|███████▉                                                                                                                                                                                                          | 8/213 [00:06<02:44,  1.24it/s, loss=0.015, lr=5.13e-5, step=2349]\\u001b[A\\n\",\n      \"Epoch 11:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:44,  1.24it/s, loss=0.0164, lr=5.12e-5, step=2350]\\u001b[A\\n\",\n      \"Epoch 11:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:44,  1.24it/s, loss=0.0164, lr=5.12e-5, step=2350]\\u001b[A\\n\",\n      \"Epoch 11:   4%|████████▊                                                                                                                                                                                                       | 9/213 [00:07<02:44,  1.24it/s, loss=0.00751, lr=5.12e-5, step=2351]\\u001b[A\\n\",\n      \"Epoch 11:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:44,  1.24it/s, loss=0.00751, lr=5.12e-5, step=2351]\\u001b[A\\n\",\n      \"Epoch 11:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:44,  1.24it/s, loss=0.00777, lr=5.11e-5, step=2352]\\u001b[A\\n\",\n      \"Epoch 11:   5%|██████████▋                                                                                                                                                                                                    | 11/213 [00:08<02:43,  1.23it/s, loss=0.00777, lr=5.11e-5, step=2352]\\u001b[A\\n\",\n      \"Epoch 11:   5%|██████████▋                                                                                                                                                                                                     | 11/213 [00:08<02:43,  1.23it/s, loss=0.0149, lr=5.11e-5, step=2353]\\u001b[A\\n\",\n      \"Epoch 11:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:43,  1.23it/s, loss=0.0149, lr=5.11e-5, step=2353]\\u001b[A\\n\",\n      \"Epoch 11:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:43,  1.23it/s, loss=0.0184, lr=5.1e-5, step=2354]\\u001b[A\\n\",\n      \"Epoch 11:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:43,  1.23it/s, loss=0.0184, lr=5.1e-5, step=2354]\\u001b[A\\n\",\n      \"Epoch 11:   6%|████████████▊                                                                                                                                                                                                    | 13/213 [00:10<02:43,  1.23it/s, loss=0.0185, lr=5.1e-5, step=2355]\\u001b[A\\n\",\n      \"Epoch 11:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:42,  1.22it/s, loss=0.0185, lr=5.1e-5, step=2355]\\u001b[A\\n\",\n      \"Epoch 11:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:42,  1.22it/s, loss=0.0328, lr=5.1e-5, step=2356]\\u001b[A\\n\",\n      \"Epoch 11:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:41,  1.22it/s, loss=0.0328, lr=5.1e-5, step=2356]\\u001b[A\\n\",\n      \"Epoch 11:   7%|██████████████▋                                                                                                                                                                                                 | 15/213 [00:12<02:41,  1.22it/s, loss=0.0101, lr=5.09e-5, step=2357]\\u001b[A\\n\",\n      \"Epoch 11:   8%|███████████████▌                                                                                                                                                                                                | 16/213 [00:13<02:41,  1.22it/s, loss=0.0101, lr=5.09e-5, step=2357]\\u001b[A\\n\",\n      \"Epoch 11:   8%|███████████████▌                                                                                                                                                                                                | 16/213 [00:13<02:41,  1.22it/s, loss=0.0144, lr=5.09e-5, step=2358]\\u001b[A\\n\",\n      \"Epoch 11:   8%|████████████████▌                                                                                                                                                                                               | 17/213 [00:13<02:40,  1.22it/s, loss=0.0144, lr=5.09e-5, step=2358]\\u001b[A\\n\",\n      \"Epoch 11:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:40,  1.22it/s, loss=0.037, lr=5.08e-5, step=2359]\\u001b[A\\n\",\n      \"Epoch 11:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:40,  1.21it/s, loss=0.037, lr=5.08e-5, step=2359]\\u001b[A\\n\",\n      \"Epoch 11:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [00:14<02:40,  1.21it/s, loss=0.0245, lr=5.08e-5, step=2360]\\u001b[A\\n\",\n      \"Epoch 11:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:39,  1.21it/s, loss=0.0245, lr=5.08e-5, step=2360]\\u001b[A\\n\",\n      \"Epoch 11:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:39,  1.21it/s, loss=0.0145, lr=5.08e-5, step=2361]\\u001b[A\\n\",\n      \"Epoch 11:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:38,  1.21it/s, loss=0.0145, lr=5.08e-5, step=2361]\\u001b[A\\n\",\n      \"Epoch 11:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:38,  1.21it/s, loss=0.0127, lr=5.07e-5, step=2362]\\u001b[A\\n\",\n      \"Epoch 11:  10%|████████████████████▌                                                                                                                                                                                           | 21/213 [00:17<02:38,  1.21it/s, loss=0.0127, lr=5.07e-5, step=2362]\\u001b[A\\n\",\n      \"Epoch 11:  10%|████████████████████▍                                                                                                                                                                                          | 21/213 [00:17<02:38,  1.21it/s, loss=0.00968, lr=5.07e-5, step=2363]\\u001b[A\\n\",\n      \"Epoch 11:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:37,  1.21it/s, loss=0.00968, lr=5.07e-5, step=2363]\\u001b[A\\n\",\n      \"Epoch 11:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:37,  1.21it/s, loss=0.00828, lr=5.06e-5, step=2364]\\u001b[A\\n\",\n      \"Epoch 11:  11%|██████████████████████▎                                                                                                                                                                                        | 23/213 [00:18<02:37,  1.20it/s, loss=0.00828, lr=5.06e-5, step=2364]\\u001b[A\\n\",\n      \"Epoch 11:  11%|██████████████████████▌                                                                                                                                                                                          | 23/213 [00:18<02:37,  1.20it/s, loss=0.029, lr=5.06e-5, step=2365]\\u001b[A\\n\",\n      \"Epoch 11:  11%|███████████████████████▌                                                                                                                                                                                         | 24/213 [00:19<02:36,  1.21it/s, loss=0.029, lr=5.06e-5, step=2365]\\u001b[A\\n\",\n      \"Epoch 11:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:36,  1.21it/s, loss=0.0138, lr=5.05e-5, step=2366]\\u001b[A\\n\",\n      \"Epoch 11:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:35,  1.21it/s, loss=0.0138, lr=5.05e-5, step=2366]\\u001b[A\\n\",\n      \"Epoch 11:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:35,  1.21it/s, loss=0.0193, lr=5.05e-5, step=2367]\\u001b[A\\n\",\n      \"Epoch 11:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [00:21<02:34,  1.21it/s, loss=0.0193, lr=5.05e-5, step=2367]\\u001b[A\\n\",\n      \"Epoch 11:  12%|█████████████████████████▎                                                                                                                                                                                     | 26/213 [00:21<02:34,  1.21it/s, loss=0.00706, lr=5.05e-5, step=2368]\\u001b[A\\n\",\n      \"Epoch 11:  13%|██████████████████████████▏                                                                                                                                                                                    | 27/213 [00:22<02:33,  1.21it/s, loss=0.00706, lr=5.05e-5, step=2368]\\u001b[A\\n\",\n      \"Epoch 11:  13%|██████████████████████████▏                                                                                                                                                                                    | 27/213 [00:22<02:33,  1.21it/s, loss=0.00695, lr=5.04e-5, step=2369]\\u001b[A\\n\",\n      \"Epoch 11:  13%|███████████████████████████▏                                                                                                                                                                                   | 28/213 [00:22<02:31,  1.22it/s, loss=0.00695, lr=5.04e-5, step=2369]\\u001b[A\\n\",\n      \"Epoch 11:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:31,  1.22it/s, loss=0.0246, lr=5.04e-5, step=2370]\\u001b[A\\n\",\n      \"Epoch 11:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [00:23<02:31,  1.22it/s, loss=0.0246, lr=5.04e-5, step=2370]\\u001b[A\\n\",\n      \"Epoch 11:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:31,  1.22it/s, loss=0.012, lr=5.03e-5, step=2371]\\u001b[A\\n\",\n      \"Epoch 11:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:30,  1.22it/s, loss=0.012, lr=5.03e-5, step=2371]\\u001b[A\\n\",\n      \"Epoch 11:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:30,  1.22it/s, loss=0.0322, lr=5.03e-5, step=2372]\\u001b[A\\n\",\n      \"Epoch 11:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:25<02:29,  1.22it/s, loss=0.0322, lr=5.03e-5, step=2372]\\u001b[A\\n\",\n      \"Epoch 11:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:25<02:29,  1.22it/s, loss=0.0168, lr=5.03e-5, step=2373]\\u001b[A\\n\",\n      \"Epoch 11:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:26<02:28,  1.22it/s, loss=0.0168, lr=5.03e-5, step=2373]\\u001b[A\\n\",\n      \"Epoch 11:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:26<02:28,  1.22it/s, loss=0.0182, lr=5.02e-5, step=2374]\\u001b[A\\n\",\n      \"Epoch 11:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:27<02:27,  1.22it/s, loss=0.0182, lr=5.02e-5, step=2374]\\u001b[A\\n\",\n      \"Epoch 11:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:27<02:27,  1.22it/s, loss=0.0119, lr=5.02e-5, step=2375]\\u001b[A\\n\",\n      \"Epoch 11:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:27,  1.21it/s, loss=0.0119, lr=5.02e-5, step=2375]\\u001b[A\\n\",\n      \"Epoch 11:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:27,  1.21it/s, loss=0.0143, lr=5.01e-5, step=2376]\\u001b[A\\n\",\n      \"Epoch 11:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:26,  1.21it/s, loss=0.0143, lr=5.01e-5, step=2376]\\u001b[A\\n\",\n      \"Epoch 11:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:26,  1.21it/s, loss=0.0203, lr=5.01e-5, step=2377]\\u001b[A\\n\",\n      \"Epoch 11:  17%|███████████████████████████████████▏                                                                                                                                                                            | 36/213 [00:29<02:26,  1.21it/s, loss=0.0203, lr=5.01e-5, step=2377]\\u001b[A\\n\",\n      \"Epoch 11:  17%|███████████████████████████████████▍                                                                                                                                                                              | 36/213 [00:29<02:26,  1.21it/s, loss=0.00971, lr=5e-5, step=2378]\\u001b[A\\n\",\n      \"Epoch 11:  17%|████████████████████████████████████▍                                                                                                                                                                             | 37/213 [00:30<02:25,  1.21it/s, loss=0.00971, lr=5e-5, step=2378]\\u001b[A\\n\",\n      \"Epoch 11:  17%|████████████████████████████████████▋                                                                                                                                                                              | 37/213 [00:30<02:25,  1.21it/s, loss=0.0282, lr=5e-5, step=2379]\\u001b[A\\n\",\n      \"Epoch 11:  18%|█████████████████████████████████████▋                                                                                                                                                                             | 38/213 [00:31<02:24,  1.21it/s, loss=0.0282, lr=5e-5, step=2379]\\u001b[A\\n\",\n      \"Epoch 11:  18%|█████████████████████████████████████▍                                                                                                                                                                            | 38/213 [00:31<02:24,  1.21it/s, loss=0.00863, lr=5e-5, step=2380]\\u001b[A\\n\",\n      \"Epoch 11:  18%|██████████████████████████████████████▍                                                                                                                                                                           | 39/213 [00:31<02:23,  1.21it/s, loss=0.00863, lr=5e-5, step=2380]\\u001b[A\\n\",\n      \"Epoch 11:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [00:31<02:23,  1.21it/s, loss=0.0507, lr=4.99e-5, step=2381]\\u001b[A\\n\",\n      \"Epoch 11:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:22,  1.22it/s, loss=0.0507, lr=4.99e-5, step=2381]\\u001b[A\\n\",\n      \"Epoch 11:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:22,  1.22it/s, loss=0.0171, lr=4.99e-5, step=2382]\\u001b[A\\n\",\n      \"Epoch 11:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:33<02:21,  1.21it/s, loss=0.0171, lr=4.99e-5, step=2382]\\u001b[A\\n\",\n      \"Epoch 11:  19%|████████████████████████████████████████▍                                                                                                                                                                         | 41/213 [00:33<02:21,  1.21it/s, loss=0.01, lr=4.98e-5, step=2383]\\u001b[A\\n\",\n      \"Epoch 11:  20%|█████████████████████████████████████████▍                                                                                                                                                                        | 42/213 [00:34<02:20,  1.21it/s, loss=0.01, lr=4.98e-5, step=2383]\\u001b[A\\n\",\n      \"Epoch 11:  20%|████████████████████████████████████████▊                                                                                                                                                                      | 42/213 [00:34<02:20,  1.21it/s, loss=0.00649, lr=4.98e-5, step=2384]\\u001b[A\\n\",\n      \"Epoch 11:  20%|█████████████████████████████████████████▊                                                                                                                                                                     | 43/213 [00:35<02:19,  1.22it/s, loss=0.00649, lr=4.98e-5, step=2384]\\u001b[A\\n\",\n      \"Epoch 11:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:35<02:19,  1.22it/s, loss=0.0255, lr=4.97e-5, step=2385]\\u001b[A\\n\",\n      \"Epoch 11:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:36<02:18,  1.22it/s, loss=0.0255, lr=4.97e-5, step=2385]\\u001b[A\\n\",\n      \"Epoch 11:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [00:36<02:18,  1.22it/s, loss=0.015, lr=4.97e-5, step=2386]\\u001b[A\\n\",\n      \"Epoch 11:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:17,  1.22it/s, loss=0.015, lr=4.97e-5, step=2386]\\u001b[A\\n\",\n      \"Epoch 11:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:17,  1.22it/s, loss=0.0208, lr=4.97e-5, step=2387]\\u001b[A\\n\",\n      \"Epoch 11:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:37<02:17,  1.22it/s, loss=0.0208, lr=4.97e-5, step=2387]\\u001b[A\\n\",\n      \"Epoch 11:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:37<02:17,  1.22it/s, loss=0.0208, lr=4.96e-5, step=2388]\\u001b[A\\n\",\n      \"Epoch 11:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:38<02:16,  1.21it/s, loss=0.0208, lr=4.96e-5, step=2388]\\u001b[A\\n\",\n      \"Epoch 11:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:38<02:16,  1.21it/s, loss=0.0192, lr=4.96e-5, step=2389]\\u001b[A\\n\",\n      \"Epoch 11:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [00:39<02:15,  1.22it/s, loss=0.0192, lr=4.96e-5, step=2389]\\u001b[A\\n\",\n      \"Epoch 11:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [00:39<02:15,  1.22it/s, loss=0.0109, lr=4.95e-5, step=2390]\\u001b[A\\n\",\n      \"Epoch 11:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [00:40<02:15,  1.21it/s, loss=0.0109, lr=4.95e-5, step=2390]\\u001b[A\\n\",\n      \"Epoch 11:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:40<02:15,  1.21it/s, loss=0.022, lr=4.95e-5, step=2391]\\u001b[A\\n\",\n      \"Epoch 11:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:41<02:14,  1.21it/s, loss=0.022, lr=4.95e-5, step=2391]\\u001b[A\\n\",\n      \"Epoch 11:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:41<02:14,  1.21it/s, loss=0.0162, lr=4.95e-5, step=2392]\\u001b[A\\n\",\n      \"Epoch 11:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:41<02:12,  1.22it/s, loss=0.0162, lr=4.95e-5, step=2392]\\u001b[A\\n\",\n      \"Epoch 11:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:41<02:12,  1.22it/s, loss=0.0559, lr=4.94e-5, step=2393]\\u001b[A\\n\",\n      \"Epoch 11:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:42<02:11,  1.22it/s, loss=0.0559, lr=4.94e-5, step=2393]\\u001b[A\\n\",\n      \"Epoch 11:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:42<02:11,  1.22it/s, loss=0.0166, lr=4.94e-5, step=2394]\\u001b[A\\n\",\n      \"Epoch 11:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:43<02:10,  1.22it/s, loss=0.0166, lr=4.94e-5, step=2394]\\u001b[A\\n\",\n      \"Epoch 11:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:43<02:10,  1.22it/s, loss=0.0244, lr=4.93e-5, step=2395]\\u001b[A\\n\",\n      \"Epoch 11:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:44<02:09,  1.22it/s, loss=0.0244, lr=4.93e-5, step=2395]\\u001b[A\\n\",\n      \"Epoch 11:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:44<02:09,  1.22it/s, loss=0.0193, lr=4.93e-5, step=2396]\\u001b[A\\n\",\n      \"Epoch 11:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:45<02:09,  1.22it/s, loss=0.0193, lr=4.93e-5, step=2396]\\u001b[A\\n\",\n      \"Epoch 11:  26%|█████████████████████████████████████████████████████▍                                                                                                                                                         | 55/213 [00:45<02:09,  1.22it/s, loss=0.00706, lr=4.92e-5, step=2397]\\u001b[A\\n\",\n      \"Epoch 11:  26%|██████████████████████████████████████████████████████▍                                                                                                                                                        | 56/213 [00:45<02:08,  1.22it/s, loss=0.00706, lr=4.92e-5, step=2397]\\u001b[A\\n\",\n      \"Epoch 11:  26%|██████████████████████████████████████████████████████▉                                                                                                                                                          | 56/213 [00:45<02:08,  1.22it/s, loss=0.022, lr=4.92e-5, step=2398]\\u001b[A\\n\",\n      \"Epoch 11:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:46<02:07,  1.22it/s, loss=0.022, lr=4.92e-5, step=2398]\\u001b[A\\n\",\n      \"Epoch 11:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [00:46<02:07,  1.22it/s, loss=0.0284, lr=4.92e-5, step=2399]\\u001b[A\\n\",\n      \"Epoch 11:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:47<02:06,  1.22it/s, loss=0.0284, lr=4.92e-5, step=2399]\\u001b[A\\n\",\n      \"Epoch 11:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:47<02:06,  1.22it/s, loss=0.0177, lr=4.91e-5, step=2400]\\u001b[A\\n\",\n      \"Epoch 11:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:48<02:05,  1.23it/s, loss=0.0177, lr=4.91e-5, step=2400]\\u001b[A\\n\",\n      \"Epoch 11:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:48<02:05,  1.23it/s, loss=0.0464, lr=4.91e-5, step=2401]\\u001b[A\\n\",\n      \"Epoch 11:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:49<02:04,  1.23it/s, loss=0.0464, lr=4.91e-5, step=2401]\\u001b[A\\n\",\n      \"Epoch 11:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [00:49<02:04,  1.23it/s, loss=0.0121, lr=4.9e-5, step=2402]\\u001b[A\\n\",\n      \"Epoch 11:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:49<02:03,  1.23it/s, loss=0.0121, lr=4.9e-5, step=2402]\\u001b[A\\n\",\n      \"Epoch 11:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:50<02:03,  1.23it/s, loss=0.0153, lr=4.9e-5, step=2403]\\u001b[A\\n\",\n      \"Epoch 11:  29%|████████████████████████████████████████████████████████████▊                                                                                                                                                    | 62/213 [00:50<02:02,  1.23it/s, loss=0.0153, lr=4.9e-5, step=2403]\\u001b[A\\n\",\n      \"Epoch 11:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:50<02:02,  1.23it/s, loss=0.00958, lr=4.9e-5, step=2404]\\u001b[A\\n\",\n      \"Epoch 11:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:51<02:01,  1.23it/s, loss=0.00958, lr=4.9e-5, step=2404]\\u001b[A\\n\",\n      \"Epoch 11:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:51<02:01,  1.23it/s, loss=0.0179, lr=4.89e-5, step=2405]\\u001b[A\\n\",\n      \"Epoch 11:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:52<02:01,  1.23it/s, loss=0.0179, lr=4.89e-5, step=2405]\\u001b[A\\n\",\n      \"Epoch 11:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:52<02:01,  1.23it/s, loss=0.0102, lr=4.89e-5, step=2406]\\u001b[A\\n\",\n      \"Epoch 11:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [00:53<02:00,  1.23it/s, loss=0.0102, lr=4.89e-5, step=2406]\\u001b[A\\n\",\n      \"Epoch 11:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [00:53<02:00,  1.23it/s, loss=0.0114, lr=4.88e-5, step=2407]\\u001b[A\\n\",\n      \"Epoch 11:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:54<01:59,  1.23it/s, loss=0.0114, lr=4.88e-5, step=2407]\\u001b[A\\n\",\n      \"Epoch 11:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:54<01:59,  1.23it/s, loss=0.0205, lr=4.88e-5, step=2408]\\u001b[A\\n\",\n      \"Epoch 11:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:54<01:58,  1.23it/s, loss=0.0205, lr=4.88e-5, step=2408]\\u001b[A\\n\",\n      \"Epoch 11:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:54<01:58,  1.23it/s, loss=0.0181, lr=4.87e-5, step=2409]\\u001b[A\\n\",\n      \"Epoch 11:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [00:55<01:57,  1.23it/s, loss=0.0181, lr=4.87e-5, step=2409]\\u001b[A\\n\",\n      \"Epoch 11:  32%|██████████████████████████████████████████████████████████████████                                                                                                                                             | 68/213 [00:55<01:57,  1.23it/s, loss=0.00971, lr=4.87e-5, step=2410]\\u001b[A\\n\",\n      \"Epoch 11:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                            | 69/213 [00:56<01:57,  1.23it/s, loss=0.00971, lr=4.87e-5, step=2410]\\u001b[A\\n\",\n      \"Epoch 11:  32%|███████████████████████████████████████████████████████████████████▍                                                                                                                                            | 69/213 [00:56<01:57,  1.23it/s, loss=0.0159, lr=4.87e-5, step=2411]\\u001b[A\\n\",\n      \"Epoch 11:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [00:57<01:56,  1.23it/s, loss=0.0159, lr=4.87e-5, step=2411]\\u001b[A\\n\",\n      \"Epoch 11:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [00:57<01:56,  1.23it/s, loss=0.0177, lr=4.86e-5, step=2412]\\u001b[A\\n\",\n      \"Epoch 11:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:58<01:55,  1.23it/s, loss=0.0177, lr=4.86e-5, step=2412]\\u001b[A\\n\",\n      \"Epoch 11:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:58<01:55,  1.23it/s, loss=0.0143, lr=4.86e-5, step=2413]\\u001b[A\\n\",\n      \"Epoch 11:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [00:58<01:54,  1.24it/s, loss=0.0143, lr=4.86e-5, step=2413]\\u001b[A\\n\",\n      \"Epoch 11:  34%|█████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 72/213 [00:58<01:54,  1.24it/s, loss=0.00909, lr=4.85e-5, step=2414]\\u001b[A\\n\",\n      \"Epoch 11:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [00:59<01:53,  1.23it/s, loss=0.00909, lr=4.85e-5, step=2414]\\u001b[A\\n\",\n      \"Epoch 11:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 73/213 [00:59<01:53,  1.23it/s, loss=0.0108, lr=4.85e-5, step=2415]\\u001b[A\\n\",\n      \"Epoch 11:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [01:00<01:52,  1.24it/s, loss=0.0108, lr=4.85e-5, step=2415]\\u001b[A\\n\",\n      \"Epoch 11:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [01:00<01:52,  1.24it/s, loss=0.0248, lr=4.85e-5, step=2416]\\u001b[A\\n\",\n      \"Epoch 11:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:01<01:51,  1.24it/s, loss=0.0248, lr=4.85e-5, step=2416]\\u001b[A\\n\",\n      \"Epoch 11:  35%|█████████████████████████████████████████████████████████████████████████▌                                                                                                                                       | 75/213 [01:01<01:51,  1.24it/s, loss=0.037, lr=4.84e-5, step=2417]\\u001b[A\\n\",\n      \"Epoch 11:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:02<01:50,  1.24it/s, loss=0.037, lr=4.84e-5, step=2417]\\u001b[A\\n\",\n      \"Epoch 11:  36%|██████████████████████████████████████████████████████████████████████████▌                                                                                                                                      | 76/213 [01:02<01:50,  1.24it/s, loss=0.011, lr=4.84e-5, step=2418]\\u001b[A\\n\",\n      \"Epoch 11:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:02<01:49,  1.24it/s, loss=0.011, lr=4.84e-5, step=2418]\\u001b[A\\n\",\n      \"Epoch 11:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [01:02<01:49,  1.24it/s, loss=0.018, lr=4.83e-5, step=2419]\\u001b[A\\n\",\n      \"Epoch 11:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [01:03<01:49,  1.24it/s, loss=0.018, lr=4.83e-5, step=2419]\\u001b[A\\n\",\n      \"Epoch 11:  37%|███████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 78/213 [01:03<01:49,  1.24it/s, loss=0.00959, lr=4.83e-5, step=2420]\\u001b[A\\n\",\n      \"Epoch 11:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [01:04<01:48,  1.24it/s, loss=0.00959, lr=4.83e-5, step=2420]\\u001b[A\\n\",\n      \"Epoch 11:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [01:04<01:48,  1.24it/s, loss=0.00816, lr=4.82e-5, step=2421]\\u001b[A\\n\",\n      \"Epoch 11:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [01:05<01:47,  1.24it/s, loss=0.00816, lr=4.82e-5, step=2421]\\u001b[A\\n\",\n      \"Epoch 11:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [01:05<01:47,  1.24it/s, loss=0.00933, lr=4.82e-5, step=2422]\\u001b[A\\n\",\n      \"Epoch 11:  38%|██████████████████████████████████████████████████████████████████████████████▋                                                                                                                                | 81/213 [01:06<01:46,  1.24it/s, loss=0.00933, lr=4.82e-5, step=2422]\\u001b[A\\n\",\n      \"Epoch 11:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:06<01:46,  1.24it/s, loss=0.0133, lr=4.82e-5, step=2423]\\u001b[A\\n\",\n      \"Epoch 11:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:06<01:45,  1.24it/s, loss=0.0133, lr=4.82e-5, step=2423]\\u001b[A\\n\",\n      \"Epoch 11:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:07<01:45,  1.24it/s, loss=0.0394, lr=4.81e-5, step=2424]\\u001b[A\\n\",\n      \"Epoch 11:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [01:07<01:45,  1.24it/s, loss=0.0394, lr=4.81e-5, step=2424]\\u001b[A\\n\",\n      \"Epoch 11:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:07<01:45,  1.24it/s, loss=0.057, lr=4.81e-5, step=2425]\\u001b[A\\n\",\n      \"Epoch 11:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:08<01:44,  1.24it/s, loss=0.057, lr=4.81e-5, step=2425]\\u001b[A\\n\",\n      \"Epoch 11:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:08<01:44,  1.24it/s, loss=0.0463, lr=4.8e-5, step=2426]\\u001b[A\\n\",\n      \"Epoch 11:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:09<01:43,  1.24it/s, loss=0.0463, lr=4.8e-5, step=2426]\\u001b[A\\n\",\n      \"Epoch 11:  40%|███████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 85/213 [01:09<01:43,  1.24it/s, loss=0.00964, lr=4.8e-5, step=2427]\\u001b[A\\n\",\n      \"Epoch 11:  40%|███████████████████████████████████████████████████████████████████████████████████▉                                                                                                                            | 86/213 [01:10<01:43,  1.23it/s, loss=0.00964, lr=4.8e-5, step=2427]\\u001b[A\\n\",\n      \"Epoch 11:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:10<01:43,  1.23it/s, loss=0.0153, lr=4.8e-5, step=2428]\\u001b[A\\n\",\n      \"Epoch 11:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:11<01:42,  1.23it/s, loss=0.0153, lr=4.8e-5, step=2428]\\u001b[A\\n\",\n      \"Epoch 11:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [01:11<01:42,  1.23it/s, loss=0.0277, lr=4.79e-5, step=2429]\\u001b[A\\n\",\n      \"Epoch 11:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:11<01:41,  1.23it/s, loss=0.0277, lr=4.79e-5, step=2429]\\u001b[A\\n\",\n      \"Epoch 11:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:11<01:41,  1.23it/s, loss=0.0101, lr=4.79e-5, step=2430]\\u001b[A\\n\",\n      \"Epoch 11:  42%|██████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                         | 89/213 [01:12<01:40,  1.23it/s, loss=0.0101, lr=4.79e-5, step=2430]\\u001b[A\\n\",\n      \"Epoch 11:  42%|██████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                        | 89/213 [01:12<01:40,  1.23it/s, loss=0.00801, lr=4.78e-5, step=2431]\\u001b[A\\n\",\n      \"Epoch 11:  42%|███████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                       | 90/213 [01:13<01:40,  1.23it/s, loss=0.00801, lr=4.78e-5, step=2431]\\u001b[A\\n\",\n      \"Epoch 11:  42%|███████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                        | 90/213 [01:13<01:40,  1.23it/s, loss=0.0102, lr=4.78e-5, step=2432]\\u001b[A\\n\",\n      \"Epoch 11:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [01:14<01:39,  1.23it/s, loss=0.0102, lr=4.78e-5, step=2432]\\u001b[A\\n\",\n      \"Epoch 11:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [01:14<01:39,  1.23it/s, loss=0.0182, lr=4.77e-5, step=2433]\\u001b[A\\n\",\n      \"Epoch 11:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:15<01:38,  1.23it/s, loss=0.0182, lr=4.77e-5, step=2433]\\u001b[A\\n\",\n      \"Epoch 11:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:15<01:38,  1.23it/s, loss=0.0191, lr=4.77e-5, step=2434]\\u001b[A\\n\",\n      \"Epoch 11:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:15<01:37,  1.23it/s, loss=0.0191, lr=4.77e-5, step=2434]\\u001b[A\\n\",\n      \"Epoch 11:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:15<01:37,  1.23it/s, loss=0.0196, lr=4.77e-5, step=2435]\\u001b[A\\n\",\n      \"Epoch 11:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:16<01:37,  1.22it/s, loss=0.0196, lr=4.77e-5, step=2435]\\u001b[A\\n\",\n      \"Epoch 11:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:16<01:37,  1.22it/s, loss=0.0458, lr=4.76e-5, step=2436]\\u001b[A\\n\",\n      \"Epoch 11:  45%|████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                   | 95/213 [01:17<01:36,  1.23it/s, loss=0.0458, lr=4.76e-5, step=2436]\\u001b[A\\n\",\n      \"Epoch 11:  45%|████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                   | 95/213 [01:17<01:36,  1.23it/s, loss=0.0118, lr=4.76e-5, step=2437]\\u001b[A\\n\",\n      \"Epoch 11:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [01:18<01:34,  1.23it/s, loss=0.0118, lr=4.76e-5, step=2437]\\u001b[A\\n\",\n      \"Epoch 11:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [01:18<01:34,  1.23it/s, loss=0.0177, lr=4.75e-5, step=2438]\\u001b[A\\n\",\n      \"Epoch 11:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:19<01:33,  1.24it/s, loss=0.0177, lr=4.75e-5, step=2438]\\u001b[A\\n\",\n      \"Epoch 11:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:19<01:33,  1.24it/s, loss=0.0124, lr=4.75e-5, step=2439]\\u001b[A\\n\",\n      \"Epoch 11:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:19<01:33,  1.24it/s, loss=0.0124, lr=4.75e-5, step=2439]\\u001b[A\\n\",\n      \"Epoch 11:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:20<01:33,  1.24it/s, loss=0.0251, lr=4.75e-5, step=2440]\\u001b[A\\n\",\n      \"Epoch 11:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:20<01:32,  1.24it/s, loss=0.0251, lr=4.75e-5, step=2440]\\u001b[A\\n\",\n      \"Epoch 11:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:20<01:32,  1.24it/s, loss=0.0219, lr=4.74e-5, step=2441]\\u001b[A\\n\",\n      \"Epoch 11:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:21<01:31,  1.24it/s, loss=0.0219, lr=4.74e-5, step=2441]\\u001b[A\\n\",\n      \"Epoch 11:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:21<01:31,  1.24it/s, loss=0.0129, lr=4.74e-5, step=2442]\\u001b[A\\n\",\n      \"Epoch 11:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:22<01:30,  1.24it/s, loss=0.0129, lr=4.74e-5, step=2442]\\u001b[A\\n\",\n      \"Epoch 11:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:22<01:30,  1.24it/s, loss=0.0126, lr=4.73e-5, step=2443]\\u001b[A\\n\",\n      \"Epoch 11:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [01:23<01:29,  1.24it/s, loss=0.0126, lr=4.73e-5, step=2443]\\u001b[A\\n\",\n      \"Epoch 11:  48%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                           | 102/213 [01:23<01:29,  1.24it/s, loss=0.00819, lr=4.73e-5, step=2444]\\u001b[A\\n\",\n      \"Epoch 11:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:24<01:28,  1.24it/s, loss=0.00819, lr=4.73e-5, step=2444]\\u001b[A\\n\",\n      \"Epoch 11:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:24<01:28,  1.24it/s, loss=0.00937, lr=4.72e-5, step=2445]\\u001b[A\\n\",\n      \"Epoch 11:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 104/213 [01:24<01:27,  1.24it/s, loss=0.00937, lr=4.72e-5, step=2445]\\u001b[A\\n\",\n      \"Epoch 11:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 104/213 [01:24<01:27,  1.24it/s, loss=0.016, lr=4.72e-5, step=2446]\\u001b[A\\n\",\n      \"Epoch 11:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 105/213 [01:25<01:26,  1.24it/s, loss=0.016, lr=4.72e-5, step=2446]\\u001b[A\\n\",\n      \"Epoch 11:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:25<01:26,  1.24it/s, loss=0.0306, lr=4.72e-5, step=2447]\\u001b[A\\n\",\n      \"Epoch 11:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:26<01:26,  1.24it/s, loss=0.0306, lr=4.72e-5, step=2447]\\u001b[A\\n\",\n      \"Epoch 11:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:26<01:26,  1.24it/s, loss=0.0206, lr=4.71e-5, step=2448]\\u001b[A\\n\",\n      \"Epoch 11:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 107/213 [01:27<01:25,  1.24it/s, loss=0.0206, lr=4.71e-5, step=2448]\\u001b[A\\n\",\n      \"Epoch 11:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 107/213 [01:27<01:25,  1.24it/s, loss=0.0114, lr=4.71e-5, step=2449]\\u001b[A\\n\",\n      \"Epoch 11:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 108/213 [01:28<01:24,  1.24it/s, loss=0.0114, lr=4.71e-5, step=2449]\\u001b[A\\n\",\n      \"Epoch 11:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:28<01:24,  1.24it/s, loss=0.0425, lr=4.7e-5, step=2450]\\u001b[A\\n\",\n      \"Epoch 11:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:28<01:23,  1.24it/s, loss=0.0425, lr=4.7e-5, step=2450]\\u001b[A\\n\",\n      \"Epoch 11:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:28<01:23,  1.24it/s, loss=0.0274, lr=4.7e-5, step=2451]\\u001b[A\\n\",\n      \"Epoch 11:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:29<01:23,  1.24it/s, loss=0.0274, lr=4.7e-5, step=2451]\\u001b[A\\n\",\n      \"Epoch 11:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:29<01:23,  1.24it/s, loss=0.0216, lr=4.7e-5, step=2452]\\u001b[A\\n\",\n      \"Epoch 11:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:30<01:22,  1.24it/s, loss=0.0216, lr=4.7e-5, step=2452]\\u001b[A\\n\",\n      \"Epoch 11:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:30<01:22,  1.24it/s, loss=0.0111, lr=4.69e-5, step=2453]\\u001b[A\\n\",\n      \"Epoch 11:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:31<01:21,  1.24it/s, loss=0.0111, lr=4.69e-5, step=2453]\\u001b[A\\n\",\n      \"Epoch 11:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:31<01:21,  1.24it/s, loss=0.0459, lr=4.69e-5, step=2454]\\u001b[A\\n\",\n      \"Epoch 11:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:32<01:20,  1.24it/s, loss=0.0459, lr=4.69e-5, step=2454]\\u001b[A\\n\",\n      \"Epoch 11:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 113/213 [01:32<01:20,  1.24it/s, loss=0.00499, lr=4.68e-5, step=2455]\\u001b[A\\n\",\n      \"Epoch 11:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [01:32<01:19,  1.24it/s, loss=0.00499, lr=4.68e-5, step=2455]\\u001b[A\\n\",\n      \"Epoch 11:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:32<01:19,  1.24it/s, loss=0.0105, lr=4.68e-5, step=2456]\\u001b[A\\n\",\n      \"Epoch 11:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:33<01:19,  1.24it/s, loss=0.0105, lr=4.68e-5, step=2456]\\u001b[A\\n\",\n      \"Epoch 11:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:33<01:19,  1.24it/s, loss=0.0159, lr=4.67e-5, step=2457]\\u001b[A\\n\",\n      \"Epoch 11:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:34<01:18,  1.24it/s, loss=0.0159, lr=4.67e-5, step=2457]\\u001b[A\\n\",\n      \"Epoch 11:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:34<01:18,  1.24it/s, loss=0.0226, lr=4.67e-5, step=2458]\\u001b[A\\n\",\n      \"Epoch 11:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:35<01:17,  1.24it/s, loss=0.0226, lr=4.67e-5, step=2458]\\u001b[A\\n\",\n      \"Epoch 11:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:35<01:17,  1.24it/s, loss=0.0132, lr=4.67e-5, step=2459]\\u001b[A\\n\",\n      \"Epoch 11:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [01:36<01:16,  1.24it/s, loss=0.0132, lr=4.67e-5, step=2459]\\u001b[A\\n\",\n      \"Epoch 11:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                            | 118/213 [01:36<01:16,  1.24it/s, loss=0.00784, lr=4.66e-5, step=2460]\\u001b[A\\n\",\n      \"Epoch 11:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                           | 119/213 [01:36<01:15,  1.24it/s, loss=0.00784, lr=4.66e-5, step=2460]\\u001b[A\\n\",\n      \"Epoch 11:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [01:36<01:15,  1.24it/s, loss=0.0221, lr=4.66e-5, step=2461]\\u001b[A\\n\",\n      \"Epoch 11:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:37<01:15,  1.24it/s, loss=0.0221, lr=4.66e-5, step=2461]\\u001b[A\\n\",\n      \"Epoch 11:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:37<01:15,  1.24it/s, loss=0.0215, lr=4.65e-5, step=2462]\\u001b[A\\n\",\n      \"Epoch 11:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:38<01:14,  1.24it/s, loss=0.0215, lr=4.65e-5, step=2462]\\u001b[A\\n\",\n      \"Epoch 11:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:38<01:14,  1.24it/s, loss=0.0182, lr=4.65e-5, step=2463]\\u001b[A\\n\",\n      \"Epoch 11:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:39<01:13,  1.24it/s, loss=0.0182, lr=4.65e-5, step=2463]\\u001b[A\\n\",\n      \"Epoch 11:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:39<01:13,  1.24it/s, loss=0.0138, lr=4.65e-5, step=2464]\\u001b[A\\n\",\n      \"Epoch 11:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:40<01:12,  1.25it/s, loss=0.0138, lr=4.65e-5, step=2464]\\u001b[A\\n\",\n      \"Epoch 11:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:40<01:12,  1.25it/s, loss=0.0155, lr=4.64e-5, step=2465]\\u001b[A\\n\",\n      \"Epoch 11:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                      | 124/213 [01:40<01:11,  1.24it/s, loss=0.0155, lr=4.64e-5, step=2465]\\u001b[A\\n\",\n      \"Epoch 11:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                      | 124/213 [01:40<01:11,  1.24it/s, loss=0.00825, lr=4.64e-5, step=2466]\\u001b[A\\n\",\n      \"Epoch 11:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                     | 125/213 [01:41<01:11,  1.24it/s, loss=0.00825, lr=4.64e-5, step=2466]\\u001b[A\\n\",\n      \"Epoch 11:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                      | 125/213 [01:41<01:11,  1.24it/s, loss=0.027, lr=4.63e-5, step=2467]\\u001b[A\\n\",\n      \"Epoch 11:  59%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     | 126/213 [01:42<01:10,  1.24it/s, loss=0.027, lr=4.63e-5, step=2467]\\u001b[A\\n\",\n      \"Epoch 11:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [01:42<01:10,  1.24it/s, loss=0.0115, lr=4.63e-5, step=2468]\\u001b[A\\n\",\n      \"Epoch 11:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                   | 127/213 [01:43<01:09,  1.24it/s, loss=0.0115, lr=4.63e-5, step=2468]\\u001b[A\\n\",\n      \"Epoch 11:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:43<01:09,  1.24it/s, loss=0.00765, lr=4.62e-5, step=2469]\\u001b[A\\n\",\n      \"Epoch 11:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                  | 128/213 [01:44<01:08,  1.24it/s, loss=0.00765, lr=4.62e-5, step=2469]\\u001b[A\\n\",\n      \"Epoch 11:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                   | 128/213 [01:44<01:08,  1.24it/s, loss=0.028, lr=4.62e-5, step=2470]\\u001b[A\\n\",\n      \"Epoch 11:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:44<01:07,  1.24it/s, loss=0.028, lr=4.62e-5, step=2470]\\u001b[A\\n\",\n      \"Epoch 11:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:45<01:07,  1.24it/s, loss=0.0137, lr=4.62e-5, step=2471]\\u001b[A\\n\",\n      \"Epoch 11:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:45<01:06,  1.24it/s, loss=0.0137, lr=4.62e-5, step=2471]\\u001b[A\\n\",\n      \"Epoch 11:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                | 130/213 [01:45<01:06,  1.24it/s, loss=0.00792, lr=4.61e-5, step=2472]\\u001b[A\\n\",\n      \"Epoch 11:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                               | 131/213 [01:46<01:06,  1.23it/s, loss=0.00792, lr=4.61e-5, step=2472]\\u001b[A\\n\",\n      \"Epoch 11:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [01:46<01:06,  1.23it/s, loss=0.015, lr=4.61e-5, step=2473]\\u001b[A\\n\",\n      \"Epoch 11:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:47<01:05,  1.24it/s, loss=0.015, lr=4.61e-5, step=2473]\\u001b[A\\n\",\n      \"Epoch 11:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [01:47<01:05,  1.24it/s, loss=0.0151, lr=4.6e-5, step=2474]\\u001b[A\\n\",\n      \"Epoch 11:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:48<01:04,  1.24it/s, loss=0.0151, lr=4.6e-5, step=2474]\\u001b[A\\n\",\n      \"Epoch 11:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:48<01:04,  1.24it/s, loss=0.0185, lr=4.6e-5, step=2475]\\u001b[A\\n\",\n      \"Epoch 11:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:49<01:03,  1.24it/s, loss=0.0185, lr=4.6e-5, step=2475]\\u001b[A\\n\",\n      \"Epoch 11:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:49<01:03,  1.24it/s, loss=0.0135, lr=4.6e-5, step=2476]\\u001b[A\\n\",\n      \"Epoch 11:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:49<01:02,  1.24it/s, loss=0.0135, lr=4.6e-5, step=2476]\\u001b[A\\n\",\n      \"Epoch 11:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                            | 135/213 [01:49<01:02,  1.24it/s, loss=0.049, lr=4.59e-5, step=2477]\\u001b[A\\n\",\n      \"Epoch 11:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                           | 136/213 [01:50<01:02,  1.24it/s, loss=0.049, lr=4.59e-5, step=2477]\\u001b[A\\n\",\n      \"Epoch 11:  64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                          | 136/213 [01:50<01:02,  1.24it/s, loss=0.00893, lr=4.59e-5, step=2478]\\u001b[A\\n\",\n      \"Epoch 11:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 137/213 [01:51<01:01,  1.24it/s, loss=0.00893, lr=4.59e-5, step=2478]\\u001b[A\\n\",\n      \"Epoch 11:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                         | 137/213 [01:51<01:01,  1.24it/s, loss=0.0161, lr=4.58e-5, step=2479]\\u001b[A\\n\",\n      \"Epoch 11:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:52<01:00,  1.24it/s, loss=0.0161, lr=4.58e-5, step=2479]\\u001b[A\\n\",\n      \"Epoch 11:  65%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 138/213 [01:52<01:00,  1.24it/s, loss=0.00509, lr=4.58e-5, step=2480]\\u001b[A\\n\",\n      \"Epoch 11:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                       | 139/213 [01:53<00:59,  1.24it/s, loss=0.00509, lr=4.58e-5, step=2480]\\u001b[A\\n\",\n      \"Epoch 11:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:53<00:59,  1.24it/s, loss=0.0261, lr=4.57e-5, step=2481]\\u001b[A\\n\",\n      \"Epoch 11:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:53<00:58,  1.24it/s, loss=0.0261, lr=4.57e-5, step=2481]\\u001b[A\\n\",\n      \"Epoch 11:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:53<00:58,  1.24it/s, loss=0.0309, lr=4.57e-5, step=2482]\\u001b[A\\n\",\n      \"Epoch 11:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:54<00:57,  1.24it/s, loss=0.0309, lr=4.57e-5, step=2482]\\u001b[A\\n\",\n      \"Epoch 11:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:54<00:57,  1.24it/s, loss=0.0351, lr=4.57e-5, step=2483]\\u001b[A\\n\",\n      \"Epoch 11:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:55<00:57,  1.24it/s, loss=0.0351, lr=4.57e-5, step=2483]\\u001b[A\\n\",\n      \"Epoch 11:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:55<00:57,  1.24it/s, loss=0.0189, lr=4.56e-5, step=2484]\\u001b[A\\n\",\n      \"Epoch 11:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:56<00:56,  1.24it/s, loss=0.0189, lr=4.56e-5, step=2484]\\u001b[A\\n\",\n      \"Epoch 11:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:56<00:56,  1.24it/s, loss=0.0181, lr=4.56e-5, step=2485]\\u001b[A\\n\",\n      \"Epoch 11:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:57<00:55,  1.24it/s, loss=0.0181, lr=4.56e-5, step=2485]\\u001b[A\\n\",\n      \"Epoch 11:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:57<00:55,  1.24it/s, loss=0.0163, lr=4.55e-5, step=2486]\\u001b[A\\n\",\n      \"Epoch 11:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:57<00:54,  1.24it/s, loss=0.0163, lr=4.55e-5, step=2486]\\u001b[A\\n\",\n      \"Epoch 11:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:57<00:54,  1.24it/s, loss=0.0281, lr=4.55e-5, step=2487]\\u001b[A\\n\",\n      \"Epoch 11:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:58<00:53,  1.24it/s, loss=0.0281, lr=4.55e-5, step=2487]\\u001b[A\\n\",\n      \"Epoch 11:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:58<00:53,  1.24it/s, loss=0.0141, lr=4.55e-5, step=2488]\\u001b[A\\n\",\n      \"Epoch 11:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [01:59<00:53,  1.24it/s, loss=0.0141, lr=4.55e-5, step=2488]\\u001b[A\\n\",\n      \"Epoch 11:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [01:59<00:53,  1.24it/s, loss=0.0166, lr=4.54e-5, step=2489]\\u001b[A\\n\",\n      \"Epoch 11:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [02:00<00:52,  1.24it/s, loss=0.0166, lr=4.54e-5, step=2489]\\u001b[A\\n\",\n      \"Epoch 11:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [02:00<00:52,  1.24it/s, loss=0.0196, lr=4.54e-5, step=2490]\\u001b[A\\n\",\n      \"Epoch 11:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [02:01<00:51,  1.24it/s, loss=0.0196, lr=4.54e-5, step=2490]\\u001b[A\\n\",\n      \"Epoch 11:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [02:01<00:51,  1.24it/s, loss=0.0105, lr=4.53e-5, step=2491]\\u001b[A\\n\",\n      \"Epoch 11:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [02:01<00:50,  1.24it/s, loss=0.0105, lr=4.53e-5, step=2491]\\u001b[A\\n\",\n      \"Epoch 11:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [02:01<00:50,  1.24it/s, loss=0.0181, lr=4.53e-5, step=2492]\\u001b[A\\n\",\n      \"Epoch 11:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [02:02<00:49,  1.24it/s, loss=0.0181, lr=4.53e-5, step=2492]\\u001b[A\\n\",\n      \"Epoch 11:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [02:02<00:49,  1.24it/s, loss=0.0112, lr=4.52e-5, step=2493]\\u001b[A\\n\",\n      \"Epoch 11:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                           | 152/213 [02:03<00:49,  1.24it/s, loss=0.0112, lr=4.52e-5, step=2493]\\u001b[A\\n\",\n      \"Epoch 11:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                           | 152/213 [02:03<00:49,  1.24it/s, loss=0.00976, lr=4.52e-5, step=2494]\\u001b[A\\n\",\n      \"Epoch 11:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 153/213 [02:04<00:48,  1.24it/s, loss=0.00976, lr=4.52e-5, step=2494]\\u001b[A\\n\",\n      \"Epoch 11:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [02:04<00:48,  1.24it/s, loss=0.0173, lr=4.52e-5, step=2495]\\u001b[A\\n\",\n      \"Epoch 11:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:05<00:47,  1.25it/s, loss=0.0173, lr=4.52e-5, step=2495]\\u001b[A\\n\",\n      \"Epoch 11:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:05<00:47,  1.25it/s, loss=0.0103, lr=4.51e-5, step=2496]\\u001b[A\\n\",\n      \"Epoch 11:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [02:05<00:46,  1.24it/s, loss=0.0103, lr=4.51e-5, step=2496]\\u001b[A\\n\",\n      \"Epoch 11:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                        | 155/213 [02:05<00:46,  1.24it/s, loss=0.00798, lr=4.51e-5, step=2497]\\u001b[A\\n\",\n      \"Epoch 11:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                       | 156/213 [02:06<00:45,  1.24it/s, loss=0.00798, lr=4.51e-5, step=2497]\\u001b[A\\n\",\n      \"Epoch 11:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [02:06<00:45,  1.24it/s, loss=0.0117, lr=4.5e-5, step=2498]\\u001b[A\\n\",\n      \"Epoch 11:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:07<00:45,  1.24it/s, loss=0.0117, lr=4.5e-5, step=2498]\\u001b[A\\n\",\n      \"Epoch 11:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:07<00:45,  1.24it/s, loss=0.0177, lr=4.5e-5, step=2499]\\u001b[A\\n\",\n      \"Epoch 11:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:08<00:44,  1.25it/s, loss=0.0177, lr=4.5e-5, step=2499]\\u001b[A\\n\",\n      \"Epoch 11:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                     | 158/213 [02:08<00:44,  1.25it/s, loss=0.00747, lr=4.5e-5, step=2500]\\u001b[A\\n\",\n      \"Epoch 11:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                    | 159/213 [02:09<00:43,  1.24it/s, loss=0.00747, lr=4.5e-5, step=2500]\\u001b[A\\n\",\n      \"Epoch 11:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:09<00:43,  1.24it/s, loss=0.00752, lr=4.49e-5, step=2501]\\u001b[A\\n\",\n      \"Epoch 11:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:09<00:42,  1.24it/s, loss=0.00752, lr=4.49e-5, step=2501]\\u001b[A\\n\",\n      \"Epoch 11:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:10<00:42,  1.24it/s, loss=0.00626, lr=4.49e-5, step=2502]\\u001b[A\\n\",\n      \"Epoch 11:  76%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                  | 161/213 [02:10<00:41,  1.24it/s, loss=0.00626, lr=4.49e-5, step=2502]\\u001b[A\\n\",\n      \"Epoch 11:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:10<00:41,  1.24it/s, loss=0.0236, lr=4.48e-5, step=2503]\\u001b[A\\n\",\n      \"Epoch 11:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:11<00:41,  1.24it/s, loss=0.0236, lr=4.48e-5, step=2503]\\u001b[A\\n\",\n      \"Epoch 11:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:11<00:41,  1.24it/s, loss=0.0308, lr=4.48e-5, step=2504]\\u001b[A\\n\",\n      \"Epoch 11:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:12<00:40,  1.24it/s, loss=0.0308, lr=4.48e-5, step=2504]\\u001b[A\\n\",\n      \"Epoch 11:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:12<00:40,  1.24it/s, loss=0.0222, lr=4.47e-5, step=2505]\\u001b[A\\n\",\n      \"Epoch 11:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:13<00:39,  1.24it/s, loss=0.0222, lr=4.47e-5, step=2505]\\u001b[A\\n\",\n      \"Epoch 11:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:13<00:39,  1.24it/s, loss=0.017, lr=4.47e-5, step=2506]\\u001b[A\\n\",\n      \"Epoch 11:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [02:13<00:38,  1.24it/s, loss=0.017, lr=4.47e-5, step=2506]\\u001b[A\\n\",\n      \"Epoch 11:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:14<00:38,  1.24it/s, loss=0.0131, lr=4.47e-5, step=2507]\\u001b[A\\n\",\n      \"Epoch 11:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:14<00:37,  1.24it/s, loss=0.0131, lr=4.47e-5, step=2507]\\u001b[A\\n\",\n      \"Epoch 11:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:14<00:37,  1.24it/s, loss=0.0183, lr=4.46e-5, step=2508]\\u001b[A\\n\",\n      \"Epoch 11:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:15<00:37,  1.24it/s, loss=0.0183, lr=4.46e-5, step=2508]\\u001b[A\\n\",\n      \"Epoch 11:  78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                             | 167/213 [02:15<00:37,  1.24it/s, loss=0.014, lr=4.46e-5, step=2509]\\u001b[A\\n\",\n      \"Epoch 11:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:16<00:36,  1.24it/s, loss=0.014, lr=4.46e-5, step=2509]\\u001b[A\\n\",\n      \"Epoch 11:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                            | 168/213 [02:16<00:36,  1.24it/s, loss=0.015, lr=4.45e-5, step=2510]\\u001b[A\\n\",\n      \"Epoch 11:  79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                           | 169/213 [02:17<00:35,  1.24it/s, loss=0.015, lr=4.45e-5, step=2510]\\u001b[A\\n\",\n      \"Epoch 11:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:17<00:35,  1.24it/s, loss=0.0098, lr=4.45e-5, step=2511]\\u001b[A\\n\",\n      \"Epoch 11:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:18<00:34,  1.24it/s, loss=0.0098, lr=4.45e-5, step=2511]\\u001b[A\\n\",\n      \"Epoch 11:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:18<00:34,  1.24it/s, loss=0.0335, lr=4.45e-5, step=2512]\\u001b[A\\n\",\n      \"Epoch 11:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:18<00:33,  1.24it/s, loss=0.0335, lr=4.45e-5, step=2512]\\u001b[A\\n\",\n      \"Epoch 11:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:18<00:33,  1.24it/s, loss=0.0228, lr=4.44e-5, step=2513]\\u001b[A\\n\",\n      \"Epoch 11:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:19<00:32,  1.24it/s, loss=0.0228, lr=4.44e-5, step=2513]\\u001b[A\\n\",\n      \"Epoch 11:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:19<00:32,  1.24it/s, loss=0.0242, lr=4.44e-5, step=2514]\\u001b[A\\n\",\n      \"Epoch 11:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:20<00:32,  1.25it/s, loss=0.0242, lr=4.44e-5, step=2514]\\u001b[A\\n\",\n      \"Epoch 11:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:20<00:32,  1.25it/s, loss=0.0324, lr=4.43e-5, step=2515]\\u001b[A\\n\",\n      \"Epoch 11:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:21<00:31,  1.24it/s, loss=0.0324, lr=4.43e-5, step=2515]\\u001b[A\\n\",\n      \"Epoch 11:  82%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                     | 174/213 [02:21<00:31,  1.24it/s, loss=0.00922, lr=4.43e-5, step=2516]\\u001b[A\\n\",\n      \"Epoch 11:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                    | 175/213 [02:22<00:30,  1.24it/s, loss=0.00922, lr=4.43e-5, step=2516]\\u001b[A\\n\",\n      \"Epoch 11:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                     | 175/213 [02:22<00:30,  1.24it/s, loss=0.011, lr=4.42e-5, step=2517]\\u001b[A\\n\",\n      \"Epoch 11:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 176/213 [02:22<00:29,  1.24it/s, loss=0.011, lr=4.42e-5, step=2517]\\u001b[A\\n\",\n      \"Epoch 11:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                    | 176/213 [02:22<00:29,  1.24it/s, loss=0.0106, lr=4.42e-5, step=2518]\\u001b[A\\n\",\n      \"Epoch 11:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                   | 177/213 [02:23<00:28,  1.25it/s, loss=0.0106, lr=4.42e-5, step=2518]\\u001b[A\\n\",\n      \"Epoch 11:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:23<00:28,  1.25it/s, loss=0.012, lr=4.42e-5, step=2519]\\u001b[A\\n\",\n      \"Epoch 11:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:24<00:28,  1.25it/s, loss=0.012, lr=4.42e-5, step=2519]\\u001b[A\\n\",\n      \"Epoch 11:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:24<00:28,  1.25it/s, loss=0.0272, lr=4.41e-5, step=2520]\\u001b[A\\n\",\n      \"Epoch 11:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:25<00:27,  1.25it/s, loss=0.0272, lr=4.41e-5, step=2520]\\u001b[A\\n\",\n      \"Epoch 11:  84%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                 | 179/213 [02:25<00:27,  1.25it/s, loss=0.011, lr=4.41e-5, step=2521]\\u001b[A\\n\",\n      \"Epoch 11:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                | 180/213 [02:26<00:26,  1.25it/s, loss=0.011, lr=4.41e-5, step=2521]\\u001b[A\\n\",\n      \"Epoch 11:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:26<00:26,  1.25it/s, loss=0.00794, lr=4.4e-5, step=2522]\\u001b[A\\n\",\n      \"Epoch 11:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:26<00:25,  1.25it/s, loss=0.00794, lr=4.4e-5, step=2522]\\u001b[A\\n\",\n      \"Epoch 11:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:26<00:25,  1.25it/s, loss=0.0278, lr=4.4e-5, step=2523]\\u001b[A\\n\",\n      \"Epoch 11:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:27<00:24,  1.25it/s, loss=0.0278, lr=4.4e-5, step=2523]\\u001b[A\\n\",\n      \"Epoch 11:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:27<00:24,  1.25it/s, loss=0.00515, lr=4.4e-5, step=2524]\\u001b[A\\n\",\n      \"Epoch 11:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:28<00:24,  1.25it/s, loss=0.00515, lr=4.4e-5, step=2524]\\u001b[A\\n\",\n      \"Epoch 11:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:28<00:24,  1.25it/s, loss=0.0059, lr=4.39e-5, step=2525]\\u001b[A\\n\",\n      \"Epoch 11:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:29<00:23,  1.25it/s, loss=0.0059, lr=4.39e-5, step=2525]\\u001b[A\\n\",\n      \"Epoch 11:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                            | 184/213 [02:29<00:23,  1.25it/s, loss=0.00885, lr=4.39e-5, step=2526]\\u001b[A\\n\",\n      \"Epoch 11:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                           | 185/213 [02:30<00:22,  1.25it/s, loss=0.00885, lr=4.39e-5, step=2526]\\u001b[A\\n\",\n      \"Epoch 11:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:30<00:22,  1.25it/s, loss=0.0206, lr=4.38e-5, step=2527]\\u001b[A\\n\",\n      \"Epoch 11:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:30<00:21,  1.25it/s, loss=0.0206, lr=4.38e-5, step=2527]\\u001b[A\\n\",\n      \"Epoch 11:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:30<00:21,  1.25it/s, loss=0.0148, lr=4.38e-5, step=2528]\\u001b[A\\n\",\n      \"Epoch 11:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:31<00:20,  1.24it/s, loss=0.0148, lr=4.38e-5, step=2528]\\u001b[A\\n\",\n      \"Epoch 11:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:31<00:20,  1.24it/s, loss=0.0217, lr=4.37e-5, step=2529]\\u001b[A\\n\",\n      \"Epoch 11:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:32<00:20,  1.24it/s, loss=0.0217, lr=4.37e-5, step=2529]\\u001b[A\\n\",\n      \"Epoch 11:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:32<00:20,  1.24it/s, loss=0.0175, lr=4.37e-5, step=2530]\\u001b[A\\n\",\n      \"Epoch 11:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:33<00:19,  1.24it/s, loss=0.0175, lr=4.37e-5, step=2530]\\u001b[A\\n\",\n      \"Epoch 11:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:33<00:19,  1.24it/s, loss=0.0195, lr=4.37e-5, step=2531]\\u001b[A\\n\",\n      \"Epoch 11:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:34<00:18,  1.24it/s, loss=0.0195, lr=4.37e-5, step=2531]\\u001b[A\\n\",\n      \"Epoch 11:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:34<00:18,  1.24it/s, loss=0.0142, lr=4.36e-5, step=2532]\\u001b[A\\n\",\n      \"Epoch 11:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:34<00:17,  1.24it/s, loss=0.0142, lr=4.36e-5, step=2532]\\u001b[A\\n\",\n      \"Epoch 11:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:34<00:17,  1.24it/s, loss=0.0146, lr=4.36e-5, step=2533]\\u001b[A\\n\",\n      \"Epoch 11:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:35<00:16,  1.24it/s, loss=0.0146, lr=4.36e-5, step=2533]\\u001b[A\\n\",\n      \"Epoch 11:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:35<00:16,  1.24it/s, loss=0.0143, lr=4.35e-5, step=2534]\\u001b[A\\n\",\n      \"Epoch 11:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [02:36<00:16,  1.25it/s, loss=0.0143, lr=4.35e-5, step=2534]\\u001b[A\\n\",\n      \"Epoch 11:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [02:36<00:16,  1.25it/s, loss=0.0263, lr=4.35e-5, step=2535]\\u001b[A\\n\",\n      \"Epoch 11:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [02:37<00:15,  1.24it/s, loss=0.0263, lr=4.35e-5, step=2535]\\u001b[A\\n\",\n      \"Epoch 11:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [02:37<00:15,  1.24it/s, loss=0.0429, lr=4.35e-5, step=2536]\\u001b[A\\n\",\n      \"Epoch 11:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:38<00:14,  1.24it/s, loss=0.0429, lr=4.35e-5, step=2536]\\u001b[A\\n\",\n      \"Epoch 11:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:38<00:14,  1.24it/s, loss=0.00882, lr=4.34e-5, step=2537]\\u001b[A\\n\",\n      \"Epoch 11:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 196/213 [02:38<00:13,  1.24it/s, loss=0.00882, lr=4.34e-5, step=2537]\\u001b[A\\n\",\n      \"Epoch 11:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:38<00:13,  1.24it/s, loss=0.011, lr=4.34e-5, step=2538]\\u001b[A\\n\",\n      \"Epoch 11:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:39<00:12,  1.25it/s, loss=0.011, lr=4.34e-5, step=2538]\\u001b[A\\n\",\n      \"Epoch 11:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌               | 197/213 [02:39<00:12,  1.25it/s, loss=0.00976, lr=4.33e-5, step=2539]\\u001b[A\\n\",\n      \"Epoch 11:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:40<00:12,  1.25it/s, loss=0.00976, lr=4.33e-5, step=2539]\\u001b[A\\n\",\n      \"Epoch 11:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:40<00:12,  1.25it/s, loss=0.0123, lr=4.33e-5, step=2540]\\u001b[A\\n\",\n      \"Epoch 11:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:41<00:11,  1.25it/s, loss=0.0123, lr=4.33e-5, step=2540]\\u001b[A\\n\",\n      \"Epoch 11:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:41<00:11,  1.25it/s, loss=0.00671, lr=4.33e-5, step=2541]\\u001b[A\\n\",\n      \"Epoch 11:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [02:42<00:10,  1.24it/s, loss=0.00671, lr=4.33e-5, step=2541]\\u001b[A\\n\",\n      \"Epoch 11:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:42<00:10,  1.24it/s, loss=0.0119, lr=4.32e-5, step=2542]\\u001b[A\\n\",\n      \"Epoch 11:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:42<00:09,  1.24it/s, loss=0.0119, lr=4.32e-5, step=2542]\\u001b[A\\n\",\n      \"Epoch 11:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:42<00:09,  1.24it/s, loss=0.022, lr=4.32e-5, step=2543]\\u001b[A\\n\",\n      \"Epoch 11:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:43<00:08,  1.24it/s, loss=0.022, lr=4.32e-5, step=2543]\\u001b[A\\n\",\n      \"Epoch 11:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:43<00:08,  1.24it/s, loss=0.0251, lr=4.31e-5, step=2544]\\u001b[A\\n\",\n      \"Epoch 11:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:44<00:08,  1.24it/s, loss=0.0251, lr=4.31e-5, step=2544]\\u001b[A\\n\",\n      \"Epoch 11:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:44<00:08,  1.24it/s, loss=0.0196, lr=4.31e-5, step=2545]\\u001b[A\\n\",\n      \"Epoch 11:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:45<00:07,  1.24it/s, loss=0.0196, lr=4.31e-5, step=2545]\\u001b[A\\n\",\n      \"Epoch 11:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 204/213 [02:45<00:07,  1.24it/s, loss=0.0082, lr=4.3e-5, step=2546]\\u001b[A\\n\",\n      \"Epoch 11:  96%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:46<00:06,  1.24it/s, loss=0.0082, lr=4.3e-5, step=2546]\\u001b[A\\n\",\n      \"Epoch 11:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:46<00:06,  1.24it/s, loss=0.016, lr=4.3e-5, step=2547]\\u001b[A\\n\",\n      \"Epoch 11:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:46<00:05,  1.24it/s, loss=0.016, lr=4.3e-5, step=2547]\\u001b[A\\n\",\n      \"Epoch 11:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:46<00:05,  1.24it/s, loss=0.0165, lr=4.3e-5, step=2548]\\u001b[A\\n\",\n      \"Epoch 11:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:47<00:04,  1.24it/s, loss=0.0165, lr=4.3e-5, step=2548]\\u001b[A\\n\",\n      \"Epoch 11:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:47<00:04,  1.24it/s, loss=0.0183, lr=4.29e-5, step=2549]\\u001b[A\\n\",\n      \"Epoch 11:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:48<00:04,  1.24it/s, loss=0.0183, lr=4.29e-5, step=2549]\\u001b[A\\n\",\n      \"Epoch 11:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:48<00:04,  1.24it/s, loss=0.0133, lr=4.29e-5, step=2550]\\u001b[A\\n\",\n      \"Epoch 11:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:49<00:03,  1.24it/s, loss=0.0133, lr=4.29e-5, step=2550]\\u001b[A\\n\",\n      \"Epoch 11:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:49<00:03,  1.24it/s, loss=0.0184, lr=4.28e-5, step=2551]\\u001b[A\\n\",\n      \"Epoch 11:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:50<00:02,  1.24it/s, loss=0.0184, lr=4.28e-5, step=2551]\\u001b[A\\n\",\n      \"Epoch 11:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:50<00:02,  1.24it/s, loss=0.00557, lr=4.28e-5, step=2552]\\u001b[A\\n\",\n      \"Epoch 11:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:50<00:01,  1.24it/s, loss=0.00557, lr=4.28e-5, step=2552]\\u001b[A\\n\",\n      \"Epoch 11:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:50<00:01,  1.24it/s, loss=0.0209, lr=4.28e-5, step=2553]\\u001b[A\\n\",\n      \"Epoch 11: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:51<00:00,  1.25it/s, loss=0.0209, lr=4.28e-5, step=2553]\\u001b[A\\n\",\n      \"Epoch 11: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:51<00:00,  1.25it/s, loss=0.0108, lr=4.27e-5, step=2554]\\u001b[A\\n\",\n      \"Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:52<00:00,  1.45it/s, loss=0.0108, lr=4.27e-5, step=2554]\\u001b[A\\n\",\n      \"Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:52<00:00,  1.45it/s, loss=0.0106, lr=4.27e-5, step=2555]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2a81147200764d2ca7694f5c2a12dfca\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 11: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:38<00:00,  2.15s/it, loss=0.0106, lr=4.27e-5, step=2555]\\n\",\n      \"Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [04:19<00:00,  2.63s/it, loss=0.00907, lr=3.4e-5, step=2768]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2aaa1118046b4497972b5936dabe08e4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 12: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [09:10<00:00,  2.58s/it, loss=0.00907, lr=3.4e-5, step=2768]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 13:   0%|                                                                                                                                                                                                                                                             | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 13:   0%|█▏                                                                                                                                                                                                                                          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step=2770]\\u001b[A\\n\",\n      \"Epoch 13:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:13<14:48,  4.23s/it, loss=0.00914, lr=3.4e-5, step=2770]\\u001b[A\\n\",\n      \"Epoch 13:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:13<14:48,  4.23s/it, loss=0.0104, lr=3.39e-5, step=2771]\\u001b[A\\n\",\n      \"Epoch 13:   2%|███▉                                                                                                                                                                                                             | 4/213 [00:17<14:36,  4.19s/it, loss=0.0104, lr=3.39e-5, step=2771]\\u001b[A\\n\",\n      \"Epoch 13:   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                                                                                                                                                                  | 6/213 [00:26<14:19,  4.15s/it, loss=0.0443, lr=3.38e-5, step=2773]\\u001b[A\\n\",\n      \"Epoch 13:   3%|█████▊                                                                                                                                                                                                          | 6/213 [00:26<14:19,  4.15s/it, loss=0.00341, lr=3.38e-5, step=2774]\\u001b[A\\n\",\n      \"Epoch 13:   3%|██████▊                                                                                                                                                                                                         | 7/213 [00:30<14:12,  4.14s/it, loss=0.00341, lr=3.38e-5, step=2774]\\u001b[A\\n\",\n      \"Epoch 13:   3%|██████▊                                                                                                                                                                                                          | 7/213 [00:30<14:12,  4.14s/it, loss=0.0111, lr=3.38e-5, step=2775]\\u001b[A\\n\",\n      \"Epoch 13:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:34<14:03,  4.12s/it, loss=0.0111, lr=3.38e-5, step=2775]\\u001b[A\\n\",\n      \"Epoch 13:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:34<14:03,  4.12s/it, loss=0.0176, lr=3.37e-5, step=2776]\\u001b[A\\n\",\n      \"Epoch 13:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:38<13:55,  4.09s/it, loss=0.0176, lr=3.37e-5, step=2776]\\u001b[A\\n\",\n      \"Epoch 13:   4%|████████▊                                                                                                                                                                                                       | 9/213 [00:38<13:55,  4.09s/it, loss=0.00738, lr=3.37e-5, step=2777]\\u001b[A\\n\",\n      \"Epoch 13:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:42<13:28,  3.99s/it, loss=0.00738, lr=3.37e-5, step=2777]\\u001b[A\\n\",\n      \"Epoch 13:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:42<13:28,  3.99s/it, loss=0.00876, lr=3.36e-5, step=2778]\\u001b[A\\n\",\n      \"Epoch 13:   5%|██████████▋                                                                                                                                                                                                    | 11/213 [00:46<13:23,  3.98s/it, loss=0.00876, lr=3.36e-5, step=2778]\\u001b[A\\n\",\n      \"Epoch 13:   5%|██████████▋                                                                                                                                                                                                     | 11/213 [00:46<13:23,  3.98s/it, loss=0.0155, lr=3.36e-5, step=2779]\\u001b[A\\n\",\n      \"Epoch 13:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:49<13:04,  3.90s/it, loss=0.0155, lr=3.36e-5, step=2779]\\u001b[A\\n\",\n      \"Epoch 13:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:49<13:04,  3.90s/it, loss=0.0199, lr=3.36e-5, step=2780]\\u001b[A\\n\",\n      \"Epoch 13:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:53<12:45,  3.83s/it, loss=0.0199, lr=3.36e-5, step=2780]\\u001b[A\\n\",\n      \"Epoch 13:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:53<12:45,  3.83s/it, loss=0.0195, lr=3.35e-5, step=2781]\\u001b[A\\n\",\n      \"Epoch 13:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:57<13:11,  3.98s/it, loss=0.0195, lr=3.35e-5, step=2781]\\u001b[A\\n\",\n      \"Epoch 13:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:57<13:11,  3.98s/it, loss=0.0312, lr=3.35e-5, step=2782]\\u001b[A\\n\",\n      \"Epoch 13:   7%|██████████████▋                                                                                                                                                                                                 | 15/213 [01:01<12:43,  3.86s/it, loss=0.0312, lr=3.35e-5, step=2782]\\u001b[A\\n\",\n      \"Epoch 13:   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                                                                                                                                                                 | 17/213 [01:09<12:39,  3.87s/it, loss=0.0114, lr=3.34e-5, step=2784]\\u001b[A\\n\",\n      \"Epoch 13:   8%|████████████████▌                                                                                                                                                                                               | 17/213 [01:09<12:39,  3.87s/it, loss=0.0373, lr=3.34e-5, step=2785]\\u001b[A\\n\",\n      \"Epoch 13:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [01:13<12:54,  3.97s/it, loss=0.0373, lr=3.34e-5, step=2785]\\u001b[A\\n\",\n      \"Epoch 13:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [01:13<12:54,  3.97s/it, loss=0.0225, lr=3.33e-5, step=2786]\\u001b[A\\n\",\n      \"Epoch 13:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [01:17<13:18,  4.12s/it, loss=0.0225, lr=3.33e-5, step=2786]\\u001b[A\\n\",\n      \"Epoch 13:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [01:17<13:18,  4.12s/it, loss=0.0153, lr=3.33e-5, step=2787]\\u001b[A\\n\",\n      \"Epoch 13:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [01:21<12:30,  3.89s/it, loss=0.0153, lr=3.33e-5, step=2787]\\u001b[A\\n\",\n      \"Epoch 13:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [01:21<12:30,  3.89s/it, loss=0.0128, lr=3.32e-5, step=2788]\\u001b[A\\n\",\n      \"Epoch 13:  10%|████████████████████▌                                                                                                                                                                                           | 21/213 [01:24<11:48,  3.69s/it, loss=0.0128, lr=3.32e-5, step=2788]\\u001b[A\\n\",\n      \"Epoch 13:  10%|████████████████████▍                                                                                                                                                                                          | 21/213 [01:24<11:48,  3.69s/it, loss=0.00925, lr=3.32e-5, step=2789]\\u001b[A\\n\",\n      \"Epoch 13:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [01:28<12:21,  3.88s/it, loss=0.00925, lr=3.32e-5, step=2789]\\u001b[A\\n\",\n      \"Epoch 13:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [01:28<12:21,  3.88s/it, loss=0.00849, lr=3.32e-5, step=2790]\\u001b[A\\n\",\n      \"Epoch 13:  11%|██████████████████████▎                                                                                                                                                                                        | 23/213 [01:32<12:36,  3.98s/it, loss=0.00849, lr=3.32e-5, step=2790]\\u001b[A\\n\",\n      \"Epoch 13:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [01:32<12:36,  3.98s/it, loss=0.0243, lr=3.31e-5, step=2791]\\u001b[A\\n\",\n      \"Epoch 13:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [01:36<12:37,  4.01s/it, loss=0.0243, lr=3.31e-5, step=2791]\\u001b[A\\n\",\n      \"Epoch 13:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [01:36<12:37,  4.01s/it, loss=0.0129, lr=3.31e-5, step=2792]\\u001b[A\\n\",\n      \"Epoch 13:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [01:39<11:04,  3.54s/it, loss=0.0129, lr=3.31e-5, step=2792]\\u001b[A\\n\",\n      \"Epoch 13:  12%|████████████████████████▌                                                                                                                                                                                        | 25/213 [01:39<11:04,  3.54s/it, loss=0.0175, lr=3.3e-5, step=2793]\\u001b[A\\n\",\n      \"Epoch 13:  12%|█████████████████████████▌                                                                                                                                                                                       | 26/213 [01:43<11:41,  3.75s/it, loss=0.0175, lr=3.3e-5, step=2793]\\u001b[A\\n\",\n      \"Epoch 13:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [01:43<11:41,  3.75s/it, loss=0.00707, lr=3.3e-5, step=2794]\\u001b[A\\n\",\n      \"Epoch 13:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [01:47<11:29,  3.71s/it, loss=0.00707, lr=3.3e-5, step=2794]\\u001b[A\\n\",\n      \"Epoch 13:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [01:47<11:29,  3.71s/it, loss=0.00719, lr=3.3e-5, step=2795]\\u001b[A\\n\",\n      \"Epoch 13:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [01:51<11:57,  3.88s/it, loss=0.00719, lr=3.3e-5, step=2795]\\u001b[A\\n\",\n      \"Epoch 13:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [01:51<11:57,  3.88s/it, loss=0.0261, lr=3.29e-5, step=2796]\\u001b[A\\n\",\n      \"Epoch 13:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [01:55<11:41,  3.81s/it, loss=0.0261, lr=3.29e-5, step=2796]\\u001b[A\\n\",\n      \"Epoch 13:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [01:55<11:41,  3.81s/it, loss=0.0114, lr=3.29e-5, step=2797]\\u001b[A\\n\",\n      \"Epoch 13:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [01:58<11:17,  3.70s/it, loss=0.0114, lr=3.29e-5, step=2797]\\u001b[A\\n\",\n      \"Epoch 13:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [01:58<11:17,  3.70s/it, loss=0.0314, lr=3.29e-5, step=2798]\\u001b[A\\n\",\n      \"Epoch 13:  15%|██████████████████████████████▎                                                                                                                   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            | 32/213 [02:06<11:46,  3.91s/it, loss=0.0132, lr=3.28e-5, step=2800]\\u001b[A\\n\",\n      \"Epoch 13:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [02:11<11:57,  3.98s/it, loss=0.0132, lr=3.28e-5, step=2800]\\u001b[A\\n\",\n      \"Epoch 13:  15%|████████████████████████████████▍                                                                                                                                                                                | 33/213 [02:11<11:57,  3.98s/it, loss=0.012, lr=3.27e-5, step=2801]\\u001b[A\\n\",\n      \"Epoch 13:  16%|█████████████████████████████████▎                                                                                                                                                                               | 34/213 [02:15<11:58,  4.01s/it, loss=0.012, lr=3.27e-5, step=2801]\\u001b[A\\n\",\n      \"Epoch 13:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [02:15<11:58,  4.01s/it, loss=0.0096, lr=3.27e-5, step=2802]\\u001b[A\\n\",\n      \"Epoch 13:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [02:19<11:56,  4.02s/it, loss=0.0096, lr=3.27e-5, step=2802]\\u001b[A\\n\",\n      \"Epoch 13:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [02:19<11:56,  4.02s/it, loss=0.0208, lr=3.27e-5, step=2803]\\u001b[A\\n\",\n      \"Epoch 13:  17%|███████████████████████████████████▏                                                                                                                                                                            | 36/213 [02:23<11:56,  4.05s/it, loss=0.0208, lr=3.27e-5, step=2803]\\u001b[A\\n\",\n      \"Epoch 13:  17%|███████████████████████████████████▏                                                                                                                                                                            | 36/213 [02:23<11:56,  4.05s/it, loss=0.0101, lr=3.26e-5, step=2804]\\u001b[A\\n\",\n      \"Epoch 13:  17%|████████████████████████████████████▏                                                                                                                                                                           | 37/213 [02:26<11:14,  3.83s/it, loss=0.0101, lr=3.26e-5, step=2804]\\u001b[A\\n\",\n      \"Epoch 13:  17%|████████████████████████████████████▏                                                                                                                                                                           | 37/213 [02:26<11:14,  3.83s/it, loss=0.0288, lr=3.26e-5, step=2805]\\u001b[A\\n\",\n      \"Epoch 13:  18%|█████████████████████████████████████                                                                                                                                                                           | 38/213 [02:30<11:25,  3.92s/it, loss=0.0288, lr=3.26e-5, step=2805]\\u001b[A\\n\",\n      \"Epoch 13:  18%|████████████████████████████████████▉                                                                                                                                                                          | 38/213 [02:30<11:25,  3.92s/it, loss=0.00861, lr=3.25e-5, step=2806]\\u001b[A\\n\",\n      \"Epoch 13:  18%|█████████████████████████████████████▉                                                                                                                                                                         | 39/213 [02:34<10:56,  3.77s/it, loss=0.00861, lr=3.25e-5, step=2806]\\u001b[A\\n\",\n      \"Epoch 13:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [02:34<10:56,  3.77s/it, loss=0.0449, lr=3.25e-5, step=2807]\\u001b[A\\n\",\n      \"Epoch 13:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [02:37<10:43,  3.72s/it, loss=0.0449, lr=3.25e-5, step=2807]\\u001b[A\\n\",\n      \"Epoch 13:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [02:37<10:43,  3.72s/it, loss=0.0146, lr=3.25e-5, step=2808]\\u001b[A\\n\",\n      \"Epoch 13:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [02:40<09:55,  3.46s/it, loss=0.0146, lr=3.25e-5, step=2808]\\u001b[A\\n\",\n      \"Epoch 13:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [02:40<09:55,  3.46s/it, loss=0.0142, lr=3.24e-5, step=2809]\\u001b[A\\n\",\n      \"Epoch 13:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [02:43<09:28,  3.32s/it, loss=0.0142, lr=3.24e-5, step=2809]\\u001b[A\\n\",\n      \"Epoch 13:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [02:43<09:28,  3.32s/it, loss=0.0054, lr=3.24e-5, step=2810]\\u001b[A\\n\",\n      \"Epoch 13:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [02:46<09:01,  3.18s/it, loss=0.0054, lr=3.24e-5, step=2810]\\u001b[A\\n\",\n      \"Epoch 13:  20%|██████████████████████████████████████████▏                                                                                                                                                                      | 43/213 [02:46<09:01,  3.18s/it, loss=0.032, lr=3.23e-5, step=2811]\\u001b[A\\n\",\n      \"Epoch 13:  21%|███████████████████████████████████████████▏                                                                                                                                                                     | 44/213 [02:51<10:12,  3.63s/it, loss=0.032, lr=3.23e-5, step=2811]\\u001b[A\\n\",\n      \"Epoch 13:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [02:51<10:12,  3.63s/it, loss=0.0141, lr=3.23e-5, step=2812]\\u001b[A\\n\",\n      \"Epoch 13:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [02:55<10:41,  3.82s/it, loss=0.0141, lr=3.23e-5, step=2812]\\u001b[A\\n\",\n      \"Epoch 13:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [02:55<10:41,  3.82s/it, loss=0.0286, lr=3.23e-5, step=2813]\\u001b[A\\n\",\n      \"Epoch 13:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [02:59<10:31,  3.78s/it, loss=0.0286, lr=3.23e-5, step=2813]\\u001b[A\\n\",\n      \"Epoch 13:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [02:59<10:31,  3.78s/it, loss=0.0208, lr=3.22e-5, step=2814]\\u001b[A\\n\",\n      \"Epoch 13:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [03:02<10:22,  3.75s/it, loss=0.0208, lr=3.22e-5, step=2814]\\u001b[A\\n\",\n      \"Epoch 13:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [03:02<10:22,  3.75s/it, loss=0.0167, lr=3.22e-5, step=2815]\\u001b[A\\n\",\n      \"Epoch 13:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [03:06<10:25,  3.79s/it, loss=0.0167, lr=3.22e-5, step=2815]\\u001b[A\\n\",\n      \"Epoch 13:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [03:06<10:25,  3.79s/it, loss=0.0113, lr=3.21e-5, step=2816]\\u001b[A\\n\",\n      \"Epoch 13:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [03:10<10:03,  3.68s/it, loss=0.0113, lr=3.21e-5, step=2816]\\u001b[A\\n\",\n      \"Epoch 13:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [03:10<10:03,  3.68s/it, loss=0.0197, lr=3.21e-5, step=2817]\\u001b[A\\n\",\n      \"Epoch 13:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [03:13<10:01,  3.69s/it, loss=0.0197, lr=3.21e-5, step=2817]\\u001b[A\\n\",\n      \"Epoch 13:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [03:13<10:01,  3.69s/it, loss=0.0149, lr=3.21e-5, step=2818]\\u001b[A\\n\",\n      \"Epoch 13:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [03:17<10:21,  3.83s/it, loss=0.0149, lr=3.21e-5, step=2818]\\u001b[A\\n\",\n      \"Epoch 13:  24%|██████████████████████████████████████████████████▎                                                                                                                                                               | 51/213 [03:17<10:21,  3.83s/it, loss=0.028, lr=3.2e-5, step=2819]\\u001b[A\\n\",\n      \"Epoch 13:  24%|███████████████████████████████████████████████████▎                                                                                                                                                              | 52/213 [03:21<09:56,  3.71s/it, loss=0.028, lr=3.2e-5, step=2819]\\u001b[A\\n\",\n      \"Epoch 13:  24%|███████████████████████████████████████████████████▎                                                                                                                                                              | 52/213 [03:21<09:56,  3.71s/it, loss=0.015, lr=3.2e-5, step=2820]\\u001b[A\\n\",\n      \"Epoch 13:  25%|████████████████████████████████████████████████████▎                                                                                                                                                             | 53/213 [03:25<09:58,  3.74s/it, loss=0.015, lr=3.2e-5, step=2820]\\u001b[A\\n\",\n      \"Epoch 13:  25%|████████████████████████████████████████████████████                                                                                                                                                             | 53/213 [03:25<09:58,  3.74s/it, loss=0.0229, lr=3.2e-5, step=2821]\\u001b[A\\n\",\n      \"Epoch 13:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [03:30<10:52,  4.10s/it, loss=0.0229, lr=3.2e-5, step=2821]\\u001b[A\\n\",\n      \"Epoch 13:  25%|████████████████████████████████████████████████████▉                                                                                                                                                            | 54/213 [03:30<10:52,  4.10s/it, loss=0.019, lr=3.19e-5, step=2822]\\u001b[A\\n\",\n      \"Epoch 13:  26%|█████████████████████████████████████████████████████▉                                                                                                                                                           | 55/213 [03:34<10:46,  4.09s/it, loss=0.019, lr=3.19e-5, step=2822]\\u001b[A\\n\",\n      \"Epoch 13:  26%|█████████████████████████████████████████████████████▍                                                                                                                                                         | 55/213 [03:34<10:46,  4.09s/it, loss=0.00743, lr=3.19e-5, step=2823]\\u001b[A\\n\",\n      \"Epoch 13:  26%|██████████████████████████████████████████████████████▍                                                                                                                                                        | 56/213 [03:39<11:20,  4.33s/it, loss=0.00743, lr=3.19e-5, step=2823]\\u001b[A\\n\",\n      \"Epoch 13:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [03:39<11:20,  4.33s/it, loss=0.0124, lr=3.18e-5, step=2824]\\u001b[A\\n\",\n      \"Epoch 13:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [03:43<11:14,  4.33s/it, loss=0.0124, lr=3.18e-5, step=2824]\\u001b[A\\n\",\n      \"Epoch 13:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [03:43<11:14,  4.33s/it, loss=0.0217, lr=3.18e-5, step=2825]\\u001b[A\\n\",\n      \"Epoch 13:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [03:46<10:10,  3.94s/it, loss=0.0217, lr=3.18e-5, step=2825]\\u001b[A\\n\",\n      \"Epoch 13:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [03:46<10:10,  3.94s/it, loss=0.0151, lr=3.18e-5, step=2826]\\u001b[A\\n\",\n      \"Epoch 13:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [03:49<09:14,  3.60s/it, loss=0.0151, lr=3.18e-5, step=2826]\\u001b[A\\n\",\n      \"Epoch 13:  28%|█████████████████████████████████████████████████████████▉                                                                                                                                                       | 59/213 [03:49<09:14,  3.60s/it, loss=0.027, lr=3.17e-5, step=2827]\\u001b[A\\n\",\n      \"Epoch 13:  28%|██████████████████████████████████████████████████████████▊                                                                                                                                                      | 60/213 [03:53<09:20,  3.66s/it, loss=0.027, lr=3.17e-5, step=2827]\\u001b[A\\n\",\n      \"Epoch 13:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [03:53<09:20,  3.66s/it, loss=0.0135, lr=3.17e-5, step=2828]\\u001b[A\\n\",\n      \"Epoch 13:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [03:57<09:50,  3.88s/it, loss=0.0135, lr=3.17e-5, step=2828]\\u001b[A\\n\",\n      \"Epoch 13:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [03:57<09:50,  3.88s/it, loss=0.0127, lr=3.16e-5, step=2829]\\u001b[A\\n\",\n      \"Epoch 13:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [04:01<10:15,  4.08s/it, loss=0.0127, lr=3.16e-5, step=2829]\\u001b[A\\n\",\n      \"Epoch 13:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [04:02<10:15,  4.08s/it, loss=0.0102, lr=3.16e-5, step=2830]\\u001b[A\\n\",\n      \"Epoch 13:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [04:05<09:59,  3.99s/it, loss=0.0102, lr=3.16e-5, step=2830]\\u001b[A\\n\",\n      \"Epoch 13:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [04:05<09:59,  3.99s/it, loss=0.0212, lr=3.16e-5, step=2831]\\u001b[A\\n\",\n      \"Epoch 13:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [04:09<09:28,  3.82s/it, loss=0.0212, lr=3.16e-5, step=2831]\\u001b[A\\n\",\n      \"Epoch 13:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [04:09<09:28,  3.82s/it, loss=0.0128, lr=3.15e-5, step=2832]\\u001b[A\\n\",\n      \"Epoch 13:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [04:13<09:46,  3.96s/it, loss=0.0128, lr=3.15e-5, step=2832]\\u001b[A\\n\",\n      \"Epoch 13:  31%|███████████████████████████████████████████████████████████████▏                                                                                                                                               | 65/213 [04:13<09:46,  3.96s/it, loss=0.00924, lr=3.15e-5, step=2833]\\u001b[A\\n\",\n      \"Epoch 13:  31%|████████████████████████████████████████████████████████████████▏                                                                                                                                              | 66/213 [04:17<09:33,  3.90s/it, loss=0.00924, lr=3.15e-5, step=2833]\\u001b[A\\n\",\n      \"Epoch 13:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [04:17<09:33,  3.90s/it, loss=0.0197, lr=3.14e-5, step=2834]\\u001b[A\\n\",\n      \"Epoch 13:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [04:21<09:28,  3.89s/it, loss=0.0197, lr=3.14e-5, step=2834]\\u001b[A\\n\",\n      \"Epoch 13:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [04:21<09:28,  3.89s/it, loss=0.0154, lr=3.14e-5, step=2835]\\u001b[A\\n\",\n      \"Epoch 13:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [04:25<09:45,  4.04s/it, loss=0.0154, lr=3.14e-5, step=2835]\\u001b[A\\n\",\n      \"Epoch 13:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [04:25<09:45,  4.04s/it, loss=0.0131, lr=3.14e-5, step=2836]\\u001b[A\\n\",\n      \"Epoch 13:  32%|███████████████████████████████████████████████████████████████████▍                                                                                                                                            | 69/213 [04:30<10:13,  4.26s/it, loss=0.0131, lr=3.14e-5, step=2836]\\u001b[A\\n\",\n      \"Epoch 13:  32%|███████████████████████████████████████████████████████████████████▍                                                                                                                                            | 69/213 [04:30<10:13,  4.26s/it, loss=0.0124, lr=3.13e-5, step=2837]\\u001b[A\\n\",\n      \"Epoch 13:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [04:34<10:14,  4.30s/it, loss=0.0124, lr=3.13e-5, step=2837]\\u001b[A\\n\",\n      \"Epoch 13:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [04:34<10:14,  4.30s/it, loss=0.0198, lr=3.13e-5, step=2838]\\u001b[A\\n\",\n      \"Epoch 13:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [04:37<09:28,  4.00s/it, loss=0.0198, lr=3.13e-5, step=2838]\\u001b[A\\n\",\n      \"Epoch 13:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [04:38<09:28,  4.00s/it, loss=0.0132, lr=3.13e-5, step=2839]\\u001b[A\\n\",\n      \"Epoch 13:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [04:42<09:34,  4.07s/it, loss=0.0132, lr=3.13e-5, step=2839]\\u001b[A\\n\",\n      \"Epoch 13:  34%|█████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 72/213 [04:42<09:34,  4.07s/it, loss=0.00647, lr=3.12e-5, step=2840]\\u001b[A\\n\",\n      \"Epoch 13:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [04:45<09:09,  3.93s/it, loss=0.00647, lr=3.12e-5, step=2840]\\u001b[A\\n\",\n      \"Epoch 13:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 73/213 [04:45<09:09,  3.93s/it, loss=0.0126, lr=3.12e-5, step=2841]\\u001b[A\\n\",\n      \"Epoch 13:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [04:49<09:03,  3.91s/it, loss=0.0126, lr=3.12e-5, step=2841]\\u001b[A\\n\",\n      \"Epoch 13:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [04:49<09:03,  3.91s/it, loss=0.0162, lr=3.11e-5, step=2842]\\u001b[A\\n\",\n      \"Epoch 13:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [04:53<09:16,  4.03s/it, loss=0.0162, lr=3.11e-5, step=2842]\\u001b[A\\n\",\n      \"Epoch 13:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [04:54<09:16,  4.03s/it, loss=0.0374, lr=3.11e-5, step=2843]\\u001b[A\\n\",\n      \"Epoch 13:  36%|██████████████████████████████████████████████████████████████████████████▏                                                                                                                                     | 76/213 [04:58<09:27,  4.14s/it, loss=0.0374, lr=3.11e-5, step=2843]\\u001b[A\\n\",\n      \"Epoch 13:  36%|██████████████████████████████████████████████████████████████████████████▏                                                                                                                                     | 76/213 [04:58<09:27,  4.14s/it, loss=0.0124, lr=3.11e-5, step=2844]\\u001b[A\\n\",\n      \"Epoch 13:  36%|███████████████████████████████████████████████████████████████████████████▏                                                                                                                                    | 77/213 [05:02<09:36,  4.24s/it, loss=0.0124, lr=3.11e-5, step=2844]\\u001b[A\\n\",\n      \"Epoch 13:  36%|███████████████████████████████████████████████████████████████████████████▌                                                                                                                                     | 77/213 [05:02<09:36,  4.24s/it, loss=0.0172, lr=3.1e-5, step=2845]\\u001b[A\\n\",\n      \"Epoch 13:  37%|████████████████████████████████████████████████████████████████████████████▌                                                                                                                                    | 78/213 [05:07<09:30,  4.22s/it, loss=0.0172, lr=3.1e-5, step=2845]\\u001b[A\\n\",\n      \"Epoch 13:  37%|████████████████████████████████████████████████████████████████████████████▏                                                                                                                                   | 78/213 [05:07<09:30,  4.22s/it, loss=0.00818, lr=3.1e-5, step=2846]\\u001b[A\\n\",\n      \"Epoch 13:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [05:10<09:07,  4.09s/it, loss=0.00818, lr=3.1e-5, step=2846]\\u001b[A\\n\",\n      \"Epoch 13:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [05:10<09:07,  4.09s/it, loss=0.00899, lr=3.09e-5, step=2847]\\u001b[A\\n\",\n      \"Epoch 13:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [05:15<09:17,  4.19s/it, loss=0.00899, lr=3.09e-5, step=2847]\\u001b[A\\n\",\n      \"Epoch 13:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [05:15<09:17,  4.19s/it, loss=0.00862, lr=3.09e-5, step=2848]\\u001b[A\\n\",\n      \"Epoch 13:  38%|██████████████████████████████████████████████████████████████████████████████▋                                                                                                                                | 81/213 [05:19<09:26,  4.29s/it, loss=0.00862, lr=3.09e-5, step=2848]\\u001b[A\\n\",\n      \"Epoch 13:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [05:19<09:26,  4.29s/it, loss=0.014, lr=3.09e-5, step=2849]\\u001b[A\\n\",\n      \"Epoch 13:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [05:24<09:25,  4.32s/it, loss=0.014, lr=3.09e-5, step=2849]\\u001b[A\\n\",\n      \"Epoch 13:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [05:24<09:25,  4.32s/it, loss=0.0286, lr=3.08e-5, step=2850]\\u001b[A\\n\",\n      \"Epoch 13:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [05:28<09:34,  4.42s/it, loss=0.0286, lr=3.08e-5, step=2850]\\u001b[A\\n\",\n      \"Epoch 13:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [05:28<09:34,  4.42s/it, loss=0.0332, lr=3.08e-5, step=2851]\\u001b[A\\n\",\n      \"Epoch 13:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [05:32<09:13,  4.29s/it, loss=0.0332, lr=3.08e-5, step=2851]\\u001b[A\\n\",\n      \"Epoch 13:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [05:32<09:13,  4.29s/it, loss=0.0416, lr=3.08e-5, step=2852]\\u001b[A\\n\",\n      \"Epoch 13:  40%|███████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 85/213 [05:36<08:56,  4.19s/it, loss=0.0416, lr=3.08e-5, step=2852]\\u001b[A\\n\",\n      \"Epoch 13:  40%|██████████████████████████████████████████████████████████████████████████████████▌                                                                                                                            | 85/213 [05:36<08:56,  4.19s/it, loss=0.00721, lr=3.07e-5, step=2853]\\u001b[A\\n\",\n      \"Epoch 13:  40%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                                                           | 86/213 [05:41<09:15,  4.37s/it, loss=0.00721, lr=3.07e-5, step=2853]\\u001b[A\\n\",\n      \"Epoch 13:  40%|███████████████████████████████████████████████████████████████████████████████████▉                                                                                                                            | 86/213 [05:41<09:15,  4.37s/it, loss=0.0159, lr=3.07e-5, step=2854]\\u001b[A\\n\",\n      \"Epoch 13:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [05:45<09:10,  4.37s/it, loss=0.0159, lr=3.07e-5, step=2854]\\u001b[A\\n\",\n      \"Epoch 13:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [05:45<09:10,  4.37s/it, loss=0.021, lr=3.06e-5, step=2855]\\u001b[A\\n\",\n      \"Epoch 13:  41%|██████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                          | 88/213 [05:50<09:17,  4.46s/it, loss=0.021, lr=3.06e-5, step=2855]\\u001b[A\\n\",\n      \"Epoch 13:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [05:50<09:17,  4.46s/it, loss=0.0113, lr=3.06e-5, step=2856]\\u001b[A\\n\",\n      \"Epoch 13:  42%|██████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                         | 89/213 [05:54<08:47,  4.26s/it, loss=0.0113, lr=3.06e-5, step=2856]\\u001b[A\\n\",\n      \"Epoch 13:  42%|██████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                        | 89/213 [05:54<08:47,  4.26s/it, loss=0.00844, lr=3.06e-5, step=2857]\\u001b[A\\n\",\n      \"Epoch 13:  42%|███████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                       | 90/213 [05:58<08:40,  4.23s/it, loss=0.00844, lr=3.06e-5, step=2857]\\u001b[A\\n\",\n      \"Epoch 13:  42%|███████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                        | 90/213 [05:58<08:40,  4.23s/it, loss=0.0112, lr=3.05e-5, step=2858]\\u001b[A\\n\",\n      \"Epoch 13:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [06:02<08:35,  4.23s/it, loss=0.0112, lr=3.05e-5, step=2858]\\u001b[A\\n\",\n      \"Epoch 13:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [06:02<08:35,  4.23s/it, loss=0.0183, lr=3.05e-5, step=2859]\\u001b[A\\n\",\n      \"Epoch 13:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [06:07<08:37,  4.27s/it, loss=0.0183, lr=3.05e-5, step=2859]\\u001b[A\\n\",\n      \"Epoch 13:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [06:07<08:37,  4.27s/it, loss=0.0202, lr=3.04e-5, step=2860]\\u001b[A\\n\",\n      \"Epoch 13:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [06:10<07:45,  3.88s/it, loss=0.0202, lr=3.04e-5, step=2860]\\u001b[A\\n\",\n      \"Epoch 13:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [06:10<07:45,  3.88s/it, loss=0.0195, lr=3.04e-5, step=2861]\\u001b[A\\n\",\n      \"Epoch 13:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [06:13<07:28,  3.77s/it, loss=0.0195, lr=3.04e-5, step=2861]\\u001b[A\\n\",\n      \"Epoch 13:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [06:13<07:28,  3.77s/it, loss=0.036, lr=3.04e-5, step=2862]\\u001b[A\\n\",\n      \"Epoch 13:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [06:17<07:34,  3.85s/it, loss=0.036, lr=3.04e-5, step=2862]\\u001b[A\\n\",\n      \"Epoch 13:  45%|████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                   | 95/213 [06:17<07:34,  3.85s/it, loss=0.0128, lr=3.03e-5, step=2863]\\u001b[A\\n\",\n      \"Epoch 13:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [06:21<07:34,  3.88s/it, loss=0.0128, lr=3.03e-5, step=2863]\\u001b[A\\n\",\n      \"Epoch 13:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [06:21<07:34,  3.88s/it, loss=0.0192, lr=3.03e-5, step=2864]\\u001b[A\\n\",\n      \"Epoch 13:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [06:24<07:12,  3.73s/it, loss=0.0192, lr=3.03e-5, step=2864]\\u001b[A\\n\",\n      \"Epoch 13:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [06:24<07:12,  3.73s/it, loss=0.0133, lr=3.03e-5, step=2865]\\u001b[A\\n\",\n      \"Epoch 13:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [06:28<06:44,  3.52s/it, loss=0.0133, lr=3.03e-5, step=2865]\\u001b[A\\n\",\n      \"Epoch 13:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [06:28<06:44,  3.52s/it, loss=0.0206, lr=3.02e-5, step=2866]\\u001b[A\\n\",\n      \"Epoch 13:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [06:31<06:44,  3.55s/it, loss=0.0206, lr=3.02e-5, step=2866]\\u001b[A\\n\",\n      \"Epoch 13:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [06:31<06:44,  3.55s/it, loss=0.0178, lr=3.02e-5, step=2867]\\u001b[A\\n\",\n      \"Epoch 13:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [06:35<06:47,  3.60s/it, loss=0.0178, lr=3.02e-5, step=2867]\\u001b[A\\n\",\n      \"Epoch 13:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [06:35<06:47,  3.60s/it, loss=0.0105, lr=3.01e-5, step=2868]\\u001b[A\\n\",\n      \"Epoch 13:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [06:38<06:38,  3.56s/it, loss=0.0105, lr=3.01e-5, step=2868]\\u001b[A\\n\",\n      \"Epoch 13:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [06:38<06:38,  3.56s/it, loss=0.0123, lr=3.01e-5, step=2869]\\u001b[A\\n\",\n      \"Epoch 13:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [06:43<07:02,  3.81s/it, loss=0.0123, lr=3.01e-5, step=2869]\\u001b[A\\n\",\n      \"Epoch 13:  48%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                           | 102/213 [06:43<07:02,  3.81s/it, loss=0.00843, lr=3.01e-5, step=2870]\\u001b[A\\n\",\n      \"Epoch 13:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [06:46<06:54,  3.77s/it, loss=0.00843, lr=3.01e-5, step=2870]\\u001b[A\\n\",\n      \"Epoch 13:  48%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                            | 103/213 [06:46<06:54,  3.77s/it, loss=0.00807, lr=3e-5, step=2871]\\u001b[A\\n\",\n      \"Epoch 13:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                           | 104/213 [06:50<06:53,  3.79s/it, loss=0.00807, lr=3e-5, step=2871]\\u001b[A\\n\",\n      \"Epoch 13:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                           | 104/213 [06:50<06:53,  3.79s/it, loss=0.0139, lr=3e-5, step=2872]\\u001b[A\\n\",\n      \"Epoch 13:  49%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 105/213 [06:53<06:30,  3.62s/it, loss=0.0139, lr=3e-5, step=2872]\\u001b[A\\n\",\n      \"Epoch 13:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [06:53<06:30,  3.62s/it, loss=0.0294, lr=2.99e-5, step=2873]\\u001b[A\\n\",\n      \"Epoch 13:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [06:57<06:27,  3.62s/it, loss=0.0294, lr=2.99e-5, step=2873]\\u001b[A\\n\",\n      \"Epoch 13:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [06:57<06:27,  3.62s/it, loss=0.0152, lr=2.99e-5, step=2874]\\u001b[A\\n\",\n      \"Epoch 13:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 107/213 [07:01<06:46,  3.84s/it, loss=0.0152, lr=2.99e-5, step=2874]\\u001b[A\\n\",\n      \"Epoch 13:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 107/213 [07:01<06:46,  3.84s/it, loss=0.00857, lr=2.99e-5, step=2875]\\u001b[A\\n\",\n      \"Epoch 13:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 108/213 [07:05<06:44,  3.86s/it, loss=0.00857, lr=2.99e-5, step=2875]\\u001b[A\\n\",\n      \"Epoch 13:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 108/213 [07:05<06:44,  3.86s/it, loss=0.0412, lr=2.98e-5, step=2876]\\u001b[A\\n\",\n      \"Epoch 13:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [07:09<06:29,  3.74s/it, loss=0.0412, lr=2.98e-5, step=2876]\\u001b[A\\n\",\n      \"Epoch 13:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [07:09<06:29,  3.74s/it, loss=0.0199, lr=2.98e-5, step=2877]\\u001b[A\\n\",\n      \"Epoch 13:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [07:12<06:17,  3.66s/it, loss=0.0199, lr=2.98e-5, step=2877]\\u001b[A\\n\",\n      \"Epoch 13:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [07:12<06:17,  3.66s/it, loss=0.0196, lr=2.98e-5, step=2878]\\u001b[A\\n\",\n      \"Epoch 13:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [07:16<06:04,  3.57s/it, loss=0.0196, lr=2.98e-5, step=2878]\\u001b[A\\n\",\n      \"Epoch 13:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 111/213 [07:16<06:04,  3.57s/it, loss=0.00977, lr=2.97e-5, step=2879]\\u001b[A\\n\",\n      \"Epoch 13:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 112/213 [07:20<06:17,  3.74s/it, loss=0.00977, lr=2.97e-5, step=2879]\\u001b[A\\n\",\n      \"Epoch 13:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [07:20<06:17,  3.74s/it, loss=0.0416, lr=2.97e-5, step=2880]\\u001b[A\\n\",\n      \"Epoch 13:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [07:24<06:17,  3.77s/it, loss=0.0416, lr=2.97e-5, step=2880]\\u001b[A\\n\",\n      \"Epoch 13:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 113/213 [07:24<06:17,  3.77s/it, loss=0.00652, lr=2.96e-5, step=2881]\\u001b[A\\n\",\n      \"Epoch 13:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [07:29<06:51,  4.15s/it, loss=0.00652, lr=2.96e-5, step=2881]\\u001b[A\\n\",\n      \"Epoch 13:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [07:29<06:51,  4.15s/it, loss=0.00847, lr=2.96e-5, step=2882]\\u001b[A\\n\",\n      \"Epoch 13:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                              | 115/213 [07:33<06:51,  4.20s/it, loss=0.00847, lr=2.96e-5, step=2882]\\u001b[A\\n\",\n      \"Epoch 13:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [07:33<06:51,  4.20s/it, loss=0.0155, lr=2.96e-5, step=2883]\\u001b[A\\n\",\n      \"Epoch 13:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [07:37<06:50,  4.23s/it, loss=0.0155, lr=2.96e-5, step=2883]\\u001b[A\\n\",\n      \"Epoch 13:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [07:37<06:50,  4.23s/it, loss=0.0164, lr=2.95e-5, step=2884]\\u001b[A\\n\",\n      \"Epoch 13:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [07:42<06:49,  4.27s/it, loss=0.0164, lr=2.95e-5, step=2884]\\u001b[A\\n\",\n      \"Epoch 13:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [07:42<06:49,  4.27s/it, loss=0.0123, lr=2.95e-5, step=2885]\\u001b[A\\n\",\n      \"Epoch 13:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [07:46<06:55,  4.38s/it, loss=0.0123, lr=2.95e-5, step=2885]\\u001b[A\\n\",\n      \"Epoch 13:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                            | 118/213 [07:46<06:55,  4.38s/it, loss=0.00643, lr=2.94e-5, step=2886]\\u001b[A\\n\",\n      \"Epoch 13:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                           | 119/213 [07:51<06:49,  4.36s/it, loss=0.00643, lr=2.94e-5, step=2886]\\u001b[A\\n\",\n      \"Epoch 13:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [07:51<06:49,  4.36s/it, loss=0.0164, lr=2.94e-5, step=2887]\\u001b[A\\n\",\n      \"Epoch 13:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [07:55<06:38,  4.29s/it, loss=0.0164, lr=2.94e-5, step=2887]\\u001b[A\\n\",\n      \"Epoch 13:  56%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                          | 120/213 [07:55<06:38,  4.29s/it, loss=0.025, lr=2.94e-5, step=2888]\\u001b[A\\n\",\n      \"Epoch 13:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                         | 121/213 [07:59<06:33,  4.27s/it, loss=0.025, lr=2.94e-5, step=2888]\\u001b[A\\n\",\n      \"Epoch 13:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [07:59<06:33,  4.27s/it, loss=0.0149, lr=2.93e-5, step=2889]\\u001b[A\\n\",\n      \"Epoch 13:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [08:02<06:04,  4.00s/it, loss=0.0149, lr=2.93e-5, step=2889]\\u001b[A\\n\",\n      \"Epoch 13:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [08:02<06:04,  4.00s/it, loss=0.0114, lr=2.93e-5, step=2890]\\u001b[A\\n\",\n      \"Epoch 13:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [08:06<06:03,  4.04s/it, loss=0.0114, lr=2.93e-5, step=2890]\\u001b[A\\n\",\n      \"Epoch 13:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [08:06<06:03,  4.04s/it, loss=0.0131, lr=2.93e-5, step=2891]\\u001b[A\\n\",\n      \"Epoch 13:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                      | 124/213 [08:10<05:47,  3.90s/it, loss=0.0131, lr=2.93e-5, step=2891]\\u001b[A\\n\",\n      \"Epoch 13:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                      | 124/213 [08:10<05:47,  3.90s/it, loss=0.00827, lr=2.92e-5, step=2892]\\u001b[A\\n\",\n      \"Epoch 13:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                     | 125/213 [08:14<05:41,  3.89s/it, loss=0.00827, lr=2.92e-5, step=2892]\\u001b[A\\n\",\n      \"Epoch 13:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                     | 125/213 [08:14<05:41,  3.89s/it, loss=0.0376, lr=2.92e-5, step=2893]\\u001b[A\\n\",\n      \"Epoch 13:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [08:18<05:34,  3.85s/it, loss=0.0376, lr=2.92e-5, step=2893]\\u001b[A\\n\",\n      \"Epoch 13:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                    | 126/213 [08:18<05:34,  3.85s/it, loss=0.00917, lr=2.91e-5, step=2894]\\u001b[A\\n\",\n      \"Epoch 13:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [08:21<05:31,  3.85s/it, loss=0.00917, lr=2.91e-5, step=2894]\\u001b[A\\n\",\n      \"Epoch 13:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [08:21<05:31,  3.85s/it, loss=0.00877, lr=2.91e-5, step=2895]\\u001b[A\\n\",\n      \"Epoch 13:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                  | 128/213 [08:26<05:36,  3.96s/it, loss=0.00877, lr=2.91e-5, step=2895]\\u001b[A\\n\",\n      \"Epoch 13:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                  | 128/213 [08:26<05:36,  3.96s/it, loss=0.0304, lr=2.91e-5, step=2896]\\u001b[A\\n\",\n      \"Epoch 13:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [08:30<05:49,  4.16s/it, loss=0.0304, lr=2.91e-5, step=2896]\\u001b[A\\n\",\n      \"Epoch 13:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [08:30<05:49,  4.16s/it, loss=0.0141, lr=2.9e-5, step=2897]\\u001b[A\\n\",\n      \"Epoch 13:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                 | 130/213 [08:34<05:42,  4.13s/it, loss=0.0141, lr=2.9e-5, step=2897]\\u001b[A\\n\",\n      \"Epoch 13:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [08:34<05:42,  4.13s/it, loss=0.00767, lr=2.9e-5, step=2898]\\u001b[A\\n\",\n      \"Epoch 13:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [08:38<05:29,  4.02s/it, loss=0.00767, lr=2.9e-5, step=2898]\\u001b[A\\n\",\n      \"Epoch 13:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                | 131/213 [08:38<05:29,  4.02s/it, loss=0.0159, lr=2.9e-5, step=2899]\\u001b[A\\n\",\n      \"Epoch 13:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                               | 132/213 [08:42<05:25,  4.02s/it, loss=0.0159, lr=2.9e-5, step=2899]\\u001b[A\\n\",\n      \"Epoch 13:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [08:42<05:25,  4.02s/it, loss=0.0131, lr=2.89e-5, step=2900]\\u001b[A\\n\",\n      \"Epoch 13:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [08:45<04:47,  3.60s/it, loss=0.0131, lr=2.89e-5, step=2900]\\u001b[A\\n\",\n      \"Epoch 13:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [08:45<04:47,  3.60s/it, loss=0.0181, lr=2.89e-5, step=2901]\\u001b[A\\n\",\n      \"Epoch 13:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [08:47<04:13,  3.21s/it, loss=0.0181, lr=2.89e-5, step=2901]\\u001b[A\\n\",\n      \"Epoch 13:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [08:47<04:13,  3.21s/it, loss=0.0108, lr=2.88e-5, step=2902]\\u001b[A\\n\",\n      \"Epoch 13:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [08:50<04:12,  3.23s/it, loss=0.0108, lr=2.88e-5, step=2902]\\u001b[A\\n\",\n      \"Epoch 13:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [08:50<04:12,  3.23s/it, loss=0.0752, lr=2.88e-5, step=2903]\\u001b[A\\n\",\n      \"Epoch 13:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                          | 136/213 [08:53<04:02,  3.15s/it, loss=0.0752, lr=2.88e-5, step=2903]\\u001b[A\\n\",\n      \"Epoch 13:  64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                          | 136/213 [08:53<04:02,  3.15s/it, loss=0.00841, lr=2.88e-5, step=2904]\\u001b[A\\n\",\n      \"Epoch 13:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 137/213 [08:56<03:39,  2.88s/it, loss=0.00841, lr=2.88e-5, step=2904]\\u001b[A\\n\",\n      \"Epoch 13:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                         | 137/213 [08:56<03:39,  2.88s/it, loss=0.0147, lr=2.87e-5, step=2905]\\u001b[A\\n\",\n      \"Epoch 13:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [09:00<04:01,  3.22s/it, loss=0.0147, lr=2.87e-5, step=2905]\\u001b[A\\n\",\n      \"Epoch 13:  65%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 138/213 [09:00<04:01,  3.22s/it, loss=0.00545, lr=2.87e-5, step=2906]\\u001b[A\\n\",\n      \"Epoch 13:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                       | 139/213 [09:03<04:11,  3.39s/it, loss=0.00545, lr=2.87e-5, step=2906]\\u001b[A\\n\",\n      \"Epoch 13:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [09:03<04:11,  3.39s/it, loss=0.026, lr=2.87e-5, step=2907]\\u001b[A\\n\",\n      \"Epoch 13:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [09:08<04:27,  3.66s/it, loss=0.026, lr=2.87e-5, step=2907]\\u001b[A\\n\",\n      \"Epoch 13:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [09:08<04:27,  3.66s/it, loss=0.0292, lr=2.86e-5, step=2908]\\u001b[A\\n\",\n      \"Epoch 13:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [09:12<04:42,  3.92s/it, loss=0.0292, lr=2.86e-5, step=2908]\\u001b[A\\n\",\n      \"Epoch 13:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [09:12<04:42,  3.92s/it, loss=0.0388, lr=2.86e-5, step=2909]\\u001b[A\\n\",\n      \"Epoch 13:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [09:17<04:58,  4.21s/it, loss=0.0388, lr=2.86e-5, step=2909]\\u001b[A\\n\",\n      \"Epoch 13:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [09:17<04:58,  4.21s/it, loss=0.0186, lr=2.85e-5, step=2910]\\u001b[A\\n\",\n      \"Epoch 13:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [09:21<04:45,  4.08s/it, loss=0.0186, lr=2.85e-5, step=2910]\\u001b[A\\n\",\n      \"Epoch 13:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [09:21<04:45,  4.08s/it, loss=0.0197, lr=2.85e-5, step=2911]\\u001b[A\\n\",\n      \"Epoch 13:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [09:24<04:20,  3.78s/it, loss=0.0197, lr=2.85e-5, step=2911]\\u001b[A\\n\",\n      \"Epoch 13:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [09:24<04:20,  3.78s/it, loss=0.0154, lr=2.85e-5, step=2912]\\u001b[A\\n\",\n      \"Epoch 13:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [09:28<04:32,  4.01s/it, loss=0.0154, lr=2.85e-5, step=2912]\\u001b[A\\n\",\n      \"Epoch 13:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [09:28<04:32,  4.01s/it, loss=0.0286, lr=2.84e-5, step=2913]\\u001b[A\\n\",\n      \"Epoch 13:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [09:33<04:30,  4.04s/it, loss=0.0286, lr=2.84e-5, step=2913]\\u001b[A\\n\",\n      \"Epoch 13:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [09:33<04:30,  4.04s/it, loss=0.0135, lr=2.84e-5, step=2914]\\u001b[A\\n\",\n      \"Epoch 13:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [09:36<04:15,  3.87s/it, loss=0.0135, lr=2.84e-5, step=2914]\\u001b[A\\n\",\n      \"Epoch 13:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [09:36<04:15,  3.87s/it, loss=0.0174, lr=2.83e-5, step=2915]\\u001b[A\\n\",\n      \"Epoch 13:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [09:40<04:19,  4.00s/it, loss=0.0174, lr=2.83e-5, step=2915]\\u001b[A\\n\",\n      \"Epoch 13:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [09:40<04:19,  4.00s/it, loss=0.0194, lr=2.83e-5, step=2916]\\u001b[A\\n\",\n      \"Epoch 13:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [09:44<04:18,  4.03s/it, loss=0.0194, lr=2.83e-5, step=2916]\\u001b[A\\n\",\n      \"Epoch 13:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [09:44<04:18,  4.03s/it, loss=0.0115, lr=2.83e-5, step=2917]\\u001b[A\\n\",\n      \"Epoch 13:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [09:49<04:20,  4.14s/it, loss=0.0115, lr=2.83e-5, step=2917]\\u001b[A\\n\",\n      \"Epoch 13:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [09:49<04:20,  4.14s/it, loss=0.0145, lr=2.82e-5, step=2918]\\u001b[A\\n\",\n      \"Epoch 13:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [09:53<04:17,  4.15s/it, loss=0.0145, lr=2.82e-5, step=2918]\\u001b[A\\n\",\n      \"Epoch 13:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [09:53<04:17,  4.15s/it, loss=0.0154, lr=2.82e-5, step=2919]\\u001b[A\\n\",\n      \"Epoch 13:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                           | 152/213 [09:58<04:20,  4.27s/it, loss=0.0154, lr=2.82e-5, step=2919]\\u001b[A\\n\",\n      \"Epoch 13:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                           | 152/213 [09:58<04:20,  4.27s/it, loss=0.00813, lr=2.82e-5, step=2920]\\u001b[A\\n\",\n      \"Epoch 13:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 153/213 [10:01<03:59,  3.99s/it, loss=0.00813, lr=2.82e-5, step=2920]\\u001b[A\\n\",\n      \"Epoch 13:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [10:01<03:59,  3.99s/it, loss=0.0162, lr=2.81e-5, step=2921]\\u001b[A\\n\",\n      \"Epoch 13:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [10:04<03:46,  3.85s/it, loss=0.0162, lr=2.81e-5, step=2921]\\u001b[A\\n\",\n      \"Epoch 13:  72%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                         | 154/213 [10:04<03:46,  3.85s/it, loss=0.011, lr=2.81e-5, step=2922]\\u001b[A\\n\",\n      \"Epoch 13:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                        | 155/213 [10:09<03:48,  3.94s/it, loss=0.011, lr=2.81e-5, step=2922]\\u001b[A\\n\",\n      \"Epoch 13:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [10:09<03:48,  3.94s/it, loss=0.00869, lr=2.8e-5, step=2923]\\u001b[A\\n\",\n      \"Epoch 13:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [10:13<03:52,  4.08s/it, loss=0.00869, lr=2.8e-5, step=2923]\\u001b[A\\n\",\n      \"Epoch 13:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                       | 156/213 [10:13<03:52,  4.08s/it, loss=0.0138, lr=2.8e-5, step=2924]\\u001b[A\\n\",\n      \"Epoch 13:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [10:18<03:57,  4.25s/it, loss=0.0138, lr=2.8e-5, step=2924]\\u001b[A\\n\",\n      \"Epoch 13:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [10:18<03:57,  4.25s/it, loss=0.0209, lr=2.8e-5, step=2925]\\u001b[A\\n\",\n      \"Epoch 13:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [10:22<03:50,  4.19s/it, loss=0.0209, lr=2.8e-5, step=2925]\\u001b[A\\n\",\n      \"Epoch 13:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                     | 158/213 [10:22<03:50,  4.19s/it, loss=0.00684, lr=2.79e-5, step=2926]\\u001b[A\\n\",\n      \"Epoch 13:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [10:25<03:37,  4.03s/it, loss=0.00684, lr=2.79e-5, step=2926]\\u001b[A\\n\",\n      \"Epoch 13:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [10:25<03:37,  4.03s/it, loss=0.00843, lr=2.79e-5, step=2927]\\u001b[A\\n\",\n      \"Epoch 13:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [10:30<03:45,  4.25s/it, loss=0.00843, lr=2.79e-5, step=2927]\\u001b[A\\n\",\n      \"Epoch 13:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [10:30<03:45,  4.25s/it, loss=0.00634, lr=2.79e-5, step=2928]\\u001b[A\\n\",\n      \"Epoch 13:  76%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                  | 161/213 [10:33<03:21,  3.88s/it, loss=0.00634, lr=2.79e-5, step=2928]\\u001b[A\\n\",\n      \"Epoch 13:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [10:33<03:21,  3.88s/it, loss=0.0256, lr=2.78e-5, step=2929]\\u001b[A\\n\",\n      \"Epoch 13:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [10:37<03:24,  4.02s/it, loss=0.0256, lr=2.78e-5, step=2929]\\u001b[A\\n\",\n      \"Epoch 13:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [10:37<03:24,  4.02s/it, loss=0.0218, lr=2.78e-5, step=2930]\\u001b[A\\n\",\n      \"Epoch 13:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [10:42<03:23,  4.07s/it, loss=0.0218, lr=2.78e-5, step=2930]\\u001b[A\\n\",\n      \"Epoch 13:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [10:42<03:23,  4.07s/it, loss=0.018, lr=2.77e-5, step=2931]\\u001b[A\\n\",\n      \"Epoch 13:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [10:46<03:18,  4.05s/it, loss=0.018, lr=2.77e-5, step=2931]\\u001b[A\\n\",\n      \"Epoch 13:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [10:46<03:18,  4.05s/it, loss=0.018, lr=2.77e-5, step=2932]\\u001b[A\\n\",\n      \"Epoch 13:  77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                              | 165/213 [10:50<03:16,  4.10s/it, loss=0.018, lr=2.77e-5, step=2932]\\u001b[A\\n\",\n      \"Epoch 13:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [10:50<03:16,  4.10s/it, loss=0.0126, lr=2.77e-5, step=2933]\\u001b[A\\n\",\n      \"Epoch 13:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [10:54<03:12,  4.09s/it, loss=0.0126, lr=2.77e-5, step=2933]\\u001b[A\\n\",\n      \"Epoch 13:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [10:54<03:12,  4.09s/it, loss=0.0175, lr=2.76e-5, step=2934]\\u001b[A\\n\",\n      \"Epoch 13:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [10:58<03:04,  4.01s/it, loss=0.0175, lr=2.76e-5, step=2934]\\u001b[A\\n\",\n      \"Epoch 13:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [10:58<03:04,  4.01s/it, loss=0.0114, lr=2.76e-5, step=2935]\\u001b[A\\n\",\n      \"Epoch 13:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [11:02<03:05,  4.12s/it, loss=0.0114, lr=2.76e-5, step=2935]\\u001b[A\\n\",\n      \"Epoch 13:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [11:02<03:05,  4.12s/it, loss=0.0128, lr=2.76e-5, step=2936]\\u001b[A\\n\",\n      \"Epoch 13:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [11:06<02:55,  3.99s/it, loss=0.0128, lr=2.76e-5, step=2936]\\u001b[A\\n\",\n      \"Epoch 13:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                          | 169/213 [11:06<02:55,  3.99s/it, loss=0.00892, lr=2.75e-5, step=2937]\\u001b[A\\n\",\n      \"Epoch 13:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                         | 170/213 [11:09<02:40,  3.73s/it, loss=0.00892, lr=2.75e-5, step=2937]\\u001b[A\\n\",\n      \"Epoch 13:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [11:09<02:40,  3.73s/it, loss=0.0357, lr=2.75e-5, step=2938]\\u001b[A\\n\",\n      \"Epoch 13:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [11:13<02:42,  3.88s/it, loss=0.0357, lr=2.75e-5, step=2938]\\u001b[A\\n\",\n      \"Epoch 13:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                         | 171/213 [11:13<02:42,  3.88s/it, loss=0.018, lr=2.74e-5, step=2939]\\u001b[A\\n\",\n      \"Epoch 13:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                        | 172/213 [11:17<02:44,  4.01s/it, loss=0.018, lr=2.74e-5, step=2939]\\u001b[A\\n\",\n      \"Epoch 13:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [11:17<02:44,  4.01s/it, loss=0.0238, lr=2.74e-5, step=2940]\\u001b[A\\n\",\n      \"Epoch 13:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [11:21<02:38,  3.97s/it, loss=0.0238, lr=2.74e-5, step=2940]\\u001b[A\\n\",\n      \"Epoch 13:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [11:21<02:38,  3.97s/it, loss=0.0282, lr=2.74e-5, step=2941]\\u001b[A\\n\",\n      \"Epoch 13:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [11:25<02:36,  4.02s/it, loss=0.0282, lr=2.74e-5, step=2941]\\u001b[A\\n\",\n      \"Epoch 13:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [11:25<02:36,  4.02s/it, loss=0.0101, lr=2.73e-5, step=2942]\\u001b[A\\n\",\n      \"Epoch 13:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 175/213 [11:30<02:40,  4.23s/it, loss=0.0101, lr=2.73e-5, step=2942]\\u001b[A\\n\",\n      \"Epoch 13:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                    | 175/213 [11:30<02:40,  4.23s/it, loss=0.00968, lr=2.73e-5, step=2943]\\u001b[A\\n\",\n      \"Epoch 13:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                   | 176/213 [11:34<02:31,  4.10s/it, loss=0.00968, lr=2.73e-5, step=2943]\\u001b[A\\n\",\n      \"Epoch 13:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                    | 176/213 [11:34<02:31,  4.10s/it, loss=0.0105, lr=2.73e-5, step=2944]\\u001b[A\\n\",\n      \"Epoch 13:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                   | 177/213 [11:38<02:22,  3.96s/it, loss=0.0105, lr=2.73e-5, step=2944]\\u001b[A\\n\",\n      \"Epoch 13:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                   | 177/213 [11:38<02:22,  3.96s/it, loss=0.0133, lr=2.72e-5, step=2945]\\u001b[A\\n\",\n      \"Epoch 13:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [11:42<02:27,  4.20s/it, loss=0.0133, lr=2.72e-5, step=2945]\\u001b[A\\n\",\n      \"Epoch 13:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [11:42<02:27,  4.20s/it, loss=0.0252, lr=2.72e-5, step=2946]\\u001b[A\\n\",\n      \"Epoch 13:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [11:47<02:27,  4.34s/it, loss=0.0252, lr=2.72e-5, step=2946]\\u001b[A\\n\",\n      \"Epoch 13:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [11:47<02:27,  4.34s/it, loss=0.0132, lr=2.72e-5, step=2947]\\u001b[A\\n\",\n      \"Epoch 13:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [11:52<02:30,  4.57s/it, loss=0.0132, lr=2.72e-5, step=2947]\\u001b[A\\n\",\n      \"Epoch 13:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                | 180/213 [11:52<02:30,  4.57s/it, loss=0.00968, lr=2.71e-5, step=2948]\\u001b[A\\n\",\n      \"Epoch 13:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                               | 181/213 [11:56<02:23,  4.49s/it, loss=0.00968, lr=2.71e-5, step=2948]\\u001b[A\\n\",\n      \"Epoch 13:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [11:56<02:23,  4.49s/it, loss=0.0228, lr=2.71e-5, step=2949]\\u001b[A\\n\",\n      \"Epoch 13:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [12:00<02:14,  4.33s/it, loss=0.0228, lr=2.71e-5, step=2949]\\u001b[A\\n\",\n      \"Epoch 13:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [12:00<02:14,  4.33s/it, loss=0.00574, lr=2.7e-5, step=2950]\\u001b[A\\n\",\n      \"Epoch 13:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [12:04<02:04,  4.13s/it, loss=0.00574, lr=2.7e-5, step=2950]\\u001b[A\\n\",\n      \"Epoch 13:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                             | 183/213 [12:04<02:04,  4.13s/it, loss=0.0062, lr=2.7e-5, step=2951]\\u001b[A\\n\",\n      \"Epoch 13:  86%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                            | 184/213 [12:07<01:48,  3.75s/it, loss=0.0062, lr=2.7e-5, step=2951]\\u001b[A\\n\",\n      \"Epoch 13:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [12:07<01:48,  3.75s/it, loss=0.00699, lr=2.7e-5, step=2952]\\u001b[A\\n\",\n      \"Epoch 13:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [12:11<01:47,  3.83s/it, loss=0.00699, lr=2.7e-5, step=2952]\\u001b[A\\n\",\n      \"Epoch 13:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [12:11<01:47,  3.83s/it, loss=0.0133, lr=2.69e-5, step=2953]\\u001b[A\\n\",\n      \"Epoch 13:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [12:15<01:48,  4.02s/it, loss=0.0133, lr=2.69e-5, step=2953]\\u001b[A\\n\",\n      \"Epoch 13:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [12:15<01:48,  4.02s/it, loss=0.0131, lr=2.69e-5, step=2954]\\u001b[A\\n\",\n      \"Epoch 13:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [12:23<02:12,  5.09s/it, loss=0.0131, lr=2.69e-5, step=2954]\\u001b[A\\n\",\n      \"Epoch 13:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [12:23<02:12,  5.09s/it, loss=0.0203, lr=2.69e-5, step=2955]\\u001b[A\\n\",\n      \"Epoch 13:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [12:26<01:54,  4.59s/it, loss=0.0203, lr=2.69e-5, step=2955]\\u001b[A\\n\",\n      \"Epoch 13:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [12:26<01:54,  4.59s/it, loss=0.0144, lr=2.68e-5, step=2956]\\u001b[A\\n\",\n      \"Epoch 13:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [12:29<01:38,  4.11s/it, loss=0.0144, lr=2.68e-5, step=2956]\\u001b[A\\n\",\n      \"Epoch 13:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [12:29<01:38,  4.11s/it, loss=0.0167, lr=2.68e-5, step=2957]\\u001b[A\\n\",\n      \"Epoch 13:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [12:33<01:29,  3.91s/it, loss=0.0167, lr=2.68e-5, step=2957]\\u001b[A\\n\",\n      \"Epoch 13:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [12:33<01:29,  3.91s/it, loss=0.0136, lr=2.67e-5, step=2958]\\u001b[A\\n\",\n      \"Epoch 13:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [12:35<01:12,  3.29s/it, loss=0.0136, lr=2.67e-5, step=2958]\\u001b[A\\n\",\n      \"Epoch 13:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [12:35<01:12,  3.29s/it, loss=0.0138, lr=2.67e-5, step=2959]\\u001b[A\\n\",\n      \"Epoch 13:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [12:36<00:57,  2.75s/it, loss=0.0138, lr=2.67e-5, step=2959]\\u001b[A\\n\",\n      \"Epoch 13:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                    | 192/213 [12:36<00:57,  2.75s/it, loss=0.00878, lr=2.67e-5, step=2960]\\u001b[A\\n\",\n      \"Epoch 13:  91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                   | 193/213 [12:38<00:51,  2.55s/it, loss=0.00878, lr=2.67e-5, step=2960]\\u001b[A\\n\",\n      \"Epoch 13:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [12:38<00:51,  2.55s/it, loss=0.0189, lr=2.66e-5, step=2961]\\u001b[A\\n\",\n      \"Epoch 13:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [12:42<00:56,  2.99s/it, loss=0.0189, lr=2.66e-5, step=2961]\\u001b[A\\n\",\n      \"Epoch 13:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [12:42<00:56,  2.99s/it, loss=0.0424, lr=2.66e-5, step=2962]\\u001b[A\\n\",\n      \"Epoch 13:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [12:46<00:59,  3.32s/it, loss=0.0424, lr=2.66e-5, step=2962]\\u001b[A\\n\",\n      \"Epoch 13:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [12:46<00:59,  3.32s/it, loss=0.00765, lr=2.66e-5, step=2963]\\u001b[A\\n\",\n      \"Epoch 13:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 196/213 [12:50<00:57,  3.37s/it, loss=0.00765, lr=2.66e-5, step=2963]\\u001b[A\\n\",\n      \"Epoch 13:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 196/213 [12:50<00:57,  3.37s/it, loss=0.00987, lr=2.65e-5, step=2964]\\u001b[A\\n\",\n      \"Epoch 13:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌               | 197/213 [12:53<00:51,  3.19s/it, loss=0.00987, lr=2.65e-5, step=2964]\\u001b[A\\n\",\n      \"Epoch 13:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [12:53<00:51,  3.19s/it, loss=0.0122, lr=2.65e-5, step=2965]\\u001b[A\\n\",\n      \"Epoch 13:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [12:55<00:42,  2.84s/it, loss=0.0122, lr=2.65e-5, step=2965]\\u001b[A\\n\",\n      \"Epoch 13:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [12:55<00:42,  2.84s/it, loss=0.00962, lr=2.64e-5, step=2966]\\u001b[A\\n\",\n      \"Epoch 13:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [12:56<00:33,  2.37s/it, loss=0.00962, lr=2.64e-5, step=2966]\\u001b[A\\n\",\n      \"Epoch 13:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [12:56<00:33,  2.37s/it, loss=0.00786, lr=2.64e-5, step=2967]\\u001b[A\\n\",\n      \"Epoch 13:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [12:58<00:28,  2.16s/it, loss=0.00786, lr=2.64e-5, step=2967]\\u001b[A\\n\",\n      \"Epoch 13:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [12:58<00:28,  2.16s/it, loss=0.00834, lr=2.64e-5, step=2968]\\u001b[A\\n\",\n      \"Epoch 13:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍           | 201/213 [12:59<00:23,  1.95s/it, loss=0.00834, lr=2.64e-5, step=2968]\\u001b[A\\n\",\n      \"Epoch 13:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [12:59<00:23,  1.95s/it, loss=0.0192, lr=2.63e-5, step=2969]\\u001b[A\\n\",\n      \"Epoch 13:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [13:01<00:21,  1.98s/it, loss=0.0192, lr=2.63e-5, step=2969]\\u001b[A\\n\",\n      \"Epoch 13:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [13:01<00:21,  1.98s/it, loss=0.0315, lr=2.63e-5, step=2970]\\u001b[A\\n\",\n      \"Epoch 13:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [13:03<00:18,  1.81s/it, loss=0.0315, lr=2.63e-5, step=2970]\\u001b[A\\n\",\n      \"Epoch 13:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [13:03<00:18,  1.81s/it, loss=0.0215, lr=2.63e-5, step=2971]\\u001b[A\\n\",\n      \"Epoch 13:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [13:04<00:16,  1.80s/it, loss=0.0215, lr=2.63e-5, step=2971]\\u001b[A\\n\",\n      \"Epoch 13:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [13:04<00:16,  1.80s/it, loss=0.00654, lr=2.62e-5, step=2972]\\u001b[A\\n\",\n      \"Epoch 13:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎       | 205/213 [13:06<00:14,  1.82s/it, loss=0.00654, lr=2.62e-5, step=2972]\\u001b[A\\n\",\n      \"Epoch 13:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [13:06<00:14,  1.82s/it, loss=0.0144, lr=2.62e-5, step=2973]\\u001b[A\\n\",\n      \"Epoch 13:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [13:08<00:12,  1.74s/it, loss=0.0144, lr=2.62e-5, step=2973]\\u001b[A\\n\",\n      \"Epoch 13:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [13:08<00:12,  1.74s/it, loss=0.0164, lr=2.62e-5, step=2974]\\u001b[A\\n\",\n      \"Epoch 13:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [13:10<00:10,  1.80s/it, loss=0.0164, lr=2.62e-5, step=2974]\\u001b[A\\n\",\n      \"Epoch 13:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [13:10<00:10,  1.80s/it, loss=0.0207, lr=2.61e-5, step=2975]\\u001b[A\\n\",\n      \"Epoch 13:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [13:11<00:08,  1.77s/it, loss=0.0207, lr=2.61e-5, step=2975]\\u001b[A\\n\",\n      \"Epoch 13:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [13:11<00:08,  1.77s/it, loss=0.0122, lr=2.61e-5, step=2976]\\u001b[A\\n\",\n      \"Epoch 13:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [13:13<00:06,  1.70s/it, loss=0.0122, lr=2.61e-5, step=2976]\\u001b[A\\n\",\n      \"Epoch 13:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [13:13<00:06,  1.70s/it, loss=0.0165, lr=2.6e-5, step=2977]\\u001b[A\\n\",\n      \"Epoch 13:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [13:15<00:05,  1.72s/it, loss=0.0165, lr=2.6e-5, step=2977]\\u001b[A\\n\",\n      \"Epoch 13:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [13:15<00:05,  1.72s/it, loss=0.00441, lr=2.6e-5, step=2978]\\u001b[A\\n\",\n      \"Epoch 13:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [13:16<00:03,  1.70s/it, loss=0.00441, lr=2.6e-5, step=2978]\\u001b[A\\n\",\n      \"Epoch 13:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [13:16<00:03,  1.70s/it, loss=0.025, lr=2.6e-5, step=2979]\\u001b[A\\n\",\n      \"Epoch 13: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [13:18<00:01,  1.60s/it, loss=0.025, lr=2.6e-5, step=2979]\\u001b[A\\n\",\n      \"Epoch 13: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [13:18<00:01,  1.60s/it, loss=0.00857, lr=2.59e-5, step=2980]\\u001b[A\\n\",\n      \"Epoch 13: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [13:18<00:00,  1.25s/it, loss=0.00857, lr=2.59e-5, step=2980]\\u001b[A\\n\",\n      \"Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [13:18<00:00,  1.25s/it, loss=0.0115, lr=2.59e-5, step=2981]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5d61c21fde6b4caf934ab954765eaf70\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 13: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [18:45<00:00,  5.29s/it, loss=0.0115, lr=2.59e-5, step=2981]\\n\",\n      \"Epoch 14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [04:58<00:00,  1.44it/s, loss=0.00782, lr=1.85e-5, step=3194]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a8ac1a4a617c400aaa715d621169f2a2\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 14: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [09:43<00:00,  2.74s/it, loss=0.00782, lr=1.85e-5, step=3194]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 15:   0%|                                                                                                                                                                                                                                                             | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 15:   0%|█▏                                                                                                                                                                                                                                          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step=3196]\\u001b[A\\n\",\n      \"Epoch 15:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:51,  1.22it/s, loss=0.0106, lr=1.85e-5, step=3196]\\u001b[A\\n\",\n      \"Epoch 15:   1%|██▉                                                                                                                                                                                                             | 3/213 [00:02<02:51,  1.22it/s, loss=0.00883, lr=1.84e-5, step=3197]\\u001b[A\\n\",\n      \"Epoch 15:   2%|███▉                                                                                                                                                                                                            | 4/213 [00:03<02:49,  1.23it/s, loss=0.00883, lr=1.84e-5, step=3197]\\u001b[A\\n\",\n      \"Epoch 15:   2%|███▉                                                                                                                                                                                                              | 4/213 [00:03<02:49,  1.23it/s, loss=0.011, lr=1.84e-5, step=3198]\\u001b[A\\n\",\n      \"Epoch 15:   2%|████▉                                                                                                                                                                                                             | 5/213 [00:04<02:47,  1.24it/s, loss=0.011, lr=1.84e-5, step=3198]\\u001b[A\\n\",\n      \"Epoch 15:   2%|████▉                                                                                                                                                                                                            | 5/213 [00:04<02:47,  1.24it/s, loss=0.0354, lr=1.84e-5, step=3199]\\u001b[A\\n\",\n      \"Epoch 15:   3%|█████▉                                                                                                                                                                                                           | 6/213 [00:04<02:46,  1.24it/s, loss=0.0354, lr=1.84e-5, step=3199]\\u001b[A\\n\",\n      \"Epoch 15:   3%|█████▊                                                                                                                                                                                                          | 6/213 [00:04<02:46,  1.24it/s, loss=0.00365, lr=1.83e-5, step=3200]\\u001b[A\\n\",\n      \"Epoch 15:   3%|██████▊                                                                                                                                                                                                         | 7/213 [00:05<02:45,  1.24it/s, loss=0.00365, lr=1.83e-5, step=3200]\\u001b[A\\n\",\n      \"Epoch 15:   3%|██████▊                                                                                                                                                                                                          | 7/213 [00:05<02:45,  1.24it/s, loss=0.0121, lr=1.83e-5, step=3201]\\u001b[A\\n\",\n      \"Epoch 15:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:44,  1.25it/s, loss=0.0121, lr=1.83e-5, step=3201]\\u001b[A\\n\",\n      \"Epoch 15:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:44,  1.25it/s, loss=0.0171, lr=1.83e-5, step=3202]\\u001b[A\\n\",\n      \"Epoch 15:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:43,  1.25it/s, loss=0.0171, lr=1.83e-5, step=3202]\\u001b[A\\n\",\n      \"Epoch 15:   4%|████████▊                                                                                                                                                                                                       | 9/213 [00:07<02:43,  1.25it/s, loss=0.00746, lr=1.82e-5, step=3203]\\u001b[A\\n\",\n      \"Epoch 15:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:42,  1.25it/s, loss=0.00746, lr=1.82e-5, step=3203]\\u001b[A\\n\",\n      \"Epoch 15:   5%|█████████▊                                                                                                                                                                                                      | 10/213 [00:08<02:42,  1.25it/s, loss=0.0146, lr=1.82e-5, step=3204]\\u001b[A\\n\",\n      \"Epoch 15:   5%|██████████▋                                                                                                                                                                                                     | 11/213 [00:08<02:41,  1.25it/s, loss=0.0146, lr=1.82e-5, step=3204]\\u001b[A\\n\",\n      \"Epoch 15:   5%|██████████▋                                                                                                                                                                                                     | 11/213 [00:08<02:41,  1.25it/s, loss=0.0155, lr=1.82e-5, step=3205]\\u001b[A\\n\",\n      \"Epoch 15:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:40,  1.25it/s, loss=0.0155, lr=1.82e-5, step=3205]\\u001b[A\\n\",\n      \"Epoch 15:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:40,  1.25it/s, loss=0.0229, lr=1.81e-5, step=3206]\\u001b[A\\n\",\n      \"Epoch 15:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:40,  1.25it/s, loss=0.0229, lr=1.81e-5, step=3206]\\u001b[A\\n\",\n      \"Epoch 15:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:40,  1.25it/s, loss=0.0188, lr=1.81e-5, step=3207]\\u001b[A\\n\",\n      \"Epoch 15:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:11<02:39,  1.25it/s, loss=0.0188, lr=1.81e-5, step=3207]\\u001b[A\\n\",\n      \"Epoch 15:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:11<02:39,  1.25it/s, loss=0.0323, lr=1.81e-5, step=3208]\\u001b[A\\n\",\n      \"Epoch 15:   7%|██████████████▋                                                                                                                                                                                                 | 15/213 [00:12<02:38,  1.25it/s, loss=0.0323, lr=1.81e-5, step=3208]\\u001b[A\\n\",\n      \"Epoch 15:   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                                                                                                                                                                  | 17/213 [00:13<02:36,  1.25it/s, loss=0.0111, lr=1.8e-5, step=3210]\\u001b[A\\n\",\n      \"Epoch 15:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:36,  1.25it/s, loss=0.0431, lr=1.8e-5, step=3211]\\u001b[A\\n\",\n      \"Epoch 15:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:35,  1.25it/s, loss=0.0431, lr=1.8e-5, step=3211]\\u001b[A\\n\",\n      \"Epoch 15:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [00:14<02:35,  1.25it/s, loss=0.0235, lr=1.79e-5, step=3212]\\u001b[A\\n\",\n      \"Epoch 15:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:35,  1.25it/s, loss=0.0235, lr=1.79e-5, step=3212]\\u001b[A\\n\",\n      \"Epoch 15:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:35,  1.25it/s, loss=0.0129, lr=1.79e-5, step=3213]\\u001b[A\\n\",\n      \"Epoch 15:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:34,  1.25it/s, loss=0.0129, lr=1.79e-5, step=3213]\\u001b[A\\n\",\n      \"Epoch 15:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:34,  1.25it/s, loss=0.0162, lr=1.79e-5, step=3214]\\u001b[A\\n\",\n      \"Epoch 15:  10%|████████████████████▌                                                                                                                                                                                           | 21/213 [00:16<02:33,  1.25it/s, loss=0.0162, lr=1.79e-5, step=3214]\\u001b[A\\n\",\n      \"Epoch 15:  10%|████████████████████▍                                                                                                                                                                                          | 21/213 [00:16<02:33,  1.25it/s, loss=0.00936, lr=1.78e-5, step=3215]\\u001b[A\\n\",\n      \"Epoch 15:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:33,  1.25it/s, loss=0.00936, lr=1.78e-5, step=3215]\\u001b[A\\n\",\n      \"Epoch 15:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:33,  1.25it/s, loss=0.00793, lr=1.78e-5, step=3216]\\u001b[A\\n\",\n      \"Epoch 15:  11%|██████████████████████▎                                                                                                                                                                                        | 23/213 [00:18<02:32,  1.25it/s, loss=0.00793, lr=1.78e-5, step=3216]\\u001b[A\\n\",\n      \"Epoch 15:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [00:18<02:32,  1.25it/s, loss=0.0302, lr=1.78e-5, step=3217]\\u001b[A\\n\",\n      \"Epoch 15:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.25it/s, loss=0.0302, lr=1.78e-5, step=3217]\\u001b[A\\n\",\n      \"Epoch 15:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.25it/s, loss=0.0127, lr=1.78e-5, step=3218]\\u001b[A\\n\",\n      \"Epoch 15:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0127, lr=1.78e-5, step=3218]\\u001b[A\\n\",\n      \"Epoch 15:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0188, lr=1.77e-5, step=3219]\\u001b[A\\n\",\n      \"Epoch 15:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [00:20<02:29,  1.25it/s, loss=0.0188, lr=1.77e-5, step=3219]\\u001b[A\\n\",\n      \"Epoch 15:  12%|█████████████████████████▌                                                                                                                                                                                       | 26/213 [00:20<02:29,  1.25it/s, loss=0.007, lr=1.77e-5, step=3220]\\u001b[A\\n\",\n      \"Epoch 15:  13%|██████████████████████████▍                                                                                                                                                                                      | 27/213 [00:21<02:28,  1.25it/s, loss=0.007, lr=1.77e-5, step=3220]\\u001b[A\\n\",\n      \"Epoch 15:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [00:21<02:28,  1.25it/s, loss=0.0074, lr=1.77e-5, step=3221]\\u001b[A\\n\",\n      \"Epoch 15:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:28,  1.25it/s, loss=0.0074, lr=1.77e-5, step=3221]\\u001b[A\\n\",\n      \"Epoch 15:  13%|███████████████████████████▍                                                                                                                                                                                     | 28/213 [00:22<02:28,  1.25it/s, loss=0.026, lr=1.76e-5, step=3222]\\u001b[A\\n\",\n      \"Epoch 15:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:27,  1.25it/s, loss=0.026, lr=1.76e-5, step=3222]\\u001b[A\\n\",\n      \"Epoch 15:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [00:23<02:27,  1.25it/s, loss=0.0137, lr=1.76e-5, step=3223]\\u001b[A\\n\",\n      \"Epoch 15:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:26,  1.25it/s, loss=0.0137, lr=1.76e-5, step=3223]\\u001b[A\\n\",\n      \"Epoch 15:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:26,  1.25it/s, loss=0.0259, lr=1.76e-5, step=3224]\\u001b[A\\n\",\n      \"Epoch 15:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:24<02:25,  1.25it/s, loss=0.0259, lr=1.76e-5, step=3224]\\u001b[A\\n\",\n      \"Epoch 15:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:24<02:25,  1.25it/s, loss=0.0158, lr=1.75e-5, step=3225]\\u001b[A\\n\",\n      \"Epoch 15:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:25<02:24,  1.25it/s, loss=0.0158, lr=1.75e-5, step=3225]\\u001b[A\\n\",\n      \"Epoch 15:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:25<02:24,  1.25it/s, loss=0.0121, lr=1.75e-5, step=3226]\\u001b[A\\n\",\n      \"Epoch 15:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:26<02:24,  1.25it/s, loss=0.0121, lr=1.75e-5, step=3226]\\u001b[A\\n\",\n      \"Epoch 15:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:26<02:24,  1.25it/s, loss=0.0112, lr=1.75e-5, step=3227]\\u001b[A\\n\",\n      \"Epoch 15:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:23,  1.25it/s, loss=0.0112, lr=1.75e-5, step=3227]\\u001b[A\\n\",\n      \"Epoch 15:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:23,  1.25it/s, loss=0.0104, lr=1.74e-5, step=3228]\\u001b[A\\n\",\n      \"Epoch 15:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:22,  1.25it/s, loss=0.0104, lr=1.74e-5, step=3228]\\u001b[A\\n\",\n      \"Epoch 15:  16%|██████████████████████████████████▎                                                                                                                                                                              | 35/213 [00:28<02:22,  1.25it/s, loss=0.015, lr=1.74e-5, step=3229]\\u001b[A\\n\",\n      \"Epoch 15:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:28<02:21,  1.25it/s, loss=0.015, lr=1.74e-5, step=3229]\\u001b[A\\n\",\n      \"Epoch 15:  17%|██████████████████████████████████▉                                                                                                                                                                            | 36/213 [00:28<02:21,  1.25it/s, loss=0.00935, lr=1.74e-5, step=3230]\\u001b[A\\n\",\n      \"Epoch 15:  17%|███████████████████████████████████▉                                                                                                                                                                           | 37/213 [00:29<02:21,  1.25it/s, loss=0.00935, lr=1.74e-5, step=3230]\\u001b[A\\n\",\n      \"Epoch 15:  17%|████████████████████████████████████▏                                                                                                                                                                           | 37/213 [00:29<02:21,  1.25it/s, loss=0.0257, lr=1.73e-5, step=3231]\\u001b[A\\n\",\n      \"Epoch 15:  18%|█████████████████████████████████████                                                                                                                                                                           | 38/213 [00:30<02:20,  1.25it/s, loss=0.0257, lr=1.73e-5, step=3231]\\u001b[A\\n\",\n      \"Epoch 15:  18%|████████████████████████████████████▉                                                                                                                                                                          | 38/213 [00:30<02:20,  1.25it/s, loss=0.00861, lr=1.73e-5, step=3232]\\u001b[A\\n\",\n      \"Epoch 15:  18%|█████████████████████████████████████▉                                                                                                                                                                         | 39/213 [00:31<02:19,  1.25it/s, loss=0.00861, lr=1.73e-5, step=3232]\\u001b[A\\n\",\n      \"Epoch 15:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [00:31<02:19,  1.25it/s, loss=0.0454, lr=1.73e-5, step=3233]\\u001b[A\\n\",\n      \"Epoch 15:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0454, lr=1.73e-5, step=3233]\\u001b[A\\n\",\n      \"Epoch 15:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0175, lr=1.72e-5, step=3234]\\u001b[A\\n\",\n      \"Epoch 15:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.0175, lr=1.72e-5, step=3234]\\u001b[A\\n\",\n      \"Epoch 15:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.0106, lr=1.72e-5, step=3235]\\u001b[A\\n\",\n      \"Epoch 15:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.24it/s, loss=0.0106, lr=1.72e-5, step=3235]\\u001b[A\\n\",\n      \"Epoch 15:  20%|████████████████████████████████████████▊                                                                                                                                                                      | 42/213 [00:33<02:17,  1.24it/s, loss=0.00497, lr=1.72e-5, step=3236]\\u001b[A\\n\",\n      \"Epoch 15:  20%|█████████████████████████████████████████▊                                                                                                                                                                     | 43/213 [00:34<02:16,  1.24it/s, loss=0.00497, lr=1.72e-5, step=3236]\\u001b[A\\n\",\n      \"Epoch 15:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.24it/s, loss=0.0348, lr=1.71e-5, step=3237]\\u001b[A\\n\",\n      \"Epoch 15:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.24it/s, loss=0.0348, lr=1.71e-5, step=3237]\\u001b[A\\n\",\n      \"Epoch 15:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.24it/s, loss=0.0109, lr=1.71e-5, step=3238]\\u001b[A\\n\",\n      \"Epoch 15:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:15,  1.24it/s, loss=0.0109, lr=1.71e-5, step=3238]\\u001b[A\\n\",\n      \"Epoch 15:  21%|████████████████████████████████████████████▏                                                                                                                                                                    | 45/213 [00:36<02:15,  1.24it/s, loss=0.021, lr=1.71e-5, step=3239]\\u001b[A\\n\",\n      \"Epoch 15:  22%|█████████████████████████████████████████████▏                                                                                                                                                                   | 46/213 [00:36<02:14,  1.24it/s, loss=0.021, lr=1.71e-5, step=3239]\\u001b[A\\n\",\n      \"Epoch 15:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:36<02:14,  1.24it/s, loss=0.0206, lr=1.71e-5, step=3240]\\u001b[A\\n\",\n      \"Epoch 15:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:37<02:13,  1.25it/s, loss=0.0206, lr=1.71e-5, step=3240]\\u001b[A\\n\",\n      \"Epoch 15:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:13,  1.25it/s, loss=0.0119, lr=1.7e-5, step=3241]\\u001b[A\\n\",\n      \"Epoch 15:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0119, lr=1.7e-5, step=3241]\\u001b[A\\n\",\n      \"Epoch 15:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:12,  1.25it/s, loss=0.0117, lr=1.7e-5, step=3242]\\u001b[A\\n\",\n      \"Epoch 15:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0117, lr=1.7e-5, step=3242]\\u001b[A\\n\",\n      \"Epoch 15:  23%|████████████████████████████████████████████████                                                                                                                                                                 | 49/213 [00:39<02:11,  1.24it/s, loss=0.0219, lr=1.7e-5, step=3243]\\u001b[A\\n\",\n      \"Epoch 15:  23%|█████████████████████████████████████████████████                                                                                                                                                                | 50/213 [00:40<02:11,  1.24it/s, loss=0.0219, lr=1.7e-5, step=3243]\\u001b[A\\n\",\n      \"Epoch 15:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:40<02:11,  1.24it/s, loss=0.0195, lr=1.69e-5, step=3244]\\u001b[A\\n\",\n      \"Epoch 15:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:10,  1.25it/s, loss=0.0195, lr=1.69e-5, step=3244]\\u001b[A\\n\",\n      \"Epoch 15:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:10,  1.25it/s, loss=0.0383, lr=1.69e-5, step=3245]\\u001b[A\\n\",\n      \"Epoch 15:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:09,  1.25it/s, loss=0.0383, lr=1.69e-5, step=3245]\\u001b[A\\n\",\n      \"Epoch 15:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:09,  1.25it/s, loss=0.0115, lr=1.69e-5, step=3246]\\u001b[A\\n\",\n      \"Epoch 15:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:07,  1.25it/s, loss=0.0115, lr=1.69e-5, step=3246]\\u001b[A\\n\",\n      \"Epoch 15:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:07,  1.25it/s, loss=0.0209, lr=1.68e-5, step=3247]\\u001b[A\\n\",\n      \"Epoch 15:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.25it/s, loss=0.0209, lr=1.68e-5, step=3247]\\u001b[A\\n\",\n      \"Epoch 15:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.25it/s, loss=0.0145, lr=1.68e-5, step=3248]\\u001b[A\\n\",\n      \"Epoch 15:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.25it/s, loss=0.0145, lr=1.68e-5, step=3248]\\u001b[A\\n\",\n      \"Epoch 15:  26%|█████████████████████████████████████████████████████▍                                                                                                                                                         | 55/213 [00:44<02:06,  1.25it/s, loss=0.00726, lr=1.68e-5, step=3249]\\u001b[A\\n\",\n      \"Epoch 15:  26%|██████████████████████████████████████████████████████▍                                                                                                                                                        | 56/213 [00:44<02:05,  1.25it/s, loss=0.00726, lr=1.68e-5, step=3249]\\u001b[A\\n\",\n      \"Epoch 15:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [00:44<02:05,  1.25it/s, loss=0.0141, lr=1.67e-5, step=3250]\\u001b[A\\n\",\n      \"Epoch 15:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [00:45<02:05,  1.25it/s, loss=0.0141, lr=1.67e-5, step=3250]\\u001b[A\\n\",\n      \"Epoch 15:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [00:45<02:05,  1.25it/s, loss=0.0229, lr=1.67e-5, step=3251]\\u001b[A\\n\",\n      \"Epoch 15:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:46<02:04,  1.25it/s, loss=0.0229, lr=1.67e-5, step=3251]\\u001b[A\\n\",\n      \"Epoch 15:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:46<02:04,  1.25it/s, loss=0.0113, lr=1.67e-5, step=3252]\\u001b[A\\n\",\n      \"Epoch 15:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0113, lr=1.67e-5, step=3252]\\u001b[A\\n\",\n      \"Epoch 15:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0258, lr=1.66e-5, step=3253]\\u001b[A\\n\",\n      \"Epoch 15:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:01,  1.26it/s, loss=0.0258, lr=1.66e-5, step=3253]\\u001b[A\\n\",\n      \"Epoch 15:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:01,  1.26it/s, loss=0.0121, lr=1.66e-5, step=3254]\\u001b[A\\n\",\n      \"Epoch 15:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [00:48<02:01,  1.25it/s, loss=0.0121, lr=1.66e-5, step=3254]\\u001b[A\\n\",\n      \"Epoch 15:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [00:48<02:01,  1.25it/s, loss=0.0135, lr=1.66e-5, step=3255]\\u001b[A\\n\",\n      \"Epoch 15:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:49<02:00,  1.25it/s, loss=0.0135, lr=1.66e-5, step=3255]\\u001b[A\\n\",\n      \"Epoch 15:  29%|████████████████████████████████████████████████████████████▎                                                                                                                                                  | 62/213 [00:49<02:00,  1.25it/s, loss=0.00642, lr=1.66e-5, step=3256]\\u001b[A\\n\",\n      \"Epoch 15:  30%|█████████████████████████████████████████████████████████████▏                                                                                                                                                 | 63/213 [00:50<01:59,  1.25it/s, loss=0.00642, lr=1.66e-5, step=3256]\\u001b[A\\n\",\n      \"Epoch 15:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<01:59,  1.25it/s, loss=0.0186, lr=1.65e-5, step=3257]\\u001b[A\\n\",\n      \"Epoch 15:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:51<01:58,  1.25it/s, loss=0.0186, lr=1.65e-5, step=3257]\\u001b[A\\n\",\n      \"Epoch 15:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:51<01:58,  1.25it/s, loss=0.0105, lr=1.65e-5, step=3258]\\u001b[A\\n\",\n      \"Epoch 15:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [00:52<01:57,  1.26it/s, loss=0.0105, lr=1.65e-5, step=3258]\\u001b[A\\n\",\n      \"Epoch 15:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [00:52<01:57,  1.26it/s, loss=0.0088, lr=1.65e-5, step=3259]\\u001b[A\\n\",\n      \"Epoch 15:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:52<01:57,  1.25it/s, loss=0.0088, lr=1.65e-5, step=3259]\\u001b[A\\n\",\n      \"Epoch 15:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:52<01:57,  1.25it/s, loss=0.0162, lr=1.64e-5, step=3260]\\u001b[A\\n\",\n      \"Epoch 15:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:53<01:56,  1.25it/s, loss=0.0162, lr=1.64e-5, step=3260]\\u001b[A\\n\",\n      \"Epoch 15:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:53<01:56,  1.25it/s, loss=0.0172, lr=1.64e-5, step=3261]\\u001b[A\\n\",\n      \"Epoch 15:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [00:54<01:55,  1.25it/s, loss=0.0172, lr=1.64e-5, step=3261]\\u001b[A\\n\",\n      \"Epoch 15:  32%|██████████████████████████████████████████████████████████████████                                                                                                                                             | 68/213 [00:54<01:55,  1.25it/s, loss=0.00919, lr=1.64e-5, step=3262]\\u001b[A\\n\",\n      \"Epoch 15:  32%|███████████████████████████████████████████████████████████████████                                                                                                                                            | 69/213 [00:55<01:55,  1.25it/s, loss=0.00919, lr=1.64e-5, step=3262]\\u001b[A\\n\",\n      \"Epoch 15:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:55,  1.25it/s, loss=0.013, lr=1.63e-5, step=3263]\\u001b[A\\n\",\n      \"Epoch 15:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.013, lr=1.63e-5, step=3263]\\u001b[A\\n\",\n      \"Epoch 15:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [00:56<01:54,  1.25it/s, loss=0.0219, lr=1.63e-5, step=3264]\\u001b[A\\n\",\n      \"Epoch 15:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:56<01:53,  1.25it/s, loss=0.0219, lr=1.63e-5, step=3264]\\u001b[A\\n\",\n      \"Epoch 15:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:56<01:53,  1.25it/s, loss=0.0131, lr=1.63e-5, step=3265]\\u001b[A\\n\",\n      \"Epoch 15:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.0131, lr=1.63e-5, step=3265]\\u001b[A\\n\",\n      \"Epoch 15:  34%|█████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.00719, lr=1.62e-5, step=3266]\\u001b[A\\n\",\n      \"Epoch 15:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [00:58<01:52,  1.25it/s, loss=0.00719, lr=1.62e-5, step=3266]\\u001b[A\\n\",\n      \"Epoch 15:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [00:58<01:52,  1.25it/s, loss=0.00989, lr=1.62e-5, step=3267]\\u001b[A\\n\",\n      \"Epoch 15:  35%|███████████████████████████████████████████████████████████████████████▉                                                                                                                                       | 74/213 [00:59<01:51,  1.25it/s, loss=0.00989, lr=1.62e-5, step=3267]\\u001b[A\\n\",\n      \"Epoch 15:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [00:59<01:51,  1.25it/s, loss=0.0171, lr=1.62e-5, step=3268]\\u001b[A\\n\",\n      \"Epoch 15:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:00<01:50,  1.25it/s, loss=0.0171, lr=1.62e-5, step=3268]\\u001b[A\\n\",\n      \"Epoch 15:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:00<01:50,  1.25it/s, loss=0.0391, lr=1.62e-5, step=3269]\\u001b[A\\n\",\n      \"Epoch 15:  36%|██████████████████████████████████████████████████████████████████████████▏                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.0391, lr=1.62e-5, step=3269]\\u001b[A\\n\",\n      \"Epoch 15:  36%|█████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.00855, lr=1.61e-5, step=3270]\\u001b[A\\n\",\n      \"Epoch 15:  36%|██████████████████████████████████████████████████████████████████████████▊                                                                                                                                    | 77/213 [01:01<01:49,  1.25it/s, loss=0.00855, lr=1.61e-5, step=3270]\\u001b[A\\n\",\n      \"Epoch 15:  36%|███████████████████████████████████████████████████████████████████████████▏                                                                                                                                    | 77/213 [01:01<01:49,  1.25it/s, loss=0.0175, lr=1.61e-5, step=3271]\\u001b[A\\n\",\n      \"Epoch 15:  37%|████████████████████████████████████████████████████████████████████████████▏                                                                                                                                   | 78/213 [01:02<01:48,  1.25it/s, loss=0.0175, lr=1.61e-5, step=3271]\\u001b[A\\n\",\n      \"Epoch 15:  37%|███████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 78/213 [01:02<01:48,  1.25it/s, loss=0.00927, lr=1.61e-5, step=3272]\\u001b[A\\n\",\n      \"Epoch 15:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [01:03<01:47,  1.25it/s, loss=0.00927, lr=1.61e-5, step=3272]\\u001b[A\\n\",\n      \"Epoch 15:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [01:03<01:47,  1.25it/s, loss=0.00767, lr=1.6e-5, step=3273]\\u001b[A\\n\",\n      \"Epoch 15:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:46,  1.25it/s, loss=0.00767, lr=1.6e-5, step=3273]\\u001b[A\\n\",\n      \"Epoch 15:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:46,  1.25it/s, loss=0.00901, lr=1.6e-5, step=3274]\\u001b[A\\n\",\n      \"Epoch 15:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:04<01:45,  1.25it/s, loss=0.00901, lr=1.6e-5, step=3274]\\u001b[A\\n\",\n      \"Epoch 15:  38%|███████████████████████████████████████████████████████████████████████████████▍                                                                                                                                 | 81/213 [01:04<01:45,  1.25it/s, loss=0.0173, lr=1.6e-5, step=3275]\\u001b[A\\n\",\n      \"Epoch 15:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.25it/s, loss=0.0173, lr=1.6e-5, step=3275]\\u001b[A\\n\",\n      \"Epoch 15:  38%|████████████████████████████████████████████████████████████████████████████████▍                                                                                                                                | 82/213 [01:05<01:45,  1.25it/s, loss=0.033, lr=1.59e-5, step=3276]\\u001b[A\\n\",\n      \"Epoch 15:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:44,  1.24it/s, loss=0.033, lr=1.59e-5, step=3276]\\u001b[A\\n\",\n      \"Epoch 15:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [01:06<01:44,  1.24it/s, loss=0.0358, lr=1.59e-5, step=3277]\\u001b[A\\n\",\n      \"Epoch 15:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [01:07<01:43,  1.24it/s, loss=0.0358, lr=1.59e-5, step=3277]\\u001b[A\\n\",\n      \"Epoch 15:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [01:07<01:43,  1.24it/s, loss=0.0454, lr=1.59e-5, step=3278]\\u001b[A\\n\",\n      \"Epoch 15:  40%|███████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 85/213 [01:08<01:43,  1.24it/s, loss=0.0454, lr=1.59e-5, step=3278]\\u001b[A\\n\",\n      \"Epoch 15:  40%|██████████████████████████████████████████████████████████████████████████████████▌                                                                                                                            | 85/213 [01:08<01:43,  1.24it/s, loss=0.00751, lr=1.58e-5, step=3279]\\u001b[A\\n\",\n      \"Epoch 15:  40%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                                                           | 86/213 [01:08<01:42,  1.24it/s, loss=0.00751, lr=1.58e-5, step=3279]\\u001b[A\\n\",\n      \"Epoch 15:  40%|███████████████████████████████████████████████████████████████████████████████████▉                                                                                                                            | 86/213 [01:09<01:42,  1.24it/s, loss=0.0193, lr=1.58e-5, step=3280]\\u001b[A\\n\",\n      \"Epoch 15:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [01:09<01:41,  1.24it/s, loss=0.0193, lr=1.58e-5, step=3280]\\u001b[A\\n\",\n      \"Epoch 15:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [01:09<01:41,  1.24it/s, loss=0.0177, lr=1.58e-5, step=3281]\\u001b[A\\n\",\n      \"Epoch 15:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:10<01:40,  1.24it/s, loss=0.0177, lr=1.58e-5, step=3281]\\u001b[A\\n\",\n      \"Epoch 15:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:10<01:40,  1.24it/s, loss=0.0127, lr=1.58e-5, step=3282]\\u001b[A\\n\",\n      \"Epoch 15:  42%|██████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                         | 89/213 [01:11<01:39,  1.24it/s, loss=0.0127, lr=1.58e-5, step=3282]\\u001b[A\\n\",\n      \"Epoch 15:  42%|███████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                         | 89/213 [01:11<01:39,  1.24it/s, loss=0.008, lr=1.57e-5, step=3283]\\u001b[A\\n\",\n      \"Epoch 15:  42%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                        | 90/213 [01:12<01:38,  1.25it/s, loss=0.008, lr=1.57e-5, step=3283]\\u001b[A\\n\",\n      \"Epoch 15:  42%|████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                        | 90/213 [01:12<01:38,  1.25it/s, loss=0.012, lr=1.57e-5, step=3284]\\u001b[A\\n\",\n      \"Epoch 15:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:12<01:38,  1.24it/s, loss=0.012, lr=1.57e-5, step=3284]\\u001b[A\\n\",\n      \"Epoch 15:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [01:13<01:38,  1.24it/s, loss=0.0188, lr=1.57e-5, step=3285]\\u001b[A\\n\",\n      \"Epoch 15:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:13<01:37,  1.25it/s, loss=0.0188, lr=1.57e-5, step=3285]\\u001b[A\\n\",\n      \"Epoch 15:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:13<01:37,  1.25it/s, loss=0.0169, lr=1.56e-5, step=3286]\\u001b[A\\n\",\n      \"Epoch 15:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:14<01:36,  1.25it/s, loss=0.0169, lr=1.56e-5, step=3286]\\u001b[A\\n\",\n      \"Epoch 15:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:14<01:36,  1.25it/s, loss=0.0164, lr=1.56e-5, step=3287]\\u001b[A\\n\",\n      \"Epoch 15:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0164, lr=1.56e-5, step=3287]\\u001b[A\\n\",\n      \"Epoch 15:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0307, lr=1.56e-5, step=3288]\\u001b[A\\n\",\n      \"Epoch 15:  45%|████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0307, lr=1.56e-5, step=3288]\\u001b[A\\n\",\n      \"Epoch 15:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                    | 95/213 [01:16<01:34,  1.25it/s, loss=0.01, lr=1.55e-5, step=3289]\\u001b[A\\n\",\n      \"Epoch 15:  45%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                   | 96/213 [01:16<01:33,  1.25it/s, loss=0.01, lr=1.55e-5, step=3289]\\u001b[A\\n\",\n      \"Epoch 15:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [01:17<01:33,  1.25it/s, loss=0.0186, lr=1.55e-5, step=3290]\\u001b[A\\n\",\n      \"Epoch 15:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0186, lr=1.55e-5, step=3290]\\u001b[A\\n\",\n      \"Epoch 15:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0131, lr=1.55e-5, step=3291]\\u001b[A\\n\",\n      \"Epoch 15:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.0131, lr=1.55e-5, step=3291]\\u001b[A\\n\",\n      \"Epoch 15:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.021, lr=1.55e-5, step=3292]\\u001b[A\\n\",\n      \"Epoch 15:  46%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                               | 99/213 [01:19<01:31,  1.25it/s, loss=0.021, lr=1.55e-5, step=3292]\\u001b[A\\n\",\n      \"Epoch 15:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:19<01:31,  1.25it/s, loss=0.0168, lr=1.54e-5, step=3293]\\u001b[A\\n\",\n      \"Epoch 15:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:20<01:30,  1.25it/s, loss=0.0168, lr=1.54e-5, step=3293]\\u001b[A\\n\",\n      \"Epoch 15:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:20<01:30,  1.25it/s, loss=0.0114, lr=1.54e-5, step=3294]\\u001b[A\\n\",\n      \"Epoch 15:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:20<01:29,  1.25it/s, loss=0.0114, lr=1.54e-5, step=3294]\\u001b[A\\n\",\n      \"Epoch 15:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:21<01:29,  1.25it/s, loss=0.0112, lr=1.54e-5, step=3295]\\u001b[A\\n\",\n      \"Epoch 15:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.0112, lr=1.54e-5, step=3295]\\u001b[A\\n\",\n      \"Epoch 15:  48%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.00827, lr=1.53e-5, step=3296]\\u001b[A\\n\",\n      \"Epoch 15:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:28,  1.25it/s, loss=0.00827, lr=1.53e-5, step=3296]\\u001b[A\\n\",\n      \"Epoch 15:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:28,  1.25it/s, loss=0.00778, lr=1.53e-5, step=3297]\\u001b[A\\n\",\n      \"Epoch 15:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 104/213 [01:23<01:27,  1.25it/s, loss=0.00778, lr=1.53e-5, step=3297]\\u001b[A\\n\",\n      \"Epoch 15:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 104/213 [01:23<01:27,  1.25it/s, loss=0.0155, lr=1.53e-5, step=3298]\\u001b[A\\n\",\n      \"Epoch 15:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0155, lr=1.53e-5, step=3298]\\u001b[A\\n\",\n      \"Epoch 15:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0257, lr=1.52e-5, step=3299]\\u001b[A\\n\",\n      \"Epoch 15:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:24<01:25,  1.25it/s, loss=0.0257, lr=1.52e-5, step=3299]\\u001b[A\\n\",\n      \"Epoch 15:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:25<01:25,  1.25it/s, loss=0.0151, lr=1.52e-5, step=3300]\\u001b[A\\n\",\n      \"Epoch 15:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0151, lr=1.52e-5, step=3300]\\u001b[A\\n\",\n      \"Epoch 15:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 107/213 [01:25<01:24,  1.25it/s, loss=0.00822, lr=1.52e-5, step=3301]\\u001b[A\\n\",\n      \"Epoch 15:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 108/213 [01:26<01:24,  1.25it/s, loss=0.00822, lr=1.52e-5, step=3301]\\u001b[A\\n\",\n      \"Epoch 15:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:24,  1.25it/s, loss=0.044, lr=1.52e-5, step=3302]\\u001b[A\\n\",\n      \"Epoch 15:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.044, lr=1.52e-5, step=3302]\\u001b[A\\n\",\n      \"Epoch 15:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.0217, lr=1.51e-5, step=3303]\\u001b[A\\n\",\n      \"Epoch 15:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0217, lr=1.51e-5, step=3303]\\u001b[A\\n\",\n      \"Epoch 15:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0237, lr=1.51e-5, step=3304]\\u001b[A\\n\",\n      \"Epoch 15:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:28<01:21,  1.25it/s, loss=0.0237, lr=1.51e-5, step=3304]\\u001b[A\\n\",\n      \"Epoch 15:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:29<01:21,  1.25it/s, loss=0.0103, lr=1.51e-5, step=3305]\\u001b[A\\n\",\n      \"Epoch 15:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0103, lr=1.51e-5, step=3305]\\u001b[A\\n\",\n      \"Epoch 15:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0395, lr=1.5e-5, step=3306]\\u001b[A\\n\",\n      \"Epoch 15:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:20,  1.25it/s, loss=0.0395, lr=1.5e-5, step=3306]\\u001b[A\\n\",\n      \"Epoch 15:  53%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                 | 113/213 [01:30<01:20,  1.25it/s, loss=0.0057, lr=1.5e-5, step=3307]\\u001b[A\\n\",\n      \"Epoch 15:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 114/213 [01:31<01:19,  1.25it/s, loss=0.0057, lr=1.5e-5, step=3307]\\u001b[A\\n\",\n      \"Epoch 15:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:31<01:19,  1.25it/s, loss=0.00983, lr=1.5e-5, step=3308]\\u001b[A\\n\",\n      \"Epoch 15:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.00983, lr=1.5e-5, step=3308]\\u001b[A\\n\",\n      \"Epoch 15:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.0116, lr=1.49e-5, step=3309]\\u001b[A\\n\",\n      \"Epoch 15:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:32<01:17,  1.25it/s, loss=0.0116, lr=1.49e-5, step=3309]\\u001b[A\\n\",\n      \"Epoch 15:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:33<01:17,  1.25it/s, loss=0.0187, lr=1.49e-5, step=3310]\\u001b[A\\n\",\n      \"Epoch 15:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:33<01:16,  1.25it/s, loss=0.0187, lr=1.49e-5, step=3310]\\u001b[A\\n\",\n      \"Epoch 15:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:33<01:16,  1.25it/s, loss=0.0126, lr=1.49e-5, step=3311]\\u001b[A\\n\",\n      \"Epoch 15:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [01:34<01:15,  1.25it/s, loss=0.0126, lr=1.49e-5, step=3311]\\u001b[A\\n\",\n      \"Epoch 15:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                            | 118/213 [01:34<01:15,  1.25it/s, loss=0.00718, lr=1.49e-5, step=3312]\\u001b[A\\n\",\n      \"Epoch 15:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                           | 119/213 [01:35<01:15,  1.25it/s, loss=0.00718, lr=1.49e-5, step=3312]\\u001b[A\\n\",\n      \"Epoch 15:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [01:35<01:15,  1.25it/s, loss=0.0197, lr=1.48e-5, step=3313]\\u001b[A\\n\",\n      \"Epoch 15:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:14,  1.25it/s, loss=0.0197, lr=1.48e-5, step=3313]\\u001b[A\\n\",\n      \"Epoch 15:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:14,  1.25it/s, loss=0.0235, lr=1.48e-5, step=3314]\\u001b[A\\n\",\n      \"Epoch 15:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0235, lr=1.48e-5, step=3314]\\u001b[A\\n\",\n      \"Epoch 15:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:37<01:13,  1.25it/s, loss=0.0135, lr=1.48e-5, step=3315]\\u001b[A\\n\",\n      \"Epoch 15:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0135, lr=1.48e-5, step=3315]\\u001b[A\\n\",\n      \"Epoch 15:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0129, lr=1.47e-5, step=3316]\\u001b[A\\n\",\n      \"Epoch 15:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:38<01:11,  1.25it/s, loss=0.0129, lr=1.47e-5, step=3316]\\u001b[A\\n\",\n      \"Epoch 15:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                        | 123/213 [01:38<01:11,  1.25it/s, loss=0.016, lr=1.47e-5, step=3317]\\u001b[A\\n\",\n      \"Epoch 15:  58%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                       | 124/213 [01:39<01:11,  1.25it/s, loss=0.016, lr=1.47e-5, step=3317]\\u001b[A\\n\",\n      \"Epoch 15:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                      | 124/213 [01:39<01:11,  1.25it/s, loss=0.00773, lr=1.47e-5, step=3318]\\u001b[A\\n\",\n      \"Epoch 15:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                     | 125/213 [01:40<01:10,  1.25it/s, loss=0.00773, lr=1.47e-5, step=3318]\\u001b[A\\n\",\n      \"Epoch 15:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                     | 125/213 [01:40<01:10,  1.25it/s, loss=0.0312, lr=1.46e-5, step=3319]\\u001b[A\\n\",\n      \"Epoch 15:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [01:40<01:09,  1.26it/s, loss=0.0312, lr=1.46e-5, step=3319]\\u001b[A\\n\",\n      \"Epoch 15:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                    | 126/213 [01:40<01:09,  1.26it/s, loss=0.00755, lr=1.46e-5, step=3320]\\u001b[A\\n\",\n      \"Epoch 15:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.26it/s, loss=0.00755, lr=1.46e-5, step=3320]\\u001b[A\\n\",\n      \"Epoch 15:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.26it/s, loss=0.00847, lr=1.46e-5, step=3321]\\u001b[A\\n\",\n      \"Epoch 15:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                  | 128/213 [01:42<01:07,  1.26it/s, loss=0.00847, lr=1.46e-5, step=3321]\\u001b[A\\n\",\n      \"Epoch 15:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                  | 128/213 [01:42<01:07,  1.26it/s, loss=0.0269, lr=1.46e-5, step=3322]\\u001b[A\\n\",\n      \"Epoch 15:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:43<01:07,  1.25it/s, loss=0.0269, lr=1.46e-5, step=3322]\\u001b[A\\n\",\n      \"Epoch 15:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:43<01:07,  1.25it/s, loss=0.0154, lr=1.45e-5, step=3323]\\u001b[A\\n\",\n      \"Epoch 15:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:44<01:06,  1.25it/s, loss=0.0154, lr=1.45e-5, step=3323]\\u001b[A\\n\",\n      \"Epoch 15:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                | 130/213 [01:44<01:06,  1.25it/s, loss=0.00727, lr=1.45e-5, step=3324]\\u001b[A\\n\",\n      \"Epoch 15:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.00727, lr=1.45e-5, step=3324]\\u001b[A\\n\",\n      \"Epoch 15:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.0122, lr=1.45e-5, step=3325]\\u001b[A\\n\",\n      \"Epoch 15:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0122, lr=1.45e-5, step=3325]\\u001b[A\\n\",\n      \"Epoch 15:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0134, lr=1.44e-5, step=3326]\\u001b[A\\n\",\n      \"Epoch 15:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [01:46<01:03,  1.25it/s, loss=0.0134, lr=1.44e-5, step=3326]\\u001b[A\\n\",\n      \"Epoch 15:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [01:46<01:03,  1.25it/s, loss=0.0168, lr=1.44e-5, step=3327]\\u001b[A\\n\",\n      \"Epoch 15:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [01:47<01:03,  1.25it/s, loss=0.0168, lr=1.44e-5, step=3327]\\u001b[A\\n\",\n      \"Epoch 15:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [01:47<01:03,  1.25it/s, loss=0.0122, lr=1.44e-5, step=3328]\\u001b[A\\n\",\n      \"Epoch 15:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:48<01:02,  1.25it/s, loss=0.0122, lr=1.44e-5, step=3328]\\u001b[A\\n\",\n      \"Epoch 15:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:48<01:02,  1.25it/s, loss=0.0469, lr=1.44e-5, step=3329]\\u001b[A\\n\",\n      \"Epoch 15:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.0469, lr=1.44e-5, step=3329]\\u001b[A\\n\",\n      \"Epoch 15:  64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.00971, lr=1.43e-5, step=3330]\\u001b[A\\n\",\n      \"Epoch 15:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 137/213 [01:49<01:00,  1.26it/s, loss=0.00971, lr=1.43e-5, step=3330]\\u001b[A\\n\",\n      \"Epoch 15:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.015, lr=1.43e-5, step=3331]\\u001b[A\\n\",\n      \"Epoch 15:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<00:59,  1.25it/s, loss=0.015, lr=1.43e-5, step=3331]\\u001b[A\\n\",\n      \"Epoch 15:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:50<00:59,  1.25it/s, loss=0.0044, lr=1.43e-5, step=3332]\\u001b[A\\n\",\n      \"Epoch 15:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0044, lr=1.43e-5, step=3332]\\u001b[A\\n\",\n      \"Epoch 15:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0229, lr=1.42e-5, step=3333]\\u001b[A\\n\",\n      \"Epoch 15:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0229, lr=1.42e-5, step=3333]\\u001b[A\\n\",\n      \"Epoch 15:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0304, lr=1.42e-5, step=3334]\\u001b[A\\n\",\n      \"Epoch 15:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0304, lr=1.42e-5, step=3334]\\u001b[A\\n\",\n      \"Epoch 15:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0326, lr=1.42e-5, step=3335]\\u001b[A\\n\",\n      \"Epoch 15:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0326, lr=1.42e-5, step=3335]\\u001b[A\\n\",\n      \"Epoch 15:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0196, lr=1.41e-5, step=3336]\\u001b[A\\n\",\n      \"Epoch 15:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0196, lr=1.41e-5, step=3336]\\u001b[A\\n\",\n      \"Epoch 15:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0184, lr=1.41e-5, step=3337]\\u001b[A\\n\",\n      \"Epoch 15:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0184, lr=1.41e-5, step=3337]\\u001b[A\\n\",\n      \"Epoch 15:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0141, lr=1.41e-5, step=3338]\\u001b[A\\n\",\n      \"Epoch 15:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:56<00:54,  1.26it/s, loss=0.0141, lr=1.41e-5, step=3338]\\u001b[A\\n\",\n      \"Epoch 15:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:56<00:54,  1.26it/s, loss=0.0323, lr=1.41e-5, step=3339]\\u001b[A\\n\",\n      \"Epoch 15:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.0323, lr=1.41e-5, step=3339]\\u001b[A\\n\",\n      \"Epoch 15:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.26it/s, loss=0.0141, lr=1.4e-5, step=3340]\\u001b[A\\n\",\n      \"Epoch 15:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0141, lr=1.4e-5, step=3340]\\u001b[A\\n\",\n      \"Epoch 15:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0151, lr=1.4e-5, step=3341]\\u001b[A\\n\",\n      \"Epoch 15:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0151, lr=1.4e-5, step=3341]\\u001b[A\\n\",\n      \"Epoch 15:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0173, lr=1.4e-5, step=3342]\\u001b[A\\n\",\n      \"Epoch 15:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0173, lr=1.4e-5, step=3342]\\u001b[A\\n\",\n      \"Epoch 15:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0128, lr=1.39e-5, step=3343]\\u001b[A\\n\",\n      \"Epoch 15:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.0128, lr=1.39e-5, step=3343]\\u001b[A\\n\",\n      \"Epoch 15:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.0174, lr=1.39e-5, step=3344]\\u001b[A\\n\",\n      \"Epoch 15:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [02:00<00:49,  1.25it/s, loss=0.0174, lr=1.39e-5, step=3344]\\u001b[A\\n\",\n      \"Epoch 15:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.25it/s, loss=0.016, lr=1.39e-5, step=3345]\\u001b[A\\n\",\n      \"Epoch 15:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.016, lr=1.39e-5, step=3345]\\u001b[A\\n\",\n      \"Epoch 15:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.00795, lr=1.39e-5, step=3346]\\u001b[A\\n\",\n      \"Epoch 15:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.00795, lr=1.39e-5, step=3346]\\u001b[A\\n\",\n      \"Epoch 15:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0171, lr=1.38e-5, step=3347]\\u001b[A\\n\",\n      \"Epoch 15:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0171, lr=1.38e-5, step=3347]\\u001b[A\\n\",\n      \"Epoch 15:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0112, lr=1.38e-5, step=3348]\\u001b[A\\n\",\n      \"Epoch 15:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [02:04<00:46,  1.25it/s, loss=0.0112, lr=1.38e-5, step=3348]\\u001b[A\\n\",\n      \"Epoch 15:  73%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                         | 155/213 [02:04<00:46,  1.25it/s, loss=0.01, lr=1.38e-5, step=3349]\\u001b[A\\n\",\n      \"Epoch 15:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                        | 156/213 [02:04<00:45,  1.25it/s, loss=0.01, lr=1.38e-5, step=3349]\\u001b[A\\n\",\n      \"Epoch 15:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [02:04<00:45,  1.25it/s, loss=0.0107, lr=1.37e-5, step=3350]\\u001b[A\\n\",\n      \"Epoch 15:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                      | 157/213 [02:05<00:44,  1.25it/s, loss=0.0107, lr=1.37e-5, step=3350]\\u001b[A\\n\",\n      \"Epoch 15:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                      | 157/213 [02:05<00:44,  1.25it/s, loss=0.0232, lr=1.37e-5, step=3351]\\u001b[A\\n\",\n      \"Epoch 15:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.0232, lr=1.37e-5, step=3351]\\u001b[A\\n\",\n      \"Epoch 15:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.00637, lr=1.37e-5, step=3352]\\u001b[A\\n\",\n      \"Epoch 15:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00637, lr=1.37e-5, step=3352]\\u001b[A\\n\",\n      \"Epoch 15:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00728, lr=1.37e-5, step=3353]\\u001b[A\\n\",\n      \"Epoch 15:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:08<00:42,  1.25it/s, loss=0.00728, lr=1.37e-5, step=3353]\\u001b[A\\n\",\n      \"Epoch 15:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:08<00:42,  1.25it/s, loss=0.00528, lr=1.36e-5, step=3354]\\u001b[A\\n\",\n      \"Epoch 15:  76%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.00528, lr=1.36e-5, step=3354]\\u001b[A\\n\",\n      \"Epoch 15:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.0278, lr=1.36e-5, step=3355]\\u001b[A\\n\",\n      \"Epoch 15:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.26it/s, loss=0.0278, lr=1.36e-5, step=3355]\\u001b[A\\n\",\n      \"Epoch 15:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.26it/s, loss=0.0198, lr=1.36e-5, step=3356]\\u001b[A\\n\",\n      \"Epoch 15:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:10<00:39,  1.26it/s, loss=0.0198, lr=1.36e-5, step=3356]\\u001b[A\\n\",\n      \"Epoch 15:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:10<00:39,  1.26it/s, loss=0.0167, lr=1.35e-5, step=3357]\\u001b[A\\n\",\n      \"Epoch 15:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:11<00:38,  1.26it/s, loss=0.0167, lr=1.35e-5, step=3357]\\u001b[A\\n\",\n      \"Epoch 15:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:11<00:38,  1.26it/s, loss=0.0141, lr=1.35e-5, step=3358]\\u001b[A\\n\",\n      \"Epoch 15:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:12<00:38,  1.26it/s, loss=0.0141, lr=1.35e-5, step=3358]\\u001b[A\\n\",\n      \"Epoch 15:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:12<00:38,  1.26it/s, loss=0.0124, lr=1.35e-5, step=3359]\\u001b[A\\n\",\n      \"Epoch 15:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.26it/s, loss=0.0124, lr=1.35e-5, step=3359]\\u001b[A\\n\",\n      \"Epoch 15:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.26it/s, loss=0.0167, lr=1.35e-5, step=3360]\\u001b[A\\n\",\n      \"Epoch 15:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0167, lr=1.35e-5, step=3360]\\u001b[A\\n\",\n      \"Epoch 15:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0122, lr=1.34e-5, step=3361]\\u001b[A\\n\",\n      \"Epoch 15:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:35,  1.26it/s, loss=0.0122, lr=1.34e-5, step=3361]\\u001b[A\\n\",\n      \"Epoch 15:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:35,  1.26it/s, loss=0.0124, lr=1.34e-5, step=3362]\\u001b[A\\n\",\n      \"Epoch 15:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:15<00:34,  1.26it/s, loss=0.0124, lr=1.34e-5, step=3362]\\u001b[A\\n\",\n      \"Epoch 15:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                          | 169/213 [02:15<00:34,  1.26it/s, loss=0.00695, lr=1.34e-5, step=3363]\\u001b[A\\n\",\n      \"Epoch 15:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                         | 170/213 [02:16<00:34,  1.26it/s, loss=0.00695, lr=1.34e-5, step=3363]\\u001b[A\\n\",\n      \"Epoch 15:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:16<00:34,  1.26it/s, loss=0.0338, lr=1.33e-5, step=3364]\\u001b[A\\n\",\n      \"Epoch 15:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.26it/s, loss=0.0338, lr=1.33e-5, step=3364]\\u001b[A\\n\",\n      \"Epoch 15:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.26it/s, loss=0.0177, lr=1.33e-5, step=3365]\\u001b[A\\n\",\n      \"Epoch 15:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.26it/s, loss=0.0177, lr=1.33e-5, step=3365]\\u001b[A\\n\",\n      \"Epoch 15:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.26it/s, loss=0.0177, lr=1.33e-5, step=3366]\\u001b[A\\n\",\n      \"Epoch 15:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0177, lr=1.33e-5, step=3366]\\u001b[A\\n\",\n      \"Epoch 15:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0268, lr=1.33e-5, step=3367]\\u001b[A\\n\",\n      \"Epoch 15:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:19<00:31,  1.26it/s, loss=0.0268, lr=1.33e-5, step=3367]\\u001b[A\\n\",\n      \"Epoch 15:  82%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                     | 174/213 [02:19<00:31,  1.26it/s, loss=0.00825, lr=1.32e-5, step=3368]\\u001b[A\\n\",\n      \"Epoch 15:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                    | 175/213 [02:20<00:30,  1.25it/s, loss=0.00825, lr=1.32e-5, step=3368]\\u001b[A\\n\",\n      \"Epoch 15:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.0116, lr=1.32e-5, step=3369]\\u001b[A\\n\",\n      \"Epoch 15:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                    | 176/213 [02:20<00:29,  1.26it/s, loss=0.0116, lr=1.32e-5, step=3369]\\u001b[A\\n\",\n      \"Epoch 15:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                   | 176/213 [02:20<00:29,  1.26it/s, loss=0.00965, lr=1.32e-5, step=3370]\\u001b[A\\n\",\n      \"Epoch 15:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                  | 177/213 [02:21<00:28,  1.26it/s, loss=0.00965, lr=1.32e-5, step=3370]\\u001b[A\\n\",\n      \"Epoch 15:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                   | 177/213 [02:21<00:28,  1.26it/s, loss=0.0128, lr=1.31e-5, step=3371]\\u001b[A\\n\",\n      \"Epoch 15:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:22<00:27,  1.26it/s, loss=0.0128, lr=1.31e-5, step=3371]\\u001b[A\\n\",\n      \"Epoch 15:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:22<00:27,  1.26it/s, loss=0.0305, lr=1.31e-5, step=3372]\\u001b[A\\n\",\n      \"Epoch 15:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.26it/s, loss=0.0305, lr=1.31e-5, step=3372]\\u001b[A\\n\",\n      \"Epoch 15:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                 | 179/213 [02:23<00:27,  1.26it/s, loss=0.00973, lr=1.31e-5, step=3373]\\u001b[A\\n\",\n      \"Epoch 15:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                | 180/213 [02:23<00:26,  1.26it/s, loss=0.00973, lr=1.31e-5, step=3373]\\u001b[A\\n\",\n      \"Epoch 15:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:24<00:26,  1.26it/s, loss=0.0115, lr=1.31e-5, step=3374]\\u001b[A\\n\",\n      \"Epoch 15:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.26it/s, loss=0.0115, lr=1.31e-5, step=3374]\\u001b[A\\n\",\n      \"Epoch 15:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                               | 181/213 [02:24<00:25,  1.26it/s, loss=0.0177, lr=1.3e-5, step=3375]\\u001b[A\\n\",\n      \"Epoch 15:  85%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                              | 182/213 [02:25<00:24,  1.26it/s, loss=0.0177, lr=1.3e-5, step=3375]\\u001b[A\\n\",\n      \"Epoch 15:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.26it/s, loss=0.00475, lr=1.3e-5, step=3376]\\u001b[A\\n\",\n      \"Epoch 15:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.26it/s, loss=0.00475, lr=1.3e-5, step=3376]\\u001b[A\\n\",\n      \"Epoch 15:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.26it/s, loss=0.00556, lr=1.3e-5, step=3377]\\u001b[A\\n\",\n      \"Epoch 15:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.00556, lr=1.3e-5, step=3377]\\u001b[A\\n\",\n      \"Epoch 15:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.00553, lr=1.29e-5, step=3378]\\u001b[A\\n\",\n      \"Epoch 15:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                           | 185/213 [02:27<00:22,  1.26it/s, loss=0.00553, lr=1.29e-5, step=3378]\\u001b[A\\n\",\n      \"Epoch 15:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:27<00:22,  1.26it/s, loss=0.0117, lr=1.29e-5, step=3379]\\u001b[A\\n\",\n      \"Epoch 15:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:28<00:21,  1.26it/s, loss=0.0117, lr=1.29e-5, step=3379]\\u001b[A\\n\",\n      \"Epoch 15:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:28<00:21,  1.26it/s, loss=0.0116, lr=1.29e-5, step=3380]\\u001b[A\\n\",\n      \"Epoch 15:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0116, lr=1.29e-5, step=3380]\\u001b[A\\n\",\n      \"Epoch 15:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.026, lr=1.29e-5, step=3381]\\u001b[A\\n\",\n      \"Epoch 15:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                        | 188/213 [02:30<00:19,  1.26it/s, loss=0.026, lr=1.29e-5, step=3381]\\u001b[A\\n\",\n      \"Epoch 15:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:30<00:19,  1.26it/s, loss=0.0176, lr=1.28e-5, step=3382]\\u001b[A\\n\",\n      \"Epoch 15:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0176, lr=1.28e-5, step=3382]\\u001b[A\\n\",\n      \"Epoch 15:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0198, lr=1.28e-5, step=3383]\\u001b[A\\n\",\n      \"Epoch 15:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0198, lr=1.28e-5, step=3383]\\u001b[A\\n\",\n      \"Epoch 15:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0121, lr=1.28e-5, step=3384]\\u001b[A\\n\",\n      \"Epoch 15:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0121, lr=1.28e-5, step=3384]\\u001b[A\\n\",\n      \"Epoch 15:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0166, lr=1.27e-5, step=3385]\\u001b[A\\n\",\n      \"Epoch 15:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0166, lr=1.27e-5, step=3385]\\u001b[A\\n\",\n      \"Epoch 15:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0102, lr=1.27e-5, step=3386]\\u001b[A\\n\",\n      \"Epoch 15:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [02:34<00:16,  1.25it/s, loss=0.0102, lr=1.27e-5, step=3386]\\u001b[A\\n\",\n      \"Epoch 15:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [02:34<00:16,  1.25it/s, loss=0.0213, lr=1.27e-5, step=3387]\\u001b[A\\n\",\n      \"Epoch 15:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0213, lr=1.27e-5, step=3387]\\u001b[A\\n\",\n      \"Epoch 15:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0441, lr=1.27e-5, step=3388]\\u001b[A\\n\",\n      \"Epoch 15:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.0441, lr=1.27e-5, step=3388]\\u001b[A\\n\",\n      \"Epoch 15:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.00944, lr=1.26e-5, step=3389]\\u001b[A\\n\",\n      \"Epoch 15:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 196/213 [02:36<00:13,  1.25it/s, loss=0.00944, lr=1.26e-5, step=3389]\\u001b[A\\n\",\n      \"Epoch 15:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0107, lr=1.26e-5, step=3390]\\u001b[A\\n\",\n      \"Epoch 15:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0107, lr=1.26e-5, step=3390]\\u001b[A\\n\",\n      \"Epoch 15:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌               | 197/213 [02:37<00:12,  1.25it/s, loss=0.00929, lr=1.26e-5, step=3391]\\u001b[A\\n\",\n      \"Epoch 15:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.00929, lr=1.26e-5, step=3391]\\u001b[A\\n\",\n      \"Epoch 15:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0114, lr=1.26e-5, step=3392]\\u001b[A\\n\",\n      \"Epoch 15:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0114, lr=1.26e-5, step=3392]\\u001b[A\\n\",\n      \"Epoch 15:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00681, lr=1.25e-5, step=3393]\\u001b[A\\n\",\n      \"Epoch 15:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [02:39<00:10,  1.25it/s, loss=0.00681, lr=1.25e-5, step=3393]\\u001b[A\\n\",\n      \"Epoch 15:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:39<00:10,  1.25it/s, loss=0.0113, lr=1.25e-5, step=3394]\\u001b[A\\n\",\n      \"Epoch 15:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0113, lr=1.25e-5, step=3394]\\u001b[A\\n\",\n      \"Epoch 15:  94%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.018, lr=1.25e-5, step=3395]\\u001b[A\\n\",\n      \"Epoch 15:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.26it/s, loss=0.018, lr=1.25e-5, step=3395]\\u001b[A\\n\",\n      \"Epoch 15:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.26it/s, loss=0.0246, lr=1.24e-5, step=3396]\\u001b[A\\n\",\n      \"Epoch 15:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:42<00:07,  1.25it/s, loss=0.0246, lr=1.24e-5, step=3396]\\u001b[A\\n\",\n      \"Epoch 15:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:42<00:07,  1.25it/s, loss=0.0224, lr=1.24e-5, step=3397]\\u001b[A\\n\",\n      \"Epoch 15:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.0224, lr=1.24e-5, step=3397]\\u001b[A\\n\",\n      \"Epoch 15:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.00955, lr=1.24e-5, step=3398]\\u001b[A\\n\",\n      \"Epoch 15:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎       | 205/213 [02:43<00:06,  1.25it/s, loss=0.00955, lr=1.24e-5, step=3398]\\u001b[A\\n\",\n      \"Epoch 15:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:43<00:06,  1.25it/s, loss=0.0128, lr=1.24e-5, step=3399]\\u001b[A\\n\",\n      \"Epoch 15:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0128, lr=1.24e-5, step=3399]\\u001b[A\\n\",\n      \"Epoch 15:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0204, lr=1.23e-5, step=3400]\\u001b[A\\n\",\n      \"Epoch 15:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.0204, lr=1.23e-5, step=3400]\\u001b[A\\n\",\n      \"Epoch 15:  97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.023, lr=1.23e-5, step=3401]\\u001b[A\\n\",\n      \"Epoch 15:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:03,  1.25it/s, loss=0.023, lr=1.23e-5, step=3401]\\u001b[A\\n\",\n      \"Epoch 15:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████     | 208/213 [02:46<00:03,  1.25it/s, loss=0.011, lr=1.23e-5, step=3402]\\u001b[A\\n\",\n      \"Epoch 15:  98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.26it/s, loss=0.011, lr=1.23e-5, step=3402]\\u001b[A\\n\",\n      \"Epoch 15:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.26it/s, loss=0.0162, lr=1.23e-5, step=3403]\\u001b[A\\n\",\n      \"Epoch 15:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.0162, lr=1.23e-5, step=3403]\\u001b[A\\n\",\n      \"Epoch 15:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.00355, lr=1.22e-5, step=3404]\\u001b[A\\n\",\n      \"Epoch 15:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.00355, lr=1.22e-5, step=3404]\\u001b[A\\n\",\n      \"Epoch 15:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.0119, lr=1.22e-5, step=3405]\\u001b[A\\n\",\n      \"Epoch 15: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.26it/s, loss=0.0119, lr=1.22e-5, step=3405]\\u001b[A\\n\",\n      \"Epoch 15: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.26it/s, loss=0.0096, lr=1.22e-5, step=3406]\\u001b[A\\n\",\n      \"Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.0096, lr=1.22e-5, step=3406]\\u001b[A\\n\",\n      \"Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.0103, lr=1.21e-5, step=3407]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4814ed5226f544a0968a99adf4908d78\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 15: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.0103, lr=1.21e-5, step=3407]\\n\",\n      \"Epoch 16: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.00918, lr=6.96e-6, step=3620]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2c779461ebcc495594cff03adac7403f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 16: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:34<00:00,  2.14s/it, loss=0.00918, lr=6.96e-6, step=3620]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 17:   0%|                                                                                                                                                                                                                                                             | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 17:   0%|█▏                                                                                                                                                                                                                                          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step=3622]\\u001b[A\\n\",\n      \"Epoch 17:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:50,  1.23it/s, loss=0.0106, lr=6.92e-6, step=3622]\\u001b[A\\n\",\n      \"Epoch 17:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:50,  1.23it/s, loss=0.00897, lr=6.9e-6, step=3623]\\u001b[A\\n\",\n      \"Epoch 17:   2%|███▉                                                                                                                                                                                                             | 4/213 [00:03<02:48,  1.24it/s, loss=0.00897, lr=6.9e-6, step=3623]\\u001b[A\\n\",\n      \"Epoch 17:   2%|███▉                                                                                                                                                                                                             | 4/213 [00:03<02:48,  1.24it/s, loss=0.0116, lr=6.87e-6, step=3624]\\u001b[A\\n\",\n      \"Epoch 17:   2%|████▉                                                                                                                                                                                                            | 5/213 [00:04<02:46,  1.25it/s, loss=0.0116, lr=6.87e-6, step=3624]\\u001b[A\\n\",\n      \"Epoch 17:   2%|████▉                                                                                                                                                                                                            | 5/213 [00:04<02:46,  1.25it/s, loss=0.0424, lr=6.85e-6, step=3625]\\u001b[A\\n\",\n      \"Epoch 17:   3%|█████▉                                         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                                                                                                                | 7/213 [00:05<02:44,  1.25it/s, loss=0.0141, lr=6.81e-6, step=3627]\\u001b[A\\n\",\n      \"Epoch 17:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:43,  1.25it/s, loss=0.0141, lr=6.81e-6, step=3627]\\u001b[A\\n\",\n      \"Epoch 17:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:43,  1.25it/s, loss=0.0172, lr=6.79e-6, step=3628]\\u001b[A\\n\",\n      \"Epoch 17:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:42,  1.25it/s, loss=0.0172, lr=6.79e-6, step=3628]\\u001b[A\\n\",\n      \"Epoch 17:   4%|████████▊                                                                                                                                                                                                       | 9/213 [00:07<02:42,  1.25it/s, loss=0.00642, lr=6.77e-6, step=3629]\\u001b[A\\n\",\n      \"Epoch 17:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:41,  1.26it/s, loss=0.00642, lr=6.77e-6, step=3629]\\u001b[A\\n\",\n      \"Epoch 17:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:41,  1.26it/s, loss=0.00921, lr=6.75e-6, step=3630]\\u001b[A\\n\",\n      \"Epoch 17:   5%|██████████▋                                                                                                                                                                                                    | 11/213 [00:08<02:40,  1.26it/s, loss=0.00921, lr=6.75e-6, step=3630]\\u001b[A\\n\",\n      \"Epoch 17:   5%|██████████▋                                                                                                                                                                                                     | 11/213 [00:08<02:40,  1.26it/s, loss=0.0155, lr=6.73e-6, step=3631]\\u001b[A\\n\",\n      \"Epoch 17:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:40,  1.25it/s, loss=0.0155, lr=6.73e-6, step=3631]\\u001b[A\\n\",\n      \"Epoch 17:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:40,  1.25it/s, loss=0.0197, lr=6.71e-6, step=3632]\\u001b[A\\n\",\n      \"Epoch 17:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:39,  1.26it/s, loss=0.0197, lr=6.71e-6, step=3632]\\u001b[A\\n\",\n      \"Epoch 17:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:39,  1.26it/s, loss=0.0196, lr=6.68e-6, step=3633]\\u001b[A\\n\",\n      \"Epoch 17:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:11<02:38,  1.25it/s, loss=0.0196, lr=6.68e-6, step=3633]\\u001b[A\\n\",\n      \"Epoch 17:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:11<02:38,  1.25it/s, loss=0.0333, lr=6.66e-6, step=3634]\\u001b[A\\n\",\n      \"Epoch 17:   7%|██████████████▋                                                                                                                                                                                                 | 15/213 [00:12<02:37,  1.26it/s, loss=0.0333, lr=6.66e-6, step=3634]\\u001b[A\\n\",\n      \"Epoch 17:   7%|██████████████▌                                                                                                                                                                                                | 15/213 [00:12<02:37,  1.26it/s, loss=0.00966, lr=6.64e-6, step=3635]\\u001b[A\\n\",\n      \"Epoch 17:   8%|███████████████▌                                                                                                                                                                                               | 16/213 [00:12<02:37,  1.25it/s, loss=0.00966, lr=6.64e-6, step=3635]\\u001b[A\\n\",\n      \"Epoch 17:   8%|███████████████▌                                                                                                                                                                                                | 16/213 [00:12<02:37,  1.25it/s, loss=0.0128, lr=6.62e-6, step=3636]\\u001b[A\\n\",\n      \"Epoch 17:   8%|████████████████▌                                                                                                                                                                                               | 17/213 [00:13<02:36,  1.25it/s, loss=0.0128, lr=6.62e-6, step=3636]\\u001b[A\\n\",\n      \"Epoch 17:   8%|████████████████▋                                                                                                                                                                                                | 17/213 [00:13<02:36,  1.25it/s, loss=0.0383, lr=6.6e-6, step=3637]\\u001b[A\\n\",\n      \"Epoch 17:   8%|█████████████████▋                                                                                                                                                                                               | 18/213 [00:14<02:35,  1.25it/s, loss=0.0383, lr=6.6e-6, step=3637]\\u001b[A\\n\",\n      \"Epoch 17:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [00:14<02:35,  1.25it/s, loss=0.0261, lr=6.58e-6, step=3638]\\u001b[A\\n\",\n      \"Epoch 17:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:35,  1.25it/s, loss=0.0261, lr=6.58e-6, step=3638]\\u001b[A\\n\",\n      \"Epoch 17:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:35,  1.25it/s, loss=0.0123, lr=6.56e-6, step=3639]\\u001b[A\\n\",\n      \"Epoch 17:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:34,  1.25it/s, loss=0.0123, lr=6.56e-6, step=3639]\\u001b[A\\n\",\n      \"Epoch 17:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:34,  1.25it/s, loss=0.0115, lr=6.54e-6, step=3640]\\u001b[A\\n\",\n      \"Epoch 17:  10%|████████████████████▌                                                                                                                                                                                           | 21/213 [00:16<02:33,  1.25it/s, loss=0.0115, lr=6.54e-6, step=3640]\\u001b[A\\n\",\n      \"Epoch 17:  10%|████████████████████▍                                                                                                                                                                                          | 21/213 [00:16<02:33,  1.25it/s, loss=0.00767, lr=6.52e-6, step=3641]\\u001b[A\\n\",\n      \"Epoch 17:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:32,  1.25it/s, loss=0.00767, lr=6.52e-6, step=3641]\\u001b[A\\n\",\n      \"Epoch 17:  10%|█████████████████████▍                                                                                                                                                                                          | 22/213 [00:17<02:32,  1.25it/s, loss=0.00727, lr=6.5e-6, step=3642]\\u001b[A\\n\",\n      \"Epoch 17:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [00:18<02:32,  1.25it/s, loss=0.00727, lr=6.5e-6, step=3642]\\u001b[A\\n\",\n      \"Epoch 17:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [00:18<02:32,  1.25it/s, loss=0.0285, lr=6.48e-6, step=3643]\\u001b[A\\n\",\n      \"Epoch 17:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.24it/s, loss=0.0285, lr=6.48e-6, step=3643]\\u001b[A\\n\",\n      \"Epoch 17:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.24it/s, loss=0.0122, lr=6.46e-6, step=3644]\\u001b[A\\n\",\n      \"Epoch 17:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0122, lr=6.46e-6, step=3644]\\u001b[A\\n\",\n      \"Epoch 17:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0151, lr=6.44e-6, step=3645]\\u001b[A\\n\",\n      \"Epoch 17:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [00:20<02:29,  1.25it/s, loss=0.0151, lr=6.44e-6, step=3645]\\u001b[A\\n\",\n      \"Epoch 17:  12%|█████████████████████████▎                                                                                                                                                                                     | 26/213 [00:20<02:29,  1.25it/s, loss=0.00602, lr=6.42e-6, step=3646]\\u001b[A\\n\",\n      \"Epoch 17:  13%|██████████████████████████▏                                                                                                                                                                                    | 27/213 [00:21<02:28,  1.25it/s, loss=0.00602, lr=6.42e-6, step=3646]\\u001b[A\\n\",\n      \"Epoch 17:  13%|██████████████████████████▎                                                                                                                                                                                     | 27/213 [00:21<02:28,  1.25it/s, loss=0.00665, lr=6.4e-6, step=3647]\\u001b[A\\n\",\n      \"Epoch 17:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:28,  1.25it/s, loss=0.00665, lr=6.4e-6, step=3647]\\u001b[A\\n\",\n      \"Epoch 17:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:28,  1.25it/s, loss=0.0205, lr=6.38e-6, step=3648]\\u001b[A\\n\",\n      \"Epoch 17:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [00:23<02:27,  1.25it/s, loss=0.0205, lr=6.38e-6, step=3648]\\u001b[A\\n\",\n      \"Epoch 17:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [00:23<02:27,  1.25it/s, loss=0.0118, lr=6.35e-6, step=3649]\\u001b[A\\n\",\n      \"Epoch 17:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:26,  1.25it/s, loss=0.0118, lr=6.35e-6, step=3649]\\u001b[A\\n\",\n      \"Epoch 17:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:26,  1.25it/s, loss=0.0255, lr=6.33e-6, step=3650]\\u001b[A\\n\",\n      \"Epoch 17:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:24<02:25,  1.25it/s, loss=0.0255, lr=6.33e-6, step=3650]\\u001b[A\\n\",\n      \"Epoch 17:  15%|██████████████████████████████▎                                                                                                                                                                                 | 31/213 [00:24<02:25,  1.25it/s, loss=0.0164, lr=6.31e-6, step=3651]\\u001b[A\\n\",\n      \"Epoch 17:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:25<02:25,  1.25it/s, loss=0.0164, lr=6.31e-6, step=3651]\\u001b[A\\n\",\n      \"Epoch 17:  15%|███████████████████████████████▏                                                                                                                                                                                | 32/213 [00:25<02:25,  1.25it/s, loss=0.0167, lr=6.29e-6, step=3652]\\u001b[A\\n\",\n      \"Epoch 17:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:26<02:24,  1.25it/s, loss=0.0167, lr=6.29e-6, step=3652]\\u001b[A\\n\",\n      \"Epoch 17:  15%|████████████████████████████████▌                                                                                                                                                                                 | 33/213 [00:26<02:24,  1.25it/s, loss=0.01, lr=6.27e-6, step=3653]\\u001b[A\\n\",\n      \"Epoch 17:  16%|█████████████████████████████████▌                                                                                                                                                                                | 34/213 [00:27<02:23,  1.25it/s, loss=0.01, lr=6.27e-6, step=3653]\\u001b[A\\n\",\n      \"Epoch 17:  16%|█████████████████████████████████▏                                                                                                                                                                              | 34/213 [00:27<02:23,  1.25it/s, loss=0.0086, lr=6.25e-6, step=3654]\\u001b[A\\n\",\n      \"Epoch 17:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:23,  1.24it/s, loss=0.0086, lr=6.25e-6, step=3654]\\u001b[A\\n\",\n      \"Epoch 17:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:23,  1.24it/s, loss=0.0164, lr=6.23e-6, step=3655]\\u001b[A\\n\",\n      \"Epoch 17:  17%|███████████████████████████████████▏                                                                                                                                                                            | 36/213 [00:28<02:22,  1.24it/s, loss=0.0164, lr=6.23e-6, step=3655]\\u001b[A\\n\",\n      \"Epoch 17:  17%|██████████████████████████████████▉                                                                                                                                                                            | 36/213 [00:28<02:22,  1.24it/s, loss=0.00763, lr=6.21e-6, step=3656]\\u001b[A\\n\",\n      \"Epoch 17:  17%|███████████████████████████████████▉                                                                                                                                                                           | 37/213 [00:29<02:21,  1.24it/s, loss=0.00763, lr=6.21e-6, step=3656]\\u001b[A\\n\",\n      \"Epoch 17:  17%|████████████████████████████████████▏                                                                                                                                                                           | 37/213 [00:29<02:21,  1.24it/s, loss=0.0283, lr=6.19e-6, step=3657]\\u001b[A\\n\",\n      \"Epoch 17:  18%|█████████████████████████████████████                                                                                                                                                                           | 38/213 [00:30<02:21,  1.24it/s, loss=0.0283, lr=6.19e-6, step=3657]\\u001b[A\\n\",\n      \"Epoch 17:  18%|████████████████████████████████████▉                                                                                                                                                                          | 38/213 [00:30<02:21,  1.24it/s, loss=0.00751, lr=6.17e-6, step=3658]\\u001b[A\\n\",\n      \"Epoch 17:  18%|█████████████████████████████████████▉                                                                                                                                                                         | 39/213 [00:31<02:20,  1.24it/s, loss=0.00751, lr=6.17e-6, step=3658]\\u001b[A\\n\",\n      \"Epoch 17:  18%|██████████████████████████████████████▎                                                                                                                                                                          | 39/213 [00:31<02:20,  1.24it/s, loss=0.038, lr=6.15e-6, step=3659]\\u001b[A\\n\",\n      \"Epoch 17:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.038, lr=6.15e-6, step=3659]\\u001b[A\\n\",\n      \"Epoch 17:  19%|███████████████████████████████████████▏                                                                                                                                                                         | 40/213 [00:32<02:19,  1.24it/s, loss=0.019, lr=6.13e-6, step=3660]\\u001b[A\\n\",\n      \"Epoch 17:  19%|████████████████████████████████████████▏                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.019, lr=6.13e-6, step=3660]\\u001b[A\\n\",\n      \"Epoch 17:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:32<02:18,  1.24it/s, loss=0.0116, lr=6.11e-6, step=3661]\\u001b[A\\n\",\n      \"Epoch 17:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.25it/s, loss=0.0116, lr=6.11e-6, step=3661]\\u001b[A\\n\",\n      \"Epoch 17:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.25it/s, loss=0.0048, lr=6.09e-6, step=3662]\\u001b[A\\n\",\n      \"Epoch 17:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.25it/s, loss=0.0048, lr=6.09e-6, step=3662]\\u001b[A\\n\",\n      \"Epoch 17:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.25it/s, loss=0.0196, lr=6.07e-6, step=3663]\\u001b[A\\n\",\n      \"Epoch 17:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0196, lr=6.07e-6, step=3663]\\u001b[A\\n\",\n      \"Epoch 17:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.25it/s, loss=0.0124, lr=6.05e-6, step=3664]\\u001b[A\\n\",\n      \"Epoch 17:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0124, lr=6.05e-6, step=3664]\\u001b[A\\n\",\n      \"Epoch 17:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0242, lr=6.03e-6, step=3665]\\u001b[A\\n\",\n      \"Epoch 17:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:36<02:13,  1.25it/s, loss=0.0242, lr=6.03e-6, step=3665]\\u001b[A\\n\",\n      \"Epoch 17:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:36<02:13,  1.25it/s, loss=0.0244, lr=6.01e-6, step=3666]\\u001b[A\\n\",\n      \"Epoch 17:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:37<02:13,  1.25it/s, loss=0.0244, lr=6.01e-6, step=3666]\\u001b[A\\n\",\n      \"Epoch 17:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:37<02:13,  1.25it/s, loss=0.0114, lr=5.99e-6, step=3667]\\u001b[A\\n\",\n      \"Epoch 17:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [00:38<02:12,  1.24it/s, loss=0.0114, lr=5.99e-6, step=3667]\\u001b[A\\n\",\n      \"Epoch 17:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [00:38<02:12,  1.24it/s, loss=0.0173, lr=5.97e-6, step=3668]\\u001b[A\\n\",\n      \"Epoch 17:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [00:39<02:11,  1.24it/s, loss=0.0173, lr=5.97e-6, step=3668]\\u001b[A\\n\",\n      \"Epoch 17:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [00:39<02:11,  1.24it/s, loss=0.0182, lr=5.95e-6, step=3669]\\u001b[A\\n\",\n      \"Epoch 17:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:40<02:10,  1.25it/s, loss=0.0182, lr=5.95e-6, step=3669]\\u001b[A\\n\",\n      \"Epoch 17:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:40<02:10,  1.25it/s, loss=0.0178, lr=5.93e-6, step=3670]\\u001b[A\\n\",\n      \"Epoch 17:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:09,  1.25it/s, loss=0.0178, lr=5.93e-6, step=3670]\\u001b[A\\n\",\n      \"Epoch 17:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:09,  1.25it/s, loss=0.0345, lr=5.91e-6, step=3671]\\u001b[A\\n\",\n      \"Epoch 17:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:08,  1.25it/s, loss=0.0345, lr=5.91e-6, step=3671]\\u001b[A\\n\",\n      \"Epoch 17:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:08,  1.25it/s, loss=0.0167, lr=5.89e-6, step=3672]\\u001b[A\\n\",\n      \"Epoch 17:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:08,  1.25it/s, loss=0.0167, lr=5.89e-6, step=3672]\\u001b[A\\n\",\n      \"Epoch 17:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:08,  1.25it/s, loss=0.0222, lr=5.87e-6, step=3673]\\u001b[A\\n\",\n      \"Epoch 17:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.25it/s, loss=0.0222, lr=5.87e-6, step=3673]\\u001b[A\\n\",\n      \"Epoch 17:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.25it/s, loss=0.0175, lr=5.85e-6, step=3674]\\u001b[A\\n\",\n      \"Epoch 17:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.25it/s, loss=0.0175, lr=5.85e-6, step=3674]\\u001b[A\\n\",\n      \"Epoch 17:  26%|█████████████████████████████████████████████████████▍                                                                                                                                                         | 55/213 [00:44<02:06,  1.25it/s, loss=0.00515, lr=5.84e-6, step=3675]\\u001b[A\\n\",\n      \"Epoch 17:  26%|██████████████████████████████████████████████████████▍                                                                                                                                                        | 56/213 [00:44<02:05,  1.25it/s, loss=0.00515, lr=5.84e-6, step=3675]\\u001b[A\\n\",\n      \"Epoch 17:  26%|██████████████████████████████████████████████████████▋                                                                                                                                                         | 56/213 [00:44<02:05,  1.25it/s, loss=0.0162, lr=5.82e-6, step=3676]\\u001b[A\\n\",\n      \"Epoch 17:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [00:45<02:05,  1.25it/s, loss=0.0162, lr=5.82e-6, step=3676]\\u001b[A\\n\",\n      \"Epoch 17:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.25it/s, loss=0.0211, lr=5.8e-6, step=3677]\\u001b[A\\n\",\n      \"Epoch 17:  27%|████████████████████████████████████████████████████████▉                                                                                                                                                        | 58/213 [00:46<02:04,  1.25it/s, loss=0.0211, lr=5.8e-6, step=3677]\\u001b[A\\n\",\n      \"Epoch 17:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:46<02:04,  1.25it/s, loss=0.0117, lr=5.78e-6, step=3678]\\u001b[A\\n\",\n      \"Epoch 17:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0117, lr=5.78e-6, step=3678]\\u001b[A\\n\",\n      \"Epoch 17:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0251, lr=5.76e-6, step=3679]\\u001b[A\\n\",\n      \"Epoch 17:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:02,  1.25it/s, loss=0.0251, lr=5.76e-6, step=3679]\\u001b[A\\n\",\n      \"Epoch 17:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:02,  1.25it/s, loss=0.0117, lr=5.74e-6, step=3680]\\u001b[A\\n\",\n      \"Epoch 17:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [00:48<02:02,  1.24it/s, loss=0.0117, lr=5.74e-6, step=3680]\\u001b[A\\n\",\n      \"Epoch 17:  29%|███████████████████████████████████████████████████████████▊                                                                                                                                                     | 61/213 [00:48<02:02,  1.24it/s, loss=0.012, lr=5.72e-6, step=3681]\\u001b[A\\n\",\n      \"Epoch 17:  29%|████████████████████████████████████████████████████████████▊                                                                                                                                                    | 62/213 [00:49<02:01,  1.24it/s, loss=0.012, lr=5.72e-6, step=3681]\\u001b[A\\n\",\n      \"Epoch 17:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:49<02:01,  1.24it/s, loss=0.00748, lr=5.7e-6, step=3682]\\u001b[A\\n\",\n      \"Epoch 17:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<02:00,  1.24it/s, loss=0.00748, lr=5.7e-6, step=3682]\\u001b[A\\n\",\n      \"Epoch 17:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<02:00,  1.24it/s, loss=0.0141, lr=5.68e-6, step=3683]\\u001b[A\\n\",\n      \"Epoch 17:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:51<01:59,  1.24it/s, loss=0.0141, lr=5.68e-6, step=3683]\\u001b[A\\n\",\n      \"Epoch 17:  30%|██████████████████████████████████████████████████████████████▏                                                                                                                                                | 64/213 [00:51<01:59,  1.24it/s, loss=0.00961, lr=5.66e-6, step=3684]\\u001b[A\\n\",\n      \"Epoch 17:  31%|███████████████████████████████████████████████████████████████▏                                                                                                                                               | 65/213 [00:52<01:58,  1.25it/s, loss=0.00961, lr=5.66e-6, step=3684]\\u001b[A\\n\",\n      \"Epoch 17:  31%|███████████████████████████████████████████████████████████████▏                                                                                                                                               | 65/213 [00:52<01:58,  1.25it/s, loss=0.00876, lr=5.64e-6, step=3685]\\u001b[A\\n\",\n      \"Epoch 17:  31%|████████████████████████████████████████████████████████████████▏                                                                                                                                              | 66/213 [00:52<01:57,  1.25it/s, loss=0.00876, lr=5.64e-6, step=3685]\\u001b[A\\n\",\n      \"Epoch 17:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:52<01:57,  1.25it/s, loss=0.0182, lr=5.62e-6, step=3686]\\u001b[A\\n\",\n      \"Epoch 17:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:53<01:57,  1.25it/s, loss=0.0182, lr=5.62e-6, step=3686]\\u001b[A\\n\",\n      \"Epoch 17:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:57,  1.25it/s, loss=0.0154, lr=5.6e-6, step=3687]\\u001b[A\\n\",\n      \"Epoch 17:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:56,  1.25it/s, loss=0.0154, lr=5.6e-6, step=3687]\\u001b[A\\n\",\n      \"Epoch 17:  32%|██████████████████████████████████████████████████████████████████▋                                                                                                                                              | 68/213 [00:54<01:56,  1.25it/s, loss=0.012, lr=5.58e-6, step=3688]\\u001b[A\\n\",\n      \"Epoch 17:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.012, lr=5.58e-6, step=3688]\\u001b[A\\n\",\n      \"Epoch 17:  32%|███████████████████████████████████████████████████████████████████▋                                                                                                                                             | 69/213 [00:55<01:54,  1.25it/s, loss=0.012, lr=5.56e-6, step=3689]\\u001b[A\\n\",\n      \"Epoch 17:  33%|████████████████████████████████████████████████████████████████████▋                                                                                                                                            | 70/213 [00:56<01:54,  1.25it/s, loss=0.012, lr=5.56e-6, step=3689]\\u001b[A\\n\",\n      \"Epoch 17:  33%|█████████████████████████████████████████████████████████████████████                                                                                                                                             | 70/213 [00:56<01:54,  1.25it/s, loss=0.02, lr=5.54e-6, step=3690]\\u001b[A\\n\",\n      \"Epoch 17:  33%|██████████████████████████████████████████████████████████████████████                                                                                                                                            | 71/213 [00:56<01:53,  1.25it/s, loss=0.02, lr=5.54e-6, step=3690]\\u001b[A\\n\",\n      \"Epoch 17:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:56<01:53,  1.25it/s, loss=0.0136, lr=5.53e-6, step=3691]\\u001b[A\\n\",\n      \"Epoch 17:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.0136, lr=5.53e-6, step=3691]\\u001b[A\\n\",\n      \"Epoch 17:  34%|█████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.00603, lr=5.51e-6, step=3692]\\u001b[A\\n\",\n      \"Epoch 17:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [00:58<01:52,  1.25it/s, loss=0.00603, lr=5.51e-6, step=3692]\\u001b[A\\n\",\n      \"Epoch 17:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 73/213 [00:58<01:52,  1.25it/s, loss=0.0105, lr=5.49e-6, step=3693]\\u001b[A\\n\",\n      \"Epoch 17:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [00:59<01:51,  1.25it/s, loss=0.0105, lr=5.49e-6, step=3693]\\u001b[A\\n\",\n      \"Epoch 17:  35%|████████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 74/213 [00:59<01:51,  1.25it/s, loss=0.02, lr=5.47e-6, step=3694]\\u001b[A\\n\",\n      \"Epoch 17:  35%|█████████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 75/213 [01:00<01:50,  1.25it/s, loss=0.02, lr=5.47e-6, step=3694]\\u001b[A\\n\",\n      \"Epoch 17:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:00<01:50,  1.25it/s, loss=0.0407, lr=5.45e-6, step=3695]\\u001b[A\\n\",\n      \"Epoch 17:  36%|██████████████████████████████████████████████████████████████████████████▏                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.0407, lr=5.45e-6, step=3695]\\u001b[A\\n\",\n      \"Epoch 17:  36%|█████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.00756, lr=5.43e-6, step=3696]\\u001b[A\\n\",\n      \"Epoch 17:  36%|██████████████████████████████████████████████████████████████████████████▊                                                                                                                                    | 77/213 [01:01<01:48,  1.25it/s, loss=0.00756, lr=5.43e-6, step=3696]\\u001b[A\\n\",\n      \"Epoch 17:  36%|███████████████████████████████████████████████████████████████████████████▏                                                                                                                                    | 77/213 [01:01<01:48,  1.25it/s, loss=0.0141, lr=5.41e-6, step=3697]\\u001b[A\\n\",\n      \"Epoch 17:  37%|████████████████████████████████████████████████████████████████████████████▏                                                                                                                                   | 78/213 [01:02<01:47,  1.25it/s, loss=0.0141, lr=5.41e-6, step=3697]\\u001b[A\\n\",\n      \"Epoch 17:  37%|████████████████████████████████████████████████████████████████████████████▏                                                                                                                                   | 78/213 [01:02<01:47,  1.25it/s, loss=0.0113, lr=5.39e-6, step=3698]\\u001b[A\\n\",\n      \"Epoch 17:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [01:03<01:47,  1.25it/s, loss=0.0113, lr=5.39e-6, step=3698]\\u001b[A\\n\",\n      \"Epoch 17:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [01:03<01:47,  1.25it/s, loss=0.00673, lr=5.37e-6, step=3699]\\u001b[A\\n\",\n      \"Epoch 17:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [01:04<01:46,  1.25it/s, loss=0.00673, lr=5.37e-6, step=3699]\\u001b[A\\n\",\n      \"Epoch 17:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [01:04<01:46,  1.25it/s, loss=0.00818, lr=5.36e-6, step=3700]\\u001b[A\\n\",\n      \"Epoch 17:  38%|██████████████████████████████████████████████████████████████████████████████▋                                                                                                                                | 81/213 [01:04<01:45,  1.25it/s, loss=0.00818, lr=5.36e-6, step=3700]\\u001b[A\\n\",\n      \"Epoch 17:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:04<01:45,  1.25it/s, loss=0.0108, lr=5.34e-6, step=3701]\\u001b[A\\n\",\n      \"Epoch 17:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0108, lr=5.34e-6, step=3701]\\u001b[A\\n\",\n      \"Epoch 17:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0282, lr=5.32e-6, step=3702]\\u001b[A\\n\",\n      \"Epoch 17:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [01:06<01:43,  1.25it/s, loss=0.0282, lr=5.32e-6, step=3702]\\u001b[A\\n\",\n      \"Epoch 17:  39%|█████████████████████████████████████████████████████████████████████████████████▍                                                                                                                               | 83/213 [01:06<01:43,  1.25it/s, loss=0.0349, lr=5.3e-6, step=3703]\\u001b[A\\n\",\n      \"Epoch 17:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:42,  1.25it/s, loss=0.0349, lr=5.3e-6, step=3703]\\u001b[A\\n\",\n      \"Epoch 17:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [01:07<01:42,  1.25it/s, loss=0.0436, lr=5.28e-6, step=3704]\\u001b[A\\n\",\n      \"Epoch 17:  40%|███████████████████████████████████████████████████████████████████████████████████                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0436, lr=5.28e-6, step=3704]\\u001b[A\\n\",\n      \"Epoch 17:  40%|██████████████████████████████████████████████████████████████████████████████████▌                                                                                                                            | 85/213 [01:08<01:42,  1.25it/s, loss=0.00681, lr=5.26e-6, step=3705]\\u001b[A\\n\",\n      \"Epoch 17:  40%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                                                           | 86/213 [01:08<01:41,  1.25it/s, loss=0.00681, lr=5.26e-6, step=3705]\\u001b[A\\n\",\n      \"Epoch 17:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:08<01:41,  1.25it/s, loss=0.017, lr=5.24e-6, step=3706]\\u001b[A\\n\",\n      \"Epoch 17:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.017, lr=5.24e-6, step=3706]\\u001b[A\\n\",\n      \"Epoch 17:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [01:09<01:40,  1.25it/s, loss=0.0188, lr=5.22e-6, step=3707]\\u001b[A\\n\",\n      \"Epoch 17:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0188, lr=5.22e-6, step=3707]\\u001b[A\\n\",\n      \"Epoch 17:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:10<01:39,  1.25it/s, loss=0.0115, lr=5.21e-6, step=3708]\\u001b[A\\n\",\n      \"Epoch 17:  42%|██████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                         | 89/213 [01:11<01:38,  1.25it/s, loss=0.0115, lr=5.21e-6, step=3708]\\u001b[A\\n\",\n      \"Epoch 17:  42%|██████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                        | 89/213 [01:11<01:38,  1.25it/s, loss=0.00844, lr=5.19e-6, step=3709]\\u001b[A\\n\",\n      \"Epoch 17:  42%|███████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                       | 90/213 [01:12<01:38,  1.25it/s, loss=0.00844, lr=5.19e-6, step=3709]\\u001b[A\\n\",\n      \"Epoch 17:  42%|███████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                        | 90/213 [01:12<01:38,  1.25it/s, loss=0.0107, lr=5.17e-6, step=3710]\\u001b[A\\n\",\n      \"Epoch 17:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [01:12<01:36,  1.26it/s, loss=0.0107, lr=5.17e-6, step=3710]\\u001b[A\\n\",\n      \"Epoch 17:  43%|████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                       | 91/213 [01:12<01:36,  1.26it/s, loss=0.0151, lr=5.15e-6, step=3711]\\u001b[A\\n\",\n      \"Epoch 17:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:13<01:35,  1.26it/s, loss=0.0151, lr=5.15e-6, step=3711]\\u001b[A\\n\",\n      \"Epoch 17:  43%|█████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                      | 92/213 [01:13<01:35,  1.26it/s, loss=0.0168, lr=5.13e-6, step=3712]\\u001b[A\\n\",\n      \"Epoch 17:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:14<01:35,  1.26it/s, loss=0.0168, lr=5.13e-6, step=3712]\\u001b[A\\n\",\n      \"Epoch 17:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:14<01:35,  1.26it/s, loss=0.0182, lr=5.11e-6, step=3713]\\u001b[A\\n\",\n      \"Epoch 17:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:15<01:34,  1.26it/s, loss=0.0182, lr=5.11e-6, step=3713]\\u001b[A\\n\",\n      \"Epoch 17:  44%|████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                    | 94/213 [01:15<01:34,  1.26it/s, loss=0.032, lr=5.09e-6, step=3714]\\u001b[A\\n\",\n      \"Epoch 17:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                   | 95/213 [01:16<01:33,  1.26it/s, loss=0.032, lr=5.09e-6, step=3714]\\u001b[A\\n\",\n      \"Epoch 17:  45%|████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                  | 95/213 [01:16<01:33,  1.26it/s, loss=0.00869, lr=5.08e-6, step=3715]\\u001b[A\\n\",\n      \"Epoch 17:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                 | 96/213 [01:16<01:33,  1.26it/s, loss=0.00869, lr=5.08e-6, step=3715]\\u001b[A\\n\",\n      \"Epoch 17:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [01:16<01:33,  1.26it/s, loss=0.0146, lr=5.06e-6, step=3716]\\u001b[A\\n\",\n      \"Epoch 17:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:17<01:32,  1.26it/s, loss=0.0146, lr=5.06e-6, step=3716]\\u001b[A\\n\",\n      \"Epoch 17:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:17<01:32,  1.26it/s, loss=0.0114, lr=5.04e-6, step=3717]\\u001b[A\\n\",\n      \"Epoch 17:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:18<01:31,  1.26it/s, loss=0.0114, lr=5.04e-6, step=3717]\\u001b[A\\n\",\n      \"Epoch 17:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:18<01:31,  1.26it/s, loss=0.0222, lr=5.02e-6, step=3718]\\u001b[A\\n\",\n      \"Epoch 17:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:19<01:30,  1.25it/s, loss=0.0222, lr=5.02e-6, step=3718]\\u001b[A\\n\",\n      \"Epoch 17:  46%|██████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                                 | 99/213 [01:19<01:30,  1.25it/s, loss=0.0207, lr=5e-6, step=3719]\\u001b[A\\n\",\n      \"Epoch 17:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                               | 100/213 [01:20<01:30,  1.25it/s, loss=0.0207, lr=5e-6, step=3719]\\u001b[A\\n\",\n      \"Epoch 17:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:20<01:30,  1.25it/s, loss=0.0116, lr=4.99e-6, step=3720]\\u001b[A\\n\",\n      \"Epoch 17:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:20<01:29,  1.25it/s, loss=0.0116, lr=4.99e-6, step=3720]\\u001b[A\\n\",\n      \"Epoch 17:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                             | 101/213 [01:20<01:29,  1.25it/s, loss=0.013, lr=4.97e-6, step=3721]\\u001b[A\\n\",\n      \"Epoch 17:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                            | 102/213 [01:21<01:28,  1.25it/s, loss=0.013, lr=4.97e-6, step=3721]\\u001b[A\\n\",\n      \"Epoch 17:  48%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.00913, lr=4.95e-6, step=3722]\\u001b[A\\n\",\n      \"Epoch 17:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:27,  1.25it/s, loss=0.00913, lr=4.95e-6, step=3722]\\u001b[A\\n\",\n      \"Epoch 17:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:27,  1.25it/s, loss=0.00821, lr=4.93e-6, step=3723]\\u001b[A\\n\",\n      \"Epoch 17:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 104/213 [01:23<01:27,  1.25it/s, loss=0.00821, lr=4.93e-6, step=3723]\\u001b[A\\n\",\n      \"Epoch 17:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 104/213 [01:23<01:27,  1.25it/s, loss=0.0152, lr=4.91e-6, step=3724]\\u001b[A\\n\",\n      \"Epoch 17:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0152, lr=4.91e-6, step=3724]\\u001b[A\\n\",\n      \"Epoch 17:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0239, lr=4.89e-6, step=3725]\\u001b[A\\n\",\n      \"Epoch 17:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:24<01:25,  1.25it/s, loss=0.0239, lr=4.89e-6, step=3725]\\u001b[A\\n\",\n      \"Epoch 17:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:24<01:25,  1.25it/s, loss=0.0145, lr=4.88e-6, step=3726]\\u001b[A\\n\",\n      \"Epoch 17:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                       | 107/213 [01:25<01:24,  1.25it/s, loss=0.0145, lr=4.88e-6, step=3726]\\u001b[A\\n\",\n      \"Epoch 17:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 107/213 [01:25<01:24,  1.25it/s, loss=0.00878, lr=4.86e-6, step=3727]\\u001b[A\\n\",\n      \"Epoch 17:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 108/213 [01:26<01:23,  1.26it/s, loss=0.00878, lr=4.86e-6, step=3727]\\u001b[A\\n\",\n      \"Epoch 17:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                      | 108/213 [01:26<01:23,  1.26it/s, loss=0.0393, lr=4.84e-6, step=3728]\\u001b[A\\n\",\n      \"Epoch 17:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0393, lr=4.84e-6, step=3728]\\u001b[A\\n\",\n      \"Epoch 17:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [01:27<01:22,  1.26it/s, loss=0.0208, lr=4.82e-6, step=3729]\\u001b[A\\n\",\n      \"Epoch 17:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [01:28<01:21,  1.26it/s, loss=0.0208, lr=4.82e-6, step=3729]\\u001b[A\\n\",\n      \"Epoch 17:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [01:28<01:21,  1.26it/s, loss=0.0301, lr=4.81e-6, step=3730]\\u001b[A\\n\",\n      \"Epoch 17:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:28<01:21,  1.26it/s, loss=0.0301, lr=4.81e-6, step=3730]\\u001b[A\\n\",\n      \"Epoch 17:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:28<01:21,  1.26it/s, loss=0.0124, lr=4.79e-6, step=3731]\\u001b[A\\n\",\n      \"Epoch 17:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0124, lr=4.79e-6, step=3731]\\u001b[A\\n\",\n      \"Epoch 17:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:29<01:20,  1.26it/s, loss=0.0311, lr=4.77e-6, step=3732]\\u001b[A\\n\",\n      \"Epoch 17:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:30<01:19,  1.25it/s, loss=0.0311, lr=4.77e-6, step=3732]\\u001b[A\\n\",\n      \"Epoch 17:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 113/213 [01:30<01:19,  1.25it/s, loss=0.00467, lr=4.75e-6, step=3733]\\u001b[A\\n\",\n      \"Epoch 17:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [01:31<01:18,  1.25it/s, loss=0.00467, lr=4.75e-6, step=3733]\\u001b[A\\n\",\n      \"Epoch 17:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                | 114/213 [01:31<01:18,  1.25it/s, loss=0.0104, lr=4.73e-6, step=3734]\\u001b[A\\n\",\n      \"Epoch 17:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.0104, lr=4.73e-6, step=3734]\\u001b[A\\n\",\n      \"Epoch 17:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.0144, lr=4.72e-6, step=3735]\\u001b[A\\n\",\n      \"Epoch 17:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:32<01:17,  1.26it/s, loss=0.0144, lr=4.72e-6, step=3735]\\u001b[A\\n\",\n      \"Epoch 17:  54%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                              | 116/213 [01:32<01:17,  1.26it/s, loss=0.0162, lr=4.7e-6, step=3736]\\u001b[A\\n\",\n      \"Epoch 17:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0162, lr=4.7e-6, step=3736]\\u001b[A\\n\",\n      \"Epoch 17:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:33<01:16,  1.26it/s, loss=0.0127, lr=4.68e-6, step=3737]\\u001b[A\\n\",\n      \"Epoch 17:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [01:34<01:15,  1.26it/s, loss=0.0127, lr=4.68e-6, step=3737]\\u001b[A\\n\",\n      \"Epoch 17:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                            | 118/213 [01:34<01:15,  1.26it/s, loss=0.00688, lr=4.66e-6, step=3738]\\u001b[A\\n\",\n      \"Epoch 17:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                           | 119/213 [01:35<01:14,  1.26it/s, loss=0.00688, lr=4.66e-6, step=3738]\\u001b[A\\n\",\n      \"Epoch 17:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [01:35<01:14,  1.26it/s, loss=0.0177, lr=4.65e-6, step=3739]\\u001b[A\\n\",\n      \"Epoch 17:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:13,  1.26it/s, loss=0.0177, lr=4.65e-6, step=3739]\\u001b[A\\n\",\n      \"Epoch 17:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:13,  1.26it/s, loss=0.0227, lr=4.63e-6, step=3740]\\u001b[A\\n\",\n      \"Epoch 17:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0227, lr=4.63e-6, step=3740]\\u001b[A\\n\",\n      \"Epoch 17:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0175, lr=4.61e-6, step=3741]\\u001b[A\\n\",\n      \"Epoch 17:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.26it/s, loss=0.0175, lr=4.61e-6, step=3741]\\u001b[A\\n\",\n      \"Epoch 17:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.26it/s, loss=0.0125, lr=4.59e-6, step=3742]\\u001b[A\\n\",\n      \"Epoch 17:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:38<01:11,  1.26it/s, loss=0.0125, lr=4.59e-6, step=3742]\\u001b[A\\n\",\n      \"Epoch 17:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:38<01:11,  1.26it/s, loss=0.0135, lr=4.58e-6, step=3743]\\u001b[A\\n\",\n      \"Epoch 17:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                      | 124/213 [01:39<01:10,  1.26it/s, loss=0.0135, lr=4.58e-6, step=3743]\\u001b[A\\n\",\n      \"Epoch 17:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                      | 124/213 [01:39<01:10,  1.26it/s, loss=0.00887, lr=4.56e-6, step=3744]\\u001b[A\\n\",\n      \"Epoch 17:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                     | 125/213 [01:39<01:10,  1.26it/s, loss=0.00887, lr=4.56e-6, step=3744]\\u001b[A\\n\",\n      \"Epoch 17:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                     | 125/213 [01:40<01:10,  1.26it/s, loss=0.0345, lr=4.54e-6, step=3745]\\u001b[A\\n\",\n      \"Epoch 17:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [01:40<01:09,  1.25it/s, loss=0.0345, lr=4.54e-6, step=3745]\\u001b[A\\n\",\n      \"Epoch 17:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                    | 126/213 [01:40<01:09,  1.25it/s, loss=0.00925, lr=4.52e-6, step=3746]\\u001b[A\\n\",\n      \"Epoch 17:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.25it/s, loss=0.00925, lr=4.52e-6, step=3746]\\u001b[A\\n\",\n      \"Epoch 17:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.25it/s, loss=0.00878, lr=4.51e-6, step=3747]\\u001b[A\\n\",\n      \"Epoch 17:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                  | 128/213 [01:42<01:07,  1.25it/s, loss=0.00878, lr=4.51e-6, step=3747]\\u001b[A\\n\",\n      \"Epoch 17:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                  | 128/213 [01:42<01:07,  1.25it/s, loss=0.0304, lr=4.49e-6, step=3748]\\u001b[A\\n\",\n      \"Epoch 17:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:43<01:06,  1.25it/s, loss=0.0304, lr=4.49e-6, step=3748]\\u001b[A\\n\",\n      \"Epoch 17:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:43<01:06,  1.25it/s, loss=0.0125, lr=4.47e-6, step=3749]\\u001b[A\\n\",\n      \"Epoch 17:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:43<01:06,  1.25it/s, loss=0.0125, lr=4.47e-6, step=3749]\\u001b[A\\n\",\n      \"Epoch 17:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                | 130/213 [01:44<01:06,  1.25it/s, loss=0.00596, lr=4.45e-6, step=3750]\\u001b[A\\n\",\n      \"Epoch 17:  62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.00596, lr=4.45e-6, step=3750]\\u001b[A\\n\",\n      \"Epoch 17:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.0156, lr=4.44e-6, step=3751]\\u001b[A\\n\",\n      \"Epoch 17:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0156, lr=4.44e-6, step=3751]\\u001b[A\\n\",\n      \"Epoch 17:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0135, lr=4.42e-6, step=3752]\\u001b[A\\n\",\n      \"Epoch 17:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [01:46<01:03,  1.25it/s, loss=0.0135, lr=4.42e-6, step=3752]\\u001b[A\\n\",\n      \"Epoch 17:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                              | 133/213 [01:46<01:03,  1.25it/s, loss=0.0172, lr=4.4e-6, step=3753]\\u001b[A\\n\",\n      \"Epoch 17:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                             | 134/213 [01:47<01:03,  1.25it/s, loss=0.0172, lr=4.4e-6, step=3753]\\u001b[A\\n\",\n      \"Epoch 17:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [01:47<01:03,  1.25it/s, loss=0.0124, lr=4.39e-6, step=3754]\\u001b[A\\n\",\n      \"Epoch 17:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:47<01:02,  1.25it/s, loss=0.0124, lr=4.39e-6, step=3754]\\u001b[A\\n\",\n      \"Epoch 17:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:48<01:02,  1.25it/s, loss=0.0674, lr=4.37e-6, step=3755]\\u001b[A\\n\",\n      \"Epoch 17:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.0674, lr=4.37e-6, step=3755]\\u001b[A\\n\",\n      \"Epoch 17:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.0086, lr=4.35e-6, step=3756]\\u001b[A\\n\",\n      \"Epoch 17:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                         | 137/213 [01:49<01:00,  1.26it/s, loss=0.0086, lr=4.35e-6, step=3756]\\u001b[A\\n\",\n      \"Epoch 17:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                          | 137/213 [01:49<01:00,  1.26it/s, loss=0.016, lr=4.33e-6, step=3757]\\u001b[A\\n\",\n      \"Epoch 17:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                         | 138/213 [01:50<00:59,  1.26it/s, loss=0.016, lr=4.33e-6, step=3757]\\u001b[A\\n\",\n      \"Epoch 17:  65%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                        | 138/213 [01:50<00:59,  1.26it/s, loss=0.00575, lr=4.32e-6, step=3758]\\u001b[A\\n\",\n      \"Epoch 17:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                       | 139/213 [01:51<00:59,  1.25it/s, loss=0.00575, lr=4.32e-6, step=3758]\\u001b[A\\n\",\n      \"Epoch 17:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0217, lr=4.3e-6, step=3759]\\u001b[A\\n\",\n      \"Epoch 17:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                       | 140/213 [01:51<00:58,  1.26it/s, loss=0.0217, lr=4.3e-6, step=3759]\\u001b[A\\n\",\n      \"Epoch 17:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:51<00:58,  1.26it/s, loss=0.0358, lr=4.28e-6, step=3760]\\u001b[A\\n\",\n      \"Epoch 17:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:52<00:57,  1.26it/s, loss=0.0358, lr=4.28e-6, step=3760]\\u001b[A\\n\",\n      \"Epoch 17:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:52<00:57,  1.26it/s, loss=0.0319, lr=4.27e-6, step=3761]\\u001b[A\\n\",\n      \"Epoch 17:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:53<00:56,  1.26it/s, loss=0.0319, lr=4.27e-6, step=3761]\\u001b[A\\n\",\n      \"Epoch 17:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:53<00:56,  1.26it/s, loss=0.0204, lr=4.25e-6, step=3762]\\u001b[A\\n\",\n      \"Epoch 17:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:54<00:55,  1.26it/s, loss=0.0204, lr=4.25e-6, step=3762]\\u001b[A\\n\",\n      \"Epoch 17:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:54<00:55,  1.26it/s, loss=0.0146, lr=4.23e-6, step=3763]\\u001b[A\\n\",\n      \"Epoch 17:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0146, lr=4.23e-6, step=3763]\\u001b[A\\n\",\n      \"Epoch 17:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.26it/s, loss=0.0128, lr=4.22e-6, step=3764]\\u001b[A\\n\",\n      \"Epoch 17:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:55<00:54,  1.26it/s, loss=0.0128, lr=4.22e-6, step=3764]\\u001b[A\\n\",\n      \"Epoch 17:  68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                  | 145/213 [01:55<00:54,  1.26it/s, loss=0.0306, lr=4.2e-6, step=3765]\\u001b[A\\n\",\n      \"Epoch 17:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.0306, lr=4.2e-6, step=3765]\\u001b[A\\n\",\n      \"Epoch 17:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.0124, lr=4.18e-6, step=3766]\\u001b[A\\n\",\n      \"Epoch 17:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0124, lr=4.18e-6, step=3766]\\u001b[A\\n\",\n      \"Epoch 17:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [01:57<00:52,  1.26it/s, loss=0.0129, lr=4.17e-6, step=3767]\\u001b[A\\n\",\n      \"Epoch 17:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0129, lr=4.17e-6, step=3767]\\u001b[A\\n\",\n      \"Epoch 17:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [01:58<00:51,  1.26it/s, loss=0.0178, lr=4.15e-6, step=3768]\\u001b[A\\n\",\n      \"Epoch 17:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.0178, lr=4.15e-6, step=3768]\\u001b[A\\n\",\n      \"Epoch 17:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                              | 149/213 [01:59<00:50,  1.26it/s, loss=0.00865, lr=4.13e-6, step=3769]\\u001b[A\\n\",\n      \"Epoch 17:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                             | 150/213 [01:59<00:50,  1.26it/s, loss=0.00865, lr=4.13e-6, step=3769]\\u001b[A\\n\",\n      \"Epoch 17:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                             | 150/213 [01:59<00:50,  1.26it/s, loss=0.0143, lr=4.12e-6, step=3770]\\u001b[A\\n\",\n      \"Epoch 17:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0143, lr=4.12e-6, step=3770]\\u001b[A\\n\",\n      \"Epoch 17:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.26it/s, loss=0.0135, lr=4.1e-6, step=3771]\\u001b[A\\n\",\n      \"Epoch 17:  71%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.0135, lr=4.1e-6, step=3771]\\u001b[A\\n\",\n      \"Epoch 17:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                           | 152/213 [02:01<00:48,  1.26it/s, loss=0.00902, lr=4.08e-6, step=3772]\\u001b[A\\n\",\n      \"Epoch 17:  72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                          | 153/213 [02:02<00:47,  1.26it/s, loss=0.00902, lr=4.08e-6, step=3772]\\u001b[A\\n\",\n      \"Epoch 17:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [02:02<00:47,  1.26it/s, loss=0.0142, lr=4.07e-6, step=3773]\\u001b[A\\n\",\n      \"Epoch 17:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0142, lr=4.07e-6, step=3773]\\u001b[A\\n\",\n      \"Epoch 17:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0104, lr=4.05e-6, step=3774]\\u001b[A\\n\",\n      \"Epoch 17:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [02:03<00:46,  1.26it/s, loss=0.0104, lr=4.05e-6, step=3774]\\u001b[A\\n\",\n      \"Epoch 17:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                        | 155/213 [02:03<00:46,  1.26it/s, loss=0.00892, lr=4.03e-6, step=3775]\\u001b[A\\n\",\n      \"Epoch 17:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.00892, lr=4.03e-6, step=3775]\\u001b[A\\n\",\n      \"Epoch 17:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [02:04<00:45,  1.26it/s, loss=0.0119, lr=4.02e-6, step=3776]\\u001b[A\\n\",\n      \"Epoch 17:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0119, lr=4.02e-6, step=3776]\\u001b[A\\n\",\n      \"Epoch 17:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                       | 157/213 [02:05<00:44,  1.26it/s, loss=0.0241, lr=4e-6, step=3777]\\u001b[A\\n\",\n      \"Epoch 17:  74%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                      | 158/213 [02:06<00:43,  1.26it/s, loss=0.0241, lr=4e-6, step=3777]\\u001b[A\\n\",\n      \"Epoch 17:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                     | 158/213 [02:06<00:43,  1.26it/s, loss=0.00864, lr=3.98e-6, step=3778]\\u001b[A\\n\",\n      \"Epoch 17:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:42,  1.26it/s, loss=0.00864, lr=3.98e-6, step=3778]\\u001b[A\\n\",\n      \"Epoch 17:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:42,  1.26it/s, loss=0.00616, lr=3.97e-6, step=3779]\\u001b[A\\n\",\n      \"Epoch 17:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:07<00:42,  1.26it/s, loss=0.00616, lr=3.97e-6, step=3779]\\u001b[A\\n\",\n      \"Epoch 17:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:07<00:42,  1.26it/s, loss=0.00655, lr=3.95e-6, step=3780]\\u001b[A\\n\",\n      \"Epoch 17:  76%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                  | 161/213 [02:08<00:41,  1.26it/s, loss=0.00655, lr=3.95e-6, step=3780]\\u001b[A\\n\",\n      \"Epoch 17:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.26it/s, loss=0.0269, lr=3.93e-6, step=3781]\\u001b[A\\n\",\n      \"Epoch 17:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0269, lr=3.93e-6, step=3781]\\u001b[A\\n\",\n      \"Epoch 17:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0233, lr=3.92e-6, step=3782]\\u001b[A\\n\",\n      \"Epoch 17:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:10<00:39,  1.25it/s, loss=0.0233, lr=3.92e-6, step=3782]\\u001b[A\\n\",\n      \"Epoch 17:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                | 163/213 [02:10<00:39,  1.25it/s, loss=0.0196, lr=3.9e-6, step=3783]\\u001b[A\\n\",\n      \"Epoch 17:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0196, lr=3.9e-6, step=3783]\\u001b[A\\n\",\n      \"Epoch 17:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0218, lr=3.89e-6, step=3784]\\u001b[A\\n\",\n      \"Epoch 17:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:11<00:38,  1.26it/s, loss=0.0218, lr=3.89e-6, step=3784]\\u001b[A\\n\",\n      \"Epoch 17:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:11<00:38,  1.26it/s, loss=0.0125, lr=3.87e-6, step=3785]\\u001b[A\\n\",\n      \"Epoch 17:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.25it/s, loss=0.0125, lr=3.87e-6, step=3785]\\u001b[A\\n\",\n      \"Epoch 17:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.25it/s, loss=0.0143, lr=3.85e-6, step=3786]\\u001b[A\\n\",\n      \"Epoch 17:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0143, lr=3.85e-6, step=3786]\\u001b[A\\n\",\n      \"Epoch 17:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0118, lr=3.84e-6, step=3787]\\u001b[A\\n\",\n      \"Epoch 17:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:35,  1.25it/s, loss=0.0118, lr=3.84e-6, step=3787]\\u001b[A\\n\",\n      \"Epoch 17:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:35,  1.25it/s, loss=0.0116, lr=3.82e-6, step=3788]\\u001b[A\\n\",\n      \"Epoch 17:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:15<00:35,  1.25it/s, loss=0.0116, lr=3.82e-6, step=3788]\\u001b[A\\n\",\n      \"Epoch 17:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:15<00:35,  1.25it/s, loss=0.0106, lr=3.81e-6, step=3789]\\u001b[A\\n\",\n      \"Epoch 17:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:15<00:34,  1.25it/s, loss=0.0106, lr=3.81e-6, step=3789]\\u001b[A\\n\",\n      \"Epoch 17:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:15<00:34,  1.25it/s, loss=0.0309, lr=3.79e-6, step=3790]\\u001b[A\\n\",\n      \"Epoch 17:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.25it/s, loss=0.0309, lr=3.79e-6, step=3790]\\u001b[A\\n\",\n      \"Epoch 17:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.25it/s, loss=0.0164, lr=3.77e-6, step=3791]\\u001b[A\\n\",\n      \"Epoch 17:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.25it/s, loss=0.0164, lr=3.77e-6, step=3791]\\u001b[A\\n\",\n      \"Epoch 17:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.25it/s, loss=0.0159, lr=3.76e-6, step=3792]\\u001b[A\\n\",\n      \"Epoch 17:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0159, lr=3.76e-6, step=3792]\\u001b[A\\n\",\n      \"Epoch 17:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0258, lr=3.74e-6, step=3793]\\u001b[A\\n\",\n      \"Epoch 17:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0258, lr=3.74e-6, step=3793]\\u001b[A\\n\",\n      \"Epoch 17:  82%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                     | 174/213 [02:19<00:31,  1.25it/s, loss=0.00838, lr=3.73e-6, step=3794]\\u001b[A\\n\",\n      \"Epoch 17:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                    | 175/213 [02:19<00:30,  1.25it/s, loss=0.00838, lr=3.73e-6, step=3794]\\u001b[A\\n\",\n      \"Epoch 17:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 175/213 [02:19<00:30,  1.25it/s, loss=0.0104, lr=3.71e-6, step=3795]\\u001b[A\\n\",\n      \"Epoch 17:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                    | 176/213 [02:20<00:29,  1.25it/s, loss=0.0104, lr=3.71e-6, step=3795]\\u001b[A\\n\",\n      \"Epoch 17:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                   | 176/213 [02:20<00:29,  1.25it/s, loss=0.00965, lr=3.69e-6, step=3796]\\u001b[A\\n\",\n      \"Epoch 17:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                  | 177/213 [02:21<00:28,  1.25it/s, loss=0.00965, lr=3.69e-6, step=3796]\\u001b[A\\n\",\n      \"Epoch 17:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.011, lr=3.68e-6, step=3797]\\u001b[A\\n\",\n      \"Epoch 17:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.011, lr=3.68e-6, step=3797]\\u001b[A\\n\",\n      \"Epoch 17:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.0254, lr=3.66e-6, step=3798]\\u001b[A\\n\",\n      \"Epoch 17:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0254, lr=3.66e-6, step=3798]\\u001b[A\\n\",\n      \"Epoch 17:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0116, lr=3.65e-6, step=3799]\\u001b[A\\n\",\n      \"Epoch 17:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:23<00:26,  1.25it/s, loss=0.0116, lr=3.65e-6, step=3799]\\u001b[A\\n\",\n      \"Epoch 17:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:23<00:26,  1.25it/s, loss=0.0107, lr=3.63e-6, step=3800]\\u001b[A\\n\",\n      \"Epoch 17:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0107, lr=3.63e-6, step=3800]\\u001b[A\\n\",\n      \"Epoch 17:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0227, lr=3.62e-6, step=3801]\\u001b[A\\n\",\n      \"Epoch 17:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.26it/s, loss=0.0227, lr=3.62e-6, step=3801]\\u001b[A\\n\",\n      \"Epoch 17:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.26it/s, loss=0.00442, lr=3.6e-6, step=3802]\\u001b[A\\n\",\n      \"Epoch 17:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00442, lr=3.6e-6, step=3802]\\u001b[A\\n\",\n      \"Epoch 17:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.0052, lr=3.59e-6, step=3803]\\u001b[A\\n\",\n      \"Epoch 17:  86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.0052, lr=3.59e-6, step=3803]\\u001b[A\\n\",\n      \"Epoch 17:  86%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                            | 184/213 [02:27<00:23,  1.26it/s, loss=0.007, lr=3.57e-6, step=3804]\\u001b[A\\n\",\n      \"Epoch 17:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                           | 185/213 [02:27<00:22,  1.25it/s, loss=0.007, lr=3.57e-6, step=3804]\\u001b[A\\n\",\n      \"Epoch 17:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                           | 185/213 [02:27<00:22,  1.25it/s, loss=0.015, lr=3.55e-6, step=3805]\\u001b[A\\n\",\n      \"Epoch 17:  87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.015, lr=3.55e-6, step=3805]\\u001b[A\\n\",\n      \"Epoch 17:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0147, lr=3.54e-6, step=3806]\\u001b[A\\n\",\n      \"Epoch 17:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0147, lr=3.54e-6, step=3806]\\u001b[A\\n\",\n      \"Epoch 17:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0202, lr=3.52e-6, step=3807]\\u001b[A\\n\",\n      \"Epoch 17:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0202, lr=3.52e-6, step=3807]\\u001b[A\\n\",\n      \"Epoch 17:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0167, lr=3.51e-6, step=3808]\\u001b[A\\n\",\n      \"Epoch 17:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0167, lr=3.51e-6, step=3808]\\u001b[A\\n\",\n      \"Epoch 17:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0165, lr=3.49e-6, step=3809]\\u001b[A\\n\",\n      \"Epoch 17:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0165, lr=3.49e-6, step=3809]\\u001b[A\\n\",\n      \"Epoch 17:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:31<00:18,  1.25it/s, loss=0.0134, lr=3.48e-6, step=3810]\\u001b[A\\n\",\n      \"Epoch 17:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0134, lr=3.48e-6, step=3810]\\u001b[A\\n\",\n      \"Epoch 17:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.25it/s, loss=0.0115, lr=3.46e-6, step=3811]\\u001b[A\\n\",\n      \"Epoch 17:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0115, lr=3.46e-6, step=3811]\\u001b[A\\n\",\n      \"Epoch 17:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:33<00:16,  1.25it/s, loss=0.0102, lr=3.45e-6, step=3812]\\u001b[A\\n\",\n      \"Epoch 17:  91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.0102, lr=3.45e-6, step=3812]\\u001b[A\\n\",\n      \"Epoch 17:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:15,  1.25it/s, loss=0.018, lr=3.43e-6, step=3813]\\u001b[A\\n\",\n      \"Epoch 17:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.018, lr=3.43e-6, step=3813]\\u001b[A\\n\",\n      \"Epoch 17:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0433, lr=3.42e-6, step=3814]\\u001b[A\\n\",\n      \"Epoch 17:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.0433, lr=3.42e-6, step=3814]\\u001b[A\\n\",\n      \"Epoch 17:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:35<00:14,  1.25it/s, loss=0.0101, lr=3.4e-6, step=3815]\\u001b[A\\n\",\n      \"Epoch 17:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0101, lr=3.4e-6, step=3815]\\u001b[A\\n\",\n      \"Epoch 17:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0122, lr=3.39e-6, step=3816]\\u001b[A\\n\",\n      \"Epoch 17:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0122, lr=3.39e-6, step=3816]\\u001b[A\\n\",\n      \"Epoch 17:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0101, lr=3.37e-6, step=3817]\\u001b[A\\n\",\n      \"Epoch 17:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0101, lr=3.37e-6, step=3817]\\u001b[A\\n\",\n      \"Epoch 17:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.00977, lr=3.36e-6, step=3818]\\u001b[A\\n\",\n      \"Epoch 17:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00977, lr=3.36e-6, step=3818]\\u001b[A\\n\",\n      \"Epoch 17:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.00614, lr=3.34e-6, step=3819]\\u001b[A\\n\",\n      \"Epoch 17:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [02:39<00:10,  1.25it/s, loss=0.00614, lr=3.34e-6, step=3819]\\u001b[A\\n\",\n      \"Epoch 17:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [02:39<00:10,  1.25it/s, loss=0.00932, lr=3.33e-6, step=3820]\\u001b[A\\n\",\n      \"Epoch 17:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍           | 201/213 [02:40<00:09,  1.25it/s, loss=0.00932, lr=3.33e-6, step=3820]\\u001b[A\\n\",\n      \"Epoch 17:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0185, lr=3.31e-6, step=3821]\\u001b[A\\n\",\n      \"Epoch 17:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0185, lr=3.31e-6, step=3821]\\u001b[A\\n\",\n      \"Epoch 17:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0205, lr=3.3e-6, step=3822]\\u001b[A\\n\",\n      \"Epoch 17:  95%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0205, lr=3.3e-6, step=3822]\\u001b[A\\n\",\n      \"Epoch 17:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:42<00:08,  1.25it/s, loss=0.0181, lr=3.28e-6, step=3823]\\u001b[A\\n\",\n      \"Epoch 17:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.0181, lr=3.28e-6, step=3823]\\u001b[A\\n\",\n      \"Epoch 17:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.25it/s, loss=0.00568, lr=3.27e-6, step=3824]\\u001b[A\\n\",\n      \"Epoch 17:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎       | 205/213 [02:43<00:06,  1.25it/s, loss=0.00568, lr=3.27e-6, step=3824]\\u001b[A\\n\",\n      \"Epoch 17:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:43<00:06,  1.25it/s, loss=0.0108, lr=3.25e-6, step=3825]\\u001b[A\\n\",\n      \"Epoch 17:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0108, lr=3.25e-6, step=3825]\\u001b[A\\n\",\n      \"Epoch 17:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.25it/s, loss=0.0186, lr=3.24e-6, step=3826]\\u001b[A\\n\",\n      \"Epoch 17:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.0186, lr=3.24e-6, step=3826]\\u001b[A\\n\",\n      \"Epoch 17:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.25it/s, loss=0.0198, lr=3.22e-6, step=3827]\\u001b[A\\n\",\n      \"Epoch 17:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:46<00:04,  1.25it/s, loss=0.0198, lr=3.22e-6, step=3827]\\u001b[A\\n\",\n      \"Epoch 17:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:46<00:04,  1.25it/s, loss=0.0127, lr=3.21e-6, step=3828]\\u001b[A\\n\",\n      \"Epoch 17:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.0127, lr=3.21e-6, step=3828]\\u001b[A\\n\",\n      \"Epoch 17:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████    | 209/213 [02:47<00:03,  1.25it/s, loss=0.02, lr=3.19e-6, step=3829]\\u001b[A\\n\",\n      \"Epoch 17:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.02, lr=3.19e-6, step=3829]\\u001b[A\\n\",\n      \"Epoch 17:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:47<00:02,  1.25it/s, loss=0.00508, lr=3.18e-6, step=3830]\\u001b[A\\n\",\n      \"Epoch 17:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.00508, lr=3.18e-6, step=3830]\\u001b[A\\n\",\n      \"Epoch 17:  99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.25it/s, loss=0.0169, lr=3.16e-6, step=3831]\\u001b[A\\n\",\n      \"Epoch 17: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.0169, lr=3.16e-6, step=3831]\\u001b[A\\n\",\n      \"Epoch 17: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.00888, lr=3.15e-6, step=3832]\\u001b[A\\n\",\n      \"Epoch 17: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.45it/s, loss=0.00888, lr=3.15e-6, step=3832]\\u001b[A\\n\",\n      \"Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.45it/s, loss=0.0105, lr=3.13e-6, step=3833]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"35d1316e765540f6b875e88bb95b3867\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 17: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.0105, lr=3.13e-6, step=3833]\\n\",\n      \"Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:49<00:00,  1.46it/s, loss=0.00974, lr=7.9e-7, step=4046]\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b0844e05bfdc47b1916ee7163bb00a81\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Epoch 18: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [07:35<00:00,  2.14s/it, loss=0.00974, lr=7.9e-7, step=4046]\\u001b[A\\n\",\n      \"\\n\",\n      \"Epoch 19:   0%|                                                                                                                                                                                                                                                             | 0/213 [00:00<?, ?it/s]\\u001b[A\\n\",\n      \"Epoch 19:   0%|█▏                                                                                                                                                                                                                                          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step=4048]\\u001b[A\\n\",\n      \"Epoch 19:   1%|██▉                                                                                                                                                                                                              | 3/213 [00:02<02:50,  1.23it/s, loss=0.0111, lr=7.75e-7, step=4048]\\u001b[A\\n\",\n      \"Epoch 19:   1%|██▉                                                                                                                                                                                                             | 3/213 [00:02<02:50,  1.23it/s, loss=0.00861, lr=7.68e-7, step=4049]\\u001b[A\\n\",\n      \"Epoch 19:   2%|███▉                                                                                                                                                                                                            | 4/213 [00:03<02:48,  1.24it/s, loss=0.00861, lr=7.68e-7, step=4049]\\u001b[A\\n\",\n      \"Epoch 19:   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                                                                                                                                                                  | 6/213 [00:04<02:46,  1.25it/s, loss=0.0373, lr=7.53e-7, step=4051]\\u001b[A\\n\",\n      \"Epoch 19:   3%|█████▊                                                                                                                                                                                                          | 6/213 [00:04<02:46,  1.25it/s, loss=0.00356, lr=7.46e-7, step=4052]\\u001b[A\\n\",\n      \"Epoch 19:   3%|██████▊                                                                                                                                                                                                         | 7/213 [00:05<02:45,  1.25it/s, loss=0.00356, lr=7.46e-7, step=4052]\\u001b[A\\n\",\n      \"Epoch 19:   3%|██████▊                                                                                                                                                                                                          | 7/213 [00:05<02:45,  1.25it/s, loss=0.0125, lr=7.39e-7, step=4053]\\u001b[A\\n\",\n      \"Epoch 19:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:43,  1.25it/s, loss=0.0125, lr=7.39e-7, step=4053]\\u001b[A\\n\",\n      \"Epoch 19:   4%|███████▊                                                                                                                                                                                                         | 8/213 [00:06<02:43,  1.25it/s, loss=0.0166, lr=7.32e-7, step=4054]\\u001b[A\\n\",\n      \"Epoch 19:   4%|████████▊                                                                                                                                                                                                        | 9/213 [00:07<02:43,  1.25it/s, loss=0.0166, lr=7.32e-7, step=4054]\\u001b[A\\n\",\n      \"Epoch 19:   4%|████████▊                                                                                                                                                                                                       | 9/213 [00:07<02:43,  1.25it/s, loss=0.00603, lr=7.25e-7, step=4055]\\u001b[A\\n\",\n      \"Epoch 19:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:42,  1.25it/s, loss=0.00603, lr=7.25e-7, step=4055]\\u001b[A\\n\",\n      \"Epoch 19:   5%|█████████▋                                                                                                                                                                                                     | 10/213 [00:08<02:42,  1.25it/s, loss=0.00702, lr=7.17e-7, step=4056]\\u001b[A\\n\",\n      \"Epoch 19:   5%|██████████▋                                                                                                                                                                                                    | 11/213 [00:08<02:41,  1.25it/s, loss=0.00702, lr=7.17e-7, step=4056]\\u001b[A\\n\",\n      \"Epoch 19:   5%|██████████▊                                                                                                                                                                                                      | 11/213 [00:08<02:41,  1.25it/s, loss=0.0206, lr=7.1e-7, step=4057]\\u001b[A\\n\",\n      \"Epoch 19:   6%|███████████▊                                                                                                                                                                                                     | 12/213 [00:09<02:40,  1.25it/s, loss=0.0206, lr=7.1e-7, step=4057]\\u001b[A\\n\",\n      \"Epoch 19:   6%|███████████▋                                                                                                                                                                                                    | 12/213 [00:09<02:40,  1.25it/s, loss=0.0227, lr=7.03e-7, step=4058]\\u001b[A\\n\",\n      \"Epoch 19:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:39,  1.25it/s, loss=0.0227, lr=7.03e-7, step=4058]\\u001b[A\\n\",\n      \"Epoch 19:   6%|████████████▋                                                                                                                                                                                                   | 13/213 [00:10<02:39,  1.25it/s, loss=0.0169, lr=6.96e-7, step=4059]\\u001b[A\\n\",\n      \"Epoch 19:   7%|█████████████▋                                                                                                                                                                                                  | 14/213 [00:11<02:39,  1.25it/s, loss=0.0169, lr=6.96e-7, step=4059]\\u001b[A\\n\",\n      \"Epoch 19:   7%|█████████████▋                                                                                                                                                                                                   | 14/213 [00:11<02:39,  1.25it/s, loss=0.0277, lr=6.9e-7, step=4060]\\u001b[A\\n\",\n      \"Epoch 19:   7%|██████████████▋                                                                                                                                                                                                  | 15/213 [00:12<02:38,  1.25it/s, loss=0.0277, lr=6.9e-7, step=4060]\\u001b[A\\n\",\n      \"Epoch 19:   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                                                                                                                                                                 | 17/213 [00:13<02:36,  1.25it/s, loss=0.0081, lr=6.76e-7, step=4062]\\u001b[A\\n\",\n      \"Epoch 19:   8%|████████████████▌                                                                                                                                                                                               | 17/213 [00:13<02:36,  1.25it/s, loss=0.0287, lr=6.69e-7, step=4063]\\u001b[A\\n\",\n      \"Epoch 19:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [00:14<02:35,  1.25it/s, loss=0.0287, lr=6.69e-7, step=4063]\\u001b[A\\n\",\n      \"Epoch 19:   8%|█████████████████▌                                                                                                                                                                                              | 18/213 [00:14<02:35,  1.25it/s, loss=0.0209, lr=6.62e-7, step=4064]\\u001b[A\\n\",\n      \"Epoch 19:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:34,  1.25it/s, loss=0.0209, lr=6.62e-7, step=4064]\\u001b[A\\n\",\n      \"Epoch 19:   9%|██████████████████▌                                                                                                                                                                                             | 19/213 [00:15<02:34,  1.25it/s, loss=0.0145, lr=6.55e-7, step=4065]\\u001b[A\\n\",\n      \"Epoch 19:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:33,  1.25it/s, loss=0.0145, lr=6.55e-7, step=4065]\\u001b[A\\n\",\n      \"Epoch 19:   9%|███████████████████▌                                                                                                                                                                                            | 20/213 [00:16<02:33,  1.25it/s, loss=0.0122, lr=6.49e-7, step=4066]\\u001b[A\\n\",\n      \"Epoch 19:  10%|████████████████████▌                                                                                                                                                                                           | 21/213 [00:16<02:33,  1.25it/s, loss=0.0122, lr=6.49e-7, step=4066]\\u001b[A\\n\",\n      \"Epoch 19:  10%|████████████████████▍                                                                                                                                                                                          | 21/213 [00:16<02:33,  1.25it/s, loss=0.00713, lr=6.42e-7, step=4067]\\u001b[A\\n\",\n      \"Epoch 19:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:32,  1.25it/s, loss=0.00713, lr=6.42e-7, step=4067]\\u001b[A\\n\",\n      \"Epoch 19:  10%|█████████████████████▍                                                                                                                                                                                         | 22/213 [00:17<02:32,  1.25it/s, loss=0.00725, lr=6.35e-7, step=4068]\\u001b[A\\n\",\n      \"Epoch 19:  11%|██████████████████████▎                                                                                                                                                                                        | 23/213 [00:18<02:31,  1.25it/s, loss=0.00725, lr=6.35e-7, step=4068]\\u001b[A\\n\",\n      \"Epoch 19:  11%|██████████████████████▍                                                                                                                                                                                         | 23/213 [00:18<02:31,  1.25it/s, loss=0.0298, lr=6.29e-7, step=4069]\\u001b[A\\n\",\n      \"Epoch 19:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.25it/s, loss=0.0298, lr=6.29e-7, step=4069]\\u001b[A\\n\",\n      \"Epoch 19:  11%|███████████████████████▍                                                                                                                                                                                        | 24/213 [00:19<02:31,  1.25it/s, loss=0.0143, lr=6.22e-7, step=4070]\\u001b[A\\n\",\n      \"Epoch 19:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0143, lr=6.22e-7, step=4070]\\u001b[A\\n\",\n      \"Epoch 19:  12%|████████████████████████▍                                                                                                                                                                                       | 25/213 [00:20<02:30,  1.25it/s, loss=0.0184, lr=6.16e-7, step=4071]\\u001b[A\\n\",\n      \"Epoch 19:  12%|█████████████████████████▍                                                                                                                                                                                      | 26/213 [00:20<02:29,  1.25it/s, loss=0.0184, lr=6.16e-7, step=4071]\\u001b[A\\n\",\n      \"Epoch 19:  12%|█████████████████████████▎                                                                                                                                                                                     | 26/213 [00:20<02:29,  1.25it/s, loss=0.00626, lr=6.09e-7, step=4072]\\u001b[A\\n\",\n      \"Epoch 19:  13%|██████████████████████████▏                                                                                                                                                                                    | 27/213 [00:21<02:28,  1.25it/s, loss=0.00626, lr=6.09e-7, step=4072]\\u001b[A\\n\",\n      \"Epoch 19:  13%|██████████████████████████▏                                                                                                                                                                                    | 27/213 [00:21<02:28,  1.25it/s, loss=0.00621, lr=6.03e-7, step=4073]\\u001b[A\\n\",\n      \"Epoch 19:  13%|███████████████████████████▏                                                                                                                                                                                   | 28/213 [00:22<02:27,  1.25it/s, loss=0.00621, lr=6.03e-7, step=4073]\\u001b[A\\n\",\n      \"Epoch 19:  13%|███████████████████████████▎                                                                                                                                                                                    | 28/213 [00:22<02:27,  1.25it/s, loss=0.0194, lr=5.96e-7, step=4074]\\u001b[A\\n\",\n      \"Epoch 19:  14%|████████████████████████████▎                                                                                                                                                                                   | 29/213 [00:23<02:26,  1.25it/s, loss=0.0194, lr=5.96e-7, step=4074]\\u001b[A\\n\",\n      \"Epoch 19:  14%|████████████████████████████▍                                                                                                                                                                                    | 29/213 [00:23<02:26,  1.25it/s, loss=0.0119, lr=5.9e-7, step=4075]\\u001b[A\\n\",\n      \"Epoch 19:  14%|█████████████████████████████▍                                                                                                                                                                                   | 30/213 [00:24<02:26,  1.25it/s, loss=0.0119, lr=5.9e-7, step=4075]\\u001b[A\\n\",\n      \"Epoch 19:  14%|█████████████████████████████▎                                                                                                                                                                                  | 30/213 [00:24<02:26,  1.25it/s, loss=0.0291, lr=5.83e-7, step=4076]\\u001b[A\\n\",\n      \"Epoch 19:  15%|██████████████████████████████▎                                                                                                                   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            | 32/213 [00:25<02:24,  1.25it/s, loss=0.0107, lr=5.71e-7, step=4078]\\u001b[A\\n\",\n      \"Epoch 19:  15%|████████████████████████████████▏                                                                                                                                                                               | 33/213 [00:26<02:23,  1.25it/s, loss=0.0107, lr=5.71e-7, step=4078]\\u001b[A\\n\",\n      \"Epoch 19:  15%|████████████████████████████████                                                                                                                                                                               | 33/213 [00:26<02:23,  1.25it/s, loss=0.00919, lr=5.64e-7, step=4079]\\u001b[A\\n\",\n      \"Epoch 19:  16%|█████████████████████████████████                                                                                                                                                                              | 34/213 [00:27<02:23,  1.25it/s, loss=0.00919, lr=5.64e-7, step=4079]\\u001b[A\\n\",\n      \"Epoch 19:  16%|█████████████████████████████████                                                                                                                                                                              | 34/213 [00:27<02:23,  1.25it/s, loss=0.00894, lr=5.58e-7, step=4080]\\u001b[A\\n\",\n      \"Epoch 19:  16%|██████████████████████████████████                                                                                                                                                                             | 35/213 [00:28<02:22,  1.25it/s, loss=0.00894, lr=5.58e-7, step=4080]\\u001b[A\\n\",\n      \"Epoch 19:  16%|██████████████████████████████████▏                                                                                                                                                                             | 35/213 [00:28<02:22,  1.25it/s, loss=0.0162, lr=5.52e-7, step=4081]\\u001b[A\\n\",\n      \"Epoch 19:  17%|███████████████████████████████████▏                                                                                                                                                                            | 36/213 [00:28<02:21,  1.25it/s, loss=0.0162, lr=5.52e-7, step=4081]\\u001b[A\\n\",\n      \"Epoch 19:  17%|███████████████████████████████████▎                                                                                                                                                                             | 36/213 [00:28<02:21,  1.25it/s, loss=0.009, lr=5.46e-7, step=4082]\\u001b[A\\n\",\n      \"Epoch 19:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:20,  1.25it/s, loss=0.009, lr=5.46e-7, step=4082]\\u001b[A\\n\",\n      \"Epoch 19:  17%|████████████████████████████████████▎                                                                                                                                                                            | 37/213 [00:29<02:20,  1.25it/s, loss=0.0289, lr=5.4e-7, step=4083]\\u001b[A\\n\",\n      \"Epoch 19:  18%|█████████████████████████████████████▎                                                                                                                                                                           | 38/213 [00:30<02:20,  1.25it/s, loss=0.0289, lr=5.4e-7, step=4083]\\u001b[A\\n\",\n      \"Epoch 19:  18%|████████████████████████████████████▉                                                                                                                                                                          | 38/213 [00:30<02:20,  1.25it/s, loss=0.00782, lr=5.34e-7, step=4084]\\u001b[A\\n\",\n      \"Epoch 19:  18%|█████████████████████████████████████▉                                                                                                                                                                         | 39/213 [00:31<02:19,  1.25it/s, loss=0.00782, lr=5.34e-7, step=4084]\\u001b[A\\n\",\n      \"Epoch 19:  18%|██████████████████████████████████████                                                                                                                                                                          | 39/213 [00:31<02:19,  1.25it/s, loss=0.0381, lr=5.27e-7, step=4085]\\u001b[A\\n\",\n      \"Epoch 19:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0381, lr=5.27e-7, step=4085]\\u001b[A\\n\",\n      \"Epoch 19:  19%|███████████████████████████████████████                                                                                                                                                                         | 40/213 [00:32<02:18,  1.25it/s, loss=0.0162, lr=5.21e-7, step=4086]\\u001b[A\\n\",\n      \"Epoch 19:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:32<02:17,  1.25it/s, loss=0.0162, lr=5.21e-7, step=4086]\\u001b[A\\n\",\n      \"Epoch 19:  19%|████████████████████████████████████████                                                                                                                                                                        | 41/213 [00:32<02:17,  1.25it/s, loss=0.0118, lr=5.15e-7, step=4087]\\u001b[A\\n\",\n      \"Epoch 19:  20%|█████████████████████████████████████████                                                                                                                                                                       | 42/213 [00:33<02:17,  1.24it/s, loss=0.0118, lr=5.15e-7, step=4087]\\u001b[A\\n\",\n      \"Epoch 19:  20%|████████████████████████████████████████▊                                                                                                                                                                      | 42/213 [00:33<02:17,  1.24it/s, loss=0.00457, lr=5.09e-7, step=4088]\\u001b[A\\n\",\n      \"Epoch 19:  20%|█████████████████████████████████████████▊                                                                                                                                                                     | 43/213 [00:34<02:16,  1.24it/s, loss=0.00457, lr=5.09e-7, step=4088]\\u001b[A\\n\",\n      \"Epoch 19:  20%|█████████████████████████████████████████▉                                                                                                                                                                      | 43/213 [00:34<02:16,  1.24it/s, loss=0.0273, lr=5.04e-7, step=4089]\\u001b[A\\n\",\n      \"Epoch 19:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.24it/s, loss=0.0273, lr=5.04e-7, step=4089]\\u001b[A\\n\",\n      \"Epoch 19:  21%|██████████████████████████████████████████▉                                                                                                                                                                     | 44/213 [00:35<02:15,  1.24it/s, loss=0.0158, lr=4.98e-7, step=4090]\\u001b[A\\n\",\n      \"Epoch 19:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0158, lr=4.98e-7, step=4090]\\u001b[A\\n\",\n      \"Epoch 19:  21%|███████████████████████████████████████████▉                                                                                                                                                                    | 45/213 [00:36<02:14,  1.25it/s, loss=0.0196, lr=4.92e-7, step=4091]\\u001b[A\\n\",\n      \"Epoch 19:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:36<02:14,  1.24it/s, loss=0.0196, lr=4.92e-7, step=4091]\\u001b[A\\n\",\n      \"Epoch 19:  22%|████████████████████████████████████████████▉                                                                                                                                                                   | 46/213 [00:36<02:14,  1.24it/s, loss=0.0298, lr=4.86e-7, step=4092]\\u001b[A\\n\",\n      \"Epoch 19:  22%|█████████████████████████████████████████████▉                                                                                                                                                                  | 47/213 [00:37<02:13,  1.24it/s, loss=0.0298, lr=4.86e-7, step=4092]\\u001b[A\\n\",\n      \"Epoch 19:  22%|██████████████████████████████████████████████                                                                                                                                                                   | 47/213 [00:37<02:13,  1.24it/s, loss=0.0136, lr=4.8e-7, step=4093]\\u001b[A\\n\",\n      \"Epoch 19:  23%|███████████████████████████████████████████████                                                                                                                                                                  | 48/213 [00:38<02:12,  1.24it/s, loss=0.0136, lr=4.8e-7, step=4093]\\u001b[A\\n\",\n      \"Epoch 19:  23%|██████████████████████████████████████████████▊                                                                                                                                                                 | 48/213 [00:38<02:12,  1.24it/s, loss=0.0098, lr=4.74e-7, step=4094]\\u001b[A\\n\",\n      \"Epoch 19:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [00:39<02:11,  1.25it/s, loss=0.0098, lr=4.74e-7, step=4094]\\u001b[A\\n\",\n      \"Epoch 19:  23%|███████████████████████████████████████████████▊                                                                                                                                                                | 49/213 [00:39<02:11,  1.25it/s, loss=0.0191, lr=4.69e-7, step=4095]\\u001b[A\\n\",\n      \"Epoch 19:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:40<02:10,  1.25it/s, loss=0.0191, lr=4.69e-7, step=4095]\\u001b[A\\n\",\n      \"Epoch 19:  23%|████████████████████████████████████████████████▊                                                                                                                                                               | 50/213 [00:40<02:10,  1.25it/s, loss=0.0161, lr=4.63e-7, step=4096]\\u001b[A\\n\",\n      \"Epoch 19:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:09,  1.25it/s, loss=0.0161, lr=4.63e-7, step=4096]\\u001b[A\\n\",\n      \"Epoch 19:  24%|█████████████████████████████████████████████████▊                                                                                                                                                              | 51/213 [00:40<02:09,  1.25it/s, loss=0.0332, lr=4.57e-7, step=4097]\\u001b[A\\n\",\n      \"Epoch 19:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:09,  1.25it/s, loss=0.0332, lr=4.57e-7, step=4097]\\u001b[A\\n\",\n      \"Epoch 19:  24%|██████████████████████████████████████████████████▊                                                                                                                                                             | 52/213 [00:41<02:09,  1.25it/s, loss=0.0153, lr=4.52e-7, step=4098]\\u001b[A\\n\",\n      \"Epoch 19:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:08,  1.24it/s, loss=0.0153, lr=4.52e-7, step=4098]\\u001b[A\\n\",\n      \"Epoch 19:  25%|███████████████████████████████████████████████████▊                                                                                                                                                            | 53/213 [00:42<02:08,  1.24it/s, loss=0.0194, lr=4.46e-7, step=4099]\\u001b[A\\n\",\n      \"Epoch 19:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.24it/s, loss=0.0194, lr=4.46e-7, step=4099]\\u001b[A\\n\",\n      \"Epoch 19:  25%|████████████████████████████████████████████████████▋                                                                                                                                                           | 54/213 [00:43<02:07,  1.24it/s, loss=0.0158, lr=4.41e-7, step=4100]\\u001b[A\\n\",\n      \"Epoch 19:  26%|█████████████████████████████████████████████████████▋                                                                                                                                                          | 55/213 [00:44<02:06,  1.24it/s, loss=0.0158, lr=4.41e-7, step=4100]\\u001b[A\\n\",\n      \"Epoch 19:  26%|█████████████████████████████████████████████████████▍                                                                                                                                                         | 55/213 [00:44<02:06,  1.24it/s, loss=0.00648, lr=4.35e-7, step=4101]\\u001b[A\\n\",\n      \"Epoch 19:  26%|██████████████████████████████████████████████████████▍                                                                                                                                                        | 56/213 [00:44<02:06,  1.24it/s, loss=0.00648, lr=4.35e-7, step=4101]\\u001b[A\\n\",\n      \"Epoch 19:  26%|██████████████████████████████████████████████████████▉                                                                                                                                                          | 56/213 [00:44<02:06,  1.24it/s, loss=0.0162, lr=4.3e-7, step=4102]\\u001b[A\\n\",\n      \"Epoch 19:  27%|███████████████████████████████████████████████████████▉                                                                                                                                                         | 57/213 [00:45<02:05,  1.24it/s, loss=0.0162, lr=4.3e-7, step=4102]\\u001b[A\\n\",\n      \"Epoch 19:  27%|███████████████████████████████████████████████████████▋                                                                                                                                                        | 57/213 [00:45<02:05,  1.24it/s, loss=0.0242, lr=4.24e-7, step=4103]\\u001b[A\\n\",\n      \"Epoch 19:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:46<02:04,  1.24it/s, loss=0.0242, lr=4.24e-7, step=4103]\\u001b[A\\n\",\n      \"Epoch 19:  27%|████████████████████████████████████████████████████████▋                                                                                                                                                       | 58/213 [00:46<02:04,  1.24it/s, loss=0.0133, lr=4.19e-7, step=4104]\\u001b[A\\n\",\n      \"Epoch 19:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0133, lr=4.19e-7, step=4104]\\u001b[A\\n\",\n      \"Epoch 19:  28%|█████████████████████████████████████████████████████████▌                                                                                                                                                      | 59/213 [00:47<02:03,  1.25it/s, loss=0.0267, lr=4.13e-7, step=4105]\\u001b[A\\n\",\n      \"Epoch 19:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:02,  1.25it/s, loss=0.0267, lr=4.13e-7, step=4105]\\u001b[A\\n\",\n      \"Epoch 19:  28%|██████████████████████████████████████████████████████████▌                                                                                                                                                     | 60/213 [00:48<02:02,  1.25it/s, loss=0.0119, lr=4.08e-7, step=4106]\\u001b[A\\n\",\n      \"Epoch 19:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [00:48<02:01,  1.25it/s, loss=0.0119, lr=4.08e-7, step=4106]\\u001b[A\\n\",\n      \"Epoch 19:  29%|███████████████████████████████████████████████████████████▌                                                                                                                                                    | 61/213 [00:48<02:01,  1.25it/s, loss=0.0125, lr=4.03e-7, step=4107]\\u001b[A\\n\",\n      \"Epoch 19:  29%|████████████████████████████████████████████████████████████▌                                                                                                                                                   | 62/213 [00:49<02:00,  1.25it/s, loss=0.0125, lr=4.03e-7, step=4107]\\u001b[A\\n\",\n      \"Epoch 19:  29%|████████████████████████████████████████████████████████████▎                                                                                                                                                  | 62/213 [00:49<02:00,  1.25it/s, loss=0.00878, lr=3.97e-7, step=4108]\\u001b[A\\n\",\n      \"Epoch 19:  30%|█████████████████████████████████████████████████████████████▏                                                                                                                                                 | 63/213 [00:50<02:00,  1.25it/s, loss=0.00878, lr=3.97e-7, step=4108]\\u001b[A\\n\",\n      \"Epoch 19:  30%|█████████████████████████████████████████████████████████████▌                                                                                                                                                  | 63/213 [00:50<02:00,  1.25it/s, loss=0.0139, lr=3.92e-7, step=4109]\\u001b[A\\n\",\n      \"Epoch 19:  30%|██████████████████████████████████████████████████████████████▍                                                                                                                                                 | 64/213 [00:51<01:59,  1.25it/s, loss=0.0139, lr=3.92e-7, step=4109]\\u001b[A\\n\",\n      \"Epoch 19:  30%|██████████████████████████████████████████████████████████████▏                                                                                                                                                | 64/213 [00:51<01:59,  1.25it/s, loss=0.00795, lr=3.87e-7, step=4110]\\u001b[A\\n\",\n      \"Epoch 19:  31%|███████████████████████████████████████████████████████████████▏                                                                                                                                               | 65/213 [00:52<01:58,  1.25it/s, loss=0.00795, lr=3.87e-7, step=4110]\\u001b[A\\n\",\n      \"Epoch 19:  31%|███████████████████████████████████████████████████████████████▍                                                                                                                                                | 65/213 [00:52<01:58,  1.25it/s, loss=0.0113, lr=3.82e-7, step=4111]\\u001b[A\\n\",\n      \"Epoch 19:  31%|████████████████████████████████████████████████████████████████▍                                                                                                                                               | 66/213 [00:52<01:57,  1.25it/s, loss=0.0113, lr=3.82e-7, step=4111]\\u001b[A\\n\",\n      \"Epoch 19:  31%|████████████████████████████████████████████████████████████████▊                                                                                                                                                | 66/213 [00:52<01:57,  1.25it/s, loss=0.016, lr=3.77e-7, step=4112]\\u001b[A\\n\",\n      \"Epoch 19:  31%|█████████████████████████████████████████████████████████████████▋                                                                                                                                               | 67/213 [00:53<01:56,  1.25it/s, loss=0.016, lr=3.77e-7, step=4112]\\u001b[A\\n\",\n      \"Epoch 19:  31%|█████████████████████████████████████████████████████████████████▍                                                                                                                                              | 67/213 [00:53<01:56,  1.25it/s, loss=0.0153, lr=3.72e-7, step=4113]\\u001b[A\\n\",\n      \"Epoch 19:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [00:54<01:55,  1.25it/s, loss=0.0153, lr=3.72e-7, step=4113]\\u001b[A\\n\",\n      \"Epoch 19:  32%|██████████████████████████████████████████████████████████████████▍                                                                                                                                             | 68/213 [00:54<01:55,  1.25it/s, loss=0.0087, lr=3.66e-7, step=4114]\\u001b[A\\n\",\n      \"Epoch 19:  32%|███████████████████████████████████████████████████████████████████▍                                                                                                                                            | 69/213 [00:55<01:55,  1.25it/s, loss=0.0087, lr=3.66e-7, step=4114]\\u001b[A\\n\",\n      \"Epoch 19:  32%|███████████████████████████████████████████████████████████████████▍                                                                                                                                            | 69/213 [00:55<01:55,  1.25it/s, loss=0.0104, lr=3.61e-7, step=4115]\\u001b[A\\n\",\n      \"Epoch 19:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [00:56<01:54,  1.25it/s, loss=0.0104, lr=3.61e-7, step=4115]\\u001b[A\\n\",\n      \"Epoch 19:  33%|████████████████████████████████████████████████████████████████████▎                                                                                                                                           | 70/213 [00:56<01:54,  1.25it/s, loss=0.0183, lr=3.56e-7, step=4116]\\u001b[A\\n\",\n      \"Epoch 19:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:56<01:53,  1.25it/s, loss=0.0183, lr=3.56e-7, step=4116]\\u001b[A\\n\",\n      \"Epoch 19:  33%|█████████████████████████████████████████████████████████████████████▎                                                                                                                                          | 71/213 [00:56<01:53,  1.25it/s, loss=0.0114, lr=3.52e-7, step=4117]\\u001b[A\\n\",\n      \"Epoch 19:  34%|██████████████████████████████████████████████████████████████████████▎                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.0114, lr=3.52e-7, step=4117]\\u001b[A\\n\",\n      \"Epoch 19:  34%|█████████████████████████████████████████████████████████████████████▉                                                                                                                                         | 72/213 [00:57<01:52,  1.25it/s, loss=0.00606, lr=3.47e-7, step=4118]\\u001b[A\\n\",\n      \"Epoch 19:  34%|██████████████████████████████████████████████████████████████████████▉                                                                                                                                        | 73/213 [00:58<01:51,  1.25it/s, loss=0.00606, lr=3.47e-7, step=4118]\\u001b[A\\n\",\n      \"Epoch 19:  34%|███████████████████████████████████████████████████████████████████████▎                                                                                                                                        | 73/213 [00:58<01:51,  1.25it/s, loss=0.0109, lr=3.42e-7, step=4119]\\u001b[A\\n\",\n      \"Epoch 19:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [00:59<01:51,  1.25it/s, loss=0.0109, lr=3.42e-7, step=4119]\\u001b[A\\n\",\n      \"Epoch 19:  35%|████████████████████████████████████████████████████████████████████████▎                                                                                                                                       | 74/213 [00:59<01:51,  1.25it/s, loss=0.0171, lr=3.37e-7, step=4120]\\u001b[A\\n\",\n      \"Epoch 19:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:00<01:50,  1.25it/s, loss=0.0171, lr=3.37e-7, step=4120]\\u001b[A\\n\",\n      \"Epoch 19:  35%|█████████████████████████████████████████████████████████████████████████▏                                                                                                                                      | 75/213 [01:00<01:50,  1.25it/s, loss=0.0463, lr=3.32e-7, step=4121]\\u001b[A\\n\",\n      \"Epoch 19:  36%|██████████████████████████████████████████████████████████████████████████▏                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.0463, lr=3.32e-7, step=4121]\\u001b[A\\n\",\n      \"Epoch 19:  36%|█████████████████████████████████████████████████████████████████████████▊                                                                                                                                     | 76/213 [01:00<01:49,  1.25it/s, loss=0.00875, lr=3.27e-7, step=4122]\\u001b[A\\n\",\n      \"Epoch 19:  36%|██████████████████████████████████████████████████████████████████████████▊                                                                                                                                    | 77/213 [01:01<01:48,  1.25it/s, loss=0.00875, lr=3.27e-7, step=4122]\\u001b[A\\n\",\n      \"Epoch 19:  36%|███████████████████████████████████████████████████████████████████████████▏                                                                                                                                    | 77/213 [01:01<01:48,  1.25it/s, loss=0.0189, lr=3.22e-7, step=4123]\\u001b[A\\n\",\n      \"Epoch 19:  37%|████████████████████████████████████████████████████████████████████████████▏                                                                                                                                   | 78/213 [01:02<01:47,  1.25it/s, loss=0.0189, lr=3.22e-7, step=4123]\\u001b[A\\n\",\n      \"Epoch 19:  37%|███████████████████████████████████████████████████████████████████████████▊                                                                                                                                   | 78/213 [01:02<01:47,  1.25it/s, loss=0.00944, lr=3.18e-7, step=4124]\\u001b[A\\n\",\n      \"Epoch 19:  37%|████████████████████████████████████████████████████████████████████████████▊                                                                                                                                  | 79/213 [01:03<01:46,  1.25it/s, loss=0.00944, lr=3.18e-7, step=4124]\\u001b[A\\n\",\n      \"Epoch 19:  37%|█████████████████████████████████████████████████████████████████████████████▏                                                                                                                                  | 79/213 [01:03<01:46,  1.25it/s, loss=0.0091, lr=3.13e-7, step=4125]\\u001b[A\\n\",\n      \"Epoch 19:  38%|██████████████████████████████████████████████████████████████████████████████                                                                                                                                  | 80/213 [01:04<01:46,  1.25it/s, loss=0.0091, lr=3.13e-7, step=4125]\\u001b[A\\n\",\n      \"Epoch 19:  38%|█████████████████████████████████████████████████████████████████████████████▋                                                                                                                                 | 80/213 [01:04<01:46,  1.25it/s, loss=0.00784, lr=3.08e-7, step=4126]\\u001b[A\\n\",\n      \"Epoch 19:  38%|██████████████████████████████████████████████████████████████████████████████▋                                                                                                                                | 81/213 [01:04<01:45,  1.25it/s, loss=0.00784, lr=3.08e-7, step=4126]\\u001b[A\\n\",\n      \"Epoch 19:  38%|███████████████████████████████████████████████████████████████████████████████                                                                                                                                 | 81/213 [01:04<01:45,  1.25it/s, loss=0.0121, lr=3.04e-7, step=4127]\\u001b[A\\n\",\n      \"Epoch 19:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0121, lr=3.04e-7, step=4127]\\u001b[A\\n\",\n      \"Epoch 19:  38%|████████████████████████████████████████████████████████████████████████████████                                                                                                                                | 82/213 [01:05<01:44,  1.25it/s, loss=0.0348, lr=2.99e-7, step=4128]\\u001b[A\\n\",\n      \"Epoch 19:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [01:06<01:44,  1.25it/s, loss=0.0348, lr=2.99e-7, step=4128]\\u001b[A\\n\",\n      \"Epoch 19:  39%|█████████████████████████████████████████████████████████████████████████████████                                                                                                                               | 83/213 [01:06<01:44,  1.25it/s, loss=0.0391, lr=2.95e-7, step=4129]\\u001b[A\\n\",\n      \"Epoch 19:  39%|██████████████████████████████████████████████████████████████████████████████████                                                                                                                              | 84/213 [01:07<01:43,  1.25it/s, loss=0.0391, lr=2.95e-7, step=4129]\\u001b[A\\n\",\n      \"Epoch 19:  39%|██████████████████████████████████████████████████████████████████████████████████▍                                                                                                                              | 84/213 [01:07<01:43,  1.25it/s, loss=0.0486, lr=2.9e-7, step=4130]\\u001b[A\\n\",\n      \"Epoch 19:  40%|███████████████████████████████████████████████████████████████████████████████████▍                                                                                                                             | 85/213 [01:08<01:42,  1.25it/s, loss=0.0486, lr=2.9e-7, step=4130]\\u001b[A\\n\",\n      \"Epoch 19:  40%|██████████████████████████████████████████████████████████████████████████████████▌                                                                                                                            | 85/213 [01:08<01:42,  1.25it/s, loss=0.00787, lr=2.86e-7, step=4131]\\u001b[A\\n\",\n      \"Epoch 19:  40%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                                                           | 86/213 [01:08<01:41,  1.25it/s, loss=0.00787, lr=2.86e-7, step=4131]\\u001b[A\\n\",\n      \"Epoch 19:  40%|████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                            | 86/213 [01:08<01:41,  1.25it/s, loss=0.016, lr=2.81e-7, step=4132]\\u001b[A\\n\",\n      \"Epoch 19:  41%|█████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                           | 87/213 [01:09<01:41,  1.25it/s, loss=0.016, lr=2.81e-7, step=4132]\\u001b[A\\n\",\n      \"Epoch 19:  41%|████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                           | 87/213 [01:09<01:41,  1.25it/s, loss=0.0196, lr=2.77e-7, step=4133]\\u001b[A\\n\",\n      \"Epoch 19:  41%|█████████████████████████████████████████████████████████████████████████████████████▉                                                                                                                          | 88/213 [01:10<01:40,  1.25it/s, loss=0.0196, lr=2.77e-7, step=4133]\\u001b[A\\n\",\n      \"Epoch 19:  41%|█████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                         | 88/213 [01:10<01:40,  1.25it/s, loss=0.00992, lr=2.72e-7, step=4134]\\u001b[A\\n\",\n      \"Epoch 19:  42%|██████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                        | 89/213 [01:11<01:39,  1.25it/s, loss=0.00992, lr=2.72e-7, step=4134]\\u001b[A\\n\",\n      \"Epoch 19:  42%|██████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                        | 89/213 [01:11<01:39,  1.25it/s, loss=0.00893, lr=2.68e-7, step=4135]\\u001b[A\\n\",\n      \"Epoch 19:  42%|███████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                       | 90/213 [01:12<01:38,  1.25it/s, loss=0.00893, lr=2.68e-7, step=4135]\\u001b[A\\n\",\n      \"Epoch 19:  42%|███████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                       | 90/213 [01:12<01:38,  1.25it/s, loss=0.00906, lr=2.64e-7, step=4136]\\u001b[A\\n\",\n      \"Epoch 19:  43%|████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                                      | 91/213 [01:12<01:37,  1.25it/s, loss=0.00906, lr=2.64e-7, step=4136]\\u001b[A\\n\",\n      \"Epoch 19:  43%|█████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                       | 91/213 [01:12<01:37,  1.25it/s, loss=0.0196, lr=2.6e-7, step=4137]\\u001b[A\\n\",\n      \"Epoch 19:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.0196, lr=2.6e-7, step=4137]\\u001b[A\\n\",\n      \"Epoch 19:  43%|██████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                      | 92/213 [01:13<01:36,  1.25it/s, loss=0.018, lr=2.55e-7, step=4138]\\u001b[A\\n\",\n      \"Epoch 19:  44%|███████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                     | 93/213 [01:14<01:36,  1.25it/s, loss=0.018, lr=2.55e-7, step=4138]\\u001b[A\\n\",\n      \"Epoch 19:  44%|██████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                     | 93/213 [01:14<01:36,  1.25it/s, loss=0.0175, lr=2.51e-7, step=4139]\\u001b[A\\n\",\n      \"Epoch 19:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0175, lr=2.51e-7, step=4139]\\u001b[A\\n\",\n      \"Epoch 19:  44%|███████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                    | 94/213 [01:15<01:35,  1.25it/s, loss=0.0409, lr=2.47e-7, step=4140]\\u001b[A\\n\",\n      \"Epoch 19:  45%|████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                                   | 95/213 [01:16<01:34,  1.25it/s, loss=0.0409, lr=2.47e-7, step=4140]\\u001b[A\\n\",\n      \"Epoch 19:  45%|████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                  | 95/213 [01:16<01:34,  1.25it/s, loss=0.00964, lr=2.43e-7, step=4141]\\u001b[A\\n\",\n      \"Epoch 19:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                                 | 96/213 [01:16<01:33,  1.25it/s, loss=0.00964, lr=2.43e-7, step=4141]\\u001b[A\\n\",\n      \"Epoch 19:  45%|█████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                  | 96/213 [01:16<01:33,  1.25it/s, loss=0.0149, lr=2.39e-7, step=4142]\\u001b[A\\n\",\n      \"Epoch 19:  46%|██████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.0149, lr=2.39e-7, step=4142]\\u001b[A\\n\",\n      \"Epoch 19:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                 | 97/213 [01:17<01:32,  1.25it/s, loss=0.012, lr=2.35e-7, step=4143]\\u001b[A\\n\",\n      \"Epoch 19:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.012, lr=2.35e-7, step=4143]\\u001b[A\\n\",\n      \"Epoch 19:  46%|███████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                                | 98/213 [01:18<01:31,  1.25it/s, loss=0.0234, lr=2.31e-7, step=4144]\\u001b[A\\n\",\n      \"Epoch 19:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:19<01:31,  1.25it/s, loss=0.0234, lr=2.31e-7, step=4144]\\u001b[A\\n\",\n      \"Epoch 19:  46%|████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                               | 99/213 [01:19<01:31,  1.25it/s, loss=0.0199, lr=2.27e-7, step=4145]\\u001b[A\\n\",\n      \"Epoch 19:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:20<01:30,  1.25it/s, loss=0.0199, lr=2.27e-7, step=4145]\\u001b[A\\n\",\n      \"Epoch 19:  47%|█████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                             | 100/213 [01:20<01:30,  1.25it/s, loss=0.0119, lr=2.23e-7, step=4146]\\u001b[A\\n\",\n      \"Epoch 19:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:20<01:29,  1.25it/s, loss=0.0119, lr=2.23e-7, step=4146]\\u001b[A\\n\",\n      \"Epoch 19:  47%|██████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                            | 101/213 [01:20<01:29,  1.25it/s, loss=0.0127, lr=2.19e-7, step=4147]\\u001b[A\\n\",\n      \"Epoch 19:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.0127, lr=2.19e-7, step=4147]\\u001b[A\\n\",\n      \"Epoch 19:  48%|██████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                                           | 102/213 [01:21<01:28,  1.25it/s, loss=0.00801, lr=2.15e-7, step=4148]\\u001b[A\\n\",\n      \"Epoch 19:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:27,  1.25it/s, loss=0.00801, lr=2.15e-7, step=4148]\\u001b[A\\n\",\n      \"Epoch 19:  48%|███████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                          | 103/213 [01:22<01:27,  1.25it/s, loss=0.00844, lr=2.11e-7, step=4149]\\u001b[A\\n\",\n      \"Epoch 19:  49%|████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 104/213 [01:23<01:26,  1.25it/s, loss=0.00844, lr=2.11e-7, step=4149]\\u001b[A\\n\",\n      \"Epoch 19:  49%|█████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                          | 104/213 [01:23<01:26,  1.25it/s, loss=0.0137, lr=2.07e-7, step=4150]\\u001b[A\\n\",\n      \"Epoch 19:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0137, lr=2.07e-7, step=4150]\\u001b[A\\n\",\n      \"Epoch 19:  49%|██████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                         | 105/213 [01:24<01:26,  1.25it/s, loss=0.0266, lr=2.03e-7, step=4151]\\u001b[A\\n\",\n      \"Epoch 19:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                                        | 106/213 [01:24<01:25,  1.25it/s, loss=0.0266, lr=2.03e-7, step=4151]\\u001b[A\\n\",\n      \"Epoch 19:  50%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                         | 106/213 [01:24<01:25,  1.25it/s, loss=0.0131, lr=2e-7, step=4152]\\u001b[A\\n\",\n      \"Epoch 19:  50%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                        | 107/213 [01:25<01:24,  1.25it/s, loss=0.0131, lr=2e-7, step=4152]\\u001b[A\\n\",\n      \"Epoch 19:  50%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 107/213 [01:25<01:24,  1.25it/s, loss=0.00871, lr=1.96e-7, step=4153]\\u001b[A\\n\",\n      \"Epoch 19:  51%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 108/213 [01:26<01:23,  1.25it/s, loss=0.00871, lr=1.96e-7, step=4153]\\u001b[A\\n\",\n      \"Epoch 19:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                      | 108/213 [01:26<01:23,  1.25it/s, loss=0.032, lr=1.92e-7, step=4154]\\u001b[A\\n\",\n      \"Epoch 19:  51%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.032, lr=1.92e-7, step=4154]\\u001b[A\\n\",\n      \"Epoch 19:  51%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                     | 109/213 [01:27<01:23,  1.25it/s, loss=0.0387, lr=1.89e-7, step=4155]\\u001b[A\\n\",\n      \"Epoch 19:  52%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.0387, lr=1.89e-7, step=4155]\\u001b[A\\n\",\n      \"Epoch 19:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                    | 110/213 [01:28<01:22,  1.25it/s, loss=0.021, lr=1.85e-7, step=4156]\\u001b[A\\n\",\n      \"Epoch 19:  52%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                   | 111/213 [01:28<01:21,  1.25it/s, loss=0.021, lr=1.85e-7, step=4156]\\u001b[A\\n\",\n      \"Epoch 19:  52%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                   | 111/213 [01:28<01:21,  1.25it/s, loss=0.0119, lr=1.81e-7, step=4157]\\u001b[A\\n\",\n      \"Epoch 19:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0119, lr=1.81e-7, step=4157]\\u001b[A\\n\",\n      \"Epoch 19:  53%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                  | 112/213 [01:29<01:20,  1.25it/s, loss=0.0361, lr=1.78e-7, step=4158]\\u001b[A\\n\",\n      \"Epoch 19:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                                 | 113/213 [01:30<01:20,  1.25it/s, loss=0.0361, lr=1.78e-7, step=4158]\\u001b[A\\n\",\n      \"Epoch 19:  53%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                                | 113/213 [01:30<01:20,  1.25it/s, loss=0.00512, lr=1.74e-7, step=4159]\\u001b[A\\n\",\n      \"Epoch 19:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [01:31<01:19,  1.25it/s, loss=0.00512, lr=1.74e-7, step=4159]\\u001b[A\\n\",\n      \"Epoch 19:  54%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                               | 114/213 [01:31<01:19,  1.25it/s, loss=0.00875, lr=1.71e-7, step=4160]\\u001b[A\\n\",\n      \"Epoch 19:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                                              | 115/213 [01:32<01:18,  1.25it/s, loss=0.00875, lr=1.71e-7, step=4160]\\u001b[A\\n\",\n      \"Epoch 19:  54%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                               | 115/213 [01:32<01:18,  1.25it/s, loss=0.0157, lr=1.68e-7, step=4161]\\u001b[A\\n\",\n      \"Epoch 19:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:32<01:17,  1.25it/s, loss=0.0157, lr=1.68e-7, step=4161]\\u001b[A\\n\",\n      \"Epoch 19:  54%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                              | 116/213 [01:32<01:17,  1.25it/s, loss=0.0157, lr=1.64e-7, step=4162]\\u001b[A\\n\",\n      \"Epoch 19:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:33<01:16,  1.25it/s, loss=0.0157, lr=1.64e-7, step=4162]\\u001b[A\\n\",\n      \"Epoch 19:  55%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                             | 117/213 [01:33<01:16,  1.25it/s, loss=0.0134, lr=1.61e-7, step=4163]\\u001b[A\\n\",\n      \"Epoch 19:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                            | 118/213 [01:34<01:16,  1.25it/s, loss=0.0134, lr=1.61e-7, step=4163]\\u001b[A\\n\",\n      \"Epoch 19:  55%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                            | 118/213 [01:34<01:16,  1.25it/s, loss=0.00786, lr=1.57e-7, step=4164]\\u001b[A\\n\",\n      \"Epoch 19:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                           | 119/213 [01:35<01:15,  1.25it/s, loss=0.00786, lr=1.57e-7, step=4164]\\u001b[A\\n\",\n      \"Epoch 19:  56%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                                           | 119/213 [01:35<01:15,  1.25it/s, loss=0.0205, lr=1.54e-7, step=4165]\\u001b[A\\n\",\n      \"Epoch 19:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:14,  1.25it/s, loss=0.0205, lr=1.54e-7, step=4165]\\u001b[A\\n\",\n      \"Epoch 19:  56%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 120/213 [01:36<01:14,  1.25it/s, loss=0.0192, lr=1.51e-7, step=4166]\\u001b[A\\n\",\n      \"Epoch 19:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0192, lr=1.51e-7, step=4166]\\u001b[A\\n\",\n      \"Epoch 19:  57%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                         | 121/213 [01:36<01:13,  1.25it/s, loss=0.0138, lr=1.48e-7, step=4167]\\u001b[A\\n\",\n      \"Epoch 19:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0138, lr=1.48e-7, step=4167]\\u001b[A\\n\",\n      \"Epoch 19:  57%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                        | 122/213 [01:37<01:12,  1.25it/s, loss=0.0108, lr=1.44e-7, step=4168]\\u001b[A\\n\",\n      \"Epoch 19:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:38<01:11,  1.25it/s, loss=0.0108, lr=1.44e-7, step=4168]\\u001b[A\\n\",\n      \"Epoch 19:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                       | 123/213 [01:38<01:11,  1.25it/s, loss=0.0107, lr=1.41e-7, step=4169]\\u001b[A\\n\",\n      \"Epoch 19:  58%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                      | 124/213 [01:39<01:11,  1.25it/s, loss=0.0107, lr=1.41e-7, step=4169]\\u001b[A\\n\",\n      \"Epoch 19:  58%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                      | 124/213 [01:39<01:11,  1.25it/s, loss=0.00794, lr=1.38e-7, step=4170]\\u001b[A\\n\",\n      \"Epoch 19:  59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                     | 125/213 [01:40<01:10,  1.25it/s, loss=0.00794, lr=1.38e-7, step=4170]\\u001b[A\\n\",\n      \"Epoch 19:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                     | 125/213 [01:40<01:10,  1.25it/s, loss=0.0285, lr=1.35e-7, step=4171]\\u001b[A\\n\",\n      \"Epoch 19:  59%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                    | 126/213 [01:40<01:09,  1.25it/s, loss=0.0285, lr=1.35e-7, step=4171]\\u001b[A\\n\",\n      \"Epoch 19:  59%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                    | 126/213 [01:40<01:09,  1.25it/s, loss=0.00842, lr=1.32e-7, step=4172]\\u001b[A\\n\",\n      \"Epoch 19:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.25it/s, loss=0.00842, lr=1.32e-7, step=4172]\\u001b[A\\n\",\n      \"Epoch 19:  60%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                   | 127/213 [01:41<01:08,  1.25it/s, loss=0.00781, lr=1.29e-7, step=4173]\\u001b[A\\n\",\n      \"Epoch 19:  60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                                  | 128/213 [01:42<01:08,  1.25it/s, loss=0.00781, lr=1.29e-7, step=4173]\\u001b[A\\n\",\n      \"Epoch 19:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                   | 128/213 [01:42<01:08,  1.25it/s, loss=0.035, lr=1.26e-7, step=4174]\\u001b[A\\n\",\n      \"Epoch 19:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                                  | 129/213 [01:43<01:07,  1.25it/s, loss=0.035, lr=1.26e-7, step=4174]\\u001b[A\\n\",\n      \"Epoch 19:  61%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                 | 129/213 [01:43<01:07,  1.25it/s, loss=0.0138, lr=1.23e-7, step=4175]\\u001b[A\\n\",\n      \"Epoch 19:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:44<01:06,  1.24it/s, loss=0.0138, lr=1.23e-7, step=4175]\\u001b[A\\n\",\n      \"Epoch 19:  61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                                | 130/213 [01:44<01:06,  1.24it/s, loss=0.00591, lr=1.2e-7, step=4176]\\u001b[A\\n\",\n      \"Epoch 19:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.00591, lr=1.2e-7, step=4176]\\u001b[A\\n\",\n      \"Epoch 19:  62%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                               | 131/213 [01:44<01:05,  1.25it/s, loss=0.0164, lr=1.17e-7, step=4177]\\u001b[A\\n\",\n      \"Epoch 19:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0164, lr=1.17e-7, step=4177]\\u001b[A\\n\",\n      \"Epoch 19:  62%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                              | 132/213 [01:45<01:04,  1.25it/s, loss=0.0137, lr=1.14e-7, step=4178]\\u001b[A\\n\",\n      \"Epoch 19:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [01:46<01:03,  1.25it/s, loss=0.0137, lr=1.14e-7, step=4178]\\u001b[A\\n\",\n      \"Epoch 19:  62%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                             | 133/213 [01:46<01:03,  1.25it/s, loss=0.0182, lr=1.12e-7, step=4179]\\u001b[A\\n\",\n      \"Epoch 19:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [01:47<01:03,  1.25it/s, loss=0.0182, lr=1.12e-7, step=4179]\\u001b[A\\n\",\n      \"Epoch 19:  63%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                            | 134/213 [01:47<01:03,  1.25it/s, loss=0.0115, lr=1.09e-7, step=4180]\\u001b[A\\n\",\n      \"Epoch 19:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:48<01:02,  1.25it/s, loss=0.0115, lr=1.09e-7, step=4180]\\u001b[A\\n\",\n      \"Epoch 19:  63%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                           | 135/213 [01:48<01:02,  1.25it/s, loss=0.0481, lr=1.06e-7, step=4181]\\u001b[A\\n\",\n      \"Epoch 19:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.0481, lr=1.06e-7, step=4181]\\u001b[A\\n\",\n      \"Epoch 19:  64%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                          | 136/213 [01:48<01:01,  1.25it/s, loss=0.00832, lr=1.03e-7, step=4182]\\u001b[A\\n\",\n      \"Epoch 19:  64%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                         | 137/213 [01:49<01:00,  1.25it/s, loss=0.00832, lr=1.03e-7, step=4182]\\u001b[A\\n\",\n      \"Epoch 19:  64%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                                                         | 137/213 [01:49<01:00,  1.25it/s, loss=0.0171, lr=1.01e-7, step=4183]\\u001b[A\\n\",\n      \"Epoch 19:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:50<00:59,  1.25it/s, loss=0.0171, lr=1.01e-7, step=4183]\\u001b[A\\n\",\n      \"Epoch 19:  65%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                         | 138/213 [01:50<00:59,  1.25it/s, loss=0.0053, lr=9.81e-8, step=4184]\\u001b[A\\n\",\n      \"Epoch 19:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0053, lr=9.81e-8, step=4184]\\u001b[A\\n\",\n      \"Epoch 19:  65%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                        | 139/213 [01:51<00:59,  1.25it/s, loss=0.0243, lr=9.55e-8, step=4185]\\u001b[A\\n\",\n      \"Epoch 19:  66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.0243, lr=9.55e-8, step=4185]\\u001b[A\\n\",\n      \"Epoch 19:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                       | 140/213 [01:52<00:58,  1.25it/s, loss=0.032, lr=9.3e-8, step=4186]\\u001b[A\\n\",\n      \"Epoch 19:  66%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.032, lr=9.3e-8, step=4186]\\u001b[A\\n\",\n      \"Epoch 19:  66%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                      | 141/213 [01:52<00:57,  1.25it/s, loss=0.0332, lr=9.04e-8, step=4187]\\u001b[A\\n\",\n      \"Epoch 19:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0332, lr=9.04e-8, step=4187]\\u001b[A\\n\",\n      \"Epoch 19:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                     | 142/213 [01:53<00:56,  1.25it/s, loss=0.0179, lr=8.8e-8, step=4188]\\u001b[A\\n\",\n      \"Epoch 19:  67%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0179, lr=8.8e-8, step=4188]\\u001b[A\\n\",\n      \"Epoch 19:  67%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                    | 143/213 [01:54<00:55,  1.25it/s, loss=0.0146, lr=8.55e-8, step=4189]\\u001b[A\\n\",\n      \"Epoch 19:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.25it/s, loss=0.0146, lr=8.55e-8, step=4189]\\u001b[A\\n\",\n      \"Epoch 19:  68%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                   | 144/213 [01:55<00:54,  1.25it/s, loss=0.0122, lr=8.31e-8, step=4190]\\u001b[A\\n\",\n      \"Epoch 19:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:56<00:54,  1.25it/s, loss=0.0122, lr=8.31e-8, step=4190]\\u001b[A\\n\",\n      \"Epoch 19:  68%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                  | 145/213 [01:56<00:54,  1.25it/s, loss=0.0239, lr=8.07e-8, step=4191]\\u001b[A\\n\",\n      \"Epoch 19:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.0239, lr=8.07e-8, step=4191]\\u001b[A\\n\",\n      \"Epoch 19:  69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                                 | 146/213 [01:56<00:53,  1.25it/s, loss=0.0129, lr=7.83e-8, step=4192]\\u001b[A\\n\",\n      \"Epoch 19:  69%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                                | 147/213 [01:57<00:52,  1.25it/s, loss=0.0129, lr=7.83e-8, step=4192]\\u001b[A\\n\",\n      \"Epoch 19:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                | 147/213 [01:57<00:52,  1.25it/s, loss=0.0155, lr=7.6e-8, step=4193]\\u001b[A\\n\",\n      \"Epoch 19:  69%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                               | 148/213 [01:58<00:51,  1.25it/s, loss=0.0155, lr=7.6e-8, step=4193]\\u001b[A\\n\",\n      \"Epoch 19:  69%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                               | 148/213 [01:58<00:51,  1.25it/s, loss=0.0148, lr=7.37e-8, step=4194]\\u001b[A\\n\",\n      \"Epoch 19:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                              | 149/213 [01:59<00:51,  1.25it/s, loss=0.0148, lr=7.37e-8, step=4194]\\u001b[A\\n\",\n      \"Epoch 19:  70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                              | 149/213 [01:59<00:51,  1.25it/s, loss=0.00941, lr=7.15e-8, step=4195]\\u001b[A\\n\",\n      \"Epoch 19:  70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.00941, lr=7.15e-8, step=4195]\\u001b[A\\n\",\n      \"Epoch 19:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                             | 150/213 [02:00<00:50,  1.25it/s, loss=0.015, lr=6.93e-8, step=4196]\\u001b[A\\n\",\n      \"Epoch 19:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                            | 151/213 [02:00<00:49,  1.25it/s, loss=0.015, lr=6.93e-8, step=4196]\\u001b[A\\n\",\n      \"Epoch 19:  71%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                            | 151/213 [02:00<00:49,  1.25it/s, loss=0.0116, lr=6.71e-8, step=4197]\\u001b[A\\n\",\n      \"Epoch 19:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.0116, lr=6.71e-8, step=4197]\\u001b[A\\n\",\n      \"Epoch 19:  71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                           | 152/213 [02:01<00:48,  1.25it/s, loss=0.0105, lr=6.49e-8, step=4198]\\u001b[A\\n\",\n      \"Epoch 19:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0105, lr=6.49e-8, step=4198]\\u001b[A\\n\",\n      \"Epoch 19:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                          | 153/213 [02:02<00:47,  1.25it/s, loss=0.0184, lr=6.28e-8, step=4199]\\u001b[A\\n\",\n      \"Epoch 19:  72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.0184, lr=6.28e-8, step=4199]\\u001b[A\\n\",\n      \"Epoch 19:  72%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                         | 154/213 [02:03<00:47,  1.25it/s, loss=0.00869, lr=6.07e-8, step=4200]\\u001b[A\\n\",\n      \"Epoch 19:  73%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                                        | 155/213 [02:04<00:46,  1.25it/s, loss=0.00869, lr=6.07e-8, step=4200]\\u001b[A\\n\",\n      \"Epoch 19:  73%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                        | 155/213 [02:04<00:46,  1.25it/s, loss=0.0102, lr=5.87e-8, step=4201]\\u001b[A\\n\",\n      \"Epoch 19:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [02:04<00:45,  1.25it/s, loss=0.0102, lr=5.87e-8, step=4201]\\u001b[A\\n\",\n      \"Epoch 19:  73%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                       | 156/213 [02:04<00:45,  1.25it/s, loss=0.0101, lr=5.67e-8, step=4202]\\u001b[A\\n\",\n      \"Epoch 19:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.0101, lr=5.67e-8, step=4202]\\u001b[A\\n\",\n      \"Epoch 19:  74%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                      | 157/213 [02:05<00:44,  1.26it/s, loss=0.023, lr=5.47e-8, step=4203]\\u001b[A\\n\",\n      \"Epoch 19:  74%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.023, lr=5.47e-8, step=4203]\\u001b[A\\n\",\n      \"Epoch 19:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                     | 158/213 [02:06<00:43,  1.25it/s, loss=0.00673, lr=5.28e-8, step=4204]\\u001b[A\\n\",\n      \"Epoch 19:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00673, lr=5.28e-8, step=4204]\\u001b[A\\n\",\n      \"Epoch 19:  75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                                    | 159/213 [02:07<00:43,  1.25it/s, loss=0.00694, lr=5.09e-8, step=4205]\\u001b[A\\n\",\n      \"Epoch 19:  75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                   | 160/213 [02:08<00:42,  1.25it/s, loss=0.00694, lr=5.09e-8, step=4205]\\u001b[A\\n\",\n      \"Epoch 19:  75%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                   | 160/213 [02:08<00:42,  1.25it/s, loss=0.00688, lr=4.9e-8, step=4206]\\u001b[A\\n\",\n      \"Epoch 19:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.00688, lr=4.9e-8, step=4206]\\u001b[A\\n\",\n      \"Epoch 19:  76%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                  | 161/213 [02:08<00:41,  1.25it/s, loss=0.0271, lr=4.72e-8, step=4207]\\u001b[A\\n\",\n      \"Epoch 19:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0271, lr=4.72e-8, step=4207]\\u001b[A\\n\",\n      \"Epoch 19:  76%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                 | 162/213 [02:09<00:40,  1.25it/s, loss=0.0205, lr=4.54e-8, step=4208]\\u001b[A\\n\",\n      \"Epoch 19:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:10<00:40,  1.25it/s, loss=0.0205, lr=4.54e-8, step=4208]\\u001b[A\\n\",\n      \"Epoch 19:  77%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                | 163/213 [02:10<00:40,  1.25it/s, loss=0.0218, lr=4.36e-8, step=4209]\\u001b[A\\n\",\n      \"Epoch 19:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0218, lr=4.36e-8, step=4209]\\u001b[A\\n\",\n      \"Epoch 19:  77%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                               | 164/213 [02:11<00:39,  1.25it/s, loss=0.0141, lr=4.19e-8, step=4210]\\u001b[A\\n\",\n      \"Epoch 19:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:12<00:38,  1.25it/s, loss=0.0141, lr=4.19e-8, step=4210]\\u001b[A\\n\",\n      \"Epoch 19:  77%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                              | 165/213 [02:12<00:38,  1.25it/s, loss=0.0102, lr=4.02e-8, step=4211]\\u001b[A\\n\",\n      \"Epoch 19:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.25it/s, loss=0.0102, lr=4.02e-8, step=4211]\\u001b[A\\n\",\n      \"Epoch 19:  78%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                             | 166/213 [02:12<00:37,  1.25it/s, loss=0.0155, lr=3.85e-8, step=4212]\\u001b[A\\n\",\n      \"Epoch 19:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0155, lr=3.85e-8, step=4212]\\u001b[A\\n\",\n      \"Epoch 19:  78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                            | 167/213 [02:13<00:36,  1.25it/s, loss=0.0114, lr=3.69e-8, step=4213]\\u001b[A\\n\",\n      \"Epoch 19:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:36,  1.25it/s, loss=0.0114, lr=3.69e-8, step=4213]\\u001b[A\\n\",\n      \"Epoch 19:  79%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎                                           | 168/213 [02:14<00:36,  1.25it/s, loss=0.0114, lr=3.53e-8, step=4214]\\u001b[A\\n\",\n      \"Epoch 19:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:15<00:35,  1.25it/s, loss=0.0114, lr=3.53e-8, step=4214]\\u001b[A\\n\",\n      \"Epoch 19:  79%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                          | 169/213 [02:15<00:35,  1.25it/s, loss=0.0111, lr=3.38e-8, step=4215]\\u001b[A\\n\",\n      \"Epoch 19:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:16<00:34,  1.25it/s, loss=0.0111, lr=3.38e-8, step=4215]\\u001b[A\\n\",\n      \"Epoch 19:  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                         | 170/213 [02:16<00:34,  1.25it/s, loss=0.0355, lr=3.23e-8, step=4216]\\u001b[A\\n\",\n      \"Epoch 19:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.25it/s, loss=0.0355, lr=3.23e-8, step=4216]\\u001b[A\\n\",\n      \"Epoch 19:  80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                        | 171/213 [02:16<00:33,  1.25it/s, loss=0.0218, lr=3.08e-8, step=4217]\\u001b[A\\n\",\n      \"Epoch 19:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.25it/s, loss=0.0218, lr=3.08e-8, step=4217]\\u001b[A\\n\",\n      \"Epoch 19:  81%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                       | 172/213 [02:17<00:32,  1.25it/s, loss=0.0172, lr=2.93e-8, step=4218]\\u001b[A\\n\",\n      \"Epoch 19:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0172, lr=2.93e-8, step=4218]\\u001b[A\\n\",\n      \"Epoch 19:  81%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                      | 173/213 [02:18<00:31,  1.25it/s, loss=0.0266, lr=2.79e-8, step=4219]\\u001b[A\\n\",\n      \"Epoch 19:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0266, lr=2.79e-8, step=4219]\\u001b[A\\n\",\n      \"Epoch 19:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                      | 174/213 [02:19<00:31,  1.25it/s, loss=0.0104, lr=2.65e-8, step=4220]\\u001b[A\\n\",\n      \"Epoch 19:  82%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                     | 175/213 [02:20<00:30,  1.25it/s, loss=0.0104, lr=2.65e-8, step=4220]\\u001b[A\\n\",\n      \"Epoch 19:  82%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                    | 175/213 [02:20<00:30,  1.25it/s, loss=0.00878, lr=2.52e-8, step=4221]\\u001b[A\\n\",\n      \"Epoch 19:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                   | 176/213 [02:20<00:29,  1.25it/s, loss=0.00878, lr=2.52e-8, step=4221]\\u001b[A\\n\",\n      \"Epoch 19:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                   | 176/213 [02:20<00:29,  1.25it/s, loss=0.00929, lr=2.39e-8, step=4222]\\u001b[A\\n\",\n      \"Epoch 19:  83%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                  | 177/213 [02:21<00:28,  1.25it/s, loss=0.00929, lr=2.39e-8, step=4222]\\u001b[A\\n\",\n      \"Epoch 19:  83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                   | 177/213 [02:21<00:28,  1.25it/s, loss=0.0115, lr=2.26e-8, step=4223]\\u001b[A\\n\",\n      \"Epoch 19:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.0115, lr=2.26e-8, step=4223]\\u001b[A\\n\",\n      \"Epoch 19:  84%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                  | 178/213 [02:22<00:27,  1.25it/s, loss=0.0249, lr=2.14e-8, step=4224]\\u001b[A\\n\",\n      \"Epoch 19:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0249, lr=2.14e-8, step=4224]\\u001b[A\\n\",\n      \"Epoch 19:  84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                 | 179/213 [02:23<00:27,  1.25it/s, loss=0.0105, lr=2.02e-8, step=4225]\\u001b[A\\n\",\n      \"Epoch 19:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.0105, lr=2.02e-8, step=4225]\\u001b[A\\n\",\n      \"Epoch 19:  85%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                                | 180/213 [02:24<00:26,  1.25it/s, loss=0.00893, lr=1.9e-8, step=4226]\\u001b[A\\n\",\n      \"Epoch 19:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.00893, lr=1.9e-8, step=4226]\\u001b[A\\n\",\n      \"Epoch 19:  85%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                               | 181/213 [02:24<00:25,  1.25it/s, loss=0.0221, lr=1.79e-8, step=4227]\\u001b[A\\n\",\n      \"Epoch 19:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.0221, lr=1.79e-8, step=4227]\\u001b[A\\n\",\n      \"Epoch 19:  85%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                              | 182/213 [02:25<00:24,  1.25it/s, loss=0.0055, lr=1.68e-8, step=4228]\\u001b[A\\n\",\n      \"Epoch 19:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.0055, lr=1.68e-8, step=4228]\\u001b[A\\n\",\n      \"Epoch 19:  86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                             | 183/213 [02:26<00:23,  1.25it/s, loss=0.00479, lr=1.57e-8, step=4229]\\u001b[A\\n\",\n      \"Epoch 19:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.00479, lr=1.57e-8, step=4229]\\u001b[A\\n\",\n      \"Epoch 19:  86%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                            | 184/213 [02:27<00:23,  1.25it/s, loss=0.00789, lr=1.47e-8, step=4230]\\u001b[A\\n\",\n      \"Epoch 19:  87%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                           | 185/213 [02:28<00:22,  1.25it/s, loss=0.00789, lr=1.47e-8, step=4230]\\u001b[A\\n\",\n      \"Epoch 19:  87%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                           | 185/213 [02:28<00:22,  1.25it/s, loss=0.0159, lr=1.37e-8, step=4231]\\u001b[A\\n\",\n      \"Epoch 19:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0159, lr=1.37e-8, step=4231]\\u001b[A\\n\",\n      \"Epoch 19:  87%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                          | 186/213 [02:28<00:21,  1.25it/s, loss=0.0137, lr=1.27e-8, step=4232]\\u001b[A\\n\",\n      \"Epoch 19:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0137, lr=1.27e-8, step=4232]\\u001b[A\\n\",\n      \"Epoch 19:  88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                         | 187/213 [02:29<00:20,  1.25it/s, loss=0.0224, lr=1.18e-8, step=4233]\\u001b[A\\n\",\n      \"Epoch 19:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0224, lr=1.18e-8, step=4233]\\u001b[A\\n\",\n      \"Epoch 19:  88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                        | 188/213 [02:30<00:20,  1.25it/s, loss=0.0164, lr=1.09e-8, step=4234]\\u001b[A\\n\",\n      \"Epoch 19:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.0164, lr=1.09e-8, step=4234]\\u001b[A\\n\",\n      \"Epoch 19:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                       | 189/213 [02:31<00:19,  1.25it/s, loss=0.018, lr=1.01e-8, step=4235]\\u001b[A\\n\",\n      \"Epoch 19:  89%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.018, lr=1.01e-8, step=4235]\\u001b[A\\n\",\n      \"Epoch 19:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                      | 190/213 [02:32<00:18,  1.25it/s, loss=0.0129, lr=9.23e-9, step=4236]\\u001b[A\\n\",\n      \"Epoch 19:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.24it/s, loss=0.0129, lr=9.23e-9, step=4236]\\u001b[A\\n\",\n      \"Epoch 19:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                     | 191/213 [02:32<00:17,  1.24it/s, loss=0.0143, lr=8.45e-9, step=4237]\\u001b[A\\n\",\n      \"Epoch 19:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                    | 192/213 [02:33<00:16,  1.24it/s, loss=0.0143, lr=8.45e-9, step=4237]\\u001b[A\\n\",\n      \"Epoch 19:  90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                    | 192/213 [02:33<00:16,  1.24it/s, loss=0.0101, lr=7.7e-9, step=4238]\\u001b[A\\n\",\n      \"Epoch 19:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:16,  1.25it/s, loss=0.0101, lr=7.7e-9, step=4238]\\u001b[A\\n\",\n      \"Epoch 19:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                   | 193/213 [02:34<00:16,  1.25it/s, loss=0.019, lr=6.98e-9, step=4239]\\u001b[A\\n\",\n      \"Epoch 19:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.019, lr=6.98e-9, step=4239]\\u001b[A\\n\",\n      \"Epoch 19:  91%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                  | 194/213 [02:35<00:15,  1.25it/s, loss=0.0501, lr=6.3e-9, step=4240]\\u001b[A\\n\",\n      \"Epoch 19:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.0501, lr=6.3e-9, step=4240]\\u001b[A\\n\",\n      \"Epoch 19:  92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                 | 195/213 [02:36<00:14,  1.25it/s, loss=0.00789, lr=5.65e-9, step=4241]\\u001b[A\\n\",\n      \"Epoch 19:  92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 196/213 [02:36<00:13,  1.25it/s, loss=0.00789, lr=5.65e-9, step=4241]\\u001b[A\\n\",\n      \"Epoch 19:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                | 196/213 [02:36<00:13,  1.25it/s, loss=0.0108, lr=5.04e-9, step=4242]\\u001b[A\\n\",\n      \"Epoch 19:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍               | 197/213 [02:37<00:12,  1.25it/s, loss=0.0108, lr=5.04e-9, step=4242]\\u001b[A\\n\",\n      \"Epoch 19:  92%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌               | 197/213 [02:37<00:12,  1.25it/s, loss=0.00951, lr=4.47e-9, step=4243]\\u001b[A\\n\",\n      \"Epoch 19:  93%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.00951, lr=4.47e-9, step=4243]\\u001b[A\\n\",\n      \"Epoch 19:  93%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍              | 198/213 [02:38<00:11,  1.25it/s, loss=0.0104, lr=3.93e-9, step=4244]\\u001b[A\\n\",\n      \"Epoch 19:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0104, lr=3.93e-9, step=4244]\\u001b[A\\n\",\n      \"Epoch 19:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍             | 199/213 [02:39<00:11,  1.25it/s, loss=0.0066, lr=3.42e-9, step=4245]\\u001b[A\\n\",\n      \"Epoch 19:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 200/213 [02:40<00:10,  1.25it/s, loss=0.0066, lr=3.42e-9, step=4245]\\u001b[A\\n\",\n      \"Epoch 19:  94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍            | 200/213 [02:40<00:10,  1.25it/s, loss=0.00907, lr=2.95e-9, step=4246]\\u001b[A\\n\",\n      \"Epoch 19:  94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍           | 201/213 [02:40<00:09,  1.25it/s, loss=0.00907, lr=2.95e-9, step=4246]\\u001b[A\\n\",\n      \"Epoch 19:  94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎           | 201/213 [02:40<00:09,  1.25it/s, loss=0.0167, lr=2.51e-9, step=4247]\\u001b[A\\n\",\n      \"Epoch 19:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0167, lr=2.51e-9, step=4247]\\u001b[A\\n\",\n      \"Epoch 19:  95%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎          | 202/213 [02:41<00:08,  1.25it/s, loss=0.0274, lr=2.11e-9, step=4248]\\u001b[A\\n\",\n      \"Epoch 19:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:42<00:08,  1.24it/s, loss=0.0274, lr=2.11e-9, step=4248]\\u001b[A\\n\",\n      \"Epoch 19:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 203/213 [02:42<00:08,  1.24it/s, loss=0.0197, lr=1.75e-9, step=4249]\\u001b[A\\n\",\n      \"Epoch 19:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.24it/s, loss=0.0197, lr=1.75e-9, step=4249]\\u001b[A\\n\",\n      \"Epoch 19:  96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 204/213 [02:43<00:07,  1.24it/s, loss=0.00519, lr=1.41e-9, step=4250]\\u001b[A\\n\",\n      \"Epoch 19:  96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎       | 205/213 [02:44<00:06,  1.24it/s, loss=0.00519, lr=1.41e-9, step=4250]\\u001b[A\\n\",\n      \"Epoch 19:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 205/213 [02:44<00:06,  1.24it/s, loss=0.0148, lr=1.12e-9, step=4251]\\u001b[A\\n\",\n      \"Epoch 19:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.24it/s, loss=0.0148, lr=1.12e-9, step=4251]\\u001b[A\\n\",\n      \"Epoch 19:  97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 206/213 [02:44<00:05,  1.24it/s, loss=0.0174, lr=8.55e-10, step=4252]\\u001b[A\\n\",\n      \"Epoch 19:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.24it/s, loss=0.0174, lr=8.55e-10, step=4252]\\u001b[A\\n\",\n      \"Epoch 19:  97%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 207/213 [02:45<00:04,  1.24it/s, loss=0.0161, lr=6.28e-10, step=4253]\\u001b[A\\n\",\n      \"Epoch 19:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:46<00:04,  1.24it/s, loss=0.0161, lr=6.28e-10, step=4253]\\u001b[A\\n\",\n      \"Epoch 19:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 208/213 [02:46<00:04,  1.24it/s, loss=0.0116, lr=4.36e-10, step=4254]\\u001b[A\\n\",\n      \"Epoch 19:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏   | 209/213 [02:47<00:03,  1.24it/s, loss=0.0116, lr=4.36e-10, step=4254]\\u001b[A\\n\",\n      \"Epoch 19:  98%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏   | 209/213 [02:47<00:03,  1.24it/s, loss=0.0187, lr=2.79e-10, step=4255]\\u001b[A\\n\",\n      \"Epoch 19:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.24it/s, loss=0.0187, lr=2.79e-10, step=4255]\\u001b[A\\n\",\n      \"Epoch 19:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████   | 210/213 [02:48<00:02,  1.24it/s, loss=0.00506, lr=1.57e-10, step=4256]\\u001b[A\\n\",\n      \"Epoch 19:  99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.24it/s, loss=0.00506, lr=1.57e-10, step=4256]\\u001b[A\\n\",\n      \"Epoch 19:  99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 211/213 [02:48<00:01,  1.24it/s, loss=0.0135, lr=6.98e-11, step=4257]\\u001b[A\\n\",\n      \"Epoch 19: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.0135, lr=6.98e-11, step=4257]\\u001b[A\\n\",\n      \"Epoch 19: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 212/213 [02:49<00:00,  1.25it/s, loss=0.00854, lr=1.75e-11, step=4258]\\u001b[A\\n\",\n      \"Epoch 19: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.00854, lr=1.75e-11, step=4258]\\u001b[A\\n\",\n      \"Epoch 19: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [02:50<00:00,  1.45it/s, loss=0.0111, lr=0, step=4259]\\u001b[A\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7a00e8b0b13e4980a4694054c635ede5\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1000 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from tqdm import tqdm\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from diffusers import DDPMPipeline\\n\",\n    \"\\n\",\n    \"global_step = 0\\n\",\n    \"SAVE_ARTIFACT_EPOCHS = 1\\n\",\n    \"RANDOM_SEED = 42\\n\",\n    \"\\n\",\n    \"# Now you train the model\\n\",\n    \"for epoch in range(NUM_EPOCHS):\\n\",\n    \"    progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\\n\",\n    \"    progress_bar.set_description(f\\\"Epoch {epoch}\\\")\\n\",\n    \"\\n\",\n    \"    for step, batch in enumerate(train_dataloader):\\n\",\n    \"        clean_images = batch[\\\"images\\\"]\\n\",\n    \"        # Sample noise to add to the images\\n\",\n    \"        noise = torch.randn(clean_images.shape, device=clean_images.device)\\n\",\n    \"        bs = clean_images.shape[0]\\n\",\n    \"\\n\",\n    \"        # Sample a random timestep for each image\\n\",\n    \"        timesteps = torch.randint(\\n\",\n    \"            0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\\n\",\n    \"            dtype=torch.int64\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        # Add noise to the clean images according to the noise magnitude at each timestep\\n\",\n    \"        # (this is the forward diffusion process)\\n\",\n    \"        noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\\n\",\n    \"\\n\",\n    \"        with accelerator.accumulate(model):\\n\",\n    \"            # Predict the noise residual\\n\",\n    \"            noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\\n\",\n    \"            loss = F.mse_loss(noise_pred, noise)\\n\",\n    \"            accelerator.backward(loss)\\n\",\n    \"\\n\",\n    \"            accelerator.clip_grad_norm_(model.parameters(), 1.0)\\n\",\n    \"            optimizer.step()\\n\",\n    \"            lr_scheduler.step()\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"\\n\",\n    \"        progress_bar.update(1)\\n\",\n    \"        logs = {\\\"loss\\\": loss.detach().item(), \\\"lr\\\": lr_scheduler.get_last_lr()[0], \\\"step\\\": global_step}\\n\",\n    \"        progress_bar.set_postfix(**logs)\\n\",\n    \"        accelerator.log(logs, step=global_step)\\n\",\n    \"        global_step += 1\\n\",\n    \"        \\n\",\n    \"    # After each epoch you optionally sample some demo images with evaluate() and save the model\\n\",\n    \"    if accelerator.is_main_process:\\n\",\n    \"        pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\\n\",\n    \"\\n\",\n    \"        if (epoch + 1) % SAVE_ARTIFACT_EPOCHS == 0 or epoch == NUM_EPOCHS - 1:\\n\",\n    \"            images = pipeline(\\n\",\n    \"                batch_size=BSIZE,\\n\",\n    \"                generator=torch.manual_seed(RANDOM_SEED),\\n\",\n    \"            ).images\\n\",\n    \"\\n\",\n    \"            # Make a grid out of the images\\n\",\n    \"            image_grid = make_image_grid(images, rows=4, cols=4)\\n\",\n    \"\\n\",\n    \"            # Save the images\\n\",\n    \"            test_dir = os.path.join(MODEL_SAVE_DIR, \\\"samples\\\")\\n\",\n    \"            os.makedirs(test_dir, exist_ok=True)\\n\",\n    \"            image_grid.save(f\\\"{test_dir}/{epoch:04d}.png\\\")\\n\",\n    \"\\n\",\n    \"            pipeline.save_pretrained(MODEL_SAVE_DIR)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"c5a46a4a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"dab1c931\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: diffusers==0.25.0.dev0 in /opt/conda/lib/python3.10/site-packages (0.25.0.dev0)\\n\",\n      \"Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (6.11.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub>=0.19.4 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (1.26.2)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (2.28.1)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (0.4.1)\\n\",\n      \"Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from diffusers==0.25.0.dev0) (10.2.0)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (4.66.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (4.9.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (21.3)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata->diffusers==0.25.0.dev0) (3.17.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->diffusers==0.25.0.dev0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub>=0.19.4->diffusers==0.25.0.dev0) (3.0.9)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install diffusers==0.25.0.dev0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"9dd64c27\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"2024-02-15 03:22:07.745503: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\",\n      \"2024-02-15 03:22:07.745623: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\",\n      \"2024-02-15 03:22:07.889695: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a1fa234b8bbb4e9d9e90280a3867f08c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model_index.json:   0%|          | 0.00/541 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"32c7e8cffec64056b9c2099824c29586\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Fetching 15 files:   0%|          | 0/15 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"abad35bffa9b4bd3a8f49bbac84afede\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"(…)ature_extractor/preprocessor_config.json:   0%|          | 0.00/342 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d9986ebeb380445f948d3daf80cc62f5\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model.fp16.safetensors:   0%|          | 0.00/608M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"11bc7894f35649c996d0902850363bea\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      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\"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c1733a295b77404a8e3a545fa27c837e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model.fp16.safetensors:   0%|          | 0.00/246M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f740dec6a7be40cca2f57a066666b570\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer/special_tokens_map.json:   0%|          | 0.00/472 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4a9abdc620a74e929232588d7650a5d2\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer/merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d0c6e7a107d64c7e9c09e1fe0b795a5c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer/tokenizer_config.json:   0%|          | 0.00/806 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6dd5166ec6064d89918cd736aeeb42ff\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"diffusion_pytorch_model.fp16.safetensors:   0%|          | 0.00/1.72G [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c12d6e9c36d647e8a4fa6a425d28b51d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"unet/config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a1ffb16063af436c885f33be26bd4b71\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer/vocab.json:   0%|          | 0.00/1.06M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c75a16d0aa1443ab9129aee1871dd5d1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"vae/config.json:   0%|          | 0.00/547 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1d700c25d7d3486b970024a8f2928f06\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"diffusion_pytorch_model.fp16.safetensors:   0%|          | 0.00/167M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"97b07a9bee3045f2b132f4cfbe73395f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Loading pipeline components...:   0%|      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Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  torch.utils._pytree._register_pytree_node(\\n\",\n      \"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\\\"id2label\\\"]` will be overriden.\\n\",\n      \"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\\\"bos_token_id\\\"]` will be overriden.\\n\",\n      \"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\\\"eos_token_id\\\"]` will be overriden.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"772caa236dfe4a15b1307d19ca6d902d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/50 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/jpeg\": 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\",\n 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dRLCPrFMOoEYNX+qXD2sK8vQPrkXRqBvVCoPKSLn1JcEnBe2bLDRg1xcllvnEx5lE5nkcrxoBmdxGsHNNmsneW9eVs1Hl9Ci9qi5WJmDK4YKFTHxGsJHHSUhr6sUogTCYNQK0PZdkiKUMHWVRuwzEmw5nGu7pL4hJAgFZL6mbO2kwJ3GyiTUsJEAnFWZdQZQzxPhh4IxZrqDSOloJHmVV1oVGl8SWljNQg4OMxIEy+4Je0bD1BDe5FiRHCVlC0S46ilpAKV1iUg7HoRh1ZShgfX7WVN6uK4q01ERhy9UQ4/pdhosiyhWRtbl4RzuI4nS0CRg8AjIn/PLMWU7SxWZhkA9EKBRq4l+VsCVAIAH+Kk5ijgrKrQuXgG8HerriVFPWzVBSc1DjE38oUoDtIFiqmLUSGMcnPwLE2j4aW57olNhozhGzekYcSRYrHFwvZ+80hZBJlkKHN4hKKy1IR3WjLszNwufmyWk3BJSLOWbLlhJGD4n1aflnuFmsNaUISOSnEfmVyBCabLxqz+mRFT1Sh9ubHXVbT4vAJI5LEdstUXSeVBVJ/3ZJ9hTLI4xQ18iU8NXaWgRZ34CJEa7IpJGtZTpbFdSQZ1hUlTkv7dZFLtwQw8p3hZBIC5+KdRJegdihkHJFHTGeoyP4uiXFYCWE1ZzuIRuemL0vEsGXGcl3movSm1GLq6SG6dOvINVUZnt5ckYTQ7kSqd5ngVsW60d2cytutKgsNpz7gNxYXS8rsGCB4m186iPOF1lg2JrrRqNwgUq7LMW5CG7GFt2pUufmVaB5vV7CIJmSMVZns1rJVrBakkt4hzE6mcjbVAugPDuZjUR5TKI/3eGTg2pbX7uFKkMovBhWZpkT0Z/Y+bxxK5WBa503gMEkDhqJI6l/S8NLU7vqgvFohiZizHBsjNslSOwI018cnK/c0esnBMHFNYrksU6VIiQJO09ay0hvSiE0T3EokZZI+MDgYR8HB6hK2weBWQGeKLOsyR9OFQEJSWjRwSEYj6oZKHznFsTQyFuq19xcBXWIJwJwRTc0qWNEAuwqaNrgyYVf/CJcmoXyROYh5qFJoqVI4YLF8A2kmxQhrqQhjJDQaQIUIVi0CWkzxEv5l7iWMUQwVLxWiOv1NlpJAlkYSPilsYnxbNostTcaY0PA0SSaUvYiyZJQcAIKRF1vutgMJIj9iBCfupPVCjKLeDFKwmfJZ0QsWRulKUYaBE+RLTQmhS7mQMRlFSbQlEtXEOW6KXEpqDpp2FSZJUnwUb+6poNmkXWWKKCtpkGKoMix1PgpleJk7y5ygsEmxVqwSDOZ83RYKtXXrVa6mV1e4+PiRWNj0PIbeFtyoqIoRwVAtlbBUgzPLtnBmt6RMyLNXgzmkgdQYfFPr45Ss1iZsZ1FonOIlnB29DU5vD/edROzctJevcWk1lGbnqH6jNgcncKFiXb4FVBqLSgHGaw666hwceyVbMJQhsyayoktUkjGlCYFzm5S4ONKyPHyj+SYS3lJgRcRdsIrBHFnhL3aJ5YoIsVrhSeXyjDVM7OQC2lhC0T1GFzNKNXEFSmLFVF5OCaCNM4hl71C1NCITMrLgaGlmObIYewePCknBl3LJmIxBjt1FJrPDz9IOHDBn2iJFulEF31RyNLF7LAsYGJ0oaGgtK1IpHJEYzEFhYV5pEAjvTSXbgcvELdRZ49s8cJdLCiy3vdvapLbvUCKgeScmN/ojI8K4AihjZd8QJtQlKXtLqdIbL7kEV9xkSg1NrFTKomaXUBl/iUuMQAwtxsoSH1UlIHWNsLBWTTrioVb6iS9mu0wxbSwXfKv5vCXJv+B/GYVUoGSdlXIo2Jm0Crv7hyonNGQzKotKjQ4TBTdvhPtf4qSZbWixEoBmXKi2rzhqQ0Rqz+rtjJuHzrJmAQV5bUoKXluitNAuqIcWlzqqLHpz8ihVGH3B7nH5MvPLzdXlOEIctrQWJVGgMjdGycvVkb8DlpAZDgBWI0tRq6N5EEqDjAnZNiY+jsUtzFaSv0FiWxzFN5YBTTCPosZMuwjl7lZlUGQILWJuGE04gzG2qKw60SiGuppFoMnppp7zeICj1pjKawymZEBOqnAumxpf4enc3FK1lNSmgUvMZaqYLpbLsNQZBoKrfKyqcgv67NRZaeJ5gFLicJ5v5NRSWjK0McYwa5mtjLF7BlaTUFm0EMRCQJU5BpDfBIIGTDo5fz+O69XRUoK6ZtEE1Nmisagq75WWvNZgW8yKB8giRAxmNB37GyQgFZHB6GS3MVixDSKTFyszAyBLKCqWeqlS58WKSLYSNeLxZZ/Nu7W0fKO8ynQuvs8w/haQwXliagWsMnZppI2ofTU8jy9zqTqbxyZZq05R4hpaMHHapVDClrjl1MBj0nkCLBRiTCwHoLRLbIIctVVcci2rh1PugADKiY+w+bNUdaaV/2XS/CNWU0MwlCofYx4iOue3T7ljFggU12pDXL0sjQHSKPSOGXxcEvO/2F0BhwK8CuMpqBS0CSip0UNKmlZ7JXjGXi6oHtB9BsYWC2bcFIIgtVuy5pcIDqQ6VMaR6mYmldMaJk6uUbRpsG9zfwfA+WIRlTLHzUWN5Xg8tDQBb6xc2sWJCZXQbYbSrJxTllxUNC8Iu9fIeTmezG9bou+MXYNKBrL9l7K6BPlRaEwomULg8XYsxtTsMKFd0ooqGPNULV7xzEhNgFX3ch8hhhflmD/mKCI1lxhfhtmkC9WBpH+Am+BcUTatympAZA3OeZAUi17J7E6jXsquPECIYDpVG1CFXefKUkDHnAXju62I26AkluSmTZdaqcMGG5soY0PCP1+j0I/6xla8zj0qJa4qmScElQ2pPakVdxKqgkNfZargRTKssHrt5sv8tQ7SWHTCf55+e3+/rIiZqLPWiG0bTDGsICO8/hUsqtgtvceIbi4mVGSwQb1bchsIMlOUilGbkGIBjdCpjSuXbgTgR+FLkLAWvJNAWlCMzDFLACUEkoTRZmgrJjsyszCL+Ocz5QNmK1I9LX5OtK1c4phlxgwUxTlLOadaVeuPsagvcP4HCd2H+Xx3pQ6eR2jDtZg3OlZ3UeFjqhNs3tsbZOl8El2+yqp81WmzNSPeUizPJnPSBktr4x01Gdt0fNsKQ5Ro6uz5I4BX7bx0584WKIm8KwvS8hXZycXwKoRaBUl5XLPINpDV0gLCB+fYbDrxxkxMoSKG3Cm3kpoYgY+xG9EGlDEqJq927ungWfikKNV1PNvo/Noqx4HJGWTHzlLNU7tkuTykkCTV8SnC4rqSxJM5enzyGKhPh2jv4Zfe5kzir7ZIjEBeLPF3JlqKVtOA1k8+WV+WpSojVGQ1EuginKrLXSLvUhGnVlsc46BlNctpU7y3ymajBXcWlmRdcNJ+Xx1cglpjUQEelVwMhanJqQoLR1XQm6KQiFnePWFTTE1Yhmcsd19Fr/DLNMNNrCnSlfzpk/b+ICWDLCR8qc8mpWMQSGmKlmHRqlXkeb+zxduD+sVzBalG4klHIGVTQgjd/6a8N9iEw9lVNvoNXgXTNqc4TMHZFGTRfhnVj1wS0feIqqSLTFlRCbyxfKlMdjZSDko7CLfuKqCaTDG0+6lf2Rz9UadA4aitWyGZeojgfw9A60lvGN3q17jUlUyRMRIDUHcSX84qWOrhsX4JkM+6FK9KM5kgu6MnwdJw2oI2jyBbRR1JsBBaHStudc8A6pi1CTDQa3YJXi8RElVWyJ02JmZhTcjYtMW2Hu4KXu+qYmOVLLJDpeBEqU3l+zeY2AqK8CanNNJYcFyi8lu40/liYFPy2N1k8dFAOqclWiGOs7A9mN4E+FHWY1HZ5kPmZFLXmMDXXDdALSUuOxdi1iq5QKZAq33sV8A+nkKqxH4Zk6x7iwHB19d0bg1aqGDwm4qZygVxkH4UVMmBwFP5pV/nWtYEIWeCRjGfEFS7pVCOJGOJdJGolca8znsA5OHYA0NIGE0gwl9SFJLYJ1bEcox5Z+SYPatGbEqDVVukQQqdIfBlLmW6NFBcchukV8FZ14pX+GXOyrXmtxGP8RuSlVNBG0LnsOSMKbiqZO5VgL9EvauRaxfJ1O14asHc2VzLJU2QmsS7oi6bbCUuiKJ/sLmIUWCQ+XOt4Gv4oNpJQr8myW3JMApPWkVDqYLMM8hO0T0IBNmft5Qu41Aqx5pSl1Spaac6VzKTCla0NeG9NoeM/LyDb61QziT44M/OCNyqZxzP5pd+q9CkNBO9hIuzUj7zyaJFtEVLpMn93VkQDwCRCVskQsfRIwoRN9lybFxW2kt5XwrsYkV1Ka+GkqbUsVOcM2XYNplj+K44x4oczMOEmO/tyiCNeS1erMxjuJOKTRB12Y7IqTeU09VRAtwSrWAWBWe6mZfZNebWyEkyl0ID7P/ohw6BZ1k83VI1s6PBb6cis+ynQtY0lCqdh1bYu1cvgdM/5SujLCRTzfb4FgR+J+JYdaxSmlJWrMoTMEw6L2BK80uUsWOJDKzTQkocXSzTUVAMHGImVRqukKPQZj6xvzmUKRvwVmq1bghjPFuFskBbnUoAhWRAXMINWPxXQpSoFBu7cJ80BmBNFkH32ITibRSS/MGvkwhW8trS2gbjRimJI0SZpsQ7JMDLk3QAjg0UT1kxNl1e3ppaRTSo7S+PCgN6f926FRQhoNApjJ2ayptKllvztDWWsbmi8/c0EQgqr83iZurIJwP6tiiovUKZMj5v/MtbWkK+J3qbqYDPKDdTKUxbVOdbHTPyrXhJANzRaktLea4ESnekQuwYVUbTiIaXHP9u+UREeAzU+ziK2Pi48jtOJYQ+UWasf8zvcWOZXyxQls0Qgv3t1Ap5/1jppOF0XgrSamLdRqu3FPguK7VGcbobkn6bucRRYiro9S9WilxUO402n+/3oQxT7ONlArxMoWoyTATQUJEiEbkGTREkt+3qyqVaYGrHPJSkIAQmKcpIc6EsxxhTrF5R4/BMr9IYc26UwaApb0yccSxWONuCSrVm2xhrq3FjDt57ayzLynuE1E2TCr4/gbbonEJzrupslAmNtVOtfBTy5Q7JU0DVAT2FEGyFIchWTGDcQZKsQZllr8W3HyrOt4ptS6xL9tZZRaqagDAPdYll+cEqW0GZuoq1Uu9DAZDyRYZK57djUP5iFNWkuWyIA7hirfN1GuNRARRFYQNvho/ycfyRbzAGCWlFQOg1Ol5KU8fQ3XoNEIQZNArgXCKNY+EZg4g+lnp7q3fIy17vsmJqCtEkg/nykqumOPqkKlg0Vxz+IKgMYOxf4efU5rIRZjUC3E2m8gpXeStuFgKR4LRY5GRNzAahBFEax3BZnAD2BkMGUyZ5ZKyGF4aAyiwx6vLyO8gYVSwSo5TKtRGgWkSammlpvhmxgav5drKA1jEHiSpVsiuA7BAJocJzi6SOmeqJEarSGiV0hULCTCJB6PydqnwhqFNsSLfCFGUrCG1AmUcm0LPrdes5NbjQuvBRNkAcnkj89w4Ji2uzWAdC+YvwcQyn5kLU8mSf9yEPOkTFi5IrV4Ushi8q1pd8mrlWKDSvAKWUdL+p6fKlPKqrSEqkqUQm9dJ2Kc4j55zeNWWEKBGdk4uaAXZiyHx8Mc6mjCfaMmMo3aXMjGJDhK/Y1nxkXSYxYyIxU6HKIDl/eRPYybowbwgmJ4ithcd0K+NFxZK6FTCbrQX47op3kFhbsrI9pbkQbqdmky7AdQSRi2bLuY5+RKYdq5zXNzB6AmhykDfttKxsgPKabwwXJcJm8/+LeZRbLBXX5cFvYzjyakjh0344ysHF9RrptYlMJgaVHTto46LPKfWTUokqcKSSr4Yc+Hg8ga9PyyMV45FeC5gD5nhDXErYlcXjdOlLzFurbZU3k1h8ScxlmOpkDe0iOKSqg1HUKG7VqH8Gg9KnmIqKdnN18kG4lG2LajWp4AGJb1YI0YLkIJlC3vtz/MUDQEZbUrTMSmxU7ZJthWuZekO4rGKxd+Sl+USKGPeOyRbFBFCrnMcwreYvc6z4S0xw8aeG6hV7m2zCJZgVag0LCmVR4fKM3ts8Y6JYTnO0BFK1lTytU2yBp+Y4lYTQDKaNhdSqlL7PdAEcRHewal+1AEy87PDayCqKSiDJNiHcLgCtkSjgbAYz05AEN0F90wgmAL067sSyCI9TDAFi7x2DRc7mITRR7hHGInidLsPcJA/wbYqy2YzFBMROw9OSKZOi0RtFEIpMwfbOSohkiXDUx1UyOwAYZFPwnZPWGC7SJsbttvLEyrVS0ShJFXVe4cEENpjMahgIJlv2CBGFLQEYkgKgyqJEsWeC21KwTEyAg1Ju8UzN5S6aVbkt9U9LvltjUpBh9A8VQYyrGssphZUqoqtaaPk5s40Th9uZirUM8L7zhqOLoCRicmwqxxofxkdHWd1o8JIsfUE92JmLD5e+p1cT5t4iIgrwFZUBqFIN5l4IEFNGDi6dAEpsvuDNxZSh0TTE5PFvY6mEJQSavWbi83FtqbHjeexPvWdVjCpijKuF8ZpNo7oizbZBxFSe3tw2ClEUrnCdtlCo2UAQipNZi6agKfU0s6ZDjA7660PJ6U9CGrggwDvQJ4UC1Cus/qrYzck7x0sNnNFFALOEDCMrRA2t8ypM6lFSgqMGsnDKjLlxxqYSisxcLBpRiXOqUt/d8al3sZTkYgWrc9Eh0gAeAc0ZiFhm0Zfj7trrIkKKqtYKbqumHmAygk1FT6hBkgy8Ka6So4LJHPTEGkXpls1JD3LYwVQTmMXXFXVhPmSnKrbi3N1zBIMAHErQ/pBgVGq0ostbFjAxByRYag5QRYUyJB/TKZUgUyYOFQVzqUzBEBUMibqK5VIkEaNunglrTFWstkEBc7KpPG/Ry1uqlnFMw5iyEMAgbpMI5UhyCTrnZCvIrgBK+TV6PI+4y0XwFKmsFuU+Ba3hTShAEgVg+gctKpkUPXADlSRczNp5bnD03OnSUlE1ipXc3vGyMarxW0P5kGVLdca8mt/qpJBtwDyKxyOG/nmAKVK1N78TSzD78FvWCJDpxM7TJab3lmE2Lkh0cE7+qod2/SOVBztvPRBCaU4mKrhwpUASmzybKShYYJEtomI29/66yxir1lTn6q5zAAwT6xPZEDuh02qrbzLXzlm40HaWk2IssOlNI4I2SaqzknCyQvFZiVmjxDQWQmCKA0GPolrNj32sFd4pIYuR0G6yaSV03Xsctyxp1EbI00lBIRG8BwtOzhBJIixs3swK8C2KOIEqaJKROiSqsMZMXU4bb11RsHLwZQBue0B4yyByf8fFrQlvj3hpCjjs6vOutIG2bSl1llopZnuLxAipacLDAs7JxaDzDK+Ng57BCXSAqPshXxJmRwVP7TsMba2q0TBXQcJJCIWGaFnZF7HM82KwMBkvVLFcLAafUklJjTrighiVImcDRzoTYXQfPqNKCIp4aPTP3LyAi6nKuIIxa3lunifntxxUyKKb1dx3E6pzqLbsxhyhQnLRFYBTBlvkYOLWzh3uMUPctlsrkPkiaExlOTghoiNMnc3BC4pSozF4oyn8dqk4b/ZLbnNRtMhLRIMlhtLACWKHgkVVAfPsbweOS0PK65PQAOJRKvpSgtpaMBpxt8pu9asCaBbFJlINvKpDVFtCMF/JOECwKgPncRcuduv1HVqI/Ez8GnocEFca/PAOmxkAwQqzn5HJa2OJehqCLQGKSY8zvvfXpeCZiqRDkmxSfbk1DmZywaHc1yVrbgjrM4MIHzcY5lsEQGKlDlmKm4oVwV19JYy5ay7x3EwquByyVo5BQqiw/Kgbw8plzcYy9kLFNYpyeJBjzIrUan0cmgdF6cv1AFACd0T5YkNt4gDbWygnDmXzDapSKY4HAOpgf1oUrxhlstXXNEnz+HCAAWB/Xr3bUqktEpx2J7KKkES7E7el7hY7oDRmKO8iafSNyLhaG4EbjHEDeT7VbXDabPI0RCkV5qY0YTPJditIqxJ1MWKzyLbzoh/zeXBfYnGNnZDiagVTg99nrOucnvokJ1101+Ocf8IT94IEb5jUDYASHNl9/0oKnyDlbIJ3bs1UuVkIJPeSyBm7K0YU8HFuuoAppolkWCK/lBgwbexUHTwIyG2PV37nmCz+LpnlVc981JwpJUB0BeA4Mqq4CHnDFUCOFH5VxiarVybE2ZXgS1RCYG4qYG5/WYCwummI+YzDeUS2SCzQbVXAGdQ6VzDkSkbNQuaYaYn4Soet8XcBWKhdwIZJszT1ToKvj1/Gu57pdmICqNpBLdX2OIKgitBEE+7Kes/E7JTlzQlfGLAm6aJDQFpGR16vNZr412hCXC4XaA1cDMiIEBtZjv70YgGTiynnyE7lFmqP54kBjs43hqiMzlF6aCnBK3GM4EpQYi31odJjZSOujO9CeHAUMRUtTIq0tFJ16pvXHk7b8gGBP/nKuN7VYmGH11Q3Z7uh6nRUsxL5HoWbmftJyC310dCcq1QB1zObGBLLpU7IS1MrzT93RzlXeQ38YdK5RkqRmWsUlLgU63MSHkXmgNLKZEr1Kc7jJDMX1xiifZyQOZ2WNzBp7crdtmtBHHPHcrmzdwhI6XlCMbjtlJlztK7ZiCCYHDgrpBhZcFQjIjCIW/ieHHyC11wt49JnUmllP+u3UfT7ciDAiXd9vaqt5vM5Tv5brdZivpS9Un/5jV5R2pDFj1nFegcpUcW4UnPKriWtdGQp8MdURbTTJL1nEVXkj5Mts1bo4mQqIJm6kAwUkS4SM8fHKoLuMileBluZz9YqcFsmKLoCqCSDYSufx3AviacNtdlsgrUEUFQZzkyagBUlm7TEHCNNlHEkai3KyOL6XVLWqFHsyB+h9E900lswS/2LcJcVi9GKGsdp4SoRxeCWc9FHG8+5RAVDmk5OFbPoWiwGDBrygEJ6VWqNN5Npq568l1ti4dPyFvhCdGGojAzA6jYviUgU786COPkTYWeAN/c9jPysmw1SYcBnvlyul+tWp92oN1eLVbNeb7pBHg0txHFgEuRKr3B6LDRRzlUKllLJJVqKTZRpJFdShM5TQGmwaiUo9A8QpavGRlUsAxkPjcpVlloaRUpYc3TWFaiyTytFl4VNdJeER1VOU01LLkQ1uZ6ulDnRx/vxAFCGSWpQUSjzY3vF+ljOaMzkc8nsj1UEl9GZIExpSZMsXG0BZGn5+FAUdCUaD9+4LBI5uGWnQppHpdfGWBVGC+BCYmF/FS67qeMs5YQ5jHaXNJeLLtQqxxoXMaasTiI+6YDHbk6BzuPdEguXSbQHSYet3bbfZoBTD+cHQpV8dx8ieMnqWcdAP68BOAI0mU5my1V7r3/0xI2LixGHg+SQgCt04rUlHYNeZFtDeQFLEX0ZaK/yjtKPucIui4jKwUM1C/4Ew6yIClyRsEBTVOzuVBE1pbQcUzVKG9yRg6VhQkZQpTfYBn7DlAvmWRFD7eVGOYIJrbHkJd5/Yv1SQHkqZdrH9Svj2qzTKhbjFTUVPEVgfN8rNIHuZBUkpi5vcTNvEIp5JGA1p6DHD+aojcCEEDMNFfQbJTj51hNKzmJuyGzHSGVhnGDlBLUxaNFoJBDiwwCQonHbtcIMXOTJNN7X+fku36FYMbVo9VjXjCAthi1K3eCOxqs31g2M86x6vb3Zcjlf1aer9aLRGI5G7VZzvVrK4QeHCWlnGS6Cn8RyafksZEmdpQMZZR+MyWRFqnabjISCFYJvWnMCEk3wCFJw3irBaatfAVBQZGGSBJ0tPWsVpR7+M1+rXkqyOSSsKbxAGil0tQeFeVbEoN0wwY2SnjtU+Dl1PfkcdOK/U9p56J2ckjBMtLIKEdIl7DXmk2fgAbqE1TtGYoyJ/ZU01sRIx1Rp3lCRitgRu88yUr2zYnkKGyqzPTw7xmjP0dZzbkqstUoxACTIQnF74AJCo4WONgNo3bfV1ew+a+5YmqpqHGucvfhwhv3W+auTz0DAzqJ80OCZTz6Eh7NyFBb19bLR+torL3earRsHh09du7ZezqHHIaDehAsx+HOn8MruCWlIJ23rWC2+KSgpGVeilUJMUw3TlCJvdYszqXaO3IK4LeWAhCThY49tweJala5goxemFG42FzgqV4rbEgqOUo10M64M77z8ZhdIIkn4fFkLOZ+7B5DkGDBB8izbltgRtkG8PY4Jp139FBo7e8JkmdJpqcxJ6dS1zC4WGIArNadxXC0yZVK3UpYk9Xer8G5E1ubT+0n+nIm6SEZ1ssgsqko3Fz9/7Ion7Q3m+O9SpOLIcV8K+M8pIVeVzc3msHrr1hWAChJF7qz4L62Fcf/Vej2fr/r9w2Z3MJws7x8P+4cH1598stFqYfin2cC8wYMsMvDHULYrho00rA+OpcTxZctOhCiF1FBeciQxFxjsL3cCTteeN0jCUogpRKEkHrjLsiQsVDIpu4/htQVSDygYUoU7vKZKrvkN/htMGc/ORa1GZWVKeTSN8mTABANXSbkdhHoAiEPq1q+xYj01SVk5U3IAEozSJHOFGMoyM02CLhbUv6jPNBFdWaKGNrrgoKpQFqwWMyUsmQZFq5HFSIQt5gQbClmYYHgsCWz691jepU6sliQJAX9pvlpnnSfeHuaXsbEEHpvjdi5zT7BZPqktKiUxtS8r53ZaWcBJ/KQQwSm6oi4w58g+JjmvxzGg2Wx2uv12p3s6W75ydzKaLJa1+gyPBC3mjUZdx4HQ3eO4wuMGE5UjgM/Y06Ynjd7qg1t5FyFJGA5a9lWsZCgBiKejE78STDkfnKSuZVZvMGKvKAP7sJW2rQYL46lCODVhnmIANIvJW+MYAPwhROxfiGIuEIrXASRhV878zDVhVn89AITDdsy6RTZaE4Q/D5KXi6zMMdVu8oHN/lKvpFTAWAgTErwvxFbI+ueNOy23u2yqXnWIy2YDPCbzioXqII9tYTSpl4aNeEo2riImwmd7QGKxQtqErlSgNfgmQb3iuUOrKo2kJvTCuZpg/DcaFQpxVQ3/1XrZbNJhNp2/fufu3fPx7VrteFpvD65MZ3OcmS0XK4IJQcN6UU5VRRdCCSrW+aiSY56oN/ol7CV/4sWgavPgyiWh6YQWMl9hS815KYMU+ZyDNxi3J8oIvFqWBbCzQu/5EvwuBTg630JkUzw2uTGETEpUwZhJDstxRoiyrXKZoVCUlxCpZqZZtlmRqHQqHnUsRh7KuExIqcKmovoqWOK1E8h5KBbzDU6brUnot1/IW+gyjBvqYDSK2QVpLm9PsAqZsIEv7kAVn3vl5YwsM4d6BinzkGLmZpCK1uKZOv/zHCojLhYzDbmpMrWODqHmoGzwl/i4+w0Ge3gHbHg+nM34Yd56u9k76GEIqNGo8UnQBi4DMAoEDqERCrktrBWhUiXMWRAUlm6KigrzhvJljIGreIezx3KfKIEqwA76OPIWuEA34iuNRUPUQKxuXAxpFN3EBjVXTIVV3YkJRLLKvW+kTsQ8h9g/AeYFAzrBygoM1YsNeAsxD2jlGJcHkzKQisG8CFal08siYMymKjOovjTYpZVWj509LY2dPbYBN+fw2PF2cdwFsy39t2PfGD8Yg3S5YNayKjiarWzmtjFaTMMOW8DWC2tPYUVYkxyMmVq1OBULoF7XVqt1bdlsNabT2XQxf/q5J67dOOr0651ardVozifzdVvuEzRWq9Uc9wk0IMlcKhYCbCECT7jjzDWaYDO1+WeC5/JLmvU8PgOG4o7MwaFcQsQ4aDlou/ZxssnbNIsCyqrMYNoYMDNqsYosC7tTka1WODMxz8pIiYEfHpEp0ToNAzhz2SKrYAaJPYFUsFyQZMBC0dAFiyhi4nKEaKPs1ENpI/UG58c0laRWoorINRvLrCgAW8wYnDGtyaq3eRTnOyZaLo8fsVjfEi4DqRDCoqx/JU67qgKbtHPC521+GThzTXCDRfZTQaD/Z4arGs7sm7XpfLFYrZvdzmoGVe3R5OL+w7Navdto4yCA3td9JqjhxnHTw447OyetUMuMdxdU8OqQ4o6S7qVx1691oQaSTZAtkil3FWKiXX12wCltOXlpsvHtk3I3ozQBeZRDNyToHHRhmVTROAAWBjXBgsA5Xkmbt3yC1TOOyQ2MxBxllInblEYqxnMIXXBesaUpVRwG2Cq6wLarlBHv5mZOxTyKmt0od0AhqgVO4UEdpBRhpQ35waRWE8zr0kJZmK25FaJc3qNAUZZIGSgOFcsF7OMrhJZtG+fkZb/06zjkoBbMtS9mUY08DNRrePsXi8V81sIt4F4bL4KNR5NrVw5wMf6nX/7Gv/jDfzOezXvtbpODQI31imAQyKWIMbs6hZihkj5U0Owkwc3+Cg6si/9zddEwOi/gd1BoVRSogWMnHmg2cGsysYNkB4XSxuQpKi1pFItV7hZTak4bMkv54eo5Xc+qRYsLuMklrGpLOZNSFCDRayHZbmO7HccxBBS3s08WWBNNiAlEzuiZv9XBBO8FxeZkPbB0WUyiqIkdNVqkMTgsWW5ZMXJ6t8RLRKyCqt6sJjx+yimFlUx4fObLeCLcJSLG0FgOW+9lYhexusXq2ZHKggmiblbxPCIRmKZlu4o74cLbvvj2J078J7PZX3zz5TuPTrud/dG6/nBce+XOvYvJsN7i4D/IeA8CSznX0mOAlGDhTqyRw5LRLRYxLFO562RgE7ynhiUhJrfQwjs1j0kZvpBCHqgAUEVBnfu5chzPIHDWP2gcEXD6Z6DLCKVRlED5XZRSTkul1ArlpRJzkUjqg65x58mrsaxIVhHBWIJ0RsdVnq5i1HkjsOgO1909NI7LKHDFBAVjgH1npDiZ0ohaY52XAqCkNa6JylupEzqgY4rExsLl2Arul1VsSYc1LqN0buatuCp0GcNldFW8Ts91wrN2LtwIc/DA8D/eAqsva01896fVbYznf/GVb7x8fNEYDPBM6GK56HV6h0c38IAoRv/R47fqNTwMxDsBvAMAas4lWduJ49QlZlBYgwRVlaSksJoQIUH7rk2hbeIQloWGtmKMgQz91twUU2Qo9d3KliWwuahsmGfRraiCZVIePUbHCHOrSgJbjWGMJAHzCiAcDhKTFYqePgm/VGiCSwpG9djCO0yXr4/HzuuxHdOmuwxN6pmU0EqXa6gi2mv8krtXEuMyqb7zWJeWZGQpmsBe8l2ZLIK2byhGASORdvx3C2ZkHvJc9rrBB/zrjcVy2e71rl49nDdqF5PxbLm4ddj52Ic/cv3a1fFkOprM8L24pTzIx30UvxHAPt+ouGI0KOY+emRlXDluULjEpGyesOhYbSlid9FkKecuW8w5PC+reykJKpLps6q5YgyCnBXzgEnZsBlzAiotZIEMk+ixUfjTAQPEgoWHEhnon2wrlg+2KvGIoVBkxTKIRwFahhaXitlOeEtxC72rVUWoy6tD3Mv7bvbQTJUfTaCChcuKW9vIHLe0T8gp6z6CIZKEFTP9M0MIZqq/HCFKxEQTkjZTrdnebrrgVnrOk0COWXXujD9uQA9GKujMmdBq3Wq28CLAeDx+4sa1977w7EG/OZqtcC72Qx//8C//wmdvXLsyXczwECh7eLkOAAdpxJs8+udUWnJpoOADOs1lFzu4I8i7MxmxCWyBQixonLJoi8FbqxK7x3JMQhlE+gdQjIvl3IceG+10yPgCR6VB8mC/D+Eyk+YPD+bk/0PSp4A8mV86Zl/0y40BSXqZqbKKCcnm0FlMLcJls1cUIAOCINNE2HdY1EAWDkJpdHcSx56ACM7wP5pCMdVHEBWd2UfUgFt8CiR/yQpNWpLw9XAZZcWQ5ztZwyQICq5sahE0IOY+si55/s5vAOEmMD7xU8cTnvMhvv+2fu7pp48Or8yWtSut+tNHB+PbD5fDs26jgRGiVqvJ7wZhCIiDQBg94pSM+mlkiy8Z+bAWP7TFOydZkHeI0vhiAY2lxbCNIxxUuH6SeRzcHJ3SNUrUNpvQsc1k8cXMNblGiAlj2by8kCfk9cVlOXIjedi6UrrICSI3OcwoCUwk1fttVz8GV56Bo4ZDai8o0u0uzSgpqSfmmEpYYqyCYk2JTFDKg0xTRdErrUyA7xSxSLeDRpOKgaaxfDV6lpu6mLIoBIASSe3jQEXZSMRklU7VRbfvgGaHFFKIJf8dSG5TiCirIEJCd++G7NmTyT/sjXgMtL5ao39f1vC+1+r87OFkPMWJ2DO3ju7fff3f3bm314bvotPptLttfjea/+TegdtWUW38MVKI5tJDT4k9Hng2jQNtyvzt2FjBt+Of+CpTnDHluOzr6sLGJmGC4hJTjPbMBXcxSJNKU2chlSJTRhylFpd9BLucmB8KJQnNRIiiulDU/5HSovnskq+BKhs3OG92q1gjlPEYoRcAUmf18erSZSWk0lBCw3Q5mc+2NBW4DVUSaReVVt8j3caDYjGcIdWkc+RWRHo2v9wJ5MGFZeQdiQXYO6mIq6oxMS9OO6RTBjF2x5hhSkMVg19e49aY8ru1JmdbpJJumILPjgcDaFFcL5cYeUWXfuPWtePzs7fuPsJPwfRqtfFo/MzTH3nx+pXRndeX00W90V6tFjyEuFvJPJeDPxl0pvQ8q3MTk9CzVR4DvPbyy6wFA3sIxUiXJ97NA5lv4Ham7dUzDhMQfrtbWY7GoIIVMyz0wu+Xidkim5CYiwXgSgPF7I+/nn0W0RCQe97MW4oZbdYgL+ZbmrP3zLhRzDQe+BjLAlNBkZC6dL3O0t7s5eHbl8JjrNvx7wBiS+pMRq6d0WVYYia8A/ErKYpBNFXMs79KCmcQP3XOoQiifzBoQCvm0HesrHFIRymUogBeyS9AOHW9iZ//WixXszn+LV55/c7D0+FZrXZ+Op1O5q1mo9ftYeQHoz/yWWgcN7DW6utGA2+Ksbk8jWPTFnQNKTqnoQpTBhfd5WfgApEyOm8Nk6guzwuPzQxR+gY0oTKetXWOiOjMBF2ZWuy6q8gZseEhJHj1xxx5eYNfxk7vhKy8ugfHMXyTxLo4XpXefQ00QPO+wRMLopLF+QfwRmSAiR+KmSaks8ESQAhWEa+aN3jHEvAVTDFqi2xBTRCHtFSmQuidTtxKmLakpGbz81GokPpWVxqWauNOUQFCHP1TWd0sm5hleyy4ZaCsGNO9WzJC8nyc/yUdSSHJw9eOSpH9uRp/A55P9DTa+OnHZb/XGwz6GAnq12oH3WZ9tpyfnI/PLnAPAD8Qjw9C85IfbwTwpQBeCXhasGo4i2xCRUKXbAplL3GKMiixRqpKhggjYgGoitJAUJboc4a8nEe8RBnRwCaEfulavoQECMlOUyxJtMQnV0korywS6eEfego4ZU/g6iYGSzLq0D2rLL2jf8ncGWUL8zZXcfPz+serWRI2o/DMPlQw5xaPKC6BtL8oVqAqukQahYFgR3zkSjHzyooerOoKo4CkugAU/5SjqFdNcPaxiksg4wlFRmPHEqsho6x/qle/KE4G316M2TajsxwjcJVFk9d5BH9XRNnhyva5KFpJntypqMa+yF+CxKeA6g0+AzqejPqDwWc/85Pf8/73PtWt/fIXfvxjH37fYjxczZbui9B4abiFV8bgjNA4bIcjgFQ4rDlJKm8EOFCVq6Nkq8W8GkqC+WXYLoP1AdVHSxqOx1lJ1BWzHNSmNSHCoyvrFjtEIGWPFCIWwAWFwHz25m6KcrzhyoQSF6Xj3J+8OUEMmIlPiWPAZ5GUsWa/COZ8scX4swzV5Jwoc6ty7hlpXCyClEo9uen666qNXDCqX8y9QU7xl3JV1t1dNFSafRo+z1MqnSiNY/ew5l9g2xw8b0dz9yvciFXQilmCUD5GjsapbFa8vOCD+6UwJIWUMzehfJkc1L3ooe3hQnFUP4+jJgdzRvbeFPEkD1oRBwB07HgL7Pjs5Far9qGPfnDcbL1158Fkst4/vDLsH63OLjAQhGeAVlg13CP52rD0a8Ln91FZbyGCrB4Ufc6yJ9NfQqeNs72kvJ5rO94QmWOUkEFKBfWLTLHCqpBvqwZiHCtELOWiIstql1k2UMKka5k0G3BiLAtVnlrQZpwJhRYiFcQMHxSaqbO7AkvwwXb5X3z2Y3jGjM54NAFqYeKVp6PEglO6QNEp1OrmoiszaDIMIn6630RosSRUSXWArNjRvA8RPkkVrOghuyw195JsKpwVb0aJaTrLQ+2x3jygDNFE4jX/bpM6GlyK0k1sdM+8gIXGSFJXy86E1O5LMUMse7sukyBbGFNPljKHrFiKh1Jr6xZJAkWPrRrzt43RHwBCNi6gU0hJ9yneBOZD12u83Iv+H/tcbdaotY8OHk4Xf/KVr3/zjeP2qvbB548++fRzB/XlXq87mq/wxOhyhbfEZG9cY7hWPg3t9lFSIWX+990iFSEBB3C7Tvkeu6XOJN8+uZAKRCFk4X134/Ho4lIZA28oazgLXXTdrKlOjCGSisUxLBPnH9PoyjCNclhxczqwuphx6K3OAANTsu+LwdxF4Izk0uXjcrQlWxAV+I9JuZyEBdSE+ymYvWbrMnZ3YK9SchdZbd6UBy6U07ial87VkrCm4G2lrM6l8FJ6UVoSBsn4tGhW0Kvsd+PSeCXKmIFma7oSbK4qtleOYNkYTUhQqs3SyIreIVXzTMPRw5CxKzQmd4DYkPn4MGGZAIpBArAgxWCVnYYn8kjWM0PrV12BgxiPY0ei33LQxXLFu7m4Dti7fv3RZPEv/+2ffOP+Aj8Bg+nao4vpk/Mnrgywf9Zntdlk3sRXQfHgKMPiECKUzAYCFxwSsp5d8vHbkgAAUyW51UWWKJneqwm5xAQ3TMwhdtJCoorNjy3nvHn57RDHdVBZ2qai9xdbCKeJeKUssZKgtZ0ZOhajFg/eXsqtKF9q0vgaSefq7s8QyCebSSD2MPsctGQZ7BY/U2kos6YCt0/9g94Eh8mIRKu6xIIA+hdTA6HbfqzM5RKyHLK1DA7924z0rZehio2zISeLE3vFcka+vZg75+VSBs0wNhU1sdXkDGZFEwxZIgDkcX6ZdElwQfZaAQBkQw4aWrE971Q/C7472jJSXziaRnqFciZoaeB/g3sZSujYh+M/f+RFYSv8IvBsdXRwZcbPQxPy1IvPHFy/fj6dn48mOGZgIIg3f+UfueWWMMlYfQmGpe9qyC0kMGAKSQCpYDXYXBE0xWgzb1AbJgQxFQSljDWPI2csKOpfypWhUuNOpbgZtjtYjSvykaY0W5ydeRaDxDBnVYoiNNF4Sr/Mebw+OJnGBH4O2goBWCVthKqRc4OZIITO5JRYiJSnHcfWdtiEiNEmuwhW3iJYlB0DgT4gQ0e0OapaMTcYOEzekuGu5pBWmQesLtsqXFU+plfBiohiclGIcjBjpBOROfnmjGXFqRv6tpBwkHKqvLw7MvKMndD5oiidrSx96xGutshRW0IyVQ6Z00/6aClhX6vX8HkfvAGGrwENh8Nep93v73X4thd/EObJ6zc//KGPTifr2Qw3iVc4PZPHfzQKXyTwLcX24J9rVlcK2egBIpRFAljx5FFn50+uy02bHDxpYCxqgq1KynxQ1D+PN4UJ3rJxCXTZpGozmuCweTmjgFn/oC+2DDT6l3ltKhpdDoozKYaSPOIN1uA5oTNgVLLA4k156MvXwhhI6WhN5wQ7/sCukFJgqTLnsnKhTmbJBAUauQkZbEsReyaJMMcijq1FU2b0KMKEuf6BIQNsCbuT2YJDcFOQvMaWirZ8oI9Sot8GXyPZRTAexpKCBi2GhlF6PrK6ZMy5KhJ9qmxb9C6EoCI5iJCYj/T0lEMonqzr/xAj+Plto47P/PN2QL3e7XTwswBnF2cY3e92Wrg+uBhN2o1Wu433xNadbhOfgsBrw/LHOMqujG5utYy1jK6GKHoQ42b0/rE1JL9Z8r6ebyvHVkBlPITin+5glagtBgtvAhxi2fuX6BC9qIUGf64ZmJ9BnM4TAucvz9THvAJiV6lAnTn6rkgaLMSBm2VHD6Phi2BWyLjiIr198ilVjFIZtVWshtR5AktVhPM/IWrhXCV1i2XVVM21LsCry86O5lFFvE0vLVwSLlYBY20dC6Y35bZoG+zKkTGFoj9FLYFpqjqHGYIlL2iWTLMhg8uauOMggGZEZxfEKYLeQYICUvEvDu+gIHzMxDNPZeHW6pPwALX42HFWDI39nyM/+K13/A5YHc/3Y2p0+51HpyfL5bSNZ37wwGcT9uWgiSc/G/wIEB4W0i8B8QjJPx1Ckn1Fwuch5XIlVUrPo+dYmQE5QaOmUBvmmk6mMAF29ZQ5ZdQ2tqYEeUm9cu2WsrR2GsOvgC2ewXxph+BKydy1AmkyYtcTmQA0f2DFGzP5w8yrDOMF7AyC9eWtS0nEZaO9f8FFCTkP1F7SN4ETF2dynM7k8X5Vp9bE3xUUwa0sm6AxNsj6ZxjD+8qYwiAlQkyoZtWYvsSHqhJyVZUYqii4Y3oic4ZgFLEMEs0ptirztlwr4sdqR2FMFipEMxsSgRwVSRRwMa1fSWo1TEALT0wVMOSJLY43Bpisp3h+xZd7ZlxaxDzoIYExkGoCwe4y2GlR9IImStCd9Gkw23VtcwBWGeDC+8A89V+t8CkI9PKt5ng8uXIw2O/31ovVZDJDvz8bT77yzW/j52KWteV8jnGgKT4DBxLWB5PVi6LETGvpSkRHtWMGcVlNosFM84OgEC1G3uqsxgglBxsPU6svlSx9I9AEuSxIwctAJhQgl1PEWZpcSm5WBFAZc/wp2OYQgjvXh64Vxca5yQbjoVgCYcwxTmTBeXDBWlTsDo22W0/DR8t8Pl6nS80x6PJysGySrKbmHmtUjitgViSlmzs0ImyKAps6GijmNKUJGVj1plTfzQxGlQjwydyMNMapUpEqY65/gJkQu+wqK12OjvPKEiyB5qpC4xJggbygaftS3A5bImo4153Rn0zKpqZiLKdPFxaaaqtxok0dtpUsbyFjSVnlbNwqWNipTCFVCr02MuHBABcCzSafA+10GjeuXXv+uaefx8eflzU8BbRoNB+cDb/9xhuz2ayN70U3avjBYHkLTA4AfsvQ+JKN1g5zJzBJTn6pJTWqzLmUrQPnfuYZ3FIXAgteQTIHRaTBAuwdkywRd7S9PLFWqCpR4zdiIEuVABhJESDuajeUUSZCbI7lBBQKmn4ob5JCWkHagMd1KKxZDqmnK23EbIiw3ZSGI15jZRG3E5UhjByC/WXMpRhTlrFCp/Z47oG5Z1q2kgneLyyz/IJhqwTPSzvztFvc6LkhqyS4RqmOpTyYS2+BHqYaCl4xYveW02Wkw2WJQ0GlCptDcBAJnKyjJPudClIFI5KS85MgPhiXKmtDBm4+tSlQtIKMDrCIui1Xtel0ht8BPrh6640Hx6/fe8CT/FptOJudnp0fXhnsH/Ua7Vqn28ZNghbeBVjhM0D8JDTd9YYJmJgQShbOJBdSDEwsSsswMEKOi6Ih3AiD5FXqknnlLB4clkWiYNsqMXslwPxxmdQ1Tjxjik1ISYuZ0qUqnkpYljycMm6P8nS6NJBXKywteVdZmkkFzP2fniGr2njzNGDw+yLp5PSEQskQENVhcowaLKi3SJrNFtB3zJy3TRRYTY+Vrjlxv6yYjB6CrqcyoLrHJMZdBt+mA1HMtQ0e2Te4eVKrkbpFHpEYKGX/xS68sUJidCOosl3n+W/0tmBAVQAr1Oa5UcicpZjVNSp6M1WyYaAHlgf48UQPTv1xzb1arxaLJX7w686bbx3uXb115fqVBn6atbZY1eaTSbOOp4Twnbg57hWs8C0gPCHKszSfBB79FxGGaL/3VtaDMrw0JczlKGQlNRLnIVi6PFUbeXvF5mUcfDPyclbZdC7nUo1GknEbxDlDryb1VlOsgd4VsSg1h7jVW3rGWN7MBVBghoTYGl7nUqjuVYIr4c6FykiUdxMFGCmlbDM1FM1ycRtf4boLySLSqHYVqkLu6u9bqorHUjRhd+aA5IlJtL7AVU6XbcfloIhWxIg4mC4tXYZlW14IXoBAEeki0WUaxY/EQjWcLfNXLeb6V/C6jMJFuIyLYZ2vUYjgcoXMHZCfaYMU74uhzCf52cWua3j3a9mor3DLF/d8+73WZD4aXZwumst2jUNAe7XaraOr7U53NpnilgC+GQf6Je4M45kgfkMIf4yAOVOIW0vkoOBe6SbJzhfiJbfeZPuNjZks0Ezni4i0yUxYlIz32mVpbgpGMVRrF/8Uo0mSJNXHGjMpOJ7DiVZbwwZN2Qr0zmxUEFQ2P9VkSlo3qdTGrUqBAVuZWBRQ0N7F/x4AynKqUEagOsy9kyOLsJEYjEV4prGkviNCnGMsP1bw9EgPOmlAv3nF9FVyGtZaRqlS42OVlFHnGWmcUsQtWBlboFJ9FBpxmR0QVRMcpMI+YDwxhj42Zekojm6VHuaaCOaYaFnQHArqSJH7ouzTYhpeVirSOY27bnElaF3vLPum20jQIeNVABwF9B1gjO93282z0WQ5HJ+OHs2Gw5du9g4eLZ+/deOpJ68MH160F8vDoyuz8RzXCvh2qNwH5vNA0szgwlckNF3JX9TaM1FkJogLGNOOkJELIIRpZVSOgKGyruaKFYd0ZgYIKX2Kc6UdIIoUYIbOiqUBtiqNxDI3jflCU1odRRbxzrHKbaeWUQ5LSoppydJ7HCHekeIVDS77GJzn1XpoKW2HyqprpiVm9bc5wwmxtZUVo+hedFtUnI+Z/hKErXm405OtONkg3M7s62FNBO9sUlOmLC2ar7ZqgjFbot1cUJ8KT6rx32VnIDtDUmo5paCYbXVqfZfmmlZKbgmm6rTk2s2wJCpNnEBXc2UAyvX8HHHHsCo/qwUVqTj8IweFJYrtGp7snE2nGA26enj04Py8sah/5D0vvPjZ9z755HOtxt69N15+/dvfPjk/7e31O93GZDaq4ffj0ZsjnPyXVtfdiLm5XBHJTkiYtTW42JNUoyqrM6z5ZAaEVDlHJOUqfg/agcJDuQRccxK/hDxnMmjsXyknVAWUxlR1HqcAThSKJrvPJ4pkVCYkvul2lJpKSz6EN0ahfLt5U8WSFQ1e8nkqQYZN3VI1QQB0LJkq1A6p1gxjSgTYKUZJ2Hdblealbau6QtJxKrmb2EL1xcw9uWyKfQ0SK8ucSnSRC0T7U6QYI0SJ/xaVOvu+Rzu33IVBd+o8csekbI2QaHcpXKaCzDXnFAXCoxK+47VzbM0qchER7YGldMNyui+M/HYbvuAGGpjx+A9eAEB3vm42MT+9uDg9Of34D33kf/9P/snf/kf/uNU5unH9YHh2dvLwDL8HPJ8v8TNhvPeL9wY4UMs3gkHPZteRIE1ail5jtWCKetBwfanmLHZ4uAnbYaRXpQTJRAUVoEAZusLoAskCkDJUDEllQ6tgRa2450x9tpWMxFoh1phyI41WWrHhGCxZgUz0nsgvN/BVQ0os4Nd8Oefak9FAVTFGiUsxNLfpaPJDQFDxPCU1BtxGanHCLKMO3lUSfapsLpmqhKrcMv2GKmXIHYqFdIvsVZ27Y6fZ19iLRqKKrLhDWjlEgwiPj0GIj6twLcHuJzMXLB6RLs3VdSMMpa7GlIVM/ctKcfOU2ct1cSXLEdXakKs0ECtldK461OkUd5iQ4euxASN1ls84U0LHzVbhw//rVheD+vXlqoFvP+DRnjuv3X7j1XtfeO/zs7sn/9f/03/2//xv/r/vfe7m1YMr164ePvP0k4/u3T+5fb+xV+t3+o1OC4eD9QqXFUu8IsBDAZ4IYr4c6sGSeflkmYlchmApMhdSS01XlPTGFFUAJamDXKuIkRpREegFb0mWwh5rTGECOSRCRhQB1A5gBDFRBeTjqun41N1QcQ7lcpUDKKJUyn29Ng6nWwRWhBJI0RNheRlaJOA9Q8P7mH4ZYotkxV0DWQRP6J8CEoNZKVghQN8VCanrX85ulcsNlypfisXqbEIUCzqv1qUvRZgASZRRIXWS7DRFSxQCQPoXOVaK5hgjdI9lsBJzmkPstqsMBvAqNTPltut6nUCuIMBKUtgQKEWnpXK3ENLbqSlqvXXTEl66Owf/hMgXrO4gc8PtnhYmnPWjL8CzO7yDS6HRbbVno9l6vpwvVsPxvNHrXdQXjYP+6GL4pT/+b7/15S911rWz05PZ7OL8/OxsdIKfg28Pur3BIS4hzieL+aI2X69azRY+IwFK3E3gw0HorUP7UPIdNUDoKyMb0w5FL4uL6bVq0NmftgH1vtq+jskyJi5AzahCNZEBy8KVuMX4JJ1NhaokSvg30cQ2cQUvqaUPlxjQaixniT2qZQGrXyVI4nF3k4MhSvrnV6OYK50TboXW/4vPfBRUYgmH1wgYGINUFmCztcyjsLHkClDu3ntoynTJacpjl2irndPKKQ4EsXq3TDU5a17ZYiSTLLbVpiTNbSpk4tckRwu8TLc0vJ6/VNJZlgWE0hhfsGe1CIbLSnHLpu1cZLJsYNKcNQ0iy+qg3IYkzDs4ZRQdGlfyVGi1yK5mGW9xKxNPbbII+HKBA0Cr3sLe1aw10YNPx+N6r4MfAnvl3t1Gq/u5v/U3f/ILn+t0F3/xB38yH4+feebZyenwq3/yp7dfe/3+m7fxk5BXbt5orurNTnc5m+JnI/HyMGKs+DDRsoaBIYwqMReuYuzB+If6MkuWWCufOhOzlGERF9c29FGj8xaocweDSNyEgkqY85kiMN8wSWgSZXTVXrFF/WJ6l5JbxJYqOY6tSVgEZbFiFUNBLx6xmxDpTmfhYnuBIVeE3VJzCnZHk6h9gTb815DBRbWh7CgUCYd1NAQUUCIR4+A5S4aUYhLZ8fst0CeZ+5mPF3TpSzl8h7I0ntv8k+TBuXlSdPBJ0LtkZHVOPLUQR4/lAE21Gi/rvQN4u0QC1kU3xBjPQEqv+z1t1BUnbQvFpgitKuYKoWsMLlJdWiPHrTiopgEeyyfE9kqLIl2aeAvId4dmpxC7uwaASkNaMKJEpXr6+aL5O5OsLETCCI064Vfc8au/i9lyggPCslnfPzpa1xb3h4/OLh584vs+/ld+83fe/1M/2+ztLcb3PoCB/lrtiY98Euf5H/nMZ1790pe++Hu//9Yr/9M3v/rt69euP3nlxnLUXS4Wk9H5Yrno9DvtVnOxXjAD7eplVMj6DTkIMFU7DmjamrEeC5C0uOsRw5XooxNtvqIU3THCWR9roc0Uz3egUbhPfAeH7RClBM5SAX0cJ6IwS6RLRM0sUWkBhGYzoQRXUKkj89GcEoCu2RI+p4qjJo4sOExYsHeXF1AKUFWUJFCBLKrN14QiZruGzprvpWngQM9ksZkks7p2YpKRyGI6qRsgipKzsBSBJGDL6B1Ej1d0VYigMNN+Wykztu1F1wEpkGSJCwOJhkJiKSmYdwpWP50Ll3dNCl6561KdfU5+Se84XrGo/MBYdAiRO0+OUXZWA7kmV283V0e0j6Ac1FFB6ftY14AuJBYagSf+K0bWgwBO1vFhz067M50tGrXlcHbx8r03VoODz//uP/zJv/Y3rjzz/lqtPTp7qzY6bu/3H731yuH5nf7Bzd7z19//1Kef/O73f+tP/+iJP/qjl7/21Tt33ug0cAOhhp+IxC9J1maL2WqND0rglTJ8WIJn/7UlMmN2zFiSl9Qwk7IUfJPofQNkSbC1iiDdzLn58SXw7jBtBbnGFKoYHOt3iKOQmKBsPRaJEo/UrCbNA/LOCUXAmELI1RYh0pAVpTRJeCeKUAiSI1KF9hoRd8IADPNxUC5Es/kKICIrERHQnW1U1lQNGrSEwSURN3qGlSS3rWP1CUlASmjMkhtKU0q2AXPdBk0T9RltDej4wzlW2H+lDthFt6WQZ+a5mEPmblQhL1MlLeY4FRbAVGc49paZyjlfdgEWRLKQl3UXvE+VGXkmytLTaYCY10NYKciYsToxwslSSRKlkzU17Bz6YSD8w2+64yM+y/Wq3W3Wa63+4f69Rw8ent++/uKzn/+tv/8DP/8L7d5NnH0t1uePvv2VZm98/dnnZhcHj9589akPXJ+cnzZWy6OXnv2eZ558z/tf+uK//v0/+u//+3uv3+62ezeu3zjo9+YXk8kCVxT4mWB2+hgPwqgQPzWKew2SPCRdKTBLBbmf+ryjdZXsu8zbJm0WV3QtEplhQCltprRkTOVCzF9gKroUITEB8XnZONKsTV0pVFSjlF+5nYdDJLrKKJc0FJIqTaeEdGPtpWvA1lni5xo0xA1SgpYtaHMyloLCMrAVAYMsk+rihjSOJHhpQaHOIfhB8vQlblUm88LOEboFRWuWGRkDSlCis4Ojuok18WIQ1bqd1VEIUQwnMPEsKSiesPh/ClSemDm15yUNirlPgKK5h3bJ/R6rbLwmxDSaSqwpyOrHuWOwxNlXc0wEeh5SuRBvP1ddTCgaNcdq5fbj7Dzr10bAEmP9+OO3nduN1QKj/PhNr+nF5LU3XsE1wQ995nM//rd+84WP/UCtdoBbuKvVxfD+N9988+svfOh9ncFRs9GcLufwXs6Ht7/xlVvv/cDB1RtXP/LhT+3tH+1f/5M//Nevffkrx2fH0/lgMV4gVLfdanTaeCcMvxuDiwF09TjicM24dcPqu9xdS0gdJFWvsLqZQB+/OZq/rO8IQqKsGLWnhLnErMBU9N0BUnRSTezqq12OVWuMl0op2FxTu2fSczTabK/3prextKAmRNEhmlriXiKQ0jiyVsbkeKINxQHLAtBX0sCsFMZzjzhPTTXTRMWYJJaT4FXBAo9Irsj9k9fHER0VnlHVKBYFgTD9yNW7YVnUQuPQauPcAkWemegjCNTwSpZA1abcavCuLJmnP1oV4QHjeHfMUNBxXOfu26DM5H0sLe+zbRnXqRobo7zsQrmFVta2T1BpZw29ImyT4MZODpkDxUEVPVL4VuXNdFdZOYagwr6n5c6P8288o8kPPi/x47712V4HD/Xw1d1lY/21N751cO3g1/9X/+n3/NwvtY+entd6teV0uRjWzu+e3H+92+9fu3KzVu/jZm8bp/Q1fACu2xnsjU5PZycn/cHR3hNXPv5TP3rrmef/9Pf/+Zf+3R/du/Nwv71/48bV0ekQ345u1XEUaDVaNbxjLM+eurpLjSjLtgBaZsm5nMukrZquPN84xOiuw4rzXTR/3Ey930ZJUpKGZyi/LeWEaXrBqt5JGQVX/6D2UrmhOqzko+ZqUJyDoQoJm8XnsmUJfPmEdcB1UVnLikAFtVdgiXFFzjRnKRu/lmitnmIM5FD1KEluejIRnIBKKqJ27+Ec80VsJunmSfdqxOLmLJt0Cd4ovYCl5kvBtn1vFQYLHWspu7LflVDUaplDSQJBpSikarmqd0B4SZEe77VYMjI7MU4+Ge3WNBfdkMTsZ8riSxVLJsL/8rfJQ8OApQjSfExvyBBSEWXO1mMrWEmMsEAVW8SDHRkEC6Ax6Ae19PGSsOv91Uo4ej+0JsdVpEN1HHwcX9YxaOGMB/QBarQ6eNoHP+bYH/RPzk7eenjvxrNP/sY/+l9+/Gc+X+9cxaD/cjpZLs6Xp6/MRw/Xy2FjOZ2PTzpHVxqN6Xoyxf7Y6fVaq9Vet9Vp1F/5sy9efeqJ68+89OwPfs/+9YPaXv+L/+oPp+fDdbPTbM3rkwk6/9UCH4yoL+srvCDAyuGNAT55KreiuQ0wW9mYWAWRpf4yY9EmjzWFuklRjomOK7JvEkvoYriadS5pxUaTpbvTkoLggIlytZNAklmORVmJLIsErgV1iufq46GegRwexQb3UxzE6yqXSuDNxqJqRhDTpt7fVq45e7awdCwJopWG9mBoE5jX+6Xbl/xqqMRiDxGbA+hC5xqiLHwWHPAEpYVA5WP43EqX/pKdxoQtRVtf79UOK4vQJ2twj/FLBW2weeCOS41NPpeF+EGGKtawoKoQ222TMIgu2pNAEsF2TMXD5IzS+wcab86XQCSJit00m/wNpJRKpPVMgxjQBN8Y5ukdIoRXaQraRtpk0k2KmkPq0uuD0O/icgTRNGDie15sYX7kZ7XE1zzXuOXb7fXX0/kbjx4Ol8ff/T0f+LFf/tVPfObnG+394RhPhY5qzVWveT5ena7qs2Z9Nnz0CLd0a40+HvEcnh0z9VYP3wttLBadm0cn5ydnk/HV97ynedC98okP/LB8F/qbX/7ym996ZdDr33ry+vRshgc55rPxYrnu4WfG5HXjOQ/+fAUZo0N8bUwvfDRp1lubQWvu20HV0Lm6screptc6VvTqwlJdWYWSlV5ACw7aQh4psjLsrlGqc8moJX1fhzgJa5VY6VtK6yoQv6cRVsaTum/BxMlRDuXSinuzO0HLQ20oV3wOWjoMbkcIVz1VGn0+WL3AKIxsIjsvLCKYBnHIKKIDRxonmq+cmBXtogneBoc+aHO3YnWBVbgSyCmf9zJO7Hn4h2LSPzoY1EbiPbcuPXV5qt4qNFLwHVTUpgzL+sBOiPORPJkvU95hct6GFLdcaVYnGLMJVo9MY8WEoqiNNZDjom9ebWQJlJoT6rKCaw26Kjf6dlmbUGCPop7kwir8WM98HJ+/7YUyvu4JUKfZ5gg+DgXrxVunD19/ePsTn/uJX/x7f/+Tn/3FRvvaYtXBJ57XjWWvM5s+eu30eHLzufdfObi+WjQ7e9drtX6j2Zpc4AoAD/u08Ytht+/cGY1GV194onOAj4Qu16f3F6cnNz7w3s/9zV/97C/9wke/96Pz1fT4/PRiOcMDQOjwO41Gu9XCGNRsseIPB+DqBP/x6VHWN2zVmj11vgm17lyrVj9tPTfnAv6sv7jtMIOLYi/jVMLrAxpZjinh9z4RtAQVWSkiYantdmTqqHgX0l1fOQSUQhk5+ChBtdO+FPEwkvgECidxDdtfwZorkswq7gHkPiVlpsOoCV2MC9tdrDUH+AlFNYG3GDLlYYnBKxNI4BFJ7GCybOQxlyan/JCtplgFxKoj59JFcCa7EDTO0zxi1iQnKyiZ8zOtcLOkTMZXgnM+BpFy2FjIL2eCDqcLjbprA2q9LLYJCWVVwYeiPfZUObZuaawEWh4NnAWUKriCsohxMrIiPQJAoNVB3reCQc+FqcW5Nvtd3MtdrJZ407dRxyf+27329GJ4d3L/ZHz287/5qz//O7919MT7avXBbNlYDE+6gx5+3mV99uq3v/ilp9/3sVrn+ebqW+Ozs/H5uH8Lozjti9lCAtbPTh/V8M4vbidPVkdXDpqN9t1vfvPe6/fe+8nv33/iyod/9iduXO/fevqJf/ZP/1vc/T07r1852Ds8OByeXMxwPMJzHR38qDy+GcrfEeDNL9kPMdcrAbYOD2/QukZBTdEwrCnVXEHexC3dT3p8UBev2768LN4YdXf0RU0jJCMryXGrNgRCORSUIC+rNgVKK/l4ybLcO4FIPps1YGGmuk+6vsIyVaPMAyhUw2cAZ2kXV9YKcM2CmH6VkzB7q65rwUPPR9fcBFVgcXaeCJVMtMJSaUyoSvypCrECICNUmnzVmGOGDjQmpTluxKNZdOWYsxcsnglytu/NuhQbdhwuGaeKLPEKhF5dnqJqzQbBp2o6JYiLsUxrXna6MrWSyRwpWpZboJHXziIoA2tS2EaxA9gSx16jHUqkIb8VNZgU2flhj9I/6rnPuR5RWOSrbIiO97zajVYL32tG94/bsPXV+WT4xvGdg2vdX/47v/lzv/4bR0+8Z1nvzdaLxfxsPL7baJ6v53e+/sd/+K1vvLp/7YlabYDT//FsNLmY19a9+rqP0aPlbF2brGfnk17vcO/o+uJi+NY3vjEaXgzH46/96Z+cP7x7+uYdHHSe+PhH3v993/NDn/mJZ7/rxXancTEdjWZTPHGEh5AwlLTAl+TwwtgCGUm90EPgYMCaSFGrJKIoxKItgbnHYSlW4mJZ/LbO1NUItuKLANnA48AZWVYsECSrNikAauWikBMBoX8lAeP04Jch1FpU5t0ZIyKEAqUXkhwyR9HpRiwiZ95F15C2WJmbOaigZwRe2fIbNxQuDcsGCsh5xUp1no5L5CAUuhR4bFZjotlUQL2YhExaOytSt0OFXUKeIXEXZTKLzo0SfVxwNTOVS0IXriBNYIhyQaGK1LS2JFdOU6kF/2MSukqkzI/JlZLstspyn41la0aPYqKWbFFQmOm9l2wntrmRlO0nnT4keYIGcwz8wIBZfb1s47Wsem08mi67jXvHx8P56Jnnnv1rv/tbL33/D/SuPLkY46Ofo3a/28Qp+cHV5nr+8LVXvvoXX7//ymuN+QVIut3BcDyaT49xunBwcIgLhPV0tFpOa6vF/t5hrXsVVwF3Xnnz49PaweAQPxC5nIyPPvKJ2sVFbTJ570dfevbJo2/8xTe/8uU/uXf3/utfeQOdfLvV3r9yZTlfjS+GzQ5v7zXqTfwGDb4ghEOC7HiyectBzbdR1A7akLqGVM0WsPa5rABP9fe8uxL4kH5Jv1gu4dFYzqDYRKUMVYkoVN1KyFmN8skiwRy7Zw5adHNZpIC4hJONuBjHhV6CYBZBnCiddegrY79M1m3AKd17APAmj48gxjQMVaYxnAuuC0lOXCVPvTTRcjwPPsYXmzM5InXxnX+G26EYUyVwM4BaZMzKo5jBzKoJXpR0HfLQarAknhTEpagOgZVZESVgn2sJxbukssqUZPO2Qxp5uhFu4TUvtLVAJTXdDShWZ6pW8/dx1jxt5smUnKjxa+nyghXQHEHh1x2WeORm2Ww11kuMriya7eZ0NH7z7mln/+Azn/+FJ55/6aXv+8mDp96znJ7cvfPqva/9wXs+9tmjJ59p7R3VasfTxXh4/vBgH7drO7Xaeb1xMjx5cO/uWy+szkajk+XwZDp5ODjozGeT+WTE7BfN7uDa4Lmn6tMzfCn6jVe+0b+6/0f/4p8vZ1/97g//2P7g5vt/5FMf/uWf/Yt/9nv/n4f/9b3X3+r2WucXp3giCf8ODvZmY/zI8LDTbfF36F3zcJiHddO5NpqYyltKW8k3jrQnPat2jwBMpMviE2cWNGZB7Vav5F80BrVKoaxYlJEYa8gpWsbAFKTQCA3R0J7BgVBUZ5szhmxYzsU81eD8yrXOaC1hwQI8ZBJnBdljo7BIg4yY4TSBYkRDg0zU4b/sCYbwZA4jBw63t0AFq2ciEAeV6kNShHVkZQtP520W3xm0nKM8WpfeBygvpoDEnUmn5qiUWZRO5pgJP3cwojaxRIQqlqYVxxJq52ZyAJiqwBwwBdN/UArWQ2qZNFWx3lpfAeUiNkXo8ScGdXUlg3qBBNy2cTSQYwDRmPCBNh4PcEKNu7149n9VOx9fnC3P3v/RD//YL/2193/yo43Gfqs3wL1cfPP/4u4rf/qnX100rn50vexizGc9fe1rr3/75bdefOH5b77ytafO79976+t3Rxdf+eIXn7n20vHozW984+W9G39x5Zmnv/nyWw8fzg9vPnHnzp1Bq117cPHgzW/VVqfHb7z2cr/2h//8X+K7oV/+8snzz734c3/tqaOnX/zwD/7U9OH49huvnJ0/uPvy66cPz3HSPxqPm6vm0cHRaoXHjFgdqb7uq6gOfrIGmyp3btaNgNIpWBTpsarX5ip1FGDY+9Xbc1R4bFJLlowa71ghfpCMxAUrsQhXRaWLcNUwdJiUIZQpmc6ExEcL/kTFnxtqkglQqQJh1IkKszSA65UZl5MYlAtiNjkvD1OrW/n+TWBmQE/wClxBMo/KJkrC3idtSc2CnuTzcwji45Ri8SpXCAt1DOVSydP5eH5p4IIizdNwQVCPNLpWOdUFD0hqgqtMDliOF/44LcC8o/MvLgwfcyZhgNjKUuR9DM3jRrEq7BzTGkYFVlerbAalSjLKbHEwycDAXlBKUuvuFAicJG54uBMNjFNogNCTdtsdnPq/9eDRcrVAL/rg5Px9H37xt/4Xv/3sj3x6ejz+i3/zr3uv1J97363p7Py1r/3Fv/4Xf3B6f/zo1ZePDm+eHt+58+bxvZfPx6ffuP3m/eO7D/Bo/9mj02+99sVHL9+Zr4aj05P2uvXkMy9++ff/dDyez07vffX/98fNwWA6f/DVP/vzV7/6ygc/dHL6xrcvXn1jNF196/ajzvcs/vxf/tOLf/rPPvSpH/i+X/3l2qK+uH33T37v9/7Nf/fPzh8ezyZ4PmjV7zd77e5ogpcPVk35iXncAZaW8ZWPmwmyto1BCKZK1YqFbM7uBNABzCJwBVGMujAymbfyVc8zIIq+e8osVRQMJrkwpgmK1mKVp+irId5ieXhFwmdWjc5zQ0wOqguDJI6wCVg9Ard3DRrPFtzVVs4bUG6TxxUA8JvBUTDzT2qi61dg0JfBq7TKp07e1ahVsJBVgof5peGCApKc/ZipQggeAVBeHW+PPCJRqpuUFS8q06tgRUAQKy5GTj5e6RI+m7Ms9XoMpSW3Qziti87N7zIxtVaBRiXZMSAWapzGUGfAKJjJd/RMgwbyuElwWnZa98QPSuj6a/iS/wIOGPSf1paj8eTKU9efePaJbrMxnowbSzy3X6+dP/qf/vD3/t//5L964bvf99Uvd3u92Te/+q3D9dnk/Pa9O4237r1Vaw0+9mM/dPTUkycX53v1xuDqo8HB4a2nJs9cb18Mp6cnjw74Wtf0jZe/XWu2D49aD968297rTyaTf/X/+r2HGGO6PVucfvvq4d5g1n7hmadef/3+K3/2+uJscXY6nIxOpif3G63D2egC9wNqjQ4G/Ot4M6CDJ0pXuD+Mh4Pw5WhMPIjJqV9UddFbg7IhXJuIwEKiEKPXqBXz3M1T+KWuBNcDemW+VFZQVU5KUIbQHCodQx0I8Qlr1j53r60mcRbNM4VpTrElyTI+BKaOUvJYn4ovc3UpPDRdHMOoTOkZoPAklPTq1+BOILj+n3/mI/K0AELRSbaQ0F16vpjO03AHkiBux4qTAMZbPbxctZM1ZEG4xHRrlDIrpzoh86Km48CWm0A2zEKTZyBPm6lDURqNKPy3cImXZWRm30pBIdd3IIEm8Q1xClJwLpgeW7EhdkU4TdjmWeSMT2EJJiCClACiAjY4lPi/elKrIoHSoleqm23AjoVl3N/Vlucet8L39/mTvLj1izd927X1bDY/m0zPx8P/9P/4v/nQ534KP9h78to3v/aHf/DqN758eHT1rbv3W83rn/qrv9M9akzP3qxNRujJ650jvM27anUWtc61J2+NHzwcj8ZXb/Zqczzu31jhQ6HLi9VkulgOh3fvzYfHJ49OcDx55rueaa+aZw/O5vP2w2//2cv/7hvnJ8NGd28xmeCtg+mydnZ20Wg1D68M7j54uGyv79x7iCeS5pPhdFlvL5s3r1w7GAz2O338BBlHq/jrMagP93DuLO45EDY0NPFxsdCo2mIFtWuxbAFChxeDFm0VSLtKPLEWkcqWkXgsk9085QgffrNXkvEWKLFl6cGtWJsQPnYx2ZmxiCazIo6z6CILHDpoCS1YrlWsZbduoY+prXNywYSPSvweQGJDTwo//LFLDZWKuaJ8A1tQGtSEgMpVaoFWIpVbXT08yJGx6NPzyxDHeEUwoANsXJQnUa5NiXzXTe0mvE/XL6NmFkLTp/T/AZQ089L8S5WFKpU0m6oid4pRMeUwA910W04BXim9EkDm4DZBd0yAhU/Q+1MLvE+LX/LCszyNdn2M+72D/oOXX11849XWSzdbg9bp6Z2Pf/p9q+b19+3/1BMf+MHejRfxPOaqNtNdCY8LNWo9dME4sDRri8Mbzx4xpXWtP13XMDo/bdb6tdq8trjXWDwYN/CDwY32wV63PTm996jRbR8Nuvvve7rfrD28e9Zu9x/ef3B2cnpy93g2n8CCl32vHB2+/tad4b3zZXt10Gv3623c/j3Y7+3vdfd73fPhGGng3jUiYtRfJ9m35XTPb6mhJYKkWFfGwvnKIkd5a7r08QytCq+O9hJTgSCWxTNWpAF8ybLzClnCTyOXm30kxSSe5QXNY3s2wTsELgZxPEYnWF+SQvB2q0mPCbqJctN0gZwnN3eqUFSLA6Loe0DdI2A1DHz+85/+iPhB6859SCDvhPgIEsh8NAbm5FWt2SBUTho3N6tHEinKj+gsRCAgId15BuPI4/gZp/ipLkaxsUI5ORjSg9ZgFo5slvhntqyo0VNlrvPB/DJFl5ZyilLQzsodA0dBI3F7FNADXxIkambDEClQbmnJiaoGKkRWRcLuMKTiOQNt+p/bjOaCsqCgURhv/FLC+VS9Pp/PWu0OPvBcry32up2zk/M//9ab/W7j+tNXP/z97/3x3/m1yVnrgz/wof7hi5PFqtu6Jj/XhZFV/coWnsdpNfh+Lz7jfzE+u1fHafzRAHeX55O7x3e+ffHw9vXrBwcHrdHJ3Tf+7MtdiAddXHC89qWvXTw4uXLtymD/AJ/9xPjTrVvPN+q9yfnFq19/5etf++Zrr9w9Gw551o/Rnvq6U2/t77dx8JiM5+29Pbyahk/ENWGdzqYTPKrkBoBQK+7b0gJSZbajHAxZXzcFCQot+BaSq22Pc0uzZXpX5NEmnkBomrA6BJEEZuRSavMOrCWqYBTJEHH4DINimkFmrzCq2lLVSKYkh9lUtnKyIixDF9XvD+zpdYU5g0tSIsQp+c076Io7jQsilwkMgA8IcjOFi1hkxgIF5YEUCH0GhaVB6FywOkW5QT3MZkzBKVdZg9LJB1QC7tOQxCOmNFlYlTDSeRJa1d2MLHqzKYUlnXlMqi2WhF4TKBqp8bZspZeDVSucmwC72zZV0bNYhl6x25ItFGeqNKUB1cS5mqWf8P5qtHYqBC9jdD5KRyK/Utd8q8tz0cDOkLsb9jpcUPNbP+t2s41PPnR7nXW9M8a3H3pdnNsvG6sXb1z7wc/9leff94OHV1+q1/rL2hzPW07Qyz94dd1sHF17T72OrhydP2JgBB5vZg1PT/5V/+hHO4v9RnPe6ayOrnUPu3v19cV6hZ99rA1uHXWPBof7e7Phw1Zv8uRLVwZ7e6vZtNPvtuq4Tjg/vL5/9er3PPXiS4Mbh/s3vv2Nb72K35hcLDCQhOMJvgbRwVj/YjbFDw30+v3JdN5a4CNFSx6AUC+e//tNWTsVazppEXYznNgg0QRlpomMIkbmUrCwB4tG8SRizGnUqMDgKNrU27M8/lLjR1V4LKosSeWoTtWHcyfeFSF9xx/zeE/ZRmVT5SrV/UQEX2L3AQI9kdcA5it4eRaAKhwAJP0oDNRWir2Uh/Nyrdpj7+CxRap0skiK0KIpwSqZcr+lkjXRs4ooHpW+Rr5esoxpiM/LQed2GL9KArm6xHO1+TgeKQjOFOrVVUv2lLtPZZzQXYrDRdvZrSzmpoxlewwAc3cBmavq0qzVDF2+VtlC4qBecC8Bea3aCBEQSelm/7Xrg032F5ytL3HuL1/35DAQPqrDb28ulrPF9GK5uPP6G4NbBz/xCz/907/4c8989PsX6369to8faGzU9zCkgy8CTbvN+RRfhpg3Go1Wuw8vZt/Ax/o7V2/+bKd3bd2ej07uNBv39g9X626t2T1E4Mnxxc33PdPp7dXGZ+tV7UM/+j2dwUF9MpsPp/P56vitt/AE0vFpfQ8vIewfPPX+F/o3D59+4ck7r73+4PajO28+xKNK+JEYXKb0cW2C1PGQKo4DeB8Y34NrNtD7+2Maayk7vzUO2wFaaZqghBRNbCqZTPCKDUvlJUB2HGlviWM+skLEThVTK5t8Zpkt0LsaZPbKYlaHrFjpdlmD8rpKSQdFOcq6usY+FMFKo3M0EctRQ1nbqM6BqZUOkccAwTt/W8uaCYvuRTBNTGHK5ZOoWrpYVeZ3QM9uHTRlSTFFsWquhLCTFi1m+ZTWyJX0qIGCBgg+MdpkXRFudZg2OHmp3AStdGFZXfLInuTtL8vT2IUXOW12TuuyCyUwVTVlKPc/QCQC1VRpPsTopBItsjc4lNkCS8A7MBX6Pi8lcZSun5JCRIuuEz+4zud68Fln/LBvvd04G03rne7Ld+6cLsa4o/tbv/trP/y5n7v5zAdXtd6Mr9hOQIuve+I7bvgm9GDvhcYBeuEOR5D4CzHT8XAEsdvr7h3gCxDY42bL9uL4jW/Nzy7qi+HBlX77ylU8BlRv7uEUvoUPO+ODoHsH7SvXarN5oz8fvnVnse7uX7u1WjbPJ+NmbXY2PnvqvU8++dS1mzevfvsr38BbaffefDCBqdnu4HViXBDgo9C8I1jHG2CoHfcN1+tjM5Qdh2O8qqKG+440SJixjS47SeNjBhZMOldBz8v0SCtGmRk0dQyARCJIgNQKufe3SAk+Ligy9jY5hlXLW+BqTvJICkgYZaAyrQZUUxTcgSONF30edCEXNzKvU4wcVfOWzt0lEfHzBwBBlGZXlncW09OHpeakFatgBdgsmRBqZBIEgvxCaqllH1Q4MDMXb6hYKs6j/bICnKoBLgaS+C5BBSROPj0qvX9C4quUeG0tXCrvrWx/yQBu9zppy7ii1dHMDpU3swE9jW4M0r9BVDNOjTm4E2/C5NHHJwjhh/2RCbvEZqvbaPS756cnf/7Gm3gN4Ec+/8M/9Pm/8cOf+1wPH/jkNtDGoMvp6b3+oDcfj3uDp+HVwjNAtbbPAD/ei0NJHQPy7W6PcWoTHBAajd7B0S0O2Jw+OL44GYwntf5gfo63h+93+50b7/2uZv89fAWtdYbfCai1Rp0jfDH6Wru/32m1hrfv4Yn+04f38Nm3wfWDD37fx288/eRrX33llW+9Nh3zxwgW8xW+CreuN/GDxPga9Ar3r5tNuTPBH67ReqPfYAVdm2jv744Bsm1rS/sGcy3n66RLQNQeqekmx5dI52EEFxxcBA0nTsivjDkmDJtJrN0oa4AoDIPYhCwKMVVnkDLBamOCoWJ2UyYxqVW/YoP5diplsUzhh7UMGmrYe+CAHjZyzxFVzdNZunSMDwBse+rEJ6SdS/Guk9vSskVK1buWYnfNi56SXmWOscH8fc0lsGkdmVcmIFFWz0I2lZg4kQSkBp+FRtUDdqVL4p8WdsgkdfjLLFkFtfay0SIfX0o3vbA+ghRtzc5NfY0k1M7FkvaR3k72DW7fsp/olt5YY/ycFBwm54kwnoTgXV982plj5t3OyfkED97cOb7z9Huu/dTP/5XP/I3fvPn8J1a19gK/yM5Yc3y1s4vXAZrdvcEhbr3y/VqM+6PPRzg8+MOv8bf29gekx90AeQF3tVg3u/jBl4N66xYuD8aP7o4vatff+9zqeq/96NHsDI97Pl2rXceQUa1+pdZfXX/vldO7txf1+tVnX0RO9WVtOsXXoMen9x5eu3Hr2Q++9Ox7X7x6dKW/t3d2jHcMxsePToYP7y/rhy28V4BPVs/xsTm8BYaXk/FUKy9VkAzuM/NIhxpIA8lOxRLbYrfJ+poSONuxRO1iqUXjSmAejLlivIFJFBJxeAcys7k5Q8WiKiOLZISeYCOxoqOUytDsoKN6eeJ0WeZoScXQGKgyyHkIjybqZSNmdeW44pDOGQvi2daaGUo4ACiHm5OVk+4uFGCQzdl5K04wm2fKsxlTbVVvn4tsOkyA/9UpLBQELQV2KpxoNoNoZJZmL2CtIB18EX4pLvg7SVqnoN1NoVmV5FaW726UW1FSua2oCMCeoiSfHbboiKRa9C2seZW1hawCZ3ArgytKJ+/uyxXLGE8q2SXY/+OhTNmUeAoFNXtGjJev8J4Xjgf4ZH97tJy3MGpfb7965+Fro9OP/NBHf/W3fv1Tn/vrve71BX7TsVZb4K4rPrpWm68Ws1bzoFkfNGvt+WKKYf96g8MumND3YzQGj5DWWxxTwiD9Aj/8WG/3D/CL8Hur3hjXEKPFW2ej2tPPPFVv3mjWrh3eem55Db/x0sfFwXx0Xm/u4w5Bo3c4eOIKs67t1dYPF7jVvJ7i9P7Gi88PBvjgz8XJ7Ye403D1iSuDg73JaIofpZnh0f96q9fvLSdzPGeKGvKHw9Dl44BX0w8c8VKAZ4yYQufL0ubJrRJZAzIrgwsot4oyQkfbl57/RDaKuX+k4UrL0FuKVXBlklbIGGJdmXchPZeUW4DNd6UZ8S5FXSOgitnEEa3GnVAsTiHbcMKq3WRYrQrXzZ9At3ezWviOrU2hBhoXZU6EcQpFJ6ma8yifoHR0QVElKdDBJVo4rhmzM2OBSQpOU8GqWSs8gqhaKVTNFuW2qJFkCUMRF5G886KFuzS1tkKhnhmP8m9useDicbplWW4ahMrHnYwKBJTjsuf0wVnWUEBR0PMW0fpdK0nFwejHyYr0k7N7dMjYB3DVjH+Y0CFidAZ/9eV80cGzPrX6eDzmuMsBHqI/fOOV118/ea056PzHf/tXf+63/5Prz34Eb3LRzO+AYpQdnK02vg1Xw/OW3Uatg7N9PPKpJ9W8MmePz1cAcCuYZ9sYmVnMF5Nxp7ePn42s1fZ7e8+O5svJqr//xLPdaxgO2q/XruKsf12fA7jAO2fDV7oHtxq1Q15qdNqrxRA/N1yfd1p7BwfXb85Hi72Do06/9eirX314B2+QzXCOf+361fkB7huPVs0nptPFCF+X4HBTs9fsTufT5WqOyrHm8l4wUuT1ClqHk1toIZ37/gaQeOUIqMxNWtmvvJSqwidjYSA2YOIrqarKGajJPBOPQoG8mbJElSGKxSSJYL5UKuq2xUWSQzRpC7YqRD319ErZymMWytzAbb0KEiptT2FkbOlhpUmjA4AmRQaTSoQyY5muxLVCpRVw1dCEgXRln4z20LrCZdNgXbSHctGlOkkIr1GAJ1RGdgnq7vt8hiQp/yc0WqhQO6SRF4USrndQpfE2JycVssR2Co6m02bQDW4nn51BmgobeXtSivAeshOEOF4dNE6KVyBkwWHmzny43vmEJ17XwuMxeHq+XsNDPvh0Tv9g73y9vpjgndw7d+4/eOb9L/zcr//OT//yr7QP3oMxHzzNCRzeDcawfrPVruPxywXUOMHGY/YYmMEdXH54X38pBrHw/pUba8EeiTP/Zg8vkmFMCHdo+XnOWnuvPbj55LXF7IKXE/jYUL2NXwWo4/R/NcNzPO3Bh5tdHBUQF6R4FBWHBjxfetgatK8+vTd6eK+J28HD82aj88x3f9fiYrLE28Sz5cnD48NbR/VB5/jh2XQ66XZacJ7NMWa0xGULbkUgPTwjhJvTSAoJolF0N4jaUFuP+5prR124QgQsF4mDr5tKVxltgAWUR8vSB9JlngYgFX4JSVTwcM/rTXnZ62WpTgqROWaeiEKVs2EStvJCTBkQnoFW1zlpv+cgatf4mHsSWWJdosHppUV/IEApmmzV4tyi7AAA/9TBfCvUzi4xDbuzUO6m2pKA0jfp+QGtgtMVoi5JXG9NlG5NuvOHzIvNi+ZTLeZSlAVaRQ/AeqBQm1ryuV+Huf5dKW9KxAXUfHbMage+x6mHi25JmFBG5hoeGMvGhBzviOzcyOzYzt3xiysOVLr6KOBHXGoYHEf3OpvhE/yLZr+LM/jhbLJq97/8yoP23vh7f/L7f/E3/u6Hf/ILGIhfYDdZN/FIPnp4nFDzMSHsOth+cAGNh3bYo6J/Xsj3FjDOrm9dyUYk25hg4desr3G0WOIoIUcijDpNER4flmaa7Jz7eKwImx6c8EMDrRa+5H9Rx+AURqeW8/V4iK9GtA8OcRSp7V87bHRq41MMW7WuLLoHe8vueDIa4/fiD48O8Lm6wfmo22gPOr3jvbOTs/PjR2cr/GBZGzeYO3i0CQ+t8jRHKiA9hmtktBGPXq7RuQvsOsXraVcf4BDBrTxxyoqJLWG1zDTHpHtMgJsLGtvICuDUYpnuWllzKBAXFHHNuUZ8/0sRYE0k5QtZ0FkckqOFoNUl1EOuBCy6HCf0ABAgZq1u/gDJJJ8h2ZiV60a9OkJrPGcosUNlELdFEuXVXLr/lqbs7VGICC7JSEJql93PQ3M/4hSrGXgXWXoVl0zIpyliMvOpJsp3p/AuhNpGqQ10yeoEUkibJmXHXHunCMp2D5tBZMhEhnL7BFa+C8d+mAQYA0cXi7N4nBQ3181lc9mutwe9dbt177U7X7n3eqe5+unPf/bHf/XvvPcj31+r38A5PzjWyxm80UnzxFl4oAYxhviFlt8MbWG4hxcA7EUZB394E2uNU3d+HhqOlPELkhwoWi3m0/FkuRrP693lEsP2jX0ZW+IXI5bzabs5WQ7vzIa313gJuNbixQU8Z5PGsNvp4SqhsZzhbP9kiR+dQcc/OkPXPzzlL4Mh8mLGXyZuNFaDQW+wv9966x4uWs5Oz3BDGANBSAq3QBrrNrLiyT8ORjJuhYRDRxpWECvCyS+15OZsZmmFUmsCrSqYZwhJqDCbLXOmUUM7aGa/XFEDF2IVFDGrRjfPkIyB1OaKqd2aOYSI0ZTVYsDEHzae2Pi1JUWqCHIFhlWZFKrEXP2EniWZ4nsAXodlROS05hGhctG8FCxFVkcnVSZ18aaypXeUcygAolXu2keqLK6KJSKeGEpVnPO/piCiFeRcTuxAKNy1FHoL6TzECxapj+eIAwkzmtrRe5OSZUpvfGeX72goJXtHKX3LKqlrKFfwDRtaBIaKVjOXANa1gjJd5ORJe3yBYrfAgQBbgR7TuVNgnJ7P6uC0Gt/zn4xnB0eDRaNzfHr+4Hz06OT+e585/MJv/sqnf/E39p5435Tn4zizb+F+L3ttPlOJ3wDAxP5YRnigwgGAOWBkn4/88ByLZcbEH0aJ1rizMMJnGfCyLw8QuFFACw4iK7yju+52OCyP98Sg5+em1xipqTWnq8mD8fHt8fkDXAC0Ov1Gp4PP/kzPRsdnr+EsHr9D2e630Z3jwmN479FsNpmNJ7gTMMX1BA5R9foCbySfjVbz5eHhTX5oot3v7fXvP3iAR0Txe5G4/Gm1Ohh3YuswX2bEWwNsQj+xKU1BFI4WkSa2ytqNfbUJHFNS8Oybl9zxihOIbNL1KUVAY4tBgrAdUahBcK6SijGLGvFVNea+TtgKS6AKcAYutLE9UpfcgLkinFU6KDYFtzcusCJZiCZCQ+jIQBGWVdkQUAaTokugzJToNKBXIUPNSZKk1gTK1hJJ2imFUNFLwLE7LVo59ShJUc06N7RwuZbSHQDkztk7uKLyqzHe+hmakzH7kiyTSsagFK7Yx5/7HI3hHaf3nQA2rrThNbQFfpuCY4vrYwF01WYBsE254RzgJDOFyYqSLk2U2oERTH8YiUIRfxgRmXeajb3DwaqDYvNVvFI7nH70E89//lf+6qd/5e90D5+b13r4gUbe721ixAhf2UEoeKKPxoQ+FP0saJRbLgOwMXATZTDGw/NENQwx4Q3h8WR9PJs2G6Pmfmd/r3+IQ0qjzjF54Fq9ffxgZLOD8XzkjYMMhpLmTRxK5kt8aHqGoRp8NW6wf+WpJ1fD8cPJKxdzfNoHT6qumsP6/tUr69FyOpq22q1Os7PXx8NIM1xztFutdWOxHC+ny8Xw7BE+YjSZLQ6PDvFK26MT/IwBvjyBu8w4XnXZ5UtL44jGYxIrh2oxfTZl2D9FwdrrroAiJ9euWrA5CYk0RSqQBZNsUTEsxUcWj89o0uLbLqXhQRdlkMgWSQDqVnCO/WGMucTfOVjVVIBJm8UX/dJiSrNyw+NJDZsYmyGvKvGYLzZTri8y08SOzUVRqOKViby+XH4AIKIwOb6CPlYAE/kyyAYvh4wchCovQxmQMZ3besUp1scJqTOt3LLFQ9oGEs96DApJNnDFOzXCGoIA+VONJytJVlDGoACNLErNw9nf6UWc8dvk1nrKlvS2mLRP3EZh7WiCeqRFXQmy2jQ9T0uYBsJcZFtXBOJ4IWoOzqA/xyOR2GnwM+z43Obwwck3771+Mpv98Kc/+PO/+/c+8iM/0xk8N8KgP8ZR8Ox9fYXuFQQkR1ycwWPCeA5P2JVTDCxwPIgf3+GgCoZ7JovFozU/ITGdL4b9g2vraavV6uIlg+nFBZ486u2hs++02te7XZyK4zYyDkR4pmiNZ3jWe6vGHCP2VwaDA3ziYe/wWq2+hw+RHl1/stVodzudJj5MPZr0rxysx5PWCl+ewBM/o8PDfVSKrwgs5xj5wS/JP7hzev/2vflkfHrnUa11enBw4+rVa6j5mF+Rw6ENhyj2+St3z4KldOIe4XYaaT5rUweLPSDr2lGb28qliZJVRZAoSO734MhTeRguuAUpzi9yitWPLSOkTRJRc4jVZvdKvzSDCNJuXiUQzvBfaDVvERXtkUlJ7eojFHrwpci+C6uF/b7ceOLjZyhTKZ0YedDGGseLVKojJZ24I5UfADxihyUCWZYk9McdumolfCIFMjXTQWtIQJDUSh2lGBRk7oMBJ94uG/q5VvBpeKDsyULpgknaCCGbI62qlyYUHoJ1F3dF6f7NJEJI3OsjNToN4WeGTIMlnXkw3RNWb9i49FVyoIhWxI2+zggKxi5OypBsywBVwo1A2SyzZP0YqFLQlWApsWjpUQCdUCs/56KVxGS/SJjRy2G/AAkH7nHft4Gz78Vi2m+1pt1+o9fB27Iv3779aL786I9++lf+3t99zw98ptO7tURHvhqv8YNftWm93cYoD1+e4vrD+7UcvsfaxNA8jwTIBocIQJcLzPAh51a92+hgd+T+uMAP++I5zPF5/+jaQWOvg3N/XBdg4Ka57g36nXat2VtMzxbLxgwXAbiq4Hg87jK0em0OCs0G1640m9PFOb4/NF+dXzQbq85hv9663u62a3iveDJt4JOjOPHrNSZT5LnGZQR3xRbuN/TwnNKV/Sca9c7B/hV8OPrK0fXX3njz+Pj2Ytbst7t4u2AyxiUJb2gvMeaEJsT5I+vFvKUBpdF1R5GqV20kaWtLCQR+JXGtuJ0pACUAZ7IHmJ5uVqBxw6RAB4+9NvhsMWUsKEoGVHtZGeISrYVUVUmDSK4YtNRr9YnxtWb1BaMVx6blQerpPLg3YRPkax6419Rs4kskq/ms3cajbC1shxKSGFkDpIjjIpiy6xwmIHY9AKiPZLxtZlBEgJylQO9ybVDrZuO2RQvndn0puw2L3BaFcdlw7KzNyYWHgi7aJAJxInoIbXkeQUlHV/wHhVsHVEo5qok0rAYhmSCKM8nFq5XGl5SLvnIIi6tmkDIhjhaHjfVlftW6LC8ClYyNUHQrUWWg2K8CrQEyPxTZ3Zs2rp4o1egO+ULBNeR7LO8Xh8eaQf8sp7iy7vGwZaM+G872brRXrdaDB2evPXgDYy0/+1M//Nv/u//D1Rc/Oa0NJuhcsWtxLGbU6ODdMPSyGgPvhaHHBA9uASzwcA6e+8RRhZ/+x+Ggic57yld9VwdNvK7F7bDVHtzo1Xrt3n6702vW9tc8x+c9XjyU2UZv3+7jSZ9avzW+uN1eTtrdJ7Bno+NudXsYO2o2u/VuZ356D/d1D24c4HvO6zGeFl0sxqPmqtfodtb84MNiMTrHBcViOel12zgj5LsGK1wM8N5AEzcnlrU+fhNgvt97aYC3kV97+dWH908vzoatDuDt2XTGG9h8G6LJqxYezXigU4krg+tC14Ko2cJU+Q1ERKfSPa6wyggJRwBvpkZXplB4NWNZIEeuC42aqFyh2pKilTrVbSml1Cx5DZeWdJHFw4oWpynxla7Hmz0/ewX84xYPlSzVlQM/fIi5jm0Jl3/zK7hFBAV3BRrKEoA6Nkg87D14jcVFfZsLi6mCZcHE48AlYSQ1wsRVKiweTl/iISqHdjJLpkEjsLX4jy2nJtFoLmgo7OHY6LGfYE/lXgwXR8GeIp3ScjB7vXNMnXylYdSKWHWcGxe6Ts1iQk4VylUQ1XurCxHcLiEVfYUVas9+CTJ1KVBmTCgqJLQtYkhE6s3MwK6/V7PxOIFIHtjVF3O3BeCRH/RtGCvF1zq7+H7OCqfbi/VXv/l13IL94S985pd+839+8OIPzPEUD54KWlzM8HjO9NHw9H6j37t261n94A82GOwufHqevxE2Ph8/WmDvW072D671Gj2cQU8n57PWcg+DSm38DhfvqOIKAj/M2212Ow0cFfj+8Gw1Oj8/ny3mg3UXPfi6gVDz+QhHj/0Ofr2LRxa8PIael78xyZsOGOtfNWvj4XoIssVqMsH3pkePzvH1t1a3s5zOcPaH35Xs9bp46QvXN7MF+vzV+PwCN6gnF2M8s7Rano8upujxW3382vDTnW7/+P4j3AduNQZDXJtcjFBlfDQaxwBpu4a7/NXVgYyd4NqXK8Da1m0PBvVoKNwaiL3ENcy4psvMovOUfhncfD6RZjexhGqbo6aXOUKZaaIGKWUMNOzN0ymwqWR2LyAW+zLXKbK/wArimT5GMjm1x9PFo+Pjw4MD/P4DT3UIkUluVaXRykp8kPntT5quRvapO9asSG2Jimq/41KWSSrvZPiQ3kJQ0F7eWYij6Ivs+UnA/xTYE6DvUCV0eJKDn0qnCQAcSrFFKoJtLGpYaKVduxUh8w0slh1mcI4nZYw0mT2y/CWJmhDyfBuZlbgabUm14kZJXCUL7dLppisGCMvO1gYErntmTSNFLPHKK2bcM/isPQZqZvi6f7fbn55OvvT6N7v91hd++TNf+E/+8bX3fP+Sn3VrL+d4jHIxnj5C/zlfz/GTW7y3xrEd3CLloDl+Bn45x11YfHcT467N9rqzh3eF8b0exMQbV60W3vaSs3xsUbhCmDTRk7dwug/5FByr5Ri/Inzj5lXctcVXQnGnt44f7u0/02vvN/g1/wlO37FH4ikdfDMOJ/utNS5H6osHp7PRKQLjVS9cNOAnCXCUWHQmi+kEo1q9AX91YDldt3t4PKgxHs5kLKeOX4PBLcKL04s5vgYxwYAPqBpXrlzBTeMHDx6OphMMFeF+Aqx4O0HOh7A76AeOpIXRfNEmwD7I7Q7atDqXJofISWW2Pk6q9KIpNqjsNbFjbKG7K0ciNVqEEYIrq7DTPPjtBM8DuJgW3KejZH7bK6U2V1i1DjlMKqyjFjEYMFp4qPSeWgsocC6D7RpbyWyxfHh28cx0cbi3h8/Bcg/htwylkTyZ8OveQSrSwgStnOtGBwDvoKhLzDNHZVf/zFRFKjnCKEvO0IGLrP5+P9el03m4cIoPJTmOYE53zEUjlUURJSHFDIdR7K28lMKz2Xgwjr/aIR6YyXWxcnHuJo1KRsFB69qZccomR5ibFAwOTm6hhbc/1+Q8TxLJK3dcah3fofSUrBDZ1CZ4SOjzrW2laloj12zYjN0aTdKUZpfVJFsLzoXx0AsO9vhGzxqj6xiMaTReu3f75GLyw3/1cz/3H/2jo/f80BLfYMbtgdlwPr9g17rXw6tUHXx6sztorGDitoTOH1sKBnzQjdfm6/7eUbfRr/cxQAQrzsoWvd4VDsDjHrMcf9ADtuqtzt4Bsh3Pzo/PToDDN4d6PbwPPFstJ7PJOZ7JxFNCGMBFvRfjk9l6PhudAdNt1Rb4dd+zEwzr4ENzs/Hw/NHx8PTs8GB/PFzOZzNcKuCDoLg7jTe7VhgCmExwY3neXeCsZj6Z4U0x3IveP7yBs8J2c3D/jTvtTns6muCTRIjVbXex4+NCZIGPhHLsGPct0GXgtJK/IYCG47GUNXYTV462qdvwqfBGJzg7tXSMilQBFOio2HnKPC1sSGBXquC6q8dOuIw2y7eMogjxnRvR2k4ydyfExPsOG6uFp/jon7Dqp3g/hec0GLzjeAZh8tsPkOKJe4lskNoZyqpxCiBx0yk6AMR+kLkhhBXHCBlAi1nAUsyOSheDjGwVjceSZiwsTIkq2ayox5FC6yZmOpkrNB4tSmk7HiHxC08Y/Gq38dw0AL1OFz/2wTeDcK4IQqjiukoTklwzkdSkKGlEWVJZPUlrgttlmwWp9nu7lrg1LsXFDMMkJcy00kkbB9BmybkayMoUPLUpDQYBSlkrunCQUqR40eJPILiJ4/e30Nvhzad2ezRe3nvr5QfHZz/3uU/9zX/8jw5f/IEZnsPHkNB0jI/t4FM7uD2LwXrsGs3uEY4VWFu4WMR9Y4yRoL9FF48bva0anujv8mudfOcLR45j3H1r4/2sNb+0g7vMeOgf3weVk5hlozbFzYPh9GTQ22s2O9j2Lh68NV9NLvDSFq4hMHjUaOwN9ur18XQ0vDg96eJNL3xbaDmdji5WjTY+UTEdjXFeh9MUnOmPLk5HZxfNNk5YFvvXDzud3gpP9o9nF2fj1WqEAwM++IP3iPGJuhpuFNRbRwfXm080Ht6/v7e3v1jMzk4uVqv59aOr63ntYnQh971kxAkDoXiBoIabATwKcK/y+z6KrqVVxT0zn3TbTrQRyjqwBFAo6F7sO7uC+W0oolzeBkvBNaY12YQCHM3Inchv6GpXOPWc0nYUm+uNsD7kvJQzqNDv491BPFOAA3qvP+ju7eED4LwFxbcbcWsqZKFBhZ2RzaDkmEcHAEvN5yNublamoynRG33m6TamUrPs3YIHKrAFKY7hEYEpqxKI2IoJFcm5EQLK7RvfQcEOWsNF/uL2vQeD/UH7EK9won0xAEqgXkPpDTHSwQ3hQj5JwcVj1DAFrF/dmRVFYZFZ8NskgTMmKYdGgWOAOVbYY2yprMFl3wxZKOvjUloczx2IdSUnbcMoGs/q4gmCwu0+6KEE7rcDnCJhnTZbvU6z3z++9+3bbzz8+Kc/+vm/+w+PXvwURn7wMYfFbLgcny3xiNBq2V33MC6KgRfuGmuMiqDXxVP1GN3BoA1uEuBKEUMsuBHAJ0GRFMRm+1C0LVwbYMJbVkyWOeBZSzx0usL7BtcOb+IZoX4Xj97PTx7d7eJ7Ptev40Xk+QyXJi38yjz27FoDn+/HYQO/4bXqDg67/Sv12Rxfqmvio0Gd2rVnnj1/405/H7/3i58pqONaodPvtHr4qvRgfIZRnsXoAlcVGNjBwQJnMvXxcMwLgOOzBT4rdzaazEf7Rxgprl9cnOEdMdxD2OvvLXDFMF3gkgJHIVwq8UNBqLe8EcCVLccHVESamP0OdgVUC0VWbudJd1F6RW4bSSIjxLc3vW2Ctxc+8rbaaxtGllBfHl4VJyBJXlR6QuN6NhTwOfBJG58AwcBlp4tve9QxioEjf6OBy8DVHN8VoausOgsrZFh77BylUyNn/CKYAqK8tq1r5ijZeR+kX0LirW6ZeJhN/FI2tVn+LKqvVIDboqsMlwJWs5RIRaW2gTQcegLsGNDh7XrsJOPp/Pad41tPNm5dv467ZZPxhB9zxB7NDV07ISElCXeGrK5Qk0tmIrCgkxajxNlQ3sglTf5/rDc58qVnXDTMZQUlKeS2C02S/C4OUt88a5R3IkLjR0DpcyK+tDmsxNVDR3q6DYD9NG6mNud4jxfXdpPVeDS+e+/RSy899fO/+x+9+OnPzWud+aK9muP5/Gbv2iGerR+eD+mxwlA4rrXR+/PxyCY+zIMf5QUXdq0aXs3CeTZPFlYtbBbojXG6hXNn3WSwY2ELAxSZsBZyzdnvNjudg0O8fIYLiuV6fPX6S409jD7yrgNhuIzg+d2q16/VrmLv5ctcvG2NDzjwRsJ4enrc6TbreDPsnM/v7x/sD89P8eW6/qCPEScMCIEe54CnOLtHREy41zy+wB2B5Wo6ubiPSiFRDBThtTJkP2Gf0eYDp5gwkIyPSw8vcBcAu8Rej08E4evVuObRhsWcDSrtKhXTFSN7EY8PYdPUXZBAQXMZT2IgW7nZ6RUgqIhayv9hzqzGSXWiaqIJWdLOReooR11sFDRwExKwfFwE569Ya9jAsGlgPU4n637nZDR+9f6j0cXwvc/eunV0OF/g96r5/ICLJ58jZ9clzU4qtzsxFIrARVcAEp96buxid5rSBVdpCS5zJMqCqy1DSCBHxP1GWoRL/C9MplOgO9NnxTCJUXZAB9PdEE2JBuFQGa7mZaPlPS98Lb3X62NQdYoHgTA6gJ/WpitGUTmgBglv8WN/p0BmLJTehZGAMoPaxQ86kUSrpgAQHpdehkex1GBKEwJdkaJa81heiKl+KlgKFgbWorKqKuaVChmBFNHw0r9whbHpDWOCcEQldn1YX7LvoGvDVMfXPvFdZT4i2W/idxRffvWt5sG1z//Hv/uhn/21Re0QiNX0DB54uUrWcGOwj9MonMjje598pQvkCI5MsMQgOQ8AqzHGevBzj+iZeTmJ031EAgLHCz4dJNF1S8ErvdTgtAwkrAq/G4q7CPU2Lt/xOlgd40xU4Vdf8LIYBnkQYYEby7hEXS4Xnc41qfcYh4zW0dU6nvMbH/euXz09P0HWeAth72jQPxjgQgR59vHDNfi4KN4g2MNngprT8XQyHs8m0153fzRbnR6fLjHk2WiM8NxnvTkc4gtEqAzSn+OzcfgkEW4D4HeHcajDO2r47jTfeuahATnzRFH3SeSqk64NyFHL0yJt5SG5UfVurttTopJClT5CAqJxomiR+d9XUWvGtqzK0NWdbe32N9mIONM6Q4/NWyn4tE+9vj8Y3H54PsVL3tPauLk4OsRdq/bobIw7Su1uiwNB/HAhjxzYgrEpSwhZnZCUVBKKDgChVS2oJpwVc2UwQ8qmYFNDSRNIrWmNbErEuScQDfcv5fEmaRIwiFmMGNCVYyQKOFnjCZqcFQHQxnk+7posl5PhfHDU6bZaJ2cXhwd7+B2P1nLRxrlQA49r4N06+aojYrGBtFE0gBxckJI77khyLrJmjyBRJcykKSvcr+MYp3XXKnis2pVQ5uwInVFz8sh3exln6jNIYmqSqlJZYbEjc4/LrgpBpS5C4kW6aKUTZzUHTw3sCNWDjYwhDXRiPHisl+ODvZvf/ObX+831r/2v//4P//rvLmuHMzz2Mz5udviiL/pveRsKh/8WxlX55A/WMZ7gkTMGdoPgwYyjK906BmrkJAtOsoHwCaBVEz8NPObPB/PGAF8jQAa4s4AHOXGjbrocrWazdmevjeMKfhPg9N54fjbAZzv3rjf5U/L4js83xqMFOvBWvYkdF4/zX38exwccZs7P77/V6eKYg2MAvuqPk/r6Yox7yDjgNFZTnOut5kP09xME7vd78hgSNvMWOvfVZHr16BquOXB6iNsSwxl+5qyFrZ/fNWo3MYg8uZjUe/gm3RxDSt0OH1WaTPAQFD6QoRc0vP+BnYkTG5Frgks2he0V2vZhLutGNvGg07UoDH4TkJWVrFSDR9pINHO+HQXDY0h+m3kM13fAhdVDU2JCHn6K6szsOLTICasBGyC6szo+YYKj+cXFeH//4Olr177+zTeXk9p3f+jFXvfgzmt3cERf47IQ37niJ6/Y++OsBSdCbp35SC4Kjg34gQiN4OYaT9cgE1A1tVFmiQcKLslc7cu5pyP15uKSgYWTM+16HShEIgZK4pQ/9MrqTD9YeLDk0904EICLE0r4Old/H/fWJuPz2cH+YK+/Pzo9aeCBDDxcwauDhjwhyiYHu4SRYJKEsFpUUaEUJs0GZfr5KcLrXuQNtozRUYtG1NokziGFG8vOgmW5sweAWosNHpqVzmMwaxHVhAyuAoYtsBpeBa4GSMSLwmmFCn0LLbKm2D9Bomq+mvNeD35KZbK4f/v14dnk+372C9/z+d+u125hdH52cn8xOh7cPEBfiUckF0t07xj9wOrHXokTZDyiOcHpAp+exO9DclgfNwjwmX6OrUoHiI/640wa7wN3sbVMlncbtf0+7xOwx+ePC2AshR+Kw/43ml7cn17M8frxkjv0cvLowdnwFENTuO26bI7mkzM88DM9na76/Vqng8dG8amf9flbyOXs+N79N986unq1jg+G4jGkKd757J7cud/GIWtWm56eL/FJiHP8fNkMKe8f9KazxXg+7LT6RweHw/UpviaEe9pHVw9wA2M0wd3u+nA4Ri8yQ/2GIzw4jp8PQ4fSbOPpqK7cDMDbBuh1xj28N4CnV5E7GxL/5K6itK1fAdDayuO60QLXh6wMCm6yLV7XGbUCpouRAOTx8VIgQREYgq5CKqXLsLtgMpdLF7MaRCGlnWTmlDzrQOfF81XXRGwUaf06XjTnsAW2Ut6qaXWudvD5Vzww9p4nrs0X977rpScP8CWo2Rx9WXe/d/rwGIfzBTbONb432MJa5BbLh1/YgNhP8A8TT5WTm8CXrBzJ/KRVCBpXpdTsSlY7V86wWncoPZtf+kZJ/LVblK7aNiFs3jiHwaRdAt/Sr/ElSvTsvYM9PK799EsvvP7aW/iu1o2nrqBhXrt991qvc3F+jssovmSBR7r5G35YuhWiQbAmuH58Gi77sJBoTLtQIaeROkltotoF/2pJfKoDVztWWoSx0uoNRVRVElVVJhO3t6pJI1Q2B83szJXAaCyv0NZmgwevfPlU4wLDehjSa+Pk/P7rb333D3zsM3/7tzvXnkBHiF4TX8O8/8Yr89ohXr06uL7f6R/h7L7BN72wHjHWir0GYyM4V8YHFjpIAd1mrTaUH2uc8h1g9Ox8MJTHB5ziT6cPDgf7ON9frkc41cBL+qsFbimt5/X58OQenvjErnt8MsRHIg4G3cN9PMFxhB8RxrkIdmc8yN+5eaPfuVjMcYBY7g0OFvPayWvfRi2HwyEe8u/x227j8zsP23gsdDkbnw7rB70ZnhI6P8PRCy+t4VUFfNYBrTQezfBdodlkMRldXJyftRqdOT4JNx/vd9sc4Vksut1e86g5HE0x6Hmwt3d8cibPImFLx6thjSkveZbD4QUaUN5U4D7IcyZu2roSXDvbGrC9QTU0x6uiar1Tb5ybfQjzNCrHGm/593GJPDV35C9ylGRUh6hXcb0Z0N7uD7o4m59PZ/glaZzY47B98+Y1fMAca2rZnJ+M1v/if/jD69evPX392rS2aE+xmXXx8RJs+/K0GPYEXkBgG+bRxecAEZzY0HkFELRitvb24JIl6dxEQfbEknp6TLy0aLHSZLMav5lEsD6YTePAOL55NAWopdPgWFkbD27gnem5vDaBt/XxcZbF/OT18ZuvP3jj7ukbx4vr+/WPfPA9N/YG5x3cksNvLuHYCRJ+MhcBeAokK5GU5JUWRFiGgYWSJCEzl6D4iOxnPruqsvBVG1NLCOX1VUtmV2Xbqi/13D12wm9c6u+KWGiGLHM3CD4OQQ23NFmdrEsCopE6ugoFSAjg6mO/3GrhKRec3H77tVdxdfe9f/1vPf/pn17U9vEzWgN8baGBB/0HuHs6HJ0cdW+06z083Cln+rxmxrM6OGx0unzu02UJVn42Z7ZCNzs+w0MYGCRvtg8G+3hpoHWl/VK9jsfyRsPjv1i1b+3t32y3DnAgWK5OcBmP7nf46H6v1biGxw26zQVO1DEEc3F8PrrbaQ/2DjrL0cXs/ByXofhcXK+J55Lq68kIn/Wv40Xfo8HBzcPaur2sL6YnJ9Pz8y7ePFsvh+cYt8fN7doMI0DDER5sw2AOnu3B+MD5+XA0usBlLq5dJueT07OTo6ML3I7Adt3H+wt7B73u3qPj9XI5xyOnGFDAoP94jF8gwHsxHf5uJa50ptM2frwMLxzjwSc2qNx7xEFPV4Q2PJtaehS2vewbWMrKkh2QhWjSNR0pKGqHJ1JmSYrRhuE2kliTQP99LbCNdNJt2rZkaRfZimHWJiWO2zxMGLzhaTsO7xjBxjegMEbZat45P3vr+EGv371y7frBrSd+5MaNF7/r/U88cX02Pn/5z7507975E1evYmAT57voytDGOKnhOTDWEU9uuMtghXJoRCLyAMD4jOonSSpVERNPClGNyDlHDE5lIpNwiVl5FIN5iBtcVApl+vuSLOkHFZ/fG43GeAtmf3+Ax6MeXTy6GJ3jwYlbT9/87hvPXD05X/7xn7fqq/PRSXM+GXS7wwuc++DZPjQWvgXJX/vjWkBfIXSYOWIN57osAkKWRFoyRbUQcL/JTDGH0AHoq0RGDUxJZKzG1K6WfB57RbYKdYRI4sX6S8maIzc4SZZL2apdZa2bKLSfRPFav0zaQxDQSEOQl23q1wRP/zGQzpv5tdrJndfPT2t/5W999qOf/cK8dnWBB+Dxzhce56wtbr34odH5o1vPHO63rnOAv4bPaXFwHR9xa+GXd/vXOXyI33Hn/VB8FLrTbl1f16Y4lceWhbew1ku8XjuvrfFpz8Nmvc9Ulqvu3gu4rYqby/jSGz723Gr0rx4+NZvca16b9DrL+fjuetHFmf7o7BwDRxin7Vyp184vxo/u487T+b038VswfdyoWtVxLV/v7509Om7gCuVsWMN9bHxFbni+12l3r14dn51hWBO7OK5R5LfK1hMcVKZLvNHAq5/ZAl+k27syQLOcLE4mkz28CIcufoqDAwY5Gy18CuJigrEgPFLUw8sCo9EI2ziOe7z9i3ZExXBPuD4a4FPVXQw+seWlcWHiroHDrOzAsmZh8JN2LkQnk25uOk8MWiB5qi5qUvtOJXDmiezk9w6CfArMQ+vk+z1qQqWlP9A+UVNGQxJPhHyVCrdwcCRo9foY3L83vDdrTz/+qR/7iV/85Zsvflf/6Hpnb6+LN1S6jenr3/4X//f/+o//h9/DXa0rR4fD01M44n4mj+Hy4SDyYZL9hPG5wtIrAJdXyA2Kqkn3X80YGPMR4gonQZtLCUizEoMjdGgsLALNRTU1GD/zmyc+AIAdd7m/N8BneRer2skM79UsP/5jn/mRz//1G8++iE+sXzx48KV/+wdf+uL/+I0//rev3bl/69q1g34P2F63y6docdjEwDD7ajab9C8Iqxu5NiHzyNJSlc191i5b6kVMquLRHqwggWQ4RZABhnD25Fk90TuwlCzJE2dgyksF0KSVxzFogRs4mxlNKoECRGBihYnquL1dcKUKBxT0/1AByJFUHPpxsoTj9uTiwd2Hn/jxj//I7/xu78bzeBiyOZkNx8OL85MnXkBv3tvbv96qd9jryYOd+GFfnFPze5pybwznUHyGAAcLvmMJDB/8xy9E1lavdwc39/G+FU4TZotGiyfReDsHD/508em3bp8PYjfQceIWBN4Xw324em9/gBe1FvN2/8aTOEVvtWd4D/Hw6rX23mBx/16jMeg2Z/juP54AWuNzbhM8zYd3fPC9KnyaaDS5g+9X49P+w/qiNjkb4uUvvNqGTxuhr8dlSgvftujgCxf4PFwdly2LCX5RZtmFrt/FBcuVxhHGdjDQdFZHh3C+nK/wE5H4MbHTsws8N4QdBA+/4Z4y7nvjA3I4bGJ8GXcxMKxEZo42YzAV7Yi80J5u9XHHQsPjMkVXHNrEtkiIuuJ0NREAb3XUghrcXA2ce0gJKPHYVACHuSvfJvR3yOYysk3YJ5YspYCZKrkVsx/Daeh6hdv72FS4tzdbx6NHe7du/M2/8w8/+uOfa+3d8F9ykxGe1Xlnv//kc7eeeOGZ03t3cB+KHBz0xwMO2IxxKaF7CFi58jjD7R5so2gHoDSytInLWHq9ykYSl+Ck1TOlp8jcAz4zWLESQUYYtT9I0k1bFhhpPjzNgx/VmOOmFs6yuncf3G0crX/6l37xZ3/7H+xdfw4v4OCm2d6TT/7UR973oT/6rv/H8PxbX/kSHpLGw9QHnTZ+XoldPy6Rtd+XJ/msRlZTy5mC5M0WT7TVBa1nivbeahNfiMDofBtZtf3tWMoS3ZXPKhJIilVhG5PQzaS6Wmepu8ZShetHdOvVA6Bb+VjwD/d+0FXhHi464PXyaP/aN175Vveo/RO/+es3P4HvPQywJ6zb471O860337y+OFrPFvh+DjYGPkyJs30MgeCnVGpzfFQNn4PGNTdepkV27Oawv+Cygp93wzMyR/xNLrkbjN+T5Dg/NjX0vHx8DOdTnTaeo+/wfUM+gQB3vl7eW68mvcMnuntP1Fr9Dj4yx7e7+u2DK7OTk9kUlwlXW91VZ7DfPdqv1fur05PVfDR6MByfnOL7RXjCiM2DB4Dwgu9oPD4b4nnNDr5ljesTfB9ogsPFAns3jmHo2fEpCETEr0QOR8PeXhe/RYDXwzDm0+33cI+40+sBOZkO8XNgeECWd6FXa7yehmPbaDTE6A+/d8EnCLHv4JbzpNNp8+sZHA7Vc0euD3T/csiUdePXlRSKq1cxAOmUA8xgZgg5yPlWLi6LryR6+4Y4Fci+P6AYT1GjiQftblMWgc0i7tzmeNXFe0X3j2/jw32/8vf+4Sc/80u12hF/zhQn8xhL5A3P8fLkjYtvffHwoIVBybvzxcXweL/Xx/gnPhaKa02cyoAf/7ExYwcBPS72MECETSjcA4A6Wx/AVU3kKEylSqAQm1XeEkC9tW00EdMIhwsnSuk15IimWlJjZ8OGC09srThyohtod7sY8Ll/chtPTPztf/C//b6f/1vr2lUcGrCr4jSr01nUx2cHT1z5wZ/6oRdeeuL+a7cf3H6Ah6SnQzxex6t3jJ1hu8ceITXQHDQxKHx3TZvvpVVyecYL7wVdJMYIkx0HFpgUbC5WpBUFBUWSOG2YGdMGjDcp/2U8vKergi/K0rG53t5MpNdDgM5pAMZVzGBRRVUMAKwH7jogksE7dF7YWzotfGbt/vGjOw/n3/uZ73/vp3+2Ub/JkVR88QPoxvrZ970PA9w1/BQwzm45FsqRDZze87oBJ/LsA3lTFHsfNyReB+Lj+YiE0yg8SY9nagY4C8b9gAU8MFbewBtcbfwCGO6rrWoTOWXGi2L4xicosSn1uoMnGzVccAzrGDLCR6HxadD9K7yfMF63Bof8rfnaCM2ALwfVmnu4W4XRHiSJjhcPja4X/BQQksRT+vjDQBS+X42ren2oA2/5zlCnJVLB4aeGd8Tm7dnxyelsscLgDl79wpkg9LgnwN+Zx/hxo3lw5cZ6dRfHJzrKwxE4BmBsB/VdYrwHBzJchchLAPhe9KgxGuzhxy57qAZR2MFwSSQ3nHXz4y5i2yJXBBqT+6BMaYkqZ8GCOEVtmitwEyKQ7kS4heodMOumWV03WrCl6DYrrSCbu7q5bgVWrBE+S8w+CGcZrQucli7Hv/D3/+EnfvLz6P0xksfm4/sci0YD/f3Xz9744tnxaeeg9+L73/Powf0v/tmXn75+/daN6zyk8+QAW7CGYGC4QqNdGA8AujqqcxaIrvDdWllCuXhKj2j5BHuiRCFyCWivJN7LYtUC1doJQIn9E82JLRR/+ObWco1HsHFi9IVf+5sf/vTPo/dHv4BtHu/L4Uvq8+nF+uLO6b1Xevs48+dI0Xg6e/rp588fPBpP8FkYvouJSboIt01zxWnTwcDwbFqZWOB/mpNJHBINCqVK0TserB4JVKBT55xve7mqcas9LUemdKkpawHx3UySeqQlbS3LX42anCPlyoEoXT+eu8TT9zglbzdf/fq38DGFz//Grw9uPccnf/Ac5GKEn9vFs9L8eV48FsOujmtZTmjR2eNn3zEQMsNnfPARBXDigU5+1BnjJPwKUJubljz/owcLQKejR53edbm+xukVzv73louT8fC4g2/99w55PcFvyYEebHhodDw7OeVvv+ACE68fHx7hKgAjTr0bjfO3vrY8P8MITu/iBH355PQRrg+wa+OTD/iez/7BAJHxsTh8taG7h48/HOL8Hk/780OejTpe/ppN5vJKMc9a8AovuuhBv4/tH2NPyB5DADiOoZsYTaYY9KldnOLKBN8zwrHr/Ayb/JSvH+CMELd9caN4ikdda4NBfzLDQ0O4epi1OjPcKtcOi+dXqA/bHf+lF5POjJtpWGtIVvYXt9OUbzsGJxEgvuyK5U47asHFFN1iR6d3BKYhpTFcBkrLgk5RF8bNSSapOzt95M3GY/Y4N8dlGIblcAmGt3vPL46//+c/88mf+pl1/ZB9N47BS3xjCmf1uD91sRg9xJnCMx98T2u8HN2b3LjxxKD9CgYg6+0mNho8nIx1jYs27Bq8KIUbMsSZjZzg6gGAKSCqZOIln54mmZZUVzkXHlZHaR2vY0+9QtRUryW1assIFzk8j+t8pR5iZDheqWOTR9O0mngu4uzi/APf/+H3fOJT/YMnWGt88Hf4EGOb/b3GYnLWal7UDtuP1nPsajg5wt2wV199+cbgEJR4Y4yNhjrozUTdpl1IqOPJ1iRrDIhP0DBpJf0OYmYVmD2jyDLjUIKERmEZx6ZiIatN4Hd69wnBy6qnmaDh0HwhK/hEJdci3HhVTRv+04etxvNTrHeeNC3a7ZMHd5cX88/+3b/+4k98vlG7grevGotZCx1pHc9SdBeNJR614SsC2CfmGPzhlrHC/dbFmJ99aOzJauQGgG+u4YSBQ+X4AXdsWrU5Nir8xjpj47DQu9nAN6F1nTF3Xmq3Ozi4YCMc8Vp7tccbCSDHJxgWeHv/GLchcHKCo8l8eLp3hFvHHXTPePZnMj2eHp/M8AlnuOF5zT6IO4fXjubTOd/4bTcmZydd9Ov7B4spv2uNTXP/2sFi0MfnnfEY6mKOX7HEc0JrvhqK54dk0AjbLo5Ve729Ge5JYHSshjsgi+l4hC261+8vxmP8lDAqjzRxzs8dB42JSws83YRnIZqtGX/vAAdFvDGAQyI+YoqKYP9C1QnT+8FRfyYrTtYze/54zYklnnG1sSwzgcZwldUmXtvoYmrK0f6Ym74jZdl6yiJZa2E7lF5YQK7uXKBZZS9AM/MaAI/xg2uJIbsbLz7z0R/9sYPrz+Oz4RhvxO382WSE9zdwpounGvECyTk2fRzvp1N8IKQ36PWP9lCcDi/QyPh+Ob4vyPXLMSMcBnAVh6M8Nk72k3oAYCJRi1OOV0lmJXrTBFcSZAy5h8aLo0onK3u4Yn0WsvRAr+S+zhiMogGl8XhpJJ/JxrEAV9l7g8H7v+9TL33yh3ATHQOoaLq9vUEHvwNSO19PToeP7t5/8/ZbL7/y6M698fnw7r07e/2rGAkedHscJVri/h5fCkXLoUZcZwxGWWsn7Sn9vUtA0haUdgrAlU2kq5jYaMLo7YyoAUXQYqKSpMTItCKs6IozRRX1RY2mGUIWEds0JQzbwtMFq7AsqippUV5dChInTHzmH6M3HK1pdU7vPXgSg3t/9de63Wf5dlOt0drDRTC68ska9zsxSFjH6TleqMJLNRxDRQ+IT0Th4VH0fTLagf0EwzXswzECgiEXdP0zvk77oNkcYJQGF+Z4VYpfh+MRCHeLcGKFfh3X43jwp73AOP0IjxJd73efxGu2oEL/vl6e9fqd8WiCs/FuD78U1sdVpvwWCz6/0F7gViwOE/jV4Bbe6d/DHitbHI8uaA28Vdbd38ODgEgXA1mIjb/+3t6qv5qM7ozx4y+Tc3zdnxsWvj2Hxzon4yUeWMW1SRO3ClBx3l3G0BXuTOPMHhcKmPhdbNR2ibeC0UI4vHEQTF4gxZgCRo5w0MNPy+P14OkYd0f6uNfc42goeiVWmn2ZrUq/OmRVMG+2uG6MzqRoVetqk3XogKIvsHl02MBNs0XQoDH5FocK8248hrIaBDo0hZqDylUHarYj9GxK5so+n70a/nFrxBgGtyp8qw8nHe//4AeffuEjndY1PKs1GQ0xDtkfDHCQXtfO64vR8uwcB+nJ6bg+xMlPZ//KUX+wNzo9Ox+OezwlwSNk2KPk+k0ea0BScn3IyO4AoI0V58pkRAulWjGPAWmVEov2lKyT943Am0QNpAjvK8zctMEVNyeyZ4shOXrBIhB01nx3E+/G4LmF5eKFj33ogz/w4509nP7jyns6PT/ZO7jCHmH64OGbr07PH51fXOAjvr1DPDiHPb9zfjHp90a4csZvtuIKCpdgcqDBvsLjDSrke2cIPCKEGkatI8jQVsFihqAiaTQpIlKoGAcqGFVhnsBmGq+wZYyFbB4GgFCqjAHbZF0fjkkr4FwsfIEiWIJEEN3lgO/UaHxZ+dyqxcr9BqeyGM/HqA6ecRmdzj/9uR+9+b6P1fDpZrwK2MbpPB7vPDt5eBuvSmL36fQO8O3mZgsn+zj/xwc7+XYtTvbRZeLggM+o4bGbOh6+wHk++k08IUpijAc1MIAkI4HYMkb4aDOOIIspx13wlj5/QrKGkXO8YX6CT0018XmF+hDZ4+HRRX2Ee3L4Wa56p7Wc4nm0Mb5E3u8cwY1TozY46HcWU/nm28UF7t/OengoB7d48eb/bNoc1I/wFCAuMNAZnGOsl4/2X0XF8cA+xjPH+IQDfrySV7Htehcb/xwvrzUO+VAoum88/HN6esZjVgNPE+EhV17E4JtHGAOAiLeKcI9DfmceBy+MBrVneJEM2z0mHJDkwIDfk8HNYBxymCqOMNL786yIYwhcE27fQ0Vlq5GVxH1Vym6V2bqOy+LOFpLjCZ1jq7nsKEjwHbE7wUC4Wz4aWXsiYRYF+4esz2JRNlhpNtKzW+Y9dhykeYPWtQjWDu5loX9fjCaLVrv33o9+fP/KU/CU4Q1slfwMFW9Zrc9O3nxz+vDhEGccD9+anI0evHn81lv3sLtguOfR6fkhnmzB71tgq+YBh70ZdiQ+DqxBOXDJifuPVkKKovJSpvfqeLlbK8UeO8hRc3I1cNvDpDMfEFu1HNeolRZkEVsuntyBgDOd93z4Awe3noMR2/Riho9kne1dPcLjgPiqF05/2r2969ebs3NckbdOD86++6Pvf+1bt0/PhxgC3sOD2NjHGRdHS7Qc3wljWITShRQ1SaYnmzDsPrUgBo34uoZ2PIAlUwAnaldxr2N9MSlH7BLLitk4V7g5OdqNLiVGdTMWh2APEKe9Izla1Gelfb6nk6UL5bscbra8NIOaWzbWF8dQ7r312q1njn7qf/YL/YNDbPKT4Tku//Bmx3x4Np9d4Is5vF+8xpcYehwY5XgqnEGAXg7n3cwZ2wbY8IlA/DYvDgvs3vncRLuPDaWBUfzeEr/YtXq4Gp/V+vgiP24Q4Pzr0aqB33cc9gYHnW5v7/AIZ/TT8aPOHsZkJvgNYrxrhoc2unt4leBsdn6K8Z167ZS/C4aPudTW3X53gbFIfKP8fIg3uzhai0cz8ZbXdI5rFF6V4GcG2u2L0/OHd+5zdGAf74pNpvgOxHS9PzjCQBY+9YPkceqOX4GfLmb4uDU+VI2HAXnVg1GixYIXvvy0hfzGAD4BjT4C97px1OCzJPxtVPZOOBbVWxhQxi1jfEgPGljxdOkQv0Xcwg+I4SIDjcxmxzACJJAkawntiInrg3PuhJRsr2DjulUIyakVwj1J4KpWlM6VIp57l0hXBY0glxaZrjiZUEaxIbKaAoA8KKGeus2Cnv/kWgCbm7LDJNs3W6PdvJgM3/PeF249/WKzf7DA6p3hjARXcDw24DoWPxVd53sntcO9wdkb919/9fVXvvI6fuwTb3LgdUC8/L2/j8FHfP0Jg0noEwGUW/5SLVwAIgIGmdyE5PCnkym9QjzCOjN1UchdjRNQ2NgERacqjaIjH7YcSNBqyotNVA4C/ghGLax43wd7R+/q4OaLz3aP8AQIDpx4s39189lnsVfMp8dnd17HxxH7e/vzxfp0ePYAn1SdLPZ6+0sM4XYauAmGzkTOdPzakKxd0EItZCW6+vk6uk3famA1V0Boa193rZAvOZSQsnK+2UzY3IwBJoRxEcyYoKmaYpOCq5Cbc3Aph5pU0sQGjS5z9C0+guRhTLKHWN8PLHsanIHXVp3m7OL47Hj0S//gN65/7w838HllPMuIrb/Txm8lYoXi8fj9wbVmE1/Vx3d75OFoDI5wI8IHf3QEVs6SsL1guHA9Rt+LAwQuITAQhOHXZmdfNj7ssfPZ5OL07mudw4M6HqTc3+cAE8bMh8ND/Drf0a0GrjyaM9xX5RDs9Gw+Om8s9zBoi99cwUgRf9AFg/e1Ow38gvysMT8d1qZjJIEBXJyko9Me7ON5UHzqGUcdHB5WU3zQbTbfv3rAp4vqq4ODQ77ggic9T/Gj8NiwMaSJGwN4JghvCuMdLxxGcPo+BVYGbeoHR3u92Rx9DzpxdOm4okEHjx4cI0UT3BbAF6XZW/MoiJOnKYb+UcbNRxwEcH2z5JcusNfhAhrvwuFWmVwZYJvknsj1w47LthndVqEAGe3pFDRBUoTbXRxcrTzOeBILIAjYU4WUVZszpxlcqmRUJmxxl0ZhZvhf4iMtRjOHldl+PGlVpTSiHBd4QMUZOi7XcAznU8aNK88+sX/1Ct5F5HnLct4Z7OFcdobPhOzVxvcf4EWRwQBfi8LvxLWefeb5mwdP4YLvm197uT7HQ74LXPSh78e2jKfesLnzBoCcz6JpEYTvl1mqTBj/pbNhPUsqUGh14rLJ8RltbGbdpXFiZVkctQcqy4athWZCq7kNEI3Fd52xb+OaFcdFbPN4qBs/kjAenT39/Au3Xnhft3eFLznicn7Vrbdw+66O0czF8O7+4f7RtStvHp9M8OOrjfX1awcPbh/j/XccAPAIbafZOrk4446Kkx0MnOFUEdfk0jqsBfNg4tLz6PkoM/NbrG79ApSqCBSJa720mZ1sC2I4cRdSRi2nc4eSYKWrSOGeLDi7xgyKcsmn6KxaLLKVOMegmCWWy91iBOSIKCmJnjMVeBTgQxAYIMfTjriLi93p8OjoK9/4au/awcf+ys+395/Gz2Lx6cw2zv8xzLEYDK73VtfxRSg56e/gdAgtjX0QexaP9xj6YR84x65Rr+PLP7P5/Bi3QlutJ3lhgS2L8yPsmhBxHoW9dDrDgz3zXvva/pWrnetX9tZLvPeFLz3wUgRnznhmEl9nxJ0kXHTPz/D27956gG8L4U3dxgqfqMN6XtRa88UYdw5qk9EYr2719joP7t09Pztr4h7B3p7UFUeQCbY//Prj/uEhnlXa399H94HjE57SHI3OcXuad7CROD7uNsWnnvEta3TS/J27Tr8/n67PG6P5Kd47wwhRE2P4eOwHxwnUut8dTBp8kgS/L4+HiDDuL5/KRUviYgOPgy47HBiq8RiA6uJRc3xzlA9Q4cgh+wLfj0FF2XPp5FajlfPegnZZoUQka9YRcAGb45GSwiKNQguKZKtRzF/GXOquVRAxysGZ2IWwB9OEKeuhANsxN0U+g8wBDD7Kxl93wYH4cP/ai89fffq5Tn+Aa0o+o4BbT605PhCFqXOAzgzjdq3hgxEu0DCA2ZjXz45xGMeWjVFBPueMqwasTzni82QZqxJRuXVirWLUT3Jkg5Y0qth0ttlqGK22gndwASRvqCimmtAyMROKkj+7Xt6jY3+J8V++eclTdjx9jS9kTS/GML3woQ8fXrnF5/iWOI5iyKyHgwVf2MfTbkuMkuL0DCeIF3fuvXZt79Z0jYv6KT4XhK/Dn87P92p9tDM+loj7ZOgbcMwBLz/ny/yYgmTO/DUdtGekQ60KvX1UCdroRg8/qU5q4ywO4XmDhxowx2RyQuZJo6XCTaF+VlShlCNzTFzgsMmcYCsKpTED1kUQlMwknvQ6aHkOnmJDxn173Nidr9f48se9B4sf/MIHrr/0YZxcN9GbAYIcOYzXbuKXUPh5D2rwdD9HO/CHhufz/jhJwjk4+mJ0alwL+K5Wk7/wjp9nxA2E6Xr2qNa4gRutzTpf7uWdNdyrvXIN+1Onsddt48tueziMrI5u8O3ZRnc6Pp6f32sObuCEor2H8fureP64u3+wxtX5GD88P8Nj/7guwbM+e1f7y4v15MEaL3ziRAVvePXxbj/Gm/CiCvpvPJiPdwfwenF/H2cg6Ma7HRxUEAmPvdYG+/iadBOD9rilzf5jfXh0iHGo+fBiiJ8KvHLl+sPjU7wcdnyyHI3x5Cg6jg7eC8NIKLdnDhNjo8b3Lng3uIcfucT3pzE2Oppgj8HTT/xBsXqTo0myV+EnqHg3GCmil5FTLl33bE+kwpUjHZtbd7pdUOsnB9KimnXuAVzK2hEywrmm+F/pY5xiOXccGqlImDsJlcLi5BRm7nFQUxapco26WU40e5VfIl8QoqXYdXHbg8yBOJzDoDtDPwaD9De4NdnEr31i8zm6fqPbxc0q/PjoDOcFWEfYANEl8TSjP8B67+53Xv3iK3i+C2vtDDeIho/mtfEcm3CjiaeG0WPyoIIzEvxDbF5i6m6BISDcOZNK6BxJYlJZxA2zEqw5QlDzBn8PKeFxXm7Vg8oRy4KNJodRnPHIbXRqcSzjXZTZcjmcT0Z4T3IweO4D73/hI9/b5rcVeZqDTRlX37iNhTuBvV791q3346mPLm4OzmvPPfkMHqIbY0fBDy0vpw8eDO+s1k8/8eSN6zd6nRZ+bpt343H23+FdF6wvNCBCMg9M7D9soihGagDyJklc66GqyGYoAdHbQ6SinkMpNBIAVvQh1FI1N3gMsFAhZGx+XJm0EfU2miI0rp54xwpXX1lgdaCv5d3KGkY5Mbi9fvVVfKSh9jN/42cGh3jTCt8BxbP06BZ5DMcNX5BpX4VTBMjw5Q99oSvEafHibLE8beGGca1xcbHCIzro0zvdq83agD/9tTjl06IdHE/wa1kTfBeIT3t29gf9/RaezJn3sOOt2hiCwufhBjijxnnzutbHx5s79dN+/zo2TVwL1HmejVM7JIPR9FYTN5nwrAKu0Dt4Q23R6eOHaOazkzHMeFcAv2O/xE3r8eji7BxZ9wZ7+Fj/5Pwc23y72cWtXfwGIH+jeMBvf9bwkUjUFC+kXeuiR8eLYHhydH//EFTYU/A7kaz/fInEcAsAOjzXg/dHGzzjwaOpY5wnLtGz88UX3CjHiQ7O7fGBCTwlxDN93ChGQ+ESG+8OY8wJx1r8vDCuSBAI39rixbg0q9vuXUG3Tzau27bckv3PLpPC4O79jWiTt4LjELrluG0m2iW9xi8dK3scm0yWY5JjjckNaULBqhzGBDtkdBnsiYVWTvzRs/OlXHzrf4FDNE7KIeNJgvtnJ6vG9Npz7zm8fgsnCvgRIZzJ4joBXypH+6/RWbX4oxH4VQnc3arjvXWMbeMT5tgbcF9rPsHzW7iTid/7xNgQf+0UvxPAVsRxAId87DbckvCkI5+Hdp1MIX2rGARDeWUV2ul1YVX3XrY0Cxvdc0WiASmwwVwG9ON/uqAPZs+O6uDaFj+AcXGBn/m4OnjhqQ/+2Bd+9nt/5idrB1dreLtnebKoz/CafH0xXw0XGG/FqzY49uHQiiPjB7/rfVeb/ZM3Hx23DruLVXM6PzubXkxnvb0j7Ej4pMri4hzv4mNMCB+Vw+bOJw15cxgjC3pIRR6yVrng1ZxkBo2sZJqi7YcHi2Qjo3NhktYwmG+bFJa1FFukZFKUGsETF1O0GtWeWMwr0W4saLiNkNhYhEc11tZUBWTBysYglcHxH703uit8+5ydbLN+8uj+Bz558+mPf7Jd28cNWZzY8vl9DvJzALSJt3wx1oHfB8BZLZ6PXPVWTTzAg7U2XjRHuPjDoA/Oq7rdgw7OvnC9hwF6fiIWIRpN/ngLenZ0fPgGZxsXkRgpxygq+ksMuI+Pz2utc3w9tNXvd2pHuOzGafzyaK9Vn9SmF7i2wFAuRiFr+Bm/KUda4Nftt/A06PQEXyWfdfY6rcXe8K0705NzfAMCJyKr7qqN3+ydLo+Pj4+uHGLcBk+hLkejVnsPV6/o0PGOFi5f+RQQfveAv/2Iz7fV8dMxUzyyg+eQpvidyza+AIFfB0NfcTAY4IqnhdNInPvgIaD2PjoF3PvAsBmuNHDYwpch8I/7Ed99xoURDx2E4EeSW/yleCjwqBR+wRh3DPawbWOf41rBDkEwWoFbuawfW00w69bDtV1cy9S6SWG6lqFy2NQlLRmxOXkuWXowlsiJczV7fQiSuPnIorQQDlIeKfX3cSxg3ASEusYht/QFbD464VQWLY7tCrff0ctf4LYRfgt0vPjED33fSz/4oy9+5AcbnSM83oDjMg7LK/x0NF7lO+LLKLXaQef592B87uMHR7WzR6M7p29++zU8p7w+Pn3qcIABpBFOdnBtseb1aQ0HcwwHYU1yw8U7kFiNHEDEHhVNvoBl1F4EqCVTRp4qanPr2WvBGBSBzAeELRJRcnmxjTi5wG5wBSWsWjQgzrtw2YRx1kmt/+O/9HM/+jOfx+kNvo13+6233njrf/zyX/zRcvgQZ4LT4Xmv0R4dD2v1zvnpRfdw0MeZD1+pXs1x8ofXKUb4djRC4bDavDk4bPTrnb3B6XB8/doRn4RuNvjE3XyOEz+ca+K6myuQXwuSRCRBydNlqRlLAdkzU845g06roLUCUEzKoG55QwSb+cRhgjmXcmbmkE2STq5WJZBhNZoq86erZSU2FzSOrbLB4LJpUjPR5qGSbgnOgP4OCA4DYgCjhQH71aK1mk5rH/z0pw6ffR96NrwEz2eCAGnwxfnFGn0k3oOa4NnKJn5isY3PbuLtXPT4uFmKR1zwEU2epuPpIHxBBW/rzqcnK3y2B90/T6geruq3+Bvv3Et5RY0zYzxThtul6DC7eIKns48jBt4BHjT7qx6yxen7FB/VbNZGuH+Mzzk0eweNGc7GZ3gDffhgOK1N6/hVAPSweD2t06rh1S2c4OGWLG5HYdAdB5D5DHcCcId2/+AIg/YDXCd0e/jUD87nJhej/h5+lrKNJ5j5a1/41Dl48FnS9eoE33bmR96Xs3O8OIxPwo3wqwHoyfGLMRgmxoAOqoefDFksBrXhGLessQfgxQJsm7Mp3moe4UfyMPqAhsVxk93Eeo1NHec8qA2eAUVLo+fAI03YWfjtOXQqWPmIp72/rFHr/dFQYeW5lR3Wq1NECw/WJXcSnVSwoldjCSTU+POuUoDBVOIlM+ouO2UuKIZAJVySTgmINMGRJfznABA7AU5yIManpZa4DuTTVhO8ENI7uvLBD303Pin1uV/967c+9nF802Zy/ia2XXzbrIcrRYzLnWFQ7wKH/cXkAqcB69EZjOPjR2fHDx/dfognAfARkAkOJevZ+ZL3hFp8HQRnSI1ufw8/F4cj/3yE7yTgeQQOATF3dmTc25FWSFgTZ9KXm3bx0DChacoiCI/kx2bDdop/csmp6eI4iJtanOo1HOh+7LOffeLpa/+3/+q//O/++b+6cw97LMb30bujm8YTG9ijcKHcmMzxnAjANfwIB54Cwj0ujLvOcN3Qwe2QGh/FnuNHM3A3pXbtAL+w1MRP8I2xC17D/eLr6DsmoxFf1+GEW4+aETLTVmO2XKucMSmZSdNKiUX81+qgGs5Mm29xmrkikkl8lDbRby0UeapduAmIVaNBNG+nqVxTqUFLWgsjDdwgFkQwVeZkmbi8QkJsYe1eiOE721gXeEnqrbtvYND+yU98slW/iQ1Dxkg5NIiTnhXvxY5rHVwqjHGhzSc1cY8HJ/KzR/PZGX6SBV9kwD7Yax8hGp6gmczOatDwQUh+J7lee6rd6CKQXA30sCdhrB4Pg+K3WGaL+V673e9f5V1kvLnZwcNFk+kcvffx6uIUPzWGkSl+kwonFOhJcVq+hx8R6C5Hc3S6GIHaO8TbVXiBczbDC7oY09nvY6wf3/zBufZyztt8+Kwbft69v3+IQ9YpTgzHF3gKEPUeDs/PT/CLIBc4duCCHg+n4lFObJrrNm8PoF8Znp1f1PCjAYvRUG4I4g2F0eSU1674Pgq+GYoxpmUH98O5tWJ4mS0JDczY1vCmMSqNAS9M+AoEwuEsEv0IHkrFmsO3QnGuivNHbpXkI4H+USFKcYrXMpG7TBU4VYOwdDInByjsRKVeooRrFWvitA0kKRRA2FB56uf3JoguHhfSFaDtOMywXGKV49x/70rv2RtP/LXf+Z2bz1792lde+ZM//f17//y/uX3/jTHG9+fLIX4LCJvXbH3+8BjnNTiXwZXlxcUQW+kYtyvr9ekMa5B3gnFqgtMifJsQJyx4oKXbaFyR2wu3rh4d4OkCub/Mozxea9dqSurYb7NJM86UW4uP51VCa0TabQYEtlc5McPJP9+gnK1evf3g//x/+S/P8YN4s9poVJti98XRDZdA+CAEbmjhYIfzIJ6wY9gYw7iLi/FszGc78G0YvvGJ73E1p/VHI14AN1s4JaztPVriBfsb/dqg9eDWYeepWzefwqV4f7DGLTyON3AwCFdQ2jNrw7H1cGj3K9k1ptXBZY+ybRBOpQvgxaZ25y2mWHYYVSUGR6Y6Y3LaykURuIEApgI+1Wkp1fnY5ioCZtJVeGtY0uJLjsloqfc8jgbnUHw1t9m+f//RU89ef/r9H8ba48cuOZTKu5p4+Ym3Ljt9nDA08GYkfgK60eMv7vJx//16Ax9pwLeVseau1jl6A9ox4nX6B7jtI2+D484nhj2ay/UZvuWA+wQddMH4jhxuxmILwK3bHkaKcEaMs+9Dbhg4Mbt4hK8qtwYY5Zlh3Ia3kMYn+PwIfv2rvtfZv3W98QDn5YvZ6XkfHriVjW8/4ONtjeu1AxwN2O938SjObNnCx0rX+NzDPh71wCfhcCmA0/hz/HoM32rAaf0Emze/9oVP0M2m+JVwXB+gx8FwEe5YoEfBt04x5I9vCePxuOEZrhZGHfxu8LrOZz9xLYJvBK3G/A4SDqI4NuFlId4NxigQbgzwTWA8iAKeFY5gGAXCa3D1NT8wh4PHeIw+C1+lRofFAyyOkdgb3erkwSDuR+KV5ftlt1L9OnZL0WKmkwkoeg7fe3pMcRl7Fa1lGnBL4MCtxQgr4QvaCEAx9FBxttL/Q+F0kh5aB0Xd9jmWg1E1rIo1Ru8/+NGPf++nP4WHAf7g9//ln3/xz7718p03H57g0vbBMb4GRI/zCUYymSm6LPRdeEAYE15r1fNgYCDjRhNGG8e8W4CjtoTirSeuIcyvNx++cLXx0hM3MBK6323hAV93AJAaSH5CKjOXdVBcRlLnjLFIsCEGTbHZM5IT/7HjYM/BsQ9nWXXc3e1gj7vSaV8bdOtHq8F+/9oVXDc38Drmo5Oz4WSKR92wz+HEH+9SPjy+wGaLDwGcX+DKqXYynpzO8akIvD3Mq3yuDOwAYzxHXcO3NNBA3z6ZDV5783ueevjR97+EX9fDNT2v4Vzbao5MCmtW66smK0o11KIrPq7V/5+1/w62bLvvO7GTc77n5u7bOb1+oV/GC4gEQRAEKEZIHInSiJJGLlv2VFlTtiRX2VUOf2gsTtXYksYa2SOOREsqWaJIigEkQRAggAfgJbyAlzr3zeHknI8/37X2Pufc27cfHkjt7nv22iuH3/qttX7rFyZdYnYKzpfbVH0ejexmpPOHU94kj0PJHxh8KDrZTSJaB7/HPjbmcUGTKpkMnIZMcj0uBX5KdHyc2YKmlZkUYvNjCCBJCNWwj2Yw2mi9GgzOPHYls3xOnuQP9V/DPGRXLkq3ZgcXQnDXBQ1nEOPM9ghmmSwiBNDRUfphphWZQgYMQUDyQe4X94B0RWjvj3r/YZQ9FXBCYqgfcOxwbEcjs9/bRnc0d7sa4LE3GpkPhDveUXPYKgF73Ob2iq3M4oLUzUFJ7HQQReOQoDktw63+UasJ5QfV/zDqM1N1NYfGaUxBjvrJTAY8zT1Bu8mOBbDT3Z1MwQz9yXQK7Ue9yLBerbNQQf2Px+Pw//R74uNE2w8mBaTaKChqMkQnTB6xlsBVNO6hpzSAzn8oyuAITr20hmuGeFSXHNSJm3NZBpOdADpDBE9ppGmjFY5q+kX3guTE5bWimZkA5DtAz5hOx8yFXmfsCDoyis5Y2tf0lxyOjWhqp2i2iElBE8eDUk2zdlxHIvI5yWPimElkwmcjzcR/kFNb/ENhk/ysvwCFOH7JqOpmJZ6EaPEv/6d/+d76eqna6LS8pfoYZuS4WMPYTkiEi+GJcRUZRBQDBmIGQiswl5IYAu2aJQUQh11YKrEGwKuPG3xqYBCRCHY8YLdBaxTwiO7IsbPWYAGgOjZwUsEPdSj68UmsL7889vdDMvphEdzw44CGMOFazXsu9+DuDARPLs2vrS6xN+dODCY6eN1EKWNXNhwnU9FEJnnqzOJcOlmr1kul+pkLvsWVPEcnJseNG9uvvXvb0xrV2QSRo9fLjGWqoUspzWm+N054PCkk+HuDaBDejJ40vcCAB2pBrgxMoHpOIGPisH10bPPdhh0b6IyEzcc0c5r5fQmOH7VJ/scH35eL8bCJZlPYKijQhNmgqefx2UxiEeyuhodiuhnY/c+hoOmH2wK93QRuqINBhJa4ewTeDala1Al4vfye89cejYTRl4IxyDabcXGqaI5BM0GdM1ZxdVz0haB1dFHaDyu8zgEicoDYIKJzrjQDh7o1NPvDo4k0LDMGyQBIIJpBcP9E0KsM+Y/tMGMvoy9QeILodGN3JbEdMmBHH0R3AzsHWDnQ44VpRk4cK7lAJoV0AZdNLBgB+IOGuoPgriAU9kO36TQbnUaTCU0ekumlZnATcXaF7N/scEFQK5VgGYLpO51OUSvahRLbHhcNHP51sdwkodTWsScnLeR8lNL5vYSztqm7MFkDpwhNDaFlLtAbjkAB7IpoHncnwukwgLYxM0Bz2CT16RHQiuEcFd8n9eDuC8sBbQTK6JH+ABYrnZroXo2S86MOwGm+iPajPuo+Pe7bfumXHO1jC7C/rt/0/ZELthFnc31AljaKEM2DH6DZIPtjYszkqiINNnOAX/hfLAwQ8ERmRGX3N7/58t3bOwgFJtNBGHxTklxhuDkVio2hCe8CazwDFPJHYFcLgOW4VvKyCxm3uhA/UEF7a32/3INLAPug4ncAKCk04vckQr5MODIXjaEOMZcIYvkqEfI2myMUT5kTgB3EmfrPNlcVV5CtvIlkvmeiW+fxvvdFczzcbB8Q7u4pDgWrBJMOh87azEF9MhujmEFjr4Zhaz9MGgCrh/u0TCZda/U3dosvfOK5L/z8j+/cXn/tO28trCyGk9FsPhdrtUeD9n/53GPnvv7yb339TXpuPp+Nen27lXKnM1xdTp1aXlhIxBIw0orHFIyjgrisY5IxaCw2blXUP7gPPVojZj1s+CGv2WDr/iHBswkemN+PkMdsfnLPtgE3Oc2W4kzuo4ke8H1MNayX/XWzdwo4FJsP4y0QODKxdPLCywSD0UFq0Co4CIT87XIlFo+tnr0QRlu6wEI8u2yUoZiDi/kAWtDjH/AgFMbEaWNxhfveYCAFbgQrowVHiYgElyfoPJSEdgL+9frQmg9BtQp1MBNcDgZjoHxDVmKGQUjvcR0KFCF/woVbv4fGf9QlpCRwC8R4Yl5/NJKKiX0b8y/RsDc47jdZAMTTA9d2ciGHYAKkJHGywZWP6EGjARc/jBliuxxAcMd4ZC3sj6DeAXoL178sdUTl1jmVSaO+kD0cBbHLj/kSLDVs/yuVKpAZicU5abBO0D2G3gOdAbzACaQP8Z5r4zY7JI5JgmHsjLFk+WAFpVbUhNWAUzVzC5oUfFGJIATlPiQf7pAZa2kJlW6gbmQoLgr2mvSzgQE7lICNO34ME2558zc7wm7MB4DOfd6TtDbh5JOIk7Jsotmg+7KZ9bARj0Q/kpmJb/2OxUY2P6d9TubTLGxdJ03Fcag0kS7AYNqiAuaopuKg6U95kmclsAfNDYkNdgOtTicUie8USjAxP/TwBY6u504thEOBpXQEgl6r248kM93RsLBzEEskvvBjT/7av/5Pv/GVV7eqw5VMNp8Msv2ApLiUTp/NZ1fn89CaWGp6vRabFekNDUv14IQENNs9x7mBZ7cz1JijDZokcSPZWTrxnnHMdsqM9xHnpM9VlGDI9J9TrLmAEg84wAf4iR2Zk62Y9aHhMKE588/PpVF3UqyWI7HIs5/6eDaT/ldf/+rND/bTywnmYKFQgdOPvV/t4x+7+ujF79/aT2V7F1byW+tbZ/Lz8FhkMolULsMmismK+r1kIsEVMJiFI5ToCyAdOkGwPvs4LTviOxvjWLdpqkKc1tll7dioE88HlmHzcHOaxJfjOE/87n/I3PpPHPo8Nur9iR/ocyT94U9hX5vSjvdx5ak2tq+UlstHSPdgJd7jUaPR4K5+buE0Ir9Qf1BoQ47s0QEObuwBDDH9qwlMO04AraG3F5AhXxgcESRmhwVChODeHvXKnmA6iNQIphLH7fGwPvZHvYEIDP/IXgJsWGrXxldGo5vwXw8HLbHkR+cC6AkKDpvtPUxNwEiKTlKvfxDNpLnhg6aODXoupMDhvbbQK7cExEfJM3e/KP+BIsS2PJxgazH0d/zxbIq9OcpqORPE4qmIPxzD9nw42ghXmy3MWlcz+dwgEceAe52n3IC7k219vQb+h+UUDfIcUuFC4hgMvXPERYLumKH4wIU0GHLKgNQvplLZCZAoEHtFkA/XAOKqED7itMRJWtFYCll6WEURDhCrrXjfpGIL/hKoUuGYFEvgY4dO40fHmGOBHcojIOMMsBM2fSnR9OtDXLOxrNuuMWTAMxvK57GeJqINPBLdDZl52xxmPO5z/pAYBqbdOBauDd6gcqKtcQZVDB08Qfjc48cTqPGUqMZoXGnC5NPPpxAO5zvsi8V+/pc+s7iYPXn1UuNe9fb33611btXG3Wq9UmnWN7d3Lp05P3/p9E984bMf3NtL7zZgBX7qyhLEv+1CORmMzMcxdFiB0UsyH5AxQJ2It0DBc6xKup15XwPv85j02sRxXxR3JI7J1O2LY9LgRYKZCDNOBdFxMz7MeeMpLj0x5Ks3BYm80OTJm4UOsa9QqN7uZC6ceejJa//in/8Pv/H7b3HwfWpp8eyVtVf/ze/T0Qu5xP/477/y7MOXEcm5t73rGzWvnV+JgQ/a2Ij39xsckiC/Yr7Pg0AOuymt2uQOFjKApyrYWmslwPf4Z8Z/pgmH4tq0to3K026cTH/MpD6U5EM+bCmzZf2ImRzKwKbFa+L4kKL/zEGmB90ybC6UZ2bQ5EtA4HwIALQIg+lQrgAa4mbyxMNnIpm8LLdIzAlPelUkUsP1L1lxeIUw+MX1agjiNwfmsbWawqkOcdcdX3gF1wDCEDNDVz+QuWG6PBiNcyhaCzJZhh2sxbBbDqDSBwsz3kG7Va0WiqFIduFUAt6aWr3YH/c4YBsW+yEbEentQqsDYgUQ66sw8PhjiSTi5lBoUA0HXh9Re4j7gJRkwcKQZtulKtUI8BFHkQNCubLqN/LGWH1gZUBjBAwh4udvNgQlXm+71dY9hS8MRoeFIYHWaGg4bN25Hea0KuafdoCtEomphO5EEA3rsssUkz/nCqQJxD+ifSht48KCX0vvIkNwE5lKgtjnrzVrGCnmCkLcsB6JHXAaYKExM2BmdrpjZOBlFmws/DgjeNzLDPhxAQ/2cyfKMTGOKW62ABs8qewsdjmSmY05m1YRpt+TPKZeNgczQIqpDYN5bFY4Be7iMDZnXKAcONb2VTgMBgMWYGQEQzF/OhXm8r5arz33wrXVSw8lBYTBP/idP/g3v/m7rN271Uap2IzGvLlU8qXvX6/36hdOn4APKBKR6PjacrbX8G5t7cOsMOh1RGZCkxwwKcIJKJPjLh4MNxVx68fbjKT7bSo9/THB+nxA+DSm45pkfF/IfR6TrnGTTmuFDwUatEu/EZH/wCDTX59qCeiAF2iALkTCJwj9Fs4qT60/KLcG1649cbCx88d//C3u0Pvtwad//PNf+uxPFsqD+sj3mU9+olAbfPW1d1s9xOU7mzul7HIe0iYEZajAIgSPUQBm+D50SoZRQiuA9pBQhM0Yqi5OffQ+2grTmEm3OU2772UjqI3TDGwzbX7WfV+yH9nj2Np9eC6zdcftPm6NZrzcoAe/jyvezchNpQzNpJ4t2PEyQRp6M6OAB+2h4EMXeyI8/OP8iYVkPg9Rm/287Ul+4fRnk67tPFdlcOUP63D2QtWRxgfASncE4Ge068yD7zjs9YcHoERKZHLCSN0bFRqNG73a7WZzH9XMWFfhCkBSsZ4ulh5homfKZufnsMHBWR5cGQ2BmzNhlDvHU7De9+qt+l6xVWs2q5XK9n67gt0O9IJymwzNyDtstMa9IcofQNkhlhxkOzsdCErdZqVR3IfjE21D9Qoay6utXrNeLSLkD7Cj7QFuUu55EfuCq4e74k6720IjtGeQTEIJiKVS8VQynErG5+azmUwS6k0kEk4mYhEugWOxXCabjPDISLAUS+i6Gcs20ifDlp9JZDZXIkiwSMA2ywoK1YnZBekZiTbisBjQP3Q+QfjjYmTMcqtRdEHCjvZkzCcOxTHPIR+Tyk3qxvhIbyebaW64ph+HszhUgP2Y/T0c+fAXLZ22TY20CRXJFni0TL6dKNOYjo88+BPG4mV6ktVdst9AnHac3DVCMpzLJXTA9Xjr7d7c8gpifP/un/3aV3/39//x//xvv/bm7pt3i7e2mncqnqUTp86fOLO13/+1f/+1927vvPX+1tt3dyOZBKRwLo/iyRhLixnaMYJQ2CNF5gAWAMTR4WQIM8qqDYXoZfah9tt4Tn40uhbfTUJxmCa7b7XI7YOpc9ILNmjyOcl54iDN7KOquF7WSXcRwfSYCWRya4tnsAV1UwH6xGkkHGHTw6TRuFRuPnrt0R+8dePubvXE6YX9W/vQxcpN3WSxM1xNL0QTsY0CmkAR+olUof3L+kdAOYBgRKNToSLVmZzFFy6Q10Ltgj5TclLT2Rb8qG7TgGkfHknu9sUR7x/+OdOPPzyyE4PCLI613Xo0nc2SX9MLTuhHKOdD2jAbNOu2mas+5G8gQm/wNpfwnGJF9GOzzwIAvXs8v7IQQukOkSFngMq0zwHTYv0qzN4A1ffowmcmoDmoWx0g84WNKwEUhH5kcgNprKCPERZrh+B+gVkHfVpSiRCcDwyaAVTutsqeVBIyETo6JY05gNhSQzNa/tTJ7MJqwJdk2chkMpFIEro/pRqmo6a4fzotrulYdDCw5Wf3Xq3BmxngfrrRG/VawBmbMVhJQyNuEYaNWhlLT5j6Aj0b5Wxodm7HE0ld/EnaIDpGHQVETk7y4bB29J5RNBZt6QzBhR7bPgRX4F7ixOJH6yk4OtiDGbBUrTURA0YCSF0BmypnpEaPy2YYSth0BiO6EOdOt93sIgQHFqKXQEd0qVYFM+dgLKW7wyidQY5GBwStjiK/OmPDvOMAMTMfNUwWfMzI8am+NjBj3BrFo4+NaSIdCro/phtMCjcQYDBIzA06/J5EVBmTj8Nx7vuaxrPIRwknjw2cRpkETB02um3P5FfBtqoMpPkQdjXACkgLsmkS+wsjMQ5sI8XNlT5wCdn+t3/vT89Xu3cP+mWPJxMInTuTu/XBPspBc/F4beSplDw39xtImtyutE9Vmol8tlMuo/EspiMtsG4F4WUBAABn6nDUQ4MCR13VzU74ad2nLreD3bcNsV80gP9Kb/67ifiaPoc+pt4Tl03N5yRLBalH3Cg4LSjZqCbE7vnNWUZJTVywNG8UK/ZQ/eZHGcqgXBsFhrn83Ksvv8oxN5/O7Hv2b9258+jFR1kAElHAG6ZpTr+U10/5g1udDj0O8zTaFzm8I2ZB1hJ0J2fqYGvhtIcPpyJKbWpvFgu573+O9ZyJ5oQ7+cwE/Lmdk178EXIytQEkzGNf9stk5nQ2bieGifZDyiHqD4nxQ+qn4jRP7COQEH1HY8Kot7sdOG8yy6uo7oExQsCtM6HYtGBuh9wDpx1cdDDGBLwJn6cXj4nZxmSG5Ad8Mhhbj0qGFpwYjsG0A48196BslPzBLDesI0j2XJJ6kAOowA4ZSMRDMOE1611umAAXQRAyWkWdPZEExj2saAnp7Pe6zUQ2x7I0jo9iiTbyl4XiAfdJrWYDuELHUKfWRC+QJxTp1BHX0hmE61oOB6FEWncHzRoKaxNxFEzHxnNzLHbNRo2TKVR+1h6YS9mPc9yNo4kOFiDdV8skZKvbGrYx+QLBHuzvBZOo3yiOK4cuzFFgA790RQS5lNYVoHgKxdImRXhhlpoASQYov8bECDt/Fh7ElGF7gn2JhYviODiUsT7VakVDUbRfsHw6+ySRMNydojtOGvgJnBgo4HPi4QznR35NEjqA4LzkLafJ33Efl+ck+STm1HFc/A/zozy3IWqRQVKmqaZObk1MJIMoTF62AgrUhDILorwErHS+g1K4hNHVrPhuIVXGxv5kLIAEr6zKtcFJwabEVDxPPHr62umL/4/b/wl+32p/0DQ38fVqLSFzh55qrQ3zYzseTcoajC8g7YGsLSpBEaU9BewGl4ArCEYlaIUzKYgx+5gWmfbO+uJ223DEe+bTJNW3qnz8cyTEKcdJqBKUbJIPbk16vfgn6o/u94wv7QNe2QGxE2ILD1mXXUwyE0Okt87lWbsfScc427MUhhJo0kZ9O7KOyMArt1wi1kjU+1BTgX8UASBThwyMSHIqWzpTmGLqnmnRZvTNJ3FMdQie1JMA3NNgEwePGf9JXCfMvu5LMvE+Pv6hxH++j9kKTwpz6jP5nm3UJMFM6J+vCkdT2xLMQNsgBsOMgTYAuvrh2CwOfyxfx4OJXAZ8ZWBCoMAAwUwBLtc6gQJ8yPgcCLRDAF9zV+rve6U6q9dvgt2YH+DJZqMEgQUxW9QsRxAEk0m4erNeglU+gsgWzMDVSrVQCLdj4EFMcYnLCFpSr9QZtcrVD7yYnmBTEfK1WiUsA8BqF4svonYignJ/liGI7fVyr9FBErHXGEVQEpqIoP85Siiiw6iBqEPcR2B4hFGBWK2CqNYIa+8IKMP/F+zBgK/52of4LkXVSUqGrxXZB/y4ueVWlgMEuiGQC0VZKFqvGi22MVwFsOwRa9DkBhiqANe/rE9jVpR2m37goItSa2TPmDecYVhbWGXRLIdB1D5CBNwV881lAecBWkZPQqZooX7FG+DmQEclC+MGAOzAHBnCQ6Bhh/P4qcF80ZCZ5JNED5gNplQnElEm0Y1j8nWkJtO8Jgj5QVGnKWdzn/rKNc3OOGcjzgQRxj/TMLdyJtSN7kY10bSZMQ8dzhCzYEPGKBQreGeyC6z1vRFLeB8ONs65P//5H1+Orv1//v1XO7XxMI7kqx54PXcE3hJG90c4G8SK/jL3vUwKASrTwETTDgndmKiQ5bRnfPihfs4Gy62UG+KkYbox7WY9HXw38Ts2IQke5D+bl+tWXFXFDqubtcWtjqeiTsbQ+NF92uuJnUN4gR3cGG6HbrPT8oRh4vBzscbdcCIqhH6wu7lR2Qqy76m31+s1RCM9TU+11QgiJFaCPcqTNsY9UM3HckJB2tO4pali6gKyMZV03aqRHo21eZmvoz/C/jz217wVecbHfn3kX6ciHzn+D4toK+a21akmY3yk3srGqfgPy9EJV1Y/WoppxkCdBsGpm+aJyUq3MDrwwajOhqnbaYXnvJk8XD3qFE0kJCG1kRenPcc7gAJOTRM67A3rbGZF4uBiF95NzyAekmAwyh4yaRSG9xrFrXZr35s/GY7GG/UKJhgh0IdCGaj3cGCGUATNooLWIU8/t3QC4WKub8cDlO+vwSSK8jSyiiYX2p4Cd0eohkMSuN+o+pEWhuksnE4sYPPrIJ5LorklmMwkFpfZN6OBNJyBj7MOv790DCGdKEVvsOpxi4s6NtqFccd2qw5Et7vtthcuIDi9h4iYIdLbrdWa0GOMdWNkf7jiZhLASoioFwddxBtRZRhu1Fsy8cL2cATnUQciMMiG0qA6CJXDDAvT0niEkDDXE3QPTxibx6wBgx5TQKV5ETLiFt1cdrAkIUcAbLgjxWQxWynn28KmEJ+DJExEJ7aFhUlSNwu9PxxMSOJEmMabupz5h8ckHqDglO8WMomu4u8LdWMdeU/yc/2V+PBjusLJ3NbgcLhTqZmEDizbNOaX7hJxDXIet4/mcMaZss1iMBqlVhZk/Yu7Lj/cXKLbPHR5rnvAua+9tXkncmLelraQi1+Hu0ynOvROdZLZVGyv4OUkwYKuecExw51P3OUgH8MFv8aIFOoqZeLUEPdMXac9Neup6D/yQwamnCMJJ34q+HAcBdlghRm36ux8aFVintM8dnO0jys/WBWgcUHDKVdL6Xh87O1VSmXkgJ984pFv/N7rb7/9weVrN2D68/c8W5Var9ZEkP/hJx/9rf/4bUlke4bJdIIDhJTkMbllDFalqxD+z9bd6TDjbSumwXMeU2H7ZZzTEBvBerqx9bYNsmVokMzjvp3Pw68PDTwc1X6pHcf5O35unYjjOJ3Y9stJbUJtFAW7n7P5OjEn2dA0Rf3oj1M81bDpzES1/U2Q7R1GBLQlzhWzn09n0vDQmQMeuJ43f7A2wgvThnqBDmP1MPo+EZlicCMJKDqgPfhk4PX0wA0kEfqoDABQZCiB7gSPN9Ko17uN1tzcCQS4YrEFNhkSwTlxyh/ot+G3L7X6YW8qt+ZF1Qob80AsEs/AsQex3xvssZzAe+EJ9SC3twv7Xiixi2uBWAajL+N4uFsrw5MHS5A3kRpWyq1iEZ4/YLqLaddaHcuT3Y6EbLH5BY7uonvU4282m6h6p80wd6IjOllLYPib43+lWOfOA3QPoz6c/mF4xbEJ4/Un4rFwhCutGHw+nH/NFh5OoX40GqMDMDvJwsCddyiKFqUgKsgwUM+1r+Actln6GovBgyFiCgweYl+wDNGB2L9hoWQ1QlURJyqmCDwXnEtYWZVQwzX7krcG3Q6nCTEDajzvg4bjfRVtNsTsucyP8rU526xwuxEn3ppUM8/sl9yz3zPR7nP+sHhuOOXPOCcF4DcTMs3diWtqLRKNvVxU37MO0LO63BK3PvJ4kUgUQlu3JwsQ7OIF6J7O97be2W4Om4My/AA212svXHvtxoHPs8WRtlVtLaUQikUhaDui2zA4wdRjCAQiJMweCV64YGwiB+BWfFq9iWu28rYtk6AZh2nGzLdxHva0Xx9SktKoBBNlUuzEYUNNxupS+Qs1KIXmO21jBfCHpQnF5235uqfOroSTc8FYkHP9I4898tN/6Zf/8CsvrZ1d/vRnPnFweyMRDX/u537xZCQKU/aX/9LPv/Ld93cKNTY9yXgSUxgwAopHmoFA+ov+MrQmVc0QFExNBPPGYVZPU2dVxQECBZoZYGOZqM7PTCzjY6MZBKdc7k/gZDmbx39ut1v9Q/kaT+vjYF3zYSrIj22HbbF1Oz7T+roeSo77w59phAfGNhsY00UMteYJkhlws6HdD0NZmVVUoYCo0Mkmvk1kQrCMJWindLgoZRysM2hiISmARH0giWQTB2Euh7XbxigHLBGeKIw/EIAi6VQkkmNcEsvXxFcq5fjok2sOfV2YZmT/PTLADF8wngLekEhDUw6EHs84Cm7lFokbXpYcNAL1umUOCCK+w3/Wwt4AGugMPw2kn0gMVQ26xQbTo2WW+6hIBDbUZC4VSyUisXALDqJqVZR3VEXAwR0MppJJIJIKwEUqshUKrzBGGQ9HQllAEETebbHfh7yvjT+U33ggARWLEnUM8mHnQLbj6Sd2/DV03wq7UxkWUA/q8GA25e4aeS/onUinUU0OBIwZGykidAcdlCSCRbgipuNZRUBYcD0x/ehYrliYhmaszRgKoNXnkwFVkAn+EAB4QLj15peHPPTpAMH9eZng+73xsemPDXI9bUHu1/RtC51+H3XNwqpx82MLnBSqbqLexnc2OvFMZC262puzAtDlurGFdgfvLxQLLzea+Xw+jhJPPrqeublslugez2//4av/6nf+kMUAIZOcH23/nozHc/XxZx5+7eZLr703bAHVQ2RLAFyf9MJB8GHrw56H+cDQMcQUDQzoMDH7qHqTejsBk++JgwCn4k6Uw1+TDJXb5ONIp8z445zGtFnN/s7EJDfBlcmUF/ezmu/8wMwEFKIji5MwOLvUKC+fWf6Lf+OvDnwpbyzx8CceOTXyrpxZ/Qf/x7/T7lYfe+qRqxf//qBeD2cXr5z7Owj0p9dWX3z40vo7d7rVVvTUUrfdS0AWsGKXZA/SZ3JoGA1gm0bxZSrGMmBq5dRy2hDbqqOVP9QhtpWTvnQ+D3faTAZ/dqet6oenn42jmtiW2DqZrTgR7AQ0frON1qCQgljqow8vZjb0gVGPBNhP09O2bH7N4qwxERqCqB2JBhUmGSXx68r+FwdBBCEhkYsGQgpdBMMQzV1mDLlfUbtDAYR1I20o8I2D2MiHlmUZTCS3QBjA8owxt5SVTIDYJsRsH0hAT6+zjqDeGZuLQt/Ayjg87HBtG0XRIPdGuofwRse+OicArg1Q4thtVMHPnl7d0yZmvQO7BvswTK80ENeqtQ6KrSZs2qgZRKGQB8Wf8G+wn+d8ww0wNB+MCsOvHQql84uLHEq49kUsDJWOVBV/NjetZgsUn8pmuqGmIsDcGYlzH00OLbRdwdogyzMiDcPUrLMNd4BIP8tMIFfkIyMuBqL3I7FsTs7+PuJuUM4E+BpY1lpYTpkN6KFBngy6Exky2hItMGgesGDB0IHM4GZ39LQGUKgBHt6HHmdID/nd/2FjufmRk57J5/3x/8w+R3JWtd2HVuiZlorrULj5dD3dENP0md6w/m6om4PNFF9F58U3/L2iA7GDQZQxGAu1WpVWt9FvlsLZhVjcg3zrkw+dqDcrp5aWTkRi0Xjrp7/04hMvPv2ffvN3I/Hs/NqpxcU8p100wHIHCpOnJMxh5GX3Q7YoAAmyWWFkpXGQOxy0kWgBsCVbh/nV8q3HbZX5OPxzOOjwlxPT5GEzsj6KdR+GcJPawMOFTLrd1FCVsms8TTDRtRSACJhz/AKvXG3DqNls15IZ3/OfefLMUx/zJXNc/X36C58MpfLBmO/yxRPDXkpcE97YkDsVbzmxEBp3A95w9vOf/8Jr3/zTFOReLEuhUlFUIM0ZZjT43ykQh6VHqypUwQ4knq7TfbvNdt+uv97Oo6DZYNd/9m3ynfX4z+9WRz5o5zRTPbXWrbFxmhE4pjqMMI9Tcb3s/L+/CCfKbBbWa6ZU5aMuN1U0GamuxkdB4Cfqxe5zhJp7BjVFbHZSOgiyp+ICoIuRPCP3CPYfNyB6x7jzwSi6HzU75lTH7ecAShFsoKEY5r24MgoPZJAdWGrDOplBLZt2yJTCZBb7L9sLXcR5hg2gjY0ULHr+aArqOjdtKJUzQMHM9cEQFEiGe+VqfWMPebLoXB6ey2G9UN/fLt/bRUI4GsYSJJSYRrO4C/9fsy5l46g3ouZhODniC/Hk3BCSr6dWqO1g6h3q/MLiMs3gioBTPDddXApAIBrCaVSvw7ffacEB1IOnlIp1MAsP175nWK01pDGAvdEQLblNgJkVQCR+7hCIDD1HQr8UKs4frtQhB2FeBlefhZVlD0ELtoxC7OoGvAyZyIfMhGRJOQQYrUTqGzPm5teMmfl0x/bQuDrBbtjsexJ0KIGFB+s1G/tHdh/K9XBqW/1ZP7cueiv0/hgzsS2Mq6JuMrkAW3zuT+jGM+HmUtEsnPQyYMcyiqoZ9uaoKRi1h51Ss+oft1OZZHQ5+fiT565ce/zvXbx66/q9z375U8lsKJNafPyFTyLx8YkvfingzyXzc1fOXI2H0QOO6tAWXBAwf8IEKmFZLQPsYmiIKEBc6wBCkgFRM1Sj6Yipn0w7rMMGmhg/2s+0L5x09yEaFeCWLpeTQtts0+EzOUyiGgSgQTGYxmw64HHg4M3pFaFFiL61TuOJx5979MXP+FOr8CuDwtkl9Thc9ap7d9+orL+H7Nz7L39v586rC8snAkN/cWf49F/46/l88qd/7qcS6JVsddEt00eUGB0sLJc6KzH9RXBgeju9Ybc6qrupo10g1AQ9Thwlsx4z7XADbcBxv06a44I+gt9sUYeiT2sz6+1Gnw11qj8bzYAqPa9+d/ztjJh8yldBboY2lhvZfplfIjzgMZG1rhx9rB/BimGz1K8TEzwEiMMsH4pk8Iaswx4VaaXRGOW5vlB4HrYvceXDYtluAyXG4AonbTi7mGyAhz8ayoTCS2b9ICtfPBnkbk20I6F0LuFkft0fzsAext2qxxfpjquNchXNP4FA1J+PYx8SPRNS4tmu+lAM5415fV1ZBYDnp1qHzaeHFQlYb9od9ij9Fje37XEP1s8gyn2qe/tYaGHFgmMfMhRmHzn/R1DzFoeLbcCNQLlSxppHLIptr05hdw/2T+wJl8r1dpPFA4QPU0/H3GUM6lVsB3QaNeSNIeO0Ic1DUKpU2sLhqMbq9huNLrv4cBjULxECWIe46ZU4BWcmXtxmcx/SgXykxYClwlA+We/EWQWGB/sDBtC7aC0ECtDTIIoSbFhIWXK07pphE1iQg/CMM0bGww6bPG0Es6I7k4f4No5NYyeZcuOPAMflRlL2epwk9uMj/9pUx0Z3ijJhti3G6UK49VKkQ3lMMIKTp222PhTVtsBthRPFrbveBqUIsYgoj+pvFnYE9NDSLG3fg95BpcLvpXMXE4tnw3O5X/k7f+epj38CRXEfxwp0JPzFvzo/DicD/jwoLptYMV0VvHJh5eLFWH29FY/Gk5CLoI1w/mN8dPbjNkl0JVAZoM/agAkCmChsZSe1m1bXCbGtcNriRnN7wb5t77hhztuNMvF2um4a+ciKYAHAdp07vS2+t0kMBiI3UxUF0PsAkQyl0ZUUJ93+Yd9DT1w+9/Sz2dVLiFPsvvt6Kh1qbN1bfOix7fdef/3V7/jbnvCr79/c2N7fGNy69T6C852ar97/N8Vyq7C/nwmFqtUyG5xIJsXaCZ8FhUCUk1ARCANapwBAkEtxuFQDp52mVpO22mo6nzM9oWTynfFyIv0ZX7bY2cJn3T8s0ym8OjUisZPettTNjLd5LExPa+98O0OjWELXbivd1EoqPzcXkxUFWYhQiM3RCVcXO3Gtv43vplJ80+8aBZ38RNgMoD0fji42qtrgdCq3w6kcpzjUXzID4JsYedtY0Gar3sf6s6+mcx62gDHWiOYoZOe9KSiisLhgV5Ek0EqAKgizqGdjkw7zhfhfQF5o0mV34EPTMmcJQALd4JDC2YBzv8b2A2OMBX8oDsEFnWnKATaCYaDd7KBNIdLqRufSqYWcTvns5+t1pKxyywvJdBL7jvFEhsbAS8SpHbSOhgk25nvrW2D5+fn5RCZV2kOvVSUcjMCAD70etT8sUdVSncUjitKIDjpG21wYyK4ZGhuoiz9IPtogIeAw4voa9tQgyq8jGIHBEA3nEYmZGoU/kvHyBiOsh9rTQzFjneAfEkPgfQkRUZJOVDBY0yGyF8M48Z+Nl8bUggAdr5G30KNR42MGuvjWoE4GehLVAIYdWQdyzIcDD040G37oVyH2MQW5H+Z92Ofw16GI0w+bnYEsQaqp1TShDcVXjqm34rkxrZtfN9gBYJPCdsSk9ZO4cjilgWB8Ix/3Lhy7xrJNPRwGB49cupTPpTOnzobyi2gofvwnPueLZ9nLeDENP6yNsSXjzxC/uvt+v3lQen/nzLVnarX6i0+/eCfy7vJCtlerYN+Qy142Fskwyow5zMH7RpVQc8t4YCrYkIBUaTMDeZkaaXpN6mZrqM/7HhNJvhPHfVFmPYjl9MrU91BKWw3V4dCDx6FoJlANEAIWM7JmTqgl89jjWDz8yMeeeurFn0D2s3TnlXe++Vuo1FvIZnrV7Ze++crXf+c7a6fO/vHXfm1nt4ji9BPLyZOZWLnU/+7rt+GDYCI8deXCo5cuNpYqzFKWYAxf1Bo1hC1hhAZ82eYwSWiD1p1DVZypoVp5OMxW3nq6Qbzvb9PhZM7Xh8dUqJunEnzETI8taerp5Gjgwc7aSatMAQqflmpctkdwWn8BkPCz80wjT3zYJNo4Zt6rKCflxGWiqrcnaeQ0GEdjz0BoWQbjQ81kyy99D1L1A7riL+HzxUX251K0V5HQEyrNPFldF4zYXDeg2xiJ2lAPq7lhc0JWhhpYgzRZEjg1g1AxHeGNZzOkNngQ9OpBCRpqF1DRxn4a9ng2+wM0bdXuBSFD+ZHghSzTQlS4W6r7Bt763gG79dx8EmqRmDVDwWQ21xoW6sVSaC6byuQ8UlolqzLROirkmsxnyCy9PtbsPFFMNyGsCcVlgBAvh5dcDesWhSILE9xEcAomYli5IUtMB2DdaZhJJFls2Nwhwwbe73fJSde4yJ110C4Qh/zF2sD2BmBGEEFYgEOAWUJplyjGkvJFBxLbf/myPICNOBnoHgDuJvSVIm2M9h/uLkIopUQjBVhEA6Idv2GPEB5xxtFMUDvEDJIO9WZ+070mhrMemFjuAJu0DIEdczvuE9CZOKaxXRdBttyZX1sT5THxFPA4qM1N6b4Vz0Q0DgcETU3ka8P0Mk1Q/a2v+5r64E8h+MthIpmG24iTQGVlHgEbTOv8g9qI9QZ/sMPQR0PlavlTP/3JL/3yXy1sH4QyOY6YYG00y3pQZ4IOwFr1zusv7XzwJy/83N+Fofmb//JX68XdrVulC0+86A2vLM+nr/3MTz/yyBVPv3br7Xe4aUIPCLpQYP7nTlhtoFCub2D8RVegWxO9TX2tx7SJsxH+/G51wSSXqeuwl4UN1892o/0yyMI4SQtcqik606Bhl9GCeafXrT/y+FOh+fzBW9//7V//Hwq37144e+nNr70L193mQe3Wu+2DrRulBkI2MrGw1Wtv3Klz98ZGLRYL08ls9rYLO6V2s3SwO59dgJkPUU/dy6CmEcW/WgDs+FrkZXrMwpQLyKqh8xxqK36ETHp44nAj/5neFG3TTbIzk0uVO+Y56mvrM62vkhBn6mETmG/98EfrZ+OYIDeNYjvJVQsbJh/7OEHTT9uT7mw3/kpj06lTHTzgpjClEyrkZFZj9quQ5cF3iPR5g7BdQv4BHBB5GSTSy3AGQFnpdxrDJuh+5A1GwV5+T9gbl6oPCKC6afNg8wRzVyh+aIkRIxwC7Y16dXSxoU8UFaHd3kZ3nA6x5cd6tu6DqF5z0C57R52gN+4NpkVkHWvrPRbjZiOAHXgwNTrdqp3AwBNPpz25ViQZzizkA0vz2ODwDzv9MdYfQ7p9GA3LxX248dEFEcvmUd9DZ7ARDGeSAewOQcZHUgHLIJBnUBkEAYqTDjKfyTp3sDDzcBUBKxO3XjwoAoX7h6UA07JYiid/DAxUa1V4/LkHlCpohIC5iQ6FsB0G6ytcPd4BrCa6tGCVBO1DI6BbWRLgL+KyHCcbfK2vqFaXIBFnCD/soGP/IBz0D7r0u47FIC71ivA/ddf6yShpfEho3PqejKl8dFIwQRZSFc+J64yzXR/wcyHI8Z94KJf7HyVwk1jgMz7TfOxcOT7xNDtbutph62fjm/aYSO6328BpSrdw23BTuDIjgV0MbAVt6KRPtH6ydjMKnLmwOQ7TACt3tVK49NjZK48+Gli4uLTw2LCzV77xJvpCYCco7W61+u1UOvn7/89/ijmX5z8Dk2ft3deu376xWSh6qu3vh9IbHCNDQ+/dezfhaNME4Z4fGmSnw7WT9q/sODAujQQsZzl3AbDNmm3MMe5pJDuCx0SZek0jT/1w4W1gxXoqknz06/bPTN9YPxvBycVMfqdztSPTSOkPvjUO2zoBD8bVneJyr1Xevnnjlevb63tnzpzd3C5ev1O0d7rs//JxDvBhFMD1PehLQrAH9TDsfnyxkKfdre0djDd3S3fWy4+Mw9mTaaTf4Y4AGTlgbSo17YCJy46yU00aQaWmbZq0xA3/kPeh9s7GM91k8KWJon6cPLb3TKmz3ib8Po9JqtkaqoomX4VOk9i+tikcXxvL/FKg3f1NchJCmaaelDT1sjV149uZ5n5RB1plEMIEIGxKfidZGDfLA1hcW32FyNgJ7hbm1UGRugbg2kt7HSgbsUBoCSNYxrotsk7oCGe5Z5ermoPm0CGB1stecxeia2C8gNIdb7A5HtTFqAMmDLDbp0bY1uVeAV674HhQbdV2mFBYBwsGW/5QCnVuwWDCl1sd9ZtwoELDRZNLyOtrsc0IcpubrxcO4DT11BHNlfVH6fj0j9PLSxj9QhCt1WyiUCGU4PoJxvx2u1aL5NKhTHrcbgUbyCVg3i4FnadRqjYrdaR3sBHG7psUwQhmwmotyPbcRnvQRN1kYZPu/kjY7OtHktWinaMh0jDtQFA6j/yeUDgKpul2+miZIx+YR2Flgu+TlY85xHomC2DmIEUPs+8BR2AkWxfDWFbmGISvtpLclaDPiKNJH0uRjIAdFIM0zQ8jbwHUjKbggBE1K4QN1mg6fxbwFMXAgcnJThjrd/TXhSD5K4uP8EyTHFoGZkt2y7a52Urya8HReh4q7MiHmjl9CDR5m37BrVKVYHZ28A0Q401P4mbIdF2LQdp2FfcX/9IvrD37Ym2/nFrod4tbv/dP/tvU/GI85H/jpdcbw9ELn3hq726pP4rce/sHgUG9tNuqlln5vXv3Ct1gEbmU9Vt3T92ahwGo12pBQJRKdLY5ItlhuFQcjaiF68PQ5S4ApicPNWHaGOtS9T/y8+DIThlEMC4b8XB0E8DPjK8b3fSYxsT2L5OcNYA+5AIOilkYVdeDVr//8te/jdqs97//DoRaRGd+4z9+hz1NNgf0Q+7neD5EZggo7la78GpE2Q8Ovc3BqNbvop6r+O7WExdP3N6rVWqecqXqO80QSUcuey8whSqlhcAZTfqD6WAQlgL4snWe1Hxab6e9H7kHD0c0XSIvAMi89EPNJv7G1w2xtXO8JnWZRHEc96c9EuNIWxQ6aY8tXJ8mGwPgpjb6tF8mN5uAbpGfjer468um1yxQEF+2F91uND4mukmurjfdaPpco8DD7hhvDIKG/OlgeGEMz74MASA9CWHHbDbJGnuQwm4MH9KUjX67DIkmmkh6gkks6IlZCHIfor/BOSnMEi0EZFjrtfbHgbloOM2tL8IBstrdqLCTl/adWBpSe6285+1vhRPZYCwPWxFXu6NxK5rKeohcqWCoq1EuDfqjbDaFuumt6zsZXdb6Rt0GYgPheCKcjA8bdcx9hdLZSDLVZguPbFnhgPUhhh7QPXZ2vcZBiTkaCUbQQsolLLdddBU4F8VgXGYkEikWDFhXufvtDHvo5YlF+Hsv9AABAABJREFU0UPnQdcQExuBYO6SmzCYerma4JqWZSrEvEAfdbVRgwWoE+oiQRCMBJtiH+LulzMJt2lwrkImE1TTEfwDdXCdoFMIQVygY8wA0hKahQJIWcMmx4aV6wV4WFGkBIZ3BtIdW97OuJrxVri+zTgap8bUPO57+qm8ftgzye+HRZyG24q49XD9TWGCUuMxLXp2kk0KI9LEbTOwn0c8nbzxNY/zti8gl7KM25LR6FwomeZaxRONhrfvVdZeyKcWfN/4p/+w1S69/8bN4sEPHn7y4s33S9dv1ntVTl9hxPf+1T/+fwWBqb4nl0qV+43SftWbity722FHUDrRRkAmn4hgBigM12M8gqpDBhdgB2FqSnKeZNPj1O6Hvhzk9kPjfdQIthMdSJhJ5GJU2zk2wOmzmVjOQHGpIfTBsZlZxT6oO4Z96qVvf/ett98tFfaRmQiGmAPdZ5+79uizl9O5fKvev/XW3Vw+5Rm2q/vFFqrg+tLvApPdbqPz5s0Kph9ffW+z3xyk4twehpjLnKE4fMNKQYV1alJ5oj5TA/PPGVbqRoD1m8CRU2ETxULWpAn4HfGZBM06lHQ2quk1k99srPvdNu8Pi2hymk1ok9hf+U8ST7ycitBOWmpaO5veJHFydWLOBiuBttx66CC13v4SwJcJkEt/7iNPFx6URiWbhwCTHyQfn2jYHe7M8FB83VnCDYCRXh0QmVMQN9h5g6oAdRTgww5TLR3cCTeS0dhCODmPFFU4GBhG87HkCroXQeL9Ftv86NjDZybAAgBXTKvEeuD15jkO+r2pUNLfa3RBm1jNimcWPL4YvNqV8l487k8unPP0m90mJBesa8GYFvGOwpgKqJdac6fY8Ufq9Ta8BfHsAmZfGqX3O9VGcmExMr/SL5T7LW80no0lI51Ge9ilyuNeGwpQE2YE6LeVQjWZybFutZpcNYsuBF8QqDmWQSggXuwNc7lIIplieUD5D7tKxMjqpTpGBVixEAqttpsRoJOjRA+eKFaTQRTaJlcibJ3EeIiZMizOh+hNjERBKdaosGBhQCOMgXquUuhaMQBpQKRjbghvETLJGJvxpEUy0lUcnc+tgIaJ6MIx/HcGzMCLGTrHR0PlPHISbcbHBhzxOC6Km8V9b9La+E75RyI4cKhG3l/Kkbjup4k7m92sm0j208RSEpO3+aLbFWbCBec2ilCFeehPjlcSV4QchNFSKH+VdvXVb3z3Y//Fl9mvl3a3X/7Gd8u7jUgi/v3v3+CuPpLxfnB7nyugSquzud8AMbGzX1iML64u7NcabTTT5n3FUnuj2Iafvdbs7W6X5mKRs6eWkIWBnw14gORIJbgI4sb0yAJg66f629p96O8k8ofGmgQeH90UNOkgW7DpMtNhNvE0pR05G+SkND1J94Fd4GKOQ4H1+3d2D3xjX6VYG4y6T7341C/+nb9+6bHHOuy9gqGHrl3f3Vn3DVsHG+usuLub661xdy4T7JeDpweejc0KNprmEv5TXNBF0bg7QNCHhRTRGqqi/aYISaAaWwVVB5epyXSFdCsm8DfuSRfc75hpig10s7NfSj6ZRbhtdBt2/68bahHlDyv6/vQ/zEd9rGba1uNwCyShxgz/41ostGEe3qZStmbk5MS2+ZleJYqD5t3+dBM7pdm0JjvWY4jVekaDVrveqRVDqRRLQau612sWUcrPHrZbK6RypwJBqJ9sE1gdoAgl2QRhNDGVy/q8wUGvDFUm6M8ix6uWwcQJq2cgDVUQeg4bC10tQQLpzYdjy94xhr0oG5NxqUhsMRSd84cWUBQUy8Jx5Ok2t9HThs6fWCKH9eBkZF6newwwhSILaColBoz94EpUcmK2IzRAJ/84nh53ep5yDRmreAxV0qkgJoJRLAoxeIgC0QwyWI1Ks9duw7vZrtbZxaPyHz4f7Ly3mjWM1UWjcei5usr1ecu1BmQbjGFzZ8V5f79QBSOz56/WG6iWrNRayTQ2DAJNOJ27kPLZy7NOcf0ocV8kh80KPUZbNfcBOvjCh2SkA6ALwW7KEtv3c96WpILOViwaqKAIBeLxcK0GfQxFoVIxT/eQox00jdV0sdeHGTd3ZJ1JhLeJqDA3yMT7KD/k6EDE1GXTzQCKKZjvo1EU0VbJLeq4GE7YTDGH07hp3bfNw2KqSSUAblsBt742D7Nc2ptF+o0LFTb20Wit0N3eWb/3R19BX+D2+jYKLQ/GnYWFuZdevoGyktRipnJQCycz2B1NZrwAPpakU7l5CP3tkadQho3dk0jG0QGOEeBKuYn+p9MPr+SziW6lWan2E1iIiED20AhyI3NkATDV+/D2ue38kd/3Zev2yCQn212Tfd7EX2N8X2qF4mmD2JuwCWHKQgRFv8VSLgfTwu1bW5mF9Kf+4ucvffLznlEshFynZ3jqk3MnK2f2rr/XbBa56eWCLt2Ntxp11sK5VPhAvJ+eiyeXL55awpqqdC32+pL5BPB1U0aZZnUyL6EL65jWdMalGj+o4pNo90XA43AqZWPbOUn04Q4lIA9hY2X2gMfGekDg1NvNQdFNXTU6Tgny4DFf/FgId7rHjWKSKZB4bl62OfSd+EZsRDkINyvI4earIVonnHg2reFl4AYS9ZZSBge5olsfYi0xzNGuW+ZyC/XO4WiWTUAIbczRnArHeLCIRTFfbH5++THEJmFrRIgqEEpACByj87kPAw41gBrOMCtPtsDgMu4SKCWcWITDBwr+EOVCYMRAJrkY9UW5toVzpgeJJhWc7zWkMwHdpJ12A13N2PKFblQrFwd+fziXKR/s+1HK1q63y3t+JL1iaSz4dhoNIKywewCmlpY3Uz7p6QhuphENA6rBws16DRRdKJfpI4NeWcK81WKRpS8YaLSgYPb6wVikUCgZLjU4OLEY3u22ON12krk0FLFisQoNae3CibVzpz54+31o/hz/MTIggyBYDBlytNGlCTJiDAS9RT5S8gNKhwjFyslSpmsVFgkcCBOEDKuVP5mIL59YKr31HlwY0VCQkwH/xJFl6XQOcDCGDKDFgQYgBDYmTCAxAQvj+dF/LFQpG1XYzYW3Cyomq8mHdZhE5udQNDeVm8sPrYVNMBNdHpOyJskdHxMoKFah6hrzCNp1i2l02JBaLFumIedOrvXb/f/xv/vHXPb6Or1QJJzOxD+4u70yn80upJdWVgv73YtPPoKJ91GnzdGQGy/Une3s7c/7YuxbNvbr0Ba5pUJl+MmFxFzI9+gjF7gMvte4A8cz5cEq1h+IJkjC+xaASd1nHE6FXZ8jn673RxlJ2wWTFNZhx4Ppb/pH3aN+m0SdONxkJib9OIEd7vIMeQb5aWnHbXbhcgqgBDHgOXkyv3ZmrY+kPbd7KG5Hya0/6EsnkIlH3crC4nwNNNLBEiy3dgNYxJdSnoVkdp5jdQh7MgA8vkGOvuwroQ+bFqpuk8dWhU+LvPR5KHwS8RiHW3/ad/8z0+jZDGe8708z7TKFKZnmhqm043FsUQo78iiVLcqmV14zj8lGP9bhhOqleW4+Z1Kbb1sT4Xjzj1g8bMihv5MKpEJiE8e8+ZnUXIiDEF524qiH2bSCotnSSwQJfh1En3qo3sx6PXGuapIIUSKz6w0bbAlk+EWjgAouRhduc6KJ7Hmh9n6HOaAJyNUq2aHHQflCREKZGg+7WbAkRhm57GwHImEx0QXZ9bbgrAQtQ3/H0AwMNZVykX36sF4dDZt+XxJlDNXCegerotE4VxG1UglCDbi1W68GE+FaqdlvlUvVBtVH6bRWHa+3hygv1qd7HeQA0EuE2ZhoMjroDqvFMjdbbNgaNXbtLawAAIaYiAErYyW4dFCkFA4E6G/Ahlg2n2XHA2dRPJ4gTywCj+H79vrr23029Y1Wf2Vl4fFnHr/81JOteuPuzY2ozBDrnpehRt8VhwZR+i0hAkFhCPyGYZTKw/YJaxD3AUgn0Vm9QRdMJbbYsQ9jZnPz837/Bwil9aOoreRKQCcJ+tECEMNtQEGIzQUMja4ART9ymceAhonh+lj/435tNAvihBuMIT+dT8nWQQ1OLH3PZGIB0dZF3oJH85hqWuexv052Jr7JczZXUqg57uMUMi2FAFuQiaOsjAMmIFHa6CMqDvABz+hZCnPQigb9rVa1uF1Ix2Pff/82XIuXHj31s3/tl85fudzqjgsbvROXl9FyWNzc2bxzu99q3XjvA08AS8Dc03PBqxU9EYhgjDoVCcynkvtb22wjOCswwFpyEDeLgirpKPG6Haqm24Tp2+khtx+nrfwh6aY5HOty+tOG2UqYsbsvsgDlQZVk6IFYLQWKJX5QcDYsDfEYtNpguVplUe2j9yoSHyDmg5aIciEQaI/bB41mG8sv6ApodFoIyucW5n3eGkx/3t4YNtpYKISoZ6/V42DOvTxzxCzVKkbkTUZv2gv4OB9Tv0NteFDd3WSHItsP8nQeuY7PYFquE3VS/MThZvKjv50s7MtiXlse20AD3MpS3X4EeDQK8pzxduo5nTd2NPVNVPKDHg1BGQRjmCFMBm7bTAEmQ4v7Jx0PgiaVdukwrxs+CjYyUrWGZRNuvIBr5Coh+KgiJkeIGuawq/LYs7M6+PoIezEdhPQVD3WYvIgNt0RV9YIjknUDjW57Bxj7jSUxN7nabFUP7nwA2w8W+0o7t03VIrXqFvY3WkU0zDb84Tg2NuoH+9VSEcpQOhHZ39rDPM3i3OLdD26cOL+UiCQ3bl5PziXRSlcqFSGhLC0tNGq1ThPh21E2n2s32rVyJZtP0bZOjXu+HmsANJXCVjGVjWWy6Z31A+5gz52O5dKpzRr3FXBz+9LJeL/dncsm0RTHOscSMhp45xfnOGi0O4NiuT7oDTHtDqfr7Xfv7BWxLNJH1E3LnJjnUBuDXmhd8zIMaJqDS1CWADAeiFI5BJFZHyVvI3IbPY8pyWazjT1KUBf6K4r7B0hZoBEDQoToyogz6SyhrqRjLUZWD9txdN4KIY7z4LQDLIfreejt+uo9wQaCRD4M/ndzMxH54dvJbJKnjTL5tPlbT7ndIqz//b82oZuJWscDENvCTE3wMLODSDa2PJx8Dbgriam/wu3DEMihowBdzCU7EUHk0GmwLIFs942bu5UKgBH9qV/5xad+8sueUCYTCK48DpDu1Qs7u4Vt1FnBnJZfTKdzUdSFJ1PJAfIwHszedbbXt1KpCJoEN240EAEMYz6UAx8dhg0LGJd5mD62GpP6mqpPq28/FWdSY5vgz/frdMpsJsrf2SSYwiZRFGC7F6/ZWiiGG0uNwS2BCjE+o+eQqdNq9jhrJ+cWAU3ouRwAEtk0hlghe3Linl9abhZ9KydWUsnM7t1NLmIyiZQHGyEdNjJ+rhMC8RD8WGaAhA3056w2k1pMHLYlRz6tJ7/4uxV1/R4U1Q0//J5koGRHs7LfHylDoh4f75iAmWLoWn25kY582qpOoruxHG8H8Jmlwr123oP0ITx0uHUUy462QOAVWzG7KVRmwtU2D1My4QozLzUCGj3MWdJJpvOZv1ytjbitiUnFg5lSlIGJK4OxlJvZa3navVYRkn4wEsEmS6vDZrzu98s0emFnC7iAY4cNdnnnThMkFoihKp/XW2+8Uyk2Ub3z4z/50zffeutPf/MP0otzTz77xDuvfycaSXBlt7f+wRNPP9PrBt55/zoMP899/JMQ8N9544OTZ888+cSLr//pK8nFzBNPPP76N75z6tLSyTP5N1/65tqlk/nzS6/96feS6exTLzy1dePuvVubqWTqwmPnsBF29/ptDiRnHrrSr7de/s5ryWR8cXG5vlPlnHH+woVBS0K/a6dWMVUD7Z5lprhfW1ya2906yM3lTp890W937t1aZyYs5LPxuXS5CW0MBjd1WW5x+ZFnPvHum++kM8m5WDSZCDZQKqGTEauIJApYBVkqODdIRwyD5dcxi6XI9B8XwjoqIYkKdkJgGKtg0HxOX7i0evnib/2b3+ACEuvkWpjpd3rc7GzNgDN0ZkQ1DgIB83UIFOVlh2ky5u7Yu28CLPQwvsrDIn4l4jGBjoOXzUzf5rkvT1uUk5/N4r44buLDb6c813PyaZNPs5oEuDGdt1m9nP4wXgbIaYxIa1pmUUXTQWS3XmtjxBObayVPLeL3XL66ev6RJz2R3NCXYLMCs/MQs6KhoS/qX8iuhP3eTDZcLhQ5s9Uy1ValzWBudgq+IJqkA/Mnclj4ZP1GTz7cbNrxUBYDzE2+ETCjHupRt6ZUY+KecbnBP+RN0iNtP+TjhB3ym+SoQBvBqYDzciLYuW8+iOUgFj6F+xWTRghB6NVCKqY3CsZii2fOheJ5JF+gYcL/wc6w3W4Xtnc4a3Wb7f29A87O1XKN3Q4XMKRhONiJGm4snWSZXuoysgfolLnKsbXi12mM3vbLhpgaTn6ceJMITiKbZhLLOA752Q8lc1K4UZzy8RWKVJF28CbRbEr7q0RTl3GbfJxMFGomk/V80K96fpK9G0nZup7uW2GqsmplC2No9M98E2Yd/FrauvKQGnptGVUR5ajOdgZ3NlubnaIQTnKdyshVa4eyq2DxpFwMz53zoMsSnl0vfI0yBiAtiP16rYk6nr7f033rm99sdSJPPX9tZ+ODg2r79htvJBYSa2dOfftPv3VnfefyhbPl/cq92+srK2v1Idv21Gc+85nCduH99/fOnV1NZhY4gP/e//s/Zubyz3/8061C+cpjl/fvvP9Gbf/n/9avDDgm/OrNhcUTX/iFL+289/oH737w9Asfu/zctUdefmXp4UunL5194sVHzj9xaXEpf+bK6dMPXTl5+tTOvbvzK2trD10MolDZi7BZ6PSlM916o3JQCEbD5x65tPP+bVhyVk+dWFyc3z29iKLQSDR+8coZhNTnONinI5xQI6EYuJvdejQYQCdEJCpuQkn4IhyJXvRaXRQbrJ8GPQsr+QvXHvVcOHvy9OLNN/2IEtOJ4vwHX4dkgYSjAFBuQYo1gCWU0jEfBvpgQULNIjyh3BdwTOAiGHUZIl8gSdPglrqmW2L45VhOERggjY5ZDCanKzOszvgbWNSwacwUYEfaDLoBDQGEiWuH3sQyA61Bdx6Tzt0wmBwVAygzuToebuSZzHCavPVj/yz8Gk/j5RRhoY/sJplMopjWTLzlIBKh9tcNmPiZABOoXlV+pt4ma3z4hx+HUOEwBbP+8os22xA37s1aG9pcLpMaDYuXr10JRefHvhTkvm6/B4cXtqsGntDJi1f79QqmpKHos3AjeYIVo3KhubV1cHAAdwOUOriEtMMCKCgeh2gmDDIjgybYiRyAUz0qYWppe2jSPLdhehOunB70KPnh536fabgT5mRIH1jAmEaYddk66lcdZ+phEsplNgVEZreingQ2UYOKYPXcQi4STwcCyKqgZQnV7f1h0LO6toyWrkKnacSHkcOP+WOBfmSw3ym2O92YPxpJxrgZM+qzhZ44+2rgzSAJ+szjNkulO14/pG/cWMd0oGmMcnZzddttv21K9c300Yf1EOyYeE644zuNalyK4fwpgpl5fJvIatw0Ok6TnQm0LguxJgcnyESfuG1qssGHrJSD6Sh9miVZWZqxNdNUUbxQoLktxIKtLKBqAM1SYWMpd6Wd/No1xBSjUxjRBcHaNgmgocVDNtnfupc9f2XQBfR3Bo0Kus7Wt67Xq2W4Zw6qFW5sEet67aVXBq3UwWaj2bsHD/R3v/7mj/3Up578iR/71n941VcLP//IZ19//ZVRNvCln/kL3/ratzP5+See/ninOJ7P7C+duHD6ocdG1ZVHH38qNp+OZpavvPjMw+fnb4xK7cpqdG3R0/KdPxk+e+2h5NryoJQ4ezp75fFzi6eWTpxbOnn5zHx+OZGNLSwtplNo9Y9nsolI1B/j7jk4iIT8mHjh5AK1JRTx12qywAWrRjQRhQltYSF1cm0F6y7zS+iNSGXTscreOogVzmSs1QyHnZ7M/6Lluon85+7GdiqNvocBt3xomJP5wF63WK0iGrY8F0mnM91aLzJuYyAV7dSQdVoNrE+KoA+S50E5KBqDtQNCCV6famgEEHIDaXAfQH+Dc1rtNiQpyMfRVMzbbG1v79ZqNdRqI/wcCUdYUYx4NoiM2UJ6ZWHzwcWjETQgoA8DIoqCtyawHs1ek8x8GZCRywFwJTfxBEDWZQKVk0lnEYiBEBVkoVAZ8Bjo4YeIxul6mUDnx2Qu90xkvlxvg3jcD5tEGRofM6Osn+tlvkyJtrKOk8z1T362myTMxKNqG2/OnRBomCAMEhIbdHi/i5LaDKddVFFh6jGYYUyQPUz3a5VuNIK2WIxYIERIrizR3KqBtSIRTLql+i1oeTB9cgfMRNG1m273KUpMRyh9MpfApl6msvwc+rCe97fPjfxR3pMM5Zh8qJzpQLj5mPYLYlTkNK4NVj+bqttwZaABxksh6n9zv6G7PDKApwpvKUZH9BF1YNw3evqVvV30mEAgqpSrbLpOnj1V2d8v9fYb7TZ6eQFe5C3TkQT6vQBldv6qg+k5VkxlqgEyFXdrd7iOTpDTmsNhGl837dTleNqE1pvf6aMAk498Z5Lpy3zalPb3UJRDkZWajJST6Ty5jHvSHH0/4LFxbH42o/si2kCnFiaOdTs+5GDK1QCxNgOFpnMZE/E+s63ULS5xTUrm8BRnGM9Deakoms4dKowq7J7Q5YBZ9UCz2njtW1+pde+8/OrLt+5uxmHbyWRRutBsou7ZEw/6srnVbCKZC2Sjp05gMrdeHj/xwlNvf/f9a08+Ezp18VOf+Uw34H/8Jz5//vFHP/jB+4//2FPpWLyO6OD8iU/+/E+svXN3+aFH/L6UP+u/9mMfx5QKPDwrp84GT55c7HrW7+17PFlPtBtdODHyxjyRvCeWjeYXvOiTQIUOixPGfoNl9D2Xq4gI9Mq1dqnSHI0PDiolbyi1t1uApL6+tZMM15LXk9yKY9p7HBxtre8hn9L3jcqVmozeGHWczU69UCminnQun66U6+BfaP2t1jibScKVX651d/bKsHbvFlsQEILdfqlULVYa+ZVsNJWo1Rs3796K7BTeevODdqMbiuW6zJKBVNslWEOkPhSlBNL8owtzmSIZhGMBrsC5C4DIhnUqlEBw5mA4UL3V6Q/b7VHuRDJ/7nxxax0tGq1WOxKIN1rtVCoGrjE3xoIUzRsHYvTW6JnHwJUNcXys/+zHrFuhBjOQ5qi/TWljTGaZUIISEFlJHFxhIlm3ZoOphY1hZ5pJbvM/vpTDxR8Tx/UCpOU0ZZkquPV2Ihh0YnpDdyxmQqty6GsCE7EAYJwcK7fMFTYECyueUEqaKcXzBsOYLmvgCahWWig/gCU0zWZ2MEKccPP2RqfZ7nX6iCIyteAbNtyetFNHADvLaDOrvI4eEDlMDVXu4Yc6Ep/HbY3TjY7v4cgf+jWbwXERJyXZQHWvGbb74lqshbebo01Jn+BnOpvOxA/wVZ+PR7FIOJZOc+oZDFEAQEuHuYVlnzc97pbR77O33kRKAlOrPfT9drkG64kWAT2UAYCbwRgQp5u5VDBdYQBlpvW2EoyhU0+3UopiK+Y2wHq4/en6Om83+dRb0Z3Mp54zrknmJunkayYGTgP6hyuiXrWPhUvjNl7KQw7zMWmQ9bivftZDSWxmk9+JlwJMVvrRh42pNZTBIX/oERgo73S6iWCSQwAmWyBQsjCYPb07IW2+dnDJwXEYX7JR1iAZlgC2QqNwPL2P4ptya4RWoGYgG0o+9tQLaP9B9PETX/zxaDD13d/+3Uc/99n82srv/dN//OLP/xd777zzL/7Fq5cfu3rh7NLSqSxoOp1t79fqvph/0DoYebu+aApEVmpVYN5p792+8+7rq5dXPJhDH6HFAa3O7Xp58/b12/H5+e+9/Hah2OW8Xq51tvar/ihJQo3RqNqplRuFUmmh3O/Eesh71WtcKPkC9VYPZZ5DLIoN/W3EgmEXHQSb/WENXk4MdTW6TPsa7BqVwUoLyo6vNcCCDDcQwVq31Syi7DYAM3693SogLIYpx/EQdrbWsBMd9DKpUGYpCWNnb9RNZcLIA8fSMdTU+dKR1NJC4fbGzvZ2uXLgp8ReHTxOZ0mMl/KkBgWRB93eSic07M6yEjyCz0e5IWXA3snHuZjFYATfJ/YFavz1+uF44Gf+6pev/dxP/v/+r/9oe6uEiIEvEWFPCbJhLeFWmeFmRjrDz4iBb/Tlwp8NwH/msVBkwEdwYx0uCDmf8lRa8pHTxDHw4cZ2PRVm/exbewvCzDMTd5I9AZNwk+/MlwlzJuZszWxubjrlahPhmsS2ZU3mn5I4H4TIRRJhaJWp1MJgcLhBwYGBt9PhoiyxkJfWc9RbKVM/lEC/Pzb2tDP5heouYoMolarT5YiDoLIsGNKEwoIbJj+Vn5kkylsTjLshSmQk+dCW2bkEVrB93Lf9so2Re9p1NuTo7zTmkRACDud5f/iDfB6QzuZIoAUo8wnNxxBqxAcNywdbSqNSUbaxQxKMBKVjIw3uzwHGPDBpHQ0jJcR+Bluy3PrWD4rwhNBVSE6SEp1ZmOQGgg3lwYyQBWSVqcraGthf0zzjq3ZahxPB/bKfDLFCieQ+1ukkmaR1s3dSK9Ike5tS5Uwr5MwwN1M3NmkUMlPc4a9D8W1uNqlydxvlwrCJPAmY1EfxrK8i2MiqmZu3ydb0H5MVhECPw1PJeqwbRzAlCillmQoHtgxJbqHTVnraLGLqIZgBt2OOSAYnCc61rM8650XaxVKj2rrwyLWzD7/43utvPv2JTzMHtjfuzq1eg9Sxs7GOZif26eGEL5xKn3x4LROPLqyd+bGf/0QuP0/W2+v3Xn/75pkLz/3+v/sPm9XOx37q4zd/8IPNSvXHvrj9r//ZP3/l5rpnLpzl+D2XePPt78bisdxiYuPee8tr2a0P3jhADdd+A8XRreJWAYHBcVek3HG9U92NJq6FPP25dCSTSWYzqTOnz7UPdufmsxevXMPmwPJC7uSpU2evPj1s79cL5WQie+bcFe+ws84hdH5x5fwVOJe3t++snbuazeSvv/tuOj1//vJj6HHc82+dPnWh7+lFw2Ef4pz7+/P5XBJNkrlyfm6x0yinU9FQJBkMRtPZhD+VCgfC40o1t3biU7/wlzsHvbfe+871l99ubRcyqJ8LjSudFqie7oZbFLo/osEwPSMNRyOQgeyjdLrTR0osicUFj6/V76CXFPuVGxudRCq5trqGYNLyiZPJbKJXbebyCaYh3BdsMdlKcf2gQTUndAdULGiZ/YkBFxdUnDE2A+3+zIQ5TmVC6JHI8rUh9pcoR2LMpnFiCiptNJt2JoVC9Gd+5W+TGL9j87YxFd3EdBYmpbPT1MwA5adHUUwM5cyjHM2fYhOAt99P18G864eZf9TupJL+1dMnQ3E0GmKqnBtKqIXiSWdr64tFA5i7QwwlnRz1Qo1icSjGZZC85tcAbn+YwqQExRRqW8Den3B8KExGAh78ODV8cITZkAf1jLpgNt5HdFM5YlIDk9h9TxI7VVM7bAThBhNfpxuxUjUaTV8qeurhRxHUZLHD0x8Os9dH1Joj0rBeJ0E8lULdSqlUQSMKvNqcfblGYxSguxkyBNmwz1RPC45tfUwVbJtMyc63eTm1MlHlMTvwyuvoc8TH5ueUYz+mRRxKa+bOxOdIpEk2Bv1OY4GUzAfhZvwnITMOk8REMz9uVnwoldPZ7sfMpzPISmTTmDzViSLzmIR0AMVqdGAgYZUFD6I3rbXXVXVMDC3e0PTdTIjs1Fi5qi/Jm18jPMOdvSQAIIxENL4hbKVzrR8Y+3KnzifTMX+kUymW3n39jy48+UmPpxjPR/q9em+8u1PshKMRT3Tl3OMXOWEvXzmP+n7U52QvPBTeLvS9wfzaqR98+5URolWhcaHW2NsoIKnF4aVchnLTi9XDsJ3G4aEejzDJnoxHFuYS6MwPB+Z90UDu5NzC3EnPODW39vC5M1cvXHkhOn/5ypVPrF18LDOfv7zVXDz7pOdEu9by5y88zZ3r5WfeWj3zWOji5ZXRwSiIcqD0yUef6tZLZ/fuZJZOLV29Ggh5TrXKl596HPsra6fOzC0sn7nyMLd6sXD8xPmH+p5OOpWLRtOh96/PrS7Rz+3I3bWzVwfNg5313XA440fZG9SkgKdQrKSz81eeeCowfyUxH3jqUz9x640PIuz+IxCXIRpL9ADBYPhp2XVi9JG+lSo81H+ORq0+hwwkhqQoNxaJjQLhnUqNyG3dIbR+8PLbq5cf4dZlfXMzFgg2kKxrYIWynUlmOGkwfpKFZhwtlVYjLZcZSw2zfewou9/OVHMDXW++HSCzMDoDZopqPwUdJqFS8edGct+zfibU5G5C+aFuJpXzYzIyP5MI069poOuyFSWqYrsfJqX7ydugNTqBSDaKk9r4TKpLGJJ3sg/jx3zbaA6d4YE4BkyBQyqJQAZdihgKGi+HvQoRgtFoMpUq7GyjRVxyJK22TtuiYhu1BWRnCjNnDDvX5EMl6Pn7FgCiOrV23k5FNS6H6uw2/NCbpLaNE1+bmU3pZjwJ/DDHpCJOqZPv2UTKUcOmttjeZVsIhvCM4OVMxCNI5o/6qhF9h6IlNKVEEIEIz43CnkTcVzvYrURCoKG9YpH1FuVZhUaR3IgO4EKtIB8Va/I2xRLoVEefch7TKTaGE0+Vs4/jmknvhkzfNlAxZzM5lFIBJtA6CMNhYhjf6acbb3YcNRn5dhLdP6QmC8ItgDiVcLKf1lKlkdb1MIVTrs1Vb7GAOMUbKg3fHK/GmDNE1ikEk0gyHn/qUy/cfu/91p11q2VJty4Uy+ixBnBa5dxKHqYMUJtxTood6QaTfcxoyO1jv9+FcpFKxKr1UmVvM3Xq0Va7+d7L38yvXlw4tebx7L/+9a8Vm61wLNxvNzqdBlSdjbdfqdZK9dL+zfd/cBC8/cinVyKJ6NWrT5w4c6bZ2INlst0PX3zquXF8MxA5+eNf/BsX725ee/6FZq0Gk/3iahlC4ZmrD2fmd+NLpz/7F8/u7e4HsvPBUP5zX/4/RONzQ18mnL78mb/8DyLZZY8nfuXzX0bPMzonnvhiJpLCrMfg6icCodwcdlvPPfVsPEcNfZFcKn/lkVQ45V9eiYWDSxceQU8FKuBgV82lspBpq1u70CgxzNuC0FUuByPh9rCNTMLe5kYm34Wwg7F77nvL5Xq3NUok/HsSCa5kc4vr27C3IlccE0SH0CZPxwXhcFVXBsahqI9jE7tF9E9IUzzSX7IZwjqMdPO4h3pJX3DQQUrak42GMS6wuV9E61Kh2vcEu3P56NrDp0NLc3dvXR+EvY8+93CvXN/Z2T/YKaQ4f8ByjsRAlPOPdHCwEDBP9F+wJ5fhEhJU8LBOqD6E2IdBx2l/HZcT4sZxINSkMdBsUzDRbR5mxppFx0SZRUnT1YbqOBjfKVgvVdAAnVvitCZu4W6I3jaqxUBujSftcMJNBZ3WuCmI6xSqXGaaYHJESJE+Qo0zM2nEzjQeTeUXuMS1xG1DvUHKhbsttE1FM0sxOEXf37lXhHksEs4v5FEfjaEIbM8hlI7aEtF7VBXVlWxtM7QkU21ZODJ1mqkNHsc9tqlHx2M2prrKiTXjfb/PTOCDnE4tD6U9popqwXRwzTDQJG05BQF9OCJQzQ45iI6kE3CgGWUwaLZKVW+3W9naLe/vbG/ucdpKZJLldgdygvSJtuqQgIMoxuIiy+5YDlXT1kNVE9g6NdXXoVgP/pimPxrnuBxMEdNCbJJJuYdTTGHbgtQ01GQw+TT1FtBpBtjqGKcynxnBSXy3njaqk+DI5CGOpg5VMJPZbPSIQrdTAhIuoHV261Cch1g4bffnY9GlpZMIll7/4BZqa6SJhs0NjPfQPU0elKJ1wGSndvEttwVgyiGQ6SARVhLC64bs6vZecWdvew3B4FgInbrzaD24+ujB3vbYF730+OPZxXlfIPT4s8/BHcC956c/9zPJ/MVnnk+U7m2S+9lLL5w55w2kFq8+/rnVU8/GYtlrz3/p6hO9YDw8d+rMQx9nJoI6mUfaWsCnB/kKVh4Oltwvn11a9PikTXP+FEyo6IxqeTxtNAKho9bnaWCf1xcawZwnTS3jPTZl5YPrUEe6nerd976fWyqthuof/Okfc6ca8YezO+9X93bvvX8rm50rbm/cevPdSqFQ3Nne29gp7JbQaX3jB+/vbqzDH7Kzs4uasHqhmcrPbWxuIC48Gvjv3N5YWMxn8+wWx2LviW0W9/Y3N1vppUwuF3vn+987/72vRmP5//Tv/s1BqXwyvwAXP3quOc3IAjxXu1ws0pWoUzFKflp1ZI/HLNis3Df3ap5YdLcOb5Bn7VTsMz/x6S/+wi+evHQlHMlwhEicPnV189F6sVbbrW1vbtUq+zff/gGsralAAkGCWDgx6MBExNixszV4R3BmZ4+gSYDjwph62EKY/XX9tUYIHJxAvA2c6W1S2Hz4tMlmI8pP89i00S3LZGcynynHSaU4MxkooRudyITYR9HMPz6N59GcJpnYJPp1QVkzz6SabbnJVs3UvAE1++lCuCaGMXSThULc16J3lQA1RWYaoqlcfjQo1Xc2t2+8C7n/zPnTaLXdvb1ejYcPdmFMAOn1FFUlqVe1j9W3PGCf4xzBEqIFwDwE2lq5Hse/3eYr06Mx3MwPhdiIx0U/mvzYbzehpr7J/1As0yx8FGpaJ/ASqLE2htGqFO1j/YL11Ego0neBENyHHPgT/WqPKwNULZIKNsSAzAFCUeMyjB0QwpBhukoYSToZbYcb8FHfHW62WycVeahqD/w4nH42mm2rk439sKUdyVmf1utQgP1QL9mRtAU5gOaUM0lgpoOdFPIzMOUArNIJDM0IKzcn7eTlZOL4m5eNZuaiHSgTRwBFpiy9OgLEMXYqe+T9ZCoBlfnt196olAvlRjUTS9DpGCeRmsJAAOMq4FUxRCsnZ1QpxAKvHKZ9hCH0zaBBsmZecG9Tqdbu3r759KiIpraHnvpCMrUa8GTmF7Pz85c0mF50e3Qe++Tnqc3C5WvMIHgtAoml/IWFoSfij59gk64CY+F0OG04ALzBBNQhg/R1BuG/ddMWhH7r8BRwLQzDZGVzL52LNAp7d6+/Hgkv5s+sbd+78b0//dbZhx5PJQPf/d2vnzx/NhZL3njr9ZNn1uDVuXfjg5WTa3Ww5d17i0snllYX3nvpjWZzGEvyL3qwtd2qdeaW58LBxOb6Jkei+eVdafEdjdbfvS2G/LGEeGv7JbRdgZ17zQ56C2OxSID7q2j01FIejY/hWBxNiF6IVEFk4kuQqG7c3YQ48Njd1+s7/rvvvV/c6KW8qLLwI/xeanVjMSwHwt2PkRox8SMm6guOUrEoBpFZxzrs/SuDQqG2shj85S9//qd/8ZcvP/MxTyBpQGMcGvXjo3FyYT4ajPXQWVGt79y6c+bi1bvvv7Nz+xZ3PDJxJZt9DBYkO84XAk/+GUhVvzKmmsDm7QKZgSp9qOcNNCoe4CQ/AxRymBoItpQjewKbi4ZJ6M4kNr+CRkVxXgaUJiXI1z5mFhDTiep6u/nwfSSRlqVjH+t9TOA0wGY1yXDyqcZRewmpi38HDUFQ0dr1hsPFI9FGuDxDUnOCbkE2TONhbj7XDg2LmxsH29tbt+91mt0gJCRIkxhBMlkpJwf9k73TPPAb1Z8sAEfaQSzbYUf83c9JxeUxaeisYzaGGZlDMd18jnlPMlHYJJdDvtMSnWoSqs2sWFwNd4hij+Px6Ilz5zkmQQVC1wAPSl161UK3sd8pl5u1RjSWzM3P7W91d3cKcNoynVLpnBeSEb1vlGYbgCNLUwsDhgZsbaWNp3XOdAEeVETdd/TB63AjjkaYfhNvksHxaYSr3OwcuHWytyOn5E6FD2XrpBEQmIhO7qZuymeSr8nAwPdMa0w05WeTKdiphqLjFHiZYkG59DdFSC4DpE8KZIUIZD3oNHrxaCIQjTU3YH0Zp7IZ78DTbNXRXiuqJenJV2hCmQL9KlUJyd4UqB9TDPcLnPfYwPZGiWiyVG4cbO2WNvfya4/EEotwLnIADniiMM5xMckGHsa5gAcFQeKGNtgcCukIT6FyqunpA0D6lV5BOC0pddhtNQ527pHM6x0U9je7vXYi5m/Uy8Nm3RsJlXb26wP/3Xdvn1zK3r3xbrXWifuiuWy6M2iUN3dq26VMItTc3a55B7f366NetZtMYud9Pp2az8+FBqP4yko2O5/LzEeffLhd51yBMvPxahyjLWiZ4wwaPXt+CWWfMC+hGGDn3kZx+2D59Fok4K9ChY8I+XAUQOczBobR9p7Iz2NHjJuOnbvrorrk2MnAUOjJj/z7hVKh4jlxKvIsJsX92T/6nX917+0aJzLkhnr9EbYdjdm7UanVktSvL4CsPKSbaDwC18l+tVMo9qqD8SMnPP+7/8v//jO//L/yeDKorUCilI4fd1F/NKJsXzA/9vRDiVggmcywwvsD+Wz6Tja1v7mxe3c7Fomypmg8xX5oJogzhhMAskBFoIbZZR4ynwIoC5oEuo9ggrR8Ah66ZyIR4CEDBtKpib+8NNaKolT6dtMYxC24mjx2rthP0kyzx8t8TmM6U0QZu/nNvvE11ZokOOpQqKmcycGE6lN/8ieQntLDDkWvAXo+8ssrwAfXv8TjJCAthVgDKu/U9+51OlXUhcMrVjyogrUWl5ZL+0Xseg463Hhyd0Pf2LlD7uQGiEsvAvqdlNV9yuBsw00tTM0O/9gaqwGHH+tjWmCbMA1WEtsq2y/TkMOuacETf5OffkzYTBlmUJ0xMnmb/iQeF73iZdao08Ix+hGRSw9J5kjTn52C9JijNToWCw4wmzqs7W82ykqsc26oB8WHXJGaQbc51wVQ2US9dOotMDzchCOftt6mtydN+JEdNk9+VdihxylNfaGuMLUxEaZJ+LRBesvFj4BZaUws64eTkddRkhD5a3opSPGUavZxMprxmmZiPJUbDpPQ5KfqacMtzA/AyVpvaBSoN1rpNIoTehA272zsRIPJz/6VX4Bf5J/9w1+988GdRDgB02E4HIEjVDlBOTKG8tTpqqOaoOqZTZ6wNDjcL2EZDHWJXdrnjQVDMETXS1VEVonaH3FJGUVdhNF3C1kJAIA5pYaZXc+4V9m5GUTfU6C/fuf1YHQZQbQ2LPX3Dur9LqaVMKToj/qLu00ufLmICwcwiYUUvbRoIXMQS4G6Eb7BPHvEHw2lovFzvjOebv/hp54+c/Uy9CHSIHKcm1+KJLNsr8SI4Q3Vd/cT84lALAuRkV2bL5RhCUFVdWJpNZxcGI+r7YNKfXNj5erZ8o33C/v3snPzYZS+BNg3h/yZpKfWoSu401o5fyqVTZR2i816xRf2x5PRViG8tVGED4q2wq/fG7V2tnew3cXKCmdzu4/qk34FXZ10UlKmKSEKe30RuuHUShZLmGD/GNw/Xm+jB0/pCPZOrEWmOKJhHSaGfYL4ve3Kft/z+OMn/u7f/69f/Lm/yUGObSmon2EaYgWm2/FF2V2xXWX31fIPOoE4BpNlpWAleJIThRQJ+UPVUoltKXgH9tOA1w+jkdCSWdsZXAvLBoIYZA31FLr5mDyKwZ/WfvsAwYAHwKEsBOICHAvxhrxh4HoSW4kNIDnZKC/3mXEaL/vNr2pjSzW/9uNQ9WxUpxEmtvszk6lyMlnZ/JxGOrmbUkyAWmPaZ5JS3SGEIG8ik2EHFdHmllWAJZTOk4bDYDQ0f+Kkt5+rH+yMuzU4weolTKI0kMgrj6owB3HnpXKFGNnUkJ1qJslJU44Jck4ATrXcmh/5dL1/tPdM8z9aQko1aWzpprq2zvJ1qmRe6kr87K8JM06JBWkPwFjoAnw0xkAGt1jwNeNPAuYeLY/mMmMsmiI82S42GvVKsdhttaOxMIobO3UMZYpFCjUyko83hTqFTZpgCzafBg5ckJqA06RakySOQ83Q36Qxxn3/j41z1J9sDz9uOTZgEmxq7XQexbnVmwk3lTDTAjjgw/zONMstxiRxQIVgRXSr5r6dqCax+ou9i6xuQTmzfacAdBqnEvH0XKpRac2tzHuDPf/OvXqnfvP7byCwunFv5/r1ew9dvMA2p9Nu+QIR8aHLaqOpOziCGjCRWVGceqrXNdklvcrNgg+aBYJgwQAccVCBEAfYzHkeev+VP1nILyAmi4a4kH/UrHMX8G6t3WUaNNvdZr2dX16t7hzsHRxkFxaDAEO33kJV+uJ8KLqQiweXFuYbc82FpVVI86iMbVar8UQWoRIU70jZlmcIGzGiUhQrKdx2q9dowD8WiOV05+ZpwhLqCWS4bTb/uYUbJlerKKDzhCGb9DzNBkI9nn6rs++NnDzh8c9xUAm2IfvkPJkE55VuG6t/OfR1dbme8rQivkHx5ka3W0e71c233qbC7W5r8/ZdNojccVVL5VKlFY7EBpgbQ8FJe1jcLnVE2xlwMghEgq3OcL+JHQJECMKoe4bTh4HkvV+uj4LBkW5RsJLTQWNdLKr7eNQoRrgnjoYOak2C6wHPZ58++df+m7/3sZ/8i0MPtDvucIgMjboHP8UYDdD1feQCxqHuoN9s7q+H4wuhoX9ubc7TiSWjXoxqbCVSG3fWt+9tYtJAOtUFJtpcCb7snxlhA254GYhzP/SpAReKt48cTkqgwHwA4wAHD9Auool4yZzVRVEAJCVWqJvTNHsnV5uRfu0ziWAKkPck6JDTja9aH85jJv4kEnVQbdzMbXVM3m5sqkuwNv7MJCF6yWkk80vRRA7QVRC4bNSDRQjLcXAucMXSKtRgt2Vzw0U7VKHdnd1mpQlbFxJODBI3XhwXQPoqVuQj9ma2N9n3qZceRAKaVvqIi6q6DTgSMvV+UBzbSUeSzX4ejTD9NlkKFmgGXaNPW4q6U71KdyGxDqvPKBCC/MVoaP/iD0WEJ6Q1ngaj08oPF14PVp9Os1apgKhYJ3uStOYkC90ZzN9vYBrJE0qnU2Sh46QSUkeNsPPo0+mFqacT5rxmx9l42TSm7uT1wD5UXJunk8DJb5LGeLsxeLv5alvkAOHhlHypy7RBAoA0+jjkdMpRkHE6TTHxSTPJxbReecjHBV8nsq2WqYZyZReoBZiDKjOc6xPZJVTPI3EHcYDD12vvvI2B84Wl5Wc//ePZ1TO7r789DvrPnl+9+vhljrd797aBb67fjeCehCEh1QhdkbUufU2V+NEMURug1JhREU5BNRZ7+rs3Nt5++bunH3kokQu88YM/vPv+D/LzJzD9EktlItHc6nw+mZoLxNMIoUE8GdTayYWM3wdLjKabBxvsqTTswOZSmRMD/ylK/WbdXBuY9tKHoi1hiEnV8vS54A1HM6NO3ZCVVMnSzTdzl5/hqmDr+3+yeukxTyy0/trvd/rei08/V9k92H7328uXX6yX92+98Y21C8+evHh2b+/O6195aWVp+VT19Le/8tVmq7ZcrCKyjjqTQb2XXpq//fYNbpSh40Dr99+6h8KSvfV9CGocVZtGuSHrHHQwHYnY1Q/GsVR8a7dCqzLpSL2F2MUITUGcJcQhgcZmpGFGnvXNajgdHfkwOtaHaZ9rGtTAweLvi0Ni8PVGXqSI3rlbeOhc9uf/5q987Cd/aexB8ohOp5w2kyUUYr6RdXXYOfD0O+MRtid7YWZTbW9v8wDqcw9RtU6vhsj9eJDIJDL1HHZWsUsLYPDIxjIZMGkFXwygIMpAlYEm69QgM/wmgkbBmfMW1M230gl+DcJk34CNHQi9yBgKsymGu9lVhiZ74tqUBLqPPMzQmiiCLfvFy0RWqBJPQuyHiacw53Hy5wsXeRz+ncl0JoHT+mne6hFUvVEU7D/MIHKJJGLBWBxVrrD0M49B3LB6YkZiMGx1iru1whZ8vNitgiOObHSHIzAQCZvDlwFiKqIeJqWdQ2SOrBQrA94PXABs7d2q2kbNfD3QqXTK+GjvyIvnmAA3K9tlJo7pFjPz3CQmtZnuGm+TxJZihl9JBQTqPFoHeoOcGsjMz2EPSbRVAYmW1BGXW/AwexLoR0QJPPqwIHR2GsFGlT1glyjBoK94sDeXWXH2m9RB/02LbNVt2U6dHfA45GeDpl6TZilAUPCAx413NIot9phEswHUki4wPyamekWlKVMNvJ0ngI46Td3D20QwOwNNMtsU06d2npHU8VZsmxnBKuPoo2DlZ3qLS3V2xezPxc8DDTo5l+7UGxvlvXKlmF9e+PQXv/zYc59Zu/asJ554KJ2udA5e/eqfXH//nUYJPfvj5ZVVeF3A/SJ3QlcQ742awvBpELUeqE32fICoEXQOghl3qYEORvZ3y++9+t4Ln91aO/f40urVz3w+hPQHaxImTKghGszIx+kU1qoVMqbqVJxc+a8NF9/oQMatAC0MCjVthkBY9QylHrpV28idOMOmu99u1Yr1Wmnv5MMnXv3qN1LJ+KnL57HK8vbXvvVQuQqx6Ku/+ccnV95ePrv63a9+u1Zr7a4fsAt+6Q+/c/HyPmK03/7jd04u7Z29OF9p1b/z9fdX5ufPf7D9/VduDHrcN4xbtQpXF4W9+txy5s4Hm2ziWFRXF/Ioe+DOttNqw6vQ7w+bLRlsierc0Aflws0JVQcaPtooymWMBffzifCZVOLdjT3oQWgHEm9EPAZ7tNgMA6MG0pGs0dD80TbDZUA0HIiHC/s18M96pcWZ5K/+3b/x6V/6WyNPBJX0nhE8o6gd43Q86DQKo26psXvHH+xyD490dKtcn19eGHc527QL1Z1mvVHbr1SKEOV8mKbhYfigYEjmWJKZwnFa1lmUBFYuXBkCOGMkmDN+QJyZ9+63460E8tJL0vsshsg9oQce3MYZhSC4PMzqYkbYRNM8Vp7K2zykxi0v5zGwNvPtpnXKsbFMisMTwWRo0zlOvdxicBHmxnGdbpE2J6cmgkj0j6lLmDvYZKPx0VhE+yCAn8ryD8baAApuMlCpg3M5TAL3asX4mbXd9XXGIZlOIgzA0VTl90dIjrGr9XJpbAHf1krZ0HKthYcXgGmNZ3sF30N94tT8vtfheDavYxLORpu4Z+I5TjPqLhDM1MDma4deVdA3DRLHIQ7uzAVYQkWwenIM7nqh2ersg0BAzxfoB8IsfkFOp60S1t6B3VELJvFoqCr6kQdyEWpeoFSCMzCsTZaALLlrtTymE6iqWx3TG+bb+ZkJw3n0cWMe8rdJjsR2Pg+VY1IZTHYovT6ciSEkqW6RF/iUGWLc7ADFYmamgPCpPDXBTCH2xyRy8pYP3WnLdn5NkLOi2GCbTtlxvtTsGLPKsnkdcc73ew9qlc3N9VAu+Qt/6288/MzHTz/zvC+SFZodDxMrK1eeemo+l929ee+dl1+rYr+qjyFTVEX4qTTq6cmRacDogYMpRfCvYjQc9pMbSyh/KH9l3GH5RZ3la9+//sJr7z5/+lnGmMsdtV8PsEFOJOI/LJsWp1PfNv006jeZRCCmcb+FHC/oC16lTruG2ehUOgXaPNjYTc3N72/eTkf8L730zXc+uPOxT3zu/OOnbrx+67svXR/2/M88/9A3v/IdZhMq22q1Zqdcf+NbL8FI/IO31rHYm8ol9g/KtXr7nXc2uZEqFiq3buwAh1tb1c27+6+/ghGX8fpG52CuunFzi+KRXR92N0s62g9r5f72BtKLuvZbyMe3b1exYz1uDeeW0sunlmqV5kGpIQXYuhQRw04EzBCVCtvgKJyfjyRQaz7uYxgYxh5ISKARTwQTwgmsvKQlIAxbHGaA0TGDsolAp9XrBPpSNxAKrtcqybznF//qFz/3S3/T75mjk5v7m8NRO7u65PWiXKvv6Tfq1S1PeOAJhmHs7Xb6zVKFGnBvMb+QxWAYVpVaydg6pzlmISBHYzjElBtoOkPsgMsNA4cAl1lktSmxwCuoNI8A0XXjkBNItFAoWFB8+5IAIQ/Me+x/2+0maioS6MAxkMJQ8wh0DBjb/b/J1uY0Kc4WZWeCU5zdFpl4KmwWI81UZVJHp95OPU19NX3s3zRQLTF5OkXbTydzgtDbzFovLZ5m78YSyY6g3fUE+8i/EBmABdSBYem4TMS9FQjafk4AaJRqQfhhbRc1AwYANkYeeKwtSmD3oFljekK9KPSvetgFYFKlaWXV4plKT5ym6pOvD3cczcAWQx1s3rNZzRZ3JNmRTzf1pGgapZVe6z2LADgb8TkAASOQMdTD01TOTmxZdB8FrazXrgGqbYQjK1U6EyV6GE2tl9uY5uh1e/FElFvgEesCOYgIIaKb3UiY2toWmKJttQwiOlrDyRBP6ug6bJPdXGYb7cS4Lys35f1vWmZi29zUUOI4PnppgLXDEj2dDTXTQIdutnbaRys2k2pSA5PA5uHApTKgAJOhKdyJO/EwDiLzJh59bE4W9Nx4CI8KSSCR7O7vtP0VNBt/6Vf+9sMvfHIYmvMEoaGj5LCPGUOm1LkXHj939Xz1+t2Tp9Zu3nzv+ve/3xl2gjK9K3lXdrhodFBlVY6o1aYMijSVEUlLDKDaDsMS4/Fi6bdcaL3x5rvXXizEF9P97na1uEuDR+3uoFvELlCvU2u1+3t3bqYX5vqtSr2yHwgP9zduV3ZqoUQCuRAuewsH+4OOv1FBJmG4dhL2UO+d9f0zF86fuXT2rb3SN35/453rjfr+7c03ywfru3u3t/2x1JuNd31VpiTztB6RLsdgt1DrDMYr4ahsSycjy5FQf9xNZiPxSLjbzop3KRJ55OFxBExaqezst2FLyORiUdl/8WXn50CovQjQGG/GO+lkjJNtp9E6sZhHoXsP4we1KhbrYFRiwLDJJbsHQ088FoIBIsaSGPL7ZN8FRSeRZCaOjeRKu13reRCNFM4Fvws/MEug1rHQDrnHoJtRIRNJBEr1DlpKK42GNxv84s985tM/91divtWeJzhoV/xhTs9MKBTDdft91Jf20idWg748gsRerh6bB51k6s7b7xS29iMcJYbeTruJElN0B+1sHXB8gUcV+wvoQoURA1DhP4VSFQcQHaBjjA1MCnYFV4JggZIG3H7rSwF2J6CNAfNUzWIB93P13QcZSDTEqLcDWoB5prEyslmaPM2XA7rKTo/B9vipQnblMXMbp62AUxtTDePp1kiJ3VpZp6m2fu57nDoYfyplsuZDTWN+4iEShijYaPZnMytCGTS5VEZaDAy/uhYFWLB0ROYqvYOmc0hD4WiysrcHxTsWi3vSLBjjVrjFBRjznHxYcDVJVbTpRlNXp3D3BHBsZSe1t61TJZ26TkIOO2w8dZ8e98tx8LI+s2ksgrFVm6aZjXGM28lGqUyWdi3TOs/xkvsQ1FyzfUSiBSGwUBQbe3SnpMDYjwYCCVI1Ow1YPpdXV0o7G5l05qDRYncII9AwhJKgfreLwi9RjShaEgIWK/LhlGtfto3G08DJtJ6Hok29Jy4bzq9pgNNZNtTN1Inr5DRJ+QCHxtLNTWBMlmZ4TXLrRs2LRDGhItAiuMOQeFNZhggrb1MXm9TJyWYqf6Vz227fBphUCrNVIKyOByK145DqDPH86ZAx2CpvhtOxL//l//XTn/tS6sR5GEi8A2IAjmisBbPL6C3GpEQJiIXPPnYplAjkM6nb77+/c3dLGjG9i75xGBzPeQxSqM53Wgf4RWsZdRCmYKSlJtOHBCuUJ6imwYP98s33bq1ff/tsuPu9P/7DV177FnqUu8X6oFs+99ijb7/23n6J8e+xXtWr9eJeIYaS/VgQteGnzmS4qgjFwoNRGgQZT0mifBRIVgrlWHLh6iNP/9Rf+UvVe5vefV8ueP2nPnvtE888XLi9G0jlG+gykq4EbyKfg9eGZG3k0NDZ2a4DdJjkDUR0MhoOW0E/3NxsMyDtjjveGJfHCbQzF6qlvfZWoRPodjJoIYHSEgy2qp1kMppIZzG5jrprmJFoNjbt/FCFeoNAYdjoNjA61mti36KP4jcMHEEOy2FABM4leJixLIgsC/SgERxH7GhgEPWI1Z/BRKUYOXk9tW4f9UEIgnEIRtkFmh4ijFwmfdDsFcrtRx4998mf/NlLj3566IGNoj7mSncuaoYK1VlcX2+KWBSbQ71SQIJviImhZSmSnl9IJHIdNlilSr89OKiWOvV+vyV6dLPRYRdizuLAKzs13U7SLZxctCyB/XRMd2AWiAOAiGZmuMXfgn4H0g1w8mOPsxA72C/DoFVr1LzoAGlx8AjLFoLgG3gjlUloJ7KBIIq1QDwBbOVuANqBdUmOULoqIPg3jwF2vvQ2VXDr4+SiIFOam0Bvx2f6Mp4q3amBUvEHOAu8cSl/9FdyA+SFFzGTCEaijG8E8rUmgJKJ2UL8B35/JB0fRsYdLGGEouNQO5HMNGGEIxgCqqkKtFEeFaGbMxWq2vNHhWgh+yfVx62lcTufRPhRH9sBh1Pdn42aaB4aQw1UNo91mN+JnwmYhLlfiqr+V6OM23QavSd4EnegulFNHccxl0pfsgBCcpTKSSiV0C6h96NIMVA+qOyu7zRRqluukyoUDPcC0qJXKpTCfuwFR7g2IxuO3uos82eqOamHW3nTDNXn0KNWTJo2G6IA821/jXPiNxvxiNsUM/FTo5WBfG1GeOgxeFEdJABm0LlzM5RzBaN3E+IGGIB0oE6dB5zepKqz+ZuJdqh9BNti7C8J9TC9SElvm0I0aRgCMBgaK7f3bq+eX/vF//p/e+bZH/P4s7CB83BFD3+yTPCi2SQc9gRi3fp+dXsbNt1YMnj24tryHNY5868HvnP75r1SrRALpaORuDKXvUEhCmoBQIO0kOuQhJ/uNdm9epu1TjQVTyWit3bK77x18+VvvFQ6KH3997712usvR4OR55++tr8/rDWvP/nUM+HUwqDVv31na2tz48Speiaff+6zz16+dDYRXqu3avC/V5t9P4qS+/XxuLfb8lTqcGhW508uj9kMZMNPfPHpc8+cXMxF6sNKyVcOeoYHpQq209lexPrJVq0mjDoebm/vjwaNxSXiRbzeYLXWpO9hWG1Uq2zd4KKESh2O+uey+RDaMxHAjY2TSaS5vLlEqoEeucbdaCp09rHT2AhAZIz7YKw0IraLAqJQr50Ppz1lTrSIiiLxi7VTiEaxYWcI804iFuYU0ao3IeqgRQBr14h2pcLBbCIIx4jpwFA0EZeCoGo3nApzvmph+NI3TkbDpR7KQ8elat+XDn/h537mmRe/MBbLPzYy255A2zCAEh1uIk/kxBkvAhaYSPagWrQyrN8Zs6mqd1EYNwr0YsERuzCYX/c390H1qKVsIwzWgZY2YL1BOyOLpJC3pqkDrgImoNXAoANn4lfRLFYQj1CfC4EGEOyJU0l8vl6vjygDJF8k7Fohf6tagUNL13stXTyYxBZlkJ/BgspRAWRvCzDRDFo0YC9AY97jq2CDbUx8VcPMGb1MFk6lbW4myITYHyW2MY3DcdoSza/N2daBUFOcVH9DPQ3q5E6jAxzpkI5kGysBb/CYON7Exj3otRpIfNe2t4sbW43yAXxpu5s7DTSi7BYwK9apQhXqI0FsO1HYX6Wpoby1dvL+aCeAmRZ9BKcKUDTbTJtALac8vZzHCZ34TBwmXBV1HErkJpWf7XA7VHwKQPBWCJ0CfMrCEVihLwWHvjD6lNiequ26CoROAadHou/rDhq9IKKTiYS31wkuwj9bZn6pl8deaKjcDZOMsrQ1EdkN52xz5OE8bjsspJj6WC+TykSyrqO+bgYzUQ558UES0tpf8z4cwQ1wYlEDU4ZTE5osbAwJVGQsSATdYS/ghXU4DJMfOojhGSG7mfwdt/EUcAB7+jM9bCOahtjWmPmiFISIiMs3FyahQBC+ZYi9G5W7S6dO/ML/5r85/dSnvN4USENHVtjG2WryEUyBHzx+tEKwTWxF4NUajwvr2+1KrVkC7XrnFleL1Wa7swMDiU6xLCkUozMwWuYN6w9nDfZH/T4qJLQK0Uh4GQ25F9r69l752996NT43f/rCud6gVbi3mVlYwRzcQaVa7AbDzX612SzBxJNdyC4sxJPJG8XCm3+0UYc21GqmUvFEONptDRFabcNT2uotnlmMRWOvvvrtl77xB8yxObTzpBPb1epOYb9ax6pcpd/opqJM0gjHz8Ggj6opNp+j4Fx25fTymZVYLD0ex1YCkSEXI+FEtbCbyESLOwVkoS88/lAU+lWzun/93Rb2Hcd9VHAmUDHR7vV9qVRn8MhnPwtllztAJBIKW1t33n4X5bbo4R+EvPnlXHouAy9ss4bVlx4a3KACAwEcXYeVQTwKf4O/1sISeC+GaRjPOCYb7pwvQODwjMAnw0SBOABbCDJDw3Rctlxg/98tVOGW/tSnnn/xs1/ChhjnDsxPtRsH8SThHswOM7nQIef3ZFmyvTKg2vB6+gEkjuG2TXQObt6qFw7IFblusVWBq0Iws/bLmLWFboGNpkiIG2PtQ5C392OFHhACNXEPLOKkwEmPgWOmHh6a7pq/FtTkoW0e5GzqIwytnuaaAw6k3gApzouPPVTZ237nle9z+OEMALTY9OwhLMJzcZDJ2oFtMtJs0YSnZFuY82tqA0Qq9fQxlXHqaTNywyy2cr+OvJW7SaofGjf5VjwhaBsM9R5LtDrTQAuCkhEIxdN+LlpYEDjrcLtmOkGnPA6GrR6KsLLZdD6X2Lp5M5/LZRPRHidZnx9LQ+yKOtjCo6s0+CB9rbw4TO+J0ESvH3sCUIWOe0zthRrsEB2J8gBvxVLLbWL7eySl/ZztYuNjPMzYuPHdPprk4hwSFU57OHFjBlDqbNl0RVk6A0xIJPwFYaya6mS2M/QIoAH26HIVhm5QegdLYaVSgWmEtSTxlEeCeNHBok4Idmx7ZyuoGsjb1oQQHIc6xnq5FXffNgvFNSltahs4m7sb/fi3sj4Um6bPPsqVOmuPzzYJAq3ICjD29YOghUiQdY5wZj5cNNRCWamFysO21GB/ZUjvqk2qrGaIHHiafT8O48YHyNLpi5WS/Abe8fbB9tLp5S/9V//L00/+mD+Y7aGjv98IxTJ+fyIYhk7OpozllR19q4cWs1IZE0ecwxq1+gevvzNsd0KRRLvWDAej8IOCJ2BRl46CUAg9ERRDSmY7W2wEx9A0wyCyqo/BbmykxRoRzc/n7+7sI+fNevEzf+2X333pOx+8/fr86ZVyu/niZz6WzmVajW4iFQrN5UHIufkUzDNYPMkujMBGGNGKRLyxBJz3uqeEY0eE7KWMtw81PQmXC/cMAYSKA+N0No8wQCAWE/uROM4kFUD6oD9Cj2AZmC5DFTnnR8NpjbAxxy4jciw80zk96gSkx4L/noSnlkwHUPKWyuWQYYF5io36ylMPFdYrkVxu/sIZTzbjaXZj+UypWKruF8PJ9Hw06R320A4DhZeSOTgMh5gHYMpLBTz4HsQNCkhHfV3oTgEU4KIFlyMYPLdUQ9rZiBOH2wnFon5vVzo4qJWIUxGkwmKjpz/5/OrpK8AM2kBRlhhE4+mgyFknGAGKOO2FwBsjD9cRe/3mxqDXRQwNbFvZ2Ni8c6uLyU3qxH10U9xBHHdgtUBUgbtYZmYolGASom03jE46cHcoylZWF1Vmv2XAzQKzNqeAloU/wBKnfjTHWA8AXhqhkyEMHoA3y2+z0jlxfvXTv/gXvv8nf/T6d77na7VjyFABruwJ2QKy+pj0WgBxmEfQrCL0K9Ama0ALeQqNqTMjiG+wv2DWxpomtZPH5IKnyV6BTkVtjvIwWSvAOB0fx9tJTdH8sxENByP7M84B7ChC7I+CETpFPaRe4NxObRnNYTQFsKZacLEMa9X9AmJ/rHpbd+4xH5FqQvCeKwRA0YsycAdP0m10nhTmUxw9g2CxFgC3Q1S1P//jdshsTraEaQfMhk3cx1fDZncY4dkkbkEaK42PELxQnjk5jlB1C0UScU2OhzSR9ZS9UatSQoGqb9TZ39hCsSJUCAQtUJiFJZ14MgGF3LsQlGGdGvxCwOuYPQqdzZ7Dokl3LO1ATSquHlRlXGzpBiiajapQ93Gb6YS63hqF2WjW3/oo6iSecTg+Bm7xmCR0o2mk6RFortyAohW425Y2rlAi2mUWIpyKPA+0IcI03QQVRLaZAGmmJvp1Hzmh79gYKtrCqnEpnTnQc2qln9hxe6Kez/3lv/LwZ34mEECF4bjXLZG92X1AlKa8AeprQmFE2bcx0d6q7MN3xbVpdj599tLJ0t5eFbMqQU8EdQiROCYuIMS1WMXZykHx8HtRigmdYegdlatlX5y1rZOIpNtcSiIrCZZjgWFbE/Ag7RVOhVLL5z7286ee+eLPVovlT/9sMpLKGF5+Ndn8F9YzbRSrkXEAKniaqaZW0nAwppCmIpwgCm3UZpU2o9hMt+vcogon2r4Be0Os4uKO3TFpmGo6DAg+tevqQL5i+nJU6nPXmg4OBvXm3nqnehCEZ+bUmg8bZNublZ3dVCafymZXU3nEcruNjq+z16s3uFwN5+ZgxmRP1252e80aAMw2UAVDq9SqYXmFhhDn201MfsEfOsik46UGZma8da6kqRMaHDz+KGqsI55Be1DrjjPJ8KIntFfjFlpNazRaDz19/sXPfQ4RAs4orCK0dYgut053zCUDRknCMQZnwMZpUK4W3ovHskHsDYR9nf1muz1EAV+jXEJRKaJ53NwUdvZKFe4aeiwilQrqSgetYJ1VHMTMnZxOIMJMgkGwmwN4NEcQBWQTRI2BL4DZvPXDmPCtsTJ3TgAd9P+gPxYqbtfCqbnQ2oWR/0/r9Q5X4Pl4og87SK/HOkFW7IdMOpKSrX4MFDtf5AeUkqcYDXSjgiUc7moYWZZ29ZyoAUpofk0Gxm1zwleN0IecimQe9y0PJ42aZj9dD5tEKTUJhWyYLPxj0SIysnpsrWgzd/6AGf3GxhROlkGjEExCDGV5bcM2h0lUuoEx57YnOOzVITLqnsDH+Ug1Eb43GVK0WUCY8oJQNs2moiaO+mNaqYn/R3ZMWnskxWye1j2JORt0JJUbhyiu03VZL0EHD4GmMbRQNG3WTHA64Bn2cglG6+hKqQFi20sHxJMp+JiHjcryqZONUqCweZfTPd0K8yxWnDgxogGxtF/ySsM2p3M4segUukq9xX+VqMIFhfo1H6YWToDiHX5sJQ/76ctkcSj9cXHk55R7NPhBGdtKmYrj9EHJYNPNDO7GMtFHn3l8697mxo2bnoEvAtYwM0I7MFVk8tgCaYqZf6qAmmoeBcntRDGoEvo8LEZQdMKRNrzgzcKLP/eFRz/1hWBgHpPUo14jJNs7qOxnQ0MtOuxTAFqfP9JrVpLzGSSC7r55PRLjZBJAYKtS8NZb9Uq5hjpi5hyDR4no69a6xQI/HEMdGdYbXBfDw/7cJ5+HPIRhwh+89na1VdH16DjUaHfQg4tietSSQYUYopqBrXhsHEplDZpX1TnWCfrZKZlzsbbEzDttrUR2tUND8aaxoEXSqdFMR4KgmoqsAEhw+UB0qa2jI8GNdo+mBYKUUpVjug4ZQ2YtVwvYWElEkrr/lgG6XogUXBXfewu2KIyXFTf3UKNf392BzX44rMGWFtYdCbrmsAnVaJVqnUZ/YXmBbi5t79UrVezcowcJZEWjKR81nsFgpM8SyHAixpuIFLAJ3h/m4jE6sdHkH1Xl9MUK5cdyDJsi5Lwi8D5E/bmoL9Ad7MDEGUp2Rp6zp89l51nrQqNRHU7oXn3X09lFLCycWeayHmiCo1Pd19udW7ng88yLo9dTD+V6qeW6t9tBgvJgazPoDcKSyCyamw9wAZD1ZZBubsKuy5lu0IiEsjFmqww5cF0p6iw7NHKmz+hMdbz6nj89elmYcxA2o2UWZbCiOQPR8XGMKSykgplEf48Z3EO/E4uctJwyMl7YjVgDpCCA0bNrtvLWqGmwzWAKT+CE8ZsCA1GG2KBjUxV3Lgk7m5o4tSK+U0VBhfU0HgaA3CATMPMjsHCiy9cCGznRbD3UAz9mi/bpPnY2MMxitgTCtpRCmGWMyLrFiVNbzBC1olHYFpq5+USnUedE18snSwdo90MPREunZYZKRfKrNqkQla+6ywMBQIUd88y07pjQ471sL9yXUiXel2DWZ9Z9X0RTefX0JBatkNt6mWDagyddplM8jBJjRAFjY+g/kDx1yhGHGAQEOF/YdwxQN4CxQAlTk1Gn3SGo2Wpw3uf6TqemLrZ4SMsJH3tV6jDQqFstHBPE6PqZtwFOjabpYFM7/E10++bXNsWJ7jbIjWK8j/uZlD0JvC+J2q/M6QR+jIuuUJcwO1EY2R40Gp35U8unH7q8s18EmYpoajuNW2JBg0lq6qftjjxM+kNlG+hX+2z36xAONtAiy/kq6Bv4PIVyFdLD5SefCyeXwJrYJm9UdlLz85BG+v0i6uA4u4dScdE3McoSDYlBr9tLxYPlEszxzQ5GyrkFKJS3tw/i2O7i/t6njRgCXEg5NiAGDfvRcKhQqaR90YtXLjz0+MPnnv/UoNCsFX/1Bx9sF9qdKOpuQuGzcCcOx8XtvcrWfmb1HCZBERLgHhWUo+4R5zunGYYKVmEuSmmvl2sGEQm9qD6WjTLpRGb2aG0Ey6JrC9McZhsmDEWnap1Q96LBlNQcpkRdlRg5JEa4jQlio88v6SPhOIeATr8l1ZyU52mVttexGOwbITO7MRwGFlcXfJ3h3vaNVqncrteXzp7TlO4ON+/cCcDLjmWoBMo9Y6jobNe1uYbkEvD4UjGUxnGS4E7D74uEueoK+kNYOMXmMEa9gqF4JAYpGA0N3lA3CIF/haXHM2q3WmnMD0Si4AWkmak4NsMxkc3wwTS6V2vEcsGrLzyXTiwTud9tRKIBr4/DRDkQy4Q8OWhWTKh2bQdakKeX8EXn2TsRk3WZPYAH+TMsfaLWbuzd3lznBjIWSzTGTY49CBxwkxHvDKGw1BrcFTEf4fSFcdfPfpbttnpKYG3WAEEjX+pkKk1uAmm6XfjaALfCtd4yEsFgGO7Pu7fX4wsryZNXPOk5xJr9sSAmNoHzZCymJJBLxESgwQcJSKqEw5tZr0125GZAn7covgF0F1IRcU6bIjUdyMVZ4YmpyCaFaqw5z6fyVjSFKtzMIfM2X3JZP6I4MRyfmdiaWMIgtBO4gmqHO57JIL4HzVMTln9Sa69jGQMOWuOKfn9jvd2ssH+Cx2p7a7dRLHXa8Omi+aqFxVG0ZcENzOZW/WmSUYaao7xUnqavKn3fg5eiftTn+ExM6vuzud/nIxRj6+h0n83BqTYdaodBpzxNcJ3amG6wvomJR/sAtYVRB633qjXfuN0o1Yo7pZA/PIBj0SseEikOg4ksFM3OzXFXRudIEEAYVB1nuog8TImCVnxsHUyg8+PUx/m6P/y4NDYjUh5OPJvtfW4HzgQQekzKyQc9wJlGR0iOtFxuQIPnbha17PHk4slT+aVb7/R72IuIRMJslozMoegYpDI5mXWB1PpSXzO96Uy6QDEUh+M7L6aDpFHYRcDRwu7cEwjD77d7sPfU559ePnfZH0ggf4IsVSafDnBz69kr3f12Yn4pmDwf8iZDcS4Od6g3N5ipbLhdDCc6mOkKHLTQBtSv7jcy0XS13EbtAfxaQL8YPT0tZFZZuWudNuSrzVLzQjS6t1M5F78ciFcWTy3TJZzgsA7GMHbGgzrGvPaK3ABRYbQmsvNFKQh9goVVrtFA1fCGsVfgjA8LJEdspj3LARFYm0Q1w8iMZKT8UIRZBnRVxoJGh1AGXEygT7jidY8xwgJWOIbKKU7boFNsJgJ+TEVudg/i2fmQNw4hRVSkMIxOCO90G3u7d975IHOink7FwLynLp1nFXzn5e8eoE36zp0zZ09dyGT9sRjWAorFg36/dvrUCZ5QOHrg29m7fa9ZhfGzl0une1QfSq8EuPyNXr/e6CEN0G9CK6OaMEHAuwPveLzdHdcayLgl5rIeSDTswWmGOKJFpfewBPuH/WKp6eVyxuttD8dXHzn/yOMfQ6ORmjsaNZvo8MhJM1BsfuxJQ77DN5paYQkUwW8Af526B9GwQDQ3SCEowNyLtEqFeqNKAQEPLIxBdDRBYk0mMI08hCRWRNrPE0AhS8aPlL5B6kxBgMFwEwBaAJkDzEJQPPwKZ1qsJVyGVA8CzL4xFnIg8HJlivDs1eef/8L/4r8KBOdOPfx0du63y4Wq9ONhf6KL/IP0JjDcaBsE+wPJJjeDwyka4LYTmnMbNthFM1EEYJzbQCoFkJi6UTPNB/uYSlIzJoqQj/U0E8Zkq/SOJ0GmAbOfxJlkZWI6Xwb7qxrwSqGVGGMMAA7Jwf7MAag6DJv2GbBQD3o1nw+TL770/CLyIq1yOeCPAu+YC0ZLNGy9sXjCy5kbda8DAJhVUIccQ82kvkA6yEE1w26Pab9e5jn8diumOOZxXvZj5lf+DwqbiWadRHSLuS/sQR4mgUE+FMSI2PXLxDYrmc2TWBpef8CLdb5QOK41naaaia5rdZ8nHEMGuIbUSzY/X9nbSsRlvgip38pBgW0mInU6NpA5GXFiAN2pTI38tNJO3WdLdGo9DcFjmuBBbZr6z4COuubDkioq4XqollOi/cafoRXqBzELaFBaCRLkOohpA32k28aeOFTlfiLKwoAmyKA4KYFgctRuFyAQcBgQtvsvwT4RhP3Ny6ja4H4EHOjt91D/wuaOTXIAkepCrRSMRj7z87+4cPJhGAQBtWQu6hlUu6Nuef373e4oHT/p9WdkLMVTL1x/uVmtoKMHfcsoN4ZxG9OMnWqr5q3ABgqN25tAibRuL9nGwtOMeexqvYNy+3q7l8yk28Peb//OH+wVCs//UvOtP/mj77/5Ohwm8JgmYpGDegu1ZJAdGp1eOJFiKEHqEUwlRWK0IISuD26J2Atol0DtRc8ZccgIhWV/gysz7ErCtw/9UEcjRZBMYK/t46JXS4bwhRho1QNEwCJTXOgB9nykkEGLXG6ImAQSiaBgzSOlnEKWXID3hq1ecWfz7u0M/EEn5jnU99ojTEp27txhSi+eWWjUSydOr3GfCcdHpVK4fe/GqZNLy2dOx1aWe/UqeuFA9AcHRawLQwSGkgLrTiTKCYnzbqHOzr/XhIiUTiRAdrU6O5w6bR/W+1v7O6Fkdr1UjQfDsEWCu+E3ZUPIwHOwCg6GUQSDI2Hyxzz2lauPzeVOUnlMxXCcSM5BfEgEKNGDofE+FjdpzVhGb1hlgnDKcpwAj8CI6g0tBuLPjaPVeGy/sI3MnTfkC5V2Sxyjs9gzod/GGC3rVwo1qFUosNZqjUZVmEXpJKP0hvshs0kVBNKlE8CWkxlu4BAw5AsUzmIL6d+sc4NapxdLZ578xItgfyp2+aHLucUTrV5/ZW0Vsb9ht8tkQMonmoi2Whg4YP2TWCiQLuA292RyjoCNkD/ib4qvBkq7NovgA2BGpwcd8lQjO/E0Ac2kwcfgCsLsVNRk0d+ReWniKbHTKmf+uvmZXBRo0qogJwOEyRPJVDyZUbdrmdR9AMGsfDARcGMt+mhoqVctNTlBVxu5HJIoo632Jl0G1OnywBQNINvWkoXmt/LgrVMBx1WcM3WzdTVVUyw5TMVxWacb4c/1FupSjh/lOS6eIMLUThkQwX5rx27INTD1c4ELQRTuEOYwEVCGAEsbDeWSJIb8NLzI/UatyMXWcHdrT8elbr9ZQRqmr+Mh17/+gLCe6Sl20sK1pijhSmVnvuyPacq0NtMQ063Tzw93mVaan0OZ35doUrLrsKuAvkitDBhWSP+g+16X/RmapNC7QusiyVR69cTqhSuZxZfj8fSg1fSirAGZTYQnaZNoGUw8pbfQJzDR7sioIKfzjGwdi6I2j7AWIHgDoWw4gi6DEF2H3qxXT1+9cubSsx5PDNUxI0ymDNvFjVeTC48FIqfmVheDwXnRXEbl4r030VnPHnPUGXeq1VQKk+mJ6kFVAr2gVXhxOPMyRUE3LEtsZr2DUNiXTLFtrBebfTQklLFTPvB86idznmB2Lp354J2bxXJteSHfaHLB0KHSvn4HjTUscrTHDxfEgA4xGJ1h5WAHqRfeMAaY/2xC2S9wVcBiCLEM5iHdtEGi1z+dCUR7BRGg8azlRdONLyLhJSEOTpYQsLWd1jaDQtlfqBfpTh+2IWELgqqIrv0AmXvEsVqtlNq1yuVrTyXmVrY++KDfaSayya1727nl+ZMPX1iYPxXPMS6d4p11uiYW8iAdnTmxBuwV9vY3tncD8cjAP94qV/bv3sNW40IGKlqMPTeUNzRsN8odEOrq8kKpyBVBc/2gwIoF+PtilN/DzmVw1BlJSEPtQp447PGAjhMR/8Jciv5gLw9b9PzySiK7SGNZ4UKJLJiWLaSEjtQ2bT6htzAi/NMSCjso6aBOq9EQ6lhuwxjXOXn6UrdYGDYbpWHHH4SjmtWxD10+kYwFy61YOFpoNPqNMlLBceSYhWrZ7AJtZjcNHGqCacIx9gZLMfvwUNcyYBZv0eWALArpuoMBllz9yTBamsbFu81K5YNXvgbFDB5TaGXs4JnvzX6TJfna049t3N6+dfsO6xWYk62M2gSVHFSi+Q2lAHlRjoJerCqiGYyHNYDJAERSI5GONMhmiqg+7p8qNjtlhSFUXfNYl/OtZjmPychAijrOeYiGF6PD8gNvrihAVDPE4YwLEkWja8BLlMZlDxdpUACRgCiu32tWinsb6zCIRqOx7Vt3oaSyRh7sFSAksgiwc2GvwrFBRRmcBARQjOa0TmmmTarQpCJuhRQ04546bczjwu4LmXjM5q6hnOb2Ia5pxImLFpi0VJcsLa6iy42DbRlbRaYf25lepx1PJHWOHcCPLNWADDf0/Wphj5NRY/9g44N30EfCdRzTBSZmhIiIiQxADQ2L9TawRgdxclDvmwI1XOYxI4zX5PtQA1Qp5++Q/0f/MMnV824Bh5Iqb+PhhtrC3A5F1A0qISjQP06Go2xllhfmdosFLgAeffH5yLmnrrTbX/0P/3Z9a2MxmQrBAthDtay48CDkQhIma+efuplyQAw6fDLbAEUhY03mLggP5vAwklfJdBe+v8G4Vu9AOD3zKKjtLFhAIqieca28NRonYvEVSM2+YNIM0aBVwk7hViyZXl1ZhfOyUaowt+rVFnK5GEvxchKIsEKHW2N/tYEW0Q4kOpZxluVwLBCGB14rWWg1Hr+xBeWIHV8wls4mUqnGrerlXK5cgWsIW7b9RCiQjgZ6nSYED+mm6MB3BMoXX6KWR+mLBbmJVA8yFIkMVlOQOdty6Gci43DcZp84gLGGu1A436Fad9v7OxvfXTv/hQjXtsydPssVSaNSpFavBRHOgqINq0bI1ywXwuksc7V6UM4szTNCHDcGnm5h4wMYRRN5ECUE/Vq3Vd+5OyxXmwvLOX84vXwxjyqX7bsvv/P6DxbZ/J84EcnOseJWCvsfvHUD7IPPsDPe3yqFoqnC9tbO3Q0kFlG+ijgL7PjBYHZ5OeMXbgvADr588fzS2bXcyVOg2W4dpuf+733l91rlEiu6OB3DnnTAfzKf6LEYDjBVM9ovVnIn5z/xuc/RcwbEoJ8kGXYvAvICrq7I7iEuDkA9qPgs1A62Av6UL5AKwizbbEUiENMqiCn5hrDtMgQx+J8efuLZVrt+UKxsHZTZZ3TgTmlU4TqlChLYAccZAAPranoaGAO0mXNUwGzPhfINZUgQzwRkWWLHBoiKcDtCARQGJOAC4FJ69Ov/3T9KLf7P3k6gUig29g4yXAgTVG+yT+bQj7D4e+/dGcEpBmNWp5sIRblHIQKEdG2TgWuzZW7325FI7LHnPrZ+8+b+/j43/BF2DCIT6Z+pFLXksWuBXKqswQjWqU89pg1yuNPU+Mx8HnLaBKRh1gjzcEpFkn6ICEYbDXdsiShPuIjDK8uCpuY4nIj3m7D3DjIYAvIiAyipmnqxQns4FHSCsIxl4M1F9w0jLIUQZAIthEmt1KoW/YlLC4BQm1tx1dk8tuKK5ProPWnOrOeM+1Bk+U88cEwSz7oniScxJ7VR2a6vfevX5mLHQcuCaQzeONAzyJoGiMAFiyIguLM5nsKmLV0QPURfdPBFXQz7IsRpAhfPdxrFbq26tDrfqtYb5Uo4GkGHbQ8AaWMNFabyiADNCEwBepoFlKde09tWyBn6SctMaxzPSctcx+FYru8D3tNBUX8cSmo+Js0mvWIoG5qn2UMFIfh2sMHIVL9++w6cmOeuXYsm2NmNG81RvVKX4T9MhXThiI+JCsR/JqROVWTFfxYQJx/DKKOtEkGw1Em9s19cJaGwP5VPoK58JBsKvna7wd3J1Rc+HohmGTR0z3hH+/5RdeXs56AnoJPDaIDg9qrq7eyeOncWL/bQlfXNrmeUSEFD9yHvCoUHNJnIxCD7t3stFBCI6zyEtKq4V9iYQ2taSsVk2BkNw2F7YBnD1x+NZQaDDe1iYQBrwPiHDd4U44fmSxYAuGAbzYqaxg1Apx9OZ3QpPehAfwABYUWGKWOv1Dg2Q8XGoAmcqhBzZIIJ8lmzEsud59jtHeeSyY+FjDYRMjMKt9ivasvarx9E4jldJHD3OxhGYmn0JCCrmU5nuH9stxooX+Aqdun8RYgSqHXAb/HM6fLuFtbQQlfCI3CjN+ZDKUWw0eyVzj/9TDo3V9ovx3InUew4GCXPXX08Hslw+7d84jFyK67vh9KB6v5ecmEB9UP5U8uNg2qvH88vxm9/742RL7hyKh+eS4cTyeSZ07pr9aThj7tz8wNsTD7LvWkgCCKv1YdLfc+Vc0u3Pti+vV5gvJ988srFq48BR71+q4eainQGYBj1S4WDjdLevY333i6Wdre2trhGK7GN2kSLRrLTDKDpAblTbA0QFVHiuXRqOY+KSghNcewT+0IgLO/i/Ml6ucphMb+YbbQHgaoXVVzNejUVCUWYdD3JLhAN9kudzqgK+FabDlEpDAbS7COCRY5QtUFzKLwbddjjR/ypGPrvDzbvPfXCxy9duPbdb/3hFmaKU7F6CR1IHVbcKLDmi3o7w0wsDTWQhV2QjjI1zTEVHPAHADmusigcVannHnkEPtqh93VmEkIqbMTBAkKWOhgpJf/tg4MszCO/ib/jp5dqbd/TuMfFNHiFiELxTElE4yiKQ0kSFiDAlYmNvn8QmdYDTl3QpqV8G52gnrav3y5w6dlrIgzYisfZ14VR3eT1xWC5AgOg+TgEMGtR4YxFPnSsjrnm16qCmGmH6jp5nMa5bTT+xzVykuBYx6Hkx8awnrYWU5zvdJgt0O0f3m6f23zpXh0fwX2wnZilnNVSYwkvULsT7gHqWsMhJwNLQcTSfalhtwZSIE6j1qoixx/gJoxVUsuD8CL21VqduMjH5mHdZSFRzgYITXVMJVX+tDemrlnvj9p2lSRYdx4nM/ttPyZhThT3e/LWIqHtK7UEkGPRBHvXdqd3Z3M3m839xZ/76csfAxcjZLv0mZ/+mbe//Y0YVJFuJwgLOhfFBvgJNQ/YX5mqWAv62nqwPJjLUoi9vlCt2mTinHv4oUhq/qU//qOdQhFmkmc+9fSlR59n+9/rN0UnaDXTCy+grEyCaK0SzOd4dmrb926+cuHxn0OApVPegM89d2LB2Lbba3Tr7TEyetDpOJd6AxG2XJSI6RXOaYFCraZVauxN5hDX8N3drCzlc9kMJwA27X5U4kfj/noDJtMRDE5l1BL0G2jSQubVbCh73lGzVSns3bo99kfPP/FkubRDT6FmBwU1oQgHbiyCFTgosuonE/PAUbOyASU9kpjz+ePB6AI3R8BGPDkHKwunwtGgCScNZy2tCgYsIoklTA/Ra9DFWG4RuBPhCtmwcLhVOxCttocZrkh65VQwfICtPuzWx9NZtBWEIrHM/EmuQYbeKG1l9J785H855BwU9p/ssG2HGWEQQ87z5ENcRyA37UWhXTCUfwzSFsPEfy51we+Bucv88ll+8uqVPmzwtd1gnCVjlSCREUyceDJyUNzT2bCPHIJ0ogZTGeg+96DUjzzPf/bjf/9XfzXooUshdlXRH1reuP2t3/6db7z01VKtwvy59d5O8aDRaI/SsSAafujZkHcf+EHzPEZUEVaG4ECeuVA1G97l2i2CEhvuXhL+dCKSy6ZSMfRyBy49dAl1let3t8WurTta6PiGNAfKBxWzfRPwCclqCbDTy50MQqQAqGSghI11Xc/pzTdKpeOeNmOR//gXvjT/+FPxvO8PK5Wb794+sTCHdQeQqTYTg/a1j//4lYce+vpXfnPrxnVogxB20P/IbGfikx9jDyWqhTKJZHTh0UeS12/IJiDUYyqhJYnJJJKROTI6s15DT3X1Zd5yH3mO+E9jCmmZdhFDCM0gMfLBk4MjBhw4mpBXUiLdHDdZtUJj2VITwZGVlkNcbb8QDA+5hylub3SqsPCOWo1GrVpqYFG6CQmjia64erkJPgO1sQsR3ZIOpospmXmt7R79DPnuQx+n8ydxpk2YeP3ndNjsp4W4HWhXBffLlmigxOIpMVfrmMaZTh7ak7F0xwOhpC8Uhc2M0xvW85ix7CybhTK605sHZdgNORWhob5RKpEWg4UcHqFro1QkEuOSRSdD5WRGR0CptZwqaIPiDLnWUgMNdpk3AaZyjretqP21gW4Tpk08EueYlPKiA1SPWWCbeJsaTMOIy9SCEIQoHPgJvtZzj1+6dOVaOD6Pea5kIrywwA2kt1atpVAzya5iNITtFclDdjrkYsqwiyy/lKuKM+U4BzPDNQ3CUiUGHSO/diKbP+H79nd3924sLyxefe5j8eyStlNsqDmnJy6jjYk6M8FQYoOdFUxgNQ7q6fmf8seuMH0D6XRslOzDG9K+Xq1uiOdqKKzC2hwKdaDKQ7HR4VcLAYg0grJbLN9ymYe60Ew+kUpARoLEN4SrvVYXc9DNjZ1sOtmGe7Hejyc95fY+yBfml36rUNm/sXD6xMKpU4FQAgUGe3eun3j4zDsvfT2Uzj967QUa2BmWC7e28ksrHhgI2dKivL9U9p9JxENx+DXHo6rHyxYX8SI26+g2HvrbdWhVYLCRp00f+2JhSEL0EkiJ2ca5wpAr2KeNYklkIcpcw4rgBHFrfg1g8wzQ047SkQTnGwhI+I6gPSEBgCAjt9FjsDlXraJwoC8PIpZY/zhE+Tz1vVsINIQT+RGFciZjiUOSLhqL6qxD+fVBq+VnoQoky6WmPw3FHNV/vXA4SSURbKq0d2WirSPZ8ETAzxra6QwisehDzzz8f//1f+pLXh4OWvvbt3c+eLu0fucrv/n7v/l7r3a1lHkz8WCP23hGFxpQvc/NQbgzxAYazeSAjemlXq8BvRlxMe0spYCTPT5cP+yjNR0j0R1uQhLR4EJ+LhlLIKCQAm2jjhp6H3sFb4/GcWulMQc1MeSkN7NNE98ApQATNwsEAomcCxkJaEPSYzrksqFTrYWM5Q84m9CC894H70NOPJ1e8VcFVjHu22Oxh5979PQnPv7O+2+89+bb2A5CUQCbRPAgJwmoUax6mNiE1BhKJzyJlC8cpwy4bYxELsy+0PqgJYI8dTARKlBtNDXMnHEqKZQqtHTkmawTJoETKE+iTprHt+6jeEDKCHhRldEojH2DcMKLVSvhHWiWKp0iQePwdYmitlfGI7+U6zZYpAfpbFIAWm+Ew4Fxd8zy0fFCAgIg4Q3m/gZuQLJhcRWpAIxGbmYBII/7HtM4087Z0Fk3Sficae8kcMbvvnzlMYk4G2q7aRI87RmbwOSpyk8Tu8UY7Kg1GgTNdQeoDJJ/LMoCAL+hFEGwNaL9aMdiRc/Mp+oHBzB9LK8sN8o7yCbCMwtXZA0VuCwRXAJiUwz9ceoejupIK3KAUHcJIE2JptFTl/nUqJjGuHWabdmM2wZPmzATJFgwAcpwNsYky4ljpiShaCVwvLS8a8Ou61uohdDEEiHkAzt//K//7aVnbt966/rXvvJbW3duzwViGMfqNjsYQmA3BIpW2cw77Q9snVQbMfiREWp2dFUwCsFKE4ogw98IlE9cOP3oC58QVbg92NrsP/X40sPPfSrgT7A1ZRwGnR6qg7WzGsB24g2nVtm6jLzh7InHB2i0DES5IvCGE9GlPBMvmjrrHy0Mi7v+0Q+gve9t743C2F5p0yj0dHJyRblQ2O+vIwgZGrc69UAUSWHoKq1GHT2UkHk7XrwC/hzWGtOxpRMLV676186tPvGxp7l5hoJRK210G+jmnE8vnBKD47ixvHaq3/HGM6vpfL7Rx/avt1mprj36ZK8xuP7aG2tXH0vmM15fKj4/H/JE9rdueUs78bnVSmFz1C0unLlClxSvv+rPnkwtnO1UCmhui+ZXYDpEkQjUVrRlsu3g2A41yRuOsN/u1PfDGEWHbuGFtTTiC6eblT2ub5uNIiySnERr1XZ2aalaLaARKJWQ8dfNO1uLJ07F48nWwYEUMHME5cKmulcpbq6evdCt39y9eQcxq/j8CU5aybUzpfVX0qsPj7w1j28x4EkX9z8Ixk56vTH02XWabT8C1aEexISDYhFiMjTxcMSzFI89egLFGK3Ccu4zH79S2X3rtd/9g3ffeOm1N9/vVur37u0UDgblkScbCXM1gn7vpWzuwunzZ85n99Z3cstL+bncjXfgaMpce/jher2XSIYqzca//a3fffe9La3+YW8+Fd9vYoIAMqp023Cbslfsbx3schJIRlE8Gsrnkr5Wk3vgEBaXdQ8Ps0IPVAUYav8PUMOZ66AYaBcGa7DoaaennQk0Dt11AW3Q2tBuApupTiA6+EXn0rVq9/bGbp2L3H4/POotrEUDqZgnkorl5lpIk3iEUlFNh/1SyCkUisggCwCAFYjH2Sjv7u6ijGpAx3GkhceXkw4VEAJQvXDYOerMFc0YzUO7JGgyEi4/YjpT08Swnqb2Tgzjg1uzjjTsOKWLAjclsrizaPPoZoDrKyorah54vo9AC2Ir7BiSuVStX+tgPOgAXTYl+KrQbhBEV8lozMkZri0wQafRBSmgWEXNpAGmayV7wX2I7gBMrW2FbXXcSpn3oQB92Moq7FCQifyf84fcDxVgPtTx/FdvGRxJA3AKv1O0VmeuSVAVCRUYgxXEBKrAg1CGZCaO0yiTkzsjrELCyAhRslXnHq4FR1mj00ESiT1asVDFXio7ExYCkBcapU159BklaeTIypTtDvZMk23FXA++PuQ5LvSwnwtFBlxorJu7+zaZW/ASmJk+USxQqEYUIIJPHBrhlYcffv/9D97+7ncfe+GJUS957+6tfqkbOZVNcUsGXpd+TrVJ1yckM+uedgamp9kgSQJFawqLInQZP+warCy5U/OptbXw8vnKbqnUKLNvX7l8Zn7lPHViLJAtCMVNv0GgEJeZ8kV8FM4sahX2hHAz4VVBjSZ1D2QWV5n0jWSptr8fGASW8ovcMubTMTaFcLDC+Z9NxrPJZLle4zQAW831m/uLq1FIChDtOIv89Jf/UoMDTTp2+twFNk2YPFk4uTa3thoIJvrjJlKiJx57OoA10GqRO4b1W9fzK6dS0Ujq5MMeX/TtP/jq2aeejERXI6GVQK6+cP5hKDXUq14ocTHtj/pS0GoS7FNj4MJ+FZ5hKcQPsyvvl+Fh7A2KYT9b7A5b7/rGzfjiChMUrZ8IvnXLTU8oxuqJXb8mluZbdSR/w+l8NLvcalY77fHB7jbHTStpMPbVivvraC9HTgKDBEg93Lte4vA0l8pF03GMyWDfpl4PzZ98buiPM9tH4dPdUXAp+2RkLcW1fCK/4vGmYP5ihUOYIr1wLRBd7I+QZ2mGY8jB4u9dzGbf274B3kNTPOtyOzRYL5ewAXOzuv+P/uW/+9Vf+4/v3wX4NZVSHs9iyrM6n7q6kOPU/Pb7m+GI78nnH//bf/tvn380880//Ha753/yk8/+3v/3Ny4/+3QuO9eo9R77xHOeWvO9rfW339sKJoLZDCdudO5yF+BrDoYlxHNH3ngKvqxerT2uttvIl23XGplkJC5NpeGFFAoluLQLwwMP6IEEtfGAWqXtDVBuwMQAucAJuOFP0MMBFq5NQA4yYYAbHkLRTOfjZt4boEg2NoxXVDRzBNXTKDQNxRKyIxfwwk0gWX9xgCNlwt5RrEqxdGL+3BmMqnmjCVQzRUb9dCouCpeurKmDxQGaYPypXga4BcLun3zwNqHyNlNK9TcxFOpGxmk/HB/lLQ/d+DLjuCDndDIYwsnCTmmEHCtYHVvlBHNSgQOhXPL6B6AsjlypfLrfrMLUWzkoYr8agVY0A7LpZ0FEYILtLKifnZ30qeiMwRGLqcfZHHTJAkBTKEs1NbU2P6rbR3xm26OROfIo+M/4kJtdap30ZKX8hZzUClNfdTW7ViCBtQ4+bm430Y8i6S7xeyIc34dX3XCCRhFtR04RenQ7mU50K1LXxYLZgZUM+xkjdNHk6rXy/FKuWqi2mz16ykgFcY2skize18BrdMzAmUF1O81UTPV0Kum85HPM42RgYk+Dlbu+zI+TkYWxqdc0XCXyqCD92R+qxo5GqFwnPpSw99thDurj0eLS8t/77/+JP5j7vV/773/jn/wLVLWwbwpHg2gS4QqEw6VmgZMh/WtyVN9SCirlUX8GoTxUrjaYUTt7G+deeOzMU58fBBbannZ4MXPhXPi5z33WLy5ASB/NemGjvn1riJICbNd2R9X9EuS0cnE7Ho1UyuXc0ol4LDEc9pKr6df+5BtB7MZ2+pUy3BrhZrkGfwnkg3gijb6bNrshqY1kC4kqU8Sv0Jbpa/QHzWZnYR55vShcjqNWnbPwz/zKX9dlYizGoR7N6aZX1ItjT71eL95967uPfuIvBLzJ4u0fcLfQ2KuuXkkGE1lIrP1a7fKLn4QtJZKMtdoHsWggPhepbb2RWr24dP7cYNDaeP17XBzOnzsnyhYyY7CHokMBXVGjdhy10uOD4s4NxHcvLCda9f0b7710Mf5io8kV5zC7mr33/ncj+QvIfmEIhYvb7NIyQBkahVDRT7fuHGwzT9F4AZEXZdD9IcZblhZWrwy6ngX/ibULmRbKjsZ+mDJZBVOL41AsBdkMAVokyzL5oRFPtHR/bEAGw6EEqI/djqfXZBNoFmJMy9AJ6G6Om6AxotZ3rm+CjAsHlUbPs1tq/9af/ADxPWTD6SuWCP5DSLq0FH/m8qUXPvbE5WefWz699h/+w7976//2z9k2XnvhqTNPnPWkk+efao3GgbmTJ376V345ks2Jlz+Y8XjmxuFdYIgTHxf89XoLZM42FnFWwC+XCtdhyvaMk4E4kxMKFIriaq1RvdMZDZtYPDyRaS6kk8vOVQFQzQTWvk2bOwfxmomvFuqaS1sS7YqFDFDZw/0d/KmymqCpwK4Fkwz9hfkMxm3Es1CvpefyPvWDf+XcxYUza7WNA6QfIEAh3GBEPbgFY8p0105ffuYzn/RElz/7s18a9Rvrb70FOwBalZiG5q4CzEnmFKJJZ+fmZGkyM1BBZk4ah3VPfsE4xttkYNIZH/vJkkIHKU/4aX0htLtCjaAL/FGOu/DhQYdjnkIP5RDESYHFMQCtE9sTYL5+F/FG7c+YLzDgdWk8FxqSZ0YNvnas9BbkSK1zIArSqpfoP/WVTgCHHltH64Wb0Fkf+VtfN5HpCefjcIjxtOmP5HLk083q/rdB9MS2hdqKOJ/Kg8dkxY8haSEIKoEgaYiE6xjBRIiqyPoHOBFA1NG1TzyMzYthu1HtdFgGmpUWEFSuVOPpVLm5jw5HqCUMcK1a57wsU5xsRnRfJJSouqg8jZCpg6muht15jmm9jXK4CyfRpikP52Azd4tQibMxXX/SODnNhKqehorDngfRVgBoEEUDUgBu+ZwvMOdJ5+vVESIjywveWr2VjBsDqtpuqQzap0fYX//5030TlOtIRNqEsFUVDBf79fmLaz/25V965IUvMNOXz134P/3D//Mf/vrvcOh6/Zu/fuvtV7bu3tu+fefWu7dbXY6mnm5L/zlHVRuYrAKSoa+q96D2gyB2DnRikHqOkSc/h+Q2bDcdtDDJLpuQvhReggRwo8yIJQi1iFWxNo/ysVB576C0t3v3rXdyy4nEmYc84zBTBN1pfk8bzhDsPngDA6S9+63S5avPouSPcjgXhBOJR1dX/f4UqIoGBlNZ1BHQ0K7noHDvdnZlLpyKVIr71ere3HwuNj8/8O13yr0bX38PMdpQJtUs1FcuXqrB3bn9/uXUHGp5MPKOVrVbr70WRW2Df7S7sU5BXhQtlbtDb2QoNXXtSHxu1KxkMmfTc2uBSFa3ieNsOn0BzdWcPhE0UF1AdToVBTxRfzJDr/iTKcgPmqumpjjoNvahIwbFnJ3wIQJjjEF22Pxg2O80y/t72+uYauF2gaAgq9WgdfPWdr06Xlxd/NM3396Gh2fMHXQQTIlMHBEPmp1YLJBIJzv1Gqocvvj5p/7Cl3/20kPXli9cCiRWPMP6hXfeXsoH0rnY4nLWn46RbbNaz62cquwXG7V2avksup0RcggFPa3aELXP9KaYaYbj3S7iV0PMqyahh/k8Kc5C7NGg3bdHjTa30B0uk5DmaDSH6G66s98oVhuY51tbmstyNaV9FuPOHS/tVic4c88cxc0BVfAKhmQHhto5XxcSPRs+Vj5BLBcD7AuhwlEeOqXQgttFcWuKDUrg7IVzkUim2N8Fj0L6JGMuJBCqBrqwrNDtdG5+75VrofSd996p7O+gQBcrC6idBXtyEqLfOS2zemuyCGzM4741PvLkZX/1th9OTPNSLD0mRBHMtNOQiqhBSsZyANRLfYXUu6JpVUn4Y6XSks42tsuqFuNomAiO2SZs1g/2dmEr7ra6+eU8+nTbzVYfpnZptRm1xwgDSzGVcBgdaff+Zk7pVpjeM7UxdXZc5mWaIIxny9bLeUwDTPCsh22zCTM/bmznPZP+SMiHfM4UotE2dVF0nVjoLSFkwQMjDnxxCqB1WujgQudiBxYO2EXgWUF9FVDngzN0hKE80Kl0Y7UgjmG7tVZutNowkKTQQgPDDDdwzLFWS2oAgA16CZIc1oG13ZDSL/pPew/da2nnb/+ZBpj2qQZmNE0djf/RHzXJ9o/9tf2C27TV/BxOYqPd52fT4U01TL+YDjK5iMQHKZ/aMsAxONiDwd4YzQbm/NjptGChS7A4QomVmIPZUeHCoVYJznCYUgWa1sGSAh9MONhvtoDAS5cf+9hzPw6pgevA6t33Xv72K1/77rd+/Xf+7c7dygi9LyRrSg9OXEk4gAeXU2xaYR0ZzJ/Jrp1eCCHNixn1e0WIR0B8tYyZGpEdmvV+G0LseLxTavl8LTqf2Y3SSk26safLgkGnoxiU++dwaHcXZb+eeCr55ndfaXVu/OV/8I+59Bl4GsXtd5dPnhiOg299+7eWVk/m106n5850S/Vhd59Z0MF2diLKIbHV2Y3HE29+7fdPXb0an89Vrt+Iry3c/c7X9rL5Rz773L1X306dXOghc/zObTQDpNZW0SsBgfXM0sow0/eHlhdOrMXSFz3e3PxadG75mSGsRLlo56Dw9PmfRFmmN8Q2Wh27eubjqr2wNld6LU8QfzahgiIY+U1fE03dDBairxHNg/9ULFloPWo3GvUmYsZIJmMYDLagDvZ/e0VurYrlTTQrJBJxhKibjX1ZAG6M0Rk6qLUqZVKiZzQ/n802qgcoq+61W3dv79J7py+cbdZLoASI7bJ4jSmvdHQhnYBzKsileyxc8/VjgfFzP/XxT/7CF4PhM/Dpo7UO6oc31MvPp2BLqqLPEy1vMHNxUELxX6W4sXOHnQXk9/nlPHDTKFdlgYBrmd4Awk7Pq90obDZomAZO0eUq2gVol/mrTQVXRMy1TtyHIAVGJql+//ZGGbQ1WszlsynQrii4wudCjAYoBaj8MRGBBfrOLgdCymi9lHi6sBmwTAoQQRNWsM6w1ZFRFAS7br35dn63/id/8NvvvfNOYgwJj80xeiS6xIYvFxFwJLBv3ryzvvHr77z1NvjgxuvvtKvVbBxrOrCOw3RMn6kWZk5Qjhyah3rprT9+qazxslPHepoQE8OmcX5NFJvQTDWtAmzyJcA4CEV98HMCD41qEQtsgTRXm6J06JigYy4tZZGAfujtDUZLp9b2793KzOdGqAaF0RsQ02JBPh04KppVpJroFpXKr7jVtNgIE+LlLgAKnantTDud2j/opUQzCc2H0zIlORT0YXmYbjsUYSaXSe1MdupjMgYZW8zPLS4aQOGdgiDYguBYKcEDgBAftDA4vdjgwvDlpT8QzO+Wy/sHsNZFE+FOQ0dqWdWA7a9eRxAsEY9WxxWIJ83aPhdLElA0/J86XnD4pkxS6phGB6qm/Go7YNrI2/gpUGGzjwm435sok6jGYTrdRnaSH5ebgmw6J7WpAD8GOjXUrG5QNgUo7MXaTW+7P8Y0GkRrf7+jQ2Qwin4UtOaYhdSupAIF85AJThfQtdYKaphwmJBhb86ec2fj7n/6F//TycuXvveVP/r9r31zq1CEootYYjIUngsHTq1yyx6fX5hfWV2BkTyaXKzt7XY6Jag3C1fW5k4uiXm81793Y3vQGEdC8UZp+83vv/PtV95o9bq5BAf2QK3WYCrT+xDT6VMwPjzMcPairw31Xm01FOTsWUpCtzmN+vvC3q4BY3G1YqwKew9Ymrn69KchEMFSA5gEUunSjXcLu7vppblKr3jr/dsnz5z2LaULm7cXF+dG1drtN96+4HvywiOPs3PqVT3XPvWFxIlVL1ZqQMWtbjSbWjgDsQm7EQwH80XbzJzwOB0F7kp6UlXBYsY3RvUY2qU8YN06O0q2E/WDMuewTpM92kZuYSHip+cH7DxgV6uUdxudcrNc4RIYzRXY7Oy1sCaGdh9fE/3NQ0yvYF8SU1CjuYXlUCJWh3stpDvvNrz2+VxgycehLJs+m8vl+3XE1qLDWify9BwWLhP5BYS+K7v7mXywXSz9/zn7DwDJruu+E64c3qucO6fJeYBBzgRJEGACkxgkKtqSLcnSt7L9ffban7VeeS1ZsteyLVGmsiVSFAmSYqZIAkTOwOQZTOiezqlyzmF/576q7p4BIFFbM131wn333XjuuSf8TzgSaJQrgWji219//PfSm8h9kRFRDZouXyqzA7EKznPT1KjDRTfzFRMbtyhjARgEqIUK4GW3LC+tXTx/+uDh6cnde8ND0whX63RUJY3JVSA4Y7fSJqZmGegg9DUC5e3xYBaF+YTFS0bopQhSD0WHcnd7VXRsqIkx3q3VWIk0h51u1V16h5hmpdJmquTTXDivUUYIuqiQBcrGGPFCtZiDsASMUiYklI6g58hGeDu8GsJPyCL3GPZgZotA2ARQUq2eq7bcrs9/6bOFpebi/BVLuR6aGMFpln5qkQcKUis4hWI7DkrAvuO3fPJXfuXaqQsbi0nUhLqXAErizExHsMyw/hgfYSXln8wV+ajTwcngwjZFNSayobjckUqImCRWnJaklrkHDglUS3ZRHQznPFrAqfvh4XG/N3XEMwXAFUh3C0fr9Lq5WUXgs15MM1C9gUAaf/H1FLDbLJHYPwnnJI7xMqnxCFAsHgWXKc+bOFL2MlIAea1iW4zaGJf+od9G+X+Up/pv+ZEeoI0kS6OhpCKcUHzOuc5eDwxHZKDsE1umaqZUtuvWUMTlT3hHRhJYhgEmhiaEpRL0lG63ynetVnFjs9xp53JZZDuFQok0NjisUhexsx3tnrmLr6nf70U/zOYYGs9AYjPJm2lRaSejX2GxVVk4EyopxVGdaZRXyiy9Kf/6Q0XVQ66/9af/vHpIpfg70/dvyjg0EqvRaLxKDhGUQjKBCGfFardhObGTQWUKVj4Rk3sYR7gRkGH9KZtoJIOSh2paVXqVF+MGRor3SKb4RjSaMEOEnV5YWDhz+sLovhliUa0uFLK13uRIgCBRQ1HtjltvnTkyEwiELE6nLxDHCRjym1nLje8dIoJWV3eFYgmEFSiWJ/YeGYrPmJwBUzX3g7/66+VU9uRrF9BSUooo4EwsTkxppBuySxe6i1YfZhRlADQSSVGrVHVoWrlKIJmUVaf0THucmmtWLUYEcxxTA5Eh8NjmT16IhoPxPSei++/0TuRdEsO2cSByAFQatz941/t/EkAoU6d+YmiX1YtwA4onQbWkf1lyWHrgruobxJAAlbmUT3dtvUI2SzuiTSvnG9Rife0atkyVYq1Yzs2/cRG4kYg/1MNUaCXJGEMCU0lnTU5vNpsplquxsdGRyTHkW5AmVj4Gjeazev3ueGIMGxM0Ayx56KsB8yKBPxyC0uNWbCVsZiAM91zIFUPxUHEz7Q9GNb+OUAKKZ/PwOmJttZB5V7J5T3zIhQEs0dZw+Bg/KDuPaRqv5W5UTE59fHIO8KVSqYJ9CNJNKB+OMsU8eN1mULDwNarn22devxAKf+XuBx71Tx9GM8KNUqEwt5hO503rq2v1TtcVmnaZfBihhqz6HbcPad4wQcyQurM0lsr5YqnE+3QNKGvgN8CQbgNF4bJa63hjisqe1u3St3j/wc9iJMX+A7LUwl5Yl3DQbWLx9cDixfK2KeuGwOph+iJPyTRSRJe5BouvBqtclFkGWRajIP7xBrENoiXpP4mQjk2MDl+/jGL+Z3/uF2vp7l/95Z/Nv/IaQNuYECCltOMpILDvhHUGCsNV7jbYPtmHJztnZktVFOR12CVYcjGTkxmiXse7hYjK9Ja3qzKoYzV31GXjtH/TSLadgiM5UaRE7nGmGC7ZhuN3AOtG7qyWrRqqSbaUfpPNw8QlHdsjm5voLsj0sV4psI+3o1JqlrEptgbcxWSKzMC8YlFs1thEsh2oo0iAFMgLxHNCtl8MGFGgqMZj5afJjI8qk6qNcU5d+5/tI7lgnBnVUQl23uZYzR++d6To57Pj54Zndtwx2lMuMA135qGWLclebrHACTchBi+QBlRgsFkmW+PYnbuPnLgbCKdCrTO552jPDOMrIkIagH4mdJ2dCed2dmqFXtM1MjwGt4ird6lo9bndV5MpIqtVMngnuv3DGsiOKE1YP/EHJtR2Q9TKsgWTNUDkaEaTStdJT6qiDiolN6XBpaTqI+0vKbavbN+RI5Wb5Hj9x7gsLzCS9JNdn6j/FPeEUMsZPD59LOpQeh7DQ6YRVN9J0BWxA6McWD4ofBsBY1ZPSOmMQkj1ODF2NlIeseomdBiVhmkESEN3uAM2Z3As+H/8wZ8y33/9X/z6/OycP+BLpVO7jt38wV/6iZE9Jxq9Ola0almEiW+OHL8VsmtLKyGn2eaPjiAQyayvlhpFL4gEwOH7wpG4LyZWPYCK0bzEL2SDL4AWCLLYcbH8oK8WSAoCr3edNTFtETO4cimPpfl4bD+ZyFbB1iGOCmEFvLCdJo9dDx66I0CEh26nZLES+T3MJMZtzKTbMdXEKIe4vJtrl1n2c5mMW3OWMym0CoV0Xux5ykVIQ6VWSS6sAjYPDcA5AJty8JjD4aiuub2BYNBjTc2vAqY0NjUFrllYC6HE3bNvn9tlL2yi9wbyAQelSmA8zr60sJF0joxGhsawmSeAB1puQrbaNCtBncCpo83haiX8uggBqsAsMdjcpQrA5TgeE6+anhySTjInxuB8SU9VpKcgGUorCfZS2+EPERYAj2l1Cx4QRwHEa3WkHUgSCJPebGRY8evlItwhwpViua7rOtVsYRdpbvCOcr392plrqXoZfv3hj8fBmWwW81fnr2Xyphxk14IcPYTROa3N3LO4fV7MyhGuoGDDgNLSBQs2V8iz+DgACAQIiYUHGbbwIha8AXGsQTyL3wmrUqNpqwudg0GxlasSIxpoM9hePwplnpYqIf1nzwqBYsxiHCmSRfkvAl8hlIoJw2oZmxkbHlKCcsTI4bpUnpwxQGL4YMAm7o7YlXrczvjQuPdI4uCZV9947gVSiVkuc4EBLvnJvp4BJlYxVE58JarwE/hjYJqPM7iaulAg6R+ZZoO5bMwfKQ7XFIWSA1WM/pdxIl2nbgweUISDS3wotkjnqYuwXHxYbCDoNuQ/BJHG5IwWw7mlZneFQRlhJIDriMTKn4iy/alkkliCmglyWynkMnkeQWcAA4QgDaEFUKK0CquBiopDZvJGoQ3yCpqcndPWAjAomSrTji+jkDsuXFe9HdflsE+C1FWjwjckkDTXtVA/e3Vx+466euOLlA5DWl44cVH0M0CsaLIc9joILI3SgRMzD336E5Gpo2bYG4xerTp4ZIQFprZ8GCTgBNPbuFCDTo602WMzVxs9v66FPHEn8ROSm8fuuPXSC6/SJqm1tTKIAoSVAj4XcxpiBOM9D94jA5AeEhZEqtqvibo0WK/ksnSpUXqjetfV5IaqSi5Gqh1Ns/MxleEgCSeDxMaDKmt5nyrL4DlhLph4kFGmEHxzDx24UjZSAUiGCt2l3L6MYhpTh1zI3chCfRuHUh2pLS3NJMO2GOuCkQP7PGO3a6Flt9+bK5fXU6VCq763WLI4kR2MmM0lXHzpJ6a3jEKT7eKplz/7e/8X2FUTMxOPfvqfxqYP4jrLBgTLR7fF1exWmeD46tMtMkQFuhTehw/jmVVXliMUjSxOTjc009ZrW0aGPITJ1Xrm6OSktZIz9ZKNUjW3uoCnGK7wxDAH+4Gyltc20mvLyU3cI5up8uba2hqAyASKbjXaHr/ealUh93gEYQRLHGCrvA9GC6lgCHQdLR73RSOhwBArViw+xP6pJhUKBKKRarESGxn2swAsrAeHo7ruA3C1vJHCINUbhkTSWu1iKqn7NZtYRkGzco65C/7x/Xa734w5qciRzFgh1Ro5FmawdWBz3U4fLUbAUiQnbJKw0vb4gizH4mdFDgJRR1MokmhmXwA8GReR/MDTqasAcjpRu0hvoblEpFPKZxAcIWtx6MiDxU+0gQE0olGMgwnB5sBBDjUxq5vTF3SBnY+8tNVrFPPF86dyvVLdp/kf/Il/0sSFD16cydQ0VZNgcrN2r7SaLqvmt4B0TT0wpJc5gcamRZsjgWNJhzkj7DBX3WBDawKvjLga34OQn9hA5tWNXKnRhPY0qxKiAbsxRF08gvgLJ01s1iJQNFqQzT3GRiBV0f1CthTNlH2LMfJl9stlZRsjlgKyaZOlgZFvfIEuhRwPeSMwUFrAD4ofGr0aXIRbQqgR5IyOQtIipveQfp5ia4dZpch6YZQJCgWYiM5KxjwSRbExHYT8yAQ33iJvkqMd5+pwkFbdN76kVPLX/6hDMpPkrCtQfHHelDjw0HikXk7smxFuYNKse2BaWo2OQ2M6I9IVkySiDGER0Sjh+0vwVHBWG4VknsGCuxqhUJg5DnZ5CDMIbtiuo4bhFYjgIF3sG4WpYzDJlgYRGgFhjMKqcu043FnYQaH7NdhRje07xtFWBlsHb5HiuudJaJyrg8GZ8bvzWxWTblEyDyFv8KV2C4uzsBrSWZ2jRHyd2mcKMgNrjY2lJhyWP9pq2XwslWjSi8mlcxdzC1cSUzNOc+/ii6/mNtNDM9Or1xZqBAgbxmOoPfv6hUa16gkEGPW5XBq78rHRoVKhCDrxZjqDkgDUAMGMoOWkBGq8UUZF+4WyyoehawwPVXguSoL+R5HlQbrBRX6NSzsSbl3jQOWzI/HW4XZ6CtPPVbKS1wtDKZNBGArmhejGrbIoivUdJgbi4SxFVaNZqjJ4Xq0kg0wGb5L8mCQGKC5BeBsVQHsk8yaqZvZINUJ9FJqmSj7LRgyBg0W4N52xigxBbTAt3/iLL33uL19guHn957yRmY9/aq8/ONzC+qNb9mgeixNxvr1SrgYCqKwIWYxVhsS8YEePZQ07eiFzGD8id2BZwJEMdonfVrdUKS2eW790+rtnll+ymurLyRU0XEGPpwFmZr3D3sHSsll1LRIdT4yMTof3TI8d8jixqiZDu+bz6H43BpouhLyAPrW7vJvqa34vzj+8C+dj0CYhnYI5KVJg2oxC8Z8G5T+t0vUfGTFOXe4eQBEE+uqa3NJchF0MmTOpTCyhKcrkCEzoyHK6JifGCJQeRBrha00ePNbRWCDI7QB0RN4ARKNIgOVXHST9yD5UvYs9LhwbJcc5VeieqFiVzavI2hmWDXbFVIy0SCyw+7cFQ3iOSr+zseg2Lc6QyQk0ghYZHgpGwuTPSPV6fW1LgyaGaFQw5EJingNUoVvPXbnl+NkHf0LDI7XcLGEPZU5Vl1Y2/8tv/I/QlCfm8px44KFb73/QZPcTv0yEiohQ4Nt5L1sMEczCzsLod/z4XuLcjCEjSwKWOW7WKKhPRcZkm2CHFqA/eB9+Ys16s0Lr9HqJWECWcGQvVrfkbbGxxZDxx5xj7gteC4uikDHywM6RJhGSYO4iN1K0QWaAmMBLSPg2YiQWIIHCAS8PXQeguA3iWOhwFfBGBj8HILhYw6B2gk5Cc1FZs6EU1lJrmgpsRXil4FBJh/EmmS3qX396DGaeOpVek97a/hj9OLiq5hdPGL8UWyahGBmKjgsBJyeoom3sg2DSEN8g30fUyabY7fOIqqPXAqeW/aHDjegPC15MGTV2Oe0aRkEel9NWIA4csXcAAesQM7TMXkj2+tiBeNwt8QygZZR5iDQezSR4tsoMVBVpu9BGcd988boUWydvk47Lb/1RHbZ9V9qg32jqgBbbvik5GGcGiVJ0V11g0WYYCCsEJFSlcPDE7uljDzoiuxq1rDV5MX3u/KVTb/gjIdBT4pNjLJiZxRXi5K2cu5o59wq4EenV4tylxf3FwumXzy4tZI7eNoVr+uUrK34PWGHB9c00W8CJ8aEGNhLikm/1edwYEtCCMFz0mOJHWKuFwkoZpU5bpZZCqlpcNxJUTYw0cp2j/u0bUxnnb31V3vXmjzBgW43GTDH2s3IFbgJuhp6Gd4XtYWsLU0DhoROyNqisFNekyqPWA5UTHIJkqgqhEjKSoCXQA5SYAKSJtk2sk21OwgmakdmzGW3ms/nUWngCD14EMkIU2PFTy141d/ryhWzLRJDuXLrz+Pefe8c7PhYITuLGAuCwxRcK+CPMsgaqGrDndXDKXMLsYk3CGoYcgcIjcyOWC/XqWYkwy9TNVmvYFs4cv7Vz6VJmLW1rOkcmd4/vvXN698FIKOwE6NStdbsNoCMRRut+pFbMIuGmcSY2GohtHKwnU5tGUtIqBh9cEUwtMgxaCLkBrHoGaEvcLZXil5TSYjWkQ2wX4KmZTLRNF7sa1ihcfJ2YY7PfAk1EPN0saLvxShXNpDgA27wiC6HRbNZKsuT0eaCb1WrVh2yLCe+E4RPXf+HrhSAwqFlpa1BvBlmr1rbrrnqjlFpbTMQn3F7ArMAJIi5wzWXyohsUEQ8gqW0vEh/oq0V3IfDECAsK2SymwRktZnNAbuvBEOpB0oKkL47F5s54Ir6+lsnUGkB0NhQ2eL2GwbktVW5vpjImU/XVp5+5fHGJhfiu26ZA9Hzt/IXcqzg1dA6/8OqPnXz2jvd8cubW+1krkUgQa2J9cXV9s5Cl1coVBP6gKup+Z4TV3WzbyOdw0M0i+mEBLHcK1TrWadBYXARw1ROlpURjaO3Zvee2W/dER+OrK+vrS4v1IkodRDk2di2QLNn5yzSj2RETQvoFuRW7JppbZGLC4KrxRkOzvmAH6LT77U7oHtsl/NIxCIT9EemnUq1bnOTIAJcxzvjCukHsJgiQI7MCYot7GcozsZNjJsjLhX2TSWHMGjV1jC81SzhUN667qxL3vwbPkaD/gOLDhSaLSMHktNnZhrDVy+czeL/QNPlGIZ6IMGyZTSK/pQgSoxyXHYwjwHDt+mKRdqOcAuAarg6LgyoOdlXIVbPcAoUJM4KGFVREaw8rL4c9R2wgJGPiyCJWQqzRsuHoQ0FIRQbVMCr1D/h+mwevv8wZ9exfkzbYeXvreOvgutf3CZFRxn4rkxIiJgAdmNDZu66b7r5bJ/gfnKTFdOW7Xz/53OlUjugWu1H/JpeT81fm0+vpm+46tL5aSJ6cdXqcQZ+OE2Ruc3MxXVvNm/xrBT1fful8PuYxaXoOmllvdnK15bjuPLx/r8fvDkdia2urxQp2jgogStEMKeV2f/bL/HZ1UNSDm6oC1zW3DCy5IE/226WfqJ+l3Bm8ZzuHQU7y1CBbWYdkRZUMZfgKHRDqKSbE0BKRhiFYlGWBMaVeqdLKm7deqTKQK/2PFI8FTgkcsOyG3ZRQANxkBQY5idEIA8uOQJydVuanjwvJZKryYsiYydo59dJLp6/Mk55rbUvn9TPXvv39b/3q/kO8QnZvps74nrEwZqmoYLtsCkStj70DvBr6e8wEkT2hDKsANCZhOuz4hUVAt7BbiJIRDo0cPuSq3PfoP/vN/6h7R5hFqtgiqaXQxn95LSy3RKNE5iFoKioGhjQQTCJSUoQXTDs24Kw2zIm2DeuRSiO1qnlMwG2uzaXGjp8IRnYxtEQcBbWzOikKvCRUm+buNrB7ESdSYl95AyHKwCtKuTK6AV2PwojVxdcE7h9OHEJdw6LQGxalLkwvBKxdrTH1WVlh0gv5gub3IxQhZjB45emNdTib+Og40CTulpfomsX0iscOmrELg7dWp4KYpAIGg9tltuqghBFCsFHJ1yvFtrUTSQw5/QnDvxvi5cRAnv4TlFusCrOwuTDmBMeAwYb0ZwjnzsZWNs5kK4qWGiF78lkWACbXwtzy1ZU8UZVzLPHVOjp/MSt4bcVe+vb+E/ew+kMoCflcz2SWV+fSNaFlWLOiskFQXSgjm1MI0SghwlF8EjvF5mIyi3Mr5LuJu6roqGz4qxLDmZUsnPDf99Ef899yV/KZF772P/9bDZcBeFRUULAtwg3IuBY2WQ0m2p7xIyRfBi/XZF/AGXVTAjKwITQLS4dgXCEtEOklS43ozsX7Sci5DBNO4HaEGyYRmgaGm2gxhLcjQ+Q+vIAdlzEx5Rn1KsnDOJAM1LBTpeD9/Y+6qkra5wZJz4cqqB/yE+0WpeAXrB7RSbTYFxaLKYB/ZkYi43v3FkE5EUm9XzaOdHmthrcjKbH1BBUb4sfmCtsHOI96Oa/pFNydT11jAGDAUswUBIALIbAYv3RLhQa2v8wLthTIzDEDAzKIjiT760RAlE2Vz5hIUtIbPqqW0syDhDfcv+7UaBKVWr6ke/q5q2tGHoPDnU+qhEZy+d7+yONGC9KqVJ4OZRA1o4FAMDpkquMfkWttXgEg6dknFmrMxLbdHSCgqu3MxVVgds//5Yu4Se+e9qWv5gF4B/r/Oy/Mw8DaAo61bLOe7FQdpo0acIaW6Zh3bqO0tFx1jnbRBI4NRYrFGsMAnxeJoQQZZbvZJ6AyEFSBKdvgV5WYohvV7bem1GRwof9rNMp2a19XWZWdekYuD27xJIfyvHHEwXa2XJNJIiRc/gkJlPHJ2AbNhHkK5YKngTliTAiVhI+iImoMq3xUnjK2RSug8peqMUzllWquYaYOr4E96CC9ePGE/Wa3Zi7k6usbWBmyLsCiQe9Q0rcrmey//83/cmUxQwFsFheMHCBlf/z7fz4cihw6fgehWnz7ol6Q3SKBSsu0ka9sZFuoB+EfaWOiVhLoA0VMvUeQ9w5TBG6wBWfeMeXrXZ/PDKgZPmP+4ShRp7CpgVqxj2ZTiIctgfysgq3WsoEp1ClC2Dt1GCsNW3QoNSsTFqK0BeaKdCqRgRgoUHZZGJFzFy6mLr7s9PgzhWYy1Qzm5nUNAhsBvQjYZvAtkTyinRXsZ1PVrvdKlfVyek0PH/J6AtVKEXGIHgzQbKwSZOfUMbEhKStFLTl3PjR+QNe8EGOaxwMcaT6zsbCMRhn4AeALBZ+yka8Vspm1yxjy14CcszcTw5OYdGk6aNSWchKO/GVfaB9iHPAre6aKxUMYmSFZ7kulNtYN6GvbtU7bXknmzD247ACxO+l6QVru9KqADaWzYODAfRZavdMLS0A0FwFNZpFhxVaCD3zXaIYSUR5y5Wg86Laakepc2cygAwMMEFrFYCBIG14cJqePccG8QM2DHL3cKnGXjxtdh+4BZhUpPP4XgMUF4r5P/PxPH5jc80e/9Ztdcwr7KgdxhNj72DrABbHKo2IIAhbldbhmDphsI5Gbbg8NfWEjte6UfQyMAbkqeq1+eaX8R7eCrMfgnlgMEIDJOBVrKklLX1rMrFY1MZ+X7QEbK8RQJIDgC/GQDYBMEuXWozYP9By/MgjECUuWBFlv2FzIxoOk/X2AQcRlZgxmHi+Qj7oiE059FKHi0uCcDPo3+GHiwR2xWFEE9ogwsuifKvW8y2f+8E9/YurQYbAoGLsdLAkmDiCUQwcm6xQ0h/94pjKbmaEdzFq0+PhoaQOBZ9MdCjeHR62mBHrgq2fOtipuIIKwVfB5fXgzI+DDSaSQKcC/YZqBMoYhUWk1lZDWKNh2WXceDe6p30Flrrv4DzjpN8HgiRtOB5f5Ne707/MjzT1YSrmtODxMrSXmK11o6a7MXWObE5lKmNZml6+lRiejF+fzl+aSydx8KKJv5iqy2emZwMHE8BFfJAR62H/iQ2EHEMRuBRMReWJQrIl67BnPrxdCWFM56oDEwRCUy9kqWIdKGs3IwzaFAjCFpA9lFPWXdaPYOwdDv0Jc6tdELsihqs32RRm41312nu88VoneohOMS/JNI1EeygW/LUJlGWl8owfjrbCg7PWZOdBnRc9JrB7tP9/PgMZWSwA/wuYgcmWNgGmVucCzdrPLKwsAwiDpF3AlXe5cpYHaPIPZe71h89mwRWEKoYS6evr87OoyM4vJzgiG7iLtvTS78a//7W/df/9t/+7f/zaE69WXzl69sklPgn0PtQTLA4VgtWsuAG6uObNFQJHpFHFZJDI6jp2FksEOojHDTILq1pr5lNPtFT0BnCzaw3YNDto/NEE3VTdT1m5JC0a62ihGN4haG9WMyFLdEFCCGVQ71ZoV8TA7FgRbwvmBBJmotqOEZAUNxok5GJQvt6oPIYSBnmA4CMQBDUV5q+uXvl83xzx+9NEYsoinRWblCuvrrsAx5MhIxJixTG+XlT0LMoUSIT7QmogTwMZKMD5idQes5lpu9okSaBqHb2FjWituQoB80aDVMuyw1QKhMWdgHFu1VnXdPzLj1UdOPf79ark4sUv3+b355BsASAeEqJV7VSICZiAnwYTX5Qt2ajAutdTCFf/Q8fDwsBKNsG9j/0RgDFyf2nYX8XqwUQJGQcHFMFREy4oI2obfVgtYjGLryvnNFcyskIrA1Nca7Hf8IOQFAMy3s+jZXODs0g74a3eIWiVOs0ItRcENGhNmPYR93Ch21rummKk3dSD+kZ//CXNHD//ZF6qdq6Vs/dhUiKbN18o1Wxd2gtiamHFjh28XTAuaFgRDNntKzIjjglWF1mQ48kIlB2JSsCGDRjOqkWaRELJmcDsyB+ACzEjYWtjQOGRDSnFYAiEAcCZizs+umFUAoEEUEDLIxUlZISXIgJYZLoySeGwzHmSiyGvUfw6FDElBmLRSX7nFoZTMSMU10qpjIQwyl9QlSSEPyUcSIGUSgx9JaUFTjYCy0azc8sCde+55wDUyhn2L2n3o4nHZasOBIAoiCLA87xQzz0o2yZbNn4iDhLp8Ya6c2oxFfcNDw7mV5dxqCnCboMdrCbXBO0Bk6494y/kKWswA8Ae5fDlbos7QvVJVppWURhVrx9dbXdtx++85lFL+KB/VSj9CQpWfFEkaUe0BhSSpM7oSDsH6/BNPvfHi83fefSi9kVrLlCf2ji8R66UGAqp1M1liwoZDzpAXe+gG0OCRkAeSVMzXsZ91oyuH3ylWYR38Xqyze8Vqg2DW+AcEnHgYOMd2T5fXM6trq8qDxoWwEr6LYSHMgZRrm/pz8qZ2VJW7rjkGJ4Pft6z+zobZeWwkFoKsjt58ixEG4yLSRcg8Ek9jD4vUQ1RbIiWFOqqRKCwUhWcIS1NKNeRcDqRCLJfGqQxtxQHxq8ik0HIz5iW8n0jb3GSRQaqKxR1cxcq1pczybGxvCDNrSEqnmHvu2adW1talsHjkC/1nLjpqRG9ZSz/5+FOPvOOJudBrf/D7/+3k6Yvdpg2rkXSuBhQ+u4AyXi89CWjO9HPbbB4oKYVzEm+KYhBuHiLcdNpqTocXdhezTe8QcltoL/j+UHP6lZiOOcIkVR29xUtLY3aLNzIlc8m6gXuAVcKIxZjnreJGs1HXwgm1Q2KNZCzgNGELTE9iIcQSisvy6qWFsemZCVDtPf5KNctMA2lQCwfWr146f/arB4496vONlgqzuvsgz0YCneTaPAYzqUx1Y/3qnuPHLr3wjO4dC0fjBKubObKLEDq1/EI5c0bzNBz6np65QSSZyNgkoP+9TrFTWtKiSIHQuiLdqbi1YbuOI8XFubOvTuy/g47ASGHv9InJ/UeAqNlYaIfHJ6GY1Y2V1OLi0trGrkP7PUGPMqUCLDXniUQ6rQpmLDi0geOKDKTCJtnaoYnQJ7MvgPMXAHWJnyHGwcJCY0iCA2qJmDBIwuqriytESmK1p5d1R++e22//l//q5wFL/8F3nskXkp6A3gSBoNJzhNle1HHOIC8Pdk5YazltIKlh5WupmUYT4dvefa/ZRfvbZg4can/7B/mWqUjk8o69xXreswH5ivkVLLCZKNMsz3bza9/92vyVWVx0Wc6ZZiJ8k6EsTLkitcICMsKZiUqUgjRFscYyLRjO4iOBrgqvhBhG3NxD5oNmRrHNMuyh6oqZRzsDR8NKAC0WYi7ieBn8ZAL5500iPpJ5IC0jL5emktzkQD4yO4wj4/rgHhdJx9lgog6IhHpUTTWZZlJqESawGlktoNOPzCTueO97XYm9+Y0lbzxOuG2oXWn1wqUnvufyxdkCh0L+ei27ub4Jg4OG49pLp295+D2aL/D0Y1+dPXnxrnfeRrDb1flZMC3Y5HkCgPe60rkcUDf4c1dKVULMxwPe3TPTSdtqYb2I/8M6XJo06KDA/cq8zc+grm9z+y0uq9Yy2uK6u4P3qVa67s5bn6g3G+1uNCyEDM4WfFTBTQUKxHnt6vwc3Fyzmc1WUxulbqqOA8jwriGgEFBHBmKB8Yl4fDhw+fSVSqWFAwUqxVwSw27kykj9bPlskXFfR05QqeVLMKk99g6T41G/21FJFhmatXpF94eZPUhJ4TQYC7LsG92+VUtZ7mlL9UVp+TU+29UVUnv9Ry6o+1up5f7Ok63jQTb9BwbJuL+VqxzLoJNRzD+ja5kRKEtkB4ClHwAh8M8iFpWbklJ+VQZqHeCYmskF5oHKSd6vkrNzRL0LEUecKjsAEdMrISK5EYUIocb52bU//9xfvvPd9ZseeLBF9K9s9tVzr0r0GCkiL8Nhx4KJMrlDd7rV+je+9rU3Lp28eikP5Qj74dVsNUtdIkaJ6YXImieHXYcPHjyydwrnMIQV+Vr+5AWwhjbSZYlNxo4Cf07yRNuhljcxPRS7NoyQsHuvbs5eOK35R5nIOHN5IxEY2cLaJcKbOK1Vf3DTxC67XFi4Oh8br8Xw/LWF67VCu5HK50ELN2Vy69lM6dqF+YjPjrKMgNGVbMbq9Gxevdpt+3yRvKVp3Tf5iL2sXXn6amw6bhrysn4g1ALgZ/HsK97I+L6Z/a5iK6olgpEAGw0EKOyLaAqn3+5LBFIrF1BJw+Ulpg8Gh+I0ZzVzqZpasjkijer5tflZkLcLuWw3tTp//gy+3HMXziGzRV8Kfa9vLqRS2NzXfJF2N4DFZ251eZHwX0Q2NSNoujpPTFhcXtA3ANAtbwRSQ8zA7GL813OxCGE+ISSt1SNnICfRLmJcI50k4T/F7aNZ7yXQQF5uF3AMZu/lcMQijne998RND77HZA8cuPfRQiYJH1As4SOJMA3TGhxUWVNMhKrXXcBydMB2AnHb5Wo/8PBtNz1wr9obmJxaMJwI55YzSVB47Q7IO2SoVkXewtwzpRc3z333m5cWlp7+4eM6QYyxZ7K3GMbcZgijDhDmhtHDAOVNUE3EO2wIQEqDgwdlFt9pcQizscMV+R7yzS52bxIKHCsIqT7sCxUkC3mMH2kOxf8wOqVvFCxofwGQNwgTzhzhbcYskEP5J6dSBFUW+eVv66I6kmeMS2oScbh1gQPylOWENVfl1oNwV4r5qX0z0ckDJoenW11ef/WH8b23tovtIuP9hZfPPDc3dGjo8OHpU6+dBp8DJmlyavj8M28EA5HJsdjShbnFxezhbP7cc6cvXlmPJTzo/fwR18Su8StXlkG+yRZwgBW54z23DB85fGjPvt1zjUtlogKh01Ll7H/1q7VV2h33jOLvuPAjHQ5WwB8p8Q2JpMnVp//qQeGgT3KLq8IPyC/7foCtvCH/3EKKbZvd55ldWL79jpve/ekfYw5cPT0XGY8cu+too1p0BYi0l/f63K1ydXV2AXVItVKmO5y6DZdzv8VaytsxFgBTRXOEg0FfNOTFeDqfLoSiQ0ghYBV4HSOHAsjqz4l8dtbSKLWUVS30clP+q2T95MbZ9reqzvbp2x71s+m/TwoySGocyBu5yOhWRYOzMHgXYevAe+EEws+2gPepYHgyjaiC7AuMjORcCsPzDEw1zYxTKihVFo5FQOYwppE34l3CDMJvh2msacDwWc8vrZ350ldTK/npvfsCw8Oig1IkD9oDkhv8uchj251oNHTz3smH3nF/IV/cWF61mPKISMqlps9hGx+Osj/IZitgMyGA+Q//+TfuetcjRA+Hl2cXk1m/8tnf/bPnSz+8tJknboxocaE7biY2cTtAAMTpD70mVoN5k6nQLq+Ws0utSskTiBOJuLNxvl7Jo08ORoO1dGHh9CteXE+7LsxaVy9fLW2khyeO25yYQyYTIwEEGO6yll25Zq01fRNxnz+aWlzaWEsNTe4KxoeIvUeD+CJB3TtaXt4s9RwNdtLVuXp2fW3+MgzJ+MFbXL4YZQMJdPr2cZOjGizViNuNEgA5RNMCUmarlF9dbDaCgVhZlsSm7uwtXzsFBmquk0ltJi+cee34sSM2v+flp54juveJ224ydzyri0vhMBYygF9katUmtjzXzl9MryVDI/EDd9zaa7TOvvpaBN8FrxsQeS8+ZNYOooI68RKwx4WzB/8JQYmpg4k48poyKBYihGafI+4asMUQWOwfAWaowJbT0Wx37TYCwtDvcs9sAVGqVWeuQTTMvjAOfeAMsvazEmOJTlxkWSpgMdZK4g7WrFbR5t+yf+bEnUdjw2MyoDot0EO94WB3OVOsNDw4krHvbnU2CjV4XUbhmXPLGxt/uAo4Y9e0ZzgOqgXOr2xT7Ljuiz8/BZExzYCFM2f1RCorEVsUD42ERAnyxdkf7Qs0PwjXQCRnDN8AyxSdFMtnXVndoABjX8GQEkEQFAReXGiJOB7CJLEnYgcA9y9SIDXZZbQbfJWaB/01g4t8pF7GkUw/DvsX1GTiWH1kAslBf6apH5KK/wEFsdsEBNdm9scTIBCYOskuWqW1hajmWb20VMcYaCl77fJqrdeZPT/7/vcduXRqqVutzmXmkI7mFlNnX70I11tsm+r5+uJyNlU0BeIAnXiW1vLJ/Cz6fQUNDaYIltNEiu2srW6G9ZCYP1ibngCxkY0PZZKmGHyMigzO3vS7nfBNt7YuSLOQTn52fHZe2XncT7czY0WgrntcPSAMgGhOiImAPMHKgKticOb1+4OudL1x+coa6sBdU9H7Pnr/5KG9Dl88uuegyMF1DZGQKxSOa16icBRSm4FEAPMVV89VLEFuKqIZbxI+HpMDJOZWn9eLKzUAhYUOUJQozbARcPAuIa8GlZTNZL+4W4XcWXwhmX/nR9VGEgndvbGd3v5JNWKNvFUOg0KQA+1C8WS0MRnkOltXNkrIRNCX0BVYgjCwGe4kQKIoBF3GJJkZZTFGu1pIJBMuqjuQfpIwa0SghJ2cBddo7qA+RaiNYU2x3iy0u4V2O5fsVk3NU/5TAIIGhseIxUVUa1IinJcaCo3pEGTrl//xT/3Mz35qbHxibXERr9K1tfT8esbpAD8C9pmonZaqvQ5IXHg8nEjsikcmmZ6ISxEFxYfsew7vvfL6Ke/5FdYM3Eohagg3sKhhZQGE0+SslZOnWtWc093VgsHp3eOrs3OlNC5O9qe/8x2/y/7eX/hJk3tqLffy4uWz9XJ5780nAGjIJNNI5p32WrVW2Jif87WD2JJmM9X1TCk2Hd13/PjIsb3NTG5zhZC4S7v37Ro9tlvq1Cia7DUdPVJmM7OxbmnN4Uh09crLeigYmY7SPJlkDnB2pK5mcyu/lsqWCoGQvwdyGwEqO6bVtSWvnsZXcWk1hzlLxOuuga3R7Lrwc642xycmMf1umavxicTErhk9EAU9bWjv3nYVP+VyMBHGxy69toIYt9myhEbHPF7f2sIspmtLi0sj99+eiEeBt0I63GzqId2HzBdZCM6x0GqCh7hdOns3kd2xfaZr6GTFXNPlsEHlJhyzyYsAFP8HB3aYpiLUnPipjVoe8I1awadZC7kGOBWBUNzp1mSvaaqtzC5dubLAgwB8bmZK9Behzm7ZPXrk+MSuXZM6wlWGmdU2NDN68PCR2YuzhHMs5YtsRYAnhAZFhzVLx9KwWs/Nrrud9pnxIWLWoOJlvjFqhOjDkghHA6UmJyQ2SmzFdQayjGIWfpgMEnSE67B2IGzYxSP5Z9fQbnQxgmPsAbLGHgApl9AQmQ8iSaIdhGXqYOiMc4KAzsodxSuJYlxYIeNjTPetU3krLSb1ko8McJXCOOWySjk4M5LJmfGn1BsohuAclIxLjN+IrUTUucq5U71czt2xffv3v5pdKrW61mtX1qJjISzR35jNoLJfWsbtsokaBziTx589j+2WgF5YrGfeWEGKhlkPxsdruXIRAF2r2eOEwjkPhiOXlpLZVB4OjnCRndK6zorvsHl1ogwYRVJ12DpUXKRc4rOjVtuV3/GUkeq6b3VXvrarv+O+8ayRLcd8JJnRvFslkNaV61vfHMHKkhAGQDgBVgHUsELMJAfMOjbSJbEXsYmznzsRtUVGrfE9gDm6/YgKO+hMKq2uFh3qVWvFdMrtC0wf9a7PLYFCi5IvFA7CnZbyoM3TkeK2zmsYMblM0YWlNkZkjC0hr7yUAWdQV7p4i0xKGdSnX17JxTjsX5dzVdL++eCnn2i7mjtqPUiz/Usy+VA0NerVoZGr0HE1IqVwMiXhYsSPluahzCj8GeeyOeCiLGPC0Ukmag1QZZNFQwit8QryogG4qx6St5EX8eAxEpfIauoO4gKB02Pi2XA8dKRkSynkHiGAikDSAUYmXypxRVYPGDUs81vNA7v2feDDH4xNgLCvudj6YvjfBuEN1BbCe4NOU9nMl8t0lYRpBIuFXwQXeLG07G7io2Oej5OTqCXVYgTQo0RAAvAA1QIpYXDr9U5po+CLugDcdIf08f17k6utv/niE1fPLj3w4K3dksPi1io1SxLv4Fx2OJWdPHyYhaQKFmVzHTSyhasLpVOXI9EEJq2L7KbvHI6OzyCgcnhbDt2/tDQLy9wFoCuOS1emspmC+cg1kh3Msdc7eiA4dvRAZiWVXF0i0Mn83CwCjuX5IurvpTeuYTmz69DBXXsmsT2orWTC4Qli8mA6OnVoBIaVrYsZqRmrmM0xirwD8YlXRzIzNGQqlQuYwIJoT0eWMplyY9WuBT24yzrCxGhDCO/yTlP36LD9xH3O5cvnN9Y24CqL5aJY9UAa8M7VWbDN7HXoCCBinIFwCahMuHWsf2jMGhSyBWAKhhDekCNMHGSH9eEPvCswGmbVoSdg7HHRAqdHI2SLo5cvVM6ePud2eePDk4xFfCZKG5tffuyxs5ewAcOa3kzg4Imh8JHp0TtuOjw2Fba69Y2FOS12EEjRGAKLRCJMLM9ee3x0BIyCtY3UrXcfO3LrsXhkdOHcxce//rfAUwwlgtiBwXWJmaKAHyBlVEy6McTFwEGk9krtzLCFkvcwoFJLkch9mKIMb4g31mBImgQaTdClGPwovfmIjbLSJbOaCJ0WEb9Ie1iamSIMV+YH7yMLWXdkmuB2TBr59KebOjZO5QE+JOz/yrHxIReZXP0zmaGKdggBE0EG2bILMbblYPc0kZZeeP3Cd74/fTCRvLqRW8rqztDJs3M9t83lxKetFYo4vvfM5XylQbAysI2DfhdUi3AajXbZ57aBgIkRtVuz5qoEEeuCZeEFSYLITDbLhUwaQ1KPB0B8O+6KqVSa5b2BwRT2W4PCDX4prVq7pOIy8dWf3NyqRj+lJBw8dMPv1vW3T6Nyk/Z5y7SKxkqmwuzLD6+XPymEPKl6BT0Ax7LICxQUBiIsA+aRsZHFbDIQC3uCMacWZ5izXmAwZ4vFcY7DWMoZJoRFl8hT5TJ+iGyBFWfaxby67XC4apUS5oaQS7glp0dnwGBeK9Jo0TAiKhX9ohoiRrF4/6ACUiyjoHKkyKm6JEm4YNyWH/Vk/3zH+FC5Shbc6j9gpFTXjOuqL4ycpBTGq7cepJD0nNzmktwV3kgyY7FHBUZTcV2UwMiSGe20sUquTkhv5CszzLjOOemZKmqjipIQz3yxfYFJ6uG/L6+RLFApAEZZRusk1KTjNZn2TAwlxsKQAlbRgEvUxfKhb2y9A+Phf/QLP3bk5lvgY0lQK1ag2R6XcFkYk+SrNbwV3CxPuLa22qVmo1DJE0oYmTXALJhpIPQN6x4ewzCR/TzSXztoMw4XHnoCKGDqFnPJXDabK6RtBOmd3Syk1kIj0wiiE0PWmakT97zntm4znT6VzmwUgK0J6XiclTYvvR4/caSQdJx75rlyMRscSpiT5UppPjZimzp4gm3H8uVTtnkLixns+fDUEK2cTqdrZaIZrnc6KaG8cQ8Q8+2WM5ssx4empw/cUsgSkruqh4cBkyDEF+V0+qNAEERHRvVASKN6GjpuVzAWYi3GIBVBZa+FgA5bIZYxZilGWoo9lU40aQFUpdKb9IzP2xmeALy8YHV4QsOAAnV9IRf0mfZAdhkaIQY9qD7iVZrdSOL85QvjNIDHaBu4fJFpICjBfhzZTU/aC892zcPLTYkwEbkcY/Hw+PSI2e06evOtD77nIwijUeMK6TE1/RHzvgOJyZlppz/cKDRjY2NTYzOw0kjhmIZ8l5t1SAm9Hw+7pmLaXQfHpmb2T+/bpWkuvLryjfKeW97HKPCFvMRpGR2KzEwP7dkzk85m2Px96OM/9uAnPsmj2QtPrc2vdXNZ1nU4fGIKSYnVUJeFhkHHEJSPGvlKDyy2DjKFcaWwo+kihRqaVhAemN1Aa4umELsBdADYnLKnwGBUHqd56RfRhMgpz0hujG7lvCJzQD59Dki9kBeTTqYTX3z354sqTn9Sy5sH80gdqqIMUshTgydlNRK3bXFkE4kTgxho1NnTVxjom3Nzlm5tbW7j4sXNnhufuRagIwWgfqoWJIQdF5wBFstdDJG9QUc4Zs+ssTza7MBR2XuVSkfHUsLlbJarIoFstfLrDVZuDLdkP2SHKcSwAvMogjUT+ATZN3tCKSAFMz5GCaV60jLy2aqEXDQSGd9bN3ZeNI6NXLaOOXhTYrlAu6uU8iUtu/3pZ6Bu9I8HLzfkL1KWQRFFIkh/egC8BIu+B25lK5cvwqSxSkKTUANh6CwQQIBBtkuF9AZRZIlYhGWkL+xDM4K1MoOLoYKAhC4nTrVEBLM3wGVUHjrQLooqQkclQFSVYXDIhzKr4knp1SV12bgn940jlUROjY9x2r+4dcI9IzcOuGh8drbJjmuDh7bSySPCznNBikFpYWXgm3D/5Qw2CgdzbjFRQa2hBeWmVEi4KkVYZNr0dQfS8ZIxlYZa0LRwWzL7RF7MNRvWebAOJIAxL9dAXBfL65r8dJh/t91x8Md/7Z8FRsZJ4Nb907smTE+8xjF+LkPx0J13ARh6t8XkWN24Eg1FKzg81csyxXkd2jsTAgdZfEGSgcdxwnvmszibUUKB9sIGzuGcmpgk8AW1Qq3H5t6OtQ8aS2FnyYRgYdlsacMX8gcjnnoSAt2tFDOaN37zXcdD4RFPwGPzx+zlfLWU37t/spRJLqxvdABMP3URt61QPNGt5by6dfKOo/Vs0RcLQuCqNeJ/IRkwo/b1AW/q1cOjM8RrL2Q3beYQOFzNGtX0wnziEzc6SWsRK94dHxUw/aFxuEaahGHVCA+BxYphnwpF2+56/XQH1TaDkkYMOyFruPDiDsYByy/mtZAmNT1gFlm++zsz7rKcm526F/crB2xkm8CS0D2RcAtTyZwIhiMOwefIIWNhf1ZMp91+dsVEtLcA+8PTvY4tNhw+cmT88O6ZY3uPNACE8zj8AnrRGx2ejE5MxqfGzKZReZOpqunWO+45fKJSmD508O477rj7PR+zEuvX2/W64lYwLIT0CzHVA7EPf+LD+SzhPLsH9+4ZnYlPTcfjQ3t6dme7BOamNe5zEYuNPCulMt4IUxOxIzcfmd43GYuFMbrbddu9UH8GiWs47vfbl9cKAJqiWRIlrwxSeQXKN/gZtbMlIUyJrAdscWT4yq5cAPOA/OEWjpo8gUaKkQQWHG7KPpcTU1KGLjcQhFMxkSGpxoTrp+1oPLgOMkBYJ9w5nSDtScZoSMiMV8hFuSx/lEfuqkN1Wb76KYxzddKnboMUO7KQ1OTMP+l03uB0Orqar5ipvPrMhVJqo1urY8XQsTgKlfotNx+8/eGbVjdz164ll+cXj9++a3zCv3RxKZSIw6yAdb10bgHQQDb5aP2Ta5u4ytdqANpXoF0llmWWfRxTxDTOTLBgidMAgDaj1Q1mXE1i3KnyUTip21t8FJ1V129MwLnUiXlHg7zVRy2Gb33LSK5oFTlste9bZTdoRqOIJJVsDRLFkWTBdoetIs7RuA7ZU+v5erHl8wKyiNcPyk40RABRBaUBwL+q2nLzS7oviKny+pUrRN2A2yQ/hNnIkiuNEkpS5D8gbkBvkBhKLwlLxsxkwGy1gCxCxomUSpVBnW4lkIoYdXzb7+2G6acky7/vGVnz+p+tpP0rUhA6RJpE2kiIPIw/RBX+j9jQWFLTTugGZdvH1pqRxxiGwMtIJFdIu3q7MiKVNlZbaPanTD0WQLVmKGtqpj1o7hBFRdMriBKAqVIQjmGHLT6c+Nlf+Ol9h283WUPkHh0dd3u9vJjlgp7YtXvy+M23Tu49xBnB6D1ux+go+GAeUSwy+eE0rbYyXQmSGUWCY4FwQfQx6+FpR4naEXVgMV9ihidcJhe+s1UCegqaprKugUR03F6MTEbjQc0TcQtGpLnp073AWOJp5fZ6cI+qZppufyA+hn2k7d6H7jr38jOeGPF0E5oe6dTHxg/urZfTLn/QE3FokQTv9cH9Yq+iB8Rs0uwSkZoVeB8rAKa+IHQEO3xIqgAVMCbBaafWKF2gi7Q9jS90otsiQADuY2xTITK0slgfIsOShic7IUa0N58dlEUGOLdlC0fHipxaOhcSRm7Vespubtjdw7K+i/FQ2e5FkFvLbV4DlLpdLlSLacCoBYoJdUKlhKAfPQUZtRDkI6Axm+MTo5/88PsefvSDw6P7sFsi/gWgMw4dHUxExQNWZUG+Yird/8Atd916BBxdHClMroDJRLeiegECjkLzDxkgR+6eo37ng/fcefQgS4JVc5jETcSv9gOUns7nU1bj06KFXe6g4/gt+3WfrZzPu9xuXyKBNJsUnWb1q3/210RQwEuYkGAMBt4gHiu0GGSascp/kjFQRVAjohx2nFB+wK1AS3MQ2EDknG0NTrfVYGeJph4jZa/Hhcks05muATOyXq+geB7MZUyeZCqTkQicnMAJScvzEpETSbGlF6TpFdWR2SddJf+5wRkfeaL/4UA9NDg3fvskixMjoeSnOl7iXVAohAr4UEhMSoZyo1BCmNmtmS7NrgHRERmLfPB//4mR/QfGV3K7Nwu55AbKgJGZcPS1y6GhWGxkKJdK2x16MBxtVbBRaXvD/oX5NZ3wEpg2srTYaxrKklYLus9rJEQYMC6sdA6swtg5MzTVGLq+wP+AM8rfbzFVN3Xaf5xj9RHiwvHgdHBVLgyabvCr0kgfq6ZSCWho+eVbZTPoN0mi2pGlX4iTTBQuyLD0u10e3cTy6NRgB8TfiXaWuKTiPI1hGBFSvRZbw2Eiyl0Ea5Z8MuV2OVmKC+ksr2bWtpkuHcBJsG0gW8VXy6+sBFIPNVn7hTNKIdd3fvr9vPNSv/7cUbkYtR8k6F817gwu3vir7ho5b90ynpACqSzkLtNbzhnvsiliEAvuB4ySNAWpRCvAAoAxCBWCjYEuyGwyKsf+mOfEGESqK8IuQwRKCytrO8ztAGzpSXxvjBFZT1rdtdVlXkSILl9Ym9k9cc97H3jokYdYbSDO0m3mluYAh9pUBCNNs49Fg3tnZkKBKM8ityMeWyVfXtzcXE+K4QXCaEAYgNNhMS53LXWUX412JZ80dfP4x//6n/7Js6+fn00vFc7O65kWYo+LaUKe5Nn8UnoRREgbWB1Ob2xkxgYnv7JY2MwQt4T8qF0FH1Vrze8HeCdsdoX2HB3qdGoA8px4ZDeu8y59CrLi9MCitp0hyiJLDpUXIbOpafVGwUIVux+4JZhzwgKQ2gzchfAWXCfmuxqBauhyT+i+ALEKHy8dQ4cIwp3wkcrkht4RWk/nkFAi3wofSBZI5oT5l+cIm1ui60D7hDjVwUxAIFItOezhdLL4ucf+KKCbfvVf/LbIiyQMLNG0so3yykvPPh4bCqQW5i8//9rIVAhgakhiGONXeprBa+7VauifkQE2zB79nY88OLLnAeRXUlVnvdpaUa6CvXzqEuvA8tVLmButr6xFxseivpjLBSferKWXTbZV4HkwWfF4ojBbZlM1v77KilfKbxY2lwP+aCSRyJVKNo+GPsfj8mGujmqfDfb6/DN7jn5iaO8xl27zRwLnzr9xxwTwi625uQubr70ycv7SIz/5i62WGQ3+PffcFGh1SpsEiASVuiGtQ5OiuYJW0y4YchL5l5EpWgFE6AxZK7ZJ4JsCAu7QAcxBXeIU0yazKVMsAYTsDDgAvAO0VM0SPEDF6B63GBSkBIkT8ahCFUIuhiefyI16DF06UWa/+MrLlDUWA9W/0qFCjWR407VCd+RLzvkzLvQnubrI83JLJiUfspMD9acmHoOIfbWYUyAL6jkIaK93LJ0C7tld+2atMpQI+PYeM3kTkcO7IgdhTrKaT7e4eqG9TpoRUQcATkP79zqclsJaenl2Cc0/AFMslIRIKVVqvJhpL3RA1hrhUgxTWgQCbAyoCCOEAbDz06/MzktSXFXD6y4OTvqtMDh9869R8zdf37piJLj+DTsfEtIuiaXVOVDf0tCDhhcMD/gdSAD8hwXzf3/ARVTYRr3p8rKBgyigkxSSSG9DZSwOJ+FPqxWwtDpun97Fv7NSKReLwKji5KXmqFBB0TLxPvpaBsGO8lAYaSSpt1Hmwb1B2VRZ5WvrM6ibpDRqM7iikgwy2Epv1HH7dOtIVds44+WqJNIOQmJUoTiBasmJXIH9xJyNdCyTKO+Y7cwaxhljXhpR6JfIdFROUhmYOtYHvqkw12FhsSqhAUAoZkYxeIiyxsoxm64VazB0JgQpgPccOTJ18/HDYxOT+w4dmDh6CASFBpaCWBDyPtmEAvkpJaaDpMWIJFXP12qFi6+9gAn6SnLz7Bvnk4pH7PSQ6aOTskgxK13mMr69C9WVhWuXT1/64ezlFwLN7KGgafgDx0bA9dWdZx9/ZTOfY0OP6RDzgBqp1Ux4cocr4E64faGJcmGjToQ8QZYkXHQQubktAEGMQ4HRLWLxggjY5hYpsTCUYnBCK6kGFTA7SA4z30mT4AMOtVGNKohFHFAVET/A3jKtBKdLoCJpS9U5VJjaM35oNGgMomlkL4jdoYdEICVqYoluyqfTbFyARsvmMhjdow9HkpnKbYJXmS8nl9feWC3mrRaNgMOlPFYf3X17br799rs7qdJL3/vu1PRkD7xMj1uYPGygkMF1N269+xbQF4YjniBxEQKOoN9HTC+YG96svEAETwD+ZmNzjV1DvlA1mbKA+zdNqWefePLUM0+NJoZtFufVS6eJyZtMrhIf9drsNXT8wUjMai4y8tC0E+uYvXGrVpwZmRpKxIQdQKDQdZaylWQqE0nEI7FhUH00bxAijbVJrVlns1NpmANeVyR6DaOuMyefu3LhDexZ14f9U7v3xaf2555+ZvXqG1Yg7d120O1nN9cODo93a0gn0Ghi2mtsTdkJ0NDyH85WGhqxpEAag9xqJ9hwx+YNR8eAd5dB3EWf7fDqWq5QwccBTBzw0dhK0DUoQhXN52lhiLmEAIA4sfQRVlUQSjpaOo1z40vkSwx9Ne/Yvql5dR01MJKqR/hST6mZqK70M1LPy6vIRr4lH27J9JPJJ6wYBXL7nYCSoBEoNsQxGNdoJH9j+yatoC3ZPOV8ge0fTpguAH8201pslBWjVCpkN9Lx0YQv4Kxmc5rLCfMaiduYn0D28g4M5NiO1ivwFMKMIPBHEtYGVZGNGi1AgUFR3SqlHEjtjVqoM/UlpVZp1d3t60ZaufvmZ3ak+hEOJQP+3uZz/Z3++6QtebE8qconh/xDatgEXAsIXyTJIhCUNVz0n6yxmPitWMz14HC8V3emZxcYSR2wQzP59aVNtEdow5hJ2E4j2660iRqm0w2Abig20JjM6pXyYl4lP/1DuaAOpTh81DcXtgquDlSJ5TYFVu0px9sf0qg8dmZk3B3ckVxUTkZKdShf8oQsSXJbDTEYdtgcCLoAPUG6e1g8sKfltoDkgCQlIgupAeRNLR7MKLHQJxNWPfZLXFVbYKL3ddOVGjMEQYvHb7eHAgg38GfKltKwxk174c779j5473vHZvZ5Y6P4hTq9DnbZhUo1ZBNNJ2zjwrVFxPN88MDHhDCVKbx26uUSu9tcyiGsl/vuu+9IJBLYjI+GPazNGP+Bioldf9hhqTlaR/YdzhZWD+/e919+9ZDfanV7wy6/uyumh+XP/OvfAVBeXGl6WLZVWP7pZbDcZHpJNMOS8MeAT9byG2vZoC/esps3K6mwY9jpQZstDDpzgGqqfTjkQVZKWogVThqCA0YNawoaRhAYRT2IkgObc5EHMkJoB0I/8k5xPm4WUOFWK1UCdRVLheWli42KAPyjlltdOF1tAQ3XDrjjgYhvYWmeYLM2ZzccCBazeaZ7cWOthEW8P+EyE827HB8e233zreO7JjSnJUIwIoertJ49u3bhUz/+yY8/+vMmR7BTyaxeWyzUs0SGgnKguOKfUxtqdxJ+r7fXrVj8/hMP3GPTraZKfXMzubpwedg97faw66LC3QCNbLWsXL72u7/zO8E/+uuZiXCmYr98+prfi7C+o3tcAX8I/MiDx456dO3W++6CJsJiuzRws5giYPAhWbFoTnsgkHA7QbduyIaxY6qkCsVsWgsSRMClY+ZPDAN8tSUcGESH0J/WTGaTgKsOlAeaBNu4eObs2uL5iDcemhwlAujQcIwhDG4jq/WZl06uB2fBtxM2X0YkN7A/EC4Gqi3gb4puymiW7RheP2gvKJptafaq9et/iT59MbVh7VimRoei0eDlN2brAEXFIwQZZgVr1ao9rAlwXwdSE06InpZ9rsxumEfYZcaFQAnJBOWGTALmkUwTmU2sMzLB0CJzKu+XSSejRh3Ij/zJFfUrx4NUxrFxQ/ER8iTvUKllOcK2TTbeVhBTorEgfti625Hv1HObxU7P0zE5wQx0uO3lMggRbW9sGFAnQovCzmLvk02VSsmkIHuqkKFAlZQ6FSzNdK8H7LJas4FwVTge3gsRkNZU7I7ozqVAagHYLrCqj1TvR/2oOqiaG7X5UZ/7UdLJCFAf9bNdyK1rMlWpBH0EITDWdiaohZ0/obIhE2Au4kzebrO9hQsLDw1bTbVeNV3IFDFgZ69QqRfcOhGdhpA8oAUuANxIs4okRMlIpEb80XoyIHirlEPeybGUjauDplJHkmTw2XnMtUE6DiUzeXjHRXVMEn4Hl7cf2D7iCaMckqGRXL2uXwzuSclU0VRWHSIuiXaScStKVOGARG+HeQArpawIIkCTHYMagZBCEiKcIZB3p9rt5aqMt87I9NShmw4nmPEjCWsszlqS3Pz3oA53uuXY+NQjH/+p3QePgbhcq3bSmWJ1pZIp5jXN06y0wv5AJrkY9joOJQAqtN15fOaBd9156NBoaCjeHU3o+u1AJLNLbbczhPrFLsHSIZwn1uouF1FDJEoLzpw9QiZifgT6EDHMKb7irGFp0axm3b5xyovpi80JQgPcnLScbFXg1cGBbmABiHdHOp+8DHkq51P+dNgLRtsU7YP1IO5LpG9iZYLmD3hFUCNhEppQbiRQAshZL+ExWC4WNktOTQdsYm1paW1zqWdr13PlUCiYzmwubBZqpSoRtyEe41OT9o7z4Y8+RJiRJ7/5t61STYtFz7z60rgvDJqFyV6dOHL8Ix95tLCZ/+CjnxiKDoOpQyvlkyt//D/++4k7dz3ykU9eee3VP/idz/zML//TfYfuoqa33Pyw065jX59eWPrvpS9NTj5ocojozKqFZnbvuzB3io5kUWXZVlhvgK8doDqd5ko5B0du8ul6tZglQBgUzaGUYbCDnlAkEIwAL8YGTYDonI4jt9w1vXuvLxTxhdzsjdWINHgdWpjxoYiDDDHanFM+dAQJPOoYU0QWYIznm83S1Y3NWXuxhOuxI5WGOW1haAIOc7sKY92wtueXruGjsbfQu3Lu8uWzV9ZWehNjQ7GRBCOQckhERkGmsMOWrKabtmYGUDj1CiqsNqmwdigB2BxCe5HZUCvhZmRvxkVEaWwdgcK/cvqCy6kVTK18IXPw0GGvCiys6+7heHhiaLyFVCmbAmgPNYrFFABBG3Qk5FeSpQKagCGXaS8TQwxJGSgMKv5TYbgiNk9qdgopELIjU5iJJ39q7KnxZxzJPSNxP2m/LupHhqnssOVpNZvViOVIJDW8i10IOLJO/CxAOJjYvxdFOhXEiIsBQzQiyoIQf/aNc/4o/kzuqT27G7hTplDAp1PptIdgGITrwP/bqzWqtW5T4m6yE2/V8YATEwPKaqxiUm5KIRq2naRkZ0l3HPerKLXqf7ZrqlqlT5ikcoOqD1K+/a9K/abbXDU+Uq6dmUmT7iiANB5tJ8ll70ZN6DxkXciCcBrFo5GhKda+SFRZxmnYLmE9cAUpZkqLV2dBLmCbCVYlPhRoRpCdlfNluEDxJZF+wN2Vp8U+BRWTlGfr7f1SybXB+9Xx3/mliqkykVIPGk9lwYkxoIwM5D3863/kZVsn6pgzubjzsnE+uCtDVjRLSkjKYBabaQavSLQgGHjCMAJgx1kZOBKtCEJ3BRREC/XKdTTogIt32h4tPDF217Gbb7rptv0nbnJG/AKkQywlU/mRd748v3LJ0m5O7799ev8xRQ6a3mCtUSqYTIFJz0G7uY80Y3PG//lv/P9+KVfwa6y+PruE/PVB3dh2IVOxas6AD7rjVTVi/oMPibaTAzrO+A9dwK6/UUxd9AYOszuARluJTYW7l6kGqj0wIJAEt+6upSs8Iqy/hv+r7OMh7SIiNvci48NuX3TxanJtfXNfOEHgQRqKhQ/H1e985/Nf+c43hsKRJcLtag6v07G8lJy56ejw/qlXv/vk+tyaR0zd481Ks1WtBMMxz5C+snTt0J5DD33oI0//4Puzhas37Y9HUb7me7c8+s4r51ZsTv3h977n4Q88dO3catfe/OYXxx/60PuGQ8FvfvXr973v/Xtn9uSKtl1T91GAzeQmA8+phyzWobse+ajbFb04d8U3udfsCoCLCRSVy44K2oqIveuwtuytYjXJU9Lp2LR4XUGfVwiGSO16mg5bI5Xiw0iwuWzJpSuZpDW1tITJph4bI3oBrCyUUvfooUDMZHV5I7EPvu/j73zwff7oqKLytDYNWCsTWa+QS22slsv5dq0CiinyKIwGc5kkLZDPpMBqY4Ywb1C70vx4IXhApdO9q8trqZX1CM43TgeCNZTgNQI9WkFhQRpjsXkYT+ZAeNRs1i0dR0jzjMVsRw8eeMcjDwdioS//rz/xhBhdstiA/Ex4b4/HR0g2aD1jnaFIT8qcZGzwVqVXZ+2TSSS8uVxiRsHfYATSqoN1RKysusut3Xb3O8+feS6by8/4nSGXbWP2DPHA1mcvIFoMhwIiR0JgUsXwViQ/EAgh/SIbFbsRZqTQR9lqyAZEWpbZI8OGy7IecEnN6P5EppzGRy5yj5Kr23KRBySHrSTqEomEKEliEkJvIFaipeClJokLxiYYqzqEYsHxcRewt12b5g3RRAiI4GQbvWJsYtrlJ1Jpo7i+lttcj46OeDzOQMBbSudoL8zk8HZiGtBaisFjkVP14GVUE6mAoteqAH0rIKP86ptiqVrsuPQWh0ZNd1ZLas65cWP7CZXd9unff7T9gGQlb5A25FsOVf7q1zgx2pamo1e4L6Ah6CdheBmpqihsD3FUohlEvYZHL4GWG7WRyQkU/o1SLms1rc0vbCytkk+LmKngtWC01mxihiy9QdfIoimbTnmdtJo64M52KVVBpHzq4G2+jMckL/n0n9/Ko3+gLu/MZvA+o2lJJSmMn+339B+QH7lHIiYGG0LR/GL43MX8DUkHVJ9bKn6qQwxtEPqw00Fe1moDFYLcFrJq82pWLRDYPTyze3p8z8ye/fvjw6OCUC/sGEKVegej/2apUG+uXN0g1Htk0ocBSb2VqZZLhKrDqZRAf9XluVIBEVqlnFmvmjpVdE0IgjWntokhIIAOzU65UsvlnErh1a7DmRQY6tjnYI3SNLtrNeaOyBncHqLE4LzaKdQqG7PL0fA5pEqpYtHn1fdPjy7kNp548eld4REqRYDetfoiYdERYSPTa4h+olVMbtaqm9D5cGwsNByzmuZXZlfcml/278x1mqLVe+Y7L8+emXvgnz1oP7fyrvc+eODwyLe+/P1HPv2TUbcl/VLyX/3+r+6dnmRV+o+/8We59cK/+a1/mc5t/tkf/sVP/NQ/iodia/HFfT/7fmt22VRb/djBo97J+L7Y6A++/8P8+Njwnlv37nf/3u/83s/83E8Mjx1dWDx5+dKKFvzhnpmpqb3jjz/+zdseOPL6s99vt2rH730AP11713515dzLL71y/3s+SDzX7z32x7c/9EjUN1JtpP7kM/9m3+33TE6HCaMrXctAEEUFqipIttSCISncrzCUWFLlrpx7PbexYaPVe9g71yNE5HLqkBooHBMEuSihFwLRIHaCZ1598eVnnjh28xEt4MMyqpVL5nMNh8fHbOkBogyYGqGQCGEmGjRd08IeZ8LkCXj9Pkg8Cu3RiV0OtxsQJrYhbmxzD3crmdLYvimX00+n0cIAgSNNEV4dFyzccAjOY9Lsmnss4TVXi98pfDEWioV9Cc3nCoVC3mhEje2W7nONDAUPTE/DwwHIJUI3M4bvjFdRwLDaQZLFel3GuXhRsQbKQq+EMvB2DHysn/KFsiMS2X34ptMvP53N1MzdNQbepbnlN4YvkFO7UPdgGMYYsFqcHqIpisSYjSMhTMUelwsdTGMQAIqyQYQ96nX80t4QAz6yzRYKqj6DKa3Kr/pH+kVdNVJQVA5UgeVZ9Sfz2shBulD2H5Ircww7aOS1hG3CWhsRGOJoon+x72WRoAG6XafmZtYSydMbHakKap7DPzbWzBSKmXW20eVCZXM9Uy6V61UEsYB3g54hrSbdT+8TpYjNrUh7sXCRtUwGDr6cUo9+EeVw8OERowZyQYjvzgscG7f7N+R0R/Lr0nJn56Mq6Q1fKrvBta23Gv2s8hXSSy7ysyMv6RU2/NRNLdZoUBBvEbHEC1y9zUkTiDmAoLbLDBDU99RGq54GNJ7wpFAiRNJgiJE2Ho8BJ1livFkxXiHEtHiQKxURb5UxN6i+lFMVZausRsmldH//R4rLp/8lecpT6kkjm7fIghtG1v0UxqomJ4a0p5+dZLtVCB5A9oV02Ikdnei+ZD+rHkRSqtUAxzL1qh3g6Zvggze7TQSfeiJ+5MCB4T27h6d2xSYmI4mAPxxEfQ4HhmMu6sq1pYUXnvvhG6k8pjK5KysET//d3/71sbHdZ84+WZaQgm7EjMQGAZdH97gDPoJhIXJzmtxObELYGuCOiFWnC08NO/Ga20DEgFYGpDCjFEU9+hrQdJD8APiCkDmPQKZly9bWam3Na7Knk9lqptzS8PGqpfFiHdHaTRw+/JlcW6ulkOfA7gkoNMwicTwIgFHJ4fXD6Ac0sZBZ8s17dh85DnfkCVaY9gTNELG/CHqaaFWtU9M/9+lfaZtKi3OpeGjq537+NpoynTn1wR97Tzg8JMbprlLP7pre78PExeWpLi8WKmjXQp3U0ua+vUeC0dFf+8X/sXf/7H/4rV8aHx2emNj9+Pee/KDb6wfOt6WlM9XEWO7lZ17yeF1er/PLf/E/Ccx48qV5b9RXadk21mu7ioWHH7n71ae/f252/qbbDr/3Aw+fPXlyfrV0j+DsN//wC19+8fW1ux6OE9/FG2ZDIHwvxA6qCNgjdA8zWbAOmejtZqHZuVTMFSGggLY5IL0EsrZComlUBOtFi9OHwisUjn7kE5/cfeBO/Lk/+dO/UtrImMVebgiCCGoOfpR2DOdYT+hPOGFIoIxYgHShQAYHZAxQqBUf6IbQDzUAGXFiGAZBVRfVfWeYwaPKzCkmU6Qkvd0/PqUHAm7Nha9qBSnV3JrbFxse3SWJmx1d1w4f3A+6OIoZW4OYvW2ENBSAgcF4xjaPsDGYrhMsTew0WR8oAXt0zBvrTcydwbeAo0/nq01nzqrrZqen2jK565ZTZ1ZRqC7NrcSj8YnxmO4M1BplAh4wTiW4CtpVpI3QV4Gcg6BIrkKLCRwudJcziZ8m+gCR/w/mmXFgTEg1R4U0yUdmNh+h8FvUikuc9gmhmr4qDUmFgBm0TfYEaNWpZQflPvpr+E5vKCCdQOgENumynWfPDkPrbJqKgMdV0muRaBhTsUAkVlhdRiqo+71kiesfFB5REahNrToGBATRoNfE3Ig8YQ2Nt7K2gQ2iFoB+0aWyfIwKqMP+Fzfk4o7bijpTcOPGdtrtJEZ9t+/8g48gcf1yDUrEOVe2MlZFkvYRl2DYWnZwKG2BJifWqi/AnKN8zBbpEZrA1guNxLotoMxL7Wq+ns9Xi8VmpeLxewiwR6QvckZV0OtqyBwI0y2Z0+UoTK4vuBBeo1z9G8aPUa7rk77pjKQyDHZ+BjU0rt2Qy87G5ZhHB08bv/3+kmeNfGUkkUiKiMyUSSJqH1YwcSyVecueoFQsZ8q1pY0MUaKi8ejU3onYyDhRM0emd8dnxvSAj/11Tdj3JFFJ5i8vvn7qHACz2OSxjyKc7DseeCj2aOhf/9r/96UfvHDoV+/92X/8y+GxA1YXvupQD9oejQMaJ8SltBtGexw50MJATlDjURoRP8HKtJpib4P5c6Nr12T1boH5aBdJq5jyAvSAaB7hMRqqrvCbpmaD+IzAUUPYwPDAb4YIxN1kZnHpGmkQHEEbZBpRe5Aj20gzdIfbGRmONXrri5euXjr3RmJ6L1AKrEkQUcGllgFj1YPB1Pkr9YZoyf7o935zZGbP/ffccXV+fXL3jMvu/P/81P9+x7sP/dIv/tKttx/6wde+hdGTq2u7+eh0emkpoWEfVbz86hMf+/hHH37//d/469cf/9pT7/zox+5698O//7v/+S///I/HJvf85C//GE6/Z06+4tWD/+dv/yco5le/8OdgTr3/wx9IrhbnVyo//umf+uZjf/WO++6c2Ttz9uTKvY8+ZDf7X3/u/OTuIx6z+zO//V/Onr76L//NP999YP+pl87h5ym1Y6crsjzaB/opDrDCX3fLwJzAEgYmdiNb6tZKBEHJ4WK7gYgcYLnVwNARgQLsNeq5vMuiIypx6Pp+fDUOQ5SZ/v0hiHRvxwciiN6b1hJTGb7hjw24PcgjSxDdhz4cgkx3w4YTkbhWLEvgddQz2MDiIEyPEJmQoLXuKKQri0woWRnbfSQ5N3vq5IuZUmozBV7UudRm9urs4srqUnRqlzcQrzcJEJA2a6OMVDG8ktUOXxzmW4+IzVY8MNx4/zHAIQ1MTmzSrCpMtKC+s0QQRLfTquuZdN3hzuQ2amaTHtYI+Cz0G7T9WrVQKq+sdIciIYCRcYBm70vsmjq6fUzUzF0WS5Gas2dFYgX9xCtAkWZmHi9SYiEahEZnpPVJkzEP+02oBuCgDZUQSc3ZfvvKwOsnN9IMnhUKRpZClrnBmBe7Bqi32UlkBjFHJKqPA+a9Vi5hum9DY4KLA76ERJYL7bG0617vVGNzaeFiHrh/FB4uqoYFjM1MkG0BfhH7T4nc3MN4g9yFn6VYNCHyAJmnagGQElFOowBG8d7+e6tCgwMeGxwOsuB865LKl7N+hd8+4xvvGA1m5CMNrorXz1Zqof4JuWNSiOuNCOwsZSA1rB2YTSE90mnderXSrpXACsVjEiUoFnBm9stdDO9cjaIlm0zV8bAuAlvWQMDJnqBGJMJ6y832gY6XpjJYnv6L1ZDod78UeFCuGwt/47mUtt/V6mnJzni2n3G/xfr5vflxeqzPW6iGlEFjNLx6Xg7Jt/8WaocNIyYT0s1c41gpgbOZTLPW8GrefQdv2n903+5DeyKTQ+GRuObxwBK3QcOZu3j21Mnk+ub6cqqYLlaqUBn9yPEjh48eGBqbCMQi3ugkBjbjEzPJxcs33Xloz01H8KzgDTS9DDQMY5QsHs/KNrupDrxmAx2uFZdzpHSUhv0YhbRqqrZYbHNJNtZ4YsgVXGVFnM1I5cNkkyFrxVlWNAeqmnIRhSSJiIvtjg6PwgKzi1cue2TDJhrqprEYwczFwe9OHB2KBE3VLp5Auo9ZDw9V7zURD1rdfn33ZODKnNboVoms++v//t/92Wcf+8y/+W1XYubDf/zxaja1e3jo/LOLpl+03X//LS99+5vNXMbhcdx208zhw7vAKSC2ki1TRQPxs//kJ5eX06+/fvadH/kQ+5/xqRFntzo+s0cPhE3m4je+9DXCIj74wXc6rcF3vf8DAvpgdxRXLq289vIL3/7rqbHEMz989md/9R99+OPdU88/OblnZjruWTr9WuuuvfiZgUl64qYjWGrW668IegMtQBuwmjsJw4lmh6ZgaEIqKj0FZN7plHJrc+1WFa/PFsIQMYJgjGAA7GZJRjD8g2996XNf/fJH3/PxRz7xk9nM4rXLl8bHJt3gZvcsmyuX2vUK/E6tVEwrR3lQEOvVqp0gD25HFd/pQpWIksidPTwApdQ1DD0h824PUHemUqHMMowZGAOOnbXd3YXLymZbLad5hDgKVvNrsxv2Zuzf/6ffXJ67BjNR65hB7GgBfVpAs4Dja544OeFYGB0VYYUBfmKrSqw/GARmoEDYsSWRPYkEiGcYwZwj2RWLVPgKXo+trtONQkhz2D0E2Ql6OoEAfATxANhQRqLRfLmGfbzd6ZlfSmZ8GmaQYR9YSh6wqACkgyYIVi0ot6JFFEtw2BSMTBnIMjr5x/gW8whsQ0SSypGi1dxU803GqjrsX5Zz4yMj0iBbxo8MYfmTXlQHKoFMY5m6A77NEA8RqQHrB78fd0jWPPZkFpfmpQBUmVXO6SRsEdFBy71OI59Mpq8ts1doYMyewhqLoHp4fDaJD0ykaJF4UjeHOADDICt+Wd7N+gltQOglC4BRKFWyt/0y0kiJ+ZMTWqL/5M7H5VidS8odnxtO1Z2dz+1Iun1IEeU5aXDJ1cjXuEKLQfBUm0mXQOCwHkf6D2MkjE+XNbJZNtt0REBuDQAAH/3X7RaJk1kvVYC8Za30R/wa3GobTOOu1+tBfwCsEtvJWr0uQfxEIsfwwlhEKLcivqoYqv9VuVRB36pi2zWQIyPFoLJbZxxsPbt1cceTgweMam/lsyPF1qG0hTqhexVHzHhhf8xlBJkMBcJu1IiXCRyDzXrktjsefOcdt977gBbC5ckiKtZCfnl2ceEy8Z+W8MpaW1tDfRdNDN9x2/Gp/Ucndu3XEyHGHT2Acl1oryU4Phx3m6uxoRhaXyJpkTPzRF4nBYFUcdYCzAt0AZk+ZrZklI8KMY9IAXuFX5NsS6A4CHUJAMbGlxWLLYNZbPOUPw68HT4alJilg87odQgNWc9muvYYCNCX19d++Nqzk5FhcoYeIa0VF1s6iZ1AB2UFypxGKBJEXmqOjnrcQVtiF0tJIZlhqy+yDfYXFlSGWberiXPYZvZKreX8+X/5S2+8747zz185+cb54/v3/PPf+Jef++xf4mnb7tbiI0Hs6tkEvPjkkyMT42O7D9737kMbCxuKgw788i/8wsXXXzWZvWjIEa/fef99o1N3tok9kMnfddfN/uFAcunMq89cfPjTn3BZXC+fPTcS9j36kUefe+a5Q0eny+UUsvurp57O4/Jmqt79zsOffeUFcyD46M/+2NlvfTe/NhcYjsNUA+HR723a2EpclwpiELe7W60lq8VlEJNBtZ2/eH7+7KturzYSG/fFh9yaF4kolqN0ivSI0zp35fLapSvRn4yh4Xry8W88+53v6g49PBTMJVNL84uENPZF4kwibJ/B4wQx1A8+UCw2NBKDt24gI00WRqbHo4mhYGzIhk4S2QuiOvD+e91iJossQSdamEMXE8aAK5vcyJbLG5nMkT2JySn/zPMny4VgeHRPIOZLbVxmAxCLxsZGRqq19uRDDyC0Hjt+DEjQkC8I8geknw5n3LIHINAYkcXYCxDMjhYQFSje7LS7SKmA3RCWAb8uIvehaRA4AKet1MLwn4I4kbyFPNqHH32YTcYTzzyPue7ufWPJTH52ca0aC8BdJyJxexgvOMDw8MzApaFpdiE7Fe6FscQSw+qprKJl8WHs8kYFOyMjXdgtodt8q486VUdyfcfUVkkMomGk7N8njVBjY14Y5A2Ok02y0DGGNVNYrF9x3aphyc6QZRqAzg1Dh9+j0LxOe3NuAXM35lcgGu/U86UsbjGME7YOTrzBpNVYsVAc8xaaS6JPcAnQBLF+JpliIwQf47qPKt51V4wT8tqu1dZ9uXrdhwvQALksFRe6ZGT4VtkaN697fPtEHpA5vd3Eql1VPnxtf6QVSSlznZ2AOAA02aC3q61WxWFHYgiYuGyfBV9ANAJWfzwMJK+5VcM6otzOss1DEg62LY0Logjob3aTBWPEXp0eBx6dKsBrifxPKiMfed2OWr1VzYyE8t1/pt8oRo0H1/qprj+9/mw7Izm6rt6cbp/L8FMfo5xQXeTgYqrOOgB4psPNstYorAHO9Uu//r9FIlHaa2Vh9vUXXwM4LbW4THCPKNAGseCefQcO3XbX/qNH46NToDiYzJ6BlIARg1+9HRYEmihG7oEodBdzzHo5ly8VG428jWDkSB9Q+TYQ00qL0Z7dpqVi7TlRwxHKUe6iZK+C0FAqFc0VFIP2Ti+fK4J4RZBGyFnWa0FawazXcN+pdC3pUsRhL/S6BTTNmUq31CqHPbZELHExlVm6eFU7QJNgm+5i+UdErJwa4fz8qKOt5lBmbWNz9cLiygqR8qaO9nTYVp2Ks4aJxoClKVkoVKsoO6sXXrj4rcef/b9/978c3j+ztrD8hf/8mcN/8h9qzQzY9vQ04oF8sUK4dT3gqRVNj/3pt37tN4/uOXHvG298/vyLr7524bTDPPboJz7CJKsUqs88+WLYbxudOmTD7DIcuOneBzWfN70x64naCez03BNPX32j/IFHP3bs4K6P/dNfyVQy939s/PLF01/+8rc/8tM/B7Nv1ggVyfaIUFrOpRfmn/vys+/7leM+j6vSY/02Pmjje4iw2a5xjo1sLZ8JDu3x+ODN9RAamuQK++FSJpVaIeKNM4SDgZW2F1nggXvuQhv/8Kd4kfuhD/54Nd95/4c+7IkGzr7+3MkXXokOg/V2HAphA0bFasUQ0xcMu726Hd0Mzl/EzJ5djIHdkRjC3Z42pAHRu6hByeBjeAhZ5j8kC745MUnxED63LXaGUP2WW0OZZURzYrnFYo9e2UbsBTNxuQnTSbRi9K7sUlicCQ4JlvsoYg4UoS4XGmlhy6psSmBmEWci2RQjXVgX3sd7RTUK08aaTwgw+rLmtGZKeQ1VdtsC1k2j1Z1bWkChNTE1vLqwhFhq78zo0vza+gad22UrMhKLuT2uhr3DMgAeLZZpAt7XrIusEloL7222wIyj6GbZUZqQ/kxjBBnUTQRU8tmauFsHXFTUS8jYjo9B1Ujfz0l+FbEWGg+PRLZckVcDxq2WOXE6IVAzNt2wLULiLYg0GrmkS9ddTt3qALU4lamkgwG/G+m3piHcJqyS2wW6NlZciOkYw3BdiikkayimbJ0s2F5QdtkB8DEKc31JjTvqmxtGXXZce8tDIx81weR+/1Ql5XhH/sadt8xDXdxKOjhQRZBbAwI8eNYgx0JwhGSj6SC6C6HC3fqQ+IfSkOgB8KW3sS0GURK7b1t2s0Doryq6zdl59C5slBgKIiZUzifAC7O5ZT7L4o+sgQ5RnaxKLKVRPd7vv7eshirhoHjGr1F6oy5v+cwguXHTSDi49g/5lW6S5qfa2DmIdLzVdgX0mcM4j4etptL8uat/+8XHMukM7oKwAKNT+xMgWM5MHzhyIDI+6h0acni8VjH0htBD34Xco6DrEvovl8nUa5Dh7Mb6+dlLtVz2r/7is4GEeXZu7dpmlrcioEU5mcamXxC46h5Wzo7eNflKFmfcbx/yesxAk5vNvgABHu3dSt0hw08Tal/B4l0H+stnj/pYNrqYfgi7Ah4EcJq6HidyV22s+9rJ1bHh3k/9xAcmdu2ZzW7+/3/lX43unmE4sHcAxoWNfM/UgBkEARnYsV7XNbu0/MqrL2JHPxEZX19d3T09NbLvuHfMiSmpzAFq1bNic+R0uSf37Nr87OfShZWwv/70D36guz12oUZQfWAYumwg6wUrMpv7Hrrv1/7FT/7Jn3+rVMi4/O3w4ZHf+nf/Zs/ugz/98+/TdJ+pl3vtqSffOP2qrbWe8GvH7rnL4Qg5/Do4ELHEdOD+YQZj6D2J9M3r6+W5sxdW3jkSOX/y9OF7bq9ZPbuPP3DzO+5nA/G3f/6113944c4PvLhrf+zT/+rD0V3j7Wqu1sw6nCEZBAwLWdHptyZ9S91Z1BK77lBAT22ne2ZkP8aX1c1ri2b0qJau7tbcHh+CFDgAkWk7QxO7DitbXmRugQO33DZz5F4oQPB9k5NTtwztmvE62dJB04mokm9UmkCJYmPVMdUZB/Ru5OYEcSYtYg0sojlUKU1QuO0I7zAu7nSgvR6f1aTTgVJUJalrNkAicok83xWqNvNcBTwQ3/BypYn8nYo0wKep0gntXqvGvpxFJYJxkM9bLlUcSMDFz9mFYslhc6KCID4r6CO1ZjOE9ANZkwgtaQmUEuz47XkQBJHxdpsYcWhuTdNIbq42q6dOnR8eGQY1fGJmClKQz5b3H9y1trK5vrpOdRCMTIwPoSMRIXm3obzKxBAUBh/vLMojk19mo2L/+VX0h7tC/fmoW3Jt58e4ZVyRY8lucJ8DEjPyjS1EnyRC2YVdV6SFdQBKDxANi52mEwsVX1TBrhDdv+wQ8OPh4Q6WWcACsjJ3a5jupmHG0MP16g4sHLDkRsIh9qAwtnbAVWGh6DNZZUSpCemi+EohzBv7C8CgfINfKeTgmF/Ku+NMHaoUO5IJ1Rk8Igecyj95TmbbdR91cfCGHXlcl8g4kUyuf7l6idEpkjUNSRfxCmF78fQlxGvArvuZMGxu0AljComDD9uoZj29bnUglK7rfj/Oor1SeWh0CKsRPI7QhuAHUAPjjIZBvSaGWYgBRQ9pUH9VBHmz8XYpG0c31su4pJIYpd/6ltYh/Vvd2kpjPK1Ob8x4kMa4buRifBv5DtrS+JWtHr2N9xvTplgshiMejOY2r7x6+vnnn3/qxVomdezWEzP33r/n4CFXcEi4LjMsDhMbIQYf5J31Wi2TTW6uLq++cfZlto21srhUgYgf1Cdg2Cqd3q5jB4iZXigVgMscCo3h3kXUQFRSHZerBSSb3eTttT2ucK03XGqCA+oIOAC8RbZmCg8FiFYqFnZWUBZctDWSX6QO2M4IZjW7hS7xCJs9kMt6LYdTxwzE3CG++rWo9sLRd31gaOoARdytuYYCMc3FQkWr4gZILjCeME0udH2dehOWaGpy1Ou/L7NwLRYer5R7TRQaogdThFNNAjAk8IZAqDW2Z/jY0f2Pf+vbw8NOU6lz6yP3kMzcrqXT+dnX5g7cOXbstoOLZy+YHrrHNeGNjXswoMw3s3t2j/3G7/zXyandZke8Ud341l/+0ec+94VmqrR0cf4Pfve/3fLcD/bfeiIS25WY2EsYRI8WhGJqu49ExkZ7l17bTC5+7n/9brPuGp4MEAfxIz/78XhguFy8ev7Mhn/8xIR4V5hu+tSjf/A//udyrx2N2H1oFIwPQ1OAThBOUhHWFJh16lBXhDtXKS61GkU9jA1EOIimE+qJoQtmf0g1sL8lNqyqMDkhDynlNrkM3XCaAvjiep1B9QYIQn19ZfkHjz1x33vetffQEbZ93U6ZUDiVfKqQTvsjADx7QOUDYwB/03Ak1GxVFi+8gqJtYs/ekYmbLDhA627R4WNh00g7PBEGLYJokDB4LQbvYNMi0UE/TKw99iU2sCyg4ChqQVxyuYrV+slzl0HuQwwFJWYNYNPAw8ggGfAYhDpwFWAjgfrbZSe2QQMJokK7tXhdnpYWcFs3U+uMGrEgVQJc3KVatcb6Zq5aLR7ev8vl0Aul3J5d4zZLN5stZIkGNduIR7z+kG512ggWi9qvWUiBmygaUqEuMm1FOCMkR5EzLlw3kUkht/odtM3Zc83YHvTnKClIanyM7QPffcqKda9sZjgV/R3vATAQ6EE5ZX8ghBtaRtFAtmcjxK57HcAtNsutQqmSStZKtV67Ws5ki5lcuVDCyRxfDBpHIl3VkN+hhyPiLYDY0AXBnuWlSEtgDlms32YBGBT07X9VhQe1JplxuF3J/i1Vw8HVrfob2W6dGgf9J97qlVspBze5oJIbSw0tLA0lV1Bm4vrocOjiAgZ8I1W0iHTSZK66h6JAWgnOBiqxVtPhsTfqltkLi6gLQIrAdJYtF4tqu+kgNBOqE4Yefc+nbwTHdnRnx7+pTIOybY0F1eE0wP/7j/HsoGGMGhtV7w8csh7kTyNIArXaIh7tWXBsYCHTglooGnvt8af8Y+PIgQ7s33vw1k8dvP02i4DlYfqBVIcP7HOtkNzYWJy9dPbU2spKbnOjCCZzleij7sM3HxmeGL/zXe8a3TUV8I+Skka+5557brvjHpjTerXEu3xBVlw4RSa50FSVLbwk+QcUw0jvcAqd4oACkwZ1O/MI7p1XY0mCPbKQHlE0gH6iN006w5PRCpR9DU4uODU0c3S/7g+qzE1gH7ab4jLAKTyADWBDgXRAwi8igWYVS5h6LB70uycCxHYLDtk17+blNxAzwCUIsyBtZnZZNVhIk6nIELHbQtVC694f/8j+I7euXC6glo6OEMfQ3ehlrKYjH/jJ9339sb/6+lcfazYzfnwjnI6Z0bsndu21mAitVZo///yf/8FnX37yqXaxyju9dkd2sfA3809/+29fhaDceu/du/YdmJjaH4nF9SDeZtbDh2/b27i5UQCTwL2SXsplU9MT76fF/vjPvj11+8FP/cy/gqd9Y+6Fp86vfef5K1af9uBD72S+UlXpWmA5AI+X4LeMSqhiIwtAXmq23VwvbsxxOrl/yjkcFmtCNllYc6Vn9eBu1nekgAhfhClSHzBEk6nVaibpDY93TYXLFy/arVcIqlcs18YnxoJuz7s/8K5qWfzAQPPrNSqvfP9bp8+cgiaPT8VHx8f8oeDoxPRocMTi0JvVJkZgzXrF6rbmU/PZ7LruZczF0CMguvAGRXSCgk0RcDG0I9QkYRqLxRL7SdYyyC42xPgbgCqNo4bd4XXbGzgcYNfRxbUZg0+0dCCN220IdFDyg3CALIRVjYxiI1G2FKIuZijhesyWpFYXKt3plbJs4Br5fG1zNQWURTQcs0X9b7wxx6hIxMKVTJnQuCura1dn5/O1SqPXBh42PhTI5yqutY308gomcIxbgWigudS8UweGdEa6wmjGrW+hFIPlYesiB+q6uqCG6s5b5EsuDMj+VBYPDyYEjD8diz0oUIro2SFA2Dix3ouhBSUBNxQ1prgOtNtIxnBYVQHQAEr3NGxohtp4bRChDiPvMlEj8mU8olgfBTJc4jixY8IonFoJa8vb4fXeZgFQdb6+uKrW/etvcVsa4MYH5BGDhRfKeWOjXZf6zc8ayY3rxtNbabYO+lmodyAcpFa0GIZfrKD9+om7OBssRJNcsVeKWI5byplcZnEVAVk9X/SGvIIHB0y5CzeiloxLFkyQ04kogRTCWMNpeumowWtVaYwzCjm4ul2d7Ytyz7ivrqkHhU6rcdV/YDv1dg6Dox159w/7zIfUuJ+b0bCSi2pkygxHjL1Tm4jHXp93fNLbcPrjew4evP1O5LnQUKc3rsS1nXYtWc5vnn7+xdkrb8A5FgnUWq5h8gfJjMWHT+w5FJ2YGkLsO4TaULgxVSo2CpaQP4CNI9aWbBzcjijwnQqqAeKuq2VAyH2zm/rh177ULNZP3HWPFnTbA8Psr1BPOZG1SdGx9hEkBqLNWOzdanHD5Wmkrj2TSZ1rNHSPlpje9Q5PMIQMoA16B5agLjCcp4hOosrQFsOtLks2pUXtK7Ek2e3RBFAZEQJZXC2nLbmymk9fyyQzhAIKjo4TkkV8UiFEtBQPMddqlfX51UvnrmrxwCuvXHz0E/eztYiGA3/23T9cWTe//yO3/eN/+3MvfvcJCS9sqpXaplNPP/upT356z7E7vAh8kBCaSgsXX/r2F750/sxrVy7Ogi8ZjUK2CI9bgX2tV9uohAHw/caffEkH89rvPXzzgfHRoalDxw/ecofuj/m0KOaHE26pwalXn1+4eiE5m3vHP/kIPIzNlP/Dz3zF7op+4ct/qpkaJ8+dbOtJE+IZSs7gwTDQhWWILKVw38X8GkosyHSraS/W8oly1+kl8HCnXk6defV8KtcdmbzL6eUhHJ5crKmSC0DPePZWKulUVgvGAeRYWF4ORLTV9dnZuav3vedDE7ump2IT5I9D3ZWTT8zPnn/thZfPnrqAeO7aXNjqME9Njb/rkQ/sP+pBSOECiswXA/cJlGjQ3MCC8oejmi8E3UGPD62RNRe2U1b9Lob8mCa2QI/AAhg9Jz3LgkwaBgX8vXzMXqc3QKzCBhagmAvLTBYmATTTFm4iDnxI8HzGE6AJWwvyn89jw1wIatloFqudjXSumMmwBuOu3KxjCCrqY81u3jMzxI4D959yrXnx7PmR6DhbFhAMMfuYXVjc2MhjMoQYZvfeScjj5QuncKzVXQ6E6Ii3EMiI8ITyy49QbZmL8rdN04wrcm/nR065w488dMNNSSh157qxBNC1jF6aC9hdCYXDss26CCfKRqCJn1utbGMeMrh1pKmWTgnb2hJcExDQutNaTq9jL4HyMpfMQvEIBeQCEB3BdweXQCJht2krxozwsowhaU6WFviKt9oB3FjQG893VvHtj+VN23d31v4t87s+uTSY8dm6bjSk5KOyVacGHYXuqWHWH9ziDCLKIjB4lZwLnyDUYLBENXBpiQ0MWjQo8bsnipvrlXQSuESADHF4x25E8SnC+cNaqfklrGq/c2QFUN1oFG1Qtf6vlGrgRVwAAQAASURBVMY4HNwYlL//S5MbnxtSbdXzhvQ3nkq6flr1Mxg028/TndI0/GMPC/4RfLXDjp/ucmrljg/f8cCnfsJs9StmHBpaLeTWzr38/MkXHod9I+j26MTU4TtuYzi4XEQ4IUBmBOdyPRSlDRVBR6ULzS3gJwP9NsnWvlTOpVRPIGmsrl6+4gt6nCH/4sWzpYbd69crmfzM9Ph7HvnQKy888/1v/LnW27jnvf/b0P674HPLxeXzr//Q2syM7tkXm5hyWvyYfzp9k+jyhuP7nbZCsxv2BHZpwajJrHXRxTeKdve4mPZrrAcsG8YAAATI6/FFEIH4gkGs/USFI1uGJsIu5gmG0AsLK5feOFNY3JyZqCTK1fDwiMwutNJmJ0QIFqpWzswtzJ489/It3pv3HMAbaYysW5DtbDJoRfMBqGx7dm7F8fTTsbHgzMium//JbfsP3cbmoGeqzF84+d2vPnbp5GuzZ4kCb/Nh3+mArWqWC+xyejikBVh+NVdIj6azQNwXM6nsSxsvvOTo+RNP3POe+w8du3V05kA4OhwMBo4Hb0dGEfN5rE4fKAWgMvzJ577Ghvbf/dtPetyjdHtx/RkviJxScz4sXs0mFpq1SjDIQZ3QjCMT03Z7NzE2tHj+lVQ6o/k0hAfFHEjx3WA0KLj6IrVn3RB9iZEJu4iRoZGRqXE0A35v9IH7P0gAvXIj53jKNjkZ8wr0fw/G/5knn3zl+19fmZ9r5OsmIn7aTJtzEm52GYPKZO7mYydvuv2e6eO3mW0ArKLtaDv8ieEpN27cMJ3IY4XKMFIZkxJxl6VXBB2YbNlsbTuCHKLXNpregIdqiM0djY92F8LX7RLfhMB1+GcA6iW7H0hXt+vmHnoe4FRxasD5LU2UtkrZq/kDPmJCWJttj9sXnInk/D59eJg1hFFQr4Ng10UfQN71eikSDeiVurlLJNBUvpT3eAMAEdot7guXr+IKurScgy8IR8ueQNwbCIKRwjIlzAqsIq5VIAyK1kqojDSiasjBrJZ+6besat/rv3ak2nlDXVZLgFAWDkQ4I1s9bgCO2rMjExU7C6pPB6LgwI+Fscc+lm1rF+MFgj2EYhFkQigx0ZlvrK7jkINEFCghAi3R7GiM2ROIIQwMHJIkNlWGPIMTdhcMMpQNO4v0VseUR6iNlLZfEaOEb0pLA/QTqFvXHQuJNh7o/xi5bZ/ITSOD66/1r8uPkb3Ktl8mJfznunAPcBLIRCVRVxyS8ICXVQGDJ3Y5ADmh1bN5/L5OjWW+zu2eeKM23bpeyTVQOjXt1lLOhIKK4LZYWNcq8P8Otd5LgymrKSkfZZN3qqJKgbY+qtBb142qG9UxkhiVkutyVWVkJDK+dybayvPvOuCxQZay9qkhI5kLt2QhWigW2G43gprFuQUiyN38jvvM1gD385vJ+aXLz3/3u2uriyAr3H7L8eN33O4Jx3RfWNZLpRECct2CdESkNwwQCH9RxAmQ52bdDpo720gUcVVMjvNIbHrdRiGzfPblrxAB6973fXg99erp08Vd+/Y3q8zRTvjBqVvf8YHEdOzZL3w1EJ5WXH/n2e98+fOP/VG9WNk9EXvwAw/e/sAjuhfy4cV9qOHwBxMPVRs5p8tP4El0AL1OoVvP9vQZ2dVTZeW2QgcwG4nVLDCQQtgcUA4IOv3Fp1Kt2PDtqTXwUZqe2FdyB81VczqV03x+V4igGdSJ8AFtk0tiwOXTybXksrN69NGPPPjKK5duvvNmp8lz58NHC/l5nKoYZg6vJ1ts700cmpzWCTPOw8nlU1/9/OdffPKJ5UuLAac7EfBAWpDMYmWErsnmBfqf7oDVsjcqjbVSFSmT12nSg2D/aDifr1xae2z+C4+P/u3knrE77n330ZvuHt93k8MddO09HBgZA+LxTz/zH774zJVf+6WfJl4wGylxcLHpseiwbG7Z5TB2+HR7GODTnk4tBJQ0AW+YAC69hMg/XyksLc5aWgRnrx2+9ebg0O12uSszhPg5PRSK8iHArx4KhVGxcIIiwRPyNhpZc9d+0833uN0MBlu3nP3uVz//1S/9VXU96XHZQ5qecCeq9XqhCWAHGDTNV59+7cLJc6+/dvKjn/zxI3c9ZPcmUHdjeG33u0R/KnMS/3v4ECYmL4W0yXvZVeOqhJ+dGG5hhG6nH+3lSgGoTt6J2oekOOiwPAiON7phMTYlxl8DKS21QLGLFzGLRTBM5GB7Pl2s2Rv2SgldKGMesaLXF2hjDKN51N4C7bEEqac0jFfGci6b8Xl9u3eN5bPBa/OLC/M4oTmmZ6b27ZlaSyURZi4tpAul2q49u0Jhv/A6wEJIidhVCusMJ04VpC23p61cMiaj2oCrsxu/SLP9QP+mcYHeNB5j6JIKyY/h8CHvMOPVDWCfZM6qaXcg5ZGmBMouu4bGml0JmJfQJnYGKNYZq/6gn3A3gUgABc9qcRXcWYD2UKyj2JTsGDWygrLGiW0Icxv3CobX37sAUOA+vRWaI+WWUver8VZV699SP9yXRwbUv39Lrqj/O5Oq3CW9fFS+6uvN6ZjkfaInpaBJBqfylFABoNWB14Z1lfUUVaMIu4V3YLFjZiIcRPQJjS8Vqhj3olsv5EoNNoRtcZRgVSBCJBQQdzzonlgWQnmk9YTuywvVxzgYlFYubd0yEhjfRg369ejfoPXeJjVZDHLkZTe02fZDcs94ofFjPKaKJ18UtOtwAuPVYhPUMRMz1ldKlU4/88PVzcVLrzxfbFcjgejP/crPC4ClO4CjZ9dkZ26JjL7bzCVXQeLEkdCuBcyYv7Vooqo7NM6sRLKCPgBAGQTN+JoLkAgMoYXQOt29YBLedC8M2+33fvS+d4eB8+JWtrKUy10K+iA0B9/zU1PuWMJkyl145vt/+ad/UqmVFpc2S+kSdn+Xr15LxEcP3nLP6Pgu4rh3mhjhrQJh07E17FB2rDCtxXavCqIRtn5w1tJ2dA26AYxJEFhxLr5+VRGV2u09GzRR6zVLcLgHjx3vErMSbUG5vr68iUYMSkcAXjEUEz7UEY5OHD549/t/6pfHg1PjaD5Rc2aq8XDk9kfu/OJn/tf6xlIsEbvl2InI9FhEo/DdZHL563/9hdee/eHiG3OVVCnicxzaGwZF7ursKq432DwibWXbjfQWfTQ2LgSqBKYAq2JYCNYohD0eYfAt2Xypulx9I3n+2rmFV469/PAHP3z0jgc9voTPE++Zwvc8cF/44P6pmbFnn3ruxN33EpVXQtW4/DxIZckMpx6Phw2GSMPgTj2BhGIp6pAO2fY6TZ0ye46C5gdAzMtccLhDTBmIK+GwMJI2Bh/R3qHCkiED3NROL19pVS9HJ+8JxaaoaSm7fPbJJ7772BeWz1+K6GAAgZkrwOjw3QSgCYIH7fRtpEFELZx86Xyj9ueFdO2WBx7yjgxbTD4AFZVPCBpLxinSE96AyFr4V6akjCx8WBVmn4i67dZqow59UQWRzTfrpB+NN7iEuUYN4C5gncCVqmONJaGsEMsA2+7RBL4lEIoxUBuVssztHkxJBb6ggp9nOd+yO6uloljxIZnC7INFFFMkJnJb5DmggESHQhgF0lMb69mLFy8eOLhv1+T41cW5Wr6cThZee/HlTo24PxJttIGmHhsqGAfpVaE9Mscobv+HIzlTn8EBvzIT+9cU9eyfXffTr7S0E4ybai6jc+hH6Wu4c+KLSh1k4ec/acRjGT0ZEg4C24lauFEppFIofAm23KmUge3D/kmhAHVB1GWeoj62l/tsA62KlbRQQtE2cMyQMnYA/aKo4m1XbEdpFdWizoNqDW5JPQfVlnvGn7qivuT24P7gme0ntq+85ZFkZryznwc5qQyN9UfOjPwZ3vQNxIH+oYzCEgr2KdQerZeozul5BILlzRVkyMCjIO6vNyrhaKhNh6dSWNNidFXDSsRFaPgGxhNNRgfUX8iNlF693nh5n31/U0NcV4F+UnXNODYqIMVVRb4u9daJvGrw2XmsrqnGMO7uyJ6+NFJupZe5gPq7i3ZNUO3cjmwKi8lfmDl0aPrYfgzFPvmpn52c2M/0B22qXMSuh32mPAwDRWQckwPjnQbxQqw2oIS8soKyK5f11o2DuXoXFiWWQiFXKOShFNQH5E1/eMjqiyMbyi0uuMdiik+va3rz6vrr2dS+6d1DyGpSy2e/+Jnf+vI3n8T7cM++obDTDQ5EebPyzNmnytXK8Qdeuvfe++995wftgSE9PGlqwuqhbAC0Y6PVuWYz3UZFZf8K8yK9ipUSlL1icC9tEIX6MXORwmg4yiKfxwOWTXKrnmNxcLoiACXWSz2nFqSm3MPw1605hqORu2+/ayq4C/lSzVTZyBTXVzfj4dGeheqlc4VSLDG25+bDSC1KpcUXnnrq83/4h5deuxjSApEQZKoShq+oV69tpolsjPrZxj5TzG6cpW4VKgszC9QRXszIXlk8UXbkCjV/VIuG/GCj6h5nvd1YTidPP/Hi4pWLh098/5FHP73rxC0uW2z3/vsj+9Nnnn32jQvLt99/n51pzd5+cXbs4IR0P1TBbsX0slmrtBx1dhp4YAGlIWg8zPt2RffYAMNBPhAbCeYza7XaysjMAcgHdprSj8LP8uHIihCpjXTUibqiunDplQO3v1v3jnCvsH7hh1/76xeffSq/nJyMx6KE98KBhiWX4YQeAQtpNDM2S9DLMm/FkHLx3OzfVD6HKeLNd981uv8EAdckCa2Mb44g7RBAgXUQRBbEUOzH0XlgwEmXgReB0xUwISYggGSqQp5VXG7cQjzY3Tc7yXSeOE3wIaVGA6rnctirSBjzAggoewn80gNBKBmmoIA3IC8CeRBZOXWDk6sViuwK3F7WGw9O/qwgFMbrdwOD22zVsrl6wKcfO3pgI5ZcXl1bXlweGxs7duDw7Px8cmNteWGzmHne63UlgiE8n7E9Fc9rpol4GzHg1Uos+kGDFd6ae0bTKiLVp4oGxexz+dLuxjQmJzlR38wxIQp8C+uGpE+tBlg1E+rGhp+HyDBACkVrxQOENQRyKxgGI1XAKdo5gjKJ8wNSoEolk0mREb2Zz+QFkLUmkHC8g9YQcZwwD6zELIW8iPwgmrId/hF2AFJCIXyDzw5aNLi085eE/RQGeeJk+9mdCd98vP3o1r0dj9743v4tIU/GG+gSBGQ9ZqE4fwn3zxmjDFglrAgs/kioUykBeOsJ+jCZLaZb+WQFHsStu0hjqkIEBYCJOUaGhvIJVpPRZHSUFGmwEG4V7+0OtoptFFrIfv+zdWdw4c2/RmISctBPbhxt3eAZdUO+pLRGB6nxyPBXkQY7bTz2Jyannn/mVcBRjx09etN9d4XHpsIj44xfzCWq+ZLb5yMIHebnuaVrVNg7POS3hJGyVksZt+6jIeCreuUSawOclmhi8UVkKW0080BN1qrMf1RshWxa9/koRrdXwZqz0etpptbVK+eeP/P4kX23TRw+joj/8sknPvt7//35x59ydl1ej9bLNMGACGBeUu/5nAF4rpefeX1hYQOZ1Tvf+2GXdwJbUvFkbiy0mxvii4EVOtoIyg3/pcTBCGeRMYEaStWxFQfdWlE22gc9QMdFOBWzM7U0e/n0U+XllaGpg7tvui8+Od3BWVfoDsNEiJG500CTUattONwjF14888bZ2aHhhOlIvVfZTHi1SCRixebdXH3uB889862vvvrimUsnF8CNDB2xvvPwyKunoDbVUrparYOlCmQ4inGPh7BrVqvP781KyLkScww3TYC9MGJkMwDLjYUkSGcN7GAIhmAyT4cTqVQ1t1R9Mf3k6srygx941y13vnNodJ9u0rRm6WOf/gC2f4vXFjHkDRHJoE8mmPadcqEAyQMjBqlHpZi1s6h2kpXCSqOeD/jMRN/0N8V7K7e+GJu4mdajp5CwwbdLkB4ZVYjo7RDLvlCj25zceyIYZiE0NVPzX/nTv3jj1Eu5jUwsGBwKeXS7jSg3yBMwP7E7Nb8PVbOY8OMm7A/jpWxhE72+lvz6Y48tzl1934eq07fdY3UJCw/Vx4kE1oFJhPhFyVIkQAUuvul8IezCYwDcGwoCzsVWqcAQtBOOgmh9xVp9M1dEBAS9klgf+IzgUgBqL3Ei291ioUQDiqW8x5vLJQHEYx3UNDdjlFUOnGqWK5AqKujhHXndHoL4wTnDu4i9EFOEEdBphkMRHRg8r+vKxWvLK0tkuHt8fGQ0tLK4lM6mUAdgdMDWDjYZdVoTpzahBVDPPkGn/GpC9teB/mw15ijfMjaNP2FLB4m5qFYFubPjo1JwTZIp8zfQ57xuQSRgyyc0CGQigfWBnqEkIKwXZp7lYjLZblSCEfTt3bUrcw2gO9CrN9uoW9jetXGylIZHAob1G94DTrKhW9hmwY4gZGP5FwtaKcXO0uw8NkpolF8qZlROrvYraCTY/u4/LNUY1LlPn7bfwR1O1PLX/zKe385854u28+4fbb1Z8lHP0KHqvSJglMVZrMmNm7LxbTUkLjYQMYQjxFECa4FWMZtl3pQzmRqBdkxmxMCsEuzYGSDcRXpos7rAq5eRIoukKiuvkJepF0r1tuuzVcJ+5bfOrz9Q1FmeUllcf884M268bS7cHtyTIcczg1PJUvaHKgN6WRRJwCIjv4SFwOnyloffefu73p3YtVf3D7OPF14/X8PVH0SR0sbc5z/7X69evrD34KFP/cI/9UQCsMN2F8TWVa9kiKva62Fzjg0VcSaSZltM4b4hi2SyiZxH4GqZw+jVTdBcT2R0AjfRhblXnn3+uX2Hb73p8L2U8luP/eFX/upPFq9e0+zW8bhvzO9bXt/wuXXkujWkVR49OpSoNoprc8uPffGxXKrwwU/8jCe6D5P9bsNlctMd7NpZAwBv0Jm5zGKkTwT5E6hIkc6ZkArjxU1P07S0SLsN8gvhZerlUsmOVnA44Qn4BQYX2ZV4A9KRFvASIILZTPaFV5/b942DH/34TwY0Z3AosJZeJ5N2b9cDH530eP3JlYWnvv3YY3/29ezyusXunEzo1UzBXq8/9/ob8OB2uxuYCMhCp2Ml3BW2hk7NRf6FOlInSoLNvVBcBiWrlod1T9cQYiB3qNabNcEfAdEFq0iT3+IjSuS5F8+uLc1ffOHV937sx/ff+cCtD/4MVSbc0ze+//3F9SXM1AfdLwOxUqsq2Qp8eQ9nhlohhRFQemNxc22x00J34MYOJwscT8umBWNC/RVDg7sTvtjGAESiwGoFShODhp1LbOIoaUy90lf/8rNP/OCb9laLwJA4X3HTrvk6xTz8JhiJraYYorDpgLAw5MJ+D8i71Rpe941rK6svFZ8DfPFhi3nPXQ91LQCLwlGByoDtNEuAwJJJSaxdl4hku8GAR6RR3Q7KYBNBANDSMarMnSpRM2vVkCeYy+PI27SCgqeQEHyEK8NnT2waexUW3VIJnpfZOTLC/jJUq1fhCbjJkiK+7yrwtbgIMwfaXfHjrXcAxOFOrVwFJ8KmOWFo8vmM06mBwBTx+0+fPX/m7Jld09MHju3BxL5SvbK2mbQlIsPeEPwkUVjwqaIk0Bg17dRcU/y8MQk5lLGoPsYVSSFH8mPQOeM6p4O0XJdrfCPBgM5ISvpVARZAyom4Ke7R/fdByRti3SAxnrDzI8CLLRSPl1MWGoXRzo4ZFTGQGERSIhQMKB0BnzebzTMtYJnYhcEjSE7918Abw7TIMv0j7ACkiNvVk3KqK8bB9d99yiQJbnxG2qffRtImqgVlwg4+6lDuDC4MfrcucHD9fXVGh4hsgFYUazHYI6zPiRIlUVipHuRQ8KpQrjRKpVS2lk8R4I99usXqs2BjjDtspezzejARYy8KPDTTCZ6FWDo8TIaMXKSX8tr+m+lKjuTTv2Cc/Ajfxrq/9VQ/F+PBnTVTKba2itdnbDw0aJCtM2FJpDz8sQDKflWO1Ojq9OAj9hzcPXFgj80dRdLTKGdw2tQCMbsdgfv6Y3/w+1/962/k8tnFtY2RsZF3vOeD9vAk3pvtVrmamW23s7Hp+zDdaTTXm81lh2PIjCS3jXsBhEPWWsggQ4gNPTmjk+y2kt//xl+cO3f+9ne9784T7+main/6n/6PL//NV6rFUjwY9ESsupWtWDkRtQsTIkFt8RrDIsQdDbhn5wvrc0vfqXzPYdM/9HO/5HAk6q4Ips4u+zT28mTu0IThZ0GmrlYnLn4i6pH2obZwpExu4wOPWQJmbJOLe/Ye0p0g4emlUiVdX2DxEeSiLkIA6Wi3LxKIxI/eeoJFJDIypMWARw2Qi9+zS/eUv/edb77wrW+eeerk6twaXm5DMY+5XqtgvZTJQhxbrJAOWxXpQ9ficrBmYs5ua+JBR4TIKkFsekQ1A6MCpylYdfCZ3ZglSLQzJ+AEACdhkA2wo5r2Xa+ra++AN+DKrZafy53sVhyxUCC+/x4g8MrFysqV08GpBDamqtfp5FYZ449SEWLc6dURByFbt+OX3IRJT2KJ7/JpkeFos9jJzs17fIRlo9PVTIA/gAojVFcf2pCYeSLohBcGf5hgO6b2ueee+fa3v4WkP+jz4M8Awk+1W3NjUWuxAOwDfcazGLUN8iuENpVqHWfKgL1rbpu8Dvv08DCWYcT7nd7zysT0pHNkL/L8nscnPBQ7KSG6rOLQOeYnGzyiGyIhA50FmQSuH6KRZHmo1ghuuNnCJTMIDFQzGHK3iYLSbDld9kjIDz1n8lFkSB22aoVyVS+WsXsMhoIMBhg4jIbQQ6MnRsUNWwCTC7l2EP6Md4ohCIYfZQEP76FMdpTqFbHcQk/dqEbjkaNHD9IIxVzu7NnzXl3fu+fguVNn6njSCEI0fYhjkMjMIceK6zDmnkGt+scy34T+yelOwtZvb6Pd+9+DKSzUrk9QmE5STBneFsRYjBPUHZi9sqaBeooVBguYsPBE2mxksGA2d6sEqqNtS5lcYWOtXMwwEFgFM9kCjhHsq5CA0ud0MB3P5ACvnQNZVQTrFH052yHe9/crgRUd6bPzUtzrKiInXNmqD4dMx8GpenRnEuN54zYD4y0ye1PuOy4Yrza+5VnJTXQyMA5QakHKZi+JCwwWGFJr9aSI8oWmAz8mEtqeywXSP+HVwFYpYRcBuKD4W7uRa6ApIkfyhDQxIJGXwV6hZpH+5n+/sJLr1tqmLu8o4N9/KAPkLVJJroOG3LpvXOyf8nP924y7xlO0+NZN+Af6mSjZ0GabDQQVIm/hCWZz+2ioRgP4QJvNG7NB/bvpx/7rf37iO98K6LaIb7hRqn7+j/4UQ/CP/cI/94/swolcwPoFgQDSAMJ4FoWcW6DxkEgiYGR9lAWAwUqz5VPr/sQegAlSm1fOXXzqwff+3E233Npqb3zm//pPf/NXnyc86dT4UMgP6kOjlM1agF3q1iiW3QoOjXejUi/nSxa/MxKOZLLZ8kb6taefHp8cu/3hj2AsYjGHXM5gr5HvWgNMZqa/sA4EsSkXEGo3m2XKwLIANWQHAF2VBPBN8N6Iq6HIxIqsEv7BzJ4AtDX38EEwwwhFg0ck9A6nGwgk1IJM6paeJ6Yfuf1It1022cpnTj3z2B//t4Uz17S2LeQz+XUb9vbSzAArWsApQ0bRMPt0MVnsWSKw9liANuAfbNjd4ouE2y1qZh1ZJK9iDwURAvUGJFCavVcVLg+2W2zMW6gm4X1jAVYj/2aqUio33jh99tlvfPU9/pBn+FY8Y3cdmvGPJIi2rAYAmfEoaw7KVFx76AJLvZFcnz/fqi4FgqFw2D4yFQYhbaOwAdwpygqxa2VsyIgAfZu/wUBBrmJBsE5DoFFEv9u6+toT//2//eeFy5f3Tk1Gg+FsKoU8HVUDrjEQeb/mQzJDZi1WYypO9Zxs/Sx5MES7HW/AEZBQE65yq0EU4gsvfW/mRNM/cZPN7pZpCleGW4giw9jVyELUBfof/bSGHkJEQLIsURfIlSmdzbtb3UIejT0mPY5mx9I0t3we8sGQ1Fyt11jz8EnRXDq+bbQ/6zEzPhKPmHUPQ1/0duxQmOo6OxUr7CC2RmIjDKVEDNXuYWCEMAfZI7jKxD1mLmeJopUrRBOxE7ceWZxfJ2D93NKsJxh7//sfzKWLqeQaCzbriMxcZOhC3eTfgMCoScBX/9OfljJd+y29NZ9JYRyrNP2EKhFJGRHCb5Iv+3fw0mWoeb0+upmKiyQHM1CxCKIP67hwI4RlDdCDLtoCY1gVeUdchWEO2NcSIpu9JcIr1k55pezXZH7wUVRXqKaYnLIf7SuBSdQvbr8e1/30y2okuu6OOtlZQ6HI150byXdcElL1lp/+wiGNwX3+jITS1NvpB4fqly+VsZyoXqFK4lGO4QM+veTHeiCrDPsCwkBjABLAcNDXLhTqxWJufY1F0Oly4JWCS7r4lNeb5MNGqVKuYAWEnI35JRsBqZEqlNH1ciKbg375BuXk2UF5tou780gVVX3tvGocb9VW1at/f+vi9vn2EWXpF8DIUj3Ybwh4BtyA6XDcAQiyoaEe09jwAnGL0begjLMQmrrPf/Er33nsb9wWy96JsV6zRxCv1y9e/va3/3b64MEH3vMhs93vwXCzgXCsirmY0+O3FTZEfyQ8CqssjHKf+4YbNdU2WTc73QKuM7/4y/8R11Nm+1/97m/9zRe/nsnWD8yMjQbDSJ+RSyTN3cLGuscF/KqrVe9lMfUjrK8CrNVCkTEicFXqqdTmUz/4fjzhHz98wu4cIjxBKX/N6Rvvmjx2J/YR8MI2SApWjaZulRZh3kjHKygIsA2QgwMF5A07unXrtdMXirmG5gkVqo2J/UcIFiRkBmtKm4lNz7WrVyqVnAOTVxNq4dpkaHgiNFJtph/7o//5vW9869rZRXvdtHt/COvPlZVyQ8VXwnzF5w5iW83iJOHSGVM+DTFysVYDEln4EBSkvIFvthhYcdMlyG95rVigwMmJ9gFNCgblMGhYosEFm5E3tnq6wzkUtPss1WKr+tQPMfwPPfTpETAtbz5+C3inGFYpIiHEk2nr8fk0oPpMWEm18qnU2twcFoyTuyL+QECEFZU6wWFYJot5oPoqnjAvlxkPoD/6YmPcyPgVfIimMMyaXq/m/vabX3/9hedDXWsi4LOZOtjpAK1cz2+KLZmym2KzUa537B5CK+KgyhILAJ+YVuCvKxsdzBD9/oDdXKvUXn35OX+M/tvXE3ARwR9gk8GCp4YvxqwSwRvfZLTWyH/QVxMLRtjfdq3RbROfIyTocPhSyN6g12rCs9HZvRohZ3D/brsJIOR00MIMbDLEmA/sOls8JkEqkQQKGy0CP6i8eHSy+aLlhSckrkSLwPb1Tp3ZCxgFNpbMfdnn20AwQSWWD0Z80zNjWBAPJ6KpbP7kqQvg67F8sFSiPoE+QIkN6iT0uk98VJ36U5+LxmykMSTBjs/g+o5L6lBdF2rCI/KNPINvbFLwNpd5LNkoeoN3GN7UEsrP4glE+bP2mjZC4+AOFtDMPXsv4EivrGMm5/f72GoSlY+9Ms0FC8I4ZPEWWaToRaXowv8wjjAJU2BwvEC9xqgGx6pUqnw/ytd1DyhauU0cr39e5btzBTBeJDR/5yslQynRjg+3pS366eXXSABpl4WTO2p5RgLk1OxOL3yW2C0w9Zh7EL5mna1BHdeRVrlQy6bAD/YH/KVcHR0ItB/kKYX6IMss04SBxDfiQ5gGo8uZ6FsdqopBBY13ShEHhdpRXKPTrm9HVWCh23z6w2jrCaMynA4qJrnL31aK7QPjmvqmEIPKS3tI5sLhCRSoANux++OcwBcoHYFOgYETUoQfp9la2rj8whPfQTI2NTI2GvEur2YszS4S3Xqu/r2vfqOar9z9gU8Ghqba3YiQLML7WdouT8IoDzSIJZXJZpSJ9RP7cYFdwe+zO64HZ8AUmn39O3/9lc8vra1GQCzEfR/y36qMhnz2ps9RL3g9YoxXwjobg3xrz+/1ovKlxQlngRfvZj4zd3X2b7/37Yd1bXLPo2BxEvKuXUhaHVOj0BTZ6mFaA0PHAJZmEEZUQDzglZj8MurpU83R1D04NI+mMovL56+AXzp9DGBLT7/MIIjBENcwdm9urqwFA1HAwffvcXdN1WtXXv/bL31x7tyKT8aRtZTP4lLJmoOeEn2Z122rV+tC0Ls2bGhsdtRF6DAhDLigYYYJ/iaaXpsjAJKguNzzEmJl1Rvgd4J5jw1qwYwAXRYIaBsDFHJebndc+P+jSACDkL7CjCW5mv3e1x8PxkeOP/yhAydua7HSSf9C3KTDbRYHpinC6gq70kDyyyj3+72gfmbSG4TCZHuAgU82mcTxAHxWqTKThLK0ERxzKo3G4Cjkc+yBhcyazMAeZZKL1WxraiSAtJjYurBMiNWZR5gSYGRDACWik7tcgv4v5kBofXA3AakZVzRBdUUTwz6HcMOuXKMCqWo1QeMoAQVKZWkIlx5w6/1Xs1Oj7gVCclWqNh0ECAtRJ4X0WQEHBRHKb6o2aSToFkX26LBorDPsRog8Z/LoAFcxIlkwUPpiq23F/pdpgHcY1l+ELJZ3mNEzY/dTxtdAZx9A8NcWMvGazQ3QNBCEOES7bDaixPTEXBL5AIPJLSZz1UIRHmV618TG2saRsYMvPPfq1fMru/cl9FBQCAmmvPjRKcGAvKRPgYwBxQUh03Ii01Ym4VtOXnVbHjZuU3IeUv0hzyjmnx02xtsSoayQr1bLZXcAwZfYEmPuBUvbqhIyyebwOE31YmpurlvHUq7cLBZREsPPO4kCSPO2ezrTzmzGVBrjF6YlKy6vUcpBDljR2MrIboB39aexlNj4qFpwyK+qh7pqXJQabl/rp+//cF0lUpUzDq9PYGRx/bW/66yfofE++b4xA9XMQopFXsMMhEhTewgRkOKMKWHFmIFsmpoYsUAlwI0PtzADZYNVL7AtwAURMxBTAAKFYZi4kmBkJrYEHnel2ISawKrImgkLpBpDmEd1YCyYlMgoolHCN1WFVxv3r7szSGz8blVJpdw6M+rKqVw2fm7MRFXeKBfJ1OMqiQxMRjbwdmLMzw4aHge6L9gY/MOZCwYaS4G1q2dKpY3hSASJIJGDbI5evVQPY0Ic9yGEeeqJHx6+7Y7A0C4alo1zq1KF3DitEaYOr6SpyQpAMl6IqgHDe1GUm5xmVGv6kARv6ea/Ag29uuKx4QpLmEgAaUvsQ3LrK+V0emrUzxAkHgiWMgTtg2tlBpbrJZfuCrm65UY94NHWM4UXn3zp+PGbJvdAVO3BQHBlfRmoOZgWaC2UrCqWIOJHyoshx1j/GzsDTkXi7LbWi3ls46LxRKnc9LotvvgIxuf0p4KkwSG2g10HgGPTEzHY03oN2/Z43Z5/4clv/eBbn2/miyGLzWvtDMe9m5ulZLEKVfcGkeATf6tnd0vlq3jXE2IBpWKjWzNZ67K6dgh5iSuvG4sR2O96q1KGwccOSMYQyARYELIvRxNL9BIyxKkZNZzZHNQQqbA1sYk5jd9j1+vm7GZp+dra808/vf+Wm7zREx0cL5BkiqzLoAMW7NZY6FiF2MI7PZ7wcCLoD4NaWsx2V2YvJYur0McOYVU8DvGH4sN8Rw7CoilY+gxkJefsWRyaBpekGs0SDwXQbLNQs6QxWTCcxHQa7hf7IVTFLNXCmKJZ6wCmjnGnBeBk1lqgvvWQVwBnQFlD0YGLpbkXnYgTEB4AdhnRBFxkKkFxZJRyipG21aHjNIfbBHSJjZITiHpu0HNYqwLZ1s3XvS5iy0hEQ1ZEUZfjOtxoo0dgy87Ypt9FEKQkv/B7HHbqLYfb40Qo1JTYYXg54K0OJ1AoNtqORjjopAzUwCGxQ2WnQCO0AaUwdRAGExcGvTMxZhjkTJCAx5OzuS+euQjoUGzYCBAmZJrtAgwGO1CZYvxTM01q1J98UtcBjaIuxlWjynJHkslnezpLCiES6kF1T9Yyao3RMC9D1REBGQnTHXh36SP5MLY9mKWyINfalRrae+JrWrzusrmXXl7FNowtQT6fpwd5XRW1Nda3ZrGqIgfKLdNUaQVY/xE3if6HbazkSxm2iyEX+PQLvHWyVQGV0uhN46b6lqtvymPH/e38jOe5RY7GfyPZDcf9Z0lt/CetyMjkY1A/OTZ6QsQ0yFVhGHCCAf8JeDLhmeQ/4KiYrwnbKGm5guwPQ0AEaOy/K5UC9hQSHhQkEPQBlADqhkIYl3QMODiG++WvT4QHBewTX+k6ubRVQqNwqoSDMhsnf/+3epTM+BjZGY/IZeOqcX7jd78kUj75qKSyJeJQmFI2wmIkj/urjACGG5b+GJXVCvMvPvuDYj6XGPIdODSF1bBUotNJxALHDk7DMTRKudnzJzHMUU2NxiRqdnhbwPkKGB72+PC1XQn/qD6Yl0ksWTFXgBPXEEqcevzpl55/vZw1jY2Eju4asQDcU8xaURBnVr0uK5bVsRDRaELRUCCkuRNeZ7dZBYIeBybgiKZHI0d3TWomc2WzWMshGi5D6aq99tzcX5iQBkPwhHmx0qXYsJm6SEUpO6ObXY8w/6jpdBEhAO+rIQQo5PIQ++MP3nXw5qN+TxBbTCmyDAPi37qD8cSJW27z+4Iut24xOTMrCz/8ytef/OunbTnTSMg9FQk3Mk1Ly9YAGdDS8evATcKZ4S1I82J3avb5kdsTdQvmnZbuAYrDpgiW0yNmOLpbqBXKRgHPgJUNepEUwXki7iEGE3SzYVOxOxDyuBEnCUFiaOKnYcdCdCTodtVQ520sXX651VoU62SmPtRCdTQSEGheBScOlh6opl0bmdgVmdivhccTw2PIYaoFYOCAN3exV6iWK1JlZUhIOEHRGhjDBJsqnx9WSaQa5GJDfMxDJk/YawV5GKF9q4E7MZsJwg83qgju21ix2tHzd3qNUq2czWNKR09QKhwTMHCCQYWo0BP4oxJqFDRSnOGQvsjQlKUaa/UcqVluxdAOx23MkOgnqmK3E45DEgHXXqlnCyWYNs3FPZYHNEwihYU2w0BIuEO7zauj0vLRyKyH0DRUK8i3cSXBjxcrVfDAUaSgLm4QM6EuznEI3LBp1jU/CgYAiFgdiKnLssggYNdIHkwUKApsHlZlxL/GSjUxEg/4g+im9x3YGwyFKBnFYGixdFAb+dAXUnc56n+rQ7nV/9BV0rDyEcIlf6rl+xcV4RfqpT4qIyHQos6kj2BPkXaGYiGH7kVIwxjjMnE0UHtxm5ZpCYRL2aHZ6Sf+KYaMytCSbbglGhZQL7akLJIMV9ZRfniV7EmNAgvrj5s9mwnDDFRKQSH6xTEKdeP39fcVkbk+CTU2qinVuf5DxlsX+8dGdluZyqu3TgYPQ5a2Hts6Mq5I2v49ikIfyhpAjAfkECYvcYVEOckCB20AhR6JYaeJaH9zo14A3BGWD/EOnAVOE+xuZcfEoopWhC5mOAN1QN6oEGkccQCR5Zfeo26DcvV/pQyDUnO8dVvqwufNDxjXd3z322KQj7rTf3pHKjncSilH8pHWlk5Qr5XRaFxX32L1pVS1LPCI+nADkHQIYlGNsUlavnYhtblazzfMQzYQwfyxYArrRouDkKKlStXp8F1Znn/pqScO3H77kG+EycE2kQBetdq65iMCCy9ltYAVksnAic8XRJ4jzAkjzO5GKlut4UbQGYmYwg5XciO1ZzrQ7tqIDU/YNqvThCwVLgwhxsiYLwuAca0ArfPYnFgvhLyaNYaBEbg/vrwFTW8FwZHJDpK0L+y9xafh/qOGBIt0q4alJ/hmvJS+l3UJUR915Iw4lC0zbABctuaPQp00l9eiBZx2P0u8XZOZID3esYUTo+VO2eUB/R8RRO/qqVOnf/hyK9kN7dM8TsCzrFBxiAYbKrEvxCoV3S+sgTDSwn0SsgA+DakMBJ7NBMayiKSw0QPGXtecOOdomgU+lAWYnThzEoaT4SjiCwzVkVMzHfFXQDoHcynW2oBj9woCcNcOhDyRhBuX9dMvPZeIx3y7P8VeAe5Q1jkY8Ea7UC1xBLED8kl2dnYW4DpdUM2zg2qHo4lcgdCo9Xy2EgAdU7pJtk2VYqGr1gyuEChSouBRJlkBUARQeFPAZ/KFwoKGWOmAt4qXMw7AlWq5BQhQJ+SwaG6Hx2XrQJw7FuoN6iLMtFssL7HURK6KRg1rW3yhQV0Hjq1ScjqxyBRbFOpqJ1gYowbPfPEmgUdF92xjMagQdUG6EY9yisPgEtRKrLLR9LJXAQzUjdiSgYOhjxPjPfwAQLeBs2MJIS5K24UFR71GVDunrsly0gLNF4FiB7YEKAUMtND0CiCkrDaOzc01HzBp+ECwMnRYcPEYZYqwHRRsZHwziuUSCi+gITSn69XXTq2urAbQtYjzsMwpSkRX8Q19kO/+Z/tocIUEMvt33lAUxLg/mLpq2BoTmNxEJqOsV8QZTgaJKFjYdTGrZBWUfTxTDAYeQ3+CmGMAQQjbRiNTrKWBMquwQDbYF2NM5gaL21YsVDQdyau1WixLDB2ylk2ggBpJEwtBo1EkBv1ABKRKvF2BAZ3ZcUWeU/TFqJdBcHbe31Flbl5XeyOZusqr5bOVgnRbxzwkd40U6uC6NYAr/XaUH/kzfljYRGhGRNo2xv66GdRDlgIJ64mQFXk1nWdyAAgVDKIbRuPTRnnWNuPrQeAI0NUZbhVSAEOPaZ0JYzKGLfMdrQvbZ1Zc8Z7gV/W88MDyUbVTJHi78OrG9pf083WtsH3rbY+2MuNg6/itMunf7DeyDEeu0Dryjx/4NGqApgt9hlSfXqaJRHrI6pdcnNtYWokEg9i9gY4NNBdzIxZ1oUEqlwnXUWXNgM8rlDaHoPQADwLxgiV+L6CwaGShFS4O91f5MNhwFpYGo7XV5pqpVWphiVw3oS8kLlYJ6QWbq14Te3FMH621jg4+kdjm2rMg9xCCGfzmapuC2s3WQqbQaJv9Xvfw7oOEIaA74Do17/CRW368a/HyFgSaUArwkH1uvxsoe67IOi+YKDCa3CuXyqYaA9sFBgiaSAI/4hpZyqx1LK3g2JSIQmG10GHYbEOjwz0LWPBjZAK/PXvhZGotOT4Kmym2c7myIJRhRoXcCIoF2WcDgZir02DmIGcQqwIRgYsK1Ol3oRolpgE22TCkJjaRBErxeoI0PgAMLZyXGs1KCYk8OgMzhAzpByQYKVab+KVMb6vOu5qEsxfO1OTxuRmOG8XS3NxGKW8KQX4ZatLBQq7T6WQ6u2648AuDYzGxmWutzzssAOeVPHi4erR6c/PK+atlT2v6TmOTxLJRz6UzwHxILnSbFWss3A+qtC4twjamXCpomh00XHsAqXPXBaiPF4vJXqnKNlq5worFKbatMtbwxZY1gLBnXicyFQxUrNxiy8HYQ2Fit7HBdgfbGiIlEezAGCCfobNgJ0xY8zMgiQxH4Rk41QYKZxYwdOfSN4ws5i+HRPVjv4XDtjQPHcGUrsNhCAxIrVmDKYZGM8dpUjbp+CoKXhWMhs3mC/hx0SCehI2BC00ADoFEYMlhhGsPNggQ7mY9ZsPJoGUgNyiTjAgxi7f4AUCEN+yCqxNgUayWSkGvT3h/iorHh7K7EkGKQbTkZ+fHOBWadOMdSSV3Bw+ozlQn0qs0J/0Lj8GeFlGiXIfOgyqLUhyuVHZWinfnKRaJFj3gDWn1lrPXbGkRUQGnAeMuFiklzZDPbaBjgvSwTDKbcXVqC3ALvSIvZaYoSRDcCrwKmnFjAVBji9fu+Eg3v8XlHSnkkLJKroOrbz4Y3FG/6rbROlsp5cbOE+P4ujfvfMPWDZkQ6kR+aD0pL/3cYutNcFO7099CMy58PUwF4V8Bdu26fB7RZq0RiS+tuxFoaxuVNXOzmUvnS7l8jaWSCLqMO7b5cKri6UNgOcS04rxDB6hVgOIxOPsFVj/940FF+wVUZd6+xdFWyQcpt+9ut4C6tpVyZ4rBU+rXSKa+t9KoJYCzfq/RKLKdIRQjuLnESOQGiwOWG7Vscn2J1UB29NjJ4T5jJ2yyA+tveDHacTzsGxsNYESoTFgYgAKYJOmdPlpCuGdsR+CxRILBp1spZBudrCgkGargQXaKlWKyksvCEkPmWHfS2UqtjNiyFQ9L9F6RqpnMiOaxxbPDw4O+IaG3msDWMNTx04F/3z+SqGsmSiBaUpFPYLIXrBPUR5QSzH07UhbkK8hAKQHTV1Cx1CInyAs24oP3bB6r14eRH1OqDE+9tr5p82oxpWyUySa0RjhQJQQQmgiGcL6QAqrH70d7hAevqdbpiLQfOu5GDWyF7WwwE2HFsEwBeRnpE+5BWCZpWsDvB0wEpQKiZ6YoY0VU0thd077Agvq9uCkhcmQEFvJF4bRZSyS4Ez5c+IEhaYRVcYu2oEesE7G1J8K5GAkV64VUHmGv0dtq2AmpQOiNnMchwXDgSqi2GeFIZjFXrWxig+MiMJtXj44kRjLj5SIuu6zTSKiw3GIQC/qCdBqnPVbKLHgBwnQiUqtUdS/gHK1qoewVGakNZGFIvKyrrbrY4iB96Oq1LsjLZuImORzsLFm5UKIiSvLg8yCWlxo4Sx1XwA8jAM1CKC/7QiFG8M29drMicJ/ojAlSUm6iKqEnyQGOFXQjxpUkVpGiWSqYtewdWG2E5EKyhI/DGqzYNveQGeESBivDEuzyiHdz1+lCIEZfi4YbYb/MVtm1qzaWWSwsdbvl0zzRaBAlAcjKEFTaQOQF8NP1rpgC077IATU0vSZEUsQ0QqOQzZTxDRcRnbCDqhnFZ0tIj9EdRmPyLnWgJn2f+m9d4U5/Pej/7CANQrn4kxQMBz4sMbQKEXCgNlbdG0LbxJCDFsm4QsKJDXEP/I8Gil9ByCvkWdzEJEGEvcg3KSRNgJKTKdrCRgLLJ5Y+VE7AQtDI5KroEC9i0rCzh25uiYCMAg8qdOOvusvTUlg+f0diVRuV6IavwTP8vjmT/lPGje3saUxaZqv9jKZSjws1kxfIE8LQUR9ENgB4MsgwFhMuUWy/mGzMc24ypBqVTKGQIQ64yzcxWljfmLs8Nz4Wz6ysi/VAyN/xe8Qx2OPGgbSUKZMvtKTWILgy3iKMFjaCQmM5lM6XN3NoLJNbVZISGR9VuO2KDC7/SL+SXb/Ob5Ne3ty/xeGOj0H95ZrMG9lBS1Moix0hXjDOeA+V8hlkv35NxzwK4QbKMvEDEuuUVjDkJ3GpDSS/E7sWyDocGyZR5ewmhMAL+9VDTYeZBwYwzDEK2kWv4PaIDoBRKrKhbnNzdQG8MzagUM+6ApBIV0pxHzQSbygPJobghRGIiSCuzOpQ0AdZICeAB4j2x/s8fn18In56/lpuM9koZrToCHc77UK3Zu8JrpksQghViplSpYjsmFf2xOib2kqr9IgqgsCZPQ2FbaAXy66urcwtLGfH9wJVRstI07KuIzplHcvliuiN0H1Wc8Dm5INB/MbMANDgb29D5yhqV7Yl9hqmPDV2K3Vhcx2sqTQqE8jshbFF8AD1s6FS9jB3RaINvaFmAiICHjvYPRbIMXKJUjHPxqdULAnbiQMKd1jhmIHEQ2A/Tqg/xEcoT2HHsVe3W5DWWQj92ihSLym1VE4GnN/rQ46hJoAIJSBhrWqVyDN2R7C0WQAyDUOaUMwzuXdPoVhjGsjjcAM2q9fvBaxfncroxUsaC1C1c0aIglOF2EGLd3ZbZ+GC3y5U4MGbbDXsmg0pEyLyZocwoibkJMAt6j4Ft4BMrNt2WzTWAKQ/DuTt/gBmx6yBeFl23Cw/HtkacgdhEdsDO8sV883KG5lWECDMpHqgNisHPTCGPF4duoSohV2F4NnTGHAfwiIjsbE1avWSdAZQTiDSQdIoKRIj8e9WrJ5wB9QUpwsWIfajfp8LEIsyjF0t1POLCkE8N5D4NkDy6GA+BeOPQMUTDdJjuHDg1sjMQQ1gxpcPzsNpw9mbdQz2n+VJ5pUMH2lC9dX/HsxB6aCtu+qikcpocvmW+30qInkYeckV6V9uCmVhoqKPFB22sO0yQriBkLCcTZqtRbupWUlmC+lkQHc3K82N5XVcXHU3ZnCOjc0V9scjowliQKodEXMBmGzJHHaCjGhYmlFeYZSIpsBAyDge1GhwpoqzfaKO5DFVx/51Ixv5VpUdZNu/+3f89N+/I8WNVziXPNVl+dqitv3L0l5ytd+C6hhOCkEeHQaQKmMObxOYNZkhcPEKFgNlLxhfZs0uSqy1ZDgSqVXr6WQWYsf4JIig7C4JDNDEOwx9KeBZ8hKh97JzohtYXaRMclm+1YKqvo3iqOTGiq4e/H/xtaMhB6/iVx2+OTcjsSqMcVNKKv8piOx56Vpklz38B0V5BcvG/rKe21jBtgwbaVeY/T3kuSWw6dUKWME+L8ysVkOuTaQSJB9Kqs9CIpwPbBPgDEL2QKeCm2tiY8RLaRG4PUzJ1YsBFylaJBRTqdowxRIaSBSNCjpoROawi+I+gD1SJBrmfhNZe7cLGn4swhbblCuWw4kQdhmVejM6Mlpiwx7CHXYIYDq1tFibldVq0ayHJ0Sa3exubOZTpaLBz9LFoI3xBtUIjPCWXfYAqBI6uNiarCAYbHp0oqTCDYnWVP6BySCpuxorh4hZm6+/+Mz82Svdas/mt5k95lJdCDRthvpRmArE71Qd4YQbfkvcilgJWTUxWwR4VaTKVqzTRaxN80PHJYgh1meNttuusTDhcOhFLuNKeNyesg/L0mzRVKRnYFXEfpUEwl3CyIpBCy9jWwZp8gZ8TWwgm5BlzBB4IastPA2Gi5Z6rSBeoHaaEMA1REw4hZaJ9GMxhUDCIFW90EDyF4rFxReMMQHzD6ts1ZrAdhvNRIWYLV08bUX0AZNUaVQR1HnYPGPPJSG6HMigMSXCYwMGSsg0nYdTMNHffK5w1GPTUA+gwMmZMNjR7Dql9+Ei7HYHAxYXIJoN1gldFKjMUtGzYYWnjllTiNpZ8aG8FRFQA24dTksKJeIsRECdQrUe8QcpFcWWeQeZEvPsFj5cQ/GQqO6IH6Bka4RtQW/KWo47BfAVyCXFSw7kr7ZASsD2s2azjKI7QLBOJuDQIaVjwCi5XNOteVmQWIiL5QKYEDLUUQ2BsNRuQl5BiWetQvoEGREfDqhxf8b1qbWMJD5cVL9y3P9cRwTkvnrQIGIQ4UGy/qOKJksmvJ0lHbkgRgdoHOCXQMLG2QXzXbtOHBi2PLqpZekGdIc9VEtmGXWJkWCjJEwA9cW1jS1ZLpMtVeu0JYUCIxu6z8oO7aL+DHtgA7mjyCJjB68VGf1UZLtMW4V7y4NBun6FpOYc7qz/zuM3Z7GV2MjISCzN1W/MG5+QZNvP9BtepRZixy25SS6IZUW0xRDGANTpZdyJ5kgyFakFoPaAJ1Y8PubUHuyENpcK8eFYOZ9JL2fGZkbLuXwDzga0iEYLzAD0LExi5nOr2hQccHJmAPMKY+mWMveLtF1qKYMqkVGkG6sh50ZdpdT9j3FoXB5c2/4lRxJsJedAOumtUqvWI6lxT9WZ6QEDRE9zDb6JWrAWUBWC7eaTK+ViDvB6Nob1Uhn8diDYGBL+gERhZdKhM7dj2N3CvkKEtuQum08gZrxhqkiOKrIH2k5ZH+QUjZ4wKUIn4eyqXVA6y6wOAeJy6N5srgZfr7QpzDqWJVs0OGo1r2MDDrHDbBkPbJhqd9tJvn6P5rMhuB8p1DuJiEB4NuoF9RJoLlYL1EIIApYdcNkOlwVEF8ogMhnMOyAlsiAhlS93KgVMWKDGDkfb4XMMTUXdhTYxlCBDUmYyYViIcIOQtmHZLHULV0+9vjy/tjccZq6jt8WCCnYXgQNmMNQLMTYKQ5cfWoIBPxsYSBaKX4CNPViXstuAZYVKIYmGmMG5CkkibC5huepNWFrkLGQaTiR0T9xXFwuWTNpZBcdYBGOyXcepladoXYmEDKWlqyH0DnAjmuwYWMk45ZrMatjdFno81gLRIIiJiB1Zi6dbx9WjE4onWtUiKtH0ai6TS43v2wVTbowiWTmAQTD2bYwK4rRrOpJmYQoVDcBSniHk9cFMIqbCPwbIASIzN0zoc/GqxgKKfbSDUWXTCPcVCbYtbc3rc7q90dG4PxzzRmN2nVheukP3dKTxGg5XiBGrVrdmA4eLRoVB2KzVoHM26mtxsv+jWgxT/AkgViDYIqei3WTr6AR2TxZIVkPRwSEW5Ig0UhgYdzQEEDXx9mL/Bc9Ps8P7Y01AwCyEUUpfSt7I7NjHoz+X7aF4cjGUGaqQWki9rOpWdhIIgQTdNlP2Bv1ODw5qSAxkWMnCxf6IuSHbCvkPEZXZMCBDJJKPJFaTVXpNTtS5MXU5pZgGwZKbctL/9FP2N2jGs3QhfkgUT1RrLO88IpINIl/giMMZGnO04F6f3jC3ykurwBu0alXGCzGBlTOThKxJbaYIEEA8MzGKsPZwDEYeibEjKztvZ4LIlJY2kEWREaXW3kGh/r7fHeXfSrpd5a1Lb3/QT3x9gm06alzfSrR1sPVeCk5XSqtKa6reEd6JOiF6BcOJiNlOJ6FNEf2A4WoVMThdDvXG5Aty4CpnEayWfKFAIQXOcHd4YpTgWDgKohzHyIBdYTgexnkSE3XNq2HyQYezeGB4R6MpSiQ/qjRCCuWfnKiyGCPBuLuz4ByrB4y6/Wjf8jwPbWVz41PX3Ric0CYGdyHdKzY/XZnZ4vIudkyq0wGsQfqAYQCDGi8BfGJQAEBcEUmbWz23EyNjD7IZgm8Fg2FMPoxiiBDeBkCvjwMaBHop/ARUSQopPhTuHlsuyossxWzT0LWwCkOZEPm43B53fgN72wZDG9Etcz5dTBMfC/GMSMqZ/YxBqxXXVnh/PRzwx4edPv+I15tvNVFWhOqNqCwtwFtdwIPT1LufqrG3D/m9zHS6mzLAyPw/pP3nkyRpnt8HhlbuoVXqLNnVXa2me7TCYPVCGAGY4c7ujDTj0c7sXpyd2f0xJPECPJ7xjkaC0MQuQGCxi1UzOzM9omdbd+nKSh06wkN46Pt8H/eIzKyqnp3leVdnuPZH/J6fFjP8VEhSoIlg6UAecCgE7sGPUJ8WzNF0ioaCm1luWlO0CvZ+2OvYeZRXix7hU4eHgX4gtQWqDyOIUI4V9le5GGWAkzsBy9IYMiPUYcLUi9MFSYlxScSsCsqGAwWpYKzEssLYSwagYvdySV7mXsdBRCDm18q48XykWKpYpEBKxtv1mtMnwcRQQxpCDQ2sYlcgXQeJGyn2MbZCi3bNGY3a9I9+SQnCzC4DCSsdTaa0b8YCOz81ARbDPIVSSAZMlg2nfXB2/JQMMelqdVeKeEQ9eUNj0iRHPCPGBl4BEkiUiPcmRwh9kl9oCjn1U5S4mSEvJCPWYIQvyoxR4JEhpMolrwbGV2y8rrtwN3Z37375reLGbqq4kSxtTxfhZDrNOkSwYJmy8FAPYWyeT0bNk0eJVEZcCEOkMAggg9CxJJKg/O3kmI8XFRdBdjELN03U9CEi7OaUfICZQEKgPXgNkMSJKphQPwAJA/5wFCJDCYYCpDH03ZR2LdhEnNmUfiIsAZ4dgMBwsCzjtQzRiU4dF58FucYpt5QoLr5FsA6kN+1022Q4wpMSbM+iQEWoFibj6JPRloFglAPIW8iglPWuxtJsnLzYNDdsuktr0qxQ8+fiFgZdNwiHCK5Fh0XhuYE2QwAUy8BCUvwmaJ0bWGjzfq2ZyUOM43AWeHIS70bwGrPCJImOIpUiZm6UW40O/A2pCRmsZDQOGuMvzIPQFX1nbmBj8LMyTLMBCL9jl5vnYx9deaHp/n0X5y/2rrziVzzQ2K3f8OKORtGMlV7HKJkD4X52IMe6zNLDcmK0ZWLZJiqhGVjKkIvBh5IJUEIUhpRL6DQ6mVRsPOihRIb4N46OcA9FT+I4fYZd5l/cBsZoxsMsDiJ/wo4LHQYmxIXxKbgGg+c0UdrMwBnk5x9pkHXWbzE/v2x76WXvdf6bLz99cfflAfO/pdHgq2LFBS0MCAE4AkQqJBsJgHk3vAXs/HSBjxxYTjRCiiI6Pi/kMhTMwxOHsPE4wJNMjCkBKCwv/j6ZKbJ4jXpDzCceGfzhg3wJns10miGBEabQa5yMVIpVGU+PD897FFvGXwFBex6g0g7uesczSjE2GeFQAS4nCobAXy1hZbrNQX5z+/VvfSMSzozhd0jG2GpJlSmsDYkng1NRagH6iP/fAlMECR41D6xklgvqd5CXJ/Cglyc4vk8i63m/cXgwcE4DsTzmA5hlb7Ro/HwKoe8gCEIyDh8/7rS6lSqeKrFutw9sAUqMH2sGAkAeNpALG/lC4mSzVlbKWBq3/zhSEDiOugYMBUp2TAHxMcOhMo1BrkMpScEsv6qRa2UoON6GZpGyOFcpJHlJMhasnUtnKWwp4wFdgypTponMHVRvjg+H7XOy8VB2TaoQwEL/CwbhZpIAOZOHFpcENyRTWgZlxRk7JOeuI9FVtjbA/rhHgeSxTfABfMAIEbLmCQ88VbsAbplNmj5eD0om4hUrNHntUsTJJkMLbNQx1ZUIYgPHQkB8Nc8i2JWrRYJwcTjduba3f/c1e/N2MHENwoQoCAUB5IzWWpCLe52K8i1YZFRky/EhBj6VSObTxOG3SL8Ch4+CUuHK/FNpUKXcabZ7uKhBFlDt2TYeszMkRYxVcOYYboBe8Xss7yjhINNB20HGQj7DfYZDYEVsm8BNrkskWyLABNkFNQhV8sTIs0RkfeFTmHsAIOg7fsOLTrufz43Cu2DXOHjTHVPKTQ6sWGvmJIQy2QHMwpYiQCjdLNDVQjQH6vF6R/v+Xd661RWDs/zTDJSAWbdpzMxLxfLzfjpH4RoE9OFsNAnFyUfJ0KLEQ1zPzXr1Ca4BLQfLkxwC8plea9brTwY9EthATBa2bWPQ6bTa4SmzaSwhogFzAIu4CEm/uot1jr4InuUiG+jlptOel29+l1528ZdcetntNGN9er233lldeu6lDKAhAB72xzMHuRRwQMcN00FQBz7YwAYcmCQpREPcFyPLRChL3hHUy8QTFirFyMxFQVgsFaitBu4IL0i6QUbDLDHtCIOoJng5i5hUVQ5ZZwcdOF/WCHoGYBcAEqJlqGgIWNZgXXVkPXgeD67mr0+xzx3e5p1cH65O83v5JT4nf+mq2dVnnz936VhvEHByimaikhCqNL4xEDBOAluE9FApiZrms76co4gQILoWB75ZNGplLQLGKaeEMwpJGrsjMC9PwZpJl86iRs/IShbfJckIA5VsAMzGdAkTx53gGNWcHvbc2lkXazqpFlH8w3FSmYRv49BCNu68HSsXrEoxxdLE0AkmjdtWOBXbIJbp3eLu3TfSt16doY5zeqW9VKN1EkMxK0UHjp5MJUpkfZNOop3DZqfALzYJ/Rj9QLhMCQr3PqHepD0gE1smWQpOIDt1MFPMqMA8koaZZzKn4iBx0fRigVkb7wnySJJBAV4C4oArNdFzdBxcCwcAkiWQKUWPwDuJGG4/RG8pDwPRUkjnUtcSuxZsd7oMFRSXrPmIVU6vjdETKbPX7BEjN5qNKKODe2UhscTdnkT2kRQOkUFATr7yZGoimB+nLOR9li1l2cCpxHLjV4UGn1ZK9pLoJZZ8GacqFPjDxHXO1IZIFM1P+/TZoNeG9li5NClNqHTebdWLRc2TnJOIZPK07UCI9ILKoS2sIdDAzTFMra1CIY9JFfxHIr5c0S7ia5uKE7NHNoWj4xrae4oMb+5sEve9OD5RWwaOLQYOXVyCPkCQDFBooerNSqgFT4B/FAPCUIfI0TAYjGBmY5QrRJIiojBB/feBaJQif9G/z9Hr45amtUZ8MbmOZbWdsVIRYyBXQtiyS0nNg0dV0o4yUyw79FaGzin0k9WOazGLVqFroD+M+hAiAI7cQHQVtwhQBhCDNxHWIoT8sagjegAyBkAZFGZOegnKsLsuORUkAjJ8ZokCYFqFxi52aYn/knWp9WgWLvfoNm+Zase/AjizahkuVFVcFV4RhVuQ04ygcsLrOA+bRqk8d0xmq3a7e9yiq+HZLIGlXS64GJLgUliD826jR/oOSqk52AGUC468oiHyv0O9EbigfXxKfINcgMTD0SsZnUwL1TDtfOGmqy+7Q736q7aX3sPLrp5/4YSGRq/WH100zL5hc5EO4WDBcCAH9US5VrqtWqNezO6RKoyASphBaDgwqpwi4iHRj8HEDWdOlwlH20rCYztju8EZy5IphZoAQJLE+AoYjd/5HMsVpFJYA2or3zbmAlYDPMCtpvH6o2ZJkGPfMAemyRya1nu36ZTXF7P34h/TT3Pamw9v1//I+nYNwmoovF8u6VGPi1BL1ASaC5xzQPbE2NKG0+YOdCP0Cq6MbPQ4+E3JSc+Kgj6SecqSH3p/zEqh1NC0PyYyq4hvpT/kGmVCSBxke8AUuiBml2TCfBpaS7CBalXzWZrGa/G9JaNyKKkYoVDRipXzCWxuW8nw5mZ+s5jL5W1upYIH5Rsj1AWDRpXT+1//Uvn2d8nELEYyGUknJ+lAwwo2KQY4DYzIQA0uiIRAImYA6Fs0gXsTXhq0ASZOwQiSeNQi1k63XRt2zwoVDI3WIGZHI9lQ3BIVFIFgC6aInj89Q8oh6T+PNOpnxJEG8UiPRPFPDQE7+Bh6U0yRYSJuEtE8FbDIdUCTiWgCKnCoVVIguGMwTAoeZDAYxiyr74xQtWfLpUG/X+ue2ilcVGdJeFQEh2CiO2gTpxoIjACjQmWvCmpUCuJ6v9tbYDIgRBZ+ZoGTC1FuJMkh4FP5vOiTtw5Yu/jJZLIZ4oDwfYWBhYvEn5sY5dlwcnz2sN08B2mUt0oozz/+8NNUmuJjGfVYEBoi/5VcezFxwRbJZRq7oKkxIsXABN0O+u8AxmPMDMQZJfCVQjYJYbMllAJ7PoYNlbk5GtTb5/PQ4vT8vNg8nMUXb5F0em8rLJoAajXwx2KQHUNis5gRKmKmYLcBDxKHRIm+7A+HMskgoMvtHWTkmaZZdqj4YzbMvtB/AEqG6RzeX4IDSQuEuTTB1CUz61WkWdGAYQIVif2QfhvDL2NC+kO6lqVo5Ea71nAUxpKK47A7hbnAfRlVk7RN0utjiMLggSxEi6XKwyMoYJOtA4KqxmBOEKsnnkMQbmDcrDH1Th0yMLf61Uh7m7nfYIAVitM9wkPCE9p42HvMfwMTz/DQBmx1YCqZ2qizRDN0HT/25tG49WzSqgfnLnwVPDzqHwIjSLgxG+G+xDIiB2q80xhoUuH3o9K0LecSZxkSvIH0cb5KP00rtKsmQQDMoOqk2nWx6X5ve/Ha6sqv9vvciy+9lFHVKKzGgiuc4ZQZKe8+XdcNao53GlaBAzyL4dRQ7ME4jZwB63H7WnX/xmYYF2nJAZAFhVxSSxm+nrQs05GD9A131ex2cHYMzqJQi06rkYwJXM7Oa6OegzsFYq+YLDLkdHpguVIu3XPAiwOES14LH6ewf8M10UwtTH/Y1JNVG2muesJ/pvGmH9ozO94lc2RuW18wR95d/p165ZVnzMHFa1avUhv0LdOY1ftQWsD1AA1EQ6qZvBnSj+mMKl941KOTBy6ABfmGkUB5QSqM/Rv71XiqT3bG8SxVzqsirxa0SB3ypxEjWGvoPYRu4d94J0vIyuWlKtCd3BqxU1lyLFP3CiMUlWUsVOJgtcmiUsiTIIEqNU3lCCMZfSJfSkctqzebZXcLhZuvBgK7cy/iCed2DTEOOlEiBYjhk3IpfGsZyKk1hpnFdmGBxU1yN/VLIXu0ViOPeI/UDxvnOs0F/PBR/fTR8f4bb8UTeSmagQnGCoZJbvgkcoA0jpv1c4qGp5TYLQwNTMUJLGQ4xfeN8UdUSklMvmmxnCD+MH7leF1MWG9iRkmWEAohPCIcxTMZUs9QGoWEvPhXz8OJZ4dnzoMHb796x6bEWHUrUAvgg6ty36l+sFPPVjbtnNVp2M0zfJwGTr1LJyR/4XGJOpJ8BniLgkSlYgL3ifiK1QcOpQZj+CWDcqdLmoXDJ8cHj40/G9cp6BDd2KkcPj0oDnqae5mPmRxki5XYGghj4JQhhytk76OAcJY8FIrDKFkFggxw6EIbBqPg9HqE1GISQ2ND2VTc74EqWOgQFcmWVDuIjHrtRPc8mc1rYAEBgyiNuCIFLBOJnUgRA+wIBpf4wmI5JwIF5AsdQuVuFhEPiq+w4PlHI9hS0mGa/HIgLFgXOP8FJAoaJeJPpKeY5KVLlAOSUyTSZ57Qg5BgdgnZSCKi4m8QBe0lE4N2N1yWUmVodOXK86v0b7wRQ48chxgZ6vnY6YwKDy8J+pXvPIokNVfIH34PYKBvwANYxzRWi129NQfCT9rMKjN73h8d+6vSYArzhKEhmmbzIh40rzFv0BlhOro2n6PhIUCbMQHWFjitur15vz0h2dPYJcAQOt2bTBxnxAvGhNm0+yxAAIaEGIwjQKrs9nNi12H3SPqtJBbcKSsKwcCoCDVRgifAHEXSuv2mTZd68OKuafCLp81QvOT0F5/yUdtqAC5u5Iy3qTH8r2H1MKvGBssH0ITXyhxgIDPSiJy/scA3fvObr7/9eq6U6/TGC8va2NxgFIyXByMwRtm6DI7IFzKfRQaNuospjLCAcRfNLFYi6CVv47X5Uh6LE8of6uhmpMoYGhfhYSmfI1fUGO9jbsK5EC5S8hqrQCwiAK8B1BCa0VMTzZ5/wgDAqkv+75VOq4svbC856UPS1Vs1kQaUdJo7jKAqaUTgCqo0IewerhBswSAxICqBgJML3BOqA1KOhJOx6XBKbbzdm9c3br3e6rSxiMCZ2rmqMAdQD9yjeZ2PJXXLvCk5GbaJb4JUSHws7RAq10V4QRqrKYEG1FwdDEuE3iRxr3YaPTQD/di8AecoczvVHBe7u9VrZISHb7EsMlTPAziDGkWScrojwVBLPTBa4OFAt2CZEdVxHoff1yADy4T445GiVC6m3wh2Rv/DosVFKFjeKoxi3UcffTYfzZ4eHLdP69t3mGi6oiYzEAoP0at4Oe/EEsm0T2x06OMx/AO9BONobDWk8ncChGipbcUx+4LUQBziILEXK9fBsksIGer1ZKaQygaT0cPT2ocf39vcq25UN+4/ORsOx7sbg1iiY5WLpc0tpwlT3+u30S/OEjkC7lL5rSosavP8FJf6+RAl5BjRXrXEZtN8yULnTjv4p5FgCjH9Uds2Q1YM0Sh6gHhGoQHYepTsqNNU4PfhQ9Bt8/wwk83STLqpTaIyblqaNTYan0hC0jCG4yeHqEykLfXUm4VwspLOUbSGksiIj9hfKUqDt3whl8bbCJKcJbFOHpkyiLn7zqt3K4UKNSlDbcoP3GERaC2wBMTti1sH0hQ8yLBLsc0GZCJpQ9+VSho3K2zuNjloB12BkkALU0h03u/RO4QtpgMXboQW4qIhTiAu3BRgQUykGM9PpSwj2neJT9eI2cCnQG5DwtPkRGs6/eZ57Zws+gaE8RINUFyTuxDrYXxYpixqmKG0laOUFsETWNc5z8O0nHjybg8LvJtLIbCy0TWAkTYCcqafZo8Tmhdv41d3sHnYXUeaIU7oEsOjmdTDusWc0kkJZRwIh2C0EUYh8aLSC06GyCsj8DkmAFw5MAZjDh52u5O2qrQSl0jCf64Bj8iyZFck6hdpAMaf1Eys0H6vjzQH7mcO6ACSJSAjhTkQAwbwit0ansi0UjhDbV9vaqDX8kvn1fQXtkvXX7j2y0/oyZc+bVC+/6z5pmmelr5kI5BbBF9goLnRau7c3vqt/8v/yd7dWwxGlXRFteXGKPGVwDJEDadODfeNRIhEuxGy3BISMSOLba0VW053toqRyAaao1EqUcgWQY+IuoTLx1PKqvH0wcOMnd4mCGg03o5vnJ2xksmbiemNCFR4as0js2li9NddMDNrmq09buE/zbg5xV3sePd6+34HL06vTqx/L2bFPMFXvefNZPm0Rt/RsuPdumgolPZhFpX5kwtS4SAyAT+obMLhbCGPQUTxSxgBWNOktichAI4CJLQplAr4UDDQcTsch3FinQCebNA8Anl4PwIEbsngZ22IYfXjk0TAsitviVELRPFoP693ao3AYE+xp+gZRuMZye4bHZJDwD9GZqjVw+HRMnTS7pdSVjlXwLIaDqFQUkiWRovkyHi6D+73ejVCdkhrH46QqJKus1RQX6i7cDuyjmLqNyOKVULdNtYCkbkhXqFkQg+3xu3SRqa6T536wKDrFLfBMnDTuAm5KMzZY0MTSB0o9AjYH1kXJGwTqjJSIIuRLqGA4FU0S6788RjORrSJ9YTPK8DW6PUeHR8fn9deuXY7X402280e2pjzWaZIpknCxBLV7VJvMG7fe0z6ua9+59tBlxwbIxxW7GyuXT9bhiu5XLlQFVoEKQ86IywRdA9ElKXKubuBogxwN73UEoZjsdKZ0sZuOG4x64w/ScGG3VN33C+WSyQHpqdo6mtnVCtu4a2J0EtvhMCCQcLlEnE6J3CE2Wb6EY0V5Apa6bScTq3Xm6d2qWGP6ZPi43A7ojEoyzF8kyMTDihBsvVYeDBy+k4P3gG6rjejrU6nAQ3BoAwT/IceRjgHwGO26RqSunE4lNXZKN9TRNuCgpAjUSay7hQIBnRKl0tmnyEuCVPejjcyWmx4fsQuTQEaHyl6cOmGtVW8q0rdKvMl1ApKHWN8VCeAkUtynfBr+PhMFh5X8hP8mwEdyAF4FsszX8O3GNWLAFyJLCg/qeQfEk1wB6DjSFd0i/Gi2fDX0msJOLQM9L/3o+E023pnfaR16V/UE9pnvLwZ4LNapJgTPFnT4ApdQ0bjH0MJsGE9AZ7D6HrAbs0GyApht93qMNQj/KOo8Yk1iNRSqWSoj1wYy1DoUFSWfI4OdeNYTgwcvW53KJ2WglnjA7SCjiPn4AUwpGQCp9abGmGaqZEyZ81fb1fHPHxxsHqMMxfd/IJ7Vveufr1nLj/pvUK0kG31Y/bUGAaK1b/ERsdiACjjWPnxRRt0mvtv/FqqcGMRtQSgQF37jOSF4/GS3Ma9+tnJpw8VtEKGN5NDlXAmbGijTqP+8OkrX30TnuHBzz8IxggdzA4ordYhKCxdsKqEDSBYBtER24kx7xt2blzfR0vw9MnhCNkKiZY2AQ3MEsYl02V/xC56dGmoTJ+4wjCt+226qT9+h/0dddzD7romEOEjgiI9bv6ad5gD/2Xe/ZwRDyFo1SM8g3IHDQbrhxXECxg51D4wr1KSwmel0jbaZlL8guSpto1/HdZgss1GkiSzR50LjwaCQQWBlYzBkOpBwInzNcZgbjDoE3+gxvlRtbipBSxwhiPDk4+s7oF8BvWP6qnhtsZzrGF8PUE6I5Y0jCUNUHI3HB5pFauLuH/TPSL+8dqaPz05eD9X3kokytAFWFQ5Mk7aMjQCC+QzwjkXUUb9gi5ImSvEIqBFoqPuQ2futHALxk8RT30SlSlKhkAkdA6MFncpRQyiIcgxOCaTTo05nlMnjUAnUIwEHf0HGVzK8V3kQAX5cBlgY0bgOskpZaWsaSjmDJuf3n/WcxY3bgTLm5uffH6/jWAeDeBTCBrKFwvlkh0Yh376k3uow7707ltbu9fcR3V0DxQt5rU4cxCOFGfQy5vxVLpbbw17Xaw02LnZzijzl7bQvwkIBAao64RVB06HMWC8Fa8xGZ4+eVB/+DnDe3Z0WCnZX//d74GUDx5/hiYIQgjJAx6YPxrjdE6QIYSLF8uR0wakk2WgJjYc4UCESTwApo+SUtMNIsFguTh7Wk+Q5DoZU1pk+GK8DsekARq2amf50sbpyfnSju1krxMZIIgE1CBQZG/t1dFkRZNELpEqXHMkvbSQ6dIEPJAXD73smIA3nqE95MqW6Ab9NalVAVi0/OBuZaGS3hXAZGrw2UeBASnAtQnrJlI7UQpjHJBRd1OhHrIqzgI+xZbRSxgUkmdFKQY3xuQHyge8+Y5JYQFjgaYMSQJVIEsC2MHpEozPGoEgjCZuMpWFRBqeX81S0IXoreQGbxVqSWqdXdl08vIJA560xJzU7VqhHDFMWuhMgtgeaBMTycYp/SN0Cz6H+gY4++Lg3GlO6g/cs/vO6RnJ72Dwd67t4qREDDAmKTzS5KoydLDOkVY8rKBArElLwilIKeLpgfEyYGCUCoIAJ4liagJkmTgYeuwRgEvNZldtW29+86+eXF99yc6ld73k6q9w6uoLGDbx8gyWmQCZjUI4UGuxxkKv3r3+2ltvEicHZg8Hxr1PPjr4+U8bp/3y9TtgoebJs63rrz768c+OH31865278PWTfnfvlVcaz5598JMPqjc3Gif1H/zR99/81pcefPzs80/eL25vZdL5yFHDadbQcj96eIb7LGx0OW2HihvXoAH9wcl5A1c0Q1iVVhp1tdk0uQycmq5f78e/5P14f68MrTm17q333PrQXGSqtPQvn1zfdvEqg/V1vwFJ/qDy04qdU7mQUECBFDwf0C28pqRmqo3He8HOkDO0+/3OBG0pqVUCQUupNjFvUioMsx4JhAlMAX8DkV5CIV6C3ANz5vU8GMzm85l8QY46gJYxvUgzM0E1OaBkLcCtApJS4shPQY5E84W7GNuLNNI9rqXkrJ0uIAAEW2gIKWMzGfbiuY1rb/zd+ajuIsaGhiTqUHT3UmnL6DX9oq46tmZhagZbjLp0U1zlS3iWYxmEp6RuSqZYoogJGqMwMZ/JKohFOk+GC4fC+SwjM8bSaXXwjIIUso+sTRAYNiMM/myW8uNpwUBX0BqDrPggkc04JSqhv7QbUqzDZZ+3SfdPHRIymS1RbeAEBWdJHk2nNzg5Prx75/b2dubwlKrv97/2tW9QCvH8lNjDcWlnczoiC1svlAtZuSIECX8iUBxpcyj8QVAiafSCccPAelBF5xFvWiePH3/s9utW9iYq62Q6Ut2upOOvPvzgw48//OSdN/Yg2YEIWTJT6DDCeapiQOLBYRje3UYLx1NUf3GABHZZTLqAdZYopCMkmCDXJ4kkB4Nev2/h6LSc41WfUM1jsulS5TIwHGJSwqxGcfkMNXVPz45TxcRstjXsd3KSNKDVwNWEZG74BZhjvLHJSIMbaD1EmXiFphLOgZZlQmgSadsTyYLASyUhaQaFiygAAFlG34fDleRWsDDYX7GHkvvNLC9CGdI7wwqg6V+gmEKLmKBUGcwuZBWuP5SglG4UZ1woNLb3p0/bafl/4lCjZE3w8nwKEApNEaIw6UdJbYRmEr0WlnqoEapz2SIITqT+gzRCeCGBgYLwnmYhCgLNYgeF01k2nTYn2fdW6vqIHXOD+fGeM+uXP4AP2h8RAkl2TAR14vh2YMYU4MLI3GCjgJWf9hvto8eBfoeUXOkcZvGEvHunwTQedYgpodBoHERsQleLEDtAHhy4BJrgxw1x6HQHQKhomzLxkZcd7SUfktWd3CT4myHLXpEA/Hb63eDI68/qtPn1+rS64A3HlRt+pQN/ZFYjxTNmYMxp+s6ImmMDF0CVmBimTsKkGFtsNuj9qEdEUe/Stb0w8fZUwXv4s2Xv9OCDTz/79PBL1PTI58+Ons0a9e//wZ+dHZ6UtzIHjw4Hzqhdaz57dn5w2Dg7Pv7Jjz7+2ceda2+N/vKTJ4cHGJ5IjTD+9L1Ps2ivY9b5WdtdTkv5ePatV4hnffbkiSJCwzFECJyvmDX1lHkyP6uh0klART/mug8aXn854/VQj2gzt3CKX11bHXJFA6tNzMJKINKxblnf6u3qtDlvdgSWYGxEE3x2QEPoScF6rC65jCsgaNHvdYuZPJY8EklBF0htGY2mY5E8klN61FOcWL9PInLKTMVzaV6EpgWnOtzdI0mGHWIs7CgtOq0jKWM6g0RhPi1E73S6pFnlxYE41lE5KQyHRK/PEOFJponLPE6GKIPqg356PCJSBYQqBx5wgpa4lkUyV4rgSA8NcYVg8UmU8I+LJChf1hcJOACudLmyCoASWUF4kUN+iOhQUiyKpCftTVAYGm9cWFy3Q070qEVwgGG0GGcQJxplYRg4bVIOy1lePowwzIrmRfZHSBLzSsZj0S+JT+JCGFr5+UPIEulKsUz9d3yum31qMpEhh4Rv1BWRwRLsxwu6/Qllgkmt0etPs+ncR4+eHT07evvLk2g8s1yeEW5ExOZi3potxxDXTHGH8iwofKj+NBxG3X4oOFnY2RLlXkS/xUebtbZY1k7q43af7tN42iRyjq9qOEDmbaKSMcEPD4/aLffx/U9wjdzPoV3Ro0BCs1UbDeDN5fmDqiSbLcp7Fd1WAPIfu3b9evthff/aNiZl0v0j0fV6o2wsnkWhjD/SSLEXkWQWI/BGsdpwCg8fPyMTA8k8CNEg47UmQnDKu+k6A4ZhpReMF0LBNE41iWQGpQowgQaD8UOBgzCKNG5nqPRCAC+PSwUEDy5Ui01ptqBoHAcEkci1JypfBVn6RZCJTUD1HYM4MpN4ioLUeJtGA6UWKG48gerHqOYoV07gCrBRVC1rNgVUs4RxIkL7CRTNKJWhOJK+49BwlECElEACgEackQAvoqZ4Xl1C5jVcu5YW311hfg2txleb2fHxocEAPHdp44hXmCXOHxYfXQHgeQqdtkIXTGIGiCMEnz6QBBsHWCQ8m5xQG4Xh6YA1jBUGjsodDJARoYmEfQGNcKXIaARPM5BYt23M6Ck0mkuiLzFud9o9vP8gJkOnjy5N1IVGaa3g7g1RE2q42tArh9zw3FV16fIp9etSN/96u5cf1lsYkvXrGUoNo/h+IVkGXTty7mGJYj2MRnsOuGVe3b2BuXzw6MHxj3/Sr7d/8cN7j580J23SiAcrBfvBx4e/+Oiw1w8cPDj5k7+4F1+GfvFRHS7r+HT8v/6L9/CV7bqBP/vz+41mj7KGT0+dsyZkJdI5G2Ty4UQm3ThvBZtj1smrd+/+9Md/airMJ4YR7MY0xptRtcobA++Hv94AmbEzK4JjnTWbuekq/HjPcXV9k3+v/+ONyvpAO+s7zUfMqGnktPGLUkWVfhD7NIQoYY3KXswNLOpCLAZmIiFc7sYot4yRkQ1pedBx7R4K8WZv0MyENxRfZYLvpSYlGmeO8VZcGPAk90UUpmyKKEGNw6uMgl4Oa+hwcbFXCjqYPdYPLpw4niZSDDCBjeQiQExHCAiQ5yw6HJQDOQXyqJKM0DufwP6ADgqeNZ7ejyXKVLudOAPFSxGmpxVDl5SsBpskQas0AWKAbYuW8U3oCCprFA+VSgXQdrtkPj04f/rY2nxtM30rrXSYMk4yLGQMQfuiASCBpcIuKTWFYAMOQmtNjV46rfwijBWsBk+Qv4HYLKNNWZyfHO/u3WSFohLBkwzXqoIVrJSKxDZXtqrf+V6y2xlt7ZQslWZtf+/Xv5mLpf70j/4YfEQte5hUK0tZZvLODoi8Ii+Eqsbgcjnuhe00vjTo0GJOjMABsjtuRK4XNreNN4hIl8E9gc3961u378SSwt1yYyGRU29Iipgvf+MbW9eKJAhKRpafffjo5OlTboMeaZqYP8CUcHcMHQbpMFzZfJGXAEngwnGnm47HtqtFG01fikTZm6PxpNlo9XAoT/So1oNCFPaeJD94QYBri+Vso5Uulyt2PkOGHUaMxoCbNTmAQK8+dhBxuqW9WCiWFt4G54p/B3QwBxAokZilUihwSHIAE65HBbfSziM2jdodlDPUbEQOQ9eBBZgIDJQ1CthBmTSBr0CVqNBWdACMEoiBhcy7wd9WNkuGO8LccPCFEvdagyr5fmCuSXqEfh+oVO10FJWod+Q9hbM/CT3QE+ILTpqKOVabZBIvOCYLxaJ0bIAEueTgDLXqDPCwp2WtNahGm7/srzdOclarVnvsr3bEP/h3816AWSw/iwfvMwUbwvAjvWIBIb2/E59aQfKn9tvTQceAN6aT8JAkWyMX9Q4zTx9dSCepy/ABRYXrYroIEuU6oMzngmR5FMZBR07ebNof7PYGfAQXR4r+MIIKLEsxBqHhhJgSvxvCr37r+PW3i73VGdPv9cH/vzur9/u/HmLkpRLSzTkwrbhCb+TgdxBcmX0IvtMeEgsKb0BOjKTlDA8enj04ODuslXKZenbyyb1D3rVRtcB9xLcQ5PTHP7hPQhHs55Q1jBI/kYg0an0iUjKZYLdNkhoAF03okqqQfHk0CQ4bA/S/2BqxhiIrOgq67p+dktCxICwD+4G602uvaagHEF4/Lv996QjRKR71n/Pv4MiDF/1wcHF88Qr/hksn1p/1HtJf2o/QDxvDf2ogOED+8rJHgf0B4Y2dXezYIGLMIlLrq2ZOHA/odqtZnlAOgPB/kpGFbEqhEggGO4fLNkF04vu1hoWo8TCTSpcmI18AzDCVIifwKNEQfhrpaKhHOmAyOkKhJ5AfudAi2+PJQCJOFBTkVaNuYqinst3gWEpxGHLCK5hf1vnUkUEC70+5GI2HQbwJ4TP7PArfhChIrSsTNyTJQ85Y6H7khQ5tSlLqiixa4eQkQGlxmUJrFHIslMu5TE6Bx6AQ8CjcH7FgqijJeETJ2tZrTyqbSQvXMhTu4mlxyTGGZel+xeKCakl6gdMiCo1qdQN9VrfbrdWoKHxSbweKNqn4nWcHp3/yFz+uN7q7O9tE5g6Hk8OD463NYvW1W5BNSvvCunVbXfQQMGlQQWgK+QYWZD+mKl3tcLGohuPpWCqrACfFgcmoLT5YwOLBgxAsrDGLd+i4ySxXQVOxvTuvLCeFQL8L5JIdJriZzZRIqhTJVUtGOaaJgZaBEykTqdXE24ws1es0koV9BpOr9dOT9z/6oHnaqG5tJGMRdzyj6teg6bYVS4UlUVCFqqGYVdWgjc0Cdh7gyjCyIFgDYHxF5GZ88uzBk0cPsZ/lN69H5UyMFiIjryU46fkcZYtkPVTtwwkzNcJNajKWCpfJRGxFPYmtz0yTi6Agl8g5RQNItod+kQ+wJBHsmHSahMTXCfYh4qgbSUnLsKLcsCl0E5wTsUAEIMTp9t5mD5CGIwKPM4eLcSgVtSU7IpMGTdZNsQU0HJLPcgDbZnIZhSYQiIB7lZR5TDsugerICqfza+DdP8E+V82q9k/rXm3mIe+PVqNu0R2AMRDGcBHFpcxLs37X6ZMgqZhNbtm5/kYlSnUetzM8fdZrno96dSWwydrEMlCBGW2t5GBkGrzQckmEJKwAcCcsAaIL0V3gPAXRIrbZisQ6nT5atfp5A6LONE0h7PNQp9FMZ9CehagXIBWQGvZcu9dt186Vzevr5VOm615PL5/+FfbXH/V2zDhdeUxnIMNSxIJ2WP+MHqRPA7fAxGOzij/9wU+2K8WTzz6rn+JyXY+TRmY8CWdivd64R1Ls2dzpuPE09ZbCaQtQCaXIJB+K8GDejitLKu5BQdQU4ExUkIkx5bBhQgmzprjQeNaonZcr+UIx//jJ027PtfM2uhBp3gAe3CPFshpzjiH4F833x0i9Wg2OOeV10zv7fD+9az634N+4vt+/efWy9bP+J7379JfWaDYZMbEwtA5sqUUp/xmWfjBE9CM18hAKEJbRAaEWAh8C5LjcwR30ux1AJzGHUxjY5E5Af8zCQFFDFnKNC98D+cvXUzobNqVHAwehseErwFKisFnBry6WalF4D4GBZTQCVas6IoomxE+DqcnBAj9IKNiCMjHonNyMKscKCGV4DcG5nEWQwlMl+Ptuv5MtZ8JUT+nL11PtWQbAQSBl4UXWEvpdcbLgNdCsKjEuZyMI1IC0d0fHqZhdufFmfud1WomAYNApqnyULlgU6Z3shnL8tcNUUmFJjGdyc4fUkUPEt6CQmIdgaZzECBidwUZP05QXDuF6QX6HHIkNoglc5AOdvrtskb6oU3MC3c5Rq9clu87R0TDx3gfURSaQwUrIfkAK6+rWTiN+xPCPxq2FS4oqa0RYxrCHvimKxsOaRDMpapgFA3kAjPczMKbfjA6DjNGi0amfJKh2EEgFwpRwiJLHrXP8tPb04eOPPy4V8l/+ta/Z+fLdr7yLeg6cyYQxOXCC9F0+z2atQ8yhcb328ca1rwAYFLOB4h88HUfmnUVI6TAZ1QHx1HLUYUjR++OAAb5lFEMkcojhCsdUKpSQWl02DtnMA81kmcIa4WeZziRKpTyKFCYfPaOwv3hoMvnNiVMFqfMiXofKguWMVKCFDatCLQ+ySaP7IjsIGA21t2o14uizIB6M+hVMjKz9VFwgU7MSQWPQhoNegPdQ8AMarjMAPIFVuEMgBMHutOfgMITZCSUi3MagM0DuxHpElBl2YHH5IBY0imgzlRNitlTeL76Kbh0JVuga+qRoKTNwBuQ8PC6AFfyZ8dQpb5IMjvIPdMa7x9xo7uHYu4pSlJcaIQASN339Kze+9Pq7BEigo3e6oz48PjMEUSdlSa2OuRfUhKzA8sgWiuP+0FHQvhsnvJ5vyzMNaKVENZQsxIzAwdhWjtpKqQGMWqhSxNYSLADlu5vtdqOUTw6diUPVMCQFrweXm+k19ov+mkV36eJzfbx05a+9Sz/0NoYd9p8DM6ISxr0j0QBt82kYn+5AsJjPd+qdP/jX/2GrkEkQPxOODGbhJyfNXKZgZ8EFYOnIsOuUS7nt69tYschWO+iOYmmYEdnr7AQOkbn6eZsQGEzq1LQDOUFfEHc7zoj6GK3OoJDkTvf+5w8QFjvd4bX9nSnlRRSoI/wHMNIcWim6fnmmNUYXI6oj0xXvz+pAZ812cac6r5uANV3xLphjHa52OC249EQkn2Ksr+osQyS/HYaLVwBP/kWZUfDLnBIxTVkMkkFKPCRBurgxBhVLXRekly4XhsP6uN8riDKIY6aPLCqqsPBJcCgud5BPgz1BHXIth71Xs+FJSAyxGEcXQ8yg4Ev4XCmYSPyIp2mMGuE4NwPzaJ3I04zL1iSxQLkMBcA2bPL6SIkwbbeOf/+f/XeTWOBa8Y3dW7tYqtLJ1BSSIASOWgalP7FPOHFTWla0DXRNfJaWPfZd/EyQ9KkBQ4K3Z/fuvf8jYnbvbl5TKptOrRDbpYI9ZBAb2GDQL5TUQxUAmi0KhWylXAyi4SeNFEZkWHDkZMkKeNOGMdtiRIBDwBWYnLIAAPnDsliG48nyZrXYpWjiIpOxtqqlr3/15vFpg1Dhre3tSin/87/8aNTvOU73xs3dXCWD/R22VzE6yyBsKX5K0UwJbTUVLmFOZ84gkgoMxugh+FQwRaEVcBaMLxZLf76ZUQQGalWqbIAax1TPpu3O8ac/+FOsFul0ptPqP/zonl2uxrI5QBV7j1CXAFsILoxB19sot9bvMyrqZmDiUEfq8JSyQK+98UqlWMkUrbP6KbbxQnZzo5AmGA1FPE4AvGKAh0w6UdwoTYJN6t6raAQ4WCp4ph7JDJoaKRaLwZkbi3LMIW3GFDwjxosvK4ENfL2CkOErMOOLmYMrNUtDjDZwBcAB3Zj5cSfFOReVDc6nDAmOmSLN6MbpB6nqyGMH/QzOCCsjbQkaPCQGxCMxLCMXDG4nMfJGHj3rpzSG4h4h+lNogDPA3gsoQ3kiFpnvXAaf0YTUSBvDLBNYaqst2CQQJmgSY0R7DSnlRm9deuOofZacv3mU+soZ7+LFYtVKkZhB1HcwkUgCt8hAle3c7/4f/7ON/VsBWCl0rc7o9Mlh5+zouHFG84b9bovQsIiYNgR5B1cstFcYfAcjtPxUHEUNlKvkO83meb1ZKpVJqcdJNADK9o78OiCBRwWvyEazQ4A6ogDlP46fnJLehOJYIgAvbitc88IVUMqqk+tr3njw92Ig1tf+d+wI+/Mm0wThewBMQMQZ0WCZgkFkYQVAY2EbTZrds/Zx5MbeJlzGk4azf3Pnv/h//pepeMrBE7bjtk9r5d0CeWrnpB2OhR0y0lhpksE1KY8XpHpqRa4cy2WFZPS8DsrPlFOGNB4g5+E4CQ8Rw63w+Pg0S3bwlE1ORxoE/LEgAG6QozcYfluvQIZ6rla/bPvisTJPGGjxicAFuGkdazM/631/x1xh3HhUDwrJkg4IlCw/bF8GAJXjeDgjdVR32B5u7O7BxOEwJ9YtjK/OaEHvglN8KPABB4tqZFAtYz0mOw5BdLBLoFgJ37xnKp85MeMgSHkzUr0dGx5fHjbOB7WzkBsg1jMRX5K0kGkjbh33ajpAwkiUJ/CTsFksdNADCnypQ8FxBhJp/+nByUc/f3TjzluhcqlzNt5+c4O0kuNJQwlReIBRnWFMxi0ItC90hq1WfJFMtlqjpB4mpRPLmOW1jFM+cHzw5F6iN89tvFGR3dToxpRfDJkEPQDYb0INsEA6XUknyAWOVyXDhsILaz/5cFAmIQ7iM0HCHvhNcofQaeKHJH0nqDZAt0gaDZuMoVSZyGFd7QRu7HmmAmy1v7nVamIvQRtGJvY5VuKBMo8H4U7x8MASigbcJoNml3p0o2EEbXsybmF7xCuGjgl94iEDvBm9lWAN4Me7I1vMk04hmcb7n7wmDumP77zzbbRc4eDk9P6jk5MnDE3X7SRzObqgZ3gZlXbSVrRL6V1kQU7A9SrFBWoZkJuye45mN29U/t4//O23v/sbnfbpD/74jx9+8EBhZmB1mAiyEkkbJQPsab0zmCgEKUk5yWASL8puizC3KeIXzCZOBudHR6fPHu3ulIE3fHNIujJon2eLMlrANCmTM5gcyyQrVpUGCAjA+Qtgg9PDFoKjgLIwmWHHXUiphLgIMAOE4gXF1yFcyX8U1h4rPiUvQIv9TiuE3IA1x7KR3fRPRQwWJECDsDGEiI+8AU45k8f/K4HCh/mCrBMqi36PygGkcs2FMxKLTXiARERkAlROfB4I85ezlqG3SjVHV7bVFbMAvYWqO71lyZ65ziyKdkvwCODHjlhNCebX33o3lUqjD1o6p0GKD511zx/ff/DREwIvoFIYejFmD0dTEtcXt6r9/lMS3OIPivb09OmYIc8WSJ6SfPr0oNemQF8XRRA16Grn9VyhNHCn29vVk1oDrgaDSvP4FES2s7WPRNQ8rZHgY00A1q1f9clr8erI67R35N3KX39TDy/21c+LbX3Ajhm3i0sv7HmjxWmNth5ksAERbcJlCpXhB3yN3oD5Q32WIU1VKtofDM46zoAc5KFg/lr11d/+jUQsK38XwoicXqaoCKYx8a21OuoGfMNO799z50JY9eY5CjTqk2DgozgKrm6sShZ6r9ONk7dgOATl7O7tEPiCAR00CiuAOYiWGd7fm1CvoS/v3KrP6stqWw+rNxrepfVJc5conQbr8mOrx82HPCDU5cu3eJPAwtCgiXTCajAzWsIo6JVRAN6LolusUky4LGu8a4y9gNul6ZT9ixRdFsl9t8JJ8j/D+skACv/Pd3DgIzGjGqbCtqBIfVoKZYSKyYB1ifMe3B+MEq4XdjqQLUIg0e2itMTRrp8gDyW89zKJXx/3YMbFfEApYOJdUCNBVUVN1Wc87kNb+9e+/uWvfeV7f2u2pA7k55PuwajfmS2h1qIAWBEIjIwlyX/rqg343RiUZtogg0c6bYVDOBdtFbK/06qdu7jJkNs6m0Xk534UIpA9IsmZag7pBti4i9yBygGWgmQqDEqEzJSwXJgZUWXRVBwhUf5P5gTnxMnGk1YuVQU19zvdTq09zlsS6Mm59fTxwYf3u/HYKc48G6UcCiNIkjz8JoNnx/XNkg2TgfIXvQfplvGGgY5m84XxoEUq4wVFCxLpcjaLFq4/7MTjtttz8NHFQcmDFVoLolQIASU+pFEBLwUnrL4ZRZYLoNHxEMfmZ+1Go9HukWRzA+NtxV/g4gLIfjd0IEx0GVRLkBoa9RkajgHJz7Nf/tav/Yd/9s/Pn9UCs8IP/ujf/tl//BHojx6e1+vYEmFXGRQ4bhufmW67WiZyNn175xqGRQoek1kVvsygAfz7h48ffXjw9NHejd+B1jPEfFpwR+ESMvWR6C2dYfzxgiXhCk3BMAONFdIXm4cTLaOMyQWTJh4DqMQwj4zGZF1FaCDwS5VtiNpJklaE0EXMW4tgDN0H6xYvl5S0RjjEqxgG4iGxomL7ZCwKcIDqCSqH/hb1N60F/FgPUAUtNloGtoDPwcEkkaIWDNZFWWX5S94dYB7mgj+mj3SIdmr0tRjMw3oB23qW2PdPablotQqV6Q6QC3+VyY/sVvJuU1Hs0ZCrVNYkHq/xyfskbzt+1rx3/7B24jAqsUiQpCkJK4uSNmXnSBlyctw6eHb0yiv7W5Vys9fjU4VSrl6vH582Z+N58+n5W1965ZRpO2+TnL3Zd8kahRx8ft5DLYmS6/j0OIF3VRffAZAo9hSzmcnzdr/gLx3Q+rxydd3j9ZBcuayD9TPre1+4ZX3Ce8vFh/SskJh4GOEz8BH3SoYPq8gUGltiGXAwIIK+XeshtzNn+69dj4N+IjYx9ZF0ArnQmZBFJL5MJ3oHAxgsilYvYqFMWYEenZPzoRPOEBI1JwQUjwSkR+l+EZf4LDMEWMAlzUZE/rBWZuh6xfOCacQRCMNqYgW3HL5ke9nJ9Qh6/Vzfonf5rzC/qwPv3NUj75wPuubAXOcVtMWgc/kYqE1w2fJ6wBLHaWVlRLOehuWMzVGVQCq07lhVchZOFqvIiYQw2al8NYDCJ5DGNU4ziEeiQtJht3kJtjSYMxa25oIziKgzUhYG0kpnDwXAezqVmwXqJA+mmEi7S/2xkY1kT3CAyCZ6W+kiMBuTZwZnJM/b2oRoee8jU5uFcwy2RL4AAe53j5qHny+jXdJGezYAuHIc9GGCWNA8I/0y3AFTIkM0nkEZzItjp7+cuBSLsmDa4/YyWeLbUmlDQbjPzDTuSnRehIccaPMpuaCoWUxnSSIbwq9JMh4GaiItWa0LMDb+sCARtAbY2fjbx05CcLjTxS0GbEJySbRQDhXQZFuX5hu3xXZvyHfP25SBRN8CpRGSgSJjsWzUl2MWpzK/k4k5gRHEcZqZbM51LOJ9sNUwvcPByCqB/XmfBl8DtKR+IsYaeY5KaZ5IpULWae1pv3ngNA/On30MjFY3NmP5KpLQDC0xrVltBE+BUiVPmInM5nLhRF6Z9q345u7++av7hc0i6buX8wYF7NuNXiFOTugI+xQtAE9TeRLD4aNHh8VcrFlvQdUoq4C3JOkKQiFEFvxxCLPQ2gRtFrETqGqxjb8VCB6bhMnPKGsNCwppEuabBAwxCiukQiB0LW6GghSDVtKp1QhgRKNETiYmA0Mdvra0GQpHlyUPwKShhpspQx/KHNREaAN4EtqGMgS4ZzxTStwRw8sDEKGWHdnxxi4pRSlak0IBhY4SYYLSRxh/AEzqKdooROEtTOo9uZmSRD3WlbVMmnr0SQy+FwmsRe8vfU2JRz50iv9XG+iA+w2mMHdr6oQwzF9dECWAT+K1sJ7KWmtZ8noI4ZOz+OBHfzmPZ49OKKW9pHfOwI32SP/MMox3h12Fhg3dVnvR746fDGttp4ck6j54ykDhv3heZ1nM66QhbA6Ziy65gaaLZ0eNVCRe6ww7ztCStQWHChTabVtFwYF4sxngWrWfTqmtHKob+qt9j9it7jEXvAPvWe/Wi8t/nb2LZ/Wu9Zh6WFWeP4wYGE3XRHTF6jEnnAG5kWLeVOZIkeyp03cCeJYlS/gwYM0bdUbkk+3Va+4AqyO2PKp5jNzOoNeD3zJ1FkIhPMDqJzV8gZkyotBxvUUsDFowfAqlgh0Z9Aei2uibYT8Nuge1wnGYAfFaY5qsmb6yvXCCq/65l126uHrlLZcPLoaJsxol/4QGzWycUSimdP3mE8hNsG5eqyFXqGonxu6FeQAMB3YjhToLDOKHwwPpziylWAi4rGBkbvJhivrqDShPU1SX48V8Rn5o8iDWqHDDxHVm8YkCrKSgoaSTClIzuM5k3ifsejDNWdm4O0Jrg9Qv5wtmjRD20DhMugEQjLwKqUDGrKrJRNiy+oh+GY37ZriixIuORkMcNQNopbxehoJtivn2uyJPbEZBhUIeN3NuCIeiCGnUeyCLWefkPqyyXdkp53fz+S1wNEoiLQBwOSZopYwNEhGjemLzALkwcSxhVYDEWPMaYf4HT6CyJrGblNGooilHTtJmCuvN0mm8eEbVTCG008tmCiTe3NrZ+O1f/1bpgyfY2bEbY2XEPZEAlWs71e2N/GhAgndUroDVOJGIjgf906PT22+9tggOCMIDMfFOSGuz0aBFpa0KVRuwnst9XtSb/xkfDREKCrQqgrwAbC5yiVKzdZrnTq2RL2xubJY3r+1Tx/2zn/20jWjyCk/QaSgkk5jcKm8xQjq1mFE3LB9Na8gXpMg6qLXOPn74ZBaeZf78P3788PMJpsjpqEBAc46YMnhtlPUgmWXJooLMvNZpkpZHrLXAiEhvKfaBJ5oEeSOaPos5N8bLwa5galKu9ePRDFfpBJoxvg8OR0sjU3w0JC8gcSJcpBIZnsq8jQpu2gBa1iYkWAwfQgEaJZhy3oHbkLto1JXMB3MdCjlScBNiIpCSLQgPERR7EAT11qK0De7x+D9EUY5PlSRH2kdqZmZUXQB3EkotBiewG7wfZINIkUikUabbVhYZmCZiYaVxxgtFbBfv91e+JsVs9MkAsJkiHeis+cOt2jUTyHPiZBWDiaSiEASn08kULNKBUOjy6S9+PHaG0Wz+5LD3+KBN2n+cjwldO2zg5oTTXISkZEAWgkksnXxy6jD5jCWBIJzJZcmQGqg1iXAOfHivAfqiNkJqRoaMEBo4PChIY0J6TCsTpfBrplSqnXZjVggCaSR5tZBtjVG8Q/6uusev6YZ3YXVWR+vThuZ51//af1cv9F7mHa1fzDe8MTQTwB/v9aqN54LGAA7UMyQOnHZnJcxXm1uTkYL8Upmo0x5nshXSafXOTzqnxywmoJWShaVKYZZJHz9+CnMF1O5c30OSR1VBrhfwApofdJI4PySJMBy5MjfL/UUsNcBCh7328XfVxBUwXBkvr5WrW/whee5w1e/1vZde6j/xxT+8a/28dmiFmqp//gbKxUAK1mbJcB5NNx2bUD50lEna+LrB1YLZ0jZ5IHHrQBqddeote+vGzFUGT3nJs9LGo0CEDEiEOIkrX4yWrUYDxboxjOk74/koHANZK/BAC5u0jIM+sfw4esLuF0tZMmfQLnKP0UBFFoSnSPHEAXAzjh2BKNVWkQogSTQUZ73JcuqSwzf2tlhCbkqislA6eLLnl3hEj+EhjhOOCXHlEJQPd6AOoqxllWLSxZKfzg4ndVIA4DATyRQh3mCMGaFpZDSCuBFOHknBgvIyRkVejhLqcCaVxhwTiLgNZc4mnwyxr0jrxKYmrHwWnSy3TZ0u7HChQJIcJRBoNYdud9TZKpF98qReq9XPlf8+kXzmYr6OHR6etlrtdmeTvCs4nAWDN6V9xxjnUDSXoDvK23f4EK4dySy+7+TgIMYpKfXl3EX7JOpDU4RcaBm72CTxU8RGyRmjXlguqRJf3di+88ZtZbvFIyedJL29Avcg6tkcVBn1HCwnwtF44NFMUZGjZ59aiTu8duA02o3zs+ODYjWVLqbv33tAZlLSzjfH/dhuCWZomaS4GhMYPa+3KtvZxskpJRdq016PQUgRF9kPDLv7IH/NDkaRAZHa4LVau2nvSHBETYiezCKrB54auA2Bs8ZjzAaI2igoUSgBPOgVmUjcYvOFXMQd5rD7p8iJJzMbXSc/A12luyjoSUWIKo8pgzVXiLDsxlCHSCqjxMDkt2GQQH9k2mAhp+1gbrTs9Pmgi9II9RSsoag4wAmkTJdMAWIAb0ctCRhBtkEofBRjDXXsIolwr9OfZ0kLT7lKsk9NtIi0aT4MMhfVAvIuluIFWuA2gNJcBi7Z4yGD/aXQwA+FTAwImCjiotT+DXTOW82TztnhIX5QB0cH7cGshycbOR6mSwsJGqWZCD8y0ExBEqR5oJIoEmskMh5gbyfMUfWvtsoR8vES50RGKMJ+0ZNCUOeUV6D4dTTaj7R3cTANx588qcGf9Yi5cH+JDcB01PzxunhxfHmPa/623vN2hIb0/2q7vM85753rZ7y7dGgG07/Z/zF3eVoWM/Ay1QJroAwMNvjJUcOc0n8EdEbwoQ5buMAAaahK0/mN0aAXjeC3M08V8yhx0PnAO8Dg4sS9ubMFOjp7fNQhuRK6RRQTccWFa6nAGwMIROAo3RKzCB/D0vWa5hN9I+WZM6btXlMvjnXSO2cuX/rzXJ8vXfGeePlT3rjo5tV1sKoHiOYNwJSADShTa/kGB/DkCEoyoU4T6BAUzY573LDV6qS2UgMXR50IPA7dhuFHkdPr1Kn2qvSx4bQInTpMunVeEcVbluUBslA1AYZIyaF9VIJ+KRzd5ntGQbd0ZoGzBjgr3qPeACtphjJmlCMrTGTKe1Bfk3jORb+KN8zUQmeHK4K6hFuM8tPIAvfR5/dPiePPbJh+ERvTR10UThShQEw4jYASozsiFsgbCTNRMg6JuWTykNVoEUXMSFC8dYs8ZqX9m6mk3To7Le3uQYF4HH0NmnekewaQiV5Ol/jmdEakVZ+i/aeSAWwmlIzmh8YLynNtVArZbIbkAHjnoIxGWs+mK6FYpNmpP+vUH3cC+chyMA81neEnnz/6/NlgEujD0I+Gga1KyukGai333rMnjNdmIfSldwavp9LRIQyZtXvdInNZr+mgcMB0GSHpPmEXOCrFiG4dI3Y02odZsgQh1Wle6SHbHPVROp/HP4GCJZwljixrF/GZQhcivTcpmwkwngSrN3h7cTYhvXefZJfIHSdHjw9rn2DmJlSCgQapKD4rEKidHGXyG9/57u/sV+5sX9+YOsN0EuUKssI8k5Oo0OgOmAGUW4ndAr41tyt34aVx2MxuVsmzj+twHE0TjgCEOvSgIz8t5hNZexPGrFV7TPrnRXCYye9FE2nx/lL3w5WgJzT5CZaRKdNFjjmECSEFrHYITaMJoa3yakStJM4AgY1ZZgzQ/sDGUfWPFSqrC6G/8itF1zd3uspouwilgBbAH3IL9O/e3qJA0WlrKuFFdu8p447vFiBgRBBSsmIrwo+W/IFo6BaZSTppW57NBV0wJX1kHJ/h8hDH4ixIk3ZHSEhLz1uBZrEJKQg/CJYNddBZNp3WntYkTAYQxYHpkkIRITwDoI7gkFqDyoVPn9VPjzv9h/WDowbGptuFopD6DDcn5bXG64VyyFhlUHvB6ZDTDLMBiqTJEHc7yhSRH1Ncfzyb4QNk7ubjSKrkjIuU0ZxJGKI+AJna+47bapz3ABKaRayT12Q11ltP2vsrNvXRv0Ud/IK7v+i8d/uld1w8r0cuvdHcI/zLWSE1cQC6mSFVEBbqQHg+4AloBiQkOyZDZKtFHMSXGeJK7i98JGBFCJaUvJ5ORHbCw2ZzDDRMZ82Do5HTG3cHnXoXFgKBkVeb4n8z/J5hLSAAID++aSgA9Mb8erNvWsQDpnGmaWrY5U1v+ys2r18GQF6889I4mDE2Pddtas76QK+4NAVqHKMEvDFSTDALhTUHiMNekSZhMiRCJF7e3qbg+GRIWp4JZdFSVpakULiSkX8Rk+xkMA1F5fnHYGCjwmAMyo6mbMKxxISiBiPJFuW3TDEWND+JFF7/eRg9Pg26P2/UiUvFk+G43kTFSwkUgL41nDSCU7xGutMF2Gs5Toamk/x2xnUaEyeeg19DjaE+kf5kTHa5rB3IlsDOEJLB4YP32t3Fdvp11AciAJLFwiSLx+NQJI7+YmsmtSQKeHocmgTCFInF+QntQhE8GsXRJmzjSpOwMxB4Ro+RkapaZQKHPKJYOIrjwe8vFqSUt9LUP5ePPH52xo1d9iYIQQDxgfSjcyyLo+mkH4lmCY5bBpNotfPpCY7IlEColDfeffsdd/xpLmuj1MaP/K03X8NYfHZylEounRrOoJvZQpI8BxbFWXJ5AtCQQNBGxmx0U2FSqsXTmSGtQl+eSPZ75+N+01Ap+ukBGlo06JWs+cwjXcA7ScFtmFPH47O2Mxt3maR0hoJrZBSAvFIbsLtxTTDBGGONIVUCAY8MGip0285H4wXGvFR9DdtYfD907SaSHMmjQjff/pt4RshYgx/XrIu1A1mN1Gv5YjFM6TQKcCJFYYpHXxYNb7odklhAiKgjBlHZ2X0HYgOPyiyp8kyQhAVJN99vnRyjgcXkg1lqAuu/hAUWfi+USmjADWJUgWkqOLRak2SEcp4wp0oLAToDFKSAojlw4qFA33XJYkMuMCQAdCkomKbLAUuXEnH5ZJ4zJJ+wUnaMgGNEEhekL1UeZdOxeALAgAALhIFNoDbEHgMiTpAlmAUwQsQH3cPuZdK4biVQqEsmFGYA0hhvCapmJjjDZkDWsFvC8KvtMl3gFnMTM2BeYv6YfbCWzPFok6jC/YM/+0k6Yx+fnJCF/eAZuQJnr752+60vvUn/ycsCsJP9iETfIHrMTtimKEwdZc6SCchlt94gVJuloaQmvE5xsvMehk8KgE+CxEvRR2gsdY2IA8Hp2umdsU5RZ5JXXy5uq2Z7fbw4WqEV9Zz/zKH6crGZKzq86PvqJVdvvHjkl+15b1m/lFvNW7zT/AW5ickU8gNfgPcJMoKHoUwFzIDuNQl7KUsNvcMvwowyMa4xirwspnFWDesNvVG308YphdckLOp6lyfUwUtFh90BXClR5qyvdNZCIYloCRaF3xTDghseE68pfH5bNVcU3jT3+Rt+2bH/whc7/kseYi5e8h29QvHSYtzNBvNPmhipSuF7WKWk6AIM0M/i6bOxuTlDS7YMFtPkYSYHHI4tIdXQJtcUhlaonrSuyMid7um/jiZ/3c7tykjLUuQHcxL8tcxkrEo2JAzsimiHcBjFf6F/Vjv+9vfeyFmVO9euk6U2VsSRD/+FXhn2lFWeLkBjhkR9hlIkaV26bTudTxUgSElJ+VoUsd/63X/wrW9/M5HJus79VqfutOD48MuLhhM5PLsgy/JnUUZW6JP4KZAb+YFpHvABXhqPW8vgRNwVTutO7dMPfkLm61e//Pd3bt2k+2qxAGsJfgeW1AM4CVU4IChtVioksD0CXmjJkK6pK8PaB1VRM5aiejG0GWiXFsQQ5RHAsTQTLIzzJznjQMpyJh9Om7UumrMKQc+RRD+AVRmuw5oMm2TathfLYbc9W+yhYiYXPe5FmGTISYQkg7MKvi92pYJRkuzZuG+TlTY56VNynjQVdJNmCPrV/rnTrrcaJ8rOQBLXANVg++2Tp+cPPiD8yUrCok7GXVKYBT56/xfQw1tfL5s0rsxnOFvaIOURwhn/Q/PQPxS3rhO7lynsiHYGFunNnNkhbdsiz03atK5EHOXxLG2oVrvOmf8E9FBWbqCNuBUFkgJpLRyeVN1QYsqYEsCGrKa8EfyJrUZ5tqiloygIxhi+BCszxABbBzwdqS6LyWQhgwMrTljiuXmzJH1pXwlbQfkjWYGxkDkbHRF5ppYWNgSKxeAVimhIKjRsT0wHKp7HJy3mk3+E8SNB9EcjHrQzVFpXsg9coFkaTDMapNgAgkS1lZE1TUJzaA/ASAAfjD8fQ0GkruPyxjAYZK/Vzq5GQmfY8bCkTnm4QOe8pepdNSPGNUaR05Lp8Hmd4gSMMH1wcG7bGL+b2xtRjBWd8TSVyZe2N4jcizaX2MDIWkusQKvehPWnYkSxVAJ3d5tt/Fkp0klkLxIP4mkuX6CYh9vtAJFEu0CfqZQ7guOYke+atZ/An5qoabRw6CgHMkO/PBmceuFttP1leE8XhfP04934wl9vhC5dXY2Yd6c3NJef8q7rAe/apUcBDZ0D9dEYAZf/WTn2AhAaTNJbE+NqgIIBBVgkSrrkankci06z5SxqwX7t5PTxI+RH4aW90rCQbD47GpLGMkouxuR8SubYEQsE0ETNgWcM/AUoE8xPTw0XoBYYYF912jTZa63317Tda7i5Zpq6/rO6Z3XiSg8v4Me7fOmiN9Cc9u7hypU3mTtNyzw2hGEy6kmIFqyiDGdS/o/lJ4MZjTzA/T46fnQNwAqawyEVdvudaXi6ffsG8Ye28IjQ/dBpUjFtYw8qaEjJzCVFAivBGQ04BW0xDVr2e+14e5bfREEKWl5+65u/+far3wpGVTs9Rr7CJC4y5PUJhlXgemYXcmFyVFOdNUFGjXm72bbLFdheCm6GEBGiwTgRs9VrgeotM8NONb1L/uRAoBsIpLqBwaTnJHAQUqgGcamqXkwblExy7NBNRgeRcDSBZ+qGC2GUT83m4cnxZ4Xdu6EEztRuiNh/UlcoFwK6aGK9xQsTMIBLJgAESSMZJuZg9MRQE5MTQiwAHH9HAbfhJFoaaAj5mcFJuMaTIQPlERHCaGcJGAQWyYA2c5/VWiedNhVdsKMny5lFxT7rtpKTMArdICWZhy6TJ0oHAV0Qm4LuUXqPGCIqnDvnySUHqwb8hWxQG/pKA3ZAPZeYUjhvcKEs82p8NInrgzvsMafl7c1263TYbxYLKawOzx4/3tqumgSprAaqF6BuTg7GHljhmzrCxk/Cal5iRD2WlYwNgJpxpuLlfBdgh0lHiMSWKzGIFzHIQmD6uBADc6ulCcTxgyZfrpLyDDaoGx6C6A6M5twXhrddwq/PxwoJj+E8it5FFlgVBmAOJcDNqXX17pe+1jk8VJ570Qe0/AamRQB4vay/PAYVwPtI3KCaPqP2LUph8oD0B118UvH3pI43Vl/C7U6Peii6en0QOsKT6mEx+7QNyjOieBYdwIpAYjjkryhu8gp3x9wMMIAiuY3gAebdoHi+TDPVbX/z1uLqSODqX7x8ky6bieIk75PewjyhMdSxkS3Q6efzeVLRRQkGmUxKpSzxhtjQIWmUtCFEgYB6yn4NyUqlgJAZBSAWWULeApORg5eF/KjoBwSVFIo4Fydi8x4sfwgFEH0hDyhZtIW/4AGpEzabIT3TCJTbYVIGLy5sAOrC1U1tNf+r8d7u5Ru8rqy6ba6s3+DtcIe5SZBiwMWMhv8cl3grBxog3cD/Ghbvd90Y727GSkNnmqH7zLMETRt8LG8qye+DXjVvFSsFUWrl+46R6QNNK+m8VIF2PLXS+TvvvBmeUD/vuH50Qi4o3FmImCDwDyjstTokAESxgJfBcDhCOcsS1KyTQoAPmi54rdKu32Svdd6xabzf7lWfzJUv/HPpad2jDl7cuzq6dOrior/nfXJ9Wu/TY+L18T+HxwG4WY+gFfaVFYpozFDywWfPsOLe2N989OCkN3QPO8QHdu9+5e5v7V0LRrLRRBkd8ZQgzynFTP5hMr0JaiDWEwhSIj5q5nR68oIxXyXSsOM2gycf7t75DXjJuF3Yo7SOusF1Vo4cNjAOM13LPKV2hXkROckuA19ImaN8FvfBKBp4nBeneCYPITkP7UQpFB4lrUnKOl+G3sSFXzKYpmXs9p5mMkT8QfhR56ITUo9BL9BoKhjzSfz9mDSI/fmjg/GwjSbge7/zW/Hs5mTSbxIetVuBpdZoSFhikCT+og3EzUi1olNTNDrkUMSoDB6GkSLqA3gQgz4Zs17iFkgJdX2ahZy0SDmX4k3OcNSezNMzJUnADx2lTn8sp0NU5hE5JfIGfD1SHYrOnDYyNFFRZjNijgxYzTGBkPkrauNtQzqM2BSfWYwGcKXLFI5MtbN2al8u/+BVzatMJaQ/g4lOMQTqLtgaM1Uw+uZ3vplKhE8fzaOJqtvvgGG/9VvfxbG5UADFa5VA+YhJoLSpARGI1ajeOjp/9ml6a/OzH/+P3XYxbxcSuQq0FQ2P8PGks5yFUhk87KrZ6h5JGkA/wlsYysGO4GOEDROYrcWhTUAmuVOyAvOiBIHEcYmcqLgoKrUeSBZtD7p1Zk80HKaNsYnMUnhwKrKe7B/RYrEQ6TtgdZP9DVTP4uZLkAfhbqIgENNRdnuYA5KADYCgR6YSsXRjdxu+H78g1j4FLXnkzp3ds8MOemDC9pQ/IjxzhoT4YP+HfMQolyg6qnYDRYorkn0BDQsQoGFa4jg0GkIVDPYXylbHzP/eSgXOdcIsUb3EP+bUajOXODCYi1XBMMmOBZajU0i1FMWUuTqNGnE8zeVzkKjT84YQGmU3YNbCSm+br+QxzBAaDTgxRBvVKtk+w4t5LpsOUNIJtBUkQbQqLQmwyTFlJ9FnLWJhaq6hJEGUQqAkewS+Zxieg7JzY++Yp0mASC7HVVP16zVXfdKm7rzYJe/apb8GGV86vry7Hh5Orl5r9syX/DG7eMD7mrl2cb8GVneaM/zqmFtEEgAypgrFMdVk5aITzaSZJbEtiOpwdNS5tq7j5RYgt/p4AJeAd/Cg5vZJFTPAxD6m3kS5Um3OG860g1SF7E2Wctz1sKbgf2EsDqA+Ra/yWdrgf9o05qLVV/Z0y5UTq4Nf9pDXqdXf1RPP/aoNz7388pdME/WIeCXDdHO3lDtgbnm0p0QHQnESKLAgHj49PGjXWrU2mfKqe1vfeeNv/Nrv/O5rd9+u7NwKRjLwmPCGoWQe13fQaxg96hgkOM+Ek7Mo+msCKVFxiH3mYrmws7nxbjhcAm2xikBWCroUWub7OAZ6b4Dd5Z/wH/lKuIpfnTeqTBelApKp2CKFXXZ/c+dtXGJQHpBSkqp8bpuAoTPsdoWNYsTGh0TVXpU/BTd9lZNVG2CLZLBRGJqAQaogkgU1HTRF5b0biWIchHF80FKGxz04UVUDBo0mkmT+0ePo/0lHzKJEjuQfHkrgerzbsRKxUFQZiuzQ8hSdOoMeiGYqh+FlIl/FE59PkX994AbqrT6rmtKRlI1h/sXOmg4PnFbzPDLodTGNtBoLMk7h/EO0EVER5Ok+Oz9ZJqM7kUkW151hovb0fNBtU92D9BrWtEwSaQfhfQKjCqccl+7BTD8sazJlYaI2qaZnWKRLmzv5LWvh1CP4esSXcSuXXqSC9WW2nLOLmcC4T9FNRh7c7AUBMFDSl8HuEBYwj919/e+l8rvQb5FmQS8zJaYdtd4s4PabTad3ikF0sXCmbgshMBZLu/1GLJWDOY0lC/NpZ46uDMv5sh8O5NE84JQpVyyWJ8YCt5cp7KN9OHl6r7p5A9xLggqwNvI1AEOcDQmoGEKRw8DcJVL18EH/5ASMBirWCjcrnxh03BlZ2GJtmA9GROpN7BpoAgXvkHSiW1wYFXzAIaYEisUS5KBFKpFyKUGapSSl77CPiyFh/kmyFE2ADBlSUCepRVCZgCXBKqx32X4WsDOgWW4GazMW4Eym3bSGhzUVGiBthjf0d70fc9bs+reYfe8hdvUeLQ7jVch3IajoNmFoAFQ4eUjiNDhHNwVYAn05FeOMxvMk9sC3fUrMRCJpD3s9UoeiXoPiYvAgpB9XCZoiR2bMwQQt4epKxLXiF1myBB/Cw7hgSLqBNG40wwqkVrSjaZz/h1bRZK/Vl9t++Z7n94WMv2DjyrrXL73FPOp9dHXdI7Kro/XzepX3LnO7mRIEcRgJhdPxz2B88rSgeJDCBo4RCQgcgG4XYEd2DSv4EKUA7H2mWIzmsQiHxp3OwacP8PFiJbAsMRoBZPiV4ygAcPFNOgf61B+AhkZx7LVi3UDtmFP+edOlK1cvHagX/iG/3pH3wOW/6weuklbvSe8575bL+xoeXqL3KM2sbobJpF8khPIWNtfDyTSBD+XNLfSL5a3N3Ru72zdu7u/vl8ubYLRACOcfHFFYahBUdPw8yfCiZllgpXQwy+EUFIvhhILUaeJ6+ByKzHDCusZXld0NQQGwxrVfSwwmHZCjGHdz0GiQo3xEiUi4yvCye/4Mf8pu6zyMtNqe5AqFXO5aYes6npbQB8KiAqzOVNEOFGeb4KSb2KJDgQxhVeN4W9wntUXSSWUDE0WBO0PbzxQpTQWaCsUH4hSYribTUfAHDprEGxMBsLV1HTaRFkKhQPQwUzBMUiGgsRBKCWxs5DBtw1Tj7xiAXMwED1BO5A16QtP5llJujSARGA/R4cgUlbFjN2Y4rahaYiIRxjtpMx9IF7OwaMNOPw0qStllXBvjs1K6GyTnDC5V+EJNJ9ShoZBVIltJVm6ktm7Po6lutx1JFQuVTIICRDGiURel7XfsUgXGxoANMCjtj8gq8RQ4q+KmBIlKpSLV4sxtdM6P+u062Lu6s5FJ544Pnpyf4ML7Sm4bzA4MwCtSsNPD7AwSlb4Kpc2bcPIyxi6w+uDg1cFrjnDbyYQ8rS3cSbuDweOHPy7nKtevfy8S2AlHtkEfUMNwMisBJIrLTZjhRWeSLhQIkYbfJ9/buH8MAwvBxZMxS7mIeReYoAt4G9MSi5JdVhp5FEpGii0i9AnWMR0ExwbPGjW3eV6ikGMIczHkBv0eigsUichlaIToOw6NQomGEODMAwInNQ4JVTHKjKLp3NbeK4FFbuiMSEUVCwfvDU5RukEmmVlakbYzUv0ZThxywoDiIQqtMi9m/plUuEBsv+SBodI8BEbWAjCJ8jd5i8xfdt6Rh/607Nh0ytv1f7zT/qIXAHnkQkwqBN0oZ+HSMHfwGlI54fOP0iEWbnVI+IyjcxQcnrBwJHPAUKBw7J1kwEPr1Rt0YUrH+DouR9T3xqjZb/UQYCiOhqiHvgMVLziAwbLJYU5mOEF7mGK3pJBDeYYgAhOFMy0c2xUC4Lf34kdwY5CKafzF+ct7/nhcPuXtG3rpD82Vq945827zcg2Qd269s7rfu0mt8FvgD7gCdJAJEaDgAJTjiTmGjYJ3AwKl/ZC3MISdpQbZnPR7bq+XrxajoVkXo9l5HT6QdGEwZoTHqTgqukll4xoiYGJbJyYATnDUl8oMUUKNU8s89G/mWK1ZN3nV2It+rM/4p/zG+6f1Lm/X//Hgwty0PnPxCvaunL36MtFCs6l1ZphEAEOTBbgGJ0aV3YOPVeIZWOdAbO/2a//l//3V6hbuKOUAXrMKMRXHNw0QG4lCRqrY5WIEMsCeiibt/OA+CLC4eyNX3gV7kJcfFhV7I6uJr/Ladu340/7w9ptwuE8/ef8n4cQGUcWknJ2NmrFY3uksu2NS9jo3bpeTyczGzu3K3rWlHbQKFcu6ARuYTnQTBXvYbHRa2I+PiLaktm2v/qxYyaazlCXbI2sduJWUPRAYFMCaBfh3U/LPW2Xo6fH7Yv5Bh/yLAfOlcDaXlE7p7PSTTz+J2flrb3wjkUEhLu7P+we0kFFS3ZdQHsySW4DYoUiUDAUQzlganXUaTyINKKuV9a+qkNCKpXKwoBqwkCGWd1/dLpZSoylZ4SJbpUplo+gOd+5s2dlCFu8Dp+em0wVM7JPZdjJNMJxDqbWdvTIOTFhg77z99vb+nerr3whnr5OuBumsYkCMWQQR8lV2hCdIkkFaCdCF0JcCFFDrpuKZuJ1BmsEFkpJuLIXzB4eQ0fLWTXfSl1N4JlfYqBCS0O31doIWNINpBT1gwzDABB1klTC5n+Nz9eFf/MtIbD9rJaj1XK/VISlYPV99d2/3+t2d4p3N9KsUrzfI3SMkzDtAotnX/GsPOIHwStFH+DxMAzfg66+aMHPEbvjTxCzQs9JdfFF0e4TioGkc1PBIBuESwdA6fqZ4FSWNtfLFKmua4B0qatJZlDyaAhAaUou8nogKxt97QRW6jJ0aYVeCqxsOyBwEAx0d9onYhmtHfGNwQHNKrRmO9ZxJs9nbd2fZTC4eWKAOQmHu4u4GXYcsYU/Qh6DOEwJL6RHMC0FjuM1AgWTZAWIMATNzQq+14LxFZ4QBf/1pPJ7fdMlAqVmzFwtXe1Isw0HwJhSawPcUZ1O5tpLqjZbQcDIUkaEE7SduTkR/44ZA+EgolmWk0IRRVoEsKmIESDNFO3mLUBgaOkV7MyNR/AS0s8TlH5YF0YH4TXhCPi2vGXoJdzaXSvbqJgx70dJVT717Lp9fPcW59QiICzV99i5enDf36E7Npr9xdX3D1U+u7vDu1U0imXpQr+CYidHy0ASD7iVMCU5wTxQBNGowCNyMOgnUSKC6G3OMK1DAHThcs/JpfGLHOC/HEvl8Np9O9Ts9xC43MsD1jbReLBUETMijCkxEEbN4+7rRtOBSo9VQDr2r3o5a6J3VRbNdPKxD71267ep5717TQ3/X3O3/8Wfl6rc93M9IeN/UoLBOmFeNDPpOQALWiEgZnAEk/sWWEevmW98jAtEgfdjncad3/vTgYafxKBafb5avJ5VDKUT6DEouU1Ow1Tm/97OfUSPg23+rlC+jRoiPCdPqwzPhUibumwIDzrg7bJ2+cfcWZSwSgUyhem2BW2hpBwY9UygjcyasSL9BLrASDgvYcnmqUGSVoaCB8CxLu6wtqgpuo8av7N4JhCwKgbmx6i/e+xN3+HF2643yDssWGhw7blGAPV/dfQ08TrY0Uy/EawM65RG0iVFAAsF/Ho9f6gvE+/Pjz7sH954FEufRVCESrVbzN/FdRAYAi8pPRPhIQgohR1RDpFRLIpErgFiXM7I9WCl060JAJKyE4yZmHtQPnxjDixy5OymrZqmYvbu8qWIh4SCJRICXYu5NqT7QHKXgvAigwwLK9CxilhVLRhX+j+M5tmMi9OLJ0s1bYco6BnJz5BymFmQg6IC3YUzMQkLO0AlhfgNowtpIVlAivRUt9riDA3sssUDtY1c3bQSb9jnGPXAppi8cYLHBsN65V8aCOBG9Gn8De6TJGOZKmyN8xqcPk8mbN26/a1X3kYby2zt9p0UtY6YbKmlgi/oP7bPjx+6gwdC5brtQvl7cQiDAXZgXxjCwYn1hFMTzyXaKKMA2MpFckB9MwXPAiX4gQSnCCmO4Q0DuABSIBgSBzHgqU2oUqkwgBlmKLanhkcVZvUbMg3bzfgVlYOUfkoyOAq94mSVRm5ACLhK30DkRxQY5xLqATJ/L2+QLefzgmRQ8eBE0OtT+zO1WpHyczkAUMA1j7BxMcHRppxNKk48DKIhkSkamJAIuzABMJIsKlMKyEnXToDOSpnPaNePKLxvnLuE2c8Jb4Jfu8e/iToMEeLXsGzJKs6F1MNnG4d3DKKPIyZWgGFgjjCCFAzs4PRUbj1BqpylwEwvPcbHWSMPyk9fawQqAIEA/idRBIY59SHYzjRmhrOPxNE51JqU2UOAsDQLC+CJedNBajwCY5q/75vVG3XpuMz33z5nuAqWXzxmgvfrMqv/eL3/Xm7fvPb66i7dpbC7eeumCP/rmh5f4TZZ9WytGKlx0p6gOSOpB8KFWDLdg5mZMMEDxjxQrRIgpBFAJABQuhMCKbyiZRrpt8jFiKYqTnoUgIHCEXqilJy0TBJNhY9a8JqsHGkavrev+eDt+f7wFfHG/LvrPrxru98A/vHSrebX3Nv/s+pFLl7wb9FoPV5hjfd0jgQwhbYQrQ77kDmAtivRoUl7jyj+ctj9//8cPPv7L4GB065133nnj63Gsmokigig3owNAhwZ4JJ36xs6N6WAQt7KIWvBUsBKyOsFdGBca1mSluPfKt9+29r/0+v43X/8O4ARaVysMnwj6cGmhtS0sYKQNxhBPFCp+ENouVpcFQMYhUnxBadEKS5uZiCT3bn177xZFsmYdqgLfe/bg+333mRMtJkNf8oYC6yMThBZTn1KUEIod/pK6U9kgENwj0yEhUbDqlLUhwG3U6SQJApDpCz93UpGqHpaYBthtdMnj0eHTdj4eS+eKMFFC+xQcoAAu7kpoTlCq4olNeC4pB7BjBh1aiQdhLErAAPJhApMvCaZQSEF6wU0ML5YAmifmEUUNmb9IO5NQTA9NJtcN6BBXT3L5UurmZmYziD0lkBb2ZDi0MHmPSIoAHa5X54yzg9QrVFqWuN/vdaYjJxXbsbKV088/Km5RvzOKbpOy7Z1OAx/TTDhHPnzMo1n8bgUf6inrHx8iGqH3QkjG1N7JbO6k/+5//o8jsXKuvIcaFesgnzZBHiwP8ZqBSf3+xz/62ff/5ODz+/QIJ0v8AUrlyub+drFY3dy7U711JxwugkmweS+WA+f042UwkS5uEYFDXa5UJsOnGXfwsBKLotiHHQnhPEpIFkg2pvrUoGz5IzEVZhVHSO6Nah6OOEg0LxgMRw24dfQ+OMZk87miVYRpc8gPOE3mM5nxkmDYGY6eIlhBpW9BwwVJESSrgoF8xbA88x9J5sB5+GvB+hAtUYiVuiqAESbRJsMjez8QLs2iCm1KL4wNGggFvEAEQK6WlwAOqPB3vRM6t94zOxe44dKNOukf8mNouw5BXiKEoHNJLJBr3NHAYCQiC9i5PFWOMWZADpQFaTbHsRgj1SI1x2+Fah64PqL2xNmO7EUsAZmAYrNOq8Vo4xuF2gpGjQRg2ISlb4J6S8ckiKC9IgNAst/wdTu5on210L/0sh+h6MuM8cvuMedW7/V+vb/rF68ucie75rQ3ymrf5cdXqFfn/ZlQOAcIA4YXvgF7oHx0MQ2yXOiaaLbgSZmTnPZTnKdJ6Z7NJSZU/6yfd3CnJZEYFbtbXfSdTDY+fbAeUprb+NUpJAqWhCXIeAGdzDcAoCbyj23VGnPIKa6b8+baevjWp9Y7ZnHrVvMMv+yve+rf5V3ShUsnzAd0wj/p/azepF/vmtnjK0y8VIwQxwXZeOWxCTxpPcA8zYc/+/7vP3p4cufO5m/87b9f3rwOV26eM094bQpgzmWPpJ7VULocKC/QfPNOfEhRkTEohJZDCrhF9tdIOlXYIZUOAGHCx3TebGgYkW2DD//i36SL+5u3d7DktUhF+PA9GHaS/cAFl/I5gH3jxuvWxlYkXCDXHpmXyJtsVa8r+UI8E65mKiU7s70XSeD8Vj96AKJXE7Hvg1tBYnxIsh8fJhcNfuNKKEaNpHa3czwbtUbjHt6LWzevE/sGosHBJqpSsSw3+KoAPuOAipyFR+i8QcqqnIdKgWTgIHNsSMJH0rjIkWJANpskACLAAiFBCzG1op0iG5sSv+EFn1QSdtI74JRAKmkkMIwNmPQwxeD4qNyh+JyEQtR1gsEFu3XPmuNeu+/0M7u34+kNZa8aHSNChIMd1DORSAbXeZX4Hs1uf+UfUK3FW3FwO+SgI8uRcYFFnBhVtraSuWX94U/qZ+NW/QczEojFCzvx63g5BEg2nbRAv8hPjJqc28UNs2FNH0cphaTSQORNwzlIEw6qQ94djjrowBCOQJvj9qN/9z//4/sPfgFCbZzWYafmGZuC78/qtaeffBBNRqkk/PXv/Y2bb3w3kt7GesNKypTf7LefHXz4e6ncdWu5k8qAbeFJ8IxCLQNSpp5JnsjkEOH6eLKgfKd6m3GG5Xuot8l0hs2cwlXUAGChUfWVIeqQqWWG/2iMqnKZrrN/fbdcqVCsiXTKMCrQkV4Hh0gXWELTAWkhfxx1xkiZVyrm+iT+GQyxz7AEIJ0QIGgY/n+hAQEBqepmtdfrjgcOLlL4GiB64JRJigWIEp4gipOC1AvKmHb+CQm8uNq5zjVdXW+rXWH51WV/Z3WoCeUZQS/u/gp0kLQhB35QGCVubCIaqGlKlBt1tilpg119Ss1CdFSDED4spDMzSA+dGN3qm2BKUTeFRpuKfphPqALP8IIH4GlJjYcqSchS4WzSAUJg6I8RD03XvE6su3Cxs2rxxRmzJzRourA6bwbhorve6Rce5sR6u3Tx0mnvPeubzBtNC1dDzzo39JOh0vDhXSwNB+fiNtwUJA4HQVUqhDCQNiNX2JxPLBYO4TwMIlrsbCk3dwC7pR0LoXlOFNJuNDgg3MeFNSGNVHzoOnCYSFL80cfoqDfxauWq32qXDv1fc+CfWe2bVq8O+NWj2szP6sA7pXMr4PJAwz/v/ZhGrM/4r+FYe9415nK1o3pG9J2pJnwdFpb1rFy+VGOZtN/71/+v4/rg//xf/d8yhV2SNZpXeKhf2JTO0GceZ08yOANsyj2GYym0KyK0wCbpXOCa4GdZsfNp/fDZeeEXe++UJ8vhwS9+kKlei+cKP/vzH+zs7G5s7OA6efOdbzA5h/c/++Sn/+nD937udqjJnHA6Dox2sVKxM8lbrzwsbG3c/PK38xvXI+lIh+R9957kN29H0xVlJJVXThF3/Gxq2k4M1GFaSW62WNwkzBcTwJwz3WoPSEsBoSB1h7wIqWwqm81VsXJvbXXrzVj6JnyUpP7ZvO90KHXFu6SEDZOeU1wDpDIht5/lnKBW+Fh41DgZZggMnqdxfspbKPehrw2yByZCpWpRxflwSSeHqik1i904AgJR3Br+hYZ9hx6BV6BCuBy6Cr+UEp5ynLHoh633nn5+bzwPvr6RHTvj86dHobBrZeFDJ+lMKW7bcGmnn3/SctxXv/r3NRnMkjFdAsVypZeuckoDcPweu/UsFprZYHPnd2EM8VzErTaM5UARLURL4M6EDAEbgGCr8TPzCuYlCzccEyWOn/Jqq1Qck6MDJn4+S+UTzeODk4cf//RHf/Lpj39IEvViNpuaLFG5pAAFrBpBeStO2stn7oMoBfX6w9e//N1wYT8aKSxtjOn5mJ0jJA+uCksAyn0CkUknjSSF1QdmHI1ZpVzOpnO0hrnQQhNlQt0tnq7WaNoUMyNxm2JbJ2QNglVjjmZE7pKRou8ePT3Zu7ZfrmwcHR+02y2bXDn48KDkzqTJ2oPVlISyAAUJcOBaoD+MFJHXdjYzGvYwHWfyaTIG4WsPNY/l0qiBa5PRYjpC+cnwAA9oS7ELIHDITk0ZO8QIhDRpm9VIb9FpcZih9AbU7HqnzBXdpSse/vevXnp29ai5T2VI4M/0beARxT0JU9HQoxvCVh8JkcI6hsJLCVIR3JRgnCgO6t9wN9HssyHASoyLLLvyv8Pey6sYypiK/7BexcmQ/Z0QCCnHwQfajPInjOnUCwRb4SK1XVjI79ulXnm76tLFngbAnPFPmx+v5z4uWt37kpc+94312Oil2la/fEL4UG32N+9logB0A7LJytfg4eKmUnTyRkG9JWtRH28KdAtxUh7G56MulHJI2CQYjGwqDiVDu45K5IVDvZajJFEMHMlHXKJUI9IpKV+hREhJZvq4aYEasmrH+lddXh9c2TGnn7929dg7uuitnjfdeW4ILw3IlU9cEAvzJrUFQFrijaOWIsUa3kcKE7DbsHe6e/fu997+bVhCOPlIEDfFxtnhvWb9jEp4O3vXcpn9wXBe3tiQ+Qj0TkJhRqfjpMt75Njg3agkCLNkeABZ2oG4fXryaHsjvUdN10hw/81v1g+evf/9/2/C3iVpPnw53lWRaLp58Mnv/av/8aPv/+j8SR0P+Js3t1DBjUbTbvMZ+PPpvUNqSl77yXvf+c2/deudb1a29kh63qk/iZ59nNn+Ov4ZMKlGxU8uC2W/gX8B2NEgQIZoA6QIvCCBR9onpPU5bm64GCaTOUqcu/vzdHmjvHc73hyRD19uqNgbWQwouYxBDBSklJvhADoiCqqSEwHj42ABpghQMA48gPjUaTmL6KJUqu7dugFwlYtZkAMVSbEZwmUjRcPCEngogQG3ITgudIao7RVSRG56Fiq8FuFLRJxJQxEJkqCaTBAVq7T9tX/4f8jdfnsaSN/4CiIVI0wXmDf+8tSiuPPaJz/+M8gnzxq7ANWS4ZKBy6DrOLEkQQYYLMYkLSDamQyR8WR24vYtYhmmEDhiGkbEC0AaaQZoDQEOIciHHpETKavgDzZvvBlGQhkOn3742d7rX0rnS8Pe4ff/8F998Of/qXV6mlqGcmmbkQwS1hcLL8ZUj3FARtlw1Cpk8BI7enDQaDQPn9x7693v7r7zG8twmbiEdO4GBd4nI9J19nHy4e2UhSGxHdQV1Iy/TmfgENiKRn5Khh4l5KHXJmxFFWPGiJpJO4j803K6IGVp2ohbzhektpgt2p3O8NPBa6+9Ui5WHz28h5NHoVAELyIeEeACpcWHEp930rtOpMhcyHhNwn07Cbajo/A3qErwHEHLt2xNSceWzRajsQH+RjikSrMAgVaToASoB9FYggcYfvgfsedaiR63ZVacGU/hebMSAU+t5fV6FsbVhBqsas7rPrPAOdJTZp0Lm7MDKGt65XmElNc6OSEhIJ4/xlhNoCv5QqQSBHKJgMJwDXcLtOJwhWaNtwF6eIBOY6lhkrQRI+XKw6sNhogy0chgdEfUEIOMUnDxNZAnpxkS00IfLtTYK8deo1dXr/yafunMurveZdOlS3f6nb905vld/wUXA3r5BnPx8qitvquGMymmS7gsy24DZQeU6BZE1M4BUkqTQih1uzYZdsmphHNE72RROzwaNroQzwmhsDwTS/TqPaijxPulkrALO1DEwyWJAsmq6B+sAYPFRabPGyJaaHYv2nyxt269uePSE1cvrI64gc37awjw828SaL3sLXqMe/0nV/siWNglMQAIOTLv8p9DIgL28f3bf+V7EzeaSpOGofv+T77/gz/8g8Nn95GXI4nIN/7m977znd9NV7ZPnj1K23HIJPls4kmSHKCqxY+Idyn41Z0MwXce9kTRUdm5tv3a1wJK9osRdFG+GSve+8Xtb34vX9jDBDVpPflPf/jP//jf/pv7Tx/3jxuZSLyUTI3OkehnuMeAR/J2CiG2ddhoHNdqB8ff/c2Dr/2dv58o3AzFc53TTzu9J5Pp48rerys9D5L8sC7hBM4I5g13ZuaGXsNAgeQUVgAZAACiqIDJg0mEOAp9Ytyc4wDu6pnqK2ErL2yCvpCwiGQGJsmwSFp5CMI4DJDlQl0MBTDZou2HacLxiF65E0pz8BUyniJHTjPZJPBA2mDF3mDJWFJtfCr2CgYNPh/yE0KfADtGkygpirpJOnCsBETyuINhgaTs0LLZKEXSy2wWFVw4kGO6FMxi5l/UWmot2M8CjvNoI4BzoQsmNBrJFUudqUM1LvIcj92zzvF9QLhx9DhfyS7qdSmp4KhJWQ0DQ6EYJX9grEQBxCnJ7AG8sEpCeKiKZ+SV8Qyro330dPfuG+l0cdo/+fN/87/+xb/7/e7hoaKFC5YVjE+oTAD+Z+gZa1IrxiLw1tkkLOSSmrsnnz4+OzxpnjS/Nw/vffl7oUgJv9JQiPkdoJYBbWEBoD/AYmAxhkBSdY4CJrvpDK/ERgKiMg0zA04IYq8P+Z4TQw7fQtE6iH06gfofbIeg0Gw2wN6DkPvpZ/def+O1Qql4fHhE1Qd0vCquIicY2kauPKI7SaW5IHiPcUNuQKUDvcQFlflGlAJ04ZGJQsB5NJOmunMBhdKAekDYl4CokCaLwZIeEMiGAM+gUSBQbwC9vxdrz0wO37m0Tv0Vy51m46PaNWeFIUUEzDlRBPhzIMqQGXAae4CCzGEgcYgTVivie7FvkdlAVEjhHAqIY9zA7LliRsnPnT6wGgtRDoOUGKSPpigyQy2kjxwFsZ8zKpLglYmaueMtdEaf9nvkN3PVMdNRv70X5/yb/B968StuXq+9d77sqdUg/ZLXXTwmUxmEFh0F7RfnIOsN1jJoJ4eK7gGVwxJCTiHmgMCU+SO2ghQIzoQonBE58aOhDKKf5Xa7/WY7mFzaWQt2A9gmWRWxYA6ZsFg3MLkG6fvNYoWrC4YQXGqpOcnxRQtXg6ehNdvqFv/QvMV/1foe/5r/iP+y1Ts9yqNbvDOrF+sM+8yjuaBREYcMZOAsTEvRJmsVyTQC5FBAsGZvbmWS+dn45Pv/7p//+R/++7MHTxHCyegLJD34SfxaufS1ne3UTv7DH/9hcTsdJwylWM4UKsaEjIefAr3wyY4pu5xaAvHcvHErVdo2DAzS/qB3/PTud37TzlYCgXb78Sf/8r/5R7//h382dShWTaKuVJHI7FgUkx1uOhH4MVKSkWgR4TwWpsp7qNX96X/49/N+9+v/4D9Pbrye332DYPeZc9xtHuRKeXIzS7FueiusCoIzbRAXIHGE9oBgooGIFQyhJQVGZEGs107ao4PRaLk/Tey/+goGVfHWcvZP8WXQH8IMHp4KX5PABxc4x4/UzllsxgWd8sYDdIwEZuJ5MhrCJ47IOG3nbEQNHuBd0yD6DYZXzAi4WuaF0AISCVsmdMwvHEkCtTKvnyXkxUGUyQwJheXYPauXK2Zuuu0Q7pc4uwbx1UPRkhWWwnhs74KSoAxGP8A8YJYk4Mw2XQY3QTXEC+YqW1SO7TaoP4l7EnbmOSXriRZA4UWdMvRUsESoDlC3eBMHFTS1UmhrYDTo9M9ONl55F1Fq5hz83n//333wox8Oa7XtfKFaTkFRCaDJ51OENSA7MQiZLEqteHvgeNIYrcRNdT6cHD169N6f/ztqbm+89i1U/egMw5GSQr4WlDAboIYyOmuCnmaO03YpGpwQSiX2DW+16WyUwJozW2DAbLW6mbKFUWTYGzNHUq+RIgS5AR+quZByt9sr5LKk6Dg6PtncKOeKWI+GVMXCqo6fFaZjtEwDt9fttUcErWN8ME6WhHuj28O8iju87MDwvxhAkknc6wf9HuwyglQmESOzB8kkQDFCL8yMiCczKxOaIF4n9ddoBURKOOCEsLh3VbtmM6fY1+1XNu8cD2ixmhcKTwEjKMJA2ehzbDRoWUhauts+h8EBa8Ozw7VgR2IGYfyhbzQLSBv3ScBnk+SCdCI4/ChXC2mDcGvBEs4ilXekuFghBbgCkqBG0U9ijRc0gSKhbsYIbFri9e+5Bl9q/aXdK/158YA7/UEw/VM3Vy9/8Wb/zEtGyrzGP7/6uMbZk5pY7jyKCKx1wlhAEcfY3ugKNgCcw/DcYLHDDOOnHM/gB5ubYPI/bgEY5KmJBhONwxOCQAiycFpdhp4vIBNhLYHmiwuQ4CxlMSGXxkfNE+5W7VCr/f3VKX7XvfZ3LnXqYtfbM3dffmQ1Dh5U8NcHqdV5/9f7uXjbldNmnJGSzaMMiyGF1LcxFmD0hotAORpLg0nf//73f/Sf/uzpLx7ub5YX/Q4hkzdvbw6G3Z/+4b/PpIt3v/Wd23fvHj/7ILtFysFNoEZKRsI6mUVsBFRfYpGZWUWmlLO80CBATF0+CAnZRauM5eHPf/hP/vv/6b3v/3jUnWzkM6HZEIY6h1QxD5TyFoGauNjg5gf5zkAYSvkITjbBuNNp/uWf/jCZzH7rvyiGIrvUGVvOzgKxMvBKwamUVdSXtO70v0GKoA2xA2BXwxFgGqACPeYCjKX9SMja2L2ZIvSPdY42XBoEsQ8wpPjGWRmRE5JT4IKCvwdMOesMnxReS0gNWtlEXtULa6d1eM9yAS8hdAl8GSdysgbAguMqrqz+mJHlZUETcHMER8FnwZwjAUgEwP6CF65C8pDPWb24wKNgwE6hbI64nKInCthYtWfULxy1rOwN5dvDiZWcRbRUSWts8I8Bdg05CeCSGG1r4HKFh3Eiv3WXFR5cDueDVjYXHo26SEZK7jTGdwszsoPinaYhhVAkGx7ZDBr8Jm/gtewExt3PM+U3QlKVd//0n/6Tj957j+JSd67twQOX85kxlZUk5wTsbArLM7QfBQoehRHLoiI7gnJlo5rf2OljmO62n37+WTj9B393cyeef038PGr/GBWLUQMyhJZZu4xFHGM0eIfkxuBRTApYlQx+wNykoGeb5Ng458N1Wyq7hhZOeqKRaxTgQ3CalWQikMCWJHXPFTLFSp5o7HqtgUk7ZSOcLRO2zTihJaGXUBUkLOwC6MLzBBcip6EUgMzOiI0K4VZEOjYzjx2i2QgVQpGHDpAWosvDsG/IN1AvgmEATIMu1AAgMH4GCRhQ1IHZVmtTDL/537uJo+c3/07zIpEZ5AC+hOWBWEYqAdRrR6q2RPqVwYDLOKfCgIDo0FgINbmk/aB+DgU6WH4kuBkRACnUT+HCATofBl+oRAIbk0RNQABX5lKQJe0QYLH0GFejAuLMS9rntddcUF+eb/4vOb56r0cSr567+vDFtYs9/4vPtctrjRa81oQvDEi9jQsUhg7j2QmjQX5HoQgYtykph89D8y7RXhNSYSFNpVKdVlMxnSQ4cHrQUiIskLhkDyOfJUX/hoo0YVzYpEEBZPifCecPzdOXvU0HGhadWJ9cXfyrftcd9V7gHf6St3DpKpx5H+U5D9DUftMa9Zs1oMZL6hffpTZLex8sXnslFkpM+4fP7n14/PDe9f38zUo5PE64owzMMraP008ffz/5B5X9vfIN8nHmqRyH3wehJOSCjognlUIJcJOuTCwEe9KUgNp4ufIxBRL5rSypBjtHP/9f/t//+Mc//nAycV9/dXdUb/dGY6xzpOlOWWAFjNTUAyM5rUtIFcEoaQp+RdEMxHHin7Z7H/30o7vfuZe7VcCLdTK/GZrEZ1bC82RTt6H64m60p2EW582mIaSf+LfgXkmEEDFSQVz4E9kUiCFBJVoaxpMMhjhuJlv6XB6T/S0IFkemQD3KO7AIwEMQeA98oVqH7ZJdgVK0tgUXNiADs9J2DSlkhUAADoLrR+lMMEPfHXXaXZKyEFa2SKQIhCYKkYhlPkU5gdJ2CceX0WCaKaP5YEGSVdRKlyGWCUbMLmwHwlv4SGposSVI+wFLiNFvBG9j+ifIk3IzGRPLjdYk4CKykndPM8EF2w00ZqMu+c1bPJhIgwYRhkCyugq+Iym6EX/MQC2xfNBZeYyFYwUrU6Ljn/7Ff3zvvT+ZjFrXNqsIRFgXndkcJ8JyLjPqNEjhADYn4gukQplZ+aujkAqEMql0fzoi9A73nePTRvTeJ5998IPXv2pFrN1QFG8xaR0oDIlcClTyn51NF3O5LLI2KWAhmaTvET4VWjIabs0S84PmDTJMcgvWKYLZYDSCC7bR8mJHJsA1ncT5llAC/t189Xok3j96fJK1smRzUQId+mWYHqECsHySAqg0JJSyyXZO0YcJFl5YPjAmOaUBaZgWBwQ8nw46XUgCBWdYQXzeslAkymxB+9R0MRsGzjSE2gzg6deszdWRucVcv3QPj6yvr69psXIeoBT8mmW1xGgJgOkEgEp9ZIWykreZgSIDUkhOrEFMKbhlUauJoEUhfZy4eFYmYAYUYorQRtIfybWc4lmoGLAkEmbggWFm4WgxIxBIuGTzGqJWXNlWTX7u9JV7/hoHvGb1xl/ylBkMb0jWd1086V3VqNEj+gRmZzFL1Ib3S5Pznfz1HKAtCMreF4uTKWXmRIe9FnrMdC4dcGP4DTNg0VK+C9G3SbORbjXbsp6T00SpRiLkbOTNDBtAgxqNmTffV9tXxGDVkV+hP+tOrHeeG871obejb11F9jrz/NSoLeaFPvD5TwBMuiChUv6rtJ9Fyo064oDQ1tEnP33v3qe/mE+GgWms3azn0mF0suN5HytZqz44fvKoffiwfOMbZJ2CAAwGnXiKRS+KCkoivpMKq2jRPboDOzEaNF2nRbISmF+WG+6RC/fpP/sf/j8//+hj/B1zkVjn9CyXiFSvb8FL2mT7RCYbU5mLfISRajmJ/E6+BEE6Cdu6Ksm5u1vpz9wf/od/8dv/181E8qvBzD6O4HC7KO+C1POiEZT1Ig5G7nreoVoD1eMf/QxRv2yMSQi3eNQ100btpDOc3X6NnAqbuKPQB2mxISBwpjBBDI2MClobRA1iRwSV814kBOAL9QPiNK9CWME9BjZKnIU8TcEbsdPT2sHBY1jp3e2dLNnZQstMgCQjOa2/5XLUG+CKRFk5ODCU2iTPaNfaMO8kG8f2ANFkoih9jlk7bsqeoOVAjhUmZG0a6VOTi/4ED05hH80q06rcZ8HQxtZe0i4HKV0TJL8x6xmNHDbXXr/Zg3DkCmWWdTBO0fPJgihRKXsZbyLPaEXW0G1eKO8GhovWxuJp3t5v3v/f/tU/OXpy/5Wda5Qo7jkECfbICEXAgzPB23KaoOgUHHE8Il0WgRdkS4Cvl550RoFmtBAlaZ/T0enwk/f+KB5J3fnK3yKDN3YTMCekIqGvCMWNSXaEORhGdYgxeYbLDmKQpCcuK1mtzAG4DzGXGD8jSRzjE/gvYnXIFHMZy2JEKP1Idlu4CHxhRv2e8l/O0xvbm4Qw4rLAOxEl6DphCcwyKWDIF0oNB3n4Qw6U3HtOYJoMhoQE4xk3lGhIOQe8kBkx4pSZYizDcwTCiZgLUKfUQTQdQFEzwZ1C2mbzcDfzw/ybRWqmyrumO7VyV5v39MUxV3kNMyBUL8QiXgb3CkEomcBzeZzfKE04gfcHoiCcFMCEwRcPPyM6TDZqNFwsHUq2iXMdk/uShlO2KDDvkw9MSk3WvfJZAIWybgtX8LjaT8J8uXVTk/VlqEUN16am8dBqu9Kd1cn/nb8Xb/VfcLkdlwZp/fqrDzBejDhgA+jAnRHggpyYsNNQNfqJ052gJ0HcEo+FYe2Q+xjYfqszg9ftD+H4CZxu10moHR0RfK1IEF6pgjCECuK+TjAZX+b1nNWIa37EVnvz6bf1opncfHGwbvIXn9TLzeY9tT5cnTZPrs+amwRyBgzX95gTF0c69NohcMSKhRFQdFHYQTg7iigcmfabh4/uO50eQ0S+VAp+qQwW6YLB8piK40F3Njh8+Enp+l8U9r5EqDxhk3hkQywECPKKmsMsm6HyWodb7XA56UtPzWKjYNak/k//0X/7b/7Fv6Q4IPpZhIcM6XEqRaBykZ6TjBxWhiguFNe4phElRPYmdFSUb1K4wmRC1G8Z3nA8P3v06Ojj96999c0l+pDROfRi4nRQGNBdpmNIORspa0BDHON+KSKNQpyTKJSDlG4JLO1chlT3xHzUGw6FkuBadTNLTYsCFRCpLzJMOCoC1rf4WokTC3A0QIVPGJoHEAHYxEw9HwXtSduEmwl/qZ7AZxv1ej5fFEyARlFXp+xiimpTMGsz13LSaSruTTi0EoSe4a0zQK0Az8XyU0PkhkGgBF2QNIJ3y2zmzNwOlS+hECh+hXR4Mdpnw4F4WmdajnIkHsljCODBzvnjaIoBLk0m5xR7oxCvIo4TFENtHj586KKv34rviJ6IEyATopQMIpMIPVTZFLBo2AiaDMzuv//ek0efht3ZkppiujLd2CoAHGBssjlI6YVPosQ3kupQdA0JRGnVwuAj0o2BW9FRZHG8SLlBPIMPHn/0s51br9sbWYZVgEtPCbnEOxakg74MxQV28PEYZ1awuXQyZibRtKCAZTCBW96L6ABvC8xjAJZvJ3V9KayDb3eQtHFB9NwYfuGFTw9PqAgcSST63RYCL2ge/R2CL9wuFivgqkPNSpQnjDBC23hIIinIghmJOSKEpOQIqZUlsEWTJIPiRny+piOVh6EAEDEbMzRSEqFBOQYJAAsMKf95a0B/6eNq9Wl2zXZ1fa7OXvnVFEtely0TzaRM54R+2FaKDqBeRBGopEmBBcZ8/NPoHW6h/U6f+hT4swBTmEOIfcRW3O1QbmNu4f8qh+8pZRFE5IFzAgLpmcw/nBD2B/r5KujAIDO8QzTZXqMu7XJCzdfcCe1o82+62NMN3m3m+pU/3rUrp7yDi695x95bNQ4vbqtrF9/xWiII8VoFWyreAR5jNkWqRrFDUh+ETV5HEAztx/o0HTS7J4ekM4RTMPEBICl4uglBv8CTQwwwhZgjFCIdAWpY59AMyeyCz4cYMulKdeCtWr+NfFV7l9q83mXHa6Q3dF/UqfVtL96wAquXXLn6zdUN3rv4S2v9rwtM5bnEypNbC3lFnH4gnGOkKGaLgpgKKhtVgmlKrGFCleAYEPDJyPz2l247gcTnn3+89eprhb27ZNNIWhVyyJFgyow5OAuvMixp2BXUTUaGG+KpEuGIxKWDVh5/8Pnv/cvfP316/sp29Vo574SW/U7dHVEfingoKX+GZOulCA1YnqLp4I3gAiuMEJTyOZO+TV4pVLztuNRxOwsEzsDo5MWCJmN4JKhRnYavAabHExARRwalsyBlzmXiIDLB2Qg/+cLurUCquNNbtPq/sDIFoo1BOfKNklaEDd5VEgCoCWiS0w40TCalJaFIaKVt2wYKwMpo7wEPOVPhB27qG6P0FzO7nN+6vi+WK55CSgCpykgiq5tEU7jL0ahPlHmGyCli06bDgQOL1gtFq0gR3CKuEjzMiw0gMXPAE+ZcuFQQNO0Qu8w/EngI0Fgh4j+E2GIBp96eUmotlp4PW3Z+g/HEp7fXwJ8FxRWNx20Jsk4ikAlhzB5caLYU6MKIiQDweSCdS3QLIUZ8+aCH8aCct7c2CqCdAc76+BAL5WGJZeJEsAlxwH8CKJsT3WboVhAWtEcJOUSjBOYzMl4M3EGz3VtisnXP7MBrUv5IO6jU89JFRIgiTODzAlEDT8NcYakXVUAEo49QYnAzulzqmaFdgnUD7zIvjAsa3kiMaccJjRxtyPdy2cX/dTmlhFhmgXYbOpEkr4VxDhZACbHyGV6BtwLEBC9Pp0uSomjUhprBuZByh1EhcgC4Va7NJdG/ZFii0gQBEXgnyPmNQyROpoF/kj21ptiHZLJjEJGItLfkOeFvq3V4GUXokubam+/VneYMb9P8IwABzPpQACvMKGlHcEPQyLCS5+B6JGDF/7GAGS/X7WPYwbJN60iBNpmryhqV7ajIDQoDVmg/SIsxJ8ABwQe+lklCGhDASQSQPYwOmUAw0zBaoFZcbOaIuy9O+9fVaE7r0uqv2f0V/3jP+Tebg6vfvfwaxhp4e16s4n7+SbUpW62RbDC7WWAH7jUKO8R7EQTsV8lYJB8nBcSgDWrAKgjvEaCkKjkIqT5F0QkCNxNxquXBBzLKFM0BKvsEzIL3WX9i1gy/y6/aoq6ryZf78Dyq93ujWy935Zftc6N3u/9u73j1Av/kpRdc3Kxv6Lr/19AA9jnFoIFjSKsLqygVMPVAWLl9p0ESGLyk81YO0AEyYL/wnrVI7xu2CuXMqIEXfhe/sulikKlsk56BdxEQALxIW44IQOoyEwZsvglDgdaB9YUIjJZp8tn7Pz89eGTHgul4bOE45L+xNkpkIE+iYFEGkmWhXMQDiDS05NZBPoH3U64i5VqwYNAgVqA8slPu3LwxHPaGzkE8fSuR2wY9z5LYAemL6S4qzSUO3WYRAgGAAQpPfPuU6ovAJCUlYwbD6KuCMOzgQsiJkwrnQ5R4D3ENpIogLDyogBsxjFjI0JDLUQeESH9AVowgakA5leNSLq0PpCAstb5K0U5mw2m1soEHE8wVqAQyxUrtd7EqoeCAiYR9ULItnM2tGNG/dCo+h79mwiCfdDqwgI5SAdhkyJC6EVGBJEP4MSJ5GAFAVMpMHyCn5/hBRTXo1D7/+EfZ0mbl+rvZzZsJ5hFyg1NTIrKgwmm7tgzniL/LVorBfoTMdQZixceASZBA6DJjSLfQCYHFhM1n5J5UmCne8fFgcoEKTZqQCEQEPKPUmQROQD8pPCiDj8BdtRSFqPBYCo7xt3QDLJs0wVY2HjaU5RxBd4hYlfDML4CBGyhfBN1TVx0iwNCQEnUEWSfpCAZJ5eIFysBNEBhAAl7E8NiEHCoKB5MJLDuTg/GZeljgPoojFsvKk8x0wcF79APiyz2E/6MWh3KDOWJBxMsgQ0yqDtkJSbVJmB+5YNGbAMeS/Igejo0Jf5PryBgmXCxRmrAHPiiphzdLb0RXkX9RfYkmMxNaZ1p2WmdmZoQVGBht5sfb1zr0z3rX/L+8zkynqLpIiWbXd8xXGif2CUJEn+jUG6gJScCFi9egNwBM+S4SFM0D0ignjOMq7D/rAFMKQuiE8p1kvHC66MOlrzO2BOgY824mji5oxvmLvZT3AHRGDapGqk1qrtl9aavXXXhZl9YXvR3e9CttX/AqNYQGmdFbDaLXMoZb468rTKQay8QYFEAptVyxivaMaUWwMXcJ4+DgFyZT1wIgC/SpZlJvwciB+sEPgxrmTeKDJBFo0aEfZB2j9ddqETsHYgEz8BVNu9qqD3od09FftfkdWD1mHvHOvfjk6n3m9f43dNdzx+srL+6Yd6rD9FoAC46UwhgbACCmCnqsfNRCIzTbIMZkBk8WqnlTPimcFcAHshlC9BPNukv1icbx8RLbZiJNUhYKzyIKs0JEC+RibmyvDAdfUVyOqqcxUhSHASNv39y0E0KmGyXqtgexp2NlQTeEkg5ySpriuI2nJuiVHJaMbpwRRZkhVyLgbwZ7iF47uFUpN9ymc9qFXY8GKssAmRrOh4NeJFaik6hvOphewSlmDBSfKezPhNPdGYgfMykYoHF2EDi69+zgIdktyRJEZcokCB9dD9hHSqRmkPoAWgtgJ3SEzDJTDU+AFM7GCgFlKfBKBl6xZuhbqaIVSmcstIT0EP4YwzAT1O91O+SUFVMpyyOfgAYI88GuMs7JJI7I2DGtbHa4pHQlPeb9rFALNCxvQ6VjkV+edCqhuMlipUZq1rhAG7RmGWvBIIosUtz2G0rQT5wyZSuoax1LpFEtpcvFQZuQtDCKNZuyZUrkPTk7IoqilcR7CqggZZiouSANOQNMyViyFySWbTlP4dyDHIGWEC9piDdKvfEIP12jLQFpR1Aay/mHOjzQRvx1DP+F6h/FFxloQjIqx4wBfmxn8yqsIiULG6BIf6NJ/HMUmI6Ah1hCoJYyPwnNkoQKqzuTqInBcuN0SfzAoBn+hUuQ3hF5LZD3SFdLGNdSRhqapxLEStmyVFmDSIywYa1iRsgElEGsIZ9S6xsnYQgJmb6sFEIafUKtx4ch+PSERYDmnFLHi0HdGTp9cklwRS43VIkdu6wnAAQAAGb4z4iIgJoaC8Ywy9NgIjOqZrHq5Hpx6s6rmybAXOYmzStXtcNn8P8xlfhQAdmUU8bE3TG1Koa8DyihwZBkpgVgQR4KkekjNCepPWZvDLliTeSqhGWbxIQkSpGsgjsQKiB9AYsmlXdAbiB5EBsyGY7I/IAraYFpj99Mg3bZ9/CvTpo+eF1dHen0xcbjL/bz4vKV1186/ct29T6N6+oeM+Q6umgHl1kicK8mUJfZh52V/5wYHLQ3iImAM3BC+sF2++gBuUDAEugAuY5NCQRPNbUhriLU20QaRNNH3sdIBEkYvwI0v8mkhbxplqu+BDiabd2i9aF/wRxf3vdueK7z6xu893B49YXq54tneJUZAF178QZ9iLP88zaeF3/CakK7AczCtUzg8WGDGAH8rbv9dgMsC7SRqGwZC6dtFc8Gh7MusaeDhpvn582jIxgKkLPxSIFPAi3yYvAHhEXspAfkLGiULiwmuOhht29lk4TSFKtVexHcqhYa53W4bXSOSlc7meGip8wk0swSqAP/BfuPiVKMNjNGAs5YBjw7JiOLqjk440gKdzc+it5GeopW+7hSuUUf+SjzwSdRzXAIK08jaYgRnsmOM4T1wbHTrbfBMJV0OXVjN10ttWtHiex+inEC7umHceE3j5v0IPJERCaAfEJF0AKRfsxF78wbUAcw/Fo1Qh7aVMQ8EHKJL+y25p0FNd879e71V26PT1onz45LG1W8WuHJEELdaYQsNRVsU3Z6RuBDgoDh8ZLS05EoMDYkI6m0ScL9BgVocWpgRSTA/tKPMR2wI5pZcZ3SBmRyG4VUsVwl0sIm+5xnJIAug98l5E3yJF5Fbph066S16HfkimoWBXZvRED2BSlgZOYF7l+pxDDPyjsgRsIY0gbRFEE8qpMpyTihLIqtA+uQI2sUcFHSYUsDHIjjkzgA6ZfnLThTvonKUEEy8MUY7TzYDBfTZcBCBcM6Jac3+RYJugUMASeUQeizaOccYzX9BCmbLmtIqAnBJUzwBrOzwOPzQK9LiTokA1VKkefKctFsNi28ijHnUnWeGliUgyTogHAp6uSSylU8L0+iGgxiypVChCw/yRQpAgmbopy9RA0kGdhtuBNKY8enPEv+PJoKRMnUyqPoVmLKqguqhUbyXUGHGUFmiR1vMNnzTuqEtzCu7JjT/q3+vr+UzdHqPeJn+Uf3ULnhpiy6N3KZJk4QBozTAy3nFsyboHErnYrPEuirVUImima1Px5icIIegM0IbWGNogqaMFDQDFYLnrisExoKcAFsdNkAM+pyIJt26OM+lhGEmG6ucYqa62/aMaTr0rG5/LKbVw998e/6vdxy6Q2XH+D0aoD9QVeL4ZxYNeKK1HysiKoChgUIfkrlI5RfEGka9g2PtWxsZ3fu9tCJpirVuTXsNiOTXh8+OFJejpLxsdMfLkOD5Yg3sFCR98imwpKHMRiOyAlDklh9hatqiEbvOTzsdULNZ++5Xqj1hn9bXzV3M9wv23T31fPefauTqzet7rl0lVtMA9UAM6V8VgDFMmOsMJHB/vcaR/c++Ent+ITzZPKaLSfkP4il8OxGcxIhrqlL+OBg2OuOcI4378NyTnEsbJnwVqhSw+6ETABDtK9eC8Sz4dODvZ1Fo0YmNDGow4k1oK46PiM2zAgWBlhmKRtR34JWYA1VvC6KhwOuN0JP2C0RFPDiIlwTz75B10kV8vnKhrGRSieDs26v86hU+jbfkDkBZ2lEddh501nVbxcrwxegTkRuYcKHF49b5c3pJBkkuz9oAVdTiQDMIxOImwlMFi1mYcDuL4YDkoJIBBZNxJ4bjpMHggcUG8oyHLkwxdTjoMiihOYlMcUZvBOdbhP5u7ixub21D7JxF5NOexBK9K/d2gf/MB0OWckGo0ZnCIYibJiCTyKiCtQAWOkBOzSCdclgIxAhEBj0ywVdEtChnEaDq65pdZIKeozh6vZXvhFJFgMBstPhpDMGtTOKyiAHvYWrp7Wzeave6DmD3Ve+E4wSe8GzcLFB+Gl2zL60XsLyAbQcZJBhEkPxDNULWDJxUqRgfQU0+DL+AYgBSDBYR2k5CIlmoqXHBSlTSEmwlB0bqSuOWsXY5VHHkWiugMcvbCtaSPWE5jPMisXFHqUBcFHyKKSEcGqsbyM0i2YmwykCdpSOA19/0k9BjsC/qF4CtoV+jE3rD80TQEVImuSZcIhEFeVqCdLTaNRF1cT8jREEwd6RDDaWDlyGi4+vUmQn3fnAm0o0jbyO+ef1fIa2kxM0Gsc7Gf//OIQA3RK4GFjRXMDgKITQGKjFJkgoW20CI468v+akd7S6vv7VQAgA/RPeA+sjZBk2WHiqTRCflqKqRAZ+FMAAgwPwenjZl9jsjkDuCENQTWmlgBquMaaMDm0xvArnOcgV0twiGMZzgSBDFIzMg7mVOTBNBmPqsyu0ozaZdq3+eG1dNdJvOT9cf25bnfF/vddzz4vPPvegd/iy29Su1Wu1e+ke/xI/6j19pfsUpcNng7BOJohZlOEW9lNusVB/Jf0JhZ12CwRjPGph9RYUJFK6aHAcrJXi+sVRwp0RRy6eFgaA0WV1+pTG9EYN4purWefrBiY5yx5/n+uxTq36YHb9A35Ecq/cfek+vWf13HoMVifMxfUd3qgYFGEGW+8U386YSI3FoqQnNJmUIM2jh6eHT8ELlWoRI3lYlbJl5MLnGp4MLEugYXdA9dExAgLAyFKWhRRn2piXMRT8Oo2F5tS/8tsAwyJmQuiHVFzQGLLVwoIYbxMiM+DW0HXz6SXZRCEwBNoDylJMSy/MpE1SRGfihx2i8C88IEgpmM2k4cKgukwgWa/Epcr2GSoW7mD103fpkoQzGBYRAFhyusloGQJAzsjsgmwCne750VNC2/7o936Isvsbf+d3dm9/hcgiscCCl3AqlTdziO5HGEo5RJCIMGKQh9KmjalsoSTONo4On0LILi4oxTzZ4FIOlRFJbLkIl7d3IpEUX58CO6OxAuXGQ7yQy9XNjc3qaa2BHTOVyaPMHs2CD588a/a6hWx252t7fAXEDtjIR52BA7FqOQpcVYReUCDA0EpVstLhlLXv9Y9bwuFes/bZJx9tXP+qlU2EAugHeAWDO1mQ+1FZMCeh886AJEiqcqj4IAIUSNXMIo8RG3UhAUiDg2wBa4wplQCYZWiGc3186QrPoQqPkmiMrHp40ZBbieq1rAY0eTiVyQfYaIqk1WFqnIkjh1oDLvI4EW8RB2eRIY68EnRBEwTR0FtZbKKATBpSNm8m7y7abpl9lV0cbi2SjKJhA3tDWOOUOiDjAS8ANZr0d/ghqSCnpFUMyMQBoLGhjo8UOxhHaCNIWmQC+ESPBZaUEQ+DC0QRlyOXlJkpaaZkyFaefcFmeAl9J58oKxrsIW4JJ3qSQJCTb7RQqWh85MSj0D8JB2ZuxEcL8ZgYXW/NaRLMLJnVqCl8YTOXzd2XL3E//9Qnqj0TDI/702xGAAQ6KPgMg/TI6qGhQhNFVA3yCVp+eCUCI+BNpHgMBt3eELsH3j0InUp6Dec/HonfwSca3g2qCOMFKgO9me8h/Uv+lkrIrwdgmk27GAbd4/3xuqFL5vhys7XvPePd9Pw184g5+UXXn3/i4j6N5frqxa7G3GxqHrv8Y9iYB/5jmtEWy4EAi5WIAl2TaQs1+GSIr3T3vDadOnCpjODQ6TGhOLNQjZsyADCKYxw0cJKBzWSg5QVEVaixbStTDQsL+UvfMl/zGuB/2+ujoUpm1Pwe+I1a3er9cs1v/aXzHkRwQphI3dB2MQ7e0WWqZ264uOnijes3GVrF6/QWQ6S4gokE2KLaESmGi2T6KpZJ4b+1gRk1iHgTmIDLQBVwTsg9+Apm8tlre1sFKpH69iGoKKgYPhn2GS/wkC0+U0vCbJRgiktGxmKAz8ZocPj48enJmVUusmq5gcxcSA5ANo621iKao8qFXPeUtwPKCluDqgTlrLTQQUwI4n7wnQcNHpycT48Ot+7exQEUgSGu1PIbMavAO6Edcg9cSEGhNkDtWLdAgAAH/fwQQy94p9E8QwF68OD0ja+9ky8WMYdzD4jQUEZss71gcIfx1sJVdjWtBym3piAoiu5SD1Y+Y8gW5AiORHpYhVATxtEroihw3U53EE0F2gO3QXSt6wzavXff/UrKip80neh5zf0F75ncfuPN7nm32RuS1/f+Bw8PThtff/dVBMtIAv8i7HhYGSnoRKPhK4X+EYvYDKQwG2AtnEHhTnFGEIQYCgGHksA7Ec6ceSSJw2zZkOt6PEMSiclQKZEht5RSE3FJ2NkIuvJAxC7wMrmvYs+BWklQlOSDYhsUiZTMAOIlgJxA4uFWp2FlsoQrTJGJ8fDt9tDuFwqVQBzMC3oBeeKgAwWGKk9aTgMHJyy58MpJ/P/Hk6RlZ5OZwdihbziqovGnP+DleBKbhFQZonL4JUdA0YQ0U+kkFbFCuK6Lx6JlQlPB/hAnluWYpSglxgi8joCoOYdoYkWQOARNwemDBKBxopvr9QbWAJAcyXwoP4RmEI8eYlLQdVCegeRMDCVUEFqITlS0CjJKmLHgaIrPUoTgDzAlxgA7g2oMdK9ArFC4NxqAAcy40/AIWFgtNEYbTZrRPmhSOKttxZ++sGDVsS/YeFak3qx9oA81vXo5ZaLmI8j5YoAZBu8dPIIEJUwW00lIF7MJz8N8hDTCcC+cwdsTwwAxrbAXRLpJMEdE6g1pFrQbfpgeaqVI+ycxlF8YREABS4BaR38u/Xpt/uKWcwWQNX+/oGsXp81rLw5f/lJu8t5mLnv4kPf757xvrd5hrmoK+F9yJXsCc5QWcLtAsxhyFjmqTQgA0Eb5pUyp4napAzZIJYpWJArix2sDClmpVPqiAZQKBUDGQgdxjRfgCzNFSLbS9xISJE9pszb5pJphmmDa4zXcO+s1kFZf7Y03sN5F/np9Wh9qIL0D/+fiinfzleMrB1cf0Gjp3XQfhM2wGF05fJvyBgMr0hJMhw51FakoARONXwSlV4AY9DtxKRMoEJmJxzLOILB34xpiNSsKYEOipP8qjypNKOMwxQtbPJy3BUNWRk51QiuEnkxx0iD7LFgyhBsMLnuE3EiTm4xnwIJpKhWSg15qZhhPuHoy8qOhA31HEiSbI7AV7wQEeOnfFmGVt5Mva8BB1yGenzJgGn0tfuYXdSy+RBwjwgPbIYUF0Cya2SX4KBwYQcY2Sjvur8erb90qbW9j64H114PAfmCheG/C/RF9ZECA85eOi67CQ2BAQJ0CnyXdkBIrsbJk10bpDyPr4j2czdjZ8sHR6fsffnI2HJ4cNYjKfPsrX2UpdmeTf/pv/7KQCXz5K7fvfPXrnz56cnp6/sqdO/XehJTGOJchj2+j3JgMkEJwPcSwzKhpLWHmEJuuHTplbABaq5l8ZTb3Y8Ek9IRmxa2tcbvUbz1KFJ1a/Tw4rJX2/gYjQahjp3bSatStJCOuNBeYRkPJDC9DSOOlCE1Sd2j4AJOQZhTdHQESsDgABKVwJ52w0yHHG0zBFI8qTBaRSaVoU/qGGDmsOKgeGNoQ+p5CwS6WyLAVtXNBQj2KBTqRIzhte48oGwToZD5PVS7osdAs8BBNoJoTf6ZVS09DlvEB48Oop6SlFlVC+cqGMpBKO/h14yBkJDMoIeYGgmBh0ZAvmGdNi+r1ZtNp9PrEReB2RS9A7KwmMHWn3R5OJ1Q97jZqeHhkWQAhBAKaIsjUIsGnOBKiZicpStNJvI6lNcjmMlBhBhl5zkrmAVhc03ghLyWjgLH4AnSGAhmlwxrVr8iAJm61+fs+WtXZy1dXa56m6JKAUkleOSCAL5VA2QiDYkclX0Kb0Jei0MDaiXaLWj3BOOEpoCmM94q8YdFZ1KoLpwkMxuMAwhAP4aWGvWRKdQEyhwNUE+xunFR0o8ESLAEAATKLHzL/ez3x8Idp6JW2qoV+Qzlv0LJp9aoTHHj3e2fXt7/kJd41PfjcxfWh/w7hUG0G4Wrfe+TSoQaLDjCjZDcVhyMLQDKXk8Ma7AmWL/l18JzmHCX3cpEYwbDhekZuLICA3HDDAVcJcsHrF9YGpRn3wq5gU0IrCSToYaR7vIXAmIZTXOF/fdpr0rrhfgtNo/0xvnRq3QNdf37jdr+7euLy9twhl/zP+jca8DIPq6tmanQLzD0cghYDgE/aZ54TczEd1A8PMfAiDLv9KGlQ0DrCa6D9zVU2Nvc3yX56dtIF4lSPSL5PGFhAz4wWYywWAhwCFwyflTWcuFqKqpRYdQJ88CDBcKc8QOVsKYualeVIE/B0xESF5iCFmycO8WHk3LE0DJRGIXt7yEX2B+8iiIKOAgToxAWUmJ/tUnxm2wtE2GkzEs3juDXD4AlnD88CYtMsy71LXZNGgs7yHKCO319+Pqlj7N7Y2EAVVKs/nd2flXf24vYGj2pGwWWy9Ek3KHIi+yl8Fe+RS0vCSqLxH06XMIDowGEFGC6IKNiTu0cUih26MQzWmYzjHP7H73/uRtG5B+xIYDCcphLRPkIPFstxIJ0pUsrp0eOTh0/q+fL2a++8EX/0sN2EQEC3YkiiaJ2R1TGhw7wKOdMXWoX6g/HG6YiRh1qRqEjpWLlMW5ljgJHUVQcnpw8r5c1qvJrLXZvFC6hXxs7ZfNoZDJtk+cDinMkpx46ZEgrCIPoSEkhEUYjAPnAAHZH2O4j+Be2TQJhJHaM9GjobKQzoyo5AGbRIMJlhepJGowKJx3AMBod1joR33nxj751vheIlDAcUvoS7R1ud32SEYzlF52lg8apAnJIlm+vhMLYQpG4NN645YzhrmSRRYE+wAKCn0ianBfJwpFMpwvIJLmNWkTs0CCEMM1H5+WARVfoJVRqSbkPG3RC0PJZJ29kskmm7TzzL2CaYkCrwhmfGm35AlTf8CqhO5yr4EdCEwYcKSc3LuLDG0emZYYdiQlgUZUKhUBzSQoQHwReSTZLCSniISsEkhCnumV9/GZrG+4caTXP+6lVz9uKP7jLL1Vv7Al2GCFLIcCGHchX1o21nyMNLdiTkUEYR6kcAHcXC+D7COWoynuItYLdokIA8eoNeBwmHNTOaDqdatLhXsTbFLxHbiLlYZQEwHXCbsa7xIaUJNpvfXn376uZduDjP/RcHuvXKDaurV2+5/Ebve+Y+/fEe8P5eerGe56TZ/HcJUnXKsEkaBPZZlpAyplFDCB0XS4gzAOgPL2Y8wIiF7OLyN2qf4k1HKiWnUZP6eeo2qX797JS8vriLjRdUCugBfP02mklgFivLGM9tj/8UlaFh/NGUCamu2qb2eEdqpt9Kv0M6c4HXzZHfR5336Yd5yPTS69oapMw588c8ufpjRsD7jv81XTFn1S7tqxkaGf6B+DH+C6+ZC6jVBRjAAqpWuomYBAyDCkHEd966G92qPvrR+9NQAsDPhcrZnA0LpsoIcE2MM0YS4anohGh5Eviqpd6GJVayJQsd2IP0WJlEoUAmNJaM9BDYabEBi+9mAYrXA3twJ7bK5ZAK11EE+VxuazuVK1BFMJmrMDK8B0RfANlRn624N+g1M4UMAl04nqM1sjngcYTYgjOEmgYKZy2biaBHkwUhZm4Pmo2nK358o/d/+v2N2t23v/Ob8IAGG8mfj5k1HtL0iFR/VGhfJiyMjcKNxJf2nK6cwpYzEmLDd0EGSD+dsa1csYg3qxPpIxOIW+4Pm/2AMw9sFuJ4t1KABNYUBGVlYm++so87Xu34nCY6DpnIkrdubX/+0WcSfwiWVrvFjAuVyh8f9lxTJtAR32Hsj3RMQD2jMII7ixQMzGhu8VDA1ejp52+89hW7tB0KFMahA1zhKB9mU3xx6VIFJZexi5vbDNJgQunwrELJeGmYUs1w9EJ8eIMBHrD/YGgpRCgApxzxMLlokbF/gpoRInHPUbCoUmYgIVqxdDaDdh2NebJYyF3bj5ReCQRKMOYhi4Yv4hmpGg1MaFKY9GAQgcENwRkQBK4QSzIXoZ5hFhLpfA6LLNwAOAqRiBawJwIAJcO/dTjI48mTIo80JmIoQdAZzeLwtIsFim8pjyjdSkRFIkjGG5S3jjtMl/MgczgRgwMUrkbjqUkPbGQz1nmTICpCqoiG7NGv6kYJ5RLLGo8PFgAwQPg/+nWCY0xoMUFnaXqCSQD9G6IHF8EtQjuaHUBN3dRcqLdCREyU5o4Ddd3b/HtWh5d/Vzdxi/eYHmSGBPqMAIiLdwNjsERIAjBo2LchUb1hB5tJFGmcqsXJBAZ0IStINamz52PCBNBM2igsiHAdkvSPKFeb2GvjD4R8C3SjFcQYo7UIZYSSIXNAV/xAMHXiciMv9k3P/Kn1znpnnrvdO/niDdzmI6f1K9Vxf/jMey9QqnCZubi+19/hglkkHtrXC7Rp/esXUJYSG7kV9QP0zesl1BpuIWTDvyfQ/U4HWE/I/KcRHCwImU/fuUbep+loyHNUJ4HpihWy2Ir4J15Ewi58A8PF0Ej1p7atu2Ia6jWVpnGPuqRN47DqkGm1Oev98Z5WVy4GwHuMK/7z/u2Xh9O80T/PXVcu6dg7IRDSnXycJSdsAuoF28qLWahXir9cqZLJ5buNc9LjoL0lvp26opTKnBDksyxX7n6zQL6pe/fikWlpZwcGSh+VeQUkhaQo2RlyghqZd/s9QExQUJjHxuIoPnF6rUGvAQOPFwbIXzMihc4UzopAXuJtWWbKjxbW2p4GJtWb11753t+L5W+Rh8nLacMHMQxgvKPBMtB0H4KosBxL4SMgVcQy0htpdPwxkd8DbRMkw7qJWcIhpXWOkxHLCERf3ijgVIF9FQabcQFHMNzJRNogHeg7Gi0sb3JEP6810RvwmoHTo5AXSlQ7ac0pjD5ZVooZAIFkFTDIcGutdnuydDNFIusoLWnlN3JRKxWZDagshXmCRNSNlmNFTgBOSBH8dbNWR9pKZmxEFLCwlD2imlTXQXxFG8/00yjxcPDZjC2sPqMNqSKn6SRICnuDXnFciVLZqpSNB+28RdkCjTyfsAtUFLDKi5s37uQz1fLOVvbOLffoHmV1SAHLgEmMI3QWrYcEO4EJn8c3xrA1MJIsFJh7nEhtIrsg82hLYIshUHIeMP4ENABsQpPRuYdztiktSc4VyAkvY4KBOmFGURdeIPgmk1zT7Z9li18CJkGj8BNm+pBtFNrAHPJPgyCORKoM8xRaw1HHGW9m4G8xes6GDDgZrMJU60SpA0s3X5JZFoqMalcBfjAEVLsPk1Kp0agRDZstVpig0bCPAtO4RhF1h1GXIh9jaAqykYppgRYVs2IWiRYIUom4RqgGG6fpCU3CbGaWD56yfIpzWnlAmjeAl5YhmNtfgt5a9A5WpzQaq40R12n+1xxoY5kKSzB+cAWMHv8AWcwj9J005MrqrJWMQhXVl5Y0IYc0ReMsYzXNJqvfAvMG0enciVIRpAWJBFxIgw2Nxe+L4Ds4Yapl8SXM/qxjcIImiqkyeEHN8pq2apbXuL/6r9dJ053VrtfFdQ/VV+/d/tuev0+nNX4++rryoLlm2saYqYl62Qq70gMdqVK5wFXGP7wHuzsBJaUxk8VDklq1SGIJwgGwL477MnWizdQypv4X6wxkA4Xn87gNssWi41hkDLBNx+S60nwbfEobzTtNS9WadUO9A7XtxU0PaamvNvOUusIOm7/nkTbvhDm3vmh2vA/4LzFf9k6vRtociRHxm0RXWJWwZqhtpF7WgEEq4e2IhEnjEJW2883TBroQlIbgQ1Tdy2gpYL2btpB+JrcSuyRWQflJTSGgCC9AqaUBKL3VzBKcSpTAAa+VsPb4HM6xXjHOuCUQxYPXAbhGYYgK+Ve8pYgr5BSjG9SVUnwkXKMaJ44dmaK9sR3N314EtuRhx820Vao4ZeLTL4K+68THA7IiC7Pqq6I98aSiWPCMphEwAMprqUswOCi7YiQZo81YGDLR4td+7bs7N951hjPSiGmO1RbQK5o9h1VHm9EGgMA0fqQnVVqbMNqQRBjeIGzZKFfJQqrgVXh1OEFTnTxop9KVfGWvN9zb2Qwc91955XogPoshMpJJGOvR1D1uDvOxSDIUfvS0dYbpdTLe3ii/+fadcavtDhxQNkjeUFLWPHKHmSLNLG1Au4FeROK8EAO9wACOl465prkMJIr7d8vXriez2+gJYFFxnE0FIFpQz2yMZHsOn+sFhh0S4p/Vn+U330KPzkAyCa4KYaODmigmF++bSAoQYQg02sFoJkeVxp14z03EyBnFp+B68KJRi8DS0pLQjZnbbLbmVjTb75XlEi9RUNgRv8thX/5UhFhaWR4Bp7brRwm7CJqhf3wdt0bmUoCJI0anS3E+dHGsL8QNugWvztRgjQVoEAbw7EE8IIaR+qzYmIlARs4kqAn//kKlhHIG4wbLFjYCVQ+JmwYjFzNBKmuxoPlPkWLYjIh0DSEHqWwQNjBAEhP1VApgYITcPkKv9B3mXkAEHYAPHLhKDQ6FhqkEO3gNQ7iAThiVi+idt5k1zJ8L1Clw8lf26ib96rTZVuucE6vV7Ok0OQJV8QmGUgoH+f0q6hz9YzTHWUWtMVRWCN8ebpMUxTJhNaAvRbqT4QrVmbS1oDq6Qvt1G5UtSJUxHqGFDUqkwVaM9xSJcJAwNG2MN6MKBPgNWjVKV71Ge39pvH/J78hFl8wV7lrfuLpDpzyMfXHG7Hl3vnSY/Pt1x+p95rtaHpzw2mAEFS0NQJMbZdYBwKQ+U4AgZkwGgdLc1AMQ/44lIDQdtc+d+pnSGE/GFJuwElRDxAll0W21AXRsJTAWMqMPzEgRbQhhxftsgkejwinEorAaaJRpyKppaqXZ1zJdN9g/43XVNFAgYg65iVvNS7xu6Q00/uJh9sytqz/m0dUNq5N6ajUYooYiJubFXBAWM5+Ak+AILSEdFK/EFRYa+t5QOEnFoFiiUsqx0qhyzll4VmQk0rMDUACfXbZR0TAwkQSFuOfcBL7UCPAhzLegVilCDZunBiu8CNoghxbh3kTGSuesArke4L9YyxLD4KnQPwiJBuPEmqHmjwZSqThO6alqKZvNombAgCPfbnFCvAxsJZuVSNQERFZLzjfJ9AEOMfym7Bl4akIfiHbXcCDoyArNDuQMSSVO8FEgth23Kjhn7r/+Lgh1in9RMg2qgfkXxprgDoPyk+EhA0yQSErWDxIRRmlFJtCQ8DydjlPA3V9RKbGo1H9Hypz1KBhHvdlUxkogb3Zdt91uo75yel301G+8c/er70Qmo7kTdHc2K+WtZ5PjVioRqzX6FPug2m1/7FK1TIVyEQNQjgki6DW/jK8IHNMGOuTHjC46E+xPLFJmEISMi1J33K3FU2WClJaBLvabwbgVnTYZZkx8vXEe5LfMlgbBZGswp3QnrkC8BwAQQw2kEwpuMujRBUJskejEK7BmFIpBiDZxci4ps3D4lCoaUszcRJk1iqXgQ0+iO6wiyXgBT308gURIDKrHCOB2WgeRqIWVMp9I82YwZmXvVewABh5hH8CzaJvoHoyCBA5UVRHyWuPuyzrV9+kvKlz0Fla1TDWULHUo0Y/hg8Skg8UAh8pmobJZquxslCpbbn9EcgT0iozU6cEzMuYUqnnyJDeaPdYvHYgmyGGItg2oCpHeU1ZStCWs/RGMgYUViShfvCVB67L6EyEAScBhdzQk1TT+I9AjemElbSiSZob1AJdjQIPx1HSYTatTc+YfmR//QCcv1r7w7cWm8/5LvF0WLLSA5Sl3T5nERVyRDsnlhrMGcg+sCWwrbK6c2UQkpMWFtoLrhdOTqrQMMwthAxMiHzOzTAImT+g9oI3bGEpfwQHPQGc01vLPpkliAbTREPVTJEjdvdQts68zpkerzuoEm3evt8tAeLfo0AyaOf+SP+unLu7nLu+sqI8apkvrodUJLnsYz6Air6EgeIQZvoVTb5QirdmszCh0j2gWBSspTwFVT+1ckbi/2ajPKiBTGIEm45CDqQt4nQyGuBGQqxJLH6ozIEamo8CSKFCGHeO7iD8qUTkaAqRmEE3vhBJXY2Uay4fU1VUv1FrvQD3RFf6ufr1bzUnzrLmqi8/d4D966cXeM+ZGzhq0YabOB0WvgTBcZpaX8BGsLY0QBBNdLH4bM5ReKpBCKXMCHmajabPRrDUH+2XuioEzsDrSbr17Fpg49WQc/QM9BTfpHMAJyHkhuGouAkAoilJGiiK4aflMJsPyUQOBC7nQH2y1aOKgB3yQFC5Y3cn5xqLLFHLRXLrXPLJwPomUZCkwAyqmGIcgzLzKmYYfmypSEv1OcUEveoWXU+9OoodJSaQUFORwBARQhywyaFBdJj20PSAEbLnMbL3GpNu5fQXJC9vRIMwPQztdNn2UWWQZSZExl6HjnXJVRFZRah9pW8X8ylCMg7aYbSQHYACQ6A17P/3ow88enT9yAsF7BxuV8LNXriOjfPzxg61KrloqLLpCbbRjOQ48OTj9+NPHy9D0+maBJHgCY3Qrmuol80MjMVyRQtQAHjDPfIECGD8BDO8kn5BuXSyH4/bHP/4f0JPfvvm74fB1xyHlED4Lt5aTKCrxWHSzev3vbkF58Q0PhHZevW7nnhGyBi8rcs0M4QdAHokpFldeRh7vntNpZa0qHUYj02u2qW3A+oHBpGloReCfKJpI2gbhcxxAY2SSiOcoxWzh/i9HVUOXNG2BKGGYvUy+qvAwYkRkZobUypYj/I1ijjz2hMQNnfS8SngWUV0k3VLoNl2XHgJoFREgIaCVzuZwMcqQzpIaxAQhKox4EVehDl40WxBvAVWziO9t9FTVA1bAcTr0F+xMm7EPMJ7IcIhNyVTK6fZzuRxGKlxiiCngg/AKrGnwpjKayjbOlCvmmFNU1ALVMrkof6bRGXlpISS0jC7QQOmVhZG0GU5r9UsXNFH8cFFr3tzjr33dbTYfl+lmPe/9b/7qQd6NVAqM8SyxDnxvMOxjN5lSwAemg9liuNGYSiKhRjxRO1FwFySducMdlsbJsj4YRqNUgKD9SC9K2ErKA1Ys76ebMnFpRmAVEAUEXow4nZMo7bXY4H/tsqPuXN0uOqbz5omLG3jAHPAqYUhvM+9jd3Xvy966uuY/8uKPRwL0qG41w0tzOaI7Ej1RVBKyAaKAucOZUBklMR+hGWD50JAZGqL8NsLyZDFq905HiVR8jocH0h+6xv4I30IqTQAxsB4sLyx7bp9x52F5/hrqiO8tX+aU4JwdD82vOmxmUKcvdtRGfVq3+DcLg+rInDWzr77oBj3n/a6HjUvavDf63Rf0eA+vL+oG8795nl3vAbURPh0kBc7XHEt3zw4doAtgVsr/gh5B56wEbEDoSgdTSmnTWXxDwTs8KpD3/iqckAd5H41kJXMW/yqycpJyw7SHUQH+lM1HGzhGwz7s9UiqB8ckoVbMNDgFw5pC82bkUmPVGQQIVsWENwrOSuOJ3O20Hvk6mdTwQZ/g/sEBIkUinROh5uvi7tUG1iTlW+SgajQYHCdSRSQYLkVjRISWUplrHndD81g7AnBUedgiJCALGSYT+UQKqwTMEl0lFIqkkuG+M8SZxCJ73FgtRB/BaiH7ALZS+trpUWDLonv9bidk2XHlUSc3i7qND3yxnCO56qOHDx9+UvvhJ7XrqeCNm5U3X92SzjoYyGVz1YL18WcPloUkbMjUHco2oZT3lGmH2PXnlC92IRio4TM0ilZKa26GG35kKgIg2gdUfvU3/h/swctyBhMpiXCArNBygjO7YEBVG1BWsAvPm4zubNbPzmAVodnGERi+nveJLjMiPaUnHRVkgsY+GJ1TR8d1rRzZZWLDfg+VjxaBJlUzD3sKHobKwskvVHiRCHuL9rP+zFftrZ23wrE0uYVkP5DcRBNI6NHHL8M8rUhUQjwgI7iS0X1CUw2ni+Zdq9WTKYkNQHtB4CqsNoTYRCaOmd8YSsoYmREGrVYgnU3HU6eooxqnp4PhYGenisMC/sa9LvIQdBvtfczK5KBcKHvRShWzyfvBDo70SKCwhVTRQeGPcgktm2LHgnJUA2xpDFgW3IrQzzEwxklWA3pUeSNpDWlNmIVgMKcWgHhNb/nq98rmHV5ZtZeva9Ua7OC/EoCDWiJzQZAQkHnvIoDhCsMXfBjjTp7ESZCqqVSHhv+QzKQauFQFA6cP5WyLr5qJ+RLGAGSYZ4QgYj8AIsDXxOeRK8RSM0FHdEzeTIIZLxLYNFfXPJSlm8wKpyH+pjt0ZBANB/TAbGZXf7jk3aHT/qjoJj3kvUYn/V3zqPeI2b305+o9ekQb7RKKlDbbtIBTcvjnqhCb2UGBMAZ9Z0BiWHpYM7Nxv0Nab7tgDZs1p9MOTkaWFafuqes6rLXpuA1Rxc0L6Uk6ofkilbZQBSBugbyorsFfPgkosMSEMn0AMM3WzJq++NO46r2ZT12kceZGb6DoujegvGP9oNcR00FO6rzZ/B6bfT6tbmvzfi8uChbNsHhD5F/wpkXflmDPM5ppQRN6HCk9YSK4EJPuEyys+Fdqw86MQ71eJ22EWow+nFwQEQmZ8D5qqOmBcA1ElU3oiQ2kQPb4TCk1nfbi0QIrH4aCr6FWQvhCD6l5Q/VDkC08CHSYcsBy8VdNI7IcokJJbOfgbURm5AU3Q+hH64HtYR5Gf0Xlq2A6XYBKzyjzQronameh1AoHSTsp87xQHQbugG3npUSS4khBHTIjqBtmCiCFGhQ96HFYRIHGQVWw3uhd0qGN7Q3UJ/DgFJCx02hF4DoJkuBm455JPkziQqeKc4aXwsUQlRrMZKtXQ/yuVsLN4/lutYhekdEkrum113NvurN8tbSxu4EOAc0DaJOSflY1kY1Hzk6bKKRhROV3GoZGBkZE+6A8Gp6hk8kUqvMx+eWytFu00MC2ndtQAVs6JRUQ51IQQtM3+s3LmWSM2IQpqMf0Xd1nEgW1El+zmbzECQCBr84ZCvKCaCgAAjAvPhEcMNogHeWWCS3iaJ9RPoSWaLiUCGpBLBXphGG4Z2jRGTjMBYtENHn9GglkDROmdHUK1sPCjOtqVAlZaYlpPKSALHG0AlBk2cKPADbyr1BuYiIE6aYwKxzrWFooGC4V6pp1qdjXaKCgseIJvL0YLDRPSCJD3PWwA8hLd9DHH3dC0m/0/nahmCYUo90jzgCRBd/NANgQjhgWOWklwYV8FhwxnPQRefD4iAUSCiKYyhsS/QmuSfAXDDLtAbOiC6IleDsjKDN04lrms+iSWGqJggIwrToNMxujp9Vi9v0TDLV3zfxcugSCSkqQAAEAAElEQVQ8ekf+s94o6SlOgPqRqjRLuqpwSMowoX3DT1emSlyocKIljxS2XMnfDCq5zmgsCBzRAYMlTWOMEA0g9iLzEhmWRNiB3QhixwcUhZJn90b/yISx0kUZEIn4Hu3SGtem3vjtv9z2S9f9m1b9VKf0Cu8Fetw7Yd5m3rvur3fKv+x9xPu7/pJeysGVt+h+bjPnxUrLnAVBA4Q5y2qE2SXzIDnZ4xGU0cylfETm40E6a4dTcQX3uCQCmRWK2fAyRd4RqgxymKK2hJXo19tuMt6uN1BwzsJEvuOvwkzg+IU2LYHXNHICgAtgaDMol6+rNd4g0enVtHr43VApDaHXC7g20zeeoE96gz9c+vWe8MdJP9zsf8Q85F0RvTOQY95hznlXvXeZE3qpvsDz3MVwGSW9qBYcJhEORlFr3DtYYSbvJoMmB/RgmHq8OFKYkFpQwpwAEio1kaKL90znjtv51MpuwfRLY6iPMDSoEHDMADPrmywPxocF5nbP4bzJ5bgYwXrAsS1URozIMfRoslehA4Ba4MkAnUWuQNJ229JuzDfLBRh80pNyGuUGmhB4v9n0jBw3QDefoMhJcCihHfUSNExfpcEpKkNFEdg4oA1wqhAmZsZroxloVhI0juu0kB8gh1tBM+zDzZrTelU0SdmYdGo+olpZCPWV1pKMtLDnUDE8KUBVSNak3CclDnbCeQotda4YGCEcnZGbDLmD16t94VAeukQSm2hgf2cfl5pk1N7bKHdrrXIx+5WvvVYq5p9+8Lk4TaQC8dyIXKChABX94D5w9+40zynqSxU1OdfIMUGsM9JJQnmbhUCZMmPmQ68NnZUCBhW57CW6ygQx0dAGrOneKNEugBlPUBxmND4Q2qjMPIa0RKPF0iZKEvQqMJMY0QAzpuP0/CQRxo4QA98QiktTmS+pQcnS1+/DFFDNK1UuIj/C0YNF8IulQpcIjoaB78JnkL2iPx70QpFpKr8lZlqDDrkMwaSK2M9nqpLJSSCETjIiOhLkcgMqrUIxh2hXqRTBc3Gptsk0FaJJsTHp3LDU2DyJoTaXTYMLcVJFcOm1u71ev7q7SZlluByis6ekDk+ixUWMJIlIYiDqMcZWDSzzLagLUiU6RBhuxk6KX0CLgpySeYCTJZMMeAIvUC/IIH0zUEQraaxZZWqzGs2v6TtzoNVvTuuSf8Xb9Q6vXvOvMDR6ynwBQwfxiBoUhkUFc1i5YHnaJsfWBPn1JBKTAo/pdtHkGL8s7BnwrAhzEFYydnMnSwnyhYAgBkFZUbVGkbr4jnqq9EFyhPPwiqcC8psmOPG7Y5plOrfuCm3Udf2YTRjMjIB/wv8xJ7mBS/qrs6aTOu89a04JG5o7zHWdWr2L2/xn/RtWh5oEhsRIaugLCBUFeugwOkaWTAo/Z2LiIxRTEy8BYwtZZa1OqLLEUEaxl43OT2o4JMODTPr4WI1bpzX5ogEKVAyeTEkg06dcsLG6KJ12LD4g5SwNkybB/GpRAjAaPY8J0MGqI4IA0w1O+t3hooEe3SIM5PXEdMtMu/Z0mv+0sWu+Y24wZzjBGd1iHvVHQm3xbueavq8TXpN0QiKhWDxJiqxevgTKAXDFCMFz4esDo8DKFzGNxOx8CjTO0u20DhsnB+3Gg82bb2bLW8SQkROBAQfaWOnqNUuUaiPRIOnxvdZxCl5k2G2Q5JdBDyfxfJE5nRQ/cKu0RQ7ITNRE9UTw5bBkcUMSh+vCTU3FO/KlqtE0INgiksLsOfrMYkgY/2LmnjmDubMsF/ZYBmAhnFEw4OK1HU5g318yW9zLezqtXrJwB14SRzDidWkzqhvSCpNKDSM2wM7wuON+mnSk4fgskDDKIxLB4yy+OD2rASlQdRhsipiAw+ZUFHPHKlsi1AAAQA/kAiRUgcuTJlTojIh7IU8y3A1Jmp+xU4lpK+h0uh8c9B+ed+++uk+/yT4OxCRT5LfBOS0RiqO6Ib+86BCoC6xLp3CKnzu948NDem9lylLAMTEadsGMGGiluqOFYm+nbuf06PNWv7GMZHHMXM472LkmIdA6IwqXyshqgjLZDKSDUukQhbe+9B0oCSnY2J/2SScpDj0WTWazVac3sMJDmCcCvVtdp9XuwGyWMrlZdI5yjmBSIAYgwmuAAAZ0c1FyfBJxkC4bcz1iYHDgts6ffXLvoz9GzV+yS6zIRv3s4NkzOxL/m3/nb19PlxR1juV5CY8Pj7skrx62dHKdAfIq3xtEtJKTqyQDySWBQiG3VSpn0ehLNwdrgswBTInCqoRQAvMsDAOa+giBD0A2IzTAIDwkadGEATNGO6ggYMn3QqgKYZmHfGlKKBmvR+807Q9cMlLLZ8msSVCtPOqRFwLjCNnqcJ7EUxmCruYoYMqsdG6GkrHqxP+xjrWaWWresmTXW5CCNO+cbuCIu3TuyqbTehWt4byUjVKy8QBnmW7EWrgOSpYTRE8TFPAFxoMHIXvHDE2aPKxgBqSh8ogra4NE36R/UAyiWH9kIwddiNC9SsyTUglHKd4tGiB0gi0MmVZKAMZfg2U2oV1mRfNwCR+tdledofHqkBk7dcFc12BoSAyKoP/qofCQ4br0633h0l/vDDfq0dVmHuPA/9XrdNXcoS9ws7hWjRn7lG0hfR9MpdL5h1zcmpnzCGWJAtBCkg/QfR7BWTxExvFsdEbKqtg8lbBd6saRSjugQPxUPIaGbblALznLpDLjxGCempAYSCPFzC/IUIBMzEqYI0KK9iheU7oneqZuGYgw7fX2zF8aqCXGjxkFybZqswbb2zVdYJdzZsh1n99r77XmbgBJt5j/1V9zBy/2zpqLGhEd6n9uFW/CL9MqNgUMgroQe6vUNRhUyfVg2kEuFq0lHG9EPlnVhMWLSUPQHExPnj569tmf8Hxxq5wIJ2emjiu8MCMunTwbj5MXQXWmtDEUoG/Myra8EpXluYcqBb9kydTMPzF4RGZOYmkL3Ecmd5BUDO6Rxqiud26ZSxQ3NmIJW8RD0Yyjfusgka4kEpl+oH34+ODT+x9vJXPlr5QNMKG0ZUFoKqAxzqDvkLYiEGg7tWnjF5X9d+AFOvVD+LZsqQQ4HD6+32mfWcUSiX0wQLCOJwvbVDycGC/UQCFXyCSiY4dsZgMQLu5ANDaVTWJ4hcsgdwzxX8jTgApQJywxn0BNqKpFdQ4i5sggSbopNvlwE8FvJRnzTqd/huK42T+ttdDh1hv9zmD+wUcP7j14iFlPFYal7zLVGYz+gcgj8ukHnTZZWMk9RErKJZkw5O8P0RVeYJ2DBj1wwhGpczb4R//Nf/3nP//h7/69/2qrXI0Hu3bCphgjptdizEYs6KIbCfaGrSfNk9Pm0fFvfusfMC0CC/A/Ru0RuE6KJMaf7zfOjtLZDVNnEFsOtlU70G6rs6wi8ulCrdU/LG24RkfhSEneEadcIaYKw2QZHBYeDKbv//DH+H8Vv/nrOasE7nn7rTeU1gmfY/mYCFTBrji3442OOIWLJ7ohZSiDForyKlwRWOVD2IrGo/7J6enCsogfISkCT5OaDXcybHIwEz0iwdC/1Ts4MYCkAWAWOwmFNra3m+d1WEP8hcIZ3JZwMYalGyJ6ogsa9KfplJYafDWKR4Kl0LCQcI2XsyZhlBAjYJJk+Cf6xzijSnpE2yO/ZG7SygUCuY7Mwp5Z5zrnLXvvsrdYGS9zsx64hNw0A9zsneMG86zOsc8/8D68udatnPOV/BnBjzWEmQa2AN0PYGCGEYRHsTAWgQK8kCV5eEIsPbOq/0XHRKZgNzjvjhFxxCQjERg3FnUWyoEnCO5g0gvQHyWIUzNMozVGBlrMocHvQiSrjqgTq23VN57gKQ2JNvMW009O6pR/WjBgrmlYVpvZ86+srppr3kO6098zbRAKgvxC/OWlAAMVDtsZ/MIIUep1Ok+On+JiAmuB0CvNq8YLhSe+J3wBMEaGjU6GeBkkqAuinOZ2kvq0hLoHp/8/tv4rONY0TRDzEh6J9PDm+HPKm+7qrrbTPT3bO95wl0uKS7dkMChSGyF3o9CF7nUlhcQIKSSSkoLaICVS4mjJ5brZmeme9l1d3h1vgAMPpPeJRALQ8/6oHupCqFMwmX/+//e93+vtHK0t3P7DE0nfF9mMhDEPHnQHSWUrvpd4VDwypHQoqZcElewu9GMvei2B3eXWYElAJcGhBDjxgZADcYNg4QFmV8TLvoV65o0EPsmeATC5rft6x42SX5KLvB+LiNfjU/EVWJPcKLkylHXv2Lb1eqrs14heOGyWv7BrjxsblRh6mDZu26Q0g7ZkrDOrY3zilTvDV3PTjbXbr2QmvNKbGhM8C77vI+wkKibpqmEmHpw8O5SRU7Mb1RMY8CJaMDFdzOcpbGr9lf2HQ1Ia4fR0Wj8BZwcpcVFmAW+0eRYnCu0pIqciBKFYecao+5N7/883bvzby1c3OieTjx4+/NkPfvq7X39LPxOB24mJhg3YsK10O4MzOYiJwa7tRHo5z0dFri1v3Arn9sTsWWn4ilaXF8PFaxtAEZgTRxcMLcmDDPRI2FOvmX6SkTthBZHpQumUncjcpvR6yXimqbB6dIoN56owBUyRz6k4dtqUobl06vbc2Guv38oXpwsL+eO99MJK6RszF1duX33tzZsvv3Atn5/bfLQZXRW4tqJvdni/iBZVYyyJBAssLXGvcdYVswGJCGq4GMBirxGdwru9HMjCnimuXbnzN+ZX/1f/i/+1IA2xbTuBW8nV8TOIBosfdU6rTz67b5Ve8sHgZw6FmAujBQrxdpKjbUI7KbubjCmyOppm0pl8zj55V8JFQ8ESeNRWXU6htUvs0sLiXDa6p8SiAOX2K29+7w/+7sraxktvfJPoevO0cTKo6eUQ+mWgv8dJu8CSBLmnh61+uJ9ldGH2jFER14hm6szYYF46o6ODgw/v1jcK9atrZ7qT2n+n1WIUcX4EZ6OBTcp7mcvn0qVCQaM3w9o2dDFh5mKi7BxRPV3fBZOENSYuOl2SfFSYJsPVXQSfjWtOx8IBnDNhbqi/NsVYHJh0t2aeH4UFsFpbvdlc3hPRkw/ZKR6K78aZBUEm+J/Q3ReUEK9AouSv+DWo85IH/ppdBNXHGwH8uA5GXh6rQ6bkX3IAzjRakvtzPptuRMCRCZr0hvtO6m0kqzlMS6YcRBQ97hVLc+IXkJaPFcdH/+h9NsdXFqhzrtn77CyhHvUerCL7j0+hhzDCWELxR7Isv8CTRJ59wekuN5W8nnwmdhjbutxM3N9XcK+4nsCM5V2CJeH/8UfsOj72xee/+JmornGXL96/vCq+//ozwdQuL/CL/2w4fjJxwwsxfdSqv/Ldb/zB3/p3kOe9T96devcnhaW5k24Zm5HrxnEbyVMoAWxlsw+6EL1fr9Bl/EPDSn9N89AdITMjjawJBwftAWeyd2CYRFoD5QOUgeZQN5gsbhdeiNh0crK2jBGGcswSixhObNMaY6liRyxIjlyZibGl+BZX+8v1iUaRvB57iht4JfmKa4NrQwcmrO+x5QhC+ArYXJJ6KE/xNC/ErV3pbb+6UryODOBO15/TLSLKZiXevAQ4S9gQ1k6zlC7R0IxbveC6sUZMAB6NpooLtwu5jfFsnt8iUqekSCrgtCKekFA3wsErIBUM7ddnenahZMoSElHLIDBBm7c3KNU+Ih2XQIKxCDRUlqgDRYkU6vNhR/7VqNTTid11ijROTG/+2ov/Rm7uztxY0QDyO9dfbrz0kL2go1WyVzpsNJPhwi8tRa+uqDNIpYr54sz5K9GRICU9CYyDrgie/HyJ22IyymWtNq5MvsAwYBkQDbSamyss4nmqk7lQx0Sa+0TjDMsRfWFUmCBBA7DAmLS0PK1Xuuni1M1bt4urCzu7jU7r7Pe/+a3e5KBYKhRL+e9+92utnbY4xrmyqyFA6j2YKmRmr7/4YrEwffBoEykmpxEAl8WEKWHIUrw9YHa2MJHkaWBGyVnPQiTyV3QBPVPWAA0nu758c6pSAVW3QPe8eLGPQDEbwqq8HH9mp5buvDR79HwXXzMt0otBozFGKrlGtZGoBtAn2kF4XybUDJSykas7q6/meDpGJ3JSUdWD6KDSBGd/WAO50AdOIUMQRbgJ56an8+rIjLTCmVCe2DrVqlUrc9JOid6r2MV0FGjPzvXrHSvRvUbitZVEJcfYAAS4yyKiN+0o8qVMamNtcXGxKCMD1ZFE5BD4Qx/0QiOj/DkX7tlOu69O73z3QAkY9JnOGlSAzAiu6E1lBBSFgTWgbWi7fQLcjtGuQ00EVuPnpjKd0xaEDMpMdPDwm4YGRVFWDs33fkYeOJqgLVAIVzB5FqT215wtAXtA3C9B00Gnv/6Kc4iPJvR/+WJcklwcf/76l3glwmzO2QdAW42FuOZcXnqD7iYn0ydAJIVaDRciCiVlmjCg78fQIaYQO57674sCQxDSkzqpjjLPWDVCtD9WKgq8zG5CNWNCgzEa3ZKDMIJ5JVzM7wEaf0K1QJTgO96FfnHJ5V69FOwq5GG87YjIxgQi4BafCBr0X7wVAElYzxeA+Os9g8klpOKm/z0oLh+RvPbXkAzwxblzEZzLPnOGEybEVZvt177/OytvfkteyXduffn13/yTveNnH3/yIe+NImqsgk+Z54/JR2nTqSqS4aR8ZWMmLLW1fXx8uL8voi6AHO3QRNllhZ5NtGs1gtSYvVwuF7ODBuc6jQQnHejBLV8s9hwdUmI/4OS0fI/Ox/FnAg1LlXiSJAuEkxa/NGSOFFGIwKAMEwyq8Zj6MKJOtu570FICEEcWwHO3eNVhoDvcM6nMF/eOEw0GEmz9ErqBMpcLiiW5f5xMshqq9vnYUAKFOztUm/AmN1peyzwpoSHXoI5aczYmXwY2PDFX5Gzx0DB1dWsOyzIFV8AMO6eoRtF15HEbWWe9Ie8FGkxc4WgKGiJQhj0hwPP5BT2nRCjD8exKd0nYsqbC2szDnYgJK8NRwt6qdy50ieHtVKk5Uqisx28+rOBU5tbVjfvpkjBtajxHPJ2d9S70f2Ghx7iQSEUVFAWD9HRm8qwDTwExwZVYSEB07Gxn+74kTdHY6dkSrTGTZZ1IbQktCXyp23yEoDuVmYG5pxKC59KB6qRFlKEGvDzV2aKy4MIXMbZsYWUp9Pfh2f5BuVo9frrXGP2z1I07pdL84uMnz378o3eePm4VipnX3ry6W21tbT3ff7Z9+/rGnZemaNaZYoEHSU6T0WmQIfKjEsuJhRFpLgwKdS3YVhyh52J74ROIpO+EFOGFiBezp6ThQRDYyPBm16iMDZ4XIOYjpjcAu6xeJo0hX+HdplPi5jQjPFuyfQIrPhkZjtxmmBpsNLF4FjgFOij8XVzH6GONkCCC/2cVg4lqh+tnytLTGSeIy9A3QF0g5ZU3v5wVuY3uHRfpbAnwnUum5Hg7mixbCHBTrcYz0WGbMLZxT4GtdA+at+omacIwUDs6c2BefXX1yuKVgpS8SYOUR4VhQYGxdbuJW0nyNlpuTgvx4MuBjaw0Wm250hx0hwsrBW47pWy4nkoqzkVR0IYnEOZ03fExE4A9+UQv2JGxFnOqIUNlijvLciKV1AeEZ1SNedJrBCQDxwieoPbwCPnFf37Gty/+8mtcFUfyxXtBi/FC8j2O5vL6y0uSNy4vjfvBVrvzYtCQOA1FTMRbt1R78KqyCicknMt/Gkpl0FWEBVCWoyaaSVIcHTVJqgjhN6newieUQhOrsaOeDOPotE7FcQqBC0HdPh6MJHAjnm1pXk6oOmLE5EHkw7CsJNiHjZpw/Ljs8p+PUOd+LRvQXghnu7iEl3dj8wGXRFm+vHnyaryefCVA+eKlX7+TwMkifn3J5euXt3C9heDdzsrm09nsi699dTKzNJeRxD5WXLt+I/UNHgrLjidHxhwWxwmAuWv8C0O8pfOBtCogoEq2b9Zraqr71YqcMU4RrcHbtebaaIOeJQG5Vm2mmq1zKsZczoq6/VEHgHkcSZRo28Eett9ESAJb8MzQxGP/aJfGi6ToxlyVhXxrdNZr93rNGiaSzeXJsIhaOJ5gQiBNqrCJHa27YPTRLZLsIuQBPXGeapJD8eDgPOf3hKBwOdH6IEKwQP/gUKwBC0hqO/2CYZ/QaKN8USJ2UGDiBHFiUR7tVFVCUCDZ4Jk8tkzFhmyBwDTSUEWiG48W6Wwt3FBTnTlOEG97tGlPVJBwJOBH6uOECkY96hs2QuDKRVAkYPvh0wTp0WlMV5G4Fhlq4TmJ5Lbw6VLNJnujmFoiaY/6Hs2np5aABZXRNEHmLJUfzxSK0WMyS4ShWGqqMIPFAwKGqdWfRdBQtaIHqwRb2HIRD8W6+Tu29uofbH4kwXR1bXWpOHtn9eUbV66DgARUPgCPtoWRUFKro0xSyg8Ew0yVOoUAkBGFBSM+3jFZp1Naz7f6w7HC0lp2af3ocP+9Dz5898Pa87PU9auEYHGMvjadHeKpqVRhcT5fWt9Yv439kCNLS0utbmdsV+PL4Vw6E+2rxxRVEXqsmxCukVWlA0+wfMiecANQQGuOHI3rrR/+6CDWCRNb+q25Apg4I2JjqnxwcFbKk+6UwE1KzWH91Zu3Xrh1HRGANO8ByRqgAdIxWSXBbELXoAzF8FuUHbW2SGN1Y+1v/M73ha31ktMazRQ1YjFyyZMwGo5w0pWjShIUJpVhT+fp4NpaSKOEdJn8okEFCfwHQXeYy8UAaKFrPC/FxZIenbSDsqMNBaph18EFFZmzFIZsfmEmTcan8gtLX/v2dwB6UKmVSkW8Gy8WEDXHg8cEoWD3agUEg4Xc/eTsc/8gjU5zog7V01Ld82Yvd7pCWsoOBu3Uk50WrxUjBetg2WOR6WwGBYfad9GLgmQ+FkcNa4w2pk1JE44TCK7DfRBBYJAPbHA4CJQ9HdALog+oxlXJ99i9l4M/xhV+D3Lx+xevXb4RV8UH/H/58cTdG/CC99gJqoksftU5qnlVchufR3wGQ9IgOtpZONBwQuIbLPRB5E+zA/zS4+SAQ1MzUl3kyQryIRDHbfVcGolXo89cICw04YoKshAuoSqFFmSl8fjE8xDyhOiHjASA50SrnFOV0hYnDpasP5buvrNTs75TEsNdyDgKh4lrbMPeQzy4+AsA/BoGHnX52l+/k9zQi/GVfCIg5/dffyJ5I/kTO8dxabseHhJ5Ymrn8c7CxtvRKJ6aHIhoT/Se+Aj696EkJZlrOPhqfOqL7xZmI6Nl3I+zSN6I9iy8INSPTlt+guHjkpSvdARFxc8NyjAYVkFQ+FWwYyozWQImvI0RGAy2HD1DEi4umkqmKmDVdkIV90Ce9Ze/+ZVWpbF5/1F569nq1Y38woJe0x6LiATkUqZI8Vk6Mq6E/qnCRQgxJTWT3ad1La4WDwXj0IC0WlPEBrCRsXAenhnZAoQ8D0yYftCKkzpcJFx+6tqN3xLz0yJGQSwB4Z8yE/rFeafTTxdztIh6q8G+yC8H3rgb5jMQWz36rFnd9rCrr38JmeCVIqjMrvCiaBZBttHiQDrhJRFtGJ81aQ5hWyF/dDqffvmFm0uTUzc2lvj+oWy4i0XRJbFI1M3mYDFSAyc5OLOlwsRGaSqTpXRjg9TbKbm3oU7YOV1v8c033lpZ4tvhIpD6uSyQIy1HdHJlsZjNBY++xA8wkgAS2iXZJdYcqqHps6tf+erXV65eX9tYyIyPlxvVYVQIC0pMIKdQu8LImhgZAl3e79YGVGDnJuUdmZOsOrgBriaaGU0hzDvkVddjsduttAc3x7OpC4Vk0w0d7FKpVrv36MHWV97+RkuV3SglsZWhIFWp2ah2+01ItrW9Xa9H66Pp0dlXvvQKzcFCwrdE+cR4yPHRsN6sn473C4XlGa2l48UISxIAnU5TmDawOnIaJsTXO82yZgg27k/BztWVZXk1ktmGU+fLC+vTk+EcgyUO3DwVWyB6nVXAkOKERyTQQfpanyFcD5Js6XUXvfT6G72jWr/Xl29oMRyioff0SQhm4vlJRxvwXOqsN37Wnc+ImmDFZsh097ce/fKXf1Zaur1oMO/5oNGrLixcXVhYWlpcU6Z6qqG1h8zMjZp1tBI+KsxGCDMcqhFXSaezhWKRaEl5WqPearZFBOSttwSXz0edjjodaaOJNwIFhmZASM/yh7GoiGpsjjrYbHQ1KVq7ck3It1qtLK+sgin9VWa4S1Y3lqIsxYAHPfOWivieg8b4wlxGYRydwX/PI+QfftczQjpUSKfPk5qAi34bPCextXHCxBH0a4YFdoAb33/NwIKF+fvXX/Gu/7/g+d5x4RdiIW4ZjC3kfjBhbCoSnjB0QktoIk4FRtIOx2dwbW86LbQczgw5/kTE2YX+1nxqaBM7krRCtSjkYDI7JtUN1Q+0z5RFuG3QlQfCAfoX9T12nbSD9nhyxhrcGnA41yBQRhatNYWsiGI5R+IzdpLsMkQioChMwHDwZZYU8ej3sLK+2HgiBRLAXPL0eDmRfV+A669h9MUHEsaffPa/l44Bt0vAhaIcJUs8O3r1zUxi1lsP7735G7/XqpR7vdr5mS5OkU/GyUxaRRUhDm3cA93hLIZhhdZLmwqLF9MLS0JnJUnWl2ejanI8U8rkFJHmC/wRetbHNGDybXw2V2B38sQSjbhkcrihH4SUo336L/645CehT/nHYGMuCkXUjo5L68tz0/m3vlFTdp9ZwEpmeXsa5bre8RxN5qTQbLutWqfdZI40yw1p7xLeDeptltuLa6XOcaPZapgO4Xy4+Trd4Wx+Tr/i/rBHxaUVnHZPNMcnJyK/I1rbT3J2QW2lnOONSmHQmC2UnFCsENoY6itpUQXuieiuAif+d3Nr24l0DB09NTszGO/X6gcHz9+9cufOxFx2dNaaSuWDAfliu/AQnMmLD0Eb+hAHxMTsoFudT94XjLp+ff33//C3eOg0ReuM6Z1JSZ08G5wEnNIz0hIVWYxNGkE8A21MrlsvGE45B7CRcHPe4pDBx8OaGmNJ5K5efWly0OYKkWc5wcUvLHE2JXq7qN9CeoFi5LHI42wq6/PuDadOzslX036s+LyUE99eyOaX3D+bn4PYIchIs2SUWBwTJWAM02g4t3Cu4Mpzc51uTSqrvQUGh4k1BR3ksMbslrE0wD/45NPZ4pWMBiP51KNW6t6z46/O35RLarY0z2S1cppv15u9+usvoUv6iXH2/f29ztxU6uYVPXx0GKRPj5nue3E21W1pO1oTIsXwgiFPG1sMPXXjSNyqo36zfXwtwnLoLnSa4aDbaBxfm3rTxhEHS8iWUT/VjmzOrKTPSnRbMWQCYKL+bCuXDteKLVAE4IXYKBsRCOJDjBWO4SAxG9W/qPnOT/7i6Onz8elc5WC73Kpir3NTpa39Y6e+t7c3N3Xx4s3VuVz+pdde/vo6kEYgB9y6/ZZh08svvLW2drVZP+51GouFhbXVO7PT2Wa9Q+p7esj0UCmdUPzTa6F/FtNJSCHmmy7N5d1n81e/pGvZ4/ufvP/R+2YWLZ+vSN883FVsER1N5IOG1ah+UPuHzDTRdnq6u7Q0v7RS5JLyiHRufkInibl0qESCEcHswGgiO5c2Ie3gsCOPIVean5rOkC9zaR0jlJVptHBGL+btpxYI//HNprmaiZxpJbjMU22i4FdinWOkYW/ESQRLwjTjXPCPYAX+9IXMPPPyKwCb8PngSf6L6+Kd+Ejox777cgzJ+xQXIfFTypxmPslD40SohgPOR+yO74aFJzUaaSdCACZEJR22DrKxRDLhzCmPG6jH7laxSEkNdkSesZUYQ3G9oUZGbIYSGV4v67F99GDdAS1+Q1XgMvn6bdClxs7n81RRKWmWGZXZLPr4WGxUeaS+8RczE41WI1HDaMBBtfZsb+EMCY3bbb/Ary+2HjcIaMXDElBcAiv57tW/ht+vL0quCii5Kx5MmwwlDQYJZfZ3H3+Ox85qYNOTy9/XtiOG7XFc0g9GJP/k2XSG7/KUb4Udx8GvYWA6F3JeUVHMpfPYIIPk6RYcLIPrI9Y2pXUvO4xhgTdR8vSvCdXFCgMC8c/F8ULC8v1i4/6P0wy4+sk8nzrfuJEWxSeG08XVdHElkt8Y5qmx5VUa3Fm2SBF0/VRxHgh5UWZSL0uE8Dh/Tp/eNryikHoBrAajQYf1xqEFU2MiRJhALK3IXONC9XSvhJbviAz+M5AJZ5Wr0W60KvsLy1cIaJqCheh6RxPKFqJ3PPcOD5Ab0MBONayJh0oiGJarTxnYL7/xB9GBRY8ESmA2muMjBuqBYe+cZexmew56Hh8v5ObTTBV7xiw1WuHePT9rnQ+Onz/r0BMuJsrHB5n0xHKxkE3ryXpWOaosrhez2cz+Xnnx6tL4cnbhNbgzdcI07d8zCmZs8kWp5xZjT5/c/eS8cfzt33vb00ap7qhfnZ29DhtSp3JDGtTeAP2Yelo54GKVU8ziSuXp/Sd3x0+bymy3nj+rnVR++1v/6rVrd5qdbkbn65KWZxzfnCpBtPgwL3P43qZSuVLO8olV6uZA2qKBe0aDURQvzIHpYkCZfIZPpNWidsEkqhV3whhqy+Yy52NTsknBWbqQaII685sbN7/26hu1k5ay2Vquyp2nsHB9PsfWS7xEAe1Wp1ns9nZ729rUrCyv55fneTicquCTOzC/G/VKcf5GnK1/IQBwoItGtx680OfhkBoADevD7QmG3kx+aHokXM+VnM0bihYJSIHro3b9IPpyEOHw3HExXKM31MlliEGsePfJ7s/+7Ee3Xvvy+aD9i59+emVj/nvfvbZ99+NCdvrwYf3WjcLaWulwt3pwuNeuH8FY6TPM4dX1jd/5k7+3vPySo58vLc3PzZQ27kxOL+szJZApmmrZ6DGqGabHBye0DfH+fghvgSC7PRNX3y0uFZKTjGmTmzuHhZmMX5RWy4c0+vaw0spk06WiPFf92gal+ZnsXOb85GJlZXF5cQnvPBkR7ay680a9PF9aVPcaspy7Q0McSpzOX232W5FOqFWddB/dyeVicSqK60E0Fm1EMxLXBQY2OBGKMCeZX5CnEVdNODUAB8CD98PG4AJhZXGuBkNNyD7xgqB+KmAoRy5xRgFsHw2mm6gUCcYmUQd3wZZwU2YmdwtIxScdi2R3jgFcjWTscbLOCFV4x0HGrZjaLsar1T8BP5nO1ROYQMVilJ1wXXBcBFJP83LxWNHdeY0UhcBPKiBFTysP6jCiRsOJf9BCEy+QRDgoUu90avXa9Y2rC1OTgv3T3Ko4Qyh8sQlqvtXEFFesi2Vwft5rNvJyKeYLVILIoiEoEzwEJze+5Jfxq6/gkwGYgMv/n68vXgzIJfCNS+PqYK6JZAHI8MGFFn920dg/LD/8dOXWbQg6yEw2Gg0W7dLKiqTGcCPTivV5DNS38lMQctLKxNjyYCVQ3DP3o93hUUPIoMHxginIF3JUnNqQm5dzOisVIRN6CgsEQjmGsB0Jg+gRSHQmhfBIzxFYaEhInBaQApzD5tHOo/z8wkympJMkBdY1Seoh740tsejCE0Bn5Ys0jZWLhHTTxA6+nXbbJ932/AqcQGPdfqeaLaqp4SpxCVyNAIKhteNp/iKgCtU+WUACuNRwPiSZV7AMrIzNEfIezOfmb3z1O3+M5grcoKGcCoqMTac1WCigisDH1Gh1/W9onGm/k3PGYMmxIjiAjgSMR2DPszldA7KeROlw3EnvbT6EMP64OfLzVz768OmjZw8x1rns4v7zo55qrkbq6pUjzzntYJcTh5WTRr/BAbZ8MjG3efSq9gHjZoiX9x795+tX/3Xtmia03bwYa7T6f/nJp6tTqbeG1UH92U9//M8K+ZOv/eb/jJDZfrI9V5qq9srCnu1mvd6pJ2Wotn16ePBwf/sJPxlW92j3gN/r5/c/OGrU5Ha+cut17U/btfJcbmEiiqdAUqIIGRpVC+EjB4VapaDppY4M6TRHm1R0FTaVgwoKKC4RvhGQn8rOLK8tH9Ub0Ar4TRFt13tZcxLzxcJsup8aLBaLiwvZ40ZZMmepmO+1eqXFjCTPUadJLscg3FyhtL40nGjN5ERgshedwdr8WnF142xsxtQwzbwiVHTuqps5hHYRyjL0A/9wC8xN9/m447wjUgIJZXM5YRN/oBWsNAKeiTs8aaWXZDxrbDdQ+oF77LSbJuaIEKaLkimwNQkOzOD2XHo+lWrqwNMatGNADLTSUH5qrDc8nRbgz47N5Ka/8q0bRNNXv/3qk4d7H7737td+50s+ggoUps1Mz85HFQiUMIasNL/88sRMIWSV0cUzxCStmhV0ns4XJdwbw2DhGKQoSKyVNDBiptFJpkqMwq9Bez9LHR93S/lCpmD5Y4PDavmgW53rtjsNVI+KX31z/eq1jepBk/tqeDYQnCYyW50OTau4yDLTd3qYMnRidoaRu3dczt5Y0NFpv1pud/ulrOBE6KW4J9UYENl9wWNEkoZnGCgGzgSzxmDu+EL4YEZ6WXE9hYcjaB4PTNgaGCZaP5RIlIlLxde9QgY4IOzsUhz4XXDWH/Gf111vBUl+F5YKtbAVbCnsdAvhu9Hh3EGPaWuBjYXbWb26XAb1Nf0+/a7HIGLZWE+n3p6YGBp4guuEOUjjksLe1w1U2obbSD2mvSnhIHRHs7PSrmZCSZjkXVPGEgnEYUEEy7JpYzODKRjkk6p2z5dOztr9PgnJ/xMaJjYa7qRAuZAeMSt1qo419U6eH5avLI1Jg+B8cQl2G9XlFhJCKyCV7NiP+DUYETAAQfwIyCe/eyfeDlHrg//9Vyhp8XmAjsSV8GLHFscuFvOF6v7uf/F/+t+tXL86f/3Kq9/8js0Zu6EG+pSC0WmphpMdJXwUqtDUJLU3JgnKP55VRapR6mmnXSHonKsDVoifW8QUzI/XUuqk06xpFIHB5ooiUeP9LmVBUoQVhgrPuxM9QlBjZCKIpEGhOeskCvEd/daGp2OD9gkVtVY9unnzhZUrLxZXrzfZSuV6DtJNzPZRuMQ1cmJist3v3H3/nVZjr39yunHlzZff+OaLL35ZqtHBzp40PA6Mzz96f2fzvTsvv51NFyHgNFVy8qJW2Y6yb/XPYriZUjo7c3LSPth5TprQCoBeORVXhxDAfGllfeMlCpE5uHj3a19/m/0TEiqOnUSkoAnfhVVFaJEsc4urCa9Hz7RUvoij1KCVJJI5iBHv4mqxuLbkXUpQT8yg3zqUHyWeCa1mCqW5bOmlt7+7ePv2oNPOzy8Oms3dh5uS1sz1hr4ivV//zttH1dr5zFlhcuZ4OPHyV7+vc7/HaxJ964X/5cTEsqQXdhhqy82s/Jt/+PdTZ43JycXBUf2sUVq+/kavdqLzzfXrb/y7/6P/8csvvhoYLC98eo3FFSdzEdNcX3v1a5m5InP2N978w1pjb3kxp+eBOHshz490Wjk+XoqTi3YX6JNIq1TqHJz7tU52sud86zMXxZnJ0rgOuvjIWKPTfXB3Z3m9ffVEdUGp2jhZuzEvEs6Rc9AW60w9rVSuiAv1qRWnO4d1+rb2egpTPvjg8699/W1KxacffrxyvbSxUjx4vmXecmoyPzFbaA5Gs/n59RdemJpZXT5V1dbV74ARrvNDNisoE5xI4mQQip5fCO+Lxq4np50zxSoJvQy3th7P5meXCgtwWCBh2OOmmDLI7LTT+MVf/GB148pqPv/+h++Y4TVWmFYBtZpfbTWOllbXId5RZef46PCo8IyyO0nWTwwL+dnvfvub3/uj36sfbqUd0JLRXzN/9K9+i7v/S6/d/uiXH+av3VmkPD/8zIK67efpXO/JvZ+fDwbX73xzMb+O61Xrh8daX2zcjGph/JjNHuoXN+yJpCOqaQF2pcZk3Qq8Fub41mJuLUdOLrMQpR5zZ9duXl1fXpi5s/D2m7cVHmvII7S1fm1iZTGv5eXWg02mbXFx9card2Zy5fufP9urlvGmXqe/+Wz7zguvL2zcKCzMS4fhGK7Ue1rDyvn8zm99t9b90ccPKq+/VlH5lJPhfSIRI5TnUqlkqHWvq/rT+PhhGh2jHaMdIhQU8SYBs/zMkHcDC0oYWehWMC14eHCshF0lDC20eBwkPDrJy/F+chl2RVaTGd5MhEJ8zF3QIOU/ovMBI64dByglQuaZqI00J/xad1NtLSZGY5mktciYMPZpn4TgI6b4Y7f5PKsowjqR8Tghu0DSanjcQrhqZagdqCpiBeTROUMDvjBguJJUw1HqKXwYUYR8IxvLvhVORMLfgLl2EtGwlE5S7terHUSU2YwxI8OLi8HjmcmczTTkyelGb1hpch13r1+MzcVsEN2VI+/Qf/hLAC2BTvwAjwQmwfp/Db5EBvg7AVpy0f/PH/HiJciCWzkTSSChYDua84V8SQOge+9/Wikf/92vffXOy1/iNlnfMJyWTtfh/JBfpyaEVaXrLYNo5dq6ie9RBjA8NQny+dPn+5XOK1/60trVq91616zv7DrUTKe6rXKlSunlNyOawITsPlFST55Sonp6Kg0uTrqksvrCMMuTQ4/umKExELsS+PCNk0GWlC7ML13NpPNzuXnOc2+dCYQdl5s8cd3Ti7VVk/GW+FTTqWdnE7e/+g2VTl/+5venJpcvUkWWRelr301QauLll17cfXrfIJWr18TW1iZTebS+PL861EiM6OI3neDe7dXLtYPdfbKr2+pO8LlM73NTyF9UAg2tL8Q0TLzUAKOvS9ISryJvoFG3+aKU7UXOG4vmBMC1+VEpq6e9XX2jpaJ12429p0+n8ut3XnmTNT3oHPVbxz/4x3/65tt74tyP7z/MF6cK87nmQaU0v/DaV3/jYursd/+V/8HJoPfs3r0r16+VijMPP36wcnNNeE1HvuJCsbicj2mdho3AsJnS1MIC/kJ/nclq2z4XKZhBU+GknpmauDV3x5/OZe3N1/7o9i3RgkRinS+k0v/6xr9KA4UhG+s3TnrrCe6was5vv/QW4rh8Kxx6Z3fCcZJ48OL7+cXqqi4XkGSgzwDPSqa4evvl17NT41evbqgNLR9VDCGbW5tvGzB4Otzbq9fq7TFq66wEspNmuy5WnF0uoph6r5PPps47HBtU3+bgtHxQO8ymoyVDJjdd6zaUo6XzE4N276WXryyuFSlhS9c3JnLppiK3/qgnuHaevhjPnp6JBllIkUdar5tI6Xay8CpymbTHaX3+7nv9k9HqlXWnfbD95NUX3zTqwNbuP/70nZ+/e+fGrc5i+azTzeXmNNfQKFQT0OMn2+aFDbup6nlvZfX6DO/bfPHO92/RBVVstesdas1E9+LLL35j9cpqt96uVXeXltf+5u/8CZJee2lj0Fq6eueF3EIW3lRqohRZ/rH5qy/NFnNLp7m/8++8UlqZKx80pjtjC4svKhpQQ/6s+VhDnk6tGc5iOuISTyO8OuGeY+A+/ORXmFen1/jGN/9obnV+96jc6NDWKoN+Q11iczSINGiO1/A/j5auX11cXiusLV+kJ/vH58s3l1968dW1lcJw1P7SN18z6WOulJ/MLC7dWjxuKr2uZbJZOt3Lb71RzC/o9vHk0ZNscckAmuzVa6++3nz962/8nb//784U1rb+N//Hvd3jjfWldo/d05c/P5XN8AZqkqArNE9Gyyx4+jcvE//3+ERHd8DRqH1yslerGBaJY0YcSyZ0VAJynkWxFTsBkfJ5UHvDQR1sj6pKeOAHYQTgG6HBJlEX1/jV31iYWKa3g0nKnXGR0KRoFAYstVe61MzY4EKOm66VVhc98iisHiR+1+3x5ITyLcdEzCDDpz2Txm/IDiE8LS78pwgzKWvGsiLHl19G1R3XGWs/ePL5edq0OyJjeIFXywTXzin4FhGHUcor0WfvRKzOTE/VGZHVMqmEvBslIeMxuUMk9hy/UAFiuloM29KEotbpH9Ua0X1OuBVuci/G04NvI7bwkBA9wJF8SwAS3+Iv//s1fku+vjAHklfjW8L/UQNZGcLECwFWlgoRqmjPTvL5hansPB5BEYgiUEGb6ZwWwtJS2E6wsbiy2m920hn8JSufsFM5lIMmseGnP/yZdMyrL7zW6zc/e+8XhaXClWs3cqWFmUXt85Yxo187+pGSJYYBnprnvbl04HBZo9IJngKesESkWVGyxHC8kHSU2jAYRVOi51Rq8srKxfmLLZV94jztZlNjE+nnJ82jdq1SrlR6Y5rFL1nn5MKaB9EXPB0PEBf9yX/zT/6rP/1/rV9fffnlm195+5u/8Rt/PDmxnCsVUiU6u/LIiEzBpPnll2699tu8WxJQww+FpcgBk/ghuqV+aigJ7+y02jx89nRqZku6jeQ6Bm++OGdYS5t0n52TjilyvrBQOnkkKncsvBDzMNLCnheG2daPnvU7PO/N119/MZ+dPxvWiYPF5bmNa0vpabc/VKK/8+S+hlxz+aWp3Mzbf+M3G7WmtnzX3nhtzDBqDmKTxuYmK1B46HGwetzA2v2DrdPzzfC2n6uPzV1owDU+c9Kvh44zox8ALWy4uHaHLDtqPJ8VCjbKdX7BLHFKcbjAxs5wHR69qejje95sH87Nzk/q26yjqEPzcfZQouYgSd4s5Kax43iKD1q0OALHK+sr/8bf+w/QnSXx9e/t7C9vFA2GqVVEcutdztB6U60pG0IWlo67Bt+vXLmtT/vLX33rf7JY0kEBiaRzuRdv3ZF5tHr91jKdcr7EeW2kydhEVkg2h0HlpBkwFydVI0ipx3He/Na/PDNXuJDqyv+VRYkccXBM+ldP0GFggIwQb79vk05I8lRhSk7H9JWvv5Vd/PbZ+Wy/c7SxuPhv/92/HaX9MY0ncTpL7aK6TozJXHrhm1+hd0LCKMUIJkQooh3/xiQ2+yWbzyIDnCxTOqeqCIyX1m+mxjKwN738eumOK8ORuBJw9Nmz5ReC8q6+hi58HBV43TWCzL4ze7xIH1L3onNxZF4MTxupiWG3drD16O4Hv/plr1qjd0+dp1/4ypumMl67qaDtmvG/zVa13R1W6t0b5/VOq5ldWfzuH/5teU0yb6ML6+R24cr14tJqOj/D8VakQxpDL589NSFRcvHGneVgUKG7Lq8VB8qDdiru1j+vSaZ4+3vf/tr3v3f1xvXcxp3Xv936e3+/fnOttFAq1vYOwCxbNCX0TBZRr9lyMMXl9czCciZvSlpkEJsHO57JvfLVr9145Q3MmvJJQaJ4ImfblOwQwgBLDvCi1vge3BzbVGsRb2F41Bh5cWBCZY++KOGj8Ut8KH5ns3FpyCUkP2Tx9g0zUIScmlLlfDGbuzCHgy4/wmIi5ZN9juFHLkRRyq/uLYphpjSz4cFmqXqi9FALcydRW/LAeqQ7R7GD5SnfjFEW4qETOTnckx1Dj/nNKMiygyLzTpWYK/0iPbbaapZb3WovdVBvvXJr6srGSnl760njoD4c5VIGUldzE2NXccm5MS2/WuIUMTEvVe6fPz8+nlwsbSyIsxslDg1OeNJCDEKMkAR+cpOECIA4PGteSmyjv/6RCID45vUEY5PfXEx+hLUkym034RIiMUNW+X86cjdIRMTSNuqOVXRy0pEpF/F64Y0kIYZnTgsw3S+mNR/gDFWcVa9Xjw+3tw8WHz+D3NDxhz/6xfbe7te+8eZ3v/ud4tLS+DVptB5K6ANrZOkM2i1LdogsOZWFWPqwNyqsXOWlsaUQcslXyKvYGy4sOkRCRBIuJVqgNcnVmjnVMPP0ROvSxtFR5nT2J//sn//klz969927mULhrbeu09PefLMwOz8LwaWRagBV3X2+8/TZuz/8fCL76Ce593/3D3cXM2sbV2+otuIsmFu6Ohm+1wASK9VGk2xs8COEfIUYsLpQnGdy5hgWcqv5uRI/VXE+K03O54hJbH1S3zRurYvz1bV1n+h3BktXbvIXM3Gk6U2tae3F0gwOslBY1Clree1qYb5QPdxZufL66LRT2X9y2m9ee/GNuVwBXu9sP+wNOkrtWZk7TzYz2VJpoUgrOe30p7PT8vw63f7i6hJ7SecwzoRGraLeQJGRMTHyWSRb9tv1UjZ7MT1bqVESRy++XHEWn3/8yXxhAabO6fE7frZU2kCGqfF+u9mSPrOytNCsNWrlQz3OChKfhiYGoLPQ13yWbxlRO87ZnA5AC4yxXrsxLcqRtPm88sIGk2hozvBgeOtrXxkr5vG1q4IiNHCPcKBYwPm4HuPBUFFKjEaZeHnsm7+TKHiWDpLM4q//nkqCdATMI0jrXzDQ5HRwycsvr+Ag9CvC2yFpP8Nog64KrkcMsuhj3t53sm4zfQ6Zu1rb33hthWKmPRVLdCrdE6jQpo3XWzkc/wn2pGcziyrQU+699UlB7Q/TqVzYE5RrT6ZQUEKwp2g/lyhVENUrp+fmS0HZdvOAhBv1BtM53q3pUF56emdN6+Ukbx5+B8Oi8Q06M3M5XiZeaal3sNR8bRxKDjbPs/L6k15t0GtrWnzR7V7M5Hg3nP3K2pU/+IO/JX4yM1fkUyeOvvu7v/Odv/Eb2Pr6zRvpSvpbv/97y0vXBp16v93OL60vLN3ggJWY1+k2ZnTKY1PwYNolIIvza3g3pe5Jkth0fiZ/qjGJWpTJierggk1+Mpk5ZfSenea1E5fmcD4qN6vjn3zWGXS+9I0vlXLq1YoLVzbwVJEYzElTKVYLgKsHVjfH8YFzYSwYrFYqV16RT2/EfNhiKJ82gQOgAhJCKnbopGAZnD6YGYPWr6St5CaGRLwUkEZWGFcUxWhEBJI05Dgi5OZ2sIQfh89c4hrtZAjJzsbjTL3bb+zvdnr1GPZguZHAo+cg/YejMVI6OIrcAN/X9iDUfLXWUXuE80ZRK3ecKCZkYEnHeUXHIOHesVarLZFMuaUggqJMNTiRV0ppRyXEiCfhho3uUDylOQxvk8w5RlB/LLXf6MxZ8NnZRjG/LKQl+aZ/0uz1KoS4fciG7qsUSeBi0vppzFNOWhEBTnDthOv7Pfm6/Mv3eBOTicqU5LXgsGD211dhUQFF7B9QwysQfmkvqLSFk2IRQMsOGw36xKy3Y8wbxB0DRIq5Wzu2EH/uKq7lNmxkPC+Z/dnpXTR5PkKUD892tnbfee8joNIl6q23v3Tl1ksSZaFzp37c7zYlkzjBpfXlXLpAlx82qmbWoSQqMAC5PznHG2ad4k4EVV8jIdPHpBPo/5XSj9fY5k613zvef66UpVRa5jNhSm4/fry5tfXOzz86LncnDmqLpal7n3y0tnD12sIqFNLjcuJ8dvfxg8O9quI35bWW8+G7n//ltX9+59aVpflCPpcrEUtyOByjSqgJCnRRzhh3sU6Lk1Pzk+l8+AfDlY8imgTS+Nhw487NaYliEbzElUJygOnpaXukjXNknGl6PLzxRtLtGWLyhYHixGw4BGG01I1IIzKKj4v+Yia9aMDwsCv2np5feDFdWJjL6wc5nFVLlVbRn5vOzBpVnl9cx3pktFkW5jIctpAcQsQnpbcST1fXX9AkAM/RH0CiUrNSHlu7yQ/LflnON6k1xcWlbr/2yo1rmVyJ54rH9uraGgxnyI+P5zOLUFmIdKqQm85mBHL0AKc4nUbNJ97tUPlGDTwJAuwEaXWO5f+pbzhNKQCJVBT+du5zNjKNbf50ebR5crRbXrt2hV4sXWJ2rjCby4U5ll0gYal5kekQTAGJ08bQVWAtvnTSw8RdR4nGgzqj07bh0pi4HlVKlfSuUxlrBJ04PmZiG9jAbCZz0qEYS45FrWDgx5kgu1rQw92j/MJiqV2QHCwZwNzbSBKNkhQzGE5ksrEhG8dVZMZph8dzaskjyBQkwo21mtJmsgLB0jQkbUB7Gdu4JD8D1zIehLjluCAUyozaD+qaWG633S0trwiAn/SjmUpwLWpjYIvKjUgxFGcmDE3tYtRjYbnCPIscXupiESUNF5lMbr1QYJFwwjHbeRY015uXIw55EkMBAQsEtIrzq3zRHNQq46bT+fWbxXBI8LwV0kvXvzQu7yT8J2OLGg/12ib9hQTC2Cy6LTl71Gu3M0sZXDtyI92u2Vy6smYwAI9Et14XxsP8El3cAYEpxcj0Ud6JG1o7cqLPl0r0DUxALRgnaGZlPSqZyOPgySwbqM1XYwGYN6qSLph04wknT9aVxF4UjieJkeE/T4gfmQQzi7u4AYvBBYllgB2w8MIZLi0Q0DjPw0UmYzukwsigGyLNh2M0rZBDBBXNYBsfbd/9qLq3R7PE4M/iqOApLsIjhNGRL9PyuYM1Rm4LqeAViU84+hQHP08RPHHaoXEn1hsHPuOLj8rknF67NV/KLa4snQ9bF/2Wks6gFw4x0k29rJEzggDAIC3gsFYrTk7X2yf1/mmjP6qfmK+MHtINLHXMcNJhw5BsqvUUfOdHC32Geb9a0tNKrSeXERU6/EqyAUIWXrIbv4XKHCjhWJOX47uVeh24fALrBv1oZuYrdJeY1wNg9C159CFk2MB4xozM8YsTk1tOjSedU85EB+HM49nQVARM0D6eRS6w3Ahih8dhftaTUzFjfsTu1mH1qt5BEwTXzvOjg+rZ0wc7hUz29p0bkaVO8ZickE3bbrV3n+/Rj67cuLa6cU1jhla9hlYLC4s2cHy88/TuPZ4EEonCTnPRaAH1IiA3YX01q43FtWW9d097Hd6MpdXFgrRBXxPTn/z8vU/ff7T5uLq4scTd8PDxrqS+F26/fu31r+LhkWNDmWp1yof1/gCTmhZ+ePRg50//y3/81S+/8jd/+9v5XOHoYNOBpnPaekgLHkyN6106RUMUBkJji8s3JAn1NK9pVQ4PtxVQ3Hn9NZOxB3XFAQva+iKzEPunSgoGHFlJVQQMvFRdHUaEVVUpoYyUnA2WXnTDSKeieaej4X93imezmfn88rU4KX/C+9R59AKIQjyq59nCciHaOmD9M8VLT0J4XjPCLbNxZd5Bu8yn4qHhMJwbz+evBn6ExnWxsuSXSLxZyHM1vRT4HMb1mQm2STzeu+4QJVKXiXnxQbBDo3HjeM8rolVUx1izrYjPtKsXBUoyNdyIwykj4YxDNhNiZZmfV9tF+sGJ0Fe/WT/a3qIRRLFSTteAXL/ZmmI5Y5Sd4EqMcFrddCabOAasQfqgZPNofDbsVHKlFZmj9dqA9aAHbdQhkUrCaLOr7D+l/mcSHU9PWH42qhQJWfWb3UnNEiKB2Iovlgrd0lVUer68xO801Wu2M6X8Sadn+7bLwJfLWj9o5RaKYOLuGMukwb7JVFQEKCLVqlQl+xLaHA2dRnthtcjHZWSWTh6UIV4B9GBkipJD0JvSpOj8YtDRPmHmpH2aKebVmcg0cx8aKzHjI/wiPCHRl411BbYUcwIg4MyFbFEhbh2Ac3d7VTjDgfBCQxBSUTktEJ/Bv2QpBH8AvpkCUg1pNOrJeZ/hetFNeq+Wzc7b3KB7wpE1Nj5/zkXtMRq4pSn9F9KT8vOS94bBSTGZk2FuYQ2jPulAufF0dnFyIq/3UVg/SJgy2jshkMhCI6HwjZlcYLVKSkWJkiQwiKh2tcPYhnqSNBYoukqFFXV389Gk3nBSyBnA0QcFBwelWE803hbbDvvK/qNngkwtl8GDUcpE4uB9dNbAQzgYiTNa9fPXKr0JYwFTw/pJCtMvhqeRWiNRWAUQ6Kczp602T5cVUd4F+SV9sQ4RjIiK27ujrBaPDfEX9d5xJevCG0pZ+MPOL9R+husevPE8/Rd9Z99EIoFCh6yhx1PKHwtF/bKiEDq8iFEXPSmr4zQGAIWDPaXo9LBc0bSwfHx0XGtLSImRY2fqhobbx5WrC/NsbAmMZNRcdqLXko8s7WKaQILoMoci8SjsIGpSBDlAGGgCXZKWzqAVCBNI5Fv8CPSKv1yTeLCDm4QsAFtaejRskrqD83PrAiIKPztdyRe4+PudNunugw4VGcg4m5uIKXTUQzw0PVdwqbEVGk+FJ06uQ7+vIrHRbJaP6uKoPM7tar0rzyeVkm68/XhPfwFHohSLBeNLRp2QMoQFPi5/rGQuU2JohIMhxds0+OyDexdGctKim/WVtYWVhfmVtdVspmRLeSqSMabdk/PCqTMjlPkiTjoDHSJS2v012/RZLYUjH7V33t2sKwj7zd/afLO6L81Li4KzVn/n6WajUWaiJag1NRyc3r+7pS7we9/+RqG0tLf9SMHR9OwCn2muwJs87Vx5UsdVtxi6NSUBb9SvHz7devaP/uGf3bxx48U3vkxd+vk//7NFeRQ3bs9kZiLgOGgel48kQq7fuFlYXjcYs7y3U9s/UDiqe45gTia7LFlF0Vk6NztoHrRqXWrL/MKVdL5E/W0cHlNBRUYFkyF6cFvqCk2ZxtYzRjHHtA9G4TDsfyp90muz9NPZMFZ4laQIQiEIEjrAZRQtUCWIBNokrJzgTywSJkuomRRR6ZsUMPRuOKRql3CqUn+QE6cQhYvPKzSxIFR+D26iqA8O5MIzZowmXk5ygiM9DnfOLWFkE8Wlq9yD1HxeOysnLKKURjH21FS71UoX5+jIKroKpTQDWwRBaasSGta06yQKUr9pvMX5K5LmSBYbmtYZYmKydHsGJWuSo5UcbRE1hMcw4ZQhygLzkUE0BkUZqaVIOiTCyDhBuRQpGReMZ+ddOSaTwPds8VKyhjsO0RSWL8WwC3wx9S5fT6SdOriVS9eTdycLq10tq9MlcAbEgB3F/vL+M8Ibcbu4VWaeQ3dycsFbmDQwBlGSAp4FiK5PCrswerpb/AnaCWjjAsfgwCQSJkzKiaTlj16IzYlEond7ZB4707Esi8IB8eLG3flqUZns5/PTyDmcU4mFjeFWzAwcTdySR9lNPILCG60Rwh7hntL4Wxzp0kEUAhELwVUuplVlWFd8OGEqvvNujztod8ZdvJPYbqHohGPnElXcObYTLDvsxoRHJ9eG5zNYV4g2KEVQhLCIs/M9Nm99cYxYrlRdMEx4bzKtJTT7uDKAgqVgO4wnaK+uEIWwHIA6fETSQKMhfZQdhKBRxCbmNpubK5SOj6tUNFqvxheEbrzL0JTbQvpL59cWwjG4jyA24hm70NmCQpes2JK1f+W0glk60OiylaY6F+bnqAMGqum7VNvmj+vzQNl5WPp2M6ANEvEBklT3NDXwzLFUczSq8mZriytyep6S+rlkVJtUeQLhgjsTM44DVaQQjQoyfN8mdXR2D48k0swXxeWzbmOXdg6IEZYOt06iugVkA3gBc78GqSaDDayF7hHODdW/ToYpNhbWx/RMrUUrHrAIcpOTG2d8KfNqZuipsF9+NiYjaRU8GEAhm+NM4Sl6nGWBOVZiFnugorVqNRkxZ4ajnDarR9v1Ttct1J3L9zw+PObMZCsYdtiu15rVevA0oW9y8dSYeLvQpmfgl2g5N4jqrDpzTPi9tPjhp5996bUXV6+usfyxv3m1K0oTtTAnA8ZGK8urWtW39mvR8qE9bFUVChyDgQ5DVKh2A0M5vfvg8feqR9duX62UG3juyVl3fEqDLUHPwEuWTaSsDQaJATq19fRpfjHHLmI1p2cMx1tcnpxc4wfrKCvV7nOOwb197+Gz5zv3P3s86PFQz+kL+YMf/NKQwu989ztXlAmtGFibe+XLXzp4tvng88+++e2F4nzh/i/f/ezTT8ZnZ7/xG1+5/cKNwvzN6VwJhcGRD009v/v54LR//c7Ld164rRRrZ/NZ5fmjpZuv3Hrjq/QseXvVct1mF1fWCJbs0sLjT9/d2z+cv3p1tbR055WXbaPbrDfqx/BSB2/JDBRe0quncdjsDCVRPz754xQoTMYv8DhdKFFDO+3jqBcQIe21pmfyOBL9btiNvonmgRhaMr+xjOUMeprJp2Vp4clwyMeRaBR5+KIURWs8X8gj+AxNTPwArwv37qCvA1cocxCPjDlLZYvzTJeF/AJ9bS5yzG8TLnjDqmSNpOgs0Ct4ru8JEwgeGmw0sdMT1Av89rS4AvolTN+BB16igvBQhqjD3dB/UIX/ICdmJJ0jhIpiBlwmitwog5wALj4Z9fpmspwNoFUrU1jAkIgcLb2564KDxOQjjBhnpj6G3aypCMlH6bUOGcCUWpqasqzgDKHayxO4EP6Prj46REpF8mHSNxhf+AZ4w1Go34PhJQX0MIGjjw5MccQosbJ4216iR01QH6tfKpNebKhYua1UjWDMpGQESxUfSvwJI5VXCvPSux2IiPBw40U3DrZIuIzYBLYw4+k8tyL+ACGuprcVXzZvYaS3KSULCRKcwkwL77ku9GvcwuuWFgdNmKTM0gqjCiCBMfh+vJ9sCXOIE3SeUnfiFUftyLhSkiviXf+wkthifMZVziGAc/nlXa/78/IVl/nFd+LHBQkDujx8BUhf3McOYEhcltxRUDE+EKkkcaXLDPuhZWb9oTNVtAQDS0UKEeSkmyYlKyaVhjNM50CHYPavQUYnYAUHwrk54tBjK1F36clYr4RXN+5iMjMmzg9pk5LRZrKkzgkLhGZqDa4WEcP7PYTGQpkQUqB0j43VxO+STVFitLmRzC0Ej5agW/eEV4oKB1zjkkwVL1Uap4U5WfVj3d6w1utHb6Z83oHRl8OsjahkQDS+heC0yOQMI5LuC+HIcNKHmfowOYh0K4WhYwhienz6oNVhJx/WmntHFc7T26tLV9ox+ZcpuH9cfel0KJ4nNYmuUK8dFXJLQXgQ4sQ4l9OJmZKNcXTwb/Cytts9M6SAf/959dm9hz/+yTuKUdT6D88nmp3To+M6TcPxNKuV3e39zz79sFGvLby2yOEfTJC2ErJymjjl+WF2pOey7cP9w2fVSn1vZuZk4vy22p/S3PzTuw/6rdbK6s3+aV/OGDlNSjspR1lcWhn0Go26XphdjEiODA8e2dcapB4/3tt+ulNays0vLz7afdinw0+et7pn+gRDGknQVEPu6q3nm/MPliJXmB09k1FyKfdpMr04NV2gAU1OHQ2bteZhFfGXq7VnT55t7+wjJhPPGN5bVrtXbZ8ONhYX3nzzpbe/8eV8/uWZF2caTYkVvfxqyUhbyVV6wkkdmM7NzJRm+4O+Phlw4qXXXzFa9b0P3qUnrywvCMy+8eW3+6/c4bxaLBSjofPO1k//+b8QOvqbv/d78onHJzufvvPJj370V+PForrNP/i973/7O38gUWcsP96q11vVxv7zbcM7z5YXmu1GYWmJS3H76eOFpZJ4bE6iOMZ1cbGem6t3GofbuxlWTgxR7xeK0i2jNyl7jspzeFgTv2DPCtlqFll5/uT544dn0upD3YgIUBjG4voxGCbqJ0kaaM+FIuTAkyhQ0lfyoh3nzLRRSvrEIfOBJg35eUxHk0gjDKcmo3RuNl0wahz2qmSONovd4diUTpxY+Yk0HjpuPxx9UmlL0gfCn28SVvCEhm1AdQka3TbbEdpMCIVnC5kIeugIpRuSEM3ZULkRDbBTbUrmHJ11+q2u3nsQrF5uTmcx2lFtd29aX9PicnXvcHFj1fXa5wgOz2+sdtvl/qCme54C35nxAkdNbmG2fVhrHFTmipl6tYrstRRuNhrTggxpbZ/TCgwW11ZZ/+XdcmY+h2LqFXgSIhNqRfxAyx2tUALtiWMNvSPKZlcyFGFimOEn2qnGm3YnIhedV1k7WJYuRNqrTaRPe4Piwnww+MRJRFWs12v2j6lIBpOdMb9UJAs5qXiKwGx2LislTJCT3wM8qfgEgwPUvAFjx9TTubxpQE5v0JW5f6IxHFOFlB/yARH5Cl97vOGGkvKGsphPPN1BY7HkukUmlb0RsyZbiQtZ3lHkRGrRP+XTDEdiHmBOkIQ6wjcXOBRBW2YK+5DAIB4UkYQDR3Qs9IlElHst0vGw70snpO3iZFac2BJT3GtRZ8MIwmf5+aEVo9AqIquF2XlGOYuchsTtM9l33tXjfkNbJEXn05q7UViS3g/hzdb+iK47ksPPHzU+NpeJwL8YGyfEFAeT3DEtqYjMyZno3xPzDakF49EIb2R6ndM+NbZUSwy5Y9E+lMTjzCJEe+DhaKn5krhou7FRnrqpU8XBTkAW/dw0rcMWCVzCGT3ZnIBBWO4yiKYmGtzQp4NrqYIY6WwhXz0+avb7K2RaSIyw89CC/YZgJglC8Q/RF8+P+DNmMc7qntaDaDSBSjqyoc/6hWxhYjpTH57u1dtnEz1Rh/1WeICmO73CbrncrjPHr9978Jsn/ampfGoWh73o1GvRozI9W680a2fHDJGFq4us/USHGc3OzT64W909ajZOUtvlzp/+w794794DEBZ3Tc9NGaN0XO5tb+69VCpJMf783sNPP31SL5evr1xrlpvXX3Z2c+ZWRRRUtIQJYM5o/0SQQLvPR59vkbtXMh/9S7/7e6uLi88nJpoHB2lQy8wFQZz35maKIneDaVm6pmjJwrpoYsMnZ9zOMHcmPyPG9eDJ8w/f/+TOjdXSK8vhRu0Nm83upSZB2ICaRIit/fo/+hc/LC7N/9Hf+f3y/iFihgaTUxmFQ4F/+NRsXqqEyaek9WyuKFvXgL+9vcOdzefXr972/sFxu15//CC9Kf1hbbn00qtvSFK6fv1m/fCQkkwrv7Ky8s4Hn9794LM/qX/vt//O8uR4tn1wAPtNuK00Wp98+IB9UcpklxaL84sL56Ncva0F91khV1pdvrq/c1xrtId/M/XCnVdF6oa9mcdbCLf26PPqamnlu9/5fb3ejWjKp/N8ztBQuHKuwMU+v3rlKl9cKVPUUIB6KQ992GzMmNu1cWvQqtXXG/PX1s4EDsvN4to6f6ZuAifqd0btVyJjuiVKnyksp2box5JDpEIWoK+6KpLTmGjxaOySx3902tNsSVK6mjp2Ao5weiYPfVgoGJEz0RmEN1ei1+SFflOdfv8ky/N+dlo7qjKfxG6Ydt1mXxh/evKsdnyok+zM1JjUlaw2n5Oz9eP9fqs2v7oOsOxHVI+5qyXHOE031l683R7w11Htz7uns4tZ7ZVpr7P5bKdyNHFxUlq+ls5lDx9R2tT84H9jK1eWpiZNtZxYvb7Wr1XG+52F66utzkVpff3anVvHu9WVqws4cjpXxI0mBlOL89nZ2WyrfSHvmQE6jhkOZ1ZeuDK3s2Oel+hT7ahevLrI4yfup+/b0sZVCmO0BHT/Vhu7lX8pQpwtqNaakiMjm4vSga1TqeUaaDk3yV0TjRzGFPfJ7UKlwQexeIM6SNGIs4eiGhlmOF24aEJUcghFljgTVg7+3JSEdnFLVubc3Bw+LqSMFxDT2KlZDOayaMbX6nBb6aOuWI9/Tv75aFxgbVqleHS50e6ZfkC28YTI+3B/5bKmvQgd0LGlQbi5AXByxroGfogj8inKZJdvbCKIOeFhVDHGQjmF85JoOIgTdsysbXRbzdDR6aNwRqqQNrDUY4l5s5d9+U/Fk50mPoqrCN6yCInB+Hx0z432fgwPKopWuJPTUkunB63GxfjpXDqrt7haVDKVAk2vp6GMzc5wu8SS+4Ozng4xa1Pns51aRS/Qbrs1bKsOkcLAw8GxG7IZnEzIDqVbNQBHq3BjmM5iVzxI9EP30xcqSqbCORMZENFfiHdN/wZyUOg/A2WTg+H9C77LAAUdHny1YxGnCQ09zKTsXLavZcbpsHeSykxH/ppkU7kWTtF58ffYKrnHAiC7BsIpgtNjY6bbWaTGApqmSPFtdLv5yCbnpgijl1oUFlao/GEA4P70JDQaJhiBANxs2tR4tdPaO67Z6gZhOzV10GjuqeeaUKV2PgSEVGqPufFkT9eOXvf8+t0H5e1HhVU5YGKrJBozj0RG3r2d3d219ZXF645bC1qmfX82n2nqHLizz+jZ2dv/P/+n/1lbKyEM3ZapWGdn9x8+eOcn77z0lVe1buagb1Xqzx9tb97efuHFl9RcsK+tmlzEIy56rcre3vNnu0+f7dGI1Oe3m6lP7x60a4PmVF2t74O7n5RW1y2Z+TM3X5iYnVczXFzm6pw5GS/rz3EiiuSsZsblZPDW0Vp295qPHz9p1N9ebLXZYmsb68Ld1uZMmEj0SNAD+M3tY6UfxWxx/Mp476RP75uYyqmowllkeSLsUHfniQE4Ls6vAXKqUu2qkVksrSvc6ZHxTvp89Plnj+5cX+/WO7kbV0o6R47OKIe66T558nBze/uTdx9c3Vj85neOF2+WFDhzdrOc7n7ysNNo/NP/+p8dbu/8/f/p/3DQbgh+sNka7Q5EFzFCFW0+JqM3JyNbMMli0E6HqXd2dFQ5PthZu35TXFp6DNxbWF7luIS2xaX1XHFp1N/BcXCFSOHVzac7WHp5XW/5melBNj/SFCw1k+vUjifmCuENGmiloDNYRrrV0Wh4sPP0VnFFKlx6YfWVrxTmiqXEVB9wE+ngz/qNxCfEGp2k+p4YOisFaBJhK/8eEjlqzYurBpgobskDeK9aR21zpRztcmGjMxN5V/P0tfZhmV6uLHDhoMKBrUgqZpZR5yRxFysojHSUPag9vZQZ/f6QXrNe00f5tFthAoxaTYhkvmN23hDzIvtDNrWmI1Nz2VyW418618LR5v2F3E2iUVlZr9O7tXQtU5zrZKYLq8sThbl5iaYYdXp8tlgSrZwrLkwWcsvdK6f96mJu5qTVT9dOF9ZWZP5l54ujFyfSuj3cvCY9fnJqdu1Nr84k08nwazPT8PDB6osvSXYa9durnVsT44okov0eFOGumc2F65xLk9rPQUzVp0vZYDo68+B5dMyxM51TOwKzOmdkea6onKJrgh/UeQopfT9cScofsjm8gvfKgLgo0QfnPNfc5GmPxSgQiitNoudpmUyZkqHO6kgwjeh97pzCzPVdQt1ps9mg1I+Efy4uJBaPuvom8xNmDTcOhh4a5jngi3DrwILLBBL2TvKLRSwdNdiydnuswvC3IBh5n7R8UmA2dHhc4DzyBoNX6b6LNQ17/jxPZyYu06VokLbPe9yRFEBIToyatbZ2zDKLNO1BdpisBs1y9P2hQCR82BSLanOuJOIdTk4xjOlsTPuRsRORDA/WuIxM5VTvnhUXVtXYSoY7TE81ySXV4dFwl8AI76XSl5ABNL4cFFLehT2feDSso1vPCH9yDGn7R+vmU9R4TH8HckDusvbrMVyJscuJ3j+XZsvBqnMaxw/rpcGU5qDQtDGJvWGHovNC4dsRHw3zVbqjpDCVxvATc29GuwUduLBtGVwRTOtybYzLK5ipn5xW24NDg1qmxpvD4X6lMru4wJ6EGPj8pVM0nCiRJsVfl8jhROTQKViV5AJmWm32nx/VdbGbSLcsbKdeq2pmAeP4Z2c5TLSrnzrsaNcXra0ebe38X/7j//z6Czf/9r/2dzIlSaqLp+0efK0dl58+fIghXn/1y3Ancit10BaUHfSOj8rMGvHq4TibSD1vJJT2RGT7J/sGxyceQHk1rXb7yfPd/eOO+iCBvZN2B+shtLqdJrWs3609/OTuvc+f7B01pYHKmJ7V0qY//v5nT5c397uj9rU7L+iwsjJ/le9xrnA1NZGHarKFiPpOlfp4KHTPxdFyijy8koAJxtOLRr9rusVSvSaOrUd0ops4eoCCsollplfUKLW1e/DRO5/nlqjixfCJT+cihZDrZBjuCGas7OKT6GCsHUPbCbZYPIdHr3TMGe0NoKRRTBcX+we1vcMj/jwq4PBwv1JrGrv69NHWn//lu3u1wVE79fDJoQg/p8Kgf6RN7qDbOumQiQ0jBA+P2v/sv/snX337eyvrJQvXvIii9ODz+wdHlcag/86v3p+eKV65crV8VO6S7J5xfqa4RHdTnhiKWvn4kOd9fmGJEUtDn19aOjvtPvzw3Ua79crrr7pZaXWBH7nxdLew1nvw4fuVw9adN1/KZnO9pg401cbx4e6zZ3feeDW7sCwZQny1ur+5vP5itljot3vpmYxjPd55Fmz5fHzl2k3shYEtL2TsbPaCmjA9ocV6mOzyN6dLRqpgZDoej847okh4MXTNLE/NcIXRry/mCxxg6r3D+Xs6pyWHkraZ5am5VWqFMbnhxHBOo1E6v8AXR5nEA6dLkzwlZg+ipcLsSuBU9ma3edgdKy9dXcFxYtkzBSG50ZkO98WZuVLo08PO2clMZukNs8rkFx0fHxuqSVqVn+zQeaVvZWevleZKvPD148PycU81+dhsfnqiOJWfJqOVp5h6O1uYu5jM9E+Uh2YodnRnKz4d656dnOSXViROyh2BKuILlAq+eWNI2o2DQbspiib+GpMHeBlYJxmUjf2KFGAh/DPhy4n0Cm56vIw782SAJejFGyVgoaSmwQ1LlZBEwBGwFGyJCHKiEDs+avAe2Ykj1o808T3NLCxk53MCR457loQfBWLqutfja+qddk4HrA2TYfCZcWOCQmvmLBEACFjSYeTFnRt8OyuBSAx7lmcA56Xsy8EjXxkL3VaDXjkuDzczfW4mgRifsT+UYsou2dVt8kSRWP3G45n01aSJutFQCvFkQzV4q2m+3G350rIqa1UslXK502wUV9akW8ukz61dC//16HRuIjuolC11NqvfTHRNPxmaKzWXX5ZgmuWOkUiSK65pYGyIqNpd2qhgHkSSHwFKAifYwKDdPZ8Zzer9pCuGDvyU+tMTDWychJh3pPQEn0LiZDC1jkMORDFrQjPpWSIMaEypLGG6jOAsfxm/WIwAc764LNcO1VUyktScs1lHStS5hguMz59bSyise3HBvJVESP33j74pBNnAQRLrjHsreLXsmtFwt3okeVbyIF00kqjC7RUDdLj9mGubtbb0qUqrizsTSKw1n5PLKbIaUtdDrZ/qEA6gIBy8gTOLU8Xmao3ORC7TORmVu/26jsXTqVqUI6eEE/oy+A3iRul8oSHdJMTSIEnOwbOn+93Oj6/dffDKS3e+8q3fyBcXDss1g+Pq1RadNx8dwP0XDjbuSvyrVqlTh/sUQnYnDwzjOeSnlOHwTGnNyKqJhOVB7+DwYL/SqOvv0+GXKxOujlmzzfOTk7OeEZNN8+R10XJvvDc0R5Vop2f/t3/wD964c/WVF1965cWXaRAc6/NLpcn0ykm3LvDJ4Bp22tWjitnW8vND7GnzSxThNEA8bth9b2d//4VXbo8PTnefbDbsBZoFeXEWhvkUHZ1Sqb/44V+VD3e/+723/uCP/phcxEwwfHwhk8l7SuSlSp6ZHu8ZdCDLMxS3VLXeqKnOZaI5XzjEZ90dCYfT0Ng/YEwV2zvYfbT9/ON7Ryf65siMEnbvQnOmLm0p1W3oHj18uNVdujJbPbyn+WJ1v/fv/c//g5nxtPiPgJUYdavfvfegejL8SGM4HPOweuxxSrSlCVQaPXEIz/J49i+9eEE3R+gQ2MDi7T78/POdoxrT0+oRyeracu2gqqXSn//zP+sqqChmb966ubR2hXNVCenWs6f8oTcnjJc3y3CQLd40dN24qiiKyk4ebR/95M9/UFzM3L7x4umSvK1eZe9gYWNd+wupX5SaMxNhT/sOX0Yir7laqnavvfPknqYIi8FETqYjZM5jq21ZS8225KVwSKvLp5P3Dj2U+8pp9mrH41EvrXGjarJaarzDVSQFQG9iTpvK80466oIzJxQXLiEJy6WNs8ni6LRb3nyoFHs2t5KeWwzDjm17muqUO1qqlQoltN09qeVza4onoGz16PTq7Y2ptIrCRe5YceFRKj2czM8QTdF2/9yslun0fOf4aGZuAcOV5nF8UBmenuTyRXxhZNQY9UlxtdDd4BQZ480aiUsvkWcR/IUX/XygeLGYuz49kZmeylFOe519SgR/NxhBPefGBDUjLwPPW20e2rFh76jV9Lv8A2p6X/RPNqFs2ay4/VSn0xgpbhpPC3NQx+U2+ZrM5bGczOL5+XGFvq7H29y8NqtaQxVgrJS5UXgzhh0Bpal0MV3QGB/HGPFCWGGvx2XPnyY6Qg3GGWN2QLchXD95lsVMeJMkA9KpCePw4YxP1I52CqvX2DdcItLbmI/ukzaqUxPpi7PSyu0p3urSi7wrScbcvr6NTENdzsWNRdbl2qXNNhsOy4eygg/PRh3lNp3uyeLVdQFH4zBbYjCjs2mtkManiMFWu8Pd32vI65hML6T0fqHcLGgDPj5G45lfvzopu3A6EwPxmp1eq5uzd3XtQkO9lgKvfH4FZtKqsX1OGTRC4SahcVmZ3jxLIgGMjmw2KxeEASG1CDOPAI14ZBQ2TytnoZzLMSKWYyJfZMHH6AWQCXFMgJx0J012C4Voot1s6xB0MhnjrdyEdcDUE90M0GE0zZNBsydpUtdP3BZz9E2qqNF8Jp6qKEFFyDZQl4aHPzCr2yejnVqDcGoPo0dK3oGHVRV+ntAz2CRRJOnmRLnLHaIbSOrXtPmig/+cspJrHtmJ2VIpfSwrWv3KOCK9OCFjT8aheOiEjFRRHB66mcws79PDzWMTJH78w19eX7u2fIdXMZpzMUwRYEfD6pPe9NQcrVO4qNdql7n5pdaDsbw0kR8owzIAq4A7L9TFzvPnNQ0ka839g8NaN7wF1WajfFjFk8IvGFA8a9daB8+PxQ7ETgTMmqxFWDM+3u6ZHVZrlWtvvPyGCPTS6ryoSqdZyQz3ZEDP5BboWGom6IhzprIAQ0Ca6zLmukR6wOnowaOternhlaMDg6cqDhQDZvSBMriR62wICed3H+83yuXXX7/D/wnxh+cDpuVcroSjxdisCLPDpUjo4BbSlNeTjuv1o1oVPOMELMOZyP3VRk6YgZ48Ntlotd/78P47Hzyo05XZGYkfr1ZtgIrJyYJBrCf/mqep/lFLgl714+eFwpIh4ecZWeeVuXxWDGb34NgH6oPRw61nbKrnRzUAdGYArAp4Z2+3ulfGIpfm57MzU+rZxDDO+n1ofbxfvvv5I42W2qLh5yOZMC++9tre888/e/z5n/3F+9q631jfKOVzt6/e6ZXrR3sH739892fvfPwf/vu5V7/7zSmJPoXlOBroDkDTc2pEn2/uTExdA5NQSy+GW08+O9x+8vJbXyleuSH8yC90diJaZDRNHnMUw8zM9rLZpXx+cXxsOlI8iTXDeqWPpaZbjRZ7Yn5J4plmTQYz6YqhHlCH1sz2p4+UExXnS3INuBMbtUPR7LzYlbKe1Pjmw0cS2F9/6y2pXxRdjCO7sAAebqBQ9bzRUftk3DCJGJWihl5qxlRakS9LjmGzciyImVzx4vqtiWx+Nru0kTpzlg5UCUGR2TQzmYlMnMDcM+r+WYECeB4Efj45p3pgt6KPRLa0OCEiHhrlBhU7e80N5EL0JrXSYAGhYF6G6flMcS0ptpmbGJMYysGu28xhq9bgQZorTMMrfJDGqFeU9laUlbAVzIfJ1Jupo+m5QoEphnJbvfyCDuT2SJ2ZbpQPltavMSsgr96gjAqdVWBfppRLp5c6rf1n99+5+sobpeU3x1NFr/O90aYGY221vQvTpXx2YW5cJ5YJ8kl/EuqsGND8Qt6oo9LyglSZRlNLx4oOoLnoBzN5luGqGoCYCa+h6BeXBs0BdjGhDc7ZoLr9fGdvR5mG9kevvP3GtRtXk9kv3InAJy3AvE+j3zC/s4tFdBlZ1DyrknHU0i6sT0T8qNtZv3V7dq6k83JwLxUe2XPJ9QtLaxNmRZy2yk+3J8ypX7/VreoVNiV4ffz4fm5p3uGu37rDIomYsewbSd4Sok4bZ6eLo8gUIOTYMHlDzeAeQp6ZLWTyC43DCl3XcsLETRrrUfNxUWzL051b0JV4b3oykn10vmHHynmjPOGoDAaqv3fDrAvWiXn4ACWet5MbK0nZ4uiJnCRnRaXgCAD9yKkU4oBQjU6Hm00+E4uQVCBCyZowL6YvcKViLp9mSNJqXDouA4w+7i9gizAyB91klpKCc5HfCl/GfQR/lJwWzmzPA4lQDE2ImuHFpopoe9yg+J+ezOWy+uJfGBQ+Jnwn0cDN1KmSUrYB99kMpI3QEwOHI0ehknEo4eN+/70PXn/h1neL88urK08fPD5VednpGPgVZRI+OhzS9YJ7G50zO53TDVx4itYQybIBAUsCK8PGz+Vw9gV5U9lcpEjzU1br7Z62/O3eRDpPkND0WU97z59/+MsPd465pGdO9FzL6tM56UIJAs6hpkJtOFq+dr2y82hkb5WtwXl2Kr/QrrfqlePOWRfLJrWDL4bDEZziRPl2ajXZp1UOQRFMc8bTwoSYfuzYbcO1ZxiGcKVUuqNjWwyL+/yk2Wp1l669BNQhc8NAiuwGE5scrByey7w1SQ4cfzQnJxY5D6en2dV56TGHO3t3arV6pbazs//xZ/f3yjUBEpYnI7vW7B7sH6hBWdpYaT6p62F9ci73ik7Ao62rhrY1nZ//5Eff//3foV1Ujhs0fYpoznD1bv+9X33w8Sf3dxo8QG6mLOW8Vh9+Kkiytf36d97q16si4D7F6O61qNj13cePnj87kG+xt3PQqZe1BEVUAuzv/vSDe58/N+/vjVduvfbaCyEMzy969erTe0/vPn26qr3ZzaszpdLajdv2GhpBLneuXLRR/fC9z6dyevSPnNTSyrJw0Y9/8OePNrd/6/u/s/bCHTJyqC43Qpcy04OuTKLXZ0PhMRRO8dRLlRHeZCqfD/TT6gzPrhQLmWJxTLQnNI8cUFLWDBF68OTuiy++cm1l9WJpvdsZzkCF4oLqSC3tJc2kp3kG6OyDJ/ceZ01UKW2c9lrV3b2uEWLN46liobQGB2YSk17L7ltYdJh6slhzi5IOKISZvKKDC9HIrCTAMY38pD6Em960TZwryjexZzos9qhZITo5Y/E0uVawrBsrC7ncqte4S8/1tINDYY+Z1pUXcxKJTdwBchvjCxDCf4UMOMtVo0TgdmZpfXVyugCnlF1iLZJi9LuLD8oHlY0yGvHCZXJLzUpNNAuOCnjzxktantFWEL5OaCkVnuhu++h00Gy3ny8W17js2fjjhSwHy5xQVkz9jNLVyN7BEiYml0oSvYg6PCW80kwxdeCRbSRp+ny0cXOdyNfUHTXniyULNftM0EIHpvMZ/Sphv5rz0Vy0uyphO2oyODSq1QZKqA9Of/HnP1178fYGFkOr5XnVqh2DpJLqZSiSqmwtoMShVEWSDGlI6kCuvnhLZVs6tzCVoQRoxXykwbU5EcXSYgzzCMVsFIUMC3mDkWcz2gFXzXlef0nsMK9Bz2y+xJshQIdL2NdsfoH3dthut+tb+ZVbmcw1jnH5xucaRU1OlVZWFpbX9x4/4cXRfCVUNW2hjM023iCOUm1DlNKJuzgXZ8aD4cMMVP3aOJgcZcR76ZlJlhNhIUUAI2ZsG85HJNASuoIhfAU+hlMolpZVJbPIHX04cEjENdHbSSpSIDgQf6pgTnxPukYPo4QUTsmNEtaUAex1UQ1rcXlUBiRVAhzcveHQXG6xCS6CyJqC3fi4RxA98keT6Q2YlSZ8tOlqq0/VVGYcgkeXv2iCwaYjtiKAh99boPTWeF4MiI3BbxieQ5IK+9ndxz/6y5+98vqXrrxwG0Kwiw3jFeQSQ8fxuHnsJL4BDIVwpJGt9Hz78hZNOUQAmjo8qm6Xj7h62k3RUMiR6lPGbJ6BPmCBcrQQT6hzYngxPKgcj8bOhYUCZxMRRVAGTU6PP3m+/X05nCtXJ4636sd7k/XpsawksLN0ZrpjKiHxn+QGWkxAQhTaU2IR8O6ChjLsjrSvkNHVihhXHHlCHOQzIsWTwv0nyeHwuHZ0dDSVZeLx4SWQZyuQlYxCKGGqhWy7vgUb7TCqNetCrOyVhNTDC+Tk2dw7u0csU65DpX87ArU10+QlPhqenaoqmWs3zkwWWyje+8HHB+Xj54dNzMLx0TgA570PH3zlhTdy/5YEp6lHj57uHZYTZ3BoLHuVrpQumIM38a/JF+QFfLy5HfMBpsb13BZMhjnTM4NuU0fIFQMw6vWGghG5oY1KeX31WqoTVcw7zzahg0ywwyp/0qEIo9Hlhkixvo7KJ3/6T/7pl9/60u//rX8plc0OmuFh1IksmrhJgD2ubD3cfPDRJ6Tg1atrHCz37j38+PPHg9bwt//WH8tfqpT32Uu5/Lz5GsHnJU3N5TjS0GlgvapeViZhPriYm55dW1ofU7o0ledz5pGYNmBcsFrig1YM4rwsQEs/m8lPZcba0tS4oVU+TWUm526u3Zoursx1T2vPDncLz5ev3OwcVp588mhhZeNwVwnKB6+92ZtfWZ0sLo5PFS7G1M2Eue0wZRvTcjRnwj7Y+1QlhhF4wmHubulTqCNqVsO1KqWJi1dKd6S8n/dPKEfmMnarg4koDIrGT1iHZHvnEDcPs9Pd7BIzwj0gbRCHcxVChJGwDE5qNl7d5lHgQ8AlOGG1D+LJp3lFEgGdRCVjLrN03hFimJ8tTBxtfj6/ekPPH/IsUJT3PcvVQLbFHGTjAO599AvF5ItzpX6l9/RXn6y9ck2MZGp6Hk2jQnQcVMqPFFV54eaKnaJP9Z2oXB+A7ILweqdRo38N9cIXfe23F6+sUuJkBPM8zywzNRSRICQ0pUyBXWW/ZqNEWlW1XDWU+Vzbwmq9VW70mrSEejZpeho6KY8JOU0BTugnqvsnilEwDnbKReje0p7YRmOz2L2rJ2cWozu7UrXLnH9sYWy8sLIeN5JldRJ9Y8uHlbQOc/m14Djnw8L8Vd2eZO7rNBdSZyI9npvlkFQYT/mUrunFEHvEVzYXx+Ew5f77rI2E59tBBKOgOeHnkcDvSBTD9oa4gQVCGg6m6DHBgSCuzt8yFRGgCHARgZgedBkN52Ym6NzDLhdfm3Wg9upU+EKrCUcWnmwcMhwOykPilCPQEeIg8CYyng0VkoI3PdXTUDGK3xBO8LwveE+S3RP2TNgKI7OZxVFDtEZykgwxeIXpf1H7EhJFxFKm3rTJYhSsCxEKUrDapj6KI9nblLgiBTicFdxdsCHWgpmH5MY3LRJ6wE5xc4KbgH/0cOto7/jmV78szOdqed/SDZiuoZH4dz4meQtXxfVCSWaOCMaFYRQtXRCIvTebg2qLudk43N0/OjzwFF+0iU5D3rFsMJCfZv2GqiLRjU2D4bAhDKilOnkkpEiz40a7B4enZ1wr5Ew/JMfEZEEbMisV0QnJZwP8sZ4c4fvYCtyPwm7y9azV7HZrbWX0W8926o3e5Yk4Kp4Mi2UCAQF/nzynBw8f3P34XmmlkAnXhOSqiMSx5YJTqJuhiLdb7KA4nVSqclC7INFiDgSx56zHas32o/tPvv+NN6O5y4lpKLVWu0+NUAUKwcTjpQtipJgL6A06zSdPnz7fOSBOQwzNTvMYNvsaI6sHkWB8wV3HUBqQ/Mp8JKXxiccQo2AlNAOY6rl0NgHcE9EeAQdd8JVOjPest1Gtix6L/hGM+4c1qqwBHoc7u9t70sHapK/l7uzUDg+riU6gcarW3zOMi52d43/653/xm9/7rtYggvMLcyunItmNcqV8dHrWqh1VPvr4I0D96re+kZlOHx5U93hFzi+yy7mb1+6kixMZmSqClhI/JBp2OnOlBVFL6L28cS1GsEcFy8WAVq96stmez5WEQVrHBwdPn1956UuyueC5mYPz5rroP6Oanmk26G9/+uFkbv7GrVtys3GrytHR0ou3cWAFcF2VJu36pz9779OPHrz1Vu6XP/7V4o1CtdV49aUX7rz19nkuB6Hj9FB8ICc8Ybnqb9mQcS1jpdfDB2ZkG0Fs/iVmf79ap1pGcgsOPRhq3cy4B/LZhTn+iqXFkgaO5Af9ILt41Txd5JKoE1QpjFIpFmyChc43kDD+iHJoNA4hVcw0k5ZAHLDjKti7UeXQLS6uiOCILjYqjKTs/MYK5Md5XHMxPiugkGCXZht0Jf3L0piJm9Ow8eIn97YK86UvfflrCoiefHrv3pMP3v72l5duwPwkvoV8gqThTBSBBrr4P7IriQfiRgsmHgNuoclBr9ytlD9//0O1G6rgS2ur1Oexs5P63pOcDigIM+g0bFzkxatW398/2nny4P4D3X92ZHkflN999xP52StXbtkyliolR+M8hhQGFs4WCiyKjTkJqBpjp5zWCAaZsOMpTqESaSZEZIG+gkWgEwKYWppOR9xVnVa/pTgzCtTCZx0JNKFnR7eatoOFWolHnHQUfyokvcusnxiyXmxKSBALp4+r1MeSnRnlaXh5UqEAyjE4OR8mM4LADGwBPYY+BlvEIuiKVk/9Y3Y76DH97cjD8I7K7B+FCDEK6FxzQ6gu1DE26xp2wXgMY1amHEDH6G2cJIjvcQShdng9GIvsH8viv+hJ/EHcfN+hx0JchgZPI54a6aZei3EOeQEAkWvDn+d4vgTweSWIAR4W+6Nxh6qOG0cDbg2vTlq6nqqCGaWyaRM8xMo9WYxcV3GM3lZ9FssNK8I7CdLGT2KATcQ03zo4OjqswmQBhegfxWHROwmQhUEEKFOa6pm0ZuAzSZs4YKR+xq0IaIgXMmCYqlar+zubDx8+Oj46smmnZEfHmjbr2OAc1MIYI1vpsCglUQm7K0uEr5zpsC4k5Fmq0u7vpRu7h/ujZ/e0k1OAs3hlQ+qVR0BMsNR4y5C2iByH6on+HHxgP4gLI5ebfO18IychfUMB8TIasxaE5DJBBLkzM7ju3cdPHj6+f/2lhTvfuqlkCdq0a/vy8vRJNYB498nTg/3DeqMdnw+n5llT13bx+YBYfLU6JyX1wudjO48fVirHT588298PHmMsU3QSBH29vmsV0TZxb0llJu20WsPc1JgynvPTsWIxxy0quaimUgknHQ29HdnNkWXOjy1VP9EUeep4POkBYyli9PGznVc3d+D6wZ6kz4zWv8zSo83t+4+2avJhU+fR/ajfe7C1+dNf/PzR022TPRJxfRH5RR2W3oVJPm0NHpXmqd8epH76q4//yz/9b3/jW79RtJmJySZXFJ9dp3+RnmXQNN7VDq33W3+0p8vhfrlTOUltbR//o3/6F195+fjNt1/85ndfwKDNhV+79bKyjnCche8pS9+0Qu3HAnAKt5xvpVNJ6flffvdn74tRGqZ57c6N7OqinS6trMsQE/hVytRs9T/+6Gln8PhrX7vAQN/72adG0L/y9W8L8u3s1hbWKpXd/Z/+5c+f7+7NTcx//PndTDnzycNdEemN27fncktqk0J7iqxoK4HlxgVXteHs1KqlhQV+AMwhdL1EyqNJupgs7MvJ8hj6jKIQhxcZcwXxiVxuUDk8Oto5GvZbr3xzxbZwIbSekBV+Qzs8lVsp0AGzEk4W9Jz8kmjCJoSkC4aCC+TC616zTqWU1Uw5kAk9GpSzmhaMcMkEd0eIvEBfxYjhJ6M62o2enE1JmPY8TIC2PDGr3Xxqbn6sffj82dZnj+9pWXXrrbMx2Y0JpSf2sJSQEEXuAfsjISOEQWK7UBIjx11nKog5Ua125DBksptLV69Rc8uV5w/e++Ar35laWluZmc2FwAEZnCGbPnxn/8NP7372aBNXePD4CQ/rn//gnaXVK9/8zmhWkoOEK0VekTMZqipVOEEDGf3hVTFfZHJCq7r9UUN8d5gp0U3l7K6whKwrto4mMcnorklc+0YvDJc43pov5Ae9lkESxqqhQfW65f19bVMWV1ejHGwq73PjugdeTmZ02jiCG0iZI0wnJEpoLNnXgMqeaUb0PwxENQVRYWliisERIIrScTzT+2hMSFIqasJOwjnMBxASKDEdYix3mjMzmGc8lw+JL5uhmp5xYQjekCXuGXwVV3Ik9Orkas9wJNYZyEHAHbcbuGeHhuNV9qgFJRIC/4vpscGrUHtIIpyRAPDh4K8SYIZQMwRKSGgmDCX1ZKgEdcow9sBi+EN86gpiwBcFM/KZQlOEc6ZO8ZLHjf3nWsjvj+CZVAPakIi0jcpxFb1N6XqK8evBoHLtVJr4IE+xVjuu45WN2oAP+v6FFRE3os9DO0Jf1V/4EDnqptORs8s8ix5UvENz0MH+iR/VK/o2cHjhRKGyiqkRvOIp4f5nrYUhhkM8vPvkyZ17FzPq7E0ulkxpVLFnWb5vNIJocxZwI77AyaZoIsmm9NRrNdU2RT+lgc40fJIIwQ7tMxQqQI/WWqIvLY24nz8+PLz5Mo4RhgwfXm9q1JVOWt41SOZhldwSAwgzM1Vvtf7qnZ9AiDg0bMZpnvItTj68/+Tg2a7LKA/qXOfk2oY9rgYnvAeUSz6+wdGh2kv7k8Iuwk9vEB+KasTB2V6l/Mm7H65clciYZgTg9lNyZ1SVaIUNmEgERJJFS5Nr1Hs/+OHPXt24cvON26qv5c2K56uyUaK1v3tQ76ZMjjxsNITuJZlAjc3nBxLBJIN7VK9ngLhCq+qzh5+Xj/e5gO2Ke2v/qPIf/cf/yfaTh//+f/jvKRodEyhs1IZn2mdOCanubx3xxv6//x//8I/+8A+nCxn1KfvldvWn72/efzo28fu/94d/XD98vLP19Mrtl3V9a1eOCXXpMk4QOJOD0chFrrw+s03MWBeLd3/888il6deatTe/8ft/wvenBEn9sDZrAoy1cufx1k6l0XZmMgV+9fN7hdWrw1q/elTbPmwWtyvZj+4/eLz3/Gh/kPpsr9zau/dMh5Yb66tf+dreeumKRvXBouE9JCVjJ0201vO3N+p1ZtdXlKf26+1o4O6wk352Z5LzT7oGsUUcKcR6YKKWHORxKIDS3P3RabM8WUXx4lSReotWElUXvam3h4P0H2jsF4MUIDiq5WWZEAKgLuTyvWzGUCln1B3PzzTKx/nSaraoi0hXX7zZuebERIY9TNlXpjvqtlMnneijNTNRPd4Jr1rmDnLVmUf2Du1b0/kUW6JSOzza33z0aG/rQD6MhinoL8ytEeEhCYf+gZHAPCLPlCpOX7rmuMkWkbGCdAYX88Wl5YXlH/7lX27f38kUVki1n/34Rx+/805r0Hvt9VduvvDlmVJBKpRwXjY1Qynd2t776NNHahEqZQHN1PZW8/7dZw8//qi0fPjyN76JshQtO3XzYNC4c48GiKOkXEDZSFtHxWqlWc+p7plfCksp/BhRjhGgA0xsIjgR7glnvE+JYqQJtSom6D5//OmVF14q79XLrepP/sWfb1xdfv3VlxW7Ld56W59iFO3EcYUAeyLrsCNB+UkOp+gYZEApJR0ICNqhHg8stNAh41IrTdCEnYJ1uoMPxmni3qKneuX6nCLEUzFL7MKfPGGWqT6BrWKPhDRU8Fn9k6d5rRTi+mQih9AkFijxNphFWIRhVwTj4uig4InW2ioZAGdoD8FTiUtKtn2H0wonjdX4rFB6oku4Js7T48UxouYjfCbwNVwphbwJeTyP9sSAjlzt+DVM0XBvAUt0GPF4VxNHCS+J7YOY/+058S1QvgCOx6/PkInClI5sMd7ZKH+z6LjMHYLx8zJy+QphJgsEBeuMOwW1iy2Pp5rGALai+RAQxT1ZKiqSlwvMOt63IR8017XiZk7otnqHlDxw5BF5E2RhxKmpyzG5s3zU1mf0y996XcSsVyvPTony69YbfgI7VLVB7Qlqx/lDc4j1JEScoqUrq1CWERovVYcrGpqFqCORwqKmkHLBw2PXm3O9srGspQEaNk14PjtX2S6fn5y3ddM/xvPbIEfTw7u7/d6PfvozURHP9cH07LSmPQRLIT/XbreajS7zXEI8FIjcc+Xjwify0AALY/A632P/RC8Dzk7+A1+OlBZSMfdxf3/12sLJgHKaDB0l6YP34P6JxaJgJ0pJY92d9sX9x894tyh5OjLd3FiilLJm2mpZ9GZgyZ6dH3daFEb5quVaLU4LUJUICSmFnjJ5Nkgd7+5v7+wcHsk7muybQD+8ePLo+N3sR//a3/4j2Hp0uPfpR7/6+S8+3N0r/+6/8nbq5OknB9VfvPfxb//+H8lTlP5FsImsGP529+5DaHK8v739+ME3fvP31NOd9GvUKJFGDmuHoUJe2HE4NurWy+/+xTvpktrL9qd379MzDo63JzPZt35Tk6LtenXvpbe/J07ZP9yp7m4+e/rw+VGz3e3A/WdHBw8ePP3knc8f7z07qDZWjmqtn9cebe8d92r7Ld0AteKU4NK7+9lHj+6/sPbyK6nUQhKqpSVQQ/RYHJkhY/zE/r3KYGVpZi6jQXzl6dOd3f3i6iqiqO3vSxa4evMlKVgafJ2PDIMTHOuOmueTpeXy801egU6dR+yYPvTKxETheh5GX5y1cTi6zqn0pm4nGkg4Br1+xTO5XyBjJMMQPad3P/zopNUofm3ZgT6991BhuxK/b/7W72bXpeDOfPbhB6jj6q2XeERCpUiNPX2ypX/10vqNuVLB8KWjvaPy1tbVW7dUpNfKx0/uPwvDrVrbf/SkUq+WK6mt588PtzZvrV4VVjsddKELYlRQNGhVeWCiPfP4WH5xkTYtXGFVQaFSTXnPhXuG448fPdvfrc4Wiq+/8fovfvKLR/c+r7Tq7//qV3/v3/x33vit39eWFdfT1uPe5w8+vvdkv3yS7UeDTOYoTHv3g090+fju976xce1Gfm3pQh9maNuvHx/sazhYWFyfzq3zE4jBBAXKlhycZBexTvnJ9DzjIobUdEl2ungk/tIgTLwEYFOwDImi1nCsTH38Fz+3u4PNvQfPHv7oz/7i+s3rlWdPbrx485sLNwykQ27jZq9i5tAfN8DiwFFkQGFBOhOTAijw6DUYB/JSFazJEh0gAjNYNI7twXg4Mhep4lQPPmnb0RM75IGGQsE5yCt+lZCz4WFCU1zR+BU9HCTJfwpsDHkkP3iooL6vhMBDriTmGZEXkpH16dMD0jqU/NhxKObhlwhu7mqiIpo3JgeJmXLNhzI5lGdplfFx/NpXXE95jqlacqVy/RE7i0oa6ghLhwLOIyT2w/fDo2BZETENnIzPgrIVJ2AJZSnZZ6wThyHPOrJbGy1BEEN5Rcqj9WZcEqaDcgj1jHHLyyxhy5MIRJ0Kth0SxLJMRuZ+67dbu9s7XXPUbDEkNNWkobmHzUQddlQOh+hL59OTh72AuNW4A2Ug7kL3neQaqjcbDB0j1Pd39in0G3l5BxhqOL+0nYn+VMmaEw9V7AroAqRaseqz3VXPA1gaEHC4hhAmABhkcZbBvcEaOOJ/M3+vv/DKlFHDzfLW3Q8WF9eHBjW0OrsPnx9s7zdqbZI5mh1wZPZOK6d1pyjAmyBaBPZYWq+/+cKVjdWHDx/Xe42AFOBKj3R48fTwhIoFcETLT+gOafd8Z5EPBqr2ytQlq9ReCT82pPAO+LViWpqhKcFiqELhng7RL98gLLyLlPSocq2xv7m/tLqmOfug1eM3E63iCAraSUxMLnUa5Y0btz6695CeQQwADFOyrDDj+a60+FpVM9VTviH9shxJL6VmqrrzbOc16Dno7j5+tvloS4s0PQgmM8w+8cUZvjLFpe7DCLUpEnZ35+jeB7/cfHb/eGuT5JO53KXAtxtGm5fWgSaLxMh0Mw8Odnd3DjbbTzVs7Tx+vi/2UG03rtzZ/OyD+x/+4r+ijN559W8QcdXd3b39zWd7z7fLw87IZPNc+6L3k/d/Lgd0upg72q+Mvfblz+59vHt02L7g0dbIaryoKLQ3ePp4Z3trt3O8v1S8Cg5IIyVP7KSF3M86TQljHV1PmjUNr/CiRx+9v7V3vPHKDaDeu//46s1bC8WF8o4C8sOFtTUdNYT1Nh/c33j5DVbgwtWl7qD94LNPHj+5p//Gm9dfSJ22u5W9uaVVjKx10Lj3sx/PlOYlTynAfjn/dS2U2KbhAzk70VD67id3qcZvffU75b2DP/uLv1go5lqN+q2XX1+8flX6wScffzCbmVlb2UhcAzKPOp9++ikt9c1X2q9859sGP3z+0cc85YyVl974kpmlRwfH3XJv/9nms3uPy8dREvJk5+Dxg8dXXnilXNV5sXH9xgtGuM2YxydsqVvNhZhLpRPhyZEB3Uh42OoygzSmxRT5IWvl7pNnB//Nn/7jza3nUs6ePitXm517dx+9+cobb/zWbzWqlWxhoVeuPnv86N79+3hlhAoYK1zVF2e7e7Uf/vi9kuL41Xe+87f+ZGY6p6TgYGvz8f3PM+nsC+NTS6VVMz8HjcqU9hPqycQqh2dKXU5rB5xuGvAoiWg3y245l83rg4Qo3J0fAPlr/BRGlJNst3laOtXG0fHh08+fvPfOk8ODWqe2MRMT2bTi3YUvK9dfmiN6mekXyYQeFI4RBFOJkEyEIOhSSsCmZF360n4HRyR5wruDcvBt5lWC5Ay4UEJVauOLmp6xYx2HtAZ8SdQi9DFOfHqyxn8aB+mqjZeQKqHq0vM9CsJHiTVIxa3CaxvfE33cq4lQ8CTuE4wdZwzWGCwyJrZz7eAersab3ULgwcBbXibPU7zhDMOWEQ0weyjcJmdSwBVMyywSvtDwYPw8Wq8wJcgUYPR4bzJiKMdkgye6bbIei7h8ALZI1rk+VmBrmBLhUa5VWTVUTgU77CChMbPL8W87j2aVOiGMIrcHypLRoBw79D8OLIXRjIGzs+PD2v7zvXq9qZhfQqR3uLarR0dKuiK8Lp1pRh8VkbWIrXm4lyxOUWGYXMHHmW0sL60eonlf+fBQuOzaW1+VyW56MstBICQmQJg5RdSDFO4WiwlI2iM5p6Xf7vb2/GxOs2bhCkaS61wZW3W5zTqSCK2zwtjz2dSpYEnq8PkjTabbmrrVOmf6xYR6MqayT58+EnAyHYlYwYj1U5SmgHknIJXl/dkndwtzmWdbzx89ei6hNs2xAr0igBYCUtsyldUGb/HnW6XgBAbqVOBMKPijC/XDsmbdjK2wv38gtUrgKvquRJ6uAsXQhKLOOazUEFomTv+LH/2Mlfb1b76ss9lpu096un+3x9wTkpP3ph5CdeloaW4CQrDfNKZ10IOzi7rp3fWOrZvq0O2fd7XY0ek1vJwpFR9HR5JHT4tZffM10jqVLL/5fLN8pPYnVR21/9P/+38VVe3C7+6mf97cxObe/v/hP/pPvv2dNwR3tJbH8vafbe9uP3OaX/32t6++/PL4bOYCREUJ9kmeo8ebRz2p7uyeVMpv//SXP/xs+0F7//i3vv/bL777yWsv3t57vrW9vStMgphaxreqwh0/2zwuH9R/tLy83D49+fDpp/vbz/sRaQp3YHj801PlzmBQG3zw+fPXH24t3XkbCxmN9s/6zUlO4Go36YrV45aDM7B81B6ZXvDhu+9/+NmnOp21jo6zxZKetfcffNzplF8em7hx/UZz0N97toWj1Wu11WvrQHr/0WNlxus37ly9fqd2tDclwFYSOk7rEvXOD34+lp+7cuvKir4fK+vmtk9miixMlCwpYHenrIZGF6Dt+9s//dG766tLSpTe/cUvVpaWt56A7u7Va3v59Ofyc6fSM5WD6oO7zxgc6dTcK9/+Tu2g8qufvT83NakxxK3br5YPiNFDc6Z+9qP37n2+uVURDUg92a7+6t1H6zef8AltrF893N7nDNQdtriS41Xr6MekdrxWneE+PeEcmG7u72Qyc4vXb3Ooc/AKalboYtv66v60XuvIUK51kGP/7r0nD9//IDef1Te0vHNQKe9p/w6f0ac+CRgVQoOle5XGz9754PqVG28dHGbWrknueu8n7z158nRteTGXLi2tXD8dNpRyDmUldqWOqF3ud4+PBvv7ZrUrd6OFNpt10XFjzxRnaaoQCr/uNeOTg0o3t7QgmaLyfM8UGu5ctubO9rEJEd0nNVVjL76pI8HgcHvrlz/9yysv3r9+4+U7r31VuWjSFikUdhwV30fmkTWiUwCJnLBNwXWi6NLNgG3IDKGE4d04A4EA7fEIcUosdGpWHlQ4ur2oozQGxy0eDq7wFIdzMFxAwYCxVd7YqKNT4EWzTdgNpi7ugq0Ga3Jz/N1vYUtYD1Ec/n0aaGLZuwHCwmW8Q+W3dkvlLy8Vphbm8wtF/VAmhg1FdFiLCzXWMHLjQr+2KclI3WheitXGcyG5CLlAeCT1kAHcbLRR9CYEnHC/uAQ545TxZ/y0vrAx8MJ4JRRlLHgS6VHDuTcingME3pY2RVYmsiNsiXBP+E4JprgHX7Ww2CEJZKv7x8edPn84s35WxhSTRCqYdAJu7vFmOwLRksyYC9RhqnA4UhlcYTRRt3E9mOHAOKyfbW7PF2YsR9WkJKzJzLlckJlZ6dP68tLuIg3P5SFw/R8xXuYOyXdWqdRmFtlKJGukJGDXiYwOjTs8SIklRLQS08+ZF9XW8sVYThuM+aWZi4ljGVX2lj81ewXqCMyYVMWCc3KxzQBS2GGhd2vbdXah/cjubmVxbU07gwBkxBsiCwVCN9r9Sl3N4emcubXpqZOR2YROincO7OMu6iEBjifMOHpae0Qp4w6xKdI1rABIE1ydBRBAE5BNz15wDOztH83OfpX2xKmm5kiy6KzyFAxRxQC/92CoDve9p4/rmufE3YjOSbL7+Kh298EDJKfgRJkkfyNTioEcqXfjE4eN2qBWZSVADw8lD6pquLVdm0ztbO9LSrO7UCkoJ2enDlNl8o9//vGNW1ey2YW9re0r6anD7YOtp7umKhSKs5qCFYorZ/3e9v37Dz79XNj+qMUdxJ0dLjSqxu5+a/N5yx+TH37Uaje+86W3Dp89fvrsmR0bDWdgdk13dUQWgYqzcr2qmvvzR58pcVTnZiEENKKuU3+Esk/Pn+yyW569snW3tL547+c/lsq/emWjsn9MeTIOCFLKqZxN57rlA81z7n/ycK/dKC4W0hNTV++8lJ/b/asf/QSnPD2funFto3q4e/ezz/YPjp7cf5xZyjK0m53h5vb+7J/9RMbtcNB6+dWXJ1Pp9TsvHR0cvPfxgzEJBcpXcdiJyZXV9dff/g0g6jUqj+49uPfgWavZv/vZxz/78U8fHJj7MaWhwj/8r/+7GV3RMvnKUeMXP/m5vg4r6idmlvvt/tOt/b3NzesrV1L1k62HWw+f7M5pSTAxXdk53nv4vFrTWuDsH/zDf4jTBKcmRyvtH/3sHQm1X3rjhZnrsw8ePK4clXUn/eO/+4eGmO7t7T6+d39hoQgjO0biiR3sPn3h5rXlG7dQe3Z6ppRlJlOYLhq9duCdwKmJXZMTP/35Oxsrq3/8b/5JqTh1tLffalTQV9jcmIOidOQUCBLZyc+e7W9tbj/49NPX59Ll57vvv/OpU1hfVfN3tVh4dLz/WInf4vJi1FYZiYKVShFst2hVmrlSVDNiqXN5E9E4WMNqFlWVPKdR0GFtTtru9PDZXYl6T6ZLi/u7hzv7RwQ/zvB8r/ne+3c3lv/8uHpw/5P7j55uvfTGtlSyjRez9NhQRX1Zrihp0kNFxA+nhb7R5gGvGI9IL9LjZqD1sq2thLKbJJApKIteb9wcUuqSuh+yIDLL4iZskmCRSU6WrCqeIlRJ21JKRLcAHigZCimlM5iFR0q0sJagJz9wqeBVuHFsM9xdwZew37Ag0HnIgeRH8GTQFmjNipClM2pVFcgx7jF4arNJ7pjfwEgBqVejgcaFlEeTuM3yEsYN3dJzwEm7TZxGuhqlkxbsZc9I1uApwS8xs1hOcprR7C/CFQWBvmiDw8nbBUgLThxkli89jE0Wn59Wxyd/+terxkd9FEv0psGbKq/vPd06bjRiL9IAunIuZwqRoicHXU5ehwzTIopAyOVzZxed8OGELyrxh9ikQfA6bM/IYm/XqjX1G7QD85FOG72z3l53/1hZTEzuNLozsDBxZ1lW7MgdwoAQXJUyrjBbh6Wks537Sy0OthFWbCLlDEAhfbjpJUGDHH91YWFNsZDZtZIlBajV/RBdcoqiGixy/4KNskhgUmSa6GseTNRY1JObN29tP93/yx99MC7FPYwoQjjOXSk1XWmhtCQY3W11Gq36xYRZrPJDJ8KdyDQDH5JleqLV63AVOYZszNlVHSaDmZhggsT5cBOHGPKVgrhcqafHPClCut3BQEslyYWd04nz/OyEtpfMKZNNoPs4XvFk97ylQ1Ocun8eFRaJQPBxk0wOBSXi2HGKIQ9ZJiK1Id008yAnBZYxmMgqJ/z5uwMASTkhLzDUoX1f9DDgs/PPP3/0J7/1e88ePFRTV9df5+Do3mefcT+wUdbXIFL98/c/eHT36faeUdkKRgOzha/sx2dD1J2nHjzd2n7ypLp/kJ8ef7xzaEgqe9OqKWToitgLgy26jDALTYaKiBaHKyXBici7cx5UuFavcXR4+Ku/YhsdN47LpcKKfrFPnz6kmrVq5Svr69NTeWiqaKJR21e+Xa2edIdlreYOdw5nzyexUQF1zpEb1zSLPvr403uN7mfPH22N57KvvPoqpU/7248/fXxwcLy4hMtMG1r6enf0y1988GT/WFXKxNz486MjVuDta9duvfRaZmFVDvHmk0e6qpDu//X/5x99/P4TcT/Hcdobfv5g872PP7i9/tKnnz04+ey00Wh/9WvfKs4xRMxP3dverr732d3KM125d55UqllYnZoh5YyxFdDqnJ/r6N6DnJBaGdDZ2dOd7Ykf/Xx1mXvp4PnW5oN7jx68d7+0ml8oXLv3wd2P3nv3za++Iai8vcvk2O7Ud9S2vPT176XCe6C1VwQpnWiYm5emqcGnY+Of3d//8oNH3zoqKxuu1I4C2WVCyvL3AYggjupUuDvEAgcnP/35L7VGvnbjxbZEou3dT+8+rldXXnvtYHI0fvfRu6++cmt+fmFGWoeEf555/DUmWShqoiadVY4qElHNGRydheFCK2k0GkZUua5eOVq5dpU7+dHjp1defUVv+f1yKxA5SYa+9+hZrfpfGD+lLcVYua7H8UT6r/7mRG71xusMCH1EjfmggdF9oXR4guAwxqhEBdHqa0kRx2VRNlvWgBbh8dD0NPehEqVxMz1MvYJH0d7oqyFUUGT4EpIBKvzJk/ysNGJInCj1VKgAZcImInwQqqhHWm7kF0QgFzFfLj/JyAq12wtBSnENdOe6x1xd4i+82LMI23x2NpeZHec8jrZzmqXoLxDcneCSoA6C7IDZuWkZKAyWGCaKSfiHCYXDh68isTxCXwrm50FuHo8IeYT44yl8xDiwZxM5udnJyvExi1hl6+HhYUuLJSFucNElR4sMjhoyRz41pSu0YXcn6sKKCVHhpvFaxFc3tw94GIvZNI7pcZGqVG9GDiaL6aSH76s/gz9aHfCqWFIANmHlmCd7KAyYAMwFZeRMnC+b71fLUzeWa4/2uo3e+bQEiUMf0JPK3WnQ4efib7HQcKnhDKlnu88rtTo/0xcHj+2EfRLbDm867TFmloQiXqlVH3/22fLVK1LDJFp0jnebmtl25CV2RNl4n9mRifgMYUOrCD9l2BBCfCCT0sTwcJ8f86JyVD867MQJ0xPwLIC/uFCnQpBSPtK6REpz1DqRFIz26Je5Ca40Dm2gXz8LmW5kYI+DSyKIkRkQjsGAMAwKJ2GIbH6NQVTtyTgw7zSX2+jMZpsQQaqPnDvuL4lJDFIgURzQTtxXkqXloUZmeSp8cfUyo83pIAQaXJSLBr4ZYH4ue/SkV+a0j1j9mcSVyKej2XwRK5s8M9AqMqMTTA5XVgJSxQFQ8Wc/eBRlEPX67tbOvUfVzNJTT9mZ3RGnqB40tp/uRnImRLEfCpFj8D02JGNonGuQhxO/m5+ZbXETYvbiYBprK8axcz43X2DObQE/MB4Ylyh5ESM5T6KPvFgXw/39rd5BZX/vcafS/PKXvyUKsvXsue6eleoexWC2kBWRb3V42rVuFMtEgNMGbxyWy/q0Hx0P26PUL3/5yY210uHuwYNne5WWgpKzT+5uzpeuUGC5Biothcm6W5zf+/zR1qOtzYeb//Qnv5J/wdc5vXnU7rbnC7ONN+q/9cn9V7+/0jk8ruPUeoOmTv7Jv/iZIXhCnyx7mdgac8ml3988eH7QhMB3n2y//87Hpdnlp48eNxtKB1JPt57/4M/+8v7DZzrdgdVBrfXJB0+3DmoEoc0n1Zkj8WQg1PBRqOxgd+ezjz9+8vldbscPPvjk8Pnxz370y8LkSz/8wQ+fPr0rR9bgo05z76PP7zXLB6ul4h+Kk86mpUFxOtCpMYyIRUVIE5qFFwGKS7R9/mTzzu3bmGKz3oNa4fiOXqXBqxLNZUwvg157qAd7bnz6939/S6uUtt7PJ6mnzw6efraz82Dv04e/nEtNfv2b35mbK3XG23o0RCfRcJegMGimxtIIIzVlTXHFhZWVTHqOXktNQUHag1B2D/b2Hj56eO35C3uHe4fVFoK9xMbz0+m7nz1nEa8sm99a3NrcqTUr6YnMv/xv3ZFOi6VFluRkjB2lvxKV5FXo2+fnIV30xIZlNkNrH41rYYnj4yN4qf5A3DuiAnw+wTIjNV+GJJ4muXk66A9tBUFGRqOEV39G89qLURR34X1wXMpNcEFA9M8ngvcmYsHp8SIkYTrIjO3iC/GFZyTve88H4wIyw5lEVQKGO5RGp3MA7wBaiH7cOIMGVCP6hNJtPSCiDjsoNZwJoe1ZYOIKDrdHEJtARzwiGGMiAcgBD/GKBfpoIpYsFnBouAMjRCfkfvlYlAJIooyPygEVDFBdpOFYK4pmUah2p0luGZ6baAXYIwwJvuPpfM1KaB1q5MBIG5hI1RsdGpfJfvKwWApylNoa1goqIk1nk/Dm2BvRhSGZJzxg62gBof9Ha+XqLZlFTz++O2jWcWqj03CeIH0rTqSo/SQshVNAXmYottVOp6nVq7sloiUwLoCQSCigCOmlhmSyOzq7/3jvnV99sLy0dvv1W9KzM+npN7788ubnCgSe6agu7xY9KOsKKRkHHKLJCYGtjeLUmXxW4dX9R0/gm5cCAcJOAo345UQMIubcjskjcSsGh+bZ0Is04kNzDth4QM9xg6kYbwzts35rBRF3C34J6S08QWTbFWeScKCpVJ8uz9KggRBClZpBZ3iy257LFOQZZ6aI07qtMJfvl3dmpjVbvUq5LrsxgCgbGiFAc5hGYDTb9QMtG3vlRr/e6CcaERksusvR5MyceIg1arjIBBjmc/Qj+nbj80eP0qPz6o/fXVkuaVfXO8cCxG5qLyxf+da3v8zhy/uks/yYaTB2GrgYXCYwVWTb5nCWZPIM8WqpsCUAZN9hJJHxQRKAAPSRL+ikJwVCzngLrNv1MJvMOKy1G5127bD60d1NuzkZpwUvPt3aPd6Rg1h+6c7LUmKmS2kOZU1VWh2aTMrwkmb/RLs8Ze0oh9w5rPT+8mfv6FfIMz0Yio2nHmxtX7u52xW8jEmX2rgosz9l4Krx+sn79/arel1F4Q4erUZNBXo6f/jpR5+/+q1vlHf2NFxS6m+vlVZrTF8c5WDcWHGE4589ea4BC5ZAluvf99/+i7882ms/fPLQBAtA2a93//f/5X/R0CeNlTk5vt1q/Cd/+n8FMDhDa4FTEdaTzk4gBtWJUl78o3/8w2JBn4zczsGxVmC/+PDhg63/rW6DDviHP/lplh/1ord7XOvVh0eHXcM5ClcWpKrV6i23hbrEKrXSmVKQY+TyWOre48fPHj//8tt1jLcjvxh6skSjFT5tJ2jJxqUpY1Xykjb3Dz97/zM4eVBt8qHUBr2ffPqOvTUalWe3nwsALKws5ZrS+WepDUw2WD49PcU2EjgRqdx/+rRW3V1YG19YW78Yzfb7ddVGa4uLvOo8k8/Lo3tPt3dqdauilJBXTNNZcZgLg75PK63+TCknXSq9n7p/897bDx7eeus3wpMsCUzKIUenwuAwcYxCizx18IqdJlQlEwseU0PBFRCQP/EFP6lw6CUwNAQuep3Q1ylYqeSCqNuKvmORSE5kcrf7KVHd5wlHYEL6QSs4ga/gM/gFLS7+gM3+C+0sYRA+7yIXBK3Hu/Fb0GJ4m6N+yiiro3K9ptx78jyv3eq0WaqKnlltoeBDAc+NjFS/IFJVxYkE0q0sn50ZjU1RLd3LLRO2lzwg9GUPStbpnTjHxJ0RirPWHqEme4WDjFmUyWqYNbBaFGMjJsMImZKk9iqPK1aJEcwZLhpKgb24d/jJkNJ5Sm8dV3iAj1Ohl7KZvA5FhlQc16Sj83coJ/ZkxlV4S4hDmVXYVpK/FWtxSlMhI+UhS71Yv7E2uVfeff704Pm2qFRrMKw0qg5Ij25xBiALPhAxlNCXcT5CJmqhJfMTVEnpXBiw6kuiygxi4Km+804FP9Hx+WB/a/fxxzeus0BR36k7RNglmmWEkGAsyKeievtIAr2owiObHQG3kuEtRzLy6u3j4xZwIiViLw6ZgQMCvDp5BfGGDChnHgmD0aQEY/U+pmKwNzXg5IVtttpMzlgYfgcLElXZzsgUenswf0wuGCaxQtaH0158HAOQXOSD4eAzYTNQKdAm2GvIUVhoDTiO60MfguXGSxxXqaVaWceJw0bbsFHFTEy8VkuDFvV5naNGz6nK5kcvuAwyczN8CwACl6M0HpHg0aCuGcnZ06db1xfyanZHYzcinK1monlSrZxM9cY3NhbL1RZW144e96H1JzBMYMkMhC+ADCjjRqKe1c8xQH4ep0ZZdmmgWUxOtO/YXST7BtmFdRyizudAxm2xBv3BaCWpbLhmy7WUmQ/Tv/xZrdHa3j6cn0rJqBQjn8raBM9epBfIP+Zzt7uP7j155YU7s5nJQVvb5/Hn27WiFspTM4NOdGqh9T/Y3G1TuRhS5KXy766OJlrDkuQW5ExgrUYs4WNkomxu7j79/FFvr6xgrWnABZSYGtN32QZYaPzKNiLDQg0QjJ8wiW4k1tV7+OzR3s5eVwdGXmlq9XC0W6mH7R1CekLKx5OdTRSJPkHHy5fMugfJSXpNPcfMkWU5pISvTYLqpy4qveZh+0ivpoychE57rOx4oyEY793uXuVo+7CwssD4F+WyMGY5sghtMHGJwD7isNUZPXu2NWiR/FIJolDfCUQWvR+h0yb6lz+oXJOpdn/wF3/1o2a/vXfckCYBJtuHdc4uLGV/90g/lNXbNzlmtd6QZ2Xz82tFbRTrtZbhves3r0hhSO0cLq6uL7366qg52n3cmpjJmqMrjEunoYR+/mDLr9BV08MgBn1tRieKCiqynU9Odw+bjfoAHT15svXs0f1bb33XjkTUKGfDKpzAGWRzcIQnDNqcQywKF2XPs3HlI2Hsg1C81PfGrU39nJ1xf0U5aWkUHLlU2GCtwc/YDcEojRc0Go8LiBnDpuPHDZJL1MIgzxBTcDWcP8EGg+tCki84vV/Cg+AVlwRaB++MggAvxtXcUuFcJon6Q4bSmVx2BepS6NNphBHTbfDbqLZR4cafrK+CkmttNIwuns2MnbXdMCN1SYvxsDku1X1UlzwpeSH5zWJiQfF0/9CS3wXfsMthjLDE5k0a6WV69ihbh/+ALwwPkAKELSJAx4AFhJgLGCYbunw9EuGV/2k7Lmw8rbOBZ8ASRJ5Mwjvjp8iIF+2XB12VutPufZlxGwtJVhsyMHGgYU6YyWx6LjU7rs1fu1bRGqByXH62s3dcroTMCZYvoGLCLGdoGE8+gq9zlhGh4UBg0iainDixKoCOM4yzUJPHaBs3p4IuWT3eH7SUJu8fbB2SogurVzG+IgdWWi1KqEdRcmGPCB67clbu4s5E5thFvdm5+/DZxtrqwVHzvFsFuniCo4zAiGGcGkiIVB15GTbnspoIGiVtKiuNRK9pEtBaZFqfMK+0ou2Y0SOUqCOLB9kOzA0BF3d0W3Y6iIukmVXHe4P+m+i+e6K/7PhENmkVEh8JqKniDDUk1BE7jz3rXaEfNTEzPp4t5scO3IuNppUs5SRwFP8iS6xbUwuGvDnNTF4RD75+7IZ+iC9c3tAT6FGYedgOUgTGU41WiwQ5OKy2J0xVPe04h5NhhLNnPEP4/jzmD+u4wv0WWkpwbXuKhdpeaDxx+Mwqoixyp6g0xD/m6oLIz4hoiBMJxQdLJgFDEgSgETQRHoJWleak2lAj9oz3yR41TkaTZ88Pd4LNunJ2slZTvs4iGmzv7dSb0XatH94XgaAJTtXZhfTF1rhSCT10tOgY1wdUJyu0Tj84OQ2Oj/FQeowbSA2VJw3ap33yQPwUSDTqkGLotyAb7qzzp9vP5JIp2zV6HfJGzyGpJ3YbeB2+XeyBWEaLwj984VJ/GxKPo0CIZiBshlsx5vWZsKWp2PVIlJsnMngEcGA08FCHlLDsJ2w5WBD+PJmdxhPruvlHKcKIFTVtFgDMx5qQErE9M2PoxDGLJzqnCs+3s7lZ6o53uSJ50QgCHvU5Rt+5eQ9nm3uH2892i/NFxbdQiZohykRwQgZ0RyMKL240BRgXiny4t3sydqYTQbZg5mI0uzhH6qdn1Xav3tTmOCJMyxs35gqLve7RRCYXCX7149yV5XNqbWZ+5ep1U2/OTzTKyzhkUszkL6LagiU78R8sLJZms32dFhwLG7E3dqLToHZR6HcMU4qRtOeb20f1RkPSgJR/qoYmLlpgR1VdaLGXvq4LBWLIOLgsu5MLKKiLCTrJ9DmLTs3jcmrYVz6jAiCYciS4DfWl4FFiXAd3cwj8otifiyKrVHtnE9D1GQmRHfwwGJczQoUJ2cT3YDqhiwNjYHvoeglPjheCZoMRh3aEaEJsgLUvdCI6wpkyMtjoRAOYVpnqWzO0sRPj3VXzR0kg74LWmZ3ofmBjwTBEz8JmjiWExenm8dDLL0+OtSTMNpbgeYkvPDSxCPQHmxOG7bSlLzKD+zgTGg9ZGXU9YVrRt9hVlzpwPCI48aVKHBtwQ5NITXRCS2xem2NlmeUjHj3snkhBU5avcocQJUjCUx4AYM347rOBqHFH5cwCXnrnTUwrWB82urllAi5z5cYNHQSPdyuNcgf3shP+HiDzEeAigGNjYSnT+INX09uszv3jVXSTcAr7BSifiohB6G8umKhXm0J/ygvwulELGz6Z07xK70ZfoZMHwwHT2CymGjpQGHV86+FM52mKcT0QIvg2TgmlSBdtCElx96rpMj+TWV0uvPrycpx2tD+SgumdqPsl7J0XT39YGcHiPcAToYF7BdfwzMQX4i+bSECGg8TACWQlatEz4FS2DmzG0i414ksODaXcIwR02DzCp3FqtVrDInWQwIZoPR5lW9QXzA46kcpxBvoQyLKMxmJBIgwlWj9ph+mDMEeNZbuV3GSqIvtQXm+gH5dCc7BXl5IWGgQNAIg0Fo9nJIRhbdTheCAUDO4dhOEIEryKsweE6L8azjBHhrJjy4Eo0AXEQ5TFUSYi1i9BKS5gNLn9mflwrdPJaW7ck0x2IjsXJV1RsJCe3imPjmr1aqUW+oL0b3O/h+cxrECFp76w8l7SnB5Ch3F2jg3Kwi0SiuyHM67nsCB9k/VrzWDQbNgokCKI3V7pwJzCepSl9ZtURNqV5aTfCRBhUmy4QBp7Bryw/pMdWDJYSCiM7pO8E1qrEnaRVoKmUFDEs8BPXwrpb2cXPW2eIsqo6DtQkWaDuFArr5xTpbSCcW52emG+BF0oN6Fn8P2Gowgz5CaYymTnPP0yY0ojOEEk2EDOB3nEJtASVh6yVooF7kz5bTTr77/zfmRPhngWhL9oRBpXNJLw3dkhI6/7sEuOdYjr9bR3ImjxU1uRIW16ZaOu/RMvwHA6l425WnO53MLCWDbHTi4tb8yv8Pssr9+6unJlNT4FrJY6XwiT1SQHnQLTOYcvwioeG/ochLReWZd0CzAl/6cl6RlqAdvGmo323u72aauCqVGMAvKaKyEfdgTmLKYo/BuIhYvz/DAKIkcenOmqcMmNQQ9aRhgqpG1A2G2DOWEpUUxwniACxk848wzha2J6Ipk+i1DdO2g2hLs/wq8Lmf0h4O3eLklgDWn9GlgALRLwxQvJrz4Nxy+/Yuj22aje7SvprLX6046lp64qlqqsY7IXrpiISiOLs+bFTJpNigNoAhGWDpUAcUMU34LjB8GFrIvHxd/x8OQ1eB7MN1KA9DoO5zw2xuoRWdo9ONB9KIKBOvZEPl6q227rEdaoNdwHieK+IIwVBN+Fm5AEink5OEv0TpJmcOldjZ/MYLlxMQfDMIBqoaQjzniXozlgAeyJTg3ATO3QdCdi3PcILZ0d7dfrB4d5SQ6L8zoM728eJlI6HWotHEz4o9PCzKNo2u1gpT8s1wHQVhIe50wIatzR65YY+EBewIPR2dzs1PKCeY66IfaLi8URQ6XfQe5CRRCcHuQjIRXjX1BUaOxx0m4X2geGQsuUyCFjKACOZqV4eZTKk8AjvRWkIowOjw4rjebT5xU+BzoXieeYvM/bZpGSWrmQtZbTxAIA4WAcbIJOgOrBoQuTEYGJwUnMVI/aEHnHBhWmRhoHD4fazYdEtF8LDjFvo8ATLi/oF4dkxa2WPM+2fvsa+lNgeRPCcoVCCYUrQxtwKflmTQDpLMI3kyBxZLaH2AwABvogxViJNUo9Ky0WW034h/kZI4FITB+aIN66xgLHZGVJVbEN6rr7SsRz09gUGOJzPONqxVUMY3CBBxYTFHjJWVwE8K52k2C3UXrvo7EADC58W0riRTisnxZ+PtVpR2aEC/RNLi0X0Wzn7Kxp6RhTvw1r1bLQleoNDUclPumCIpI5KTkBj6RViwua2FGan4vh5sYxhWS0sJHG1iIvQ9w4Jp8EAkhzClYESmH4B1FZlkcDUVeiaFV7iQJTFC3QLKFMeK8CZVwVxBIuusDVhIA0BtQAWpVSuGJCEwAfS7okLZuW3BHkzP0FccMJhv8GXGhPQIcvswvN4ciHqxjTnDabyBrd3kJBQLwSE/M/Xc6STXisqe2I1JWx4Wk/CAMb0zBU9JJSYDugGv5L/dLLT59tLxhti/NI+QHkM0Vu4QqzX3t3RpYK2jRPniARaRa9RD+OIh51iiXHi5ZdWmAhTh5FxV8MkdBKGA+Z3OLaasKhjOMyg30Gx+TnmSkUsqXl9pGAwRwrXUozcav4VFcXK064DSqL5neYku1HjsS46BKZTv/o66fr4pi7Hu2FZC1wBxDjkVUMlwAHQuIOwZq54AjRmK8X2ZXexs8DrQApFB+0CybuzpjG5pwymtYgOuH+FGUDZHBMZOzUYhIYnA3VCUiC8mBtqKXxl2OHH7YNXtDjkmq8ESwEKOJf8NK4CiYF8cZ3v8fHra2bNGaITnM6PlM2yZSL8KG3dHypye6l8YxUrtqGxA7ZG0E/lCdfdCN/RGAtHOTJV2ANPSQOIZhU8iSXiT2aKX9+XmmdgBhVHzyUr2IbPPOQlvZHsnc1ia+UKZxJa6bQUQKQsREXOQgwiT0FEYTNHlwYiAjp2fGL2Yuz1eVA0VkeUAq+eRre1vImDMnQ4gEPF8cRYt0hiPCLMXKorT6l1+8023P50sLGlfxi6fqtF/kIsBbn4bhgYFwcrrdgd+Ac+q67kQr4V8hfb7upRwRJWHCwErEgfOH0PJ+ZWSjlr17fWFle14WwerB/PhwsLs/DV1igKQUoIbc4j+B3AUggDKQLv0xwc+U2e8fHQZZxbsCB4Qa/Mzy6pJIQCKbOasp4DKg5aPiIlQZahb1FjVAfqV3STC+afgQX0UMhouYe8AXWhKnhYnuj2cVhRFL/hI/oVx/uqLDhqU3gpvosNAirCPEckETJId7dKc6btjQ5sby0eG1lXVtgsApvYvDhOMnwpYR9HUwePyXOoLoPOlBPDe07dCLabtwv7q39VxjX0knoX+n8fN7vNLO8+VRa7PJigD/tc1ortEADkBdYwfFwYUsJXhgkmUR9A7BWEdWIyVq9h8Y9J4JSlh6tUhLIWwrvkE+iVMvz3friOWF34qDAFjMWuSxD6zIkbFwuEh05fF96o8opDBvgZAgqNL+ApzvifqZ+xvEGcQYOErecyDpAia2EmLrQmSl5GRHQBOOc7N0JIRxfjseN/B2JeJOpaq25t73jY1iMKIDuLHCUj9dltqyM1rQq2wztBHcOV6o9EnEJfycA5FtSQJBPIiPcJ0HfeJZ1BYbHUsO/FxQHFFPkB1hMbh4fbx3vcYVlTbTxrlEcYangzFG5yHMo6JVz9/NzTYIJf9wWJO3aoUsvltYow0Wwh2PQcooxnmik+KzaqswZ2UZ02II9xOEFkTmQ5NQC5ZiAeC2dJwQiGIJbxK/Eq1Wh2x9nbHC0SR1AMVluefxGjuYp0dB038lcYXIuQ6cJiyKmhBrtaGomiRNxOhagG+vQByqeaRXB/QNRL9PxZF7THQIcTp1HmFLm9KEwCOLazgVzgy50jTCP4pfADrDE68k2WCclQNAL/wz2FWVYkWcC3C5wY1lD9IMgnyCzODx4h8AteFxSNu0Ww02Ef0DkEplRlj8AMowl0AoMd/M4ywSKyQ/gTH5C8tCFQn0O+AZk45TZFrSP0BOBmLYTGAbvUDUqkEs7OZXPRgyIm8YIL8NgI2dFaRvCRmzp6EKQrDMEtWcHJcX3hG/FJvyarCAs76BPWhrGcXh41IwebaPwEsxOC4ywnVv6R+o1LwASCfUxwN3UByw0dEzSE1YF/7DmkKSkFLCEMpP4FjLsSnL6PNU6Pu6XmyiecXe8pz1ok2iNj+E3dhtftI8EvyxtelyIji86OoVlsojFcE3Gx/z6tfm1NTG3aGVg+TjEJeS+4HgB0VBq7MSdcFu+7zi0+Cu4Z/Bmr+OMgc4Aix3AypsvvGgIqT6mR5Qju6Ldn6qdAc84slhmstTkW/A/uIcCQ5OQj6s1M6NZ+QJ2ksyChkOOiXKB15cPDmHozOTsiFMraVsI7JbjdCCi1qix58jt07QtRnt6kOfHgcTiE9bt+uSkEmjHhxvtKFDQmYs2LEZiQJioJgUydpwcLIUOq3Bp8C245cxDtwsIuFO7cdJoDIyGtrXYSkIdDpDOqawh6UAapgysd7MQ4SFEgxe7Jk44ViwoHD7e0Kn5Xk5OZmbCQ+I4QnlEUWbPTqZ0G/bBcN5ljFJxxLzxujAmPjQkGioAQMaSgI7eEGw+ngjocTEIJ4wjITdHFpuLwwkTi/HuisRmh0GBBdZjkDp5gwtE1pMwBLdQqheNu9EqE8EIPCWVMzQ34o5q5fEO0NzoZzvHAJaexQ9DyU0HH2S1hNEYNuWFVCi7CUYtuBRztBNSDTwKDLpkhXRey/JXQsOcaKcXbZPnPDuKMyJo7gycQ9yPpLq4KMyp7UlUKNwj6buV1k2Noo1M8XrqZrATJKJGD2hFvMOojTF8ATErB2Y9YsJWwr08mk+4Ua/JsDejuZDRQgLeQX9V6/jbGRU5/Hj6DRuuwn7JZjXLoR4HcwBqkA2WFdQBVsLFibMkdWSkqlaqrc6lLoUZBwkke0RlgQYBMqokf4q6F+PDDDbHDewh4YAMDmV0zQYHfXZpeUYxfyA1HIrEg8BYdyMvBBly6sLGe52WC3heKEE0eDE6Lryp2ah+B0rtv2hMwZrPLkx1T+waWIqjhFcqPOUeDyICXcoKNKIRHXNt8M9ovG+L4ekkkvi0DQPS4yQ0V0sBzUj0tC/iGbcPOvZFePoKzmR+ssk/Phf9n/UNY7jDdJcForg9PcufAb/gMnG7L1AVqmNPgbZewIdgQGB/cOK41hN9wRvwjyOATy6PdcRfgU5sPkqoNeYLuWj9H6/bhpG6p63uSVMYIPITxiscQBoM87PDmxjXfqoVpmupKU7MIYUFmdw/HhjLSSBvacmrTtCy5fbBLA0lqscVAgCAstmM60QvVfV0qvWj3UNaJwIjY+zA/e2eSshodVUgYlgnIOZ7aAKeMjsxzmW6tlDiUteE/vDh416z7aQZNIq8iHGiLAQ/gRmLQmQAGXoJTiqbUBRg/cqV1Vs34IT6HaKO12XvcJ/XJZm3Y9l4ZsAkNuc/62DPRfKvdQR0w9Txot/CYRTaCLHu95D4YfimmooFsEaHfCbcPzGXKwiHHuiQWW60BgNI9mudN86Jkyy4krvGLulBji28tyEposd5BJygAozCbkIC6n0+X9KTtlFvaLFQmMvR+ywzTJdwcyTQStLzjQoQzkH22JrylthCgMTKgy0Gj4zwLDpz3vRZCbvSl00ck68YzfXaJwNjIBIwxKlA2jjYYNzBwS0olhTUIDOvVu/W7UVWVzCjoJoAWiDoxEXD2HlBmkh3C1JNdKqAauBJXOh3dhamEndHBJAZDFGFxkFgofeewjOF07i86XtrC4XlYpH5yGmAcAVRnS+LhUSxFGtIaMjmwmNJo0cc6M6DHBE0joXBMDCBS5dyMXSO5KjjtAM8CfcNCwHiR20ESzx2L/wjHH3RMqbe3JCsohmeYcNGu3rmtCX/80RBOZgR0mysUuuEiycM+oA5RIE9ENhZW0O/341KaeALZ7fUaJpSglsJUGIL1Am7ig5jWo0ZiaHc+GBuNidJWwBMP6jQE9QP8OGF7Wgro1JB7mJocyHQqJaJSUc5BSi0mot2aWFI2mOoZqHNhFvMgp0FZhPL8xfCkcSVZlFIMA15P5AifTZKT40v5vMaYl6qQeEzCBEr2nHekdRDPPdOUxJgh3o3hNIQKCcuTBImQpEB53HuLERM97v35BGklGMTKJCgUwAiEc9I1T98wCeiwCrOKK4CHfAKAIscdI2VODhp1zPzq1RzNfyWTr+DeKpSLo23S+4naVOA0CcDntMT/XYdM+AcI1kBQDyfNs0hpAVMeJKkkEVqikMihoJnej51qlmtlvf24vipoTxX0WUHqUVuGZZA24sIUFi+YVgpT/Zn4kVIUAmkw5FgC7xVtHccP6HhGDQSgZSQz7F5DMp4HXHOaXpitNsk+9w1EMFqXOGloBroGTp1uIIubYH4HmZbPDz+JaSVfE8+Eo9OqAImeTsBToBS3yLDXiQuKdK0QLQT+qOQjfAc1JuaECsphTYdVOGIkK02KNDnCyXpCxkUa49FJUgf63JA9uf7r98gECWDd7ee7e7tHpq6HtuKSYTNHQ1YTJc6Oq4cVDjupT8kxxy10yFhvuBSifDDRiwiNA8UgVVGcUI+KwRlrOjp7uZmvykEUFbwQikwMMVZsOdDuwtdNfDJ/S6ZZ2hDs9EXOvJCxgzB3szl55yRgimtE5gItig6ii2G0EuIJWK6USBgnwHA5P/EwoBygB8yIHhiMGZokxAwktcYzeAXnYFlVmXzmVazdXxYhtfTkqIw9ITeQiZBdB8PnhzkEbB23mFgJt4s1yaZ6USXl9nXtGkAgBXHx8e4hnBCrdmJ84uDh0LQO+HO08a/nO1XqvqvQjsDbpmb2FwY+0QgFArFITgkXZA6wxmPgcpMEJKBkQF9VztrPwjnEPYhQf1zEJHMeinmOVq54Cm2o1GhkNvYWM9m06QRFAe+S60fIM0xFcxrYQzI2W6BygNix6En+u4r2FgsHx7GC56iC4XKaseFAmzQ46nkJnwKIse4NwgX7eyTzydtBEHIi2zIgG/gaewvrB9fju3yT0TjreCAlwzZswL3gyqTgJ7NxgE6BSTlDZO0zQHqhtI3m44QRadrWLJpE1HirhWNPGDVucw1KftOJiFVE9Mn+Et4xnA9UPCiuzEtw/EXBMgfQkXo+gjqszE+gdh11KTFQxm+2IJ/QE5GIEzmwUBVnvBqX2XGMNQFbin3ZRQmiQP8QoSFQcca05M5CmNBDBqDBDHoKdCyyzUUVBTEGkDxS4I0cbrgfslBgmW5ebzHdxOYoEXu5ISwi2VUWwLRwHPZdMRGIs6J1gLxpyaVHJ9ELonhEyNz9qJWhy4bdjMTNfgSYNKnmd6cV3sHtbk8LkwdtuHwsTlfgtz35AvEEgUKcdlQJOAlgtCsXgoWQRsyoGs7M4Ui48PaQvMZ6tHXE502hAPBhqhVlQB/QuRGsrnVDtoNt8vFrK3YI4APevKMotUVh5VeInQECn3oA+G6DEeftdOMiW4lHtBJmFRZrx/QUmlxiOrkTD2Fvq8PBFYTrefiyvh0qI3kVnjLVCCG+euwwCS2CT6R4xOn4TaEQch6k+MkKemvJbEaKEECiHAGgAhuYdl+Ri4QFhY+wMCuQFhk5b3Lf34gM5juNGOj0C451/gllisvU4VOR/KWslzNzHtyB2HrjBr2rLCJOORId8oIgEXWEPRwmwAxA8iGLNezANjtoG0sLxbo3pdfwfsDTQKN4mV/Nuqtg4PD/f1DFVjA4agBhIZyOuyZ1d5rdiW+Ok50aOUMKw/Adf0L6gcoL7hZQpDBRRK/M8pwIUNB5VqwjeGwZnB7t4ZKw7+AlViIlYRyFJoXDJFh4jZyKY52t48f3uPWvXLrrZn8gpiiqiKSCVvio4B6NghxktN1qwS8CXADwMmXnw6G7mjBbh4MMtzmFmV2FXaSMrRra2tPqIGvYv3aRmkhT2KFppt0/PMpj7C6+I+A9188JSgRziSqGJ5HjtHBxI9CTiM0V9oN0qo3yU0CgH/H+FwVFtAttDrX4CQg6R74i25bik3kXUHsaUPOEyUzQBDKQ8An6A1wYwkp6hgdHbryP4ap60uRhaoZi4MJsTULi7NLsMuug83HwhMkU+dLpVD+Qv0L2zUkFlrDJQcsH6OK6BScJJCUmRU9l34tA+LZidPCrXDhkFCeeJ4i0Pt6kKAW8QldskepPDLVuGJ84trKMm+kDTCsfZqSm9WliK0B4+MWsVi3pdhQ09wigQq0CI0okTSknfcDNWwGzGFIHJ/PJyIFRNAzeQNUViMe5iSIPReoJ8BKPKHdS3wIIwYB/h99FX0qyuaDsn1QS9TwX8eBwlsz1ygYw6Gapzh2toVZ6jFcKDhuQDjpAuIpjiSxm3woNAISFx5ydUVumLlJjRjVgOKE1JGg44MugUkEsygb7h+ZlzFTzD193nPjeDGdAGvYQMEkqBsRx7FSRxRQ8NhAiaAvZx2uvdh1nIOwBV2RS4RlJxU/JqwFO8N+AoPJGJeLHmE0FP9u8C/aXQj4OFOgdXWUGoXwlsEx1pQG2O0uLy8CbrXeCFYQO+BmSHQCfye4FPoBmmVyhcrvNaeFviL0BMAGE8u2sj2hrrGpHLSzeDY1KBr+VSgsaNqcnDUiCDUHHlefP9VLuLC4aBp5So/VjJIkPdDYYeazaIc8ub6ek14UdRw4XtjTcQhsbwPEfAdybRFDdEeUCM4lMSuLCj4fMV8cBwLBs7CJ4iUApGXxO1lyIimlPkvgjgQgyV9klNBDDLcIdOUkYCe6IIF+Opue9FEZprpBocYAqJ0FREKvcpyJBh40HAgbpxaH4QsexB/J0uPvIKL4siqbcRPi3+MdMmxl5YA5GYbZxfyv1qA7qWDBYzxujJtVrKVJLJyHI4JslDTm1pEG7v4wJJ4TGO9/f8djk5cSxIs3YmVOLsaWjMf8VO6jbovfwNa6sqHr7d1ne+uLS47TgJBCseR0eX1Vp4QgBSpLTzS1ZG/xIPfHs8QLqHhgwSqHhhok8/Px7GkpIZ69vbW5f1zmbMGY6VS2g8dB8EBFYpmnj1ebZWo6WLX87NGjtVdf3Lh+Mz9fPNwWomhx4Dg7jDFEcpBmuPlCFrgRYAY6ByQBO6HxZFEuCkkbKwR/dqVfAZBRfFSVYdtkH8r8YFAgYAR/qO1LlO/ai2F1kb4SvC92ao0Jv4mb4wZh7smVugStu8dDwYZqOTvZafdaJmmdm+NYx3qSbjaIK4KYoIYb2oQ9gkNMZY3GhHLAbSbBlQRVkm+XwOFBYPOpR4w6gJieMztndBf/Ur3WONg/ltwS9GeHURsBlFYcFAk0+DmI4im67zmMLc0ranV+QpacRXpoU8sPjLI7eKJnA4mf9NTDv0L/oAElQHPDBHtjRXRyIIA2ItgAGc6N0GYnaUH0Kr7lYX8oa3qppKYqC8qhoAVjUANlXB0VGy0nJCCvRgE1RE5YHroiFD3FKwkcQ2IljirHG9w/NhhHSpIE0oETfz4J5d1AdLt0f+2bONkjHY6GGP5LuqWrtTZhd0pXhSiwhtKHfXAEhQIbin14tjw2Asgaq4QPOnzrOEZyRnG+5EqcPIQIXh9wsJRLGeDz1oPtTcwEVrQ6bS2RA70SQsDSIQ2gBo4jXB23MunwOVt41NbgzhJ6k7TGMbxJkCyiaknVWOhUPhHsmSPE6QcjtgSyIGQCPgNiAVtbAgQ6YMjuSNplvdhdmCuhWEeAPYgrWl32u2zoRpMe0Aofsn0FEw+JClfs0JPADvVNGC5lGH0HPcZWgnnE26AEOLH30HMhQ7JGMinYIvem0kIIEkLeXaFEz3iIuWycpHI7qTnmQenCkZ6aKxZp59Q8uKOgSvISK+Nktzc1e3zjS1/iLIJYs1MzMnqjYZ18PC3IRAekGg0vjBwXQVBMCXqRFxlkHS14MRMFTEIlBrNyt/Q7vXTgJpXBitltQG2TlozLU8hmA+UiNha8J9iOnQUqG2zlRKMQzePAkNcIvBPWHt5juklweYkOLIhpXTVcTfpGvoYbxbYhTXJTf3sNsC4Zk1MESVAOREjIKr4lLDSYZnw2NOX4QGwJaSkjPBvJDtQnPswnezDelL8/MU18UC44s124Ug2hk7Z/96NXWl5gSujXQVEeGgR0SVVhCrjAs+K7K2L3Phad7oUNoi2tkQBYtt7djWpzWYsTU+n7ZxsbG+4gSTRJ5g7fpezQyAALWnSD5J+HxjasJR4WNtpFKq8NqJYvzboZJriedkMHx4cKUuzRFoOUhf4iehjYAwthvY+H55pROT2Ju00YITpXjNrPqWn+PLyJm8i3S80/GJMNBgVgNmgiNm9rscf4shx5ByDg5APOnpl8JMCCXDg8kI0qo8rR/t7TLSlVNBFcpee7WwagEBJUCVdTQDXgGOcTirl1ulvQHmiyX6V2RGd+LJdC4K68C7TLXqt5dKgkshWsyrIYT2gWixEFwden41gVrKcV4cQRB+gwPhcEe40SaYcXNJxsLaQR8/b61XUYSlZRuo2Db3YVJ7kOm/XBkM1ARCEOeIbVIdeP52GWPmqSCt+vKx2UNI2kYZylhzhF/1SjyMEAqGA2NhzY62hjjwFSlo47hXlBh2DbBnTOzxCqq6IBXzSlNhCbQqoq20CqcDFgeWp43CBJ7k1IIJies+bGtVuamchtQNo64mgCSL6C03kxeQV/AfBA3UuCCqSmIHOCy6aKwIHNx478FRBOFKBQoxPEl9FmBHFUX+NWrh7XfVKhr9vDvDGts6n8biCN3Wc5xD0j3K0+Gzu/MF0DNC4xy2EgEzkukcEWEAnNPXDCBYEnzKahUTxKgpnr2BNewaKK3JNA0dgkiMEufobQWYI+Anm5KIADSocw8DEHEWuPz8CBOIbAr/i0R1hY4tj3O9Fi4dHgCqMDIZzI4tBIdMKH1EBGbYeLib3FJgf2VrfbUMHY7UHggJx8bo4KW9JnO5Jhgs+Ah5aBPugOWpjgfRFDjiyYy/Uk1Bb4YG14Y9jUthPIFkRN2yDV+fmlGrj5jK6VoiH54hLfpQ7hDDMYSs6cn7DOWsaJyUo2fImoOtzeqR8fn9SbUNxhRGwm2CpAWEIcQqMVJS8CBuZng4MTxg28SdhJOpIare+0GAA51+s0ubLINacSAp5Qc6aROBoIqf88/g6V+T/9SAAbVBDHHE4IDp/E+EI61ooWwlyOJGCoouwqAejZJBaJ04Q/m99Jo0eriICyGWGhHwXZBp4Gd/w1xwn89S9OMs41wOd7vOSxQWnJn3H26Cr8v0HT03qCy6c9pYYbIwqbtctjneDTcIEHSr6FM3PkcQ8PCzwL8Rw3QUTuHlhyKXe8GM+PdUHvRCo4RL8LiUqKv7qydn1tpXx0Vh6LxKJ0Wo750EiWbrXNY7C9vQ9I1K5IrYGkcSzkHiB7RDwu+QoODiG5nO0G2qoq63Ta+jpJO8gtLzu88MOqEEqaOTthsILW1gIsCYMNYg4vkOdMnDeaVSJYaLHXUP/WY5dRkeTBxBMDhj6E9i4hivdFbyIChMc8yDsxlIHF3fGz5AGgHTTrU46Ni1oC87HWcbW2wYGdWqOwKAOcbhXCwSeo/zJO4upQAGJDcV9/AFoi5OJenP6ImVMbT5RFF5VUUaGDfUjdHXRa0rWihxKnKGkRIedgoNbkM8jPwdmU9l6DdrQiITWD+1P0QDMeFvLKV8ibEF4wTcHUiI8uNz7VVWnJa69epM1mSxhWfAIQIHIiQtyIHipJJumuwVFQVPegTfvpYDrohA4ceAAUkfZwkapUWtJXtJ80JI36FrzVmhK/hOUki0qWEWIvrAr71E6jNTtJXlLosDXVI+4jsTCbzyOI6E9naCG/w+AMm7MaN/MfNRcUIQop6viALs7HIzwwDjM5pSCQ+HLD5ID9lbyXXOY3FycKcVR1gWH3JHw1wB7UPmEqZni3fZyvst3rHB9U1DaK2NkO32Pi2mJsmMJrIVApdgoPHVwAD7htHP86NSNUowpFykSp53HijxMIHBpWBd/wlUs5ASD45umkBhLNgTa4fR0lATDSp3BAt7feS/XOnaLrWajwccB0T8zF0zGdwKtgQJQbzP2LG9snkMUSA+nC5eJG0Ne9E5VmXAaHWRRx0hyP0WZHzj+rK/z7oBRy1TIhhLOenOAt7OjK3JXwoRcReeGD7ksUhThJSCmkZLwTMNDyM6jPZiO/MDkLMI0Dcfw+GsjjqpCQgUrhiAoigSWW4+kn02P9fr+Q6o+6hkof1PY2r7+OBXcPNx/n5+dXaOvzc43D7WGnMXdtYzTolrePDUm4/aWvgJrqeLhsKXEraxPp6fTTOvXHrrzqPEKChniDajLc2p1GpSpdl27hZDhwjF9W1xa0TrQPhlpnUhGwm8tPuWvUu9hOpJxwfkbmT8Rp7D8W72Ph+vEbiBAfbOIouGHNzZq5ezpebyoaFz5n+YbYdH6xTGtKCAaFsxysnZKQAM63L75AEIzCMInr/XPV5Wf9JDyC1yQvR2ceIVSVKcHCLINvcmJS6op+Z6gaM41bxQEktkncPm7MdRpkC0y+gChW4QZxDwBlGEDcRIfzuVAvCMGQdJqjjl/cWFnKTI1zVmdNo7CU0ZmGzIcHZeEBXnE2MgqC07DQbZObhBYfD0uIxsrjV+QMs2kNysgjP4Tjx1c07+HRMj4e1K3cx12amBGx54BFwCrEsNtLdGm120srVzqVelhx0rGi4vpEL2gmZAIwrDSMd3cLhEs+CyXj3tG/wKvxor/jK4DglThTFE482TpQaKeEF4gB0LzAWcSyZVKRpNXI/fIpX37ExcG9kl/95Wew8gCec4kQiO/O3cNZ8TAhHWPLIrEsndZlaMSficzpZ+wzd6P5etHxUBc8F73YPwd18po7xikmEIrb4kURmYrr4iiFZNTtP3ryzHKKpVL5uG6IO+yP44glulcA1sq+AG8E24MmWW9iZLYueQE2JwZ4hAF8NNS3lMwZKeKh+uqQHXhDANCXgM/e4XfY9YE2OBfY4lfUYTQoBOkOPD+5LClmWJJVci5NbayVxL6PaxXDOwDc1CJsC7+1U8/0VOZGAuEIlGkeEDDFsJy0nYeBBKY2gSXFK+7pfa/ERl3gNF2ehJ7jlYSWLnUg5ZDgYBGUR9DmxrEFDW43Hz6u6rGDLJPXadxugnHIXAreKGbO2xyHA0GCwwQ88W6RLG40jUPkhwYK0Rx9SCAzIZ1wcMcFGEDgrEVPptZWl2UTwswQGMEZQSsWiBDdFhQ6XUmqeEMwcS1lPEpKUKA9RdWP2GvCekMMBX65SbKw2KZzDFINigs56s2QWFigJXvOBJdXPCyxWENEeSIOBroWHaFWHktT3wZdC7ADTjzAoa6xIQJ9E5Mifg0TJK6wyPCXxj3tM/5FvC1h7pdbCmtEwb9r5IMlSJtQAVQkC5CIrQz7jaNU5WF165P9xx91W/XW0Za5m0IvRlT3qvupUU9bBhVPEpek8W4/uHfaaEp0CxowHRnhJIlSISSdQPAW6AEPnWysJRhvhB1UFfFJyNMKMRUsFHJh6OLW8ieDMDh+kaCLRYlYuZp72DHwhB1vY0LGQT2YUlRZwz+6vWPF5yJMBbauVfIlMx5d6bTGOJoU6RekUoUlbw6J+YwuG4KThImLIBInTHIEthL/xWkkgASW+PJKvB6AjcOLr3iMJdpAoClGEgV6Kit14+1zxaimFp3B3SCtEB3rUsK+/rbsDp+09Pg8dkgFdne39n/cPPktnpdwrkscQ8K2G4+LQiAG+/+Xpz9rsTVLEzw/G7ft0aZzjg/hEZmVVZVFdVfTNA1CiAZ9BV33R+gL0aIvBI0QtECgK10KXejrCHRTCEGrRlVlRmRkeLj7mWzatrfN+v3X9izz4+eYbXvfNTzztJ4lnqAF0uZO8F9KkiB4+vzTT5/+9NN/8S/+s9/+9vu//eOP4tgKzhDZ0KAt2LLbR/83cxsyi58Hg0K/OgcUent3/f705G1yIP5zq5Wict0MloCBSrzS+5mAfWfvbHw0cfv15vjt+K/++q+/fvz6r/+f/3L57sPV588dZNlq16xUIs3XZEOgQHjvZpQNvDZk8qUlheCxtLFWC8URoRozPO85QGizCsV0PwOFTz9/FiP/4mp1+YGkD41VQCFR5YXxp4BscGs6n+zgiMY4Z/3+sLM8fokeLi/Olov557vPTsazErA35toRAWmV3MXjxNCTyzjXBLOgKBl86DxTEBkMHlAGAsd+4M8RiDsR0/Ua9F1E/NMffmbYmteOUGuui8VGOiHIylOSogRsbJ6UJjDYfxzMVmxH7iIziSYyfPP08Dc//VjrRGJa0FOMVfNzb+bujY33U9rVTDS0FRJwgLBcrFaOzpYDCAnWgGOJsr/7mz/cXsyg2/1Jlp0wTRblM7bEwnT+TcgZEbv0jwr9jlsOljQWCPRpvxriv+/Nn6VV2BDyHUxpLSQg4mTWVnpQSRG5ED365Vy7+te3PzlPfnL2+fMXe0nIutoBYU6IOVCpr1zDJmiHZYMtzUHE1K3WpglKIeZWkYD35TRD1eVyg+4ArCdRmxD79ssQQUGhshzjYJJ/BfFpWkul7a5EyvziHywzguaKH++xoRcpEtzhjYSDf3eUGuzjKuMGg34dhQ97AnW2EqikBq4coE85FEC2VUxl4igB1CX2n59/oQyvrhEj4QIfOsQVJDGwnEpw9VjgGPOmiUvdWZC1QUaft0cg3P3dvq3EWEwfTx13yyliY69Ygz/K6n7+2/94cfR0/8tP2np9/9f/+eEJ9bJ9227/9O9+71a12fT15uefPv/409uW++RqLYXV2hb+vPrtmRMkqR92EuvbxEXSgoEgA2McoDAdFrUA07OnsKquWpEswHc4hiOg0Cx/nsimL4gKgk22YMgaHCriL7BTwMp+BzunCZL7ARoxp4J3W2ayxbzFjHTzUvD2evT88KIu5qaeU5LACrKPxKdpBhYIgZIMtHhqBWwiujAX6NrRf/oaiDTFEFuWFWZ7MkJEuJoFXrvj2JVSXSgsML11nUJRwse3BaPLNceakj48Hz/xdtUI1qx1DDBUr0nGhKZs+sG9durj8X9k2ad82A5waT279/Xm6t/+/veOfgiMKmvXM+D8/OzD5eXX6Sf4F5Bx1EaiCnwUhIEYBO+2Y2upPONmvvk7cyYpiOCFODgyri5YLD59/PT1mtdEvXNupGJAhkz4dUEkB2DJcECtz4RpJ7Ojxcn+31///Pu/+Tf/1f/yv/n4889qk+/XaybqHbEVDUSov24P9AYnZ9IDqP2lotougBLjEXUoaZaedW5r+8J6/vjLl83dw/m7M0jgHGvuz6amBC0M6oxglp1RaT/J014fXD32ax5DN/iEaV95u295htLqipu/fPkFlf14fSUMTqBoHGKWChRsvRCrlJWIZIfVBRG5V4TqDmmtefwZWt2w/BW2aOL4bqPZYkUQGtNAk+2Re2OPkZilDAevGPMAbVCi9YTNum7v6oqsd4adM0co+85q3btGKXx9upsv5rSvXKYwKWbzy/A7xkGQJEM/2m/xin5Fl6JJgg9ffKaVBzJBGAv+4e9++VHIUMcbTKSgOSbLTyekAmxIaS9R4MCRXRfBBlrjBmWPDBGC7UgZOyPI2mTS1o+D/ioZkHhRooBlrcGguTmIZygPWBPTXk2O/uJ33/32N+/O/2Zx9CkCUPAiUMUNjPzbx44uLM6yBuE0vLlgmsZwhgE/tmw02bkwJaEHhZKs0YPAi3TxnoXJ0Hz8LI62rl4mUktkt2B/GRFL7B8wsCyRRrBgE1p0eb5qoOIdy0noeTCWg8qW5KtdxSgDYpbtLoQTB5d0LxHaTxmrshG011LEsURGfWwkZzPeJtmDmKDTs/6S1798/EkagFg3sF0lE6wPN5rAYwJ73BVtCF4fBEPomEOxfU5HmA8LVtWSehOiUjBQjOTIVV5XhTM8MCdC8uNeF2p9lP3efXr+8mlx9q2UggqhVxErlUg6jv3y88f/IPs1cS5CewNN45az+c3Hz3/8V//2ny++1ZTe5bH2zWIE9KaEWTH28EJSHOrUSzFZBeKMtOjQ1B2qckNGRi3g4TLmnuw4VNIIrXxIDPvqC2BaPCOsc7+yBKSeDXis3LttF/kZx86zlRw1e9HE/PlAH5fjo8vLCzUjh48yae7ZE+40VanhCvR5BHLN6NEfqM9KDJW7PfhhwLL9jK8gOkTW+H2qzksZU3xnSZgbZPXytJx17RfW3cfJ0UfjIaMK0cmRSiJCDZwkjwiSXyfrn0i82VsDIeZbrOTLz0agAhEQXGsFoycl1SkarZQg5fp6/NMfHBD+mfU27b40+/JKIIxvG8l3GX1BM4dwN/AwQkiRMOe25TeXLIsya1bODUfu49WYKi5pJ0aK5kMP0AyqojilJX/888ff/+2f3Pe0+vCDSkFsklGFskboFhwg3vNBdGzHkEZEChaZihtfPvKdFdtviAds3qPH3l4cP1QBKSD6/HJWy9L9VxG2mzr3O2efRqn4puFhv62NP0F/8CC8gfn4zN+8wE74RigFgF6etXrWJEt/7JKMmaUZFDLbFoPf7B2OLEr9ihgRsz5Pz44sG0DHpvJxORNm8AqSEFR9fJvTvm5mfxTVWUfZBRC0Q+gQbOwAJQOQZrA4wAclNIFADCm0NXKeI4Q79pGM67RL+ltpKXyRY5DZ+vwiUe1XwTIRNQhoRzbJDE2iHFZ81fWlnli8bGnP3nx7/fa7Dz/88O5f/4e//fPDk1bPQWBg1vIMZaX9Hx9bbMKF9AeOdph/lg9Ly4xYzvh1O4OIpHNEIxPb07uTw65i1h3D/xyRk9qaDngh7MTBoO3N5v7nT3+uf1IZCrigC4u6GBAMCEqIAF4UEoyIA10EWGCw0+psdCC8dhdGZbhYihO6AbaV71o8JTGIzn2l4e7TlGonMcxlQ4M07Dr82Xu0PrgxCQ3CvmdOAgRSI3lxfRgc1LtjCH+PV9p+SsJqqxAQ1XDDLdUPL7oDtMo31U3FYokskc6d0vC8+Shp/R52rtJnxwF1BdqqQ61QR6gX5yruaDdBB5vYW/Qg2OXbWL0/LTqa2P0V6fZWyiwkxpR+SgJ7UwqXkXewdzk7+e7dmZNA8pd/+v/+my8/flUgeP7Nt3T25fnZ1R9/ufnl68LROGc/P369vbpaX9/+8W/+EAM8bqWzO2aDtseJUa7DIMPIdQDOJNjPdsEvuMLvWv3ow52mVvV28NShEiOSvQBSA8WiB7ubTuAFLuWQhYWEdgBVpEWZ0ohUR4ZGBLlgrxXRU50znMR02KJCGGnI4iZvr84wkzQdnXHREtoKjfWrNiFYMwrAiolC95g8tGNoHw0IYuzx2YBwsI3ZCH1o8IzlGspMIlZ+Za3Vh1mMApWNLmAayT5Ol1PayKFHtWSsrwGE1t3ANgQ/DTW++ic66K8QB6nj254dhSYHh9J0p8eaArVWLfVt9QsBdnX97/6Vbq9/vn97UmtI1XPt+M7IbAwyhtuBtkXbwq+zJr1VtUNgPPh0c3Olt+jzwUTrNMcZ0HEnce0w+YJ6onhf2AVrReaGOj7+/e///nwyuXZByeb5//3/+peT9++ldc4uz28/3lSrwviyGxbU2Be4WU3bH5sm/vwc9Q/Pmutt/B0YKi3wPT450gQbA++7p769fvkCkdnWvmKCZE1yZFS/xHgFwKysSfKKkxdj081p6yXirYD4kDwXZhR11OXh48ev+sWphIFc8/qrp0g3qRqfjQGUpUgBtFo0nVzN/tihyLoZLk7ItxAOLDS/vPLV/vinP0kP/fyZthKXND9BkHLHf1601Sz/NpK8841z9RaO4jsHG1NVb56NxMJSZK2B8P6BG5N00rZGwiMJMwRyKRY7beABWRgK4jplhnHrFdLglLoiqlNULorYF1DaV3d5ebGMOZMNh3e3L7PVUKI4YmwssWLMqF7wokmIOQAQFvc3LQ+YKAKs0Yh/KOO+orjB+QnwbDpb8Jviv7bfcw0r36wkmr1GmzFo/vjzz1Cin656cpmDuifgKtONPG2zhO2W4kPr8u9uq4ANWObkaWAM1F8Sq9RYwfhItV4eUQuoC6kvpjNWlDBlmk2IZUzxK6X0fcuLkPY7YeP1RtgJ2CARYXU4ZsRzhiZqKWPHg6nN482AQ0f6fRZwFhEyJGw8aVjkCyzpnQFcow/GgKnwrrx7u/l0fcWO7gGgVw7MVPKOn8cfEtzm+hb8R8mvuYNDqAgjA0heHl+J6ih/ALPNRL+ZSm4YfgX8zc3mp//fn5+X+gO6yHL/659+EcK9+Xx7/v33KP7f/M//7tvffEuU4bLPP33SjGWjo9/T0+X7Dx//8HfuuHfqyfYtprZuXVLadqwBqBAIjMd01Kac+aARdatbPYTdMvb8NHcdAg9UoattiH5ChmsE2aQinAX6NZY6eNn6NJ5pC/JkdV4oOY8ZzdT5PBReSXRpT0XtCauOShTuO1K8ypbdvB4apNPYtG636gie6DvaQhsCwtKoMP8rHsd2Yq3/BEHbCfrBN5zFVDbJqVFFe/90+7LRRe9NXe/jg1MQVmIg4Sa2FgRxOrAx9iGXR1ghOdTIA0hjbyHDqA3dHIik2VF1n45/IQ1xCGCyJZUzbbbHy8WUserD//lf/+t//Bd/uZqtbm++smbZC+lGJkfiJnaFlt2I7SJ2iKn8bXBGpdJvtCPU4YSRYoC9k3kXL4BLZZ9Rv3MsraMl9VIDRkVuJmDiP39iDvz9H967qnjx3bhg/dGxp7JwfB1inUCmKsZODGElvge42uNahhUOMBDf5pKhCrDmHhou8f6iwcjeh3f6GJ7A2i8fP+qDJLknWI/OlHkIjYEwjWtV4SblZLId8EATPIOlkWLS8R9TRQGmOeL8ysDVASHuYz3NlYh4ATloGIAcGJLeSgAVClB4VAsd0xW1HcQMKu2piYErZ4WpU3iAw+YWXBdgfb1eLX4QdMTL5lLjToR7OFkAMlaFVvOQvJXggAVOcVAD4bE8TxtKtIRpERdIYpXj4VgwlAwUHYWkSNKfvnzY90PVUSF6uDv2ig8RhtALxCUkhWDfnl0W9v7L9MdfHLNQbPRWr2B7+AdDyK4AzyL7mwhiL8SJchVByfcJkVh9gKDHwl3gbjMDKNDaG6ifV7T95CbT6yu1N0xWVwezi0HLyDboxm9XBC8WZ5eXl7+4sUDd9IPtZK2DZ5yYjI9f22qTBo3+HTonJqJ5S6T3W+EIVJ4yTnKWjoVNa8hbieV9U5tJlFDh07Q8ExUFrl73a6KbiNwplYFW9IgJGJdYCgpGFDPjBHwC9BiSvGrfIAUy8OqdAhLAT625zF7xq8PM0QXtciiH4AHD9oVsORSCOL0fBpUC3HV3I2qTO0mx+cZ0gDGGtkb8ktnD8Bi0EgEl0k04/vpVJw2F4Hc+7K8hd0zLzsFrg6uDhk63f/4Pf/dyOkGn08VqcrZ4frt62jzfuG74p08OMqpo+OlPvzh3dnqq85BLm2+my8mXX378V/+ff/nxz78wq5NL5avHiaimgqUQEXqH8okmUbUP3djz5erzjz/frW+smEwn65l0pD/THnk+3DqzuwUu+2BKdRidNH90D5rYBorQL9bV0CWXGa+dYD884jOKtEuoIBomBgKeK9cunfByRBahAWVJS3c3CPp3Ciu2G6nNQT7D9iTqBu1an9XvvmC2byKpWCsQIlYfjj9QgdO6fQk61S+/d7aS/hrOLNLwCelvE4qi6KTDk5mqBNvHYEBgHNMBh29aD9QmbFtZM46xm3K3AP9EJDvLQm7MVRLUAPWIoJQJHzgK8Hn6+Wnh2Mi1eLMriusXjBwqVcyUa6KIjRjMrkz4JZyTFPQ/Aw1uVMQ4/AgY1/dqrQQuj3R3M4jHeWbeRdV++FU85I9G307VO17bNHsv118+nZ6//1KdDJmfIIiHKnMf+7Sb+DH+MVK8NmSEz7BxmwaLsU+DoRavBSWSj2YVkdcc6mRCMJM2BKu7mbyhkFE9i+c8+dJjg8IDqIkHmGNEY+3mjRSx30CfCSNQzynvpKjZHKC1C48hLcDHXcjEfn1In3WyhMNSUAjosnTMW5IwxtzRSM6ErSHivBYdZhzZeny4vblWerPSue/wiiG0QybUGXzgPioyhDX6RkxDK5YS1J5wSLQvgmy0ZAgeTUX3Wb2KioAMRD72TxDs7z4CY1JB4DRpU1G8/EVHFD3ZsoVQD3gGz7OX0/mCs6dwwZ26L7f3oNHbADFkvQkBIOkJHnRaGSNSJ3I1qUWbwr99QeZOsFpP8DDKWIHlUm+OenXGdcYkKf82zKZnXc7yO6P4MYeqCHcE8T9BxsqdHQ07I/ebyk0dtq/dJl38sUOtH9txg0RZVo7K7N0DXi/0MrKMSWKyd1jSOjXdq7BUmeVUhLWhHGIlkPqT4kV1TU3Lmo3AoL92GhDHWb0Pw5c3dl8RQgsb0Ivy/JjeGio/U8m2rc+QwndoQuQTwHaEadmo39ZivVLSVms55ivB5YVOt6XKAEBt66gAMkQBOK8SN1HRWAtEmMIPpjKpVyPKpvP7RED0hYl7InhzRnYcHc/78EhYTCvyr5Pl5nh2dkTCHh26I+jTR9dH6mF3ff3p7m//5u9++0/+QiWO/OPxYubo75M/Dw+O31kzc1CnDLPgGFEuO8oyMz3RY15hkJJokct86caem9eHB1Dq0jGtSuazkWvhf8RshrB3ZTUYSj8V7YYcEQE+yQaNP33IrYCCEh/7LxWU1c0kQWa7+ug5yUoOI367Fcvt3g99N7wTrEyQZk6OWYz/B+9GroG0T3aYhaIe9pdvxkfhfPdI8Eq6+R04Z1boPqceX/rm6X7LrIBgRrM9j6oS0x0JzHie8PAVZzZThIMyLGtMNWaJoX6Ne47Zm3T366ROBBlvI0J+0L2coPboeOtoouZESPHpUStt8G6nu4xT9OGjPjNMWzGeeUfo05DtMiJ5ftOoXTG4GibBbsBN9o+pZR2q92+g3dtjPVnugag4EXfnYP/z9ZXe19PXw8+PevfKIyBxpJU4iCWzXpkcNhvIhuRNP6HYOKE1tfOgb2BL9tf4hZ+qbnjbOzs9o2jdf+PqqAo0+IiRFqRbQQH4NkhKDXYyknGaaLDEDoBhdoDaX3Zug+iD/uaea3mUlwm6wSglYZyWEO+3rjSDagUomOqY5nMDRNSBLwyFTFIyFm4zmbyErGAjsma/mAD179ZiCpDIswj8GdHoIcgmBca/8oSaJDSo1wf+AMRMY7+CG1jLupqCIWksj+5wMv75VTb52CP2T5Km4LT9cbjXQMOM6NZDkuVYnGHNr9eXapj9HrYJGUL76KyDAUMJCkI/hXGsOTgnZXeLrhkLQdn+PZZH4qHg7NfENqCaE5ChejFbaDr6y9dP7CHLsHFzBgcvgLACpLfXP/7pzz9//qpHkzHEMInLMXlghZWBHyMF7qjaFG0psgHKncxFElyoyIZR7d8hlVMl0OJLdNT1OKfOkcaCfsmbadc2NMpoMUI0Wo2eM3dkRawUoQYH/2d1m3I36e63qAKdG95iBxGGWoaCJ3txfB+SchRbBBBFIr0xqMtHATbi3REJwiZXdZ9NkB7oRJTbVlYso22QSrTgpQTsEGOGh+vWFYhM1DQDD30nETs4rvlbAck0HjOfOTnPLkxef7l/mr+8/v0vtw9/uj7/9kXT1hr9Hzoco2aCTH7MHnmVSV3P3+ZuZL+529AH6/sDheeOlZiGwMGKlmJ7WH+31x1y7RObJSpE9gium9s//92Pn7Ux/XrLC0sx5NMedx0ec+dYo7c2p32i1wHxaXvrNuAOlynpPDsRYIC5vKsXLYUmHAJhIOKexwQOUCQEretglgXRyBrV6E/mWwIgckCaoro+41VZkDeAZFD6QHGUHCZtyNf4xi/HZsYnQ/ZHVUYqxYRFgZ64VwD/yEbykCqjTrN1+j7zGp8p1DncurXTZgQOUpCRxaC6QQxjttZgpmbbraep+rYlon//IxpBUbQuzAZqtIsKfrLp/cXZu3P38mCIl+X0+Hw5VyXq2qdR/B2pWSysywXFV2O/yXOfYgBNRqXDWe26M27cd+hSg30mEmzbpWcpLsnSLMmYbSxvrCtjENe57/Tt7dP19epIz/DTNfRqbKuP0wvLKSVg2VTIjjptCA23lfDUdgdr/bq7ftwB3MIQPsK145qTv83U0a63f/rxR00Vfvr55v711a0ijjURr0PLDNBkVqG8QIjgmqNpxooDWmvPzWvfQA+QWC7hy/90Uk/14RW2q2qndYPUboAkmVfpA9QzANDxlWDYdGMX/dT4Y2bgQgWFa/b2TxeL3/7mO71pv8jMaCRXJchQdJbGw1AwVFQp0BqnwA8mb2D0iTwI3CFEx+y72VIyaXhbKZEbHqr8GFLw18VYb1gbP3nWlhGAWjrqpaNDWN+WUQSkkIHX68ej1ezjpxu3yDOdDh8eJlNpHVdNAMQY11DjG7TrEzMicaUjoJLfYiU7gdx8yU0rRN++TWlbR/ZuATTnyxw1qBOZxVRsmggzYCuME5JQN5sHncnX64qlvFyAF3+ansFE+yKdIfStIgy0Ly8inuDXag3IJmR9YYNR2GaNgtJj/WlyXoUzcV4/O3PyfRqlgj0FMOwAWysfM0Zvny0hQZleAfE0GSmcBI6Ah/ymEc2ekI8aUid9Z020iCiitmiMM4IT3tMoUQmcsvV7fSy8Wby0g4NPfMPYf3txmfuX+zuRDZo5DCapPWc2wBoapP1n+oCzRQWCYCD03Q48NaYYi2kvhRDNNaiyFdN3wEU0epj1fvv08vdfb+dPTz9f3V7fPX3qAl53GBz9xV/+Y1eeO5CIr++eN8dnJ25rUZ4pc3Agf/u89/U//rvDi4s6M7h+Z+zE+pEWbOZelFpCF5aRx5OdMHTq1eeb3//H//Dznz7efb1z8ZTo8+PjLZFJK1skLvABPDoU1rWdx059svbCtjjw1y9fBWNJpPvtvcWvb8v40yI+JIGBlUNvQuJR1Icjob7t5WWzt9FUXBWeQBO51F0M5ihqDLi/Yi1SMsUg2t1f9jPA6uMmH3985tOQBsQAurte6tAxROeAR7j1YVsgpb07SqXy78XViW8H3cEmGkIOAnqICjXQFXAAihqAG2CLmCMKYGsRMZW3SbWWEi0Su6iUUYxU8KEsA+sAr2xutaB/cRme2O5ic480S9MpeRQV9WweXvEoI+9oL9wPZZCk6b46lFmhkfZMlBZguZZ7hFFb35HW5SpFm3oIZeNYDN5yPseB1ZrLLNSQ6pUjhlHHNDl4Zgtks4NGN3AATCGzmr2bxWRjXo0lEnZ5tmARrfjHL3o4yYglKu4cnLz/7uJsve8Czu0dG5ELYFMO+1VcEdykfsKMIQa+fv3WZuw/wdF0gLybluXNTmQWzycnp6uVTBcs6PXiRBKSGEoknVWFVbfYV6cMUN5Hh4GOOeqhENSISb5+anBfZgEbfbYFazitbumzKUW2Yd0zw5OAHOQxSHDsUTDEy9gmFvd/monHY9XQa1yzVeY3FJF4UOZDGgDTgVFzk1VjMS0glPt4tz7SUFyV8NM89VhdQImHSKi/905X5/PF5LffHXyi/LYPmhy3NwWVgJsMbxEJE8MJXLQIGaOuXySXhgkvy+rDYJ4wbn9jT4QlRAM46IXKQqWMpBt9mw01fmUr/AO7tcuqY7pd9enCXZHd/rqxwTZgy+wAqsaPyTkDmiiIBaOx0z5MOsBFblKcA9vxUgazfcdo6Hd4G96BYglntz5TSuoGaGu2ho2CkndtmFrrO0M1Vbzf0P3dkjzmE9P7FQHS4gewE3e9YOO2HttCj2/GT2PZpohQ/G+zScsx5NjDjoAKMRojgwDRuj6+OyyMWa3kSCAXG2oKXzmoJg7/AJR5kqYbK/QD1b7Tv1EFkzMOayNw6RnLDnxIYXg5TOnbpwfXC5/q8Pz8tD14dR8ZLjy/WF39x38rEyF4+PFv/153vEoT7tcHJ6SZTgOU0kTPqCfXvYv+IMKo3H4Tq/yS8t5KjMYNmiNXEc7IFHSrkeXk7qt27ndXdwijfT+7MmvjvBQh7ksEn9ms0kHh8PqO5+riP7flHN3s32mHR/L8dHW71HhYiPVhq4mNqbgpSsvOTudkI/gSTHfq5DXDhxCFt90RhIs6NnKYXxkUMhZDcmgKpqG0nyOhPh6g9pnvQfPXJ8cvBgBj155xLOjp+fplLS41R3CiioqCKj2aUYRQozbW7YUb7kfnlWIMxGw1FmPaQTZNH7X4bJBWn47Zh39A6FiSV8jbDkYye10yRkNqrkfXaKol0Kx1WCGzCUffQTguCCHU6KwPsbiqoOQKKvkxV+wxqLjJLIsAVaB3drqgirUfub51zmxjAaIEosYYGP2VpLKMIDcg0ptEidzd893R9nm1AnS1qcrXFRLFs+YB5OLy9h3xtUcvM6TxemDbmcmoWEijX/bK2LVf55cOrsD5283b+u5+lCrB2fG3313KpP6sTJUJQi0BgSMtRWobgZtlVztV2ooT+c3WXzkU/mViJKo8D0LMXocfLFAMTwjNtKBdyCLOtUeyhoUBMTXRzbqNqhNbAxDNZdgea59xGDEF9oLINJSq/o8fPz3cbRyxpRMlsZg4HmKJO1y3T9WGVQvr31xaWxDi8UTEYFGiTEGLWELKkWVh/2pKBkf5HYdyLNRfLMUhoAcsB5LKADE4wXlQKgjH/9g+MFu2d6Mf37brdHDChOgvAUAEBaUBvYBmJJv0qg4nCK1DChXZ7x2Vg0w5etj6uWHBabwGxtnITSao9QiC9/q0CBmxU20OjbQjfyWSUCzj6O6OWfeG/zXAsNwsaTpx+KlNEIsm/cOeTYzs8SDzwpDRtWWg+SgnqvNv0TLPsfErOHZEuJykMhDn3gm20WcnPZTID+IDkYMOA3sftzE78DuRXUaqH6y2AY0P4PABgD3Sbrw1tt/fwDKbTOxCn/z4veZ1DZ1rPSS9MY3QH28NPkGt0GGPGjWKs5eToG8r/tBl07utpkhY2ga1JExYdfyhUXxotQhVuOMh3SOK628jEjQm8SuaxXcD1JZqJZbiMZYUm0R4Yv30cvv4uO7CSBbP/i+k89WNTLny68GPWgVPYJipQiDo9fNZp5Oz0/vrIV4H40h2yILB4w5ZaCltZklV0OaxAjWb9fLy7OXhDkpOFtNbV7VuHuxa8tItioKV56crDUYrcyfAC4dqHneymMxUfeh/6mSfjpCHZ6QGi3Sf0akc2iSOgbiYRtRVusB15LACGculTopTSYJkn4pTYSXIg7UkaoQ3EBJ6fQs4EU3f9Vff+nxgB7TD7qDyXvd/pAaoOkwgCdUdokBHj9NRqEHL6eDI1u83sUi65lFjOsNZ80BC01tFZNdEO9nXb3ZzWIFPB730RGwyTJiQjZ+zifGPCOdwDqCwc3MvLKfDt8+3D3rlmprJQ2YkY8ZWMVMM5wN/DWrdUbtZjMKsdgMAS4o4nO8v5i+Px1qaYj33cUuvR2MUa/IjARdP8ULwkZYee48upXdY/+V1sVhcPd455eOMhedGCWnLaF/2MlgZFaDJuHhH93bXikCgPSMcxQmZdDROwCH4MopFT375/PlyQkgmKRCHegwH0dUq4QjHDtIhI8getSP7lBN2iFPG9uEq0BOwJo8cjJOmVhhw6OYWFNZbUsEgN240RHoD67Gur2wl2B6G1Q4fhk2JDtx5hoz2RlsjcmNItP5Gj66lMpGH62jQK9jBmnJp/FDneu/bovf0L4aFTFHoAGkf8mshzBTgQmJYdABykogIDv7G8Au/8kJ7NnY734mn6MT/xRr8ZUNCcol4rwGYZ0ivPA546NJaDh9oizjsHHUoswtUGwh6Ktyhf1t2s4W9BvFSF5ZpyHZqukFpv1IbwsgZJCtERCUhZPkO64oIxAzH2Cxv1GLtoVl8ZP2TIiZm8hN1VhbRdXpkiEetoo/baJv0KpD0oR+rEgB9rxK5vTk0hB3GoUPp20LZzwCRA1eEfb35woLILUiwIrMeBPyBonaBGP12UOWYsmNrHhJ8LTtt76GmDZGVLWnQMQ4DN4QOPSV+rRTuGGG+5zCVWgfoAdHWZOn+8b5ZoQS+sYd1FvjSZOXJPZfGIdwrHUlwOURmqOxEf0hvnIgm7YB8o5yU0wi/EA5BTMTFFFBWR/paErVEE2aUVcyGN2JslTWc+OeXP36+U4LzdOcS01e2q9dv3DvE7p5OlJB9/+07w1qSkzjzyaGOXy6Zmx8f64GvGFTVUpSvvYKoENglfTMvyOjinOlcboiOGuWdJY2+fPyYzBlWnUdOjtIo6k4Yst7WXRpNitw4mECuOUiJFxazmWqa1cpBhUMjgM/FauUo6HI+hzsO98xdjMcnLEUFvl5YTI/fKiByTiorCZoIJMai5I8QVnRO1VrZIPGBiVDdV1In2kEA/o2sBxgHjkF1PBDixjPhsgtgD97Ppx+WJ0u2oo7B2f2vj2sJ6wod6IOvj04iH9adilbSERDDl7REFWPSgVFYiQ9gY/zxTTMMPR9Rjm984g88y4H6JYlCFciNSAKrkZIWGGzgfckW3gb/VvjeRuuCv2MWWzJU7n9U54vo3OcSiaZrNewyO71c/bZQHDlrkqak6PwzKJ6Dh9t2BJgLKb4vDzMl9DrKVw+kKLAsR2wxlh2Eot1WAG5jHYMaG3vA0W8ScWzfdten8dFg45EAYDwo1b9//Pz5BkOdzKfE4a41gqUzySuGIhspp9aK4kxuGuxBaESMITKfKM4oQo1dUKQulR7nLG2fVIlJdmVQd1YziWxhQ8AZLAAawJqNpCxtfJceN89uIr9uXz3RlrxtJiLSmu8mqm2enTG7VUtHNoGMSpRxdiRj3jSN1Dt+oKR9gyVaevtHnqlBywUgOCUHJoiHbLbBEdT2qtGImmi11STgMk4tpI20TGE9+SGEL83TykZQhmPKy6aibm6eXNKyeSMmihXk69h0OdfW2cLGXyZNnfq+6CaLowql9u9Pkar+azFEsX9GSNHcRqrZ2SszQnXBVHgwj9/CvBnUIoSoJf2377SaUhGA6iTLiKETUaY0rb+N6gWyBDTsztvQND7vF4ELAAJYVqgZPTm03ZBG3k5E4Tsp7kNGru9F/MaQGd/20lr8aV1FgUCgWQdss+D6H9HUlD7IgtX4LfPG0sAmt9LscGphDRy0LExpc+qxAxkxC6yhQThAkTRMmchBNUi3NxusOnoYZAxk2XRzVIacLTpJ0gBGANIWVj4PF3sLSZf6KEzLlJZaUpGOmFXNeVz8spCIfWIkYoct4CX84u/MCKCoR//DhIBaPzGJHGGUfuCjeuv+emPvN9vHM+7MdH6ra/rNGpxdAy7mrfzxp09fmYDCHfcEcR2bB0t28S3tZI9tiqSwWmrH+kUPb758FkQSwIhu0YYz32864G6QiIevb2+ij+dnyUzmHR+OHFfYQy12o/OBkEDnoNZ3dzUG0vpeGRIRnDyXusAXXEwaIdeduj2q/CItaXcGJ3SjolAL8AAXo4z/QScOCkx9+THs9Se57EPvDYHNiCCRsZmRYJTtxoYhUhzz0y4KaPzOa1pNae1LFl5dCZK/rN/etulF4YV9MfPssUgs0miKaMaSB8WEyLGwf1hJiykBEMohjAmpSJa/MwisoqnJ7MQlza7nRQhOQ0jnOfRp1F0q0sZbUEMOjhmi1cQ+GyGaGtVy7myHQtHwwB08luRfagMJaUlQammocRxlpeCw47iWZUe6tRy6tOsqStt3SE9rZc0W/MYwscHY5NjM+GtwWUIqkHrI7tK24cev+rKSguDI229QrSqqqQU4b6ER8P2dWqfXs+Xy9nkjc8SxgVXC1P6SzeKsxkw69G6saMnBc0cfiDlhKpRrfoqUdyqiQW37BYtVCaLSqhqn1fyyXbeAgkXGBLjaPw6fxPF9o+zQNHblYyPasE1J7sZwe2KaVNchonXaad3BhcLNjix0LhztFvzcqY0ixYCb6RuiCyKChSUPM2GAyk/aWFjFAGcc0rzttXnbZz/73jdtv93nuStFe310GUcGPYxbfy8U8hanEj99Vn7+enKvy6nafHFeu4KKUSIxoA+6rQVvQxCZxeU2KcoZS08WJvKTQg0MG5bYDIA2xCYpRixy5M/OJmyW+5saW8SuzRN8zWJRYUrExlHpyQSCDcpeM1y/3rGHwRO9bRnxtIagEcTCqQ/82ufZ5g7rDU4sHTtkny34aFhSEOInxqyfkRYwZEWZyap+ZXmoz/wb+sPi2yzGM5F4CB1AOZoFAfgoT6jz+cmN4GtTthfJxHBgE2HaYyK6X9ISuCnvOeFFmXiq+k40gho9O8YoySduS10b30c4kQJzdWsFtaObUKPGryjZuqrQaoYSrVB8KKbJRmRk5uPYF7iZHZQiDNwSxHBmsTJ09+xqtofXzcPU1X/J7LdnZ5Afn1xez/TJ++gqjEfH0haLN6WHwvpuOwcfmurz7e3Z2XQxORSV1eLt+HTijNcOEUl20ApIwEFZROmgdCptuVFp3vWerK7lYtZiZOYXHPKoWykllbB34PzPOL63p2lj3rDltCkWkmYHe0runeR3D4xWZt0rI6AkH2CMyiNc9SGKMeJWla+444nKYw9TQ0mKIXtM6YviRXlQGp0N08mPUVw0abWhsj8hqud36gFlGMobeKp22t5PDBGDWk7k4zjOSQF4CQpP5rMD2rIeZPv6hG50iK2vd6N5ZyeI+8n4zf0PXz6Cy4jHJ2MhiDtsFtJ1uYC8vGJ4IwAG23l5+f7ml7+/dRexEKBPvcjvAq1YErXYn4/aS8QYlaAOS+5TM3TqBjTNsO8iUC0hNp4pdKaOCtAy8uh2FJxMSqpDa30oQ9iMcDnSXM/tNI9HM0w2bncTG7LqQOoRA5vcML/uLpT3YcvxG9/FL+OXO4PImniFQMAWHCB6ldk44GrIfMyn2+eaQLTLqjnBs4q9ZgHCcGETyaC29p/+9LlpEjQetBDrn84PfvP+3WLCBlF9TiYmuDGzM0rjNsuS3AYxHIsNGPGGv0M9S7nV/wq9MWB/ge3YWHoah4smBazXV4nW/buQSai1opYKRTl8WRLtMC6FX4l9ctO3PsseHyeAPDDIluVHx9m4wSvfI3MSoWPKgeMhL0IzbVKAzm/ZTXVxmU9l+rcvmj20SNKfwl9MJ6awhnuVX54dx1BDOXusOSK2JDskcqusETuL9I0NZPkaZ/zWggf6EsGBnawcEBmGeNqAa1bR0a4p3EBA0jLMhvhowN+I9fBwY+G0o87GnUTM/rWZ3tg9adhRU2TPoGftLTZgwspQRBYcS6O5nhgjRMBJ2/6q5oqjd5Jp0ZJJgyjFL+jTQR/+8oFXqATrI37tnZy2X9gn+ok/VYhg6Ger49/AUbGUjkv6e0fPwchayr6o1NcBbGjc+SFZVgT3bl0xSsbchIkoeN0uCGfmG9gHPBDRs9LJf6cUIhvvq6OdPHbQilNFbwmE/ioxCEDzFot2SFDNXjS633WWqkBc3Pj05Pe2w/8ETPUe/olzhYDE3dVowGmKoo6KZ6cn7xbz6zt3AABve3cCk6hdHfv7efP59r1TAAwUC52cWImUvbPlrk+bKw3Q5eHxze+5LAiC9DjZUbaLGUT2VzPOm7Mxq+nht4u5NnOu6zpYukmm4CjKgkU5TWMKemnTy4ollESf+Kn0LsvAD27jAPjIBbTqVHZS9aKT1gY4eJ0bbWcVRThuIoosdZnAJLqs5pxkk5S02uEuoPsuIjMeTgXy3cDRDjj9KpuiPmjZ/Y4Qa6k96Vk5PZPozP75aYOq1reVabIWl45UOr2pafVsquMA/Z317yWhdCM1YeHRYn0GN9mg7zGk37W3GN0cg9LiFl/IJUNkT/7qVGRHgbn2dsK2/nE0yA3S0iYi49ISqKA32ByNa+ImjCUiy91GhnUTm5N3lXKfaKX9en29mZ+tsI+3kZPXGNgkWJa1D0meQWR+45vgkGWrOd12dXj0uw8frif39w+cwaMJqey4dmajxyQGiyfgjiAQNNvY2KYN9aPPd6tiLORJ23ZLLmbKMnewrKMhKAvZdkBmzcYZbA+vSZGcXMM0ztiWxe7wCoLtvNnGHD2SxEyx6Qb6onETA+Rxc/Pjn34h25kPrmvtTAm/jt2XLuvtYWbq5pfEFfeq3BRog+VOdjXfDlOwYwproSy9ZWGcNXe8K1QiwQ0I+65iDDulPBmQGbOMau9Ljo0Sr34ZaEc3OpUzZBkt6AVYAE/p7nGnQdY1kKYlBnp2+y992x6tEOSLhexMHKcmtPcDdHNgGPKkPN4Lf2/vYUiS4jCFg1NXttwCOyy8U1q5YGkDVOEJK0nCEI5g0LKQKuPT1N4cCq81p6g4NMwd8jSdhgu90VVorU9oKMOi9z2NNg3vygQ6KUGc8ZHuRwkW4sfqnlIkWbA7so7sB4PGTWgGcTLtoIwZhtAsz3k3Ys6vtZcMDAImLgR+68THEYv5gCowpHUqWBshUzD3OLs8tyBCQWTFomwmyFjjTlAKpfGzkaD4itdtgwi1TuiLtvuvYhPeEvKpClHAPfDsLY73V0f7dWTSF3J096IF5RCYbE+yhC9uabZbQ0XPQ48+54zi76e95ZLVLeoXfgVjZDnh1IqFQcqUwN9YssUK6HMeNMbWL8r4S731n1/0+XajpgbTKTfe1dv+qc4/9Q3TNdXBz7fn9aN8S1d7P70sjw4rlI0e3W31IC844ywdTdUzrtxtNOm6gpPXg8XZwmi6D+ewe8BSjrsK5XilYbt6dHcqD1ujnrXHChRddskQ4ZXpZegiN03b5Bc9iZIFHo1TkAddUgMgoE2LujW1nEqHgfT1WWyKs6VEjW6w3agUisWIJm4Ec06TiEdLMFeqzAC5HqJSurvQIMrSheD5NX4XmsNpQIZZTBhuMftOMEdvcVPEOf6O8ENqP/nbi+OHjALTuODTPleCU5SUZW6f3MyAtcR/KOPe3Xt1tnmNPkYjuDiTzc7FG98YK8pFw0MumsTgiL0l/MMPfkKNwItqkSRgUcsOl6qNEmVmrUs2uGAI+K5lT0wJHaP0HvXuWKtJraMdx6LlyWgdctRn2TjFKYXKpIZc8/jgNIAyzp5vo6xgUX27zqxDoP+gC/GM5Whfft/ln4jH2aJD/S8tQS1XqyjcWSYwnZ0NlZA0r73E477zacBolnSgf30HEfF9dnFhij4CLtktcS9NxDSGfujKusazDQvvOyN6pbeKkcJvwxuRa2BxDRJjp61tyu9iI0eTnD98dnjk+csXR1nqS3M+c7ZZWvvJbLjWdZ8IRvQEzShicdxszGIPafQkZRuIlBDUrz8OOJXUOlCsMNHU1ys6QDi0ACSeFLOCqZZKbOZOjfISbIAkUgi8EEHMhPvQd9Qb8SSjbunPyiLQmT3UkogEioRMG+XaI9JPIfJDy4OM8ZMiexrnKc/jMXB6ghoxIlUiAa4z3evbV5lqRpKdeB+5ZGSEsP6AV4pwRz5JRb+NBPJCklDmAk6fNHlgCFsWA/J9TvyZVFUPVoZLpS2wDG6D4CPUDO3x0kH6VemEmKi5mwABAABJREFUskINlzpKHummYzyd9iDcw2kt1Q3eglgjCGkwcsuy4dBMizU67ep3hbHjmUgLJRiWC4kIiEXLyQ1AJSoI2TNo2SjcG0jx/kAQ6Z8NMCiJtjZ6zWaUlmvG1WVH8ay1eRzNWWpUMlaYOwJrvGcNXIWjdKQ5IiVez2aShZOfX10S+ryAvXGZpZ6b2jxlbZDnhy8LV565uK010e2vZIuSSFbu2YLEfL2TGZ4evD+bftYqA6FUqnfwm/PjP39+FgkQyg8mhy581oHOHl5dTMQenexNlgoRX/Z+WT+sn5kQe0Inl1NXV719ulW1rcJy78P0YPL0drb36vZEovbdxRJQ6d3t7ATEUerSZQurGaxP3ScxP9pn5r0+y/+tb+Kpd87kny4GqSxZOS6+nassPHDTeP3QCBbCgV3PyhGiB7FsqO50OWDG0rgWJbuCrgA7Y0Phsr7TWkO4swGm9vddgaQIdXW61NiHpqsOzXklTkDawtXNNQ6S4pLXD4hi/j4dhHDE+2XyaKv3lOWQgQSX3h84HoZsdDUMsaQiUum/HQVHxr6PvuIOr/hd8YWe8VUQz1BOWp5qKS/IxTQgrVys9OC2JjH6SizQqWja5/sSPggEQ6ClLA0j7hiGnh3SBD396h0QFMnN1hvdUTP6SQpAP75QzJczcRbjiJVJrB8uYEaHr+09UraKm+0DdaCTG8LPjjHmjl0Hp1l7C/fZYKGORihG5AZ14bBGMM9ucomgHQ22Tk9LinaUzRjtGYIa0udZbQUBwc6hcfimk5E0DYK5Ro6QC5aeS8kMXQDlue0gaQnAR9YNbR30aaO4ciwNcBBUM6XNO02jpl5QfHLg/M7WnffX9/ZIGgFCS8KT3iP4SfrdCLGyIdKWsDdw6rkx/tgRsbvjt4qJaonw+M/+8v2PqiCE8/YP//Alwwo/St8ZGUKJfaKB5U6qWCi3b0jIABKdDJyliHzAJAhKiSeWxuXylHr56eePB3NRpWS1bnH8syMNGNuyIlT9lLw2olgkIOGebAN7v/dPUUyb8Yc0SbhjeKYnVVThM7kTAUMvGOZZ+p4usCqCIU3oINJEZNkAX+7voYl8iBXVJnT05pkOWE2PP96Tg6PeF6ZyptrCTvDFpjlkWi2PDyv0YBUmIm00MrZOpCuZaY5CMrvQmPgBe8VPAZ+ctQ0hAhgUJjZQuxsYKjwfnhqFRs6grs9rdaJoglAtNhCTdC7BprlRmtbCe/nJzPYw4EVSnufdN+7PzHPySsrDXohRbgPjiSkuxAkNlAPoO95KtnKphEw97EE6cpw8K2W6I8CkiNDzRGMVK6kq+nI1++WXtVIme3G+Bw+QWs1rO5zUdFV2hy36PP80KxSdV9v2w7uFoIkiFOJqcSA+zkinFl/O5+UL2algcfL0tprtXSwn19u9bhHfvl6cHQiO28RMc2zG9fzw/flsMZWZe3ud6grF6sImREMNWQo0rnOtZkgEJyaF9uZHe99cXC5O5h9dqKT3RW5MV+h8fdrszfa/mRyef3v6cHv/w+WC4V+I5E1XGOnMEXwUzJh34+6ImT2NyjTWx8PpYobU3HFKWSvFhwfpLplIcFRTh35OORf1ZnP6b3Py/CwqIFbA31QDkETr9IhWuYfyyUoRzKV8SEJDmDftix0ElJjTCkPqhkSUifYcHav4eHpltNG+4FpEh57gfGmKrlb1dkuyux+Y0GC9Ib3N3YZ8dvF1mfte04pQU2/uWBIH0qPp+BdnRoZWlRhOXvjGPxHQiBaEYe8QDgWdiXEvWpzPuB2WeDArnWfgcXjxeQEnWHe4NpRbbsLb26mzwA+OGiGoUoCGJFdi3V1syI+Dz5smBwBXZ5PgaqQLWuBFHvfZIS/s6MPZXALl653onrrYAz0yT/ac4nyarqpWRO5v7q+2F9ILl1qCf+Izf+yJbTLMJIxTiD9kiL5ZjUoN8ptvnAyxxxHHsACvVcECCrJMA15jgLSIdN3r5GV7t57uLWzIBbNvVDX2M68CpBE5SBinYo36D9u0lIRI0s4q/Y3cPBMTWClBg6PxH0MbscYtJxdnq6v1jSIj/ugvX2/uBntLV4tTiGN4N1PYCLaXnoFAuDKyOTPuCjv0g7Ab/7tIMD5ZLaXpD7/cbn/4sPztwdm17PW9tvUuo+j6JBydHAJ4F+q6uUjuThVNlq7PSPk0SjIvlLXm1l7RS/A2la4aHhGHvDif3xBq1tUTjdy+BsZlwjrGQZ75SB6MmHw7+OmO/I46haXxkKHQAemP8BhhH96t1jcPd5DN1qlTqanbeZD0Zf++/OD8x3A07HdHyliFxZgaswt01VG0wCRYwXluAgPZkvngDtbBDaEPMwLWLZAutzkx4NoNMr5kOAYLCc4pt1ZWDzC8qBoVVR9xsBAQeHFv+dvi5PjDag5wWP3m59uwQPOJxLCfBnH0vEqQVwEH2JYXGbTLK2Uad5iIO/U2g4JcG10dnxnC3h3Cdadv4SlLwHpIfwwtAl4pdF6dto8qx0nP6k9XBOhy0a0o0+ezJRNt/093TupVFGlrjLjp4cTJJyd7cIVWDEAFRVYp9erSUJ2eWN9fNw/i2dN5vEZs4XQZTsUt9k9jifXgHYlTQlPmCOPT1eIpdM/k+eU925zyneMdFztPRZHcNn15vvjbj/ez5+18Eg1Q9uerk/vpZHt/9815EX6RVTfoTp5B8mB24GKiB1rkGwb2pBu5dYVZ2nwX4LxdnhFZ++9OjufHs5ftg0Tr6fHxd8vZYjLVbO/h5GAl0fLq76MV1/Nt/9uLxW8/XL483IPbyO6Wk4B69ZdXd2t+5+gjQr4pv+B0PjmUSw6BKtDZuFoU9qfDYlGhgMyIszF9dIfg+YjweEYHcNiheeQsp8sjkSHCbXO71jlmBHNMiHQzZ+CrJqCUtQNnGfhZH+J4zgOfna6urm+1HpqvJrPF4t7lgIWzEl/UgOYQGQgahcJyhpYgHiGQ7NX4v8K+Oj+/HYmRE07kGu4zp5niWyREUMY+aQIfoskyA0Ni5DYYcXigVoPPPJBhS7v1TBUjrhR4vNvMuxKsoguGzMLhtBdux3q2Ws1pupdHdoIEjXS53LwTagglJ7UgJVEYn1lH6wH8ZIxFDZESGyYHqS4bMTzP6GGhK/GDbCErPbbdZ4w8HMzUTjxsrx+F4mR0MD1mYQ2xNZMuQwYkbweHYyqfgN04TIoWaVN2lczOiSi3a8O5VNnqWfpETNFYwMGWlurd5iRkyDInyBB91tWrWIemHPPz0yRIcd2myP/wJoQMKfSfwOt9a+rvlgnAiWuL6n/j16LDz/S4yE+ZEoVwN3ePsxU7b+/i9FRv7fvbOxFBOysSAlrprdT1DotGzCMpNGJb9GA7gURPpnqLSifRbq7uVm9v5Onv1w8//Pbb06P5x0+f4up8+ZSuTKmlHWiqYSlPz6XUxjWDCSLY8xfwWvHQkRFS8yVxpZSFV92MeeaS98095qjEc4ACKZqbpbqYFZ9wNWyH9BTsureLJ8eHonDkQetxlY0A5vI76BAomP8ceGUPb91ejJA9Ec2k6Abp0hKJRSup3wnLMBfSkigf5tlqJpFzuMZkBDhL7+VlebqYrycu6QJZUzH/oLJgqiXyVAQQRAey2i3EyQnQpimQe6xO6hXkftufHRzN91UPmzD1ZgGhAkKGtS4m8+356pvF5I8/f95uuALqvruBtdOjATGgRdlIwXnFNB/7tFNyJLxZTueHZ1V7gCYZzdHMNmHjKKaweXxBYGJwq4WIjvw8Ps8YsFSaoo90mdKpSKoCqr09tdrfO0G63lycL8lZd4TcHLze77uamGl59E9/8wEWtFZc373ykx7fDv9464zEC7l/cXL8/enCHb037N+hL//6w/mf3fO8ecbXkznlpP/Zm27ed3svpB/Pn8eg4A8DdRjMLV9HL0wkN7kuV0e3t3pbaqOiRyQdcfS0eflwfPDhQj+x+h9//OX28nJ2evh6/P5CSb47APUZ2NvuubDTrW4RiEjDfqrioZK9Q/0Rsf92yazv5O1qOludTNSfPauPFC0R6jx4m+P2xck3krAERCH9rjZysZLxJ2/botMvz+urzTXbByJQL8OcQPA86zqXSjQnkwGRoHjGvZuuIA2aLcdZK1mvAj0vj4z3yu4PtFjfkviKPRVBomj0X8xxcqzYF4v4+WSZJjCJEYzPX1KLiBoQ0rMQytHRXLkHViclDg5ckSQbISRE1t1pb6vGTOaDEVHpAA9Et1AamCAx4P6+HsXs74ynHDhT6CIhpnv49eFF3raHEmDjL2YmGk73JCUTML4bTI2WEDJRkLwDzWGDJZsKf+Eu8ZIoXZnRy+aR5Tt1GQtuZLBopMRWYAagbspeBlzqXyD4UAebKqAIoQr6yNoqwMopJhua2evDevQjfss4opxGRONBWYTGMRWN6GRoJpnRD3P1r1OC6Q4RYc3j420B32eKHR3gCHvMOc/pjtcMmdU9zo9Aqbl8ZMsQqw/4yetRwba9A2O6T2Hy8mw0MqT3snWHYLekEehsvOGVq7CqfZAokNjXlsR71pYOdKXr13Dnu7GvoYVYb0R0Q/VnfCVH03gkjKEHLigp8yYVzF2cBNXh+qu7eyfcKL0z3uZ89u3B8fbLNRmjIIhJMjL+6WrrEjoXDoJ7YV8/J81pI9vYaR0/JICTDXTP+XL23dnvXDiPcMqe053uK0zrsOL3t9VgHKzE8vcPWTqf7zcsVsDwPqJi1VtlIcGIBw1ZCFFDG1n53opdLC75uGUi8HsJ0hwR8iAUvM4P9xh3+u783baLF3zyw/v5b96d/au//6XIbsENqPeoIDX/98D9X6w/HILx8FV6LFDCuzXkbwVrpu+OkBg/LIyuDHq2YHy2mB1/EIZyO7wXNf7Dqw97bMuTN4FoCRzhBEsXtCheH2O/vM1FiEvhYCi1oeLUntMGjFH/uMJa0JRirhvX5XzBu1fg6igpncvmgD59XWzZqCLBy8nb/vb+og7MB4vJwcPxsQsXFXjzw6gxnSMd2HS4451bqQ4OEPB1J7sj//nx3vfLkx/OltTJRmJKonJ6dDZb/CSWscZ748q7wUwPsG0Dry+Lo/1vFlPf8v3XBS8CBPWp5tVltrO91/PJ/mKy1NIAAahxZDvfjKOqZ+qnX5/OZyfz08nd/guT6Jebx7lLmJ1xeX5eOZX2+roiLl+OPt3I8O1/P1c9fPi61Wjmbf9esHvvN98sKdaPD67kE5J9MvKKSqDWKmrdU7smDXo5NdLJ6VEn+Tf3rydyRcdZbLPLOS3OzSRGjxcPpzP1OTOaLpLBLoIzpweumqrXCwU7F30ilieaNPhsJya4K05p5SIfH57OBOvZ0Q8a0eCuCH97t9rfp0/IbOZeR5TYc/pgb++0wPJw7Xy03ZlaUVIgdmfOd0W27WiP9Xp7L7tJXVEEJwwLVVriiCUOUxluMS3/kGBXpHSsb+9h+t3MQuSH+9Zzcyf48lTbGFEgjb/RiHvIXbuWZSP+4E0rHnFdBbA02nRGb7DUHA80OxifLASLJo6UoyoVZcdHJC4KwuDDF6cQ0k0o9lBB/LAqCmweMbrlDB/I2YcNYZo8jUuTtHFsbJ5owMBDyBWZICBipMFL/sJMO/aCD+3VrMrvRbhIY6krJVy/Oz/X8Wt5IA8j5/gKM1mIT3uOrfGb0N7l9ODdyUyTq5/vNsALh66yzdIhQ8Ea3MiGAQI/9mVBlhDmMq4toEXRRi+MuL1vT6fnJwfOSztr9swf4O4Rv9zew0M3XMwcGLmrjxRySpAm6KIQ20maJCPamP/Z9aIKxGVD6ydEO77tfffNd5u3GwE8mHcHEfTH62M5EMYoTsoaeRzo9LpKr/cksiCbRMF0787F4iJwh4rFU63FJzw+INky/MyuK6JN+RCGneACeUHXhL0Hg3R/K4qY7OlAVAXdaIiVrMHipuec66XwsEZgB+K2iJ4PAIbJdPvyQPsCKrZqqLJjQ9aIL/9HKgUJIOPn5XRv5iIE6zyYXF6+c9uvfBg4EVsKmEbzVl5lG2TDoj2JF0Y9SrEnZG7B7WjAFaVmCg9QhTdoJe5bwNPUvXSHRyoikPMVM/DJfdTMsX1i6FJi5+WIaaZ/xvlk77vz2Zly1K7DrkaHoObaAwePAWkjrcuLThWojWNbsb4jnkxsBko8Dpr+ZX+nyXgNpWJaIcP/m+Xk/eLk7OS4eKl7kAYBoNuV/1+flwdvp8IfoiW534AcSESK5pPjO5lZJX0Kseul+MgH1cKXXBPh7Vwb258wOD4+PTpc79/PWQRwdJL5d7+nz77ziqVkBYLOjw++mx4/HU2+bl6+Pr996lbwovUx1htP6HB+sH/qsYUL1MhE0ujlyonTk/0Py+PvFtMP8wT6lsZgHx8fimVfa92RMhOjG34+G6BrhhmTb+9Xk796t7DzHz9dr788yqm6mk1QFkUeP5Ocr6hKgY67AkXRBKZOT49nt9s/vF2pYPv7P3103und0oUeE7lxSgNeDp8f7+Fiu/n8vP0wP/onZ1O2LgnoDN3i5fEf2fbJxDXciPBdoee9xYoN3P2qHHLxG3L53ImQk8mSJytrKl4gJt7hma7hQG80hKfxSmxW3vVo8e4SGbhOnLvlVIT4zd5kAfUmZZtJBE4Wqkuo2KdH8d/qgSGbw0WAKrjcf9WNRv8J5j+n5OEBO8KoNLKiyef1RjJt4xgvUNGWdfMQpGKxMhIwSrRN2XMRXJnC7/BbQm97ezcMjP3PX7/SAQeHFaCid42g4QPsx3mE/eG+1QpMkRmW3jJY8ZHo0NvhLcc5syKf74XQFGR2twI7zLK5GCwo8VXFcBlHCFAjSdfXgXrqY+vhkmdcH6Pyx/xtXW4vWQ/BomypvHZ+dXfP4aCdX2xIwtg5agfJ3Mib49qWex2aOoRjC52KzMC2xrxL+4cFcojk+zUulL9gIQRRi08i9TTZ/MZnd5Hu8/zw6PLk+HeXp6dMQieUFGDJiMIFxyfzMJ32frVYnl78eHOHSY8lQjbPn9ZkTWJLNgCyfW5XSDjBEhkkrjoIQYyJXeSVl00A7N+eTv/F9+++p8r3X93NJvKpy2RT7R+dUnfzxR9v11+Fex09RlInJIGvX1cNuzlDtkEzkMWyRuWICBMCh23toAejQ6rHg/ucTqdOARF+2s5zglVgweYDWWm3N57k9xenv7tYnZfGqd1At6ZyHBJbNehJh4FsaRPTFYKysbY3/C/ca/8GTLKTqlnSxW2CNyQcPtu1JxWzuZ/o4912OTl6d7a6U0RdAa5ZuiFrnrVcrLZxh6Vvf3BNMtDCzNulm2ToGIH2JIS4rcWUr7T4Exy50QPj2tv3r/tXmxt2MYnpzfSCvJ+wDPvlSCyIreo6bKIV4RQQ6W8bwdTjMEeTU2pQ7sP2JFUmFV/n6PN3l6pgrhyweNu4xmBxKGf0+m52dHbk/o29E9fGPr99c3nyw7leqhEoGGMwwoQ1Tb6rMccn/GJaxPmc9Y2b1UfUNOkRZSLlQvJA5wOypxqVOolS7aLPF4u9//qH97+5OFEG8dPV3e2GAsgtlFrnHLtS/tvV5PR0/svm0QVztw8YRVQB8JIQnlEOnlhTbHe4r4LPb75/f7okhV16rMEtu9GzL9t3x4crkvFy/3h6LEV/RXbVmoB+2he5Pt6+vL84udOmaq9CpvNiCAk8ekXySSJ68rr/w7eX0/3DX+7Wn2/vaH3Oyqnj/gcHQtUH2ruTSqMaCkgpJTEJNOOPJOcH0Y/Hpyu8buTDvX9ysfzHZ5pO3xy/Pr2b7J1yIN4trPGXTzfL44Nz4hBz3N6i3svLU2c1J89759O3u8Xsar113+3hy3p9df/tu3PHAPUX/uZ8dklkz7sNApwPNnTOyweuGK3VBc7PF+92jUWtmN5lIalXPjy7FM2Z0xlPWuk/7K0uz0VXH9d3uAwo9foePSRVVnS3LbEjU0obETrSu/gcPIjmm6/XoCB+rnamg4FZpcgViz3c721QmThwPqzy2dtbyU8awgqoVmWadb6sdrroJ7NAbCR8GsLJ3pv1yWI28tzoVen95L4eHRlkeYBEpK3e35M5ibjkvdiy41/uYOEjHMuOgAMT0KWMkhPEp4PDDwSf+4nIqqqd3H0u2blm2CFhUlu/OPve/Wd8jxEOfESiQuHJ5Ghq/O3T1lwbYX0BNcQvlztWU7uvZAK7hV+85zpu8ggQ3Vh1oMUisDpkKShIQpEdWP7t8F6/b9GhyhA0SXs5kvuVW5YmLNE8mMSIXouHUggZiVlrgiFEXZP4FrjUoPQTlsJo5JAd5Bp5gWRl6WR1DWH2qpqC2ckF4o+3lCJTCg9OxP4P13e8xdl0FZW4fo9WtyN2bGGYZJaVgCGPZgDc1D7yeXLT9BngfmMacpwM5b+xXzhBCrLYJB9OT6+ch+Y4g5w8VtcSHMz5Ifuvc/UAbroYhXrJqdZsvbbsh6Lbtj87EnQ+OjueXC7ml2q7yDICOmOi4D5zT3knMQcqtAYwFW0QP6j6gkxCMKWq+KqXC4Mc/PRREw6rkNTk6OP9IksGMHtfPd/xE4hJ1RWazLHJQUjQHD+YytQm6vrTELIDrr8HWWpSdIKbFANTYqJMQhxYh1UehHAdC6MRk7xRDGN871W84tsZ5+Rt/bivSxKDMYFtQDYK+tjXN2p/SlrD4GQiuPvDcrk+2qLZP6uYR78A7hoTx2SRPANqwmCm2Mi7AUmANBili/YGeQcdUXJXF8z2/unl6X/23bu/OF+5x/JktXT49+er9fHjk3kletmI354JxB4JFn+/nKwOn8+P9h/v72/vR4BbysepHrwrhfP6YgdsLDHlT19v3k2nX2EQbzQpPYE6bLdvwZlShzwBVSTrY++T4O9nSn3cM+8Uz9eTw+NvTpcSqw8P6/Pp8e8+vFOscf9FI8EHifY/fL0+VDei1uV0/t352R8+uiV59DAkanlJhwdsf9HFdxkMe789W/70enPHyn9Wrvd6Pp3/9Q/fyiYrTvzbj9d3NcMTIngVdZk+EeUHqkZ4+5JktKm487vpgizA/fxOtv+Cai9idhyDz094SO8mkihiX4fPm/t72FCcLq6ihuL4UNTueW9ztX4SkHh3evzDaqYIkqMH9Rde2X+mUbYnh98vDt9J6U6naBuupueLd6dLWv/u7hZt3+t+tZg5aEfk6CrwfnrMSfr86cqV2pxN19V+f7r83bu5Jk7EMQ7m9LBZt+stqJ6/O1Xzcvf1Wiy+EhbOxKgMQ/2v7p19e5m/voCqSgM5WCXrc+5ZByWOTpd2dvCk0iKzTyhMtQWxo6LxUZcbop+kM2Ae9Gar1Ju0dIlD0TxKD2mRWt5M8EToqE6VFwI4q0OOGHDvyxfMjt0Y+ji6NiXvPKyRZ4pvs1GZrGGaqBuDGnURQ+vOSTnJpHGABE/aosxix26tyx8lQHwCmuhos33hBcsICD57zLCYjNravkIL2DzrGpQGou+h2eZJOiJaJD6ZDm/5HMjWQQIOCZ+JQSZaKMMoYl+khJ2+/6YRvWNf2umcLpbY3J4I6cS9+HHNGotWKd0oPJDN4wQGSMrzTICJWboW9cQTiTlMxB+V7JNvUwkHHnvOIxwC6p2KK1u1xmf+OGnvuupjG6D+jEgXUn3Z+SBE/Ay3gPgDjfQMYUN28NPoAzM/i/OUcKrb++Oj8w6StKxEtiYJdzGf8opvlC5yaF6//P7jl3//WVqdU1OOlOBPM4/zPpVTAh79weOyIvqDcmMCioUoHhh1XYv9vcvJ3vJ4b3N3/ecf1TxtFyfTEYDTxi9VxmBe371pPWmoQSfJCLtjLgnYJL5phqPX00klmxQRyaUq8EF26U1v9BKvNMCDUwBg8fRQ3H1AgRVptexi7eItbm0staM8g+D00IlUVrT8m3zdyeT0ZHlqd7dH7tveMicRuEnDO63eqojvrGvKQCBn1MygRYc9Nq+VdniAPAMUELZ41ihyZG/+lq7jScyPz+aiuLJqXhLrFOg92u4dfnl8/OMVWdQ0uMem+m7/7XR+/P35/PTkUEXduLdTJYKZqR/Bn73ffv/uH4mLb24PWXuvR98sT79/f/aHTz//8eZm+/r49ZHO85iU2qHcnV5+7+YnIOPcC4UrB9oyKQT54dRNajUd7UOx7NXJNxen/+T772fAtVl/+fTlRpj18XE2lIrSk+nzq+jwmSCyVrcneuiq2ZDGm7C8ZtO9c21vKe8RuV6sxEb2tuM4GK/n9rHrf5QuCTAqH5XXNin1tFNAhdMrhnGKGQe+qlT5r//R6ner+aU86d7zX3x3drT68Mfra5lPg4xLMZAMkbC3fD1QIfLt6vh7gnm+WLnBYO/wft7VjeyQ9fbtbPLy/lxs45AJIoaNC75uxcmfxPZvbsiFvbu9++XhN3C61ToSIoUTn19nL3sXs73fLGe/O1/ZIOH53bvT0nBKaSUyX59WpQGPtTJ/uLq9edmsKRAyZX3rBOxiVVGyRMJCR7Btl4zq+3QmLbicFzZ+cxOGtiNOO709rbe/OTn8QFVsn6RY5cQ+f/xFucdvTmdKrG7uFQywRqVhuC+Ksnim8hFP37x/h729S6ReiniKwkxnf3V+Ji3HNkS733x7zoC7vrnRZitiqxRJL0mSQXgaCEgozX9doXrvdNnr4+NyISM30VNXgINHfj5jfjC1ym2OHg97M0B8eVbKwsFMLCIid+7FJrU0iG64692C4L9KboT+JDwrpuS2iitMC3lnexX0ioyPHb918ub+bnV6itGBNP9vdkjwIl0xjeV8oVyOEEcWlc97n/Pg1YPmTYiTOhZBsPJuK6gfrMN3VMpmKRZTrGZn6Yhq8fJt7blgO1ElHJidgsMZIsps8q/9UZhZOcDOovVbSY7pVIpIBseOytxW1EY0syjsWaG8MD70cAhULqr04s89I79PVzdWxxS26haJvQyaZ5z9I0edPlCNk9tRuyT5A/MbKcuedDooyaFogtA4dvCmwDxRzm8Rp7D1xEBWP5tzvuSgCwgcYbNEc0MOWRx3AVYSScbGS+V+n7IKKQZbEndcTfaImN+czS8806nuN9aL/h/YiSq+3VNqtnDP3pVGdlRC+Yq9Lls4VkM2yd/HvuLd2dz+BEtTCwuoOdNMAnuLNfL73s+Of3s2u5iSVCKze2elBN/u7xx6gNPXDZXDFzs6nC1mJyfL9Ztisu1iefz1ZvQeTjiVCSMSzQeSUkAoTWZmvn9wMT367nR5vtQNYV8ROsFJuKyvr6fb9XLyUrV6USnrQSVdMc9/p4EHujsEf6Yg9WJxPp1xUM9mSy7py9F0ubf31f0EjCMyiI1cfJnArn5WJY8qLfBPZCrMUIvGBU0pJLAVTBQzEs7gQbByBAmKBOxdzo4vZ0e/Qc6EYdvYf7s8xVeUlos2r9DP1fXnu301/CyNAUWQq+rjbMasm/3l2eyXE71Onj5d3eVh4JWXve/eL+z9bBR7Oud1/KrxFAO34vVzRytf9oSqPeizd5K/yNth99nMGdyrt4cvTCCuDdTlmWV0YxXbLH6neSpSpeFESNkBahAnk3fTsw/vGM8/Spj9fLvVEWchNmV78oTFivd/3m4kFzs+t9mIxCs6Wo6qc2TAhL/58iTe5/ubq8fL72ZnB7NfrnTjerpczNZ6ODzoM7H/3dnkl3X2SbdIMa1897L3/dkh+vyrby5WB9Mf//x7nQo0/HFg5mm7od3ZTjefv0yfp5uba3A+mzBseYPT+eIUPj7+/Oe3p3tLPXBoYf95LrS9/zp9ef0nv/1BeevXzx95arIXp5T324tuLlKWVzfaR967ZfHg8e3D0dHF+dHi6PCbxeIHxvPe4fWtUNs9c5div7pdE6mCHarNXST9Np0/rF67AJG7Mz359mLJ2sLgvCvZwPfv3rlJ+GnGqn1w7bSJyFE19IfLpeJl9P9wvBHmxTgnZJRmXK6HZT3It2JfcTzhO9WWlKJizeqI9j6cnZ/OlsZnQpO1WMgDYg4eF5GaVXmyfZQE2Hvb3NzJjZNBz/fbj1+uqw42jzTb/Zr1Ii7DLFWoyjQU0Yhfn16/3t3PFksRkefnrdB6OVLfiYMjzOStfQm/RDNOURXc1n8NOSF39REjzixgwjHGM8gvk42j4IyARaJN/0hKzZyNKn3uJ/KKFIninl70Ui7l6pSWaImFMhHubkjH9YP2/QbRIzOiJYWTub51oq02XjzdTi3k1eJKZhc5muzpTKh4vZ/NZMkJfUw5HkD1VkhKpqIxQ7EFVmYNjoyBkj0JlUKRidhhOPcrVOsZXkIn7HgstKAdqth5Ut1Q+wK1EvDLEdwoljxw8a996C4D0EQlLaXoXQs5Yr5Cx8rMLXWoB4YjZ2RI0Mqs06+etAp3AiuZUWNwItYqGaGSD+d5clhw5P5s8u7sTI3t+jWbV1hgaAc7L6uWSZdlJ2AFIlqZO9JV+ZQNYDI4/e5s+tuz1T//zbesDGvsWFGld/t3h7cTZ6WP0turQ3GSBVzr1/Z0cPty82gfQmmKZCqueH1jWwAseBbIRgd8JmEEpyJZ+c+PYo3fTvfPjt6c1FD2o/LAaZTqMy7OVV5erM6u7ur7piGPyiy+2d3L809oZ/ukLCdzJePQ4pmrB/d37BtZj0P/KJX7djH7fjV7P1eX/PDytE80y7y7h/r9anZ+8e13auE6xvaG/Izx/nTxe804aW/ExF07IZeZGFNn75SUWM+p74uUHX36enX0+ngxB6WZxl+jOcQxP0knpgensZPRRHiC8vvl4h23wq2Wb883qtBAmvBCYwoURQPB/+WZelgqWdNKbKWYavLbb98jHPDHV0NtHv3JcZSH9d9jd86ViIMuPkXxoEnQNjLduLJu7QrY+wqvQJkSUA64mPz1t++nL26hwDtmfOUa1jqcBX24fz4/vB9e2sqPrnc+OLg8W304fff28fPZ/eRm4+RkdUeM2DQXMvS9IdRZOqVxdHB2fPh0e/Nv/82/cT57sXdwfnrxvC9ltZkfvP1meXRO1Ry8LdH3RO183ejo4PcX53fbzbsTOaWnrdKcI3VcKexuId/fm8tkTPbfXe473okk3h8fClkoASeTNq1k7/h5/7ymvm+MA9E3lrEz8z+spounva1ADn/GzV9sX8m1l+f3PLnT59XkyLkkti7/7f7hVf++s4vZ5PzYyY7M82e+ztPlfHphb+J+m418u2yhYAHyZHjb4znTmE/w7hS0Wam3snbrB9GZCyHPxWy5WrB+kLeq95++fr26Wv/45bGbhUL/3uWS9uy84vXjNQNX6c3l7NAFKATx8WzJFBXqxYxEDiidL6bkgfMvghvKZFHZmTYy2ZNcrNdbfPPyeHr6zikUlwYKcJMU3NLLizMG7N3N/VQvJDH0uNlyXsVYljXIU0kJg48VxIh/Hh5ofSxyslAwP8Er2Y6LC+UuUSO+Vuj+okpz7rfpJpkIwUA0uN13zuAY4bo5nakr6/3x6yeOcsWat2WiIDHHUeZZKRl6zL42yJTIuru9JeMkbXwvXE7yBh25h/xjaoXTwmZk2ufPuZVLZMbObh625B65e1gtaMhxyJw8FaQgrFi43rRxoEj9EYP1y9wYOup3cMeCWCcKx6utZSY7F1ItCgLGF35XtOkpO1SfZtSCDTOr/akwXzheElPkgioi+Z+qc+sy97RDxrW6bTnnpJqNpO7MlkSXFkVU7siSvSNT7Ksi5OpBmYMElNQxRnKuSg2P31n1crHA41AyrbPMLtCbOKuzutwW1lBGIMZIc7xsQE8IgkYclqoFV7vuYIrjGXpTPJPy5hyqi8aAAG7VsXQc8cuZu1xNb9+ebu/ufM8YZYhQx0EeRPwPcIbBeKN9hw0keHI09lTync7cwO7ugg5HeFnzEZrK0Wdyg6ZhdrvcVE4F1ESfEO3FCbvphffF9c9MxNvHBxen07s7Nx6qiihEDqWpRRqGzuOhvOz93fXDZw8QZHjA3c2PT/ODQy4LD+fzw3Z5GoDc5Cfwy+j8cLm6Y4k4BrPJOFEoRJiiaJsZFktlUvko5j/Yv+c2i7c901/r2zso+woL82UFgI4FplSPnS9T7LR3dfNwxN/qKIpE15OgDbwKLlr0/XbtzpP1jWyfWgyejQrcowfh7fsHvuuJgvcT5+AmynbgVpaGvJVpkAAUYBEWOtJ5QUehF8mzAtCq+xzxYPBWe1ve+4Vqe3+2VIpJPXz8fC0qREvc3l3NFSoei+W8kCrcI4ndTT5EMUXwC7hv+x9Oz9RmuOryN6fd2SAq+PVuK+ghaIr8p9P55/3OTyiScLGZKnyXjjlh8o3M/muFehyXikXUEswny8NDhvnZ294tKqz9S3lR5nAE7JQmEkAXT0+KBd8zAg/39WAhsFRjKH7jPH0wwvQM2Gz+TJncyOU8brZCvyJJGuS6nx1fiXw/Ej+zvFL+NtpzAoo4O3cHt4Z9WBXqq3M4hlhboBl0G2UCvlcYrRJLceFWOEJ+90Vg491iNiXsNo8n0q5vx5fvTpmbVCPXh0f27dmSDSdq8cPR9OFyi/3IJtd/U6LfL5fk+2S+QI8iMPd7hywMN/raoFQpI/MiC5qo2X9/vrRvUvF5sTz47juzSwkKFIjrOq4qu2fx6qAuRaLO5nKnlog31A4lcgpDj5LtOO0FXWL5V3TLx6d5cfPr4R0Jn2mK6ATM7nUHkxOVqi47PllnrEIEE+cJ24mQKYucEj846PNXphbJ6KZZspN0dtmba0S3nXnEZOw1ghaPCbIXeyh/QtRiOeZ07Ic4C1MUTLAFnIiBIL1lrGkENpAODSOn93pPYZOV0hWUiuAqewObVT7B2nR26ulZeaUxmLfwR9iJh+TyM3EyR8n37rPNfjVbESJ3TL3UV8avNizc7G5zsWssufP/xs0mrlKALqGHMqzKHBJC+o6osx1nI/r85fhkXvSigxc4sIoS+FVNjIaqM8jgZZurVilEYWAgpSgJtEwqotQpNica1AJt1PzoxV/PBqJrBMaMAHOkpZWwAbhYGZgiDoRtlVGyQ3saUxPulD5LtDgV8QaZfA6hC9+hAlWE4s6IxSkHhaf4kStHQwexqeCVmB9tw3VQz+kujDpR+xtryL8QupleGesV/IgY5B5BfL7OsTN9R9PFlBHaJeqUvFySsZK82fg8RKwvEaq4+KGQFAnKaPOEVQFmICX02ACUFlLwIQLgXpBOttlOn9/cUP77Vi+L6Qma0plD9UzmkIkmZQ8lJ7kTFgupPz6+3OGWk2Pn8e6vHkyxOt53+ISevSPAzGiyjEp7kl7rJAsQ3/P0Xvd+v1l/2ltj/rOZqB5UdkobJ3w4nx6WTCF+hSlP3iZzlQcKqw2lb060c3J4+WHx49XDPXg7JCEMULby9YvOrTe6yD2+O55KXaAF5MUp1mxHJQCCunI966HGdvOzs3OO1R/fvqo/v+vUhv3RwE4gr2Wd5nhCvQMF9iyM4zCA3kcv1+RTZyb2BEpX9uAu0c2TAMU2k+jNUfiLxQkD0xVQD+v71Wy2PFuev+396e7u7fZBN6jLpe6D3DphKB6CqwQ3LqDqbpE6QKBy8Hh9/lyZBCVE4lwuZ+/OZj+uH39ZvxbLlEcVSH16+nx9fc1Nf3hQW0J1M5nKyD3tffrzl3+t4W21wdtkueDmy9vZAmxebh/WrH+WO03cQSh2Ckpc3358+dG5nsXbyzdCB5RDxgYYMw9fJZCVT8sfquJA4wL/cj/H784xq8M4V3ebq68Pi7Oj04ulDuVMLE0N2Qzbu61zv8hG4fbisA7el4cH7y5O4XTX4kTqWi1mZfiodpCerBlSEMxmZ/FcDs9mcuNq7/VGF2MCxqy8AgEeiICo1f17KDiRW+z8t9P4t3ffzWdvp0uyhibXhkxHUHFVl/kwrqWzjgl9OoYxO3/UvZbx4FTW+UWZyVwyt59qQvBK78zIGtL95suN47tu1FkttYIRFF8TE5JIcCBgSNAWfKcfFMIqOHO7yBBACBsRCVaQoAxHD9MiipoJiMmcMUzJ06/qkjMhkaVOAGiXwDpbFTWEEBDBR0UnrKtCjdrmzJwYEJSKXxWt1T8KlDyJm+lgdw3SSwU2QBjvUSujgqv2ZlXTOi5woE6LMEUVehwrzUn0FA0ol5ZdifL8jMmzCSuGJjSE8YyuPFeInyQCKUoIIfEl25eQ0Vx4la1NTgruj7RlTjkZK4zuFd93GQPMYWpgoQAVrfFJmZCMD+yG2tzDSbtkqqMYR1YqJKd35UULnTGRiWBqz87JITCZHs5sV5TpZr2mpxUncDfZCikewpFFaKxRr1ZwpsoGfJBOpnboSOTv5bBkeYJDjk4LGkh36/lclTvqgUNqid1f/kOIxf69TmwzbXlTp4uzLOUODO/cCOeleY96nwg9sdYtWEC3s40JzczTY5bfnoTFkQM5zBGpSiQsooLy5Ic92dXlVjQsM/JtyEnhB6VplGdCAWSAs9JOX2PtnVupj4fhKd5unecLoemy9gHCHGLJZN9ZJ6TVNU7WOB5EkVc77Cm6jQC3ihCsQgYu9g/uLe5xT2nH3ac7gYcsdduIpFLLtCqFJcJjwYbJ1+Bb7+0RwRwzm4VLJraUNnQ8MBA7QYNsESVzprlIl9nx4+XZYXFZysYyEAGfS3AgT06OueQBE3UtIvHq3N3TWuTRut6UUDulOb101Pxk/2HyMl3xyykxyYisVRZtdKHWu15UDroABh0hDp+VwVag821IuvVCwp6Pv1idnp/Kht0+3G5fn69dPfwmMViVH4Pm3cH87FRHcMa74Bxh4gDIi5pq0CaDQFiCi632cfPw03pNq5RIPiCF5QYX/+zy4k/3DtEdf/Ph/Ozs8t9fS5aq7yH9yF8BmX3zoff7u4c/PT4tQATPJClykJKAWgJs2WXXMnh+LtPz+Dy3LHoZlZh8s/3ll58cBHEqp2AusAqkSnCq02Bn1TYgf2h5seRFO812frbC3cTUh4uVpoPELlqfTxzxflWT55jQhWMyb8sfll2hg8ztq6V0AF0sGedDq2QS23F4M4QihiN8nAl/v4XRDo5JDaDjh+eTg+PfvvvG4TZFeEeTzEOMoIgZ71uSwwcu5z7mXOuYInLgrJCMep2/juWLURPtSgSJl+Qi7L98uJizuUWDihtUtDGk1d6hiD5xocpQ/aV2Goj+fLXCGtK7sI6sn9yx0JmvThgY0+txptObDqfSn501OJjrv+6jiZ4H6i+PhFaIA+IMp2E7YHKyATRurjlyJa2cZ/728oxCwv7Ep1+Zlxdx9emBWOIDybfyaxXnmcufrE1CyJEx3cIw3OvTfDZ3ip26UDd0L2MBhM/7T85/qgmeTLpBUJuB6VTEZr2+C6gtR4SGW8Xjkm4jxWp7aUwNYcgrEtnaoMeqRpleNfSwwM1icoKF+MQQm7Uu4Jadna2kW/ELYhqGOp3Yic9EDK8u6yePgZShyJ2FJMqRPyfJppDEXGDnWRFdZgqqAzxgAEwkbExEIl/oN8KtEwf1iOIuj9RtpDB8sszZl/RFlGMIzzdQvh/MgwHY+DQZh68ZaqVdrQxJa/WaxsKFOHs4L1bTo12oQjTadHpmN2BSoMRDrI+iQGxnpcs1vD6fLhdObhF3rFiCW8a+FQgqGbipWVMCdxORG7aCcJ04Ksb2pkg0vabahntNetwzTU4K1wmRpW6VktrgcDUYL4a07ND3XLafxrIa67Zlk5LD4KEEi5AGLs3ldMSAx2JKzrJxOmks1TKjOMrzUOkTdVbcEyzzqJW0lIhkqS5HCsYPnvyG6GcKpFp9kGp5fVW2PGdQi6IUSRrJAdPSUamvDAUhFFIMFRMKcrACDmG9fLUqt8PF5IUlm1HcITRdGer60HXAecggUlViNgVafNu/chedSi9FHEDlkbomS+FKVwARzGXjnjsONCknrlvKby9P5dDkfp2msT8Y4Nd//514gLAjYnKT8sOXm2sod3x99lgRN98HLg8XJ0pzmDB11j4++njHkWLNWyXlVsc6TMOR+8ffvP9GR8z1+jffOOPiRgVFxpiBYcNcehY/OFN4sVx+vd8qnru/UlHxIosvLk/JqWHTfuRkpar9xGE0Z7H12nh3Pqc4v304QdzEE7OFmcFjVndwPd1MX7eLxUpW7HI++4vTM1GXjcNR4Pn08svtl+trhfgbxykA8PXGAMXHyB7WiLCAk6Zy3NAWWmyT4cF23+4plKaLWQGaup29iFwdfXcGcknobxYnK22q9g71AdQ9lRighgUAEYowwZkAx+zocS3k83hz83a41J9lb3W20s80acgO4LmTN/TKw2Z6Kd+JNNXqcKh5gNAKXRQwraTugsuJgjrasizlyE7BDQ4HNZsoCI2N1B0xE0Vm3Cmapm9wt2gwUpT8jYOBUzcn8amCsM+OnolEkHHVwB0cKM55eN5iFoc7MYlvSJTHu4ehYup7nBwcIeAUJDrmu2deZ0Gqz8Xagn5Yn4xZSt0Qwa9O6jzLbFOljnryRZxs6qJNcuH1SaxGd3Vzc0GJehb9Rh7dlKQ198IoeIe9qbRpeqLdiTMjmGIOKOZ9ebrpAOqar0XQC3dYsIgbz2Ax4+oQC7rccKKIV4dH4I57YBZL5sai0f08S+ReeKEbOuVFGZh6EcxXC2AS+gX7zeOW5X66f+pYhLQtm5ccBAMsm3d1v+U7Z0bVl8t52G7OmCkhk13Ke0huVuuSaQhx0pZwzXERhVATIGzG4Ux+52ZVQimhROQpaMH3B0xGvY2RAekGwi83N6enyyGwpRGh3X23pfNEhoUQSH+ISoP1RFlwe3RivlyTqk3jWwSap3+obiFg4h/+hrA2MzG+s3hJEh5SBjVJ7AfugniMf4EtR6sa+WKCDacRvYQlW9Dbne+NMuxTMZsjuE6MKgDPsk09ddQlOPN3CT2m1HS7FQt+Q7Q+E8YCUYyO+NWciFPZg2ITWyFbzO1sHrpNURU8EOpBmrp+ToG42J5KDUT9KB8enTOQMALsibuwJDyT8zEKRnuR51S/RW5A1SskUfmg2i9Z6B7Ui7iIfd0NG5/SMtQo8kyWtVEtLoj5o0JVqap8DMh+ONLCyeGTl4W+2Cp0JAghiHUpkmPPxDsL/eDd4ni9vfsoJsX+Qsts9oBnVYDP7GKMHgnSi2b81er8XlYROg8O/sKRwYPny6ODH6RNiQkrKbQkqiiKxXdRU+KoJCXZwUo8f/u096crszxOz+d/9xlfCFYwSMjTg+8FiF8nf+9ktEMMqmtmJ+wpuUBVcloGOsqopkERsnwVOH5V3/O0ZL2j5S/Xt1LB+TPEhIKZs5UjAT9/vNpnEAsWrZwKcBPIiyqdc4SkeFky0ICLE0RsZPkr7sAlG3455wdyAIRoaTzDOQHCvPr08dPqfKGBk0YRH6Yrxs+F3ATET1y56Yjk5sYx1u3jqYjBk3TNwS9//phOV9qskoLpPKGlHa5CKvt/NZ0/iiadngKOkrnTw+Pbu+vOaR9rwfy4nE4uyd9LFcvpd8kQZ5YIYo0faC3iXvtyEUJyDrdAiDifAMT92o0LymVVyuu19+AsCcIVkEUOovcf9CWX/nrZW9+tCa3l7Gj95M4gZr20w1a47vomka0SbxCRHbzcVwh7L2+GgO0R3kttecCBPrDCgm6qO1aDXHSQfNMKPRkrk6+tw1BNhRtzRUhvzs49cahaaZw9ZMG9njoQonxNWtCZCaChxhV9M3BQdBW1/GWI4wt3P5dQedIgB+CYHWRYHIgWa7NnfZlcmmpxINKYsRcmlpxQg8E2JEz9lwgbdsDMGdg3DRRIAexiZiPMp5Ob69sD6eFtTnqylXxhIPAlNWcnDshqhyiFBjJXqZ/+JkwIGZskFomtnMJ73XRIj+BOOhcpFr6sq5fkiOxrvvnzhpaiYx5ebx5AJvuHQeMdV1DLARasslzRJsuLn8EMJuDZSsVaNutb8N/eyQfkd1i8WAobEc/bZHGB9Eexr8BihSeaszFKhT6yx115qZCUtPQmhYo6KAf+Gw1nWAuu4IR4zcom6HXiFAgSfBfDoWjxAiikzo7qP9/5edb9ar64zXBWce++0s4q+Is5jBpBku1oDrRKlieiM1qISdSLho+r3KP54LUEEOqjnjQx4iLujE5gsEb5MtZxpXGmaaQh8tE20xKYDFo4SldQHrBijeeNsjpEC4OEJIyWwB7FLOSS3TKKaCN6wL5Ic6+QukNLQVDIHxX6uf9AV2PjaKXaHqG6IWL9WNvajGJfKiH5E+lQ5MQSEK55VELMdhkRHgTGPyN503AsTjVRUFDFhQSAqMAhxnl62VLlYji241wbUyBlLINia9q9oPGOiD3qmyQIwvgvccmEzYSvKUGkTj+mzIoHTq9vYK0VQekrn1fHKk0D6R4dNJyl/nQnR8SRqeKWTrEUm2cJad+Podx7j15UqVblbblaaJ/otHxyfc29l4dMf6kMY5GtCO0Uw/P0SZmE3lIMy25dkKsEOI6BRg1KrQ8PZ3gQyMiC5bsL507Ei/72Zyc+Tr57f/bp7mU+fVJY/Zer5W8WAuCTj05eW9Rmw4DEn5Th0+b1/fnkm/eL++u7K/W8LE36CXe9PH29XcMvUaM2Q/24kmSHeB1UcnXDQi3dci5QY3fni4U2JIpQAXR50ZnfbWnNCbs+m1XLPaWFFO7s5OOnz5JXNAI6gFI4hxWJOmGNj79c+ehksjibTs4ywHP8ZSd40KqU+eNq4DTAlktQtAgdQ/PVNI0lJCWmUxBThCX3fnH4/vRMtaP2Czc/XW1lOymJt0N1tEJ8pOxfXyzYbjw0MTzNlB4XIgD7IkugQZGSVtEjcrNzrWE7XXj4uEDHL0vpR+ch7+D+SJEKE8k1L4SR+At03K3XDnwxmYmR6f7b3ecv88kJjXJL8guHST7PlB0XSlHG56QtZpOAyTlkZacgcGYH6/E1amS4sWiQ78YlpcKyTBhWH8vlWffDeigCa3JHhwPXzRzzXt+2VzdL5w026rNe9L99Vh8Wv6v0EB/KXAVTdSH6p/O3hSZIMILbJY4MkA8fVqPk+UB5PSs4H1ycnhLI4dcYTkgwjgIWNe8Masa0djJENuRR+wSM2OJCTppFSVQtgaXYrrp6ogans/53kps4wKtYVX5aJ4y87Mq9SSvT5e+T8OSqcg5yCu/J2mlJxF62E2F2fQMaWe3/ydFyNQO1uH6UHTNaiNgBtCpA4MHyGszOmVmQKhxUSKZYe59bCgpMiHbWE7OiNxDTsI0goy8xL1+BcV+LIk4oO9ZbYqSao43u+6Aqf+093Gocl3gR9yfTBR4mUJC2XZCCZmKDgiRBhubJV/NKXSYLE50cZp4LUWpyJS7BoEvJ6cWa0fO+HgrpHEy1asAKVCb5i/7ZI5uhowhJQ6H8iDo52qCwZ94sFFQz3BzbtWDZFEtiaJPrQzbbOn4H0UF7KogOymBxPHPCorCMZk6SBg+QZXgHTnlNhCr7E0lbZWa5E+nYJyDmsMKFI1DCsyIptLLQvXFSmYlQhqJjX0pdw8zbUVFK7xfXZHq8vDqDsb9cwL5YROWL6kw5VU4HSJOl4RUKqJ86VE5oAbQdmvRk1VSc4Mq/xTYroxfou1xd3DOmavmpImP0UaumSCRcH7O9ywUZpjJV3Ollc7dGw+qkajoNW4qIFH+LslaY40g9M774AdG9WFK9qKXr4HNwKfXpiYavzr1bvb66z0qqnY9dSCWSIcddgx4PdCxWJRRslDrQmUUUUmhquhDWcEB9j35XxPFy9LRCNC8Ojh65jUsm8/OX7eT14b0CleUZxnPWB0rZFODrUmYKClXjG/UQCp7XX2/fX660bnr/m3f//ucvq/uHd4tDB9Xez6b/7IeL31ycr6+3ouTvpsdXApIPzze324XbWZ/3frnZCNTzVL5qBXe/namnUWqFTicLQX9MCjYHsxNJcZc4i4Ww3PgfDItbl0Y+PCpwEebTRWt7Kw6m+x6JJEX2Mk1TcWgPJerXD5uPsiCjCLXa67jpTZdB5S+w/G6JCA5ub5wszD5UdNWMRRG7ZwgDfHN5poi6W3pnRWZNSkCTi0s5I2J3//Xu4Pk8G+FtxWLkFu+iHczJZ2WIrPU9oSUd0zQvQo7VCMC9qN8InqBNWsrbuALVxst1JxdV8MqDsCkO4J9f3VwFata523i0vd13OpEstXH12pX5nRxNkNrd+p5fACQ6JyEv0x2tTpUvEd/ktt2p+M8+kBBJduUpksQZqQ6si9oK0jnlXvKrwILoCcJD07CDuhGl5AedJOIJg/IoptscVGq9XM7OlxPpSWKC1U92XFycIV8BF9SMSXBYfs/+AS2uwHK9vpnO5zxZ/LsrSauzUoEozkhtpN42gpxuHdINUdxSnVtlEjJqGvPQXSUfuW8mxqNlYuTRsY0o0P7y9NT3j+6HEb709fQ6PyMoSb9K6AhRZA/UlI3F2C8hRL05vUpVrmYLQgIencrC1lYtP6DwHx93INF8xbKKqicONJXzWpUeVM2eoAGj7/OXLxWKOD8jbpCUhaPSxBYA+ETd8IG4MoQyfFKpo2wmn4BNSibj+dQthicoE61Fwsk+FmXGrnb3CKaUKvFT1hCVNw2qIGHpUd6zXJFcCOUEix2C7BRKJcXIzi94BbZPe9NEuf1M5fRM8nFGXbK1uYZDc9Fk9OiKpDlVhkgeMgnKoJMlhB8hCndgiOINSATVpYnhXgykND6hSdiw85GQvQse+BtMRchTFZ3wEVBO7EpTAiys+TvidB8vq6KMAsVP51YiIc5CrZiEFgF6/5OslDQPo/SLGMuDpFWCnZk6Aj7lSIAdldo1aw9GLLLFgb29M9fTmlRX+n+HHoKJsoGt4oo7fA1tBXjRb3pRTk0W/VoohzMhLsoGQkCIRImBkYmyeo+ogdzekVwsBPEpQnglGQCcNl4wyh235Y1BMy1DvSFntDhSsdYEUMr/Kk4itajEocYlWmhGJDho2VnbkiOv9083KtYUIJLqDzTIy/NfXri5y7H37s/85estN+Kc67d9UkkOLTwuKlbM6YyTP6HYT5RmF+82rEMTSlMWHWlBt1J+Z+/EAbo07++urqnKn7+sSe3uQCN96GA2iAoYMR42rBOCU0kYYmHfrQUq7v/mjx9tTWbWfWhg7ELl33/85evT8w3TzTlikkwDpTdR4L0vnzdd41rfM/7O68utXOL+7W1ZPhWTUvpuBNu/cTj3iX3MuE9o2AM55BDjnTBrAZMsdifzN0+bfQ7AsSqfq9t7/jdSYOScSdVUDpy8w/E8qzi5TGL2rK0wBJ1ZhR+YcGcMTY493q1WmKNGyeU+HMpP7iQZkVKGiZtCRZax6OP37wk1weLX2683jDclC9ISp9MzNpBl4+Qa2sU6TO6OHsI6ehWkRWWtZ1rOlo1meZbz8nVNvY2KNMKcdYvf5IoopJJf+csiA3mU2PiVy5McnHbIq8yw4zjdTmXA4uNSlkp21W8dvR0ZUPHr4fSEIKQhkBUIAl5hZUJFWm/OuWfpINJOIYMW2FnzbijyFOWqfpOQkOMk48TbZXfxE6ZGybA5Yrzc0vxpqiF1wu1lH1pTtyapaDy42953MAz8nzT6c7Jtf/11s+fs2Qj+dvqlTloEROYYyQT0AIVFSEXuwJGLAoe1zF8aWSbxCZJT5HCi2T6puLnqYLmdiXnLTQoMAAK3Cd+SsRlo+fjHNts4JKyYTOY2R0Fi2k5EMinw4oR+sFBZ4Q4o4cmThV0Id2chsh1eH67Xa5aBX8kqUVAyfk6u4gJSy4DWT15T7CRNwkwMTWYMvOynyBb2GSk5EiEu3zueF0pFE4hZDVUC4dUNrGtW8elqVQhCgqSQ8gHyRv7yEI7REnGLxZxtBG2ET3kb7sKw6QNpWC6LK2DvhApRpFDUf1lNYhuFWxPB5DOws2QdvKCxiGIWrnISVsEz89epIM0FpFjpsuPjOyrd0Q5c0i4cPKaOsv/pbwYpeW2rw3a3J64mzVQoTzQiEc9Bz35S4fPkfHb0V7LNGElhJhrMQwfCo1rHW3XIKUIeXahbjUlT7QRALRlYU9VZGmX4O4gawXR+N0WT2YFJ3Vr4olaErPt6fRPt7fHt0G3yp5ocnVDzg3A1f50zyzQcpQxEPmFOBNAH+EjQ2CecaDGVE74r4RcPwpFBpC0vHDZ6eb1+RBKaf8TH5CzgSsGa8e1xnZ+BQluy7H8Xq+ioPV9MSVyBVJERBFzsU86Cv/O28W7lg3JSTaJ64tghLVeKGi+DYvit0H349rj/3/9XvxFA+lx5sqjudLt/dFPvgpcr9dlvjvIeOsw1Z5fcuZ9ANDtVbssCOlIQn928QEg8OKX+cn46FaeWwSG3WIOYWkZFD5P0PsVN2x4ef/16WyL67WXl2KrGfS9C4fW4u7r56nmVoNduC5NE1U375Oj6bqsFuaYr8rlC+U/7U+0HQX+5ZAgXjy8950pPkXX6xKnL1bICWNdlb6Cw82g0MWNV0JntgpCA8HYjKprRSwGwFxERK6k2PxG+XZXTRDsb8QSrYy6NkNku00J8IkQ6VvccgkkOH7g1YkG+JIjxS9pomlZFCREgkOMTNoe4VMkuRk1GlpDU7BhhyWQwkVCvq5/RL7P36vMdA4rtvdTIdK4l4VZoHg+eni5QNjFBRSMj1pwOMJUjDfMtY7IjmiBBbqpWFeWIMxUdYuhdgpSBCe2VvqDEbUfVZ7MFR+bW0dQ7GdD0C74TKSLNiUskZyNCXjiQs1zURR0DSQaZnog+xa8zoLDxze09bTjhPGKlrEAExl+FEqIsK5OiEnkrPKmpMn/VxMxSnjD4UpWYsgoTkoRr7Hup4M5q3j1KNdsHcXigXMjOa+brPFrnF4fOBop8KGKtfG88HF/T5wibNdn5HWevyCfAphNIKgyUGuKty3aZRTi2/rqlxuwLRLEL1oSlnnGAWE3wRlJNVogQNxLVTrD4qR5hhIX1NSlGIo2UeGakq8ap8QkoJI9EGPhij8A4M5k1EKlwOmIMT9M5r5lpae/cYiPltrfO51dJJ+kEEh3c4bcRcE6ddPWR6eiQhViQoezUBPpL0evlBzU7q7dPVeFmBlF5bCNQudhQUBF4h84QaGKHhRG/y3ecZQ4bAPVYj48MTXAAttgHyjcd2gM0OB7TkpByFfL1W6LZr2Ra7JCXhrkQFRJAScRhKgUaiz5Zb3U4GfPMZw3OvIX44f5V5E90LrOLTrai1D6MgKZB3uRj2I7hXaBPVz4jJmLRH38f//BXRCygpHNICDGjBGbHOw0TLwvBhcTEvTHJZSRaBrh+lLXOJxCoB7oTXA3FV+bVsTatqFSEf1ij/QcU4Ap4ImMgMIKMyAp+/TeKRBERGWOHdmyHg8yKy5RHEQixrhzN1Fsc8yK1PvVvuBZaILKFVFAyKOBNPg37BA1WQI8i4nzUiElXDpdOkyO2Bf7Sw/ZdYmyIeNACQ8c4sqm10mIbad5V6LKL7Bl87FO8sv/f/vZUaEQ6iTqQZdHY9NP63iljhC6/sDo6+C7Dfl9+Saza7pGao0zVuB0cXG8J3JaFD0HOhOpsEfKuIvd0ZT/P+oo49wg1di0Iz6cRoOVzEIJWwKLphH/l15tjDd0nQnKaYTnzpPxSncxE0E6ocqqaaTIX96KLnL8TXjQdxnPIkSZilFLCDE5uXmk1EmZ/fzWbX3+5U25FQCfu7+8yNYHAIaBuB6O50sbyNHEvaepouypJKour+PCkFISxUnmwbFUJQCZSVzoAvjtICfUT93MTFSwG50RjRQfcfCwV4w+TMEMO0ZWvNU6MjxoJEBrSbLjRNUYoX8pMfal6Q5RFN5ywIFxZLZSEDsaCa0ZjO8QHwKJtvCNT6nEkjiGYdpYmSCDowTKCFQ+wT3xCf+Szw02WBy8YPWREkj4SEkJZMCLyMHJGzHYVfiN8WcHlYJzUX+QOQXw0jNEaFUDF/1LuT4Wu64dOnqtmOqFsREKiU55dYo41hzDsLPurMp7CuMRlrE0NtHWvsqw7xF+h0BgQFqsnls4tUuZ70VujT1UYVKs+hJXAa4Hzqp4KRgjfCz0phun3AEBYgYAAd8zMycJ2rYH0FGcgxhP69hjL8RJIjmTSECS8UlIe6mS5TmYgp+SDYQB53sy9wX5oPHlC10TwRotXKZCgmwo0qc+jTaSL7oWJDqSXOUsVepsx8TKUQUZ80Quuas0dPQs+gGw66rZglWHxKFxXDz4cmoQjFZ7TRAz4daDc1T8WGUjtogsqcad+LYiOsXCUhyuACM6Qk4DE1n0W5dhtuSy6t3gnIn52LZRBusviEBFiEZR3qrUvBIfYoz1GGL8KPMkpv7NpKGOyV5iCG0Xw7h+RCtmX8SvjRyjiBaiCfiGp0jVdhm6se3oKX5Qn92W1xoaj1D/YMt+1/Ro2nN2BzzO3FGXSwR1QYOE9SShyAPYfzEgnhfxcWMqFXwoLLDwhArId8AwNB8CX81rYCZ08WIsRgM6kiIrusilCv9VorSibhdHrsAuMXAls/XJ7e0v+0Mr0G5MBJgyOREtfYByLYL9TNirUGwephBBEDlm0ZT4J4YJVaJ4n5e4ix9xoprhjoV3vKMpXwqCLAVIR1XXWXxi2EUl5gry1kUWQX2SH4GVyNHJjRyhJcOJPcj8JVVLaMYytHFeMuP/Ll5svksMcNIKaxPzfuPzYOo73yL4v68efrx9uefHSClmvMjZ7v/0g3q4JhIDmkyK1bDEZRRekMISdWSCpNEKZnkRbaqqiLbyJQfeWc+wn/JukynCTtFJfzUKRNthuZS/VzlC5YuYombJRfEdIdrBvX5GCf+YR2f3t5vbOBcJRx4nbJjxblyWHJoX0mgY7JWRc2/kglGY4n6ERfI921dtn03ccaeNAHePXH3YmqJkcVji6kDHMlErZSlLs3N7ubd+nmjJuDg512eW1KYiONDNhcbV/5Up21kZFhH5l4+JFcoHwxF5IDkoMuDgKCyT7nLRcL2err58/JUPwtjBxBRLEj4zT3P31iF/9C8Zgng6PuDB3PKbQwhfzoS+yFX1jKVyC6kTFHilLtEgaMGiRqQMDdkoaptg0KhCfsXYxcamHAv0OYJXZG76uVY5YTeUxQGIWGEu2AKu5Ra7Rn9UmKo3gEM2wFh0jxIB0HGhYavZ0JUAFSrzlfVLBWpknPh9SX7rVQb3M6ETSyHSSZzbC4TWzScVJ6S5sLCqBkiUMAYqo9TeuFnVN1peNFJxLBPLDMQVuY+aYJa8IW0KxA1z1/nBcTvLDX7DGegCitgPeqR7cr9WDOzc43NOpwHKgpqvUAQdkqpFiCM7EllWTYq0h6IAcJVHpiCZFfDP8wuov7kHGY19Pgl7xSgVuKutB3jW5zMIa9ysnRed0gQ1SFCQUojU3QW9f1DOq9mUSs5vMVyJAgvmgs1c8hESJTnMJig47E8RpHB6nhKr/YW/YvHZSL6XRESilICFsxCLsoMe8y40zkeV2sgk1qti8v4K6meMRwm73zE86Si9P3lhha4syMNETjwzRabU8b0EtnEr9CCOvtVOeCZ5QatmBVgWKdo2gxjUJUjRmCHW8JzFuwE+T+ZCN4gWOi/GbCSp4mbwzEXQ7RgA1AiBoW21iMX1JPqJl77ItR6hWG7ETzo3SBqRodw9a++EIA2cfOkWVvLJbHAny0UP5I2M6a9I6qBR8xWyIaKr7AKTDm7VkbOn9SB02rHDErEaQ0xKqzpSCwgXkWaG2DOJKWgmfKBwSKB2qWzUtjBy5D0f3C6X9XHP1WmRU24Qj8YkKgLgB2fJq2zqYdnt9m33WuTbMK85cUZZLXa5vb+PBg8PvvvneLzaPTlxzCFiBXL2X6+u1LYu0zOleyejlbPPy+PnT7ZePXtuSJ4CS2vzv/vk30qfeNKmg4PXmZS25lBUpgKB/J2F24JBRFkKmCecuXkTfNL2EJNxCG02IpV2zST/gRpQp+kE0+oKDbIKjQ9sgJmlIPIRxkR1aIo+hVnWnsLY+a/rxEqvFk3lDnZF8Vs+smjhdfTBxmJhNhdNR12LCQleDkk4kAQxZLWY15kri7rW/M8TmDu5PdAzyH9mNbkRvKXysRdkUsmEIDOlAZEB+UUI0oYQJV2/YBfy81k/l4xZ0k13DyibhbWdN5lLvwdCiuNMcNeCOsaXMiQ9hohEnRb3cEuBeyHWknbC9Ml4Wc05oAYdiOBmUxb7H8Q2ChpiwWqvb3DmgKBdNLhjYzvI0Wbp25CXOHeq/uVMyz+bIaaCYrNxiyTtQk5LSfuDEkYFRqxCdZd/j9xKbBBn2pBIIBQCxmNIsdJOB0mEEBO7I7bCkfFwAcW+fXM9o6N8ItLVzPSZyKDbVqLiYp+Q3tD5IcV4hlaFHbT/pFZNbI/NSDXIeHiJRcSQr62wH2V2YiDvcMziTR0LFYSGF6uQ7HIyKhmqrLSprDArpAyJGr4zdrgFYFxDbJCNAFtwARBMngDFmjgaHrIWmLNlGghW1/ut+QoYS/eF4nJjYsFe5dY5QZC+GZLLVr8db/BaKoOP+uJnms1ssrmFJop+iSAKQUB0ZG3zf3Riq7K3r9vYGdFlwwNLyEwoFiGmWESrxXpot1ZtgYZNgE8XZqrjLIoodqFohMogtPxFbonXGILyJplwQTF2bAUXPw3mS0Umw+BwyU28eIFq5ghE8U1ahwUC0uQZdVZyKhuEaZMTBEIHoKOEOpq0Mx6d/GwQ1KNiFL5YWzURJmCkCI8CqMRMVtEWxxIT+ICVJow59Usa0rY88CpAZbZQ625qrTZHhZVRjG0mPXDbGMnSSOxbMQsd98spWCGzEDX4kykeD9jqsZbw6ma7tBKKsNO3B8GyXQpoWwToqikcGgJBatXtjOAsE44oO2OPMeTBHnsAFDRCUCUvDifiLECJlgKD+3PY6nVQBXG9U5ko3X4m2kZdWQP8RBEQVyY9ysG2+jp/rbGoFkS7jc6iitC/kkF3g6HOHLrtTLHdBPS5YEYM0IugCc6zLlM9o0A3CFXDX4ggPq9WpKWhSrSDyDU3Ig4Kl4+PNes2kPJrNnAtxdc6BVorEulr4Qg+Bb/9//198JxWbsfz0pBkikPslpQGbIEZy4yyBBkk3XAK+Yr5DB+o6LYp/UAZJIuveiaoT+cNqMBEsyYr+WfOI2uFPGhsQdVXWAwcba4AzjvDf6AQEFaMSVFG8ZZOjPpAeGKIHyKSbBCNrH2QaTYb9hwx5G0BttPv66kEPP/7ZUXqEZeKnhw0kQUCxDkgXHKikNVGxmM3kCcg79WpSBRgXpxNV1hCPFJfsvojiKMU3O3iieszLgdIBywg1n9TcrAzTxRKqblwQEd9iPBVmjg7QU8x9yNs71bbX70TAUEzUsA9QBkYV3HzzodeC6+njagVYuYgJjgk+JgBVYdmoEJoFCi0yVRSGR8sg1TjIdM/9fdr8VJLsjC9LlnSwGnM4wsoo1iTnWhdGhkD95ipeYqfSsmYk9PGBFTCQiCQ8aZBCKH5nD6NC0TZtnJgwMq5DjikH+oAaYiS5o4plbUlBzQijqsSvZfnsP07zoUTR0+39HesHW3uPhVya7fVAdW6BM6xOTxY2KU4CyQLUihE7LXnE2LlDAPgPDTHSRQCIZ7KAFLQypM92W2/v+Ta2JkAnCiA4CsKu0UAYM9eTlyMhxF5ls/EbZJvTFm3EjqAA5EhYnkFxDTuUDxNrZIPXzCAnBz2vZWVzL8TKI26LGSLVZ7gtbWpJhjQNo3qI171uJ87HV1/Igu7Epq0yC3d+a9sOnsKqxYXoF+NQKoQKqvYbzxnNLCyPADyUGrkvfCGkRKOgc6RSkGg0/EGc/ohuWwel4AVT0zrUqI0MjalQbYEes0Atl1JsJ00NCyQfbTc4gfShxx1Cl+rgUxipWIeV2AJMxYdqDMz9wLmc4QHhU1ls2udsMRM3967VRjRDU55o7kZJEWkuTpkcWTlfIcZ3PlCxj1kHocRNzDKkOAx1UAJRFFuax2+UbJH9xsyGyXzMPBj0hdrcBmYvqD4D+lcPiLhXUZ8hSt5F20UXJVSEdApwoU/leaUjOSIjwlMsXMFCHsuuvWgUDkpYz00yATPCZ3RaQs4E9rHHIlHU2BFiJjchMHs+bUkADrTteBMBIwNEC47kXtYFC6DDvSwk4p83UOcVfEF9EJ7GJ2kd+JKJYUd1IsZmgHXc7Q74fBnkTZqLIkAf15MRaT1xUEUYwdSmlciWGtYS5np9e3WLrnJTMa2k0cvj/v/pv/wN+3XUM5XF6tCjjWUZlOmg8GgWwjSlIfPKfK4b2lRUoSNITNcDB1n7MJDwuzNpXSKUMLUe4CP1oIlwKeDu6hy1ZSmr2kizNJxLYj8ymagWRoRIlWYj7B7NHL3L2iUFoY5Fk6zXHfd0oazvyo1FE60gih8hKeIfECcnM38J2BUrRjf8gER/ljvIWT3SQR+8MKb6IOVoAuABFG4F4MwIrAXHgphVA2EyEMhEiP0yw3hkb0CJeUtaKH+mm6AfQxDVCWIR7YbBG1U1og2Uqhhp5E3Qn46+xIny5Dx42MU8SviZPKQeIAZ3wcRxFWougkAIMI4auGDqAXYySZjHkDFSKgohcTkzSaRDansrfx8RkKgS12+iTOk8MhEAOtOZ3VyvCPdGGAZ1+mryF8YF0UMEEBeeGcGEpES/BjH2Dta2NtijpOg33oHnU1RFHxnVxD+hht+KGvlV9otT09U0k3Q7G7MN8Edk/Kw2Xy/nmlmAu7DGwT1ToyY21A9XQ1MMrRoYJ5ooRGQiICitalx1YmoWWhJ0C5ozvqsOyl/TvIETSRtRcCJdYjhcATWv3DKSwoG1vCLKWnQ4Z0L9CdxmFFGNAKWNj+tty685u+R6GGV/HTPitNmucBVmISOsB5dnA7ute8r812ayoJNmT7hGZiT7PZqDH4eGCrR4nRQI/iWfedVkPdtK8oMTk5cDGp7GKVjPC4CAHPBLy2Z0W6uvBFpGRVkl/lnRc6ndtBfYm4joFsASgSDEEUDhvb5sjzzifGhSRKqKpMGeneRd111H7UYgDIs9Sl+MX2WNpA4LndCCxLdZECH1zCGk9yzDIq00bz4Zm68hHAVZhW6gkew5cRDSYhoiulXPQ/YDevpGN5hUuD2O/9CAuBmRajF2nnWPncl08TRmgVhBlOwTj0eqyLIL0aYnqXnAzOiL/okitoeDxxIYHubFVvNmoVWJsUEVKXUaEUMRYpCV9IMc1ROIE7WDtWd8A4wUSZ5y4YtORXgRQ+BauTpSDgGDDnSLX2XFPotbr03B1IirGNWqoh9fnUOCX8No+2Vl9TKotedI7I+4A2YUvYgjaX6n8LRiNa7l4a98tWEN1j4nHojuHgWsfu3mxAYlSAkjC3aSGdcJc7FsqLzp4tile4vp2U9/+pMW66LMjNSD6Tx+dD/5/+GfXgpJ0CACpwggp3UUrrncBwodnCl8wTYoq4baNdLLZOW8ozy4oduJPlVu7AQ2g7ISOpa8IJWLzZB/6AKv2Dd7hGSsPWzCVCqfOFMUOHxb7siBcgJyUPAaogaHaIO3sP2O2Lu7kzdAE6ydd3BY/wQWiD4TWVrK/ODQVRg5tum30XDCD5igExHOCQvSHTjzLMaivgLcv365wvatSkJ4W5t9AwE7qUxstYbsQUgcCdYIlwiQ1VgKuqEYe8n3VxAJH8+F7NF9cjkHDXYqkjMmexZN8TmY+EzgxKjCEodsGQsnevDdE/ESv7GL9rmyTyrzeLva3BeS5gQoZckaRVkqj8lQUyDvNATKQFwMZ9JwMNVcVIHnziJIlmYOkM2sNzxO1Z+6OYX5lmzu1wJZuAtVZcT6Kj8J+Z26kjs1JNTs9HQfDg6xCoPHVEeIL/89q6JGU8gkzPov/tBMqW5WWDNpnrZI7VqL3afUTUliIVwjWA7ONDOflsC0u2D3qrLIkY6Jdk6YgZwmX8j32XJCaufFdynKvSoLhwNM5GJB88ig0CLuSzIKAQHS7kUCkHaEegIiwjDVke8Rsu4GoN05HUtlLe13DNg6efHEgVoc9Zf8NXllPscIvvFaFMlIlqn17MpcIjfGNhqOYpWbIf/gaJT8MaFo9Hw5VKoUjUAvwJC0EwTIodD1QTm4RFHVPvYMHGiI4VLQqchv6o4wpeGIEhzI4yGk9fALohQJwW1FkSh0w0QcVFWujzg9GmOzPHxvaE+R1+peYI21XdqfKAc+osri0VFYOBHVEQ1v2GZOUHuEAIUaZjZBaE11F1CGQrkje58jgRQDUyGbhGvirjg3sYxqcINWBuNXDZQY9ZCA4bFGrYkOyC3FpBYzn21QEAoTSnW3XK04hLVK6+wULYmP7dgRRLGJ0PDgsmq9VLJhM5yLr2kvY2MBK/ZF+YLVoHIml8g+ML9iVpAUK7LBah+rYU3O+vKZ5Y4YrsC1W0K1IE1bE/0EGYL2ivl5XvmZL4/MQBE4UsdZil4TBBVLD1CAQheQP6ag51AtuRIHkkjYhz3IojU7p5mXS5XYkEEESLpqmNooWSVaWSGA+F+cOhR21CB4QB/7+VmTeS8XrxRlIZ8TWoQCcLjOdmj6Xz59dbYBKEp9vTpdW2PRxdnKQVaMoffe/v/lv/yWU8Qg8Wsq4epKO9PaRsgA0HtoMapg0B1mHlLpZJoJzU5SmCf51d2YVXsCv7253p75qd9ZchI/5BEzxaO6dDpdoLC1SOwIkoy6xmGrceIKxmFs+QbegJeYCoIZyMB+V8sZegUe/IPyhMjZTjIH2oHYH9SyECh3VTyEUnk4AbWxJbBOFZHB9Wx6XS3FK0q+sX/l5hk7iExyGs1DcZQuWzVfOm1kXhW7SZDNE24Yfl0PUNBQbAioJYiL2wzdbG85n0VAcVHMDH7ECgiM7FNmcjrCpINAESo0C1m8KbNUaq3cmjPOuOCD4Q0Px9+jThI9GDNL1sj0ReELZowwAWWPzVlM9AEY7IxEqxoWEmkPQV3VIm6Kw27uOxIBFpCIEMvnEwqYooojeqQYa/uNh/TIIC3tCAMNLeMb0qEwb4XY4ZQeS1RAe4GUlgvgpUNeuFPsAHVuJC4F5cwXCtlB0NIg19C+iIYsz1yC9IXzyaiz75X2qA3QSSJjK34yoCUmNViZohBwzdlnYltbIE0oVHJbcVcnZcweZQ6XX2iqzi2jdpMhQ4u4uZZBjR8XriXtas9hVI06eV5orh97QSC47IhSfTbXQwdA9pz5OFaXAbEnlbBl+MKXBziGBJIMQ0sxqxxQ4gYU3S4jdsf4c9QOQQFxkHcGmnEMC4TAiNYRFomvFBN1y7FjO1DzKe1s/yzoxLhtdTIuOhGWFKGCEfjgLyJCIoM4C5J8I9CWdwlK0VuBrEYDpzKlCDLzdkp7oRkYIluGB4tyB+E6ssB1w5s69EEtW8os+NZZKVNmcNDi+Z1YtNUEf/4NJ6bKIpE7pGwr0ECfMxmCry9DWHDlTBFKdi6ZqcTIN3BsoVYbNglPXOTvguYkvk/8j0wtFRdbD7ARS6kqnr3nMz4qAPUJx1qMziqTOq5l1g6A/pRxIeRlMtBP2RYy4FdVB8KV/RrBJRldWiWmX3BYp69xCGPUlE+6P8c+Cfk0Smnw/eVqKrrnc1PTAp6m8xWnuO6YZcLMZ2fQoLbQEoeOE5GsLaJNDtQopWO+2BhPCFgY0/w0EYWhJzJKPA2yQAP1HqMoEP9Q/dmrsAcm5vWkmLYtJdOqNfFehKE/+qevn4lcR13tVwmGfDlihXX99hzyiYn/b/+rf1QeUUFriTb7q3CQRrQkSon2w+c6CEK9o1p2T45TOOiTcCEyrA2RFo4UG1X5ZGsgS0UnH51ZIJQtrNgt05n2lBzGX97FI+iAhGEPGVYhPNQiRxESAOENAB9HPvmqD+KRU/qpbJY4xeiX/COkBo+OqJhBSSzdjFH5eGQCnwx5IV0HXD1kugxz8k7Nvh7HCoSMJWYi1EB/C0nxyxh7JbvK+y+dYn18yEYYUXhbMh0067tpL7qAMSXDOcBbOjsHh3GTESLIdtvSXHLJp7F0iSxErVCHjIzKqLj1XT1nVLvSYfw4lNbdJFGH+elmEkYTXSK7cyXogvmoX0VNpjr5klQ2FsVG3zhQ4IvE8bbniSQUQPHgKBPKBiSO0myhaussqOWh8ngbh3col6OazTJiEb5H9JaZneHveruCUw5ZagaFc/cllZS6jmA9LceRsiIWHWosR1CmrWwEAURscbno4jgfcw/7IG0TQY9OPskm1kXxVsPaOPPw9nbNFyNYcCA009BWAkmJ+oo0CnwYLtFlkdVE+sq3AEoRNHEfBGYK4ICbat0pUR/qX184kd2RB0CUI+NIb48txp+uns3hZwqAoKLGFTtUyT45Pj89XW9uxKttlNxh9xOdkc3hgeIQIOasZKp5ratR9ImcY2aRrTCurJ75VhKE6c11sKFXplnV8UpdcX9gQ19pWRI+M40QgxrnNoOq2t+K8QPxUNERcGxQktbaEV6OG5hke/gvMQpeEVyaiC+teIzA9HmeKWYtXtfvA6BHuWg0ofUhAO+X7kkVJOSGnwD22DTCDo5sAGZKSyXUIn2CHUDIc9KTkAK5dADfx5OqVgT4tfyriAufDiMSALOhydyiLhAKnRGfANSInaPUQGJKGsAQwNoBoUEeY6F2Tw+kuTJZU0riMB7qCvhgB9qpI7Sd2W2FW51cinUyrk0IpAWmSEeLJZbByQR0/6BJM0RfPtnnzCUkYpshXqwSo50tdcd3kZmg95tCcMV1dmEZVD3a94A1hHbKr3fdxXdNIFE1WU8xoo34V7DkWc53NLNy6og97HWZV01YqLtDF3DZGKboWKRqeB9LU1XOfiCwCZjBHOEOLq2Rl506dzbVOgBbJUUhNmHArpfpA+VOOyWyNvcc0BN9JtCG+nT73P+//zd/RSgDJ/tBnZiucvFVoeoa1PFv8SKlKgiPQDxpvBwD28YNcbuiMRcssYAUCD2v7+9EBmRy0Csj6Ne0hfFiR6rLoV1HsbLbRAYRPhgDMAOcIRWNEdSdoyFva2epufknmWJBdkYWVVHzYBKs7BP4YgpiCDXTgcr2matWLroL8X5FANl3kMg+ygN16ZLFcw8L5Ef3VcigHoVANksUS5byKWw2rHuxpaEQoWArD6gonERG5VBs6NyCGA35oQAnaBCJG+k6eUC4YBA4w4Zms7Aoma4YgoLY9W70XCl6bSFO2JR6w7oFDWuBS1FRprQTHM2AP4soVCRAYqbhLMti2REoqVLtyQytKO9SFkhr/jp0MjL/xovAi8GQBFFBVhhWIypbaWbtvfikvOu6SvHkEp9YqCYFOhbQwUX8+TqW7JQT4vb7locqhaoy5rLIRikLkrMnup/lVdVud2oNcizkXTAD8O0isKqRIKVyvUGPXZbsqyyVdYAZWGqF3YlpK7dlG+EQq2y2tyxS9eCMk7S1ck8ZvH7wQPxjBW5nPJkbBnmAGtIDBINzUdEhaDqAlVVZqvnXS5e4k2yVE8eNlPzeP1J0klnrezdwacVyfHPzheOockwbTgxmxrubXMZEo4aG42pDjic2RvGgTqnRO4gJxHLS3U7z8PD1+s5xMHZNiY1uodIzwhFx90NlgjiBNeqYGLzdLiICaSJABnUgLD0rVtB9sBiwogbp/ZTI8A+kDUDdLr0iNwYOa5FSMCWqWI62KuYmJPhGq9USsaAFqwp1e82L3Mshvo0zyBVqUg45mglh4UH+Bwij8CSrQjmSFfToMMpJesxerNk0PAb2Wdk95l2tw/BO+Acm+oTgS+5jpsrim5tbZkaGD/7oHNjQCTmTrpmr6gnBE1P182inAI7R6CEzZfmOwmhKC834OVayvnwj9KDyMi+qyHasKwjIOhG45iPCvciVJ+1aIJeehyZLTSgMFQiwGrgWLqx0p1NrHFDvgp4CLPLNi6Q47KBkYCwymQrjDaYD7u9E9UhY2TuK5ojYsbrr23tyXIqIqe5vMEd7qEWMwekmUsz57YJcel9WMa8wcmMSzIU9KmixNdkddAxWld4ZEu+rRjnhjitZNKMFCzUAq1f0vMHDKFV/X4skPqUHbERs8NgBWiadtj2EjF/+n//zb8WAyUi0n/3GuHPwClGn520+mQuA4IWUAJH8gHvcBneWQj5anSdRtgnMryII9xIOdm8IAlLum7QSI9vi2JMCI4gFcA3FcsTOOIc2QnZYnQ6HfqC0ZfoFXBPZyYp2JGhCaqthwBCex2wI/XSpPEbHijwStEM1upuJeENrUEWmiIEYjZDDh1hCQHknQ9f3sg6DetDEYW2Gspz2ddi/xzDiIMiTTETAIdvYYh0jh56IBAbDR8NZD2P1+D63FMRzOgknwnyYvSaRokBjEN+nQh/P2SwW0iFATgKOQ9ZjlIjeQ2Quqcn8Zk6q4k/7JE2hCKYZL6xCKjsb2WJ8BC85JVlmQATnGINE4Fq1Z8aCoiBwOJo4wSa0kiix/q4ocXpLnqDIR32DU587eEc6fpWV6gg05uYqlT12JgTX+80Qtgf7X2+vM1oBn1PSgjvnQg6PaDjI0EDFIuARJSNP/zVItIOwKxP0LtzZv10zgVDNGB47KLSoHGK7ufcs52nu3tCCVMU+wIT29VsKFc9YJ/IedjFNJji7p/MTO2B4HppVRopMsIop21Y8RG+Bl+gwocjg8FtgVFKB2aaThcoWK4Ib+OX88Pr8CorkG25clVkhnWRbh4fRoW6ACiVWLurZf726ukGLVzdbR7A5BCBAmqIUr6CocvXIoFtcMl2LoOXmF6gBNwqaeQwdfUNSl1OhnJLUyIEyI1XiiBEmQl2kjEEYLlkrRdXpYAOBN0jUcjlRqwUC+8KpY4IKmHCxKFkXvqrLyPBDfGbN5attQCEFU3s3g9byQgdqZrUkO8SoueDQkevfBfDZ9IUGkSg6s7We+zXobxmo1uqBPZM8volhRLwxmhcte7xXaCviUaiW4cISZ+NpXFHrQMkcPDikjQ1Gir2L0XxovogtU2bEM2v2IPoC3JVgKtycuG91qSvGQHjQ5nlTMXJmGWEW0yGA0R8tswrJo7gnzZnwDyACpDuT2CkcOMTB3GZ9GtYaiWJud0hhFyvnw4/dR0IuFV529A/KVt3SDHsFzJ1LtyOwtl5DscagAo+LVVoPr2ipTqw+TphDzU8hK1TqtFtWsnTsuCuYUcNxZNFQV9nZ7ntiztCi4pm60aUpycjuWRJ7cxVKii8YFbxj3MNkJBfQXbLE4Nrf/x//8QXyd4z2VBG/ckOtAog51YEOQFuvheITpg1R4vARo44zgjqSKYbJrOcMJCoQkcw4vwGlSyQfcP9vQMrUEI+yuNReWNsBJc72b9lKPmREnchIcw2PEv4sJ2QhPGM67axQ1qprntaBLEeLt5BvFI+ELmuuFONFoY120eScN9C4G1gtztSoRJ9zpydsW5wH/Z1fdMgTbmP3iDNfEy0Zj/2Vm89mJ1jrFMbei0gof4tHoIQpqMTSbyaiHVB+8pCF0J1exY5Fb7oMDx0M6Cn5f5KXt1RQxZliXPptWT96xTfMf0+KFOO+tEantKhBOCQtmO3WXXaOma3awXKQB/YBMXjBmUIKSR/l3ntvLq+ni/GDD1N4GjnUOQAKEqzuN6s+ucopmVUulKB5NY/mjLsjdYpXcoz2nyYk2OE+JJloaHIipBBIKSSCz6u+JW5wiz9+YIW0OGfjRnjBpBmMON4oIUGTjE1zIxNWhY/Bxw/kihp2fMnYDKrISUUTJ6yCGT8xfj1AbOUwE7Ug8cAWUWFJstNWdg9YT6QX6IGVqYR08Db4REjlh14xEFuvuyCAEuYG5dDcpAk8i+FI/WqKgZgMZ7mASyakYDhjiQKRBHKYAnP2uJWDSarfzT9KrgI5g4alEWlYBYIEMBvxyKiFU+31/P7dRXnhgiuJRPsYkR3nYPVh1q9ClJmduCYOSF6rTb8OE9MakFgUlWVcELxdaYlTTT0SQNt8O8UItP6Q+HFPtTewR8Mxxk026L00OBje3K6zbwqHUoWHDpoYEBnQgvhCBtbG/ZapQUK2JRDs6BMNH63w7ZknWfRduVMDH0YNGFhS6MzWAMbCE4RUbD1iTTmXUBJ87ceQcVn8Sf7iQlqlzpoqIaifrn9BOsYk/MgT6o+8sRgvF85JSUvcVCUxTOBO9TOTURLhYJDZbMYHms2PnQwFjPOzc/BUuAcrrDocl3OTucPzFfF71XxF4N6GMQcaUHbcIQ2tvLdPV59v11y1uzUKFWlwRQZkWzOIkoqxsPO0hMBIU1HkEMPboEp8Xd/cIteEktYsK1U3QxLvXiwIeSB2IjovZ0YYmFcHGCwDEpAEbu5xQKk+UV2PbTkOQkSns2WhTr8mIPEIg03bm4wj9MdPFG4SLNnXqnNzU3sfqiCCw+ayA9JnD9WtqRMkqBikm+16///6v/hLdfWeSBFLZchrE3NOdVdmA+BlbotcirazQWnjqfbGouoRMeLIDZQfHjFQe8Z+fKXYfvhrWTrRhP+kgV+1rEKJVzd1jzFYzTsV0etTxXbT3sSuYJttIhwc2XXimS4FBcNCCQ5pAYhu8DoTAXWNF/STGLxip7SiJmUFPjr0kT8UOyQMJKhzSgi7UCFxnynRd3XSIOI7R8dgBDNyE5Px6UpQMFzIMtrDKWW6f9BsNkcWbg6T14WPebXMMb/GowR8urMMJLpNsCUujCPoMWKs4ES2uYgKzeHn3gtQAhT6IpT1BWj2Bt72HpRaPM+a0Ghg8dDRrQgIKSAMZQYQxBhz8QRM4GdnCE5O7u/XOQaEXUxUD3oSXwLM7p24Axm+A6bClobCiJBOIJP+Y70JSiItyJPGGbAoPnct796GEF9Nj8qmAJSrazIEqatSR52zJ0dIl56LaaeON7veAGkhMEDgtxpYn85dcvJUNzrt+NcbFgatTkUOCnJOoJm4uGHt5RXrMurxPPcFF+YYwVZXFblCQA0l/nX1kthLMX2Uk17MmuHpdhESUPi+d6mELtOYZIxUBorfEly3axEe7KzGzO4DkLMtOBMQWXDo6my5gihShhmknhJSmB3J1pS9MvATni59EBDgs0IpZFCoCxVKh0Af3KAl9Ik2LMivLdWKkJ4pOQe7FAhARWOZ3hE83nVjFG40Mk6ez+fVf+vYZRayBid06xNbnmdcNyFEQQiSv4SI+wiRpS51Yk6jGqDwj0gmy1KAeTBJV2B3QIpZOCoskAHUQV7GZ2lFvlT9ToSaLHyESpBlRMvcZu7H3wVB5MlKmBMGgpnalI2IlJ1VLsyskIf20BAqCZYsmYwqyClbw7sU1RncFAOxxqEeMrwydscVq0bROB62U4KYfMewZvS5smuo86tGi1pwjqh9h0VMtnBp9uSEOnRrMrygVUET+/n0088TiFwszU/lVaGKk48P9JevV86Bnri4yuAV75elcBcbRkC95eEZLvucZpyFYtFecbksmj3X2YuoKttXdZ2aEco42D9jgO/t8wmsE/1fu2TcdT7u0DMNk7/iUdqruWCWit9qxFs5QRVHiHfHuQSmu1xhgR4VhuvEGuXHOzmpZRCTjd5EL5C2uXlciOcL3Y0yWZDMPxspaLfcUaDFePR1l37lIYJJeOLw2mh1zYJ3chHZ6opqlnP36G5d5A09qESd6HMhPlDO9KGJtOghckeeM6+M4ZZLWcSgvCsKpXKhWj03LNYRaKks0nlLzW1e8QTFwND1unQ5H9MdVFVAlDF2qoiilcslUsiSBDl5hK1sMffU/yIbynUrEOB5uAAgzxCt5u6L/4wEANxkRi0p9eo2jGYZby6th3V1qSMFpCwvoq+cAy/rIrev0oXOK9nMZbbLEnI0fVXDMBCT4NiWoUAVLNJEZkE/+HS0+WGgCUHSkVYtf2L2oNI2h9oh+pNMqWe/11ek9uIwUTtNJFwNADOhwJ/IG8oWvMA+KD45LruDJqKPDrDqND64wkJsPEl3dX1jzfw/oZGD1yo0Qhy7xyuF7HgYqLK56SISEdEQ02rY0QLhChQE/YClCtxqNJGXh+k1tM9wQnNkme1aEtz4MWmbgxk3AzhdGrbGEUd4Q+txpCekahiPed57CqOZ8PaBqhyTDvhkSU6sO0Yk/iHI7fbaU70udFEgQKRzdWmvjnAkAKpGY8PngxiWXV4sFvmxDASbNZ7miqYAkqVkfVC1AkK0xkdPt0/OvI+Tn0UjSANIhxMme7SUEXNQ8hWCGI7UuUCw/izHbprbcz2hJiLBjsujw0PejE0iG8U/I4ZAJXAsZWdsiJSrloJFiZZt3pMd1uXPpYbtmucUP6P2rGc91EYrQ0iph8F4saBOaoOtiQkd/KH5+CeW6z3b8x60dopBNs5ADuvQv5jXscMqkrixNUZkWhKUnj9fObWpHlQNpVNRjmZlg7P2YSLjEGUpt1DNrEPYyDUO04btUNY+xxHAWfcIV9PtXOs8QSYTylvKn4gLqQrZpevzAyzqYKlNi2vPoN7YSgE1BUUsFUyTE/ZEknkSH6TlFlONHddcARk14WxqO7szGPHOCm9ieVBkktOnDKfyW0CF7mUiO0t1MFUV8nbw6dOfNQXx/s3XK4AkW4UcdGMmMCmi3A5WVUVg14x97ci4QAyUnDA9vWXO+M0T3kB8hy6Z1Vxc5D3s16HX+SVHR3e3a6EWe4RWRohFsM25BwKSwkxkoVxZQgQKtY909AdxySdfrK41Zvhyp88uBc6wVBB/f7dGfrxbpuH7797bGyFN4sFrjqb6C5q4w9r55xWNZiBxgLeH7koiJgiKg/0b3TzdE3A4/fL1Bgdie5Y06woZcE/ZLrPl4syt1+6bwcgrzTVrZtKpQp6fx/kICSKbOV3ANiuFAjg7O6PMlqsF77bMtBJEcgEoGCEYHSKV8DNASJ3YJzszk1mhAgooZ1D5ujv1mIcgS0Bcns8Qop6XXZIo0iF5lV6jT1yWUhscJMa/5WIbHEhTRSd1YqjcBT3KhRTj4dE43km4IiOsnUkV4RJMrkln4pq905V0qM95S7kmWEL9hOVFO5y56JAqCdZA29FEJi/AVvab6jKCBcjr4c8MeQKVwMslMT7XG78pens9lu4n5OByeCsJwBRMLT+9W/lXqhHFVI9cfPLZ7LCKkvoCKEIDRhORdgs6CvZL+IRtu80Soz9GVNQygTRJHqIE4SAi0SsayeIT/+O+EfBEC2jttohtED6h6AUDdjBTbEXv+xrUkD4UmL2XYvWu3RFZUW1fjtUcyt9YXulGs9GLTF7StQMpYVZLb6TV40L2na4iSQIguJUx8pWpUNqGwtq+0h/5KB5xMMUoHrWPqobgUjib0TVXa5EhL5xi45Z7cbZSBKyZtDo5gpRvtprObu5ELFs23AvSuPydDcQw4oujzO3Nbah8ehFdZSMlYekb6yDiswXK2YpNl3FlKxbtZHUSaw5DCFdGqPS+oB8C4GZnyGG+ds/1gcOt/UZxvg34nMJsT5b1WhhRGKo+ZXn3BcJUAYFtzsqxcRCg7DoR/PXr9cqNSdPF9ZdrQMUFl+crQhuT6WZQmGuvRnIcN4Xe4lRi7pZnOSKgaAacssPQBV6oNgjE7dhCEtzEMoDL1ystt1t5TwrjcbP20KBYAiPMpnfqF0m/xmi+cSElhmPfqecQ6qHD0LmVgJpy4ywr1UToW8fvASI+wdEMyimzeq9Wm87X8UG+VlLPvGhvGKogJJNRRxELx1fRiU5Wzp+6tE4vrAqjJ3wZgWA8fX91Uyyo9iQhzWibzY0t98PR4fXXL5GX2kjtPZDZyzPfyASk1v6nr1VVwkw9ytSDlg7e6YiyVQ+e5D2/SIipsrMKcS/eMrahLckHV3OSGOokIAtq7rZMeZ2sKkAnkxkm1k34AIugN/MBOgQYeXIvJ8hmHJQRcXTLeo6CqzHk8CIuKgr1AidpTxpDNGR++fIzmqFK8bdXdt0qh4kvyM5xZ/Drj2DT2S7wguQ0RyQMzSz0UlLHui2lDjf8FT1dns7mLoBZURDcQtE95H1xdmHdUr2I8eL0FHLIp8l8tf//+F//lVgRsyNcIHqSV2Cj/kSdxuQRox6qmn0nguxvDCKTLmAKnpzlhCUxx5cHhkx+VOY7AZYRNR7Ky+o7G66mRUdT3rdcLsCqNOezYHFxZ3EedICI8xW8jghRr1OLx/Vy1MmW4RkZ6mvm7jxrw5IVHWI7f1F8cujexkJO+XkYjQKz8ExWGEM4L0TZUvoN4v0aP7DsUJhZUW9BlxAabfcPKCNTGX/HKwhQFR2d9mKuZtVAe7YLWR8JIHKi2ssYkNGcxUejaHXWDrhRo4CHdFGBqyePeqWmokLGaTsjd9CG1qjAqbRoHDNidthP4BMnM+Kg3TIyvB6V2Cv9J/gLAdudBYN8RpbjUVS0S2gXCxtlqPGSWV20Gu4xOFeGm2p/lETlHfAI9AZcOIrgZkcir5HIbsMZ1kbgGlUkRih8vOQ3Ll2fqUXJdMLRBB93mPyVXKBevSKkQw+xLExJ3eU0T0/EHNVySbkvBXedjVhvUfBS1FXVMweUe6Ei1tXwuvBzCstwKleYQhAyA2PK8/zs1MWcLF8ONXApKZ5N0us0H68FY3RQ7qW7NZgSuEFCjIntQN+yQJCIsliig9AHNARc2TziwSoYMikZCROm+RDIkdpm8pOGuNErrAzoI8FUuFkLaUtloAkRABqBSPAM+Q5MtgP3qh+YG0Zs7JSC5RXZQHTi7LSCWe2XSpJbd6+nJB7869liOGfsw6/g7N09s28nNEgAx+YknPhey9WSRHWJ3O3NLZozFNrDFyEJvmLODvSiPXDLiI8KKWDo4E+cpOijowg90nN0qKpQr1km3UZxjhqNNLyCVxWryg53uWiyA7z4HGwDwtZexJapgYgmiczlopFQRPcRdY800WEjSI4fQ2JicGoDssDEhHROPIuydTvQrmcUEzNOrQ4tM2sqFK3UPXPXH/sBubaJfNUv4D2r2d+/d2c1nsGkhAuphg3obDeDsvN+rfU6gnt0as+c7NubNTnAApV+u7u/s00Wm3Fkvobp+rpcLuGdG/Hx8xUj09R8LTU8RfnyVfBl9MI7tkIbxMioQwiDEI8UjqoWxXyxkMqC9QP/YLpQXqxyMRoQ8vf6QYxyOF+efP/NB9R7fXNFn2bkqgB6e73+4gB6RAl2EJo1141PDC3QKxJi0k7bhVyC298ZvhhvJ3tArmSf2+dH3aY0fGHDxSxrRK8H5BEVUgWHatX3/4d/tELklTRCEjNzuKUm5C+gIXLHou/v7+bcmOUMM5TxrhhJZ3x2/HG5G+aE67/JrwiW8V1oyPIRPzokOYzMuMJaGVTF/KBfA5NMJqxCaGZZiiQ4jaWadbQNobqYPOzGC75qwrF9k1fEEz/aYs2C4cSRvI6g7IqsYfQyEokpsoynzW7qcs1OrnZDBZKVjpFAwC/kZZyQWlADIPPD4A4GEEym0At50c2Q6JSr2GWeE41kf5oIb3WIiZkEGv1MXdXCaRjvuRPMKrka3o/fNRg3R4/W1fysfIn4gbSelEKzDLWTJ9tNyyOIX3yJKITPp61bqKZlnnc1UY/FPbPj3nSJaPnMlhydFmBdGCcXXjWjkQrEAwt2ogJhjlJK+InUMbEp6l5BInQayuF+xVQMMwZMJGVzbFi/9YpFRmS8TzFxlA3fw+OyP+7XRhsTXK3cCP2JDMEyYdC5pxr8kasjihWYhm4XPHFhbBdCzsXcod7IJIogNb2VYBSaoEOkBKqS6qSxYOi7C46iJkvZ5upTT5enKVn4Db0nRUOSDowgAKGzk2vVvhT9F7rt1jbg9a4yMMgiy6ioAkfFYORXGAYjYgn12QHIwy8oVGqgEE0BSiFAziYTnn1YICbsiJGmGzIg2MJlnJwd5fYTJk6tSME9P3RNkNHimcJ3VUPi6rpmHXNn9e/F2nwU4cZfK2tZL+Lumei8K5avXFjOvXP1LD8FYeoXXqkHJhSUoDdAtTlimvovbwTFRDlKTSIIB3fnjHozuGXosMNyGtAuJLE33fOTb2Tl0TWrE7oQFZWJ9lGBxZNr/iYTxQARD5ENCBET+hwlW2gh51CoQPSZN1/dgdTjI9FvWLPboqPPoMb3VXdCntoiw2/QSZySyYyVgDpVkhXMD0EVtpebkwKISmwteW2paYQciL5F8Jp0ZV0XmgdxZTOaIshYXl19Taeu5lzSMMddw45EdA1zXYN8HzqwQ2gmuhpRQI87xo4jWLZcbSJKDHMgjAEq/OJ16GFYWNw/8C/xIusTHyMKRwR2vW0WqznJibzOLk5BwQahRAWM+U9Pl7VmZkQNOpeKp1XVCF1d31qtJb3cU29cs/3F2SLDi/1Vux5bqIMk4y5IKmA1uhHqF5B/VHRo5NJl7IbTqZ4F5TyzyKdLVq867Er3sTL669RTbPO8/9+ezQgxx7/wvRmq7ZFdHNkYgOaY66iM9lxRAkyyBw7jZ9bwgTky454B1mESwWY9nQrIMwKPAjJ9kK/BxvQAZ2tInPCZVkB63U5QnSV29H56JUYSiHjGweKXZJlNsp2JOIxnH8k7e0beVkDx6/UIQ0QVp3AkkNna5GDyXCgtwW5dL7ogoWMjx8yDpFAMUxEhgqDgg9ATjkLh/rNqCpiWTC9hxIND1h/qQDuMJav0ECoRpaaWhGKs34bJZSQ7yLcSOpscFQtuU1KlEOsgkTINNdXYRriCG7k6JQxBzYyksKktk4QC0sjd5U35eAU+Fdjg69PZoh5cbgkzY5nzaAB8MAtTorDP4YE7kCEesXCz2jSFlkgrl0S6gS0giHnI6/DLmMZDz6UOSDQWKKGg8KE+X/NU4lr50FR2V7tE1YQntoxfrFCHhbQUcmQ0iDSQpw1f3IaFlVHcBb0i5iSvpgssnFy0Qr5xMP4J3lVMWVB/sC6gSiSQCaxvZmJttiQ/wYTbgdrhyLzuoWPTK3SDSACRjjUFhec0CRfEijH2gHlmOZiADxoAQ86i4KUhoAM34h4hMBhIeUeEsN0dTB7gSvv78+dr5oJBsHfGjF+RPBk+xzqDSc1lMCmLdNRTsbzC6aFzqHpCOzMF2FFeJxhAJtkAAto/JCg6UaHQOYMC3yIJXONJUtIndgd2I3SHekVL3rS4IJqT91BOjhD2dQrJHid7oYb8g8tsO/yQsMwdAWXGjdmY5UCVf2YIPNB9n52OQsuJeDcOaldXgZbCUOwRlBgONAPaIk1QfRoGCdFSTxL+shcwcaiVCSIjKLFPmQY0MvQH/NFY1kB+Bpq8UbFMBG6dFsLM9710Tl009OCsbczh8Y2+6NFyxXKkbRK9ytSKSi2M+YgejAnlzBr/oHwjo4dMcJBVq+IK+wP3es7JHD6NyDZ/0bFNqxJHSCYKL9AtXukN3NyhSO2iWDCmoFYp7NnyZCXVD7XIt9s+tuzc2WLODrBgC0SxoCRn4AAXHu/zKs41pOH31MEQhTALrMjnlpzd7BuokkVz2mvhmpnajtke+NNeEvVUJiWAQK75Greb03cXvlIq2YF5giaOVpxhLR2NgQ5dwUQB5KanBzA+ISNvJzL/Ys3WU0cwiqHwgUOASjDkUlgGKlmdzfRhVaRuutz/P/5Xv3tYr4GW/+U2q4K7AD6O78OShMg9hD8+nZ4vIeNKkO6xw9ZxLL6yP4SVaZ9/hHNbHLjRAKrTciZ2mKIMUrw5SeJF8DTsz34YT5PX9gKXnHb28EghaltawxncSQsbCpYLIu3vX8zmV05kGN+ICAqB+6+Aj2Pf9dtz2Iz1KSfPvpUtEKpmLBAWlJYkjiVRpZw7K8z3VQkjkJnWimCJCTTvV9mBiXWva7vaf9SdFQKlJRErHcwiv20nfmuDrYImK2Ql0caKl9hcoP/KH/ESAt5TA67qn87IAyB/qEnSGeZMiSLKazh8S9EVL8YDqRlObrG8DsGy/roOXjEF+yVOKLfXToBBKQKmdUbXXBhy3JJh/yO+y8QS33gowmbNmfgEUAU05Vqts30idgqgFv+CKBwIDkydTTEZqtjlOVkQnm0lQN+1giLLLuSpZlQuAUgKa3TvcvdyTAQJhD4qf4jTQNQxjJg1T9feAS0lRFgTB9gbyVFwDASinOZL1ne0m4dJxpOSG1SVAUBni2YQwaJBW5m6NBPBZEtqJ2ShTAA78LK+1c7eJRsOBPHKqahqmTJBjthK9apEDHFssYy0lECFmaFwHFHkQXYKGlBgBIUjCz6xWrXOsoiPvYnmaxoYhXMsUDl6GcZNIo6h7Xsb8FrFdPCI/oDu0TlBzQdFK1/rAzjqF+yd02NAOo+I9SQLh/wI2qiKw4SeRCNpooII8tAIJiDh00Qf/s40NmRXSpGvzOGkP3AxdBn8x50w8iytIRdiP0xk9ldU4NcdI6jgjdVPRhGZ5k431vCHAqjsxzMUGVYA2Z1xxxTNnHIsP/ldRyz8SddgZHSlAIZLiePtABkDG1lcfRTDibVYvKubNaAV1ZFLtxtmi21wXjMuYTBZFkQHGw4CZXR51YGNzjWQ/XZXTxCyL+rHHlVj5DQIVXlz1/6BD0diZOflYYTxKs4RYwSMblxRxS3pwnQXnCMEJUN4AlMI8NA9xFqyE7UiEQIOboQyGDKex0isK/SfKj3pzKzfbpmJupFjUdhyx4WcrSuAr25vru46Ml6r45GAFDeClyrmsLA+DGysO+x8suBJ79+4i3cQNMFP38DssJlQYrSonli0qoib2svEhTylhUORc7LQnyASJXuUvnjVu2xRYzfBFb6z0OV0urlx1R7rgYsmJ5fs2f+f/sUHSS/HUzAfmkgoJ4jJLOF+CXQ9G2ItEoK2AWCgKYJEO1MyrAPAUp7YhsvHsmn8mtTLaYGq7FyOfArD8nsRehN9XmcZdXYsX/y50qUQD9bEbE6NviKZT4RzBzqjhFrOYhDzMicAFK2zsbgIeGa4pRZCAeD6LkKzDlJG+FtunQJg4qEaaDc1eQVwNgVkKVPgTgLypq0EVSWQ04GSYCKkoPS2f3VzszitkncUwAgvnqhosSZf3iQfSHeqwh6Z1TjSjNBPhLVudiKjoKnLhoEh1UMYJ2SRMD7MXrBIIaC6FEBmpo5L6RGnx567b8S7kAuJDoJYnCftYowWzJezJcObcrKYsfraNhRGEZIutlfOELFIduIH35rCRuCXuU/w4CL/ezEy2kmVqvK6qCT79LUNEspkufEDd4ess+vtjvACCffL50V1kYtwM1evhkuJYvfMEalj+xK8xD0CRY4ArfwOBvU+Ax8Cg0BBE/ivX7HRBPRIES58STZRoGqXnWaiq+nsSHD/4PpqzcmjiUmsfIWOVqIOuB0ykdnrh4EhPIC8VHZhTr80rLmGxmc++zcnmrtLcRgv40hZvYJxDWJdraPV4FBaNGvSOY0Av2DIB2JTOmTg/np33nqPTWCD4dwJz6KU5GgsAxQJNbRpbSA8+Joow5WAii86H4eIKjKB2XESBanzCIdZFbMR7eq3+VVDZHWy17uEb8nQhJzeNSda/skMNb4NuHGXwiTXjJlhnWjGa4xQ1AtBCEg9BdKzQmvgRduW+kWjWW4gwvCZJgh7uFA5ZYjVWipRN3C/ywopBlschQ6jy7F+LQfSxAnVBIYvzpyDDpybI4UkxAPFQGbwQrC7JQpyGg3kUJpBwdj3OYqAmnlTmhfh+QHAWC5m8xQCu765I7B0fCH80a44opw5HVwKfEig2GI4iMwoL2exGtkOc4lgryvNUW6btlSpcyIABbPtyagjrewFSOAk7zxJCw1+4zXS0oKOFHfqhplQStOy88ahFi4CSEnbgLRlbmQC7m7FBgIoG0P4xaeYhavjZ7UwTo1vixBqlQdYWCDSPShjLGJs36YCDTa74640aZUmh29u5XLLL1XffX0ameg5dVB9lISElaE9Tkf53XGJxXw5JxCe6ucA/I7yOTafR77/P/xuWcBc/Lo+M23Op0LHakgRE3yol0YZynUEIMZOq10BlJgNRC2EoBGf8XLRyjiN722vaBCcbRvxMUmYCoQC8z7/SKRp1NgMkGeZZmWYGFcSychOVgqUg3PfbOW/9l9tRQAMrG85XwzhLEfN3bClY3LI5UCtb16hYxnPaaMxRIaLNZrVE4gEWXM8s1kMRMmag9PbUiGiDsPGrFNrIVQLsjNFfyZITuNzV4OxBGezOSuJLmWWoFtyk0KvZjYqjqTsuhPRbIlR3Gk/vh2cRnmjNjSAUeIwNrDYDqmJqlgbzKQ+7CCSE4wrhjxBP6ara6mpLJiEUAMm4IhQEDn/+9yNEMjI0eVhDHoGxeQ9US9DFeF2im0YC9m2A8ZEXtwQEWS8Jr6IIbNXkZVWKByHp2vqMTtRWDaov0yJwZEX1wRIEaYBJSsWixWBY/3EivZ5xCVchvHBbhkWVcV01xByWd/dlwY9OhbPAWSAI5OsgoQiXws3JzE7gVhSVPy92onYDvn6bUL4yUpyG0hXaS6xEmxvSGQovgmaUGMXqMLDUoyoiK2FaCmjxFpOY+4RciUQGIhpuCF7dmYBm5UsB0GaMvjwT4ZFjDDtCgrI1iYtpkFJu/889S9oCRiJrPyKRE3EXb4qBPvOmOA34OznnLxB4p6E275oQDLIqnoL0We77efvjziDGkNgwUNiqsmGpzoL8VSiGbEpZinVxjo82L9d34QbsxsIMDmUQIPz9SImXVOgtceJlrTGzC8hccBTxAaMS/LZNUvJPzTMEMEPVdfpmCFwsVXmL4noP5e7drlN5kyeVg7xcokjyyZ1FkHC/PV5I5mfN0CwqLNViVibZe1bhoMdkHUs10Qy01SsPL88FLbGIgvRRrypOlMtD0cDt0KGPehoZjH6WbEjNXGZRq6Cl3SsSkZpkpxL58WEuaYCJoWchG6Y9yk2VpgYicgzbS0mYz4Bjy5OEC4jgnk89JyqBFYNSnYx5AYKwcMzAEQQ1yulUlS1A9Is0ML2T8zIATAifFZhJUTGMGieOGb5U5ZWxzNTbv56I0HcjTRoJFthnTVEZL5IEmgqRz04Ug5oDA5uuXK7pUP8k8UN5skiYyzv/fz12iXqwxHVS9GpvNq66IwgBaVhmQLTW2XUd2tWFIBbolo4EgLTDArNE93/3/7lUjEFt7+clRTGyD9n1tHqtLRs+TiixfNPAVdhZercsUFXBQexFlQgNiLWjb/5v0xO8NSHmcLslrvYy5UyYqtwBr/5V6OrRGSY4gWx3DRyUBQbgmWM6hjMKtJH2yktfiiLgyaQdrOeUS4JZOUGoIMuyUoAiEx5f6N44Enm+BAr1vcsc0voZfC5AfKViD6+KHCQVDAA0lkjgnoK8pyuJIDIEiViaFkpp8b6vCeFYuy0aidYZx17xUT4qeWZKmFibpZF2VwCEXtjMiZv8SMFoKN+wGIqZrXXl5fVfGEZ3EMZDHQxJCCzYFyEe3IiykEAOWXiSCkRgL1tn+8k/IbEQAm4eNxKAsSyERnTeggx38jr1CGiGEQBg0wYK/ANnoQ7YBc+FLhcVoEHGnJKXRczDmrVs5rwKZ5WdSbXmeKpnQASgXqs5R/bwaLgyXQFpdK3h8f6TDHwvZUBFplmxwVqAAGbpGU/uq9OhQk8qTJgFSY6y/F6vh+hlEg1CHCZhCFCxg0VlWtoFwqFbYq8I0DROmw6rmmzXs1wRQRGStxkJVBqZ6u5ABQbBCHWtiCq1Lc50wSLtloVtJmlSWOG8zC/Mhhiax5MlM91czDFftJpOABnk8PozjMErrXY2ngBh5nHk9FYo9AYZThHIxBzeYy03Uc/KNcbzFu0CTomr4DC0on4rKAK/6ELvtTm8Wb1AmO5a2ZFq9cLlvVNJACdluO3d7cGwWqR4kEZS1NZPCEITah6vsrlYjpaUGI/08dZvMcZeTGdIfX56dxyrEHTwPpZZ5EySPEBQGUsaUcoboPXhWcZKMwH4EX1hA6oUF3EnTFZvSjD+7KxrEOg6VK/11cVOHb++ZebBqutC4e1NCzuY65drzdX11qt7q0UTRZ36uAVkY226AxqjCqk8siVlFpElWRVKIhlpa1osro7jBPNyl0tOO5QzdhJ0qhdNVqOpxCt1Gi9/6I60hCB5BLNToRVUdSgTxZqs2SpcQ1bpKRUUbYC9g+u6ptOF3OQFSkh9HGWUD7SyGRk7zLJdSJ6ebu/WaOlLgrFONZzcqwX/YLecz/X7U3N4EvluFINvqQhZ+uHqsLQYfnyo8nt+s689hnviE7Tv8B6eOTUpBmv7wREJNTLEbl5RIqR1kbwqJnMt3KtE9K2MsyHkwrixn0VI9ohKoIyOEmEyf7+/+6fnspYCftgPDfl4iXbU7EHs4wv6BcKAAx2nEAvLV9OBz+P/DA+8RhLhd9BZ2MT5DXEb8dZuVL16D6u2xoSHuRd4ASqMI2/sTTnYZBQWyOmqBXD+5x5lVkxInDsa/zhdywNUpULBf60WKK7G0RJ+qZjLerUwe1HhcU+ORvIPua2YZY15WLyHGc0CaZCeOEMDVduVVpIPqYOxrnUjCxYLyBNNIvBkcCgQMqgOsRE0pIBVpj2ISMCEKfNGKCKCvRdwhFcAbXJURjJVmYFRfEVip6jh9JUtiYm7dgMWiTHiRm4zy6piq6Qs7cZR5ZR2VH1HVV0ZdQeKLDhVByv8X19pqYGNPFwRwjrDl7AmXeMwMpkfACUR2wfted3Vzgvtcwkt2PUA4aVSifyKETWIOD53qoJKnSoJUP76H+AowB8gveoZKiFMu6FVeIAcDA44w2ccyE7hl4Ugj/LEPMiwQZexBYCNrhUgNMx6aqMkxe5xydZIvotVIVxY6AHEgt5Gx80CAghStYegDIMd9Y6ik9WdZlaxgkmEhwnJhI3HccpVP5QT6G8OWDkTpL+2bnos8WDX0FCHXLsPcRg5SqPtwhPIpCQ9YQDMwyG8MHpY2bVBhmAe1kLGq8gAFPlZFRuVMSpvevCliVywD+iBgW0MKzQigWDXlKpCnRU09k9+yMy8Ho3PT0+LZbCdHhO6wKnfUrnx+taEokwCKNpDqG5073c4drBIwgquytC5SCQe6wYDYInwmv1OVAHOXGnBXoTdFYE2V7U5wjeTbWsCVDJE+T6KC+dWE/uwuvR3q0QnzIsQKdy5ktblBVDCQlUbuu9DA1qgRzRjdQ34jVOlZMyv0cnmFMrBqWuPqOf2IIIBsCZV3c390ZeAfpc+KsuMohnc7NeXYi0L3XTpJnEN7T3QI3Q1rm/x+44YkIBclzBJM/7zoqLeEW9q93iGePVFAVHJAOL6UM/0TBigFqHDn0tkubF/BJac//t5ura+LbsIdhSTGzH0hdsdskb9IactMVUjIv7XFfnjqCmLexeMTcBxocBcuRKX6xv7paM9uUcQuktHhjckdqm2IRZabNY9ElJCK05m+GxasJJQvz19HxFo4+YGLmEMnEBqeOgA+cPlm3fbyfzOROq8Ins40bHCi3khtva+Z7u2lNGB4GjdqWMI6MNZZM/yBBaAWT/v/thzmtdP2gCQ9jrT+KER656ha21tOUEpc/BiciUySHIGJlwpvYhB3Lr9gkSRPYZMRscyMUJa9ESVyi+9xcyYkvweuoqlkD0PFQMb4sFDXFsn9gd+v0ABFYGUEmKSrcieqRlfhLKpKx9iLHV2ZFEhRdGfBOEkvHjT3wlSUcMq3BiwKBNvkJBWBqeX1HI7kDS2+XI5A/hAsIicVsTEyiBH4UWQ6mXlTUJqrHSUhkdBTOCygfWn8lkKWQ28w9E4EE8zyazj2LL8idosZ8YHxhyQwAlMacx07juGZDoQU0clJptXzZA4Jf5z7Ric4vPpBUNNKIQPgtU+NReqp4aV5KhOJsi8zwMUYbaiZXcOKJtHAFHetaGsOmYAen+JeuZk2Dbiirr8kBhjU5LjSeBkgTHXF6zVF/ABEFGINHQMxR7niICeq/Yl21bIbsVBCBkNHDiAo4+a2lWSvGQuUQPQrGxzWNYvjbQWgmAcjcqSh3lOp4JyMUe+TrsYr/sCL6XudNCTWWOjPCwZaygVxwF/hHGzN5RIXmtORUbSoNCkcFil8MT8yTYsK9rmJMOw4H5cPR9fHI8LVRpj0JPENjOUeX+wb0iBZsoMOFn+5c9wq79HszLVTEnSF/KncODFxJJQCQZyPmgMl3+XjmgP17gipCYcQwzBEpTS9pDnrAO2r/L3QJyJgu5oiIw86Gky1QY2BrgV+gX1tCbChYyj0Eg1KNNseBYh1A12X7o7Cu1TzZrhI4MsEDLNmMeL4Kq3/jxRIPSpI4CxyyqHD5W05sb3nEhq3+miPlJDzLJ824+YHrbg23CBTDaMoky0mzqf56Vw4AeXBDEnefgHR5Nzt+fYZkvX77YO+MGwaWuInTJpK3WcByRaKKEHBh2kTpfU+DAWRDA+PT1hnVWMIfqtjOUYBMoEw+4zOe2O7Y8oGIFnVtTatg+wYscX8yNu1nzEYu9Whs24gbYPYqjxslnQs/6QXuEOEaNoenFXrIH0U9ageQpqVKJbGU/MXYilGhEG7V5hwAshRXX61vGN2Dmhs5Vz7OoKBm1FSM4LHDhkI7oWm6Nb4dhhKXpgzr/nMSaii866IMSxLi26pToMI4c+6r14Am7Q/Da6NY5dSTMasbsvq9I0URFxrESSIT94pZdWIn2ip8dqUx0jJq/uP8//osP1oAQHLIpKqunXe4h15vPi7+zCpnAvJSC+Aj18NDNSYK0XbvE5deihNCr+GSmxJpsQLlNwkS3Rr8bOOCTYu84gY3NJ63DidWox9Cw7ATmsAd7K206tK9f6fvoQLYcMhNqOLZ1Yc2cmQz9FAuxQRs9ueKh6hEdloF8Issn0hqQJ9bTqX0JUrcaeB44sNQEWo5Obh7ksqo9tOY0skSM8BQNYd0ZL6l0Bjc8m4E1iRUTGcVFgzJ5bAUsTUEPvGlwkc8hCs0ugMaMJbm8Sj9hTKw6jJRODyLiusgSwdIVy+UKPd0/buhIFoTpxF6w1tAvNbYNI/F5whLWkUJpjC5+7mZqOCu7YEE0Z8pXqFLsTvSJBiFeAJscz10yHbeAQiaRUTMyGZCDq75IWEwBnhmBrDhCT2C6KAHRDHtBAU6TR61bMFSAhWWaTmXYAiNoEAwoOX2JckczA5+UGKSKeGUmBBucBQ0EpXb5GNS4GQMd6Lcbm8oEksIq9V3TaZOzRoyPOGkdf3eKQE9NG3DiDGzH2XUro7FIbXD2HLlLr5CR8Kc2lY+LNKZL0V60rc/XBjANi+cS5zk0ba1IcPxTD22rEm41K53KCiMxbU3Zi9WbGkBQld2yRMX80l5gwTzCPbbU5WKjzEXaBrwIiVokkNfIn9FdMB9jspO4k+nzKvkMi0xS/WSrvbBFHOsjdvGpQIGjOBcXZ8TfltN3f4tdkr4sQQBAdknzvBkrt1SGML5nn+k1N8x4cBeHZKmZy+RUqDJWnEWq0guCkzrEqvjO7vE8AW18sLMqTIz0BVhy+Onp4iXi3IUm7AU6e55nJ/okeg7t5AGaETrpik0SknjKtzPUEFhYuIBbgS0SmqeV4oUuoirG0x3EOnkXjOhmKPqgI8g9UA0TopTb6ekqhd/VOtog7vGowF4zxs4Sp8AO7rZrm/Rk9kvp3EjYERK8C+ApwfbpxJ/7lyrXiVgFFU/UUywQMvNQT7RSsF0iXSq7IuMSLW6lt9nRYo9mq/Ij9kc8vso3I/FYAi11/vTz1VfLw62ji1xywDtpe7OzCx+fbq/veZqr0yWFMOSuI43dI22VWJiWheGGq3HbVlU81h6vJ12RFKADrzJbsCxSyJaNN3MXQI88hCD7Qsn0Rgeu8LWNwpQvhtb2WQjoLI/WVGpCtLQcVq7FpYyQSi8OkUgeWPUxDe/wAMtFqGu4iwNpziuBMe9Q7IFcQFGOn+8EKMcHh3rXp1E2mucoOfPZGewJD7KKadKQoyeewGGVbacsJfTYMKNATYYcn0hK8oXB1f3mxnc+TPNTIpRL4UXbJBrsXf0lNyNaJNnNTfrFXG8Cbn6Lp+GD5Si5zx/wnDAumW5V6+dSOkxQHJ5N41w1O6WcM1jDmLhZUlDI/svd9VBgU1WHJk0EADcjAgyH80J0gDtCRA1oG235hnR0ogIw/ShHY64SbZ1FOrIVP95t7gHak4V9C9ZTRXCAT2OAEpQJC6ZKYAyiNWvwdp47JYi/EuflOepdY7NEPBQiuYi7FxF9BkVE2PFLaS4daDt1lewrDVDnrKIQKIb5VkM6+zFoV+URVyjJ374sCdpTgZbLqK7MIGvXdNYMFyUDPRypDYGKAC0+QZ/nDY5cblqfaAc/mpwckD6lzARIjaNU2eCEhYd9Dau5Un3Dy7xJVCASE6WgTN1tB08uHSM1mIf2zlPGUdIi0vWdSBc7SsiLWVcoQhUK4yIqiPFA96NSrPzoChkJ6zIxRs4BfX2FKTvMWNS9R0sGV2zXvk0kUFlBUSMQAhQWbnMMxjZLSBnqiCFCHNk3QEEiSkOdAFtDEX5CdQdmCValod5wrx+DWyqReevSjoBYaiJbyG8zTOJ7/zWiDFPHlVmjbMmK0I0dTK1khLB2IWz2I6nz9FokIVqgbDoYC/CpnrEMe3wGV0OysGEHJYz6WDckd72HrP5wdPV0OxDITqFLy/e6XuslgTMXEBZ66PSZpp7ZylXXikKczFgWSFIwDdideJLexPVMNvWS9rdkyZLtwiPSXUhfqEAtjekrPxBoU70lg8Hh7oS2wBRkIead8hSMAV5hbtuWc0x1xg2KEe6xHuaVmaJ8UkskAzVuj08sErDy8CtFZj2+MTLjUuBUfktlp3rjnSmC0Wg9MEHgcGF50MEm2cXleBUV+jmdKkpjiloAlYUWL/IiCAunU7IGz5t/rqsHtdZRDg5yVCFyEDvy3qQ5ozgQRSAv+jdUoxv1U8LdFJ1aleHIeCJrMHWiP+lAXQ0vIgvPnRVxccqYoxx+vA/f0VZVpwwDo0eAKAeEE1H//T8+TXqhpuBVLBJL4Hp6vU5GWdbOlOaMeKlCCpInG8MwaRYKW2VZ9JQBiLET8kiYIoyF7JLbUsZMFC3/3aYFrSp6svGMg0akO/J4hVvkyC02sYLtAG3/dDaHFL0kfWgiiB9cNm4HLTyTxGR7E9B4Bp9GgWTuQwhGQX5vuJQEX49+J6beXhyr8VyGppB6CXnV/Y80BFkltAcyQe5FO60KSOulN27rNBJLkBvLhVEurUQnNuDC5+Bl0TCIjFruOp1RkKpm3FV2Om8px8CqqiDXfoUjrMRG6UPfJwQHeBEDwWpBych4sdx4shhRwxDVBRM4J1GaaCCAwVoyKAyDp8po+jmrLZ8JNdN4vWYUVudJIQIDmcQa4E7ZZtKEbyH/nAVTSMeUEJii6lxShlIhOMZmuqx5sQIkZXPaMFFjcuKy8h4Ge3hje2D+EDMqOsyNf8bwiUjUT14xqlCinfKfALNfsy5iMwZHPXMQDzpGaIDsl9QVm8BjnnHiQVyRIEaDpERpwPpqlGcS1bQ7R/RAzGRiuK9bkT1+NsLfm59yZVPtViuEcH97i1acG4IHOfX79RrA0D+9wRrCtGwkwgiXMXcAEO3RH/6yIxD7/9P0Lz2yr9m+15eR97hl5pxVq87emwM6luB03KCDJRqcDpLdASFAooFooINAwk2747bdskTHHXeQEOIN+HXwDmghZBCwd9Vaa87MyMjIe/L5jihWVa2aMzPi/38uY/zGb1ye8QgZMcamg3gKtAKhnHmqAOSmnLd1rsq5bJM9IMFiGwTZtvo40IlcMH4ui8/Y+08uciaA4hxd8MxrphZM+H+LFSckslWyxnvsHjsa3aS8RI5XVEPvAuVgiIRwFU0cHpHDPcuZOcnpdM6XqHmILbhew98Fit1vrJ4NMpaPL1l6fqKrUGStdGQhw17kSkuxWOO0Jh17dqZs23FBg2RhrDN7sFXDsjwXIBZoSs6jscWWAdaMEwdyIDl3lXnmP6kYIU3Xl0tCK2QHpVQ9OQBfuHChgdjH7/c/Bf1pG3EnoxJ6Mgom71vWVL5kRJygeuT0lMxRjsV7u0PXHClyTNLUR+53TxKY6LzKOsB8tT32S4gDRYm6g49wyVnwhWQviF7pK6aBkQt8JAV9DD6YbytBL7Mi6JcM0AAeKnMNzC/cInxx9u37LcRH3skVV2T/tLcc4iwYd1DlNq3lhZ7MKTBREeQ3Ix+dtjXZLPZAcHJ9XSq8PaPiXdKFBbBU4EYqO+uZEffrmKhhUdv19VKJYjoK2jh5YDy/zzOifaDc/wiAhy/+7//KHTcSN2GsSZ7tsKYf6JEtmfjX6mqpVg8Btz2cEa8ww/LWDrOULFoohMbWtAqiGOQ/XK1aK1T5+YDKAcc3OQOAq0VWITOKYRSO9T/XkU0Kge9TCYlEg/SXjAXueSqrbAjUfg74aHmqq8z6GqxImFELfMiyQidfRw2wPooXYcbj+ETNM5ikL4X63OO4vEr+welCQPaZbIDFGIscEv6LHavxr/+4Tq2vfky9qWUpPM8SeKNgBfoTMpzLCMUgnEC2WD6W1lo1Gzd63IexkMmqmayNBqMD3C6mX5lUOEpmPbGCAWdz0ktPJlU4kkU0zlS/bAUbZwiEkF3rc4aHAIMlIuQTJpd/OzFNu2n/yV27S5PDh+qjjq+rNng0kCQUK5EZHiLvvCQnCJgaqSHFD0q0uy5tMkhdlsKjh5nvKv8Csnws8C12F+JT0cSkYxkQqQ6UM4/JweArULA7ymvSigOMCRn6YSX9QPdW3+3d/qeuTm7QzOOwIj9Evtkpw/3fT29bhygDhKLoQmDOT2pZuMG/aaaaAyy3OaxW28f9A5Jv/5XYek01rNwOf7m+0FSd1DlHphGCda2G5vxUqYbowTMMxISiOszklNNYKnycD0ln6KMr4dVq5+TSXuk7UNocKzicG36uVkvG10IYut2NnhlpSZ10z4O4IuBzWWKQehZ5CMTiENUjIRheDneIKb5MSHjbobLXEKrMz0Bq5/P1vIz5K4KsMNL869BQ8j9heqstklWNqrEukTMwa9crsjAwZx6I0yCdpPEMkeSwfGw5i+KuC9WOcwrdBhrP6mpNcSAm+WQtxV7AEKO4XK0shWFVwKYAn1HzNeJgkv7b7hfnQgysiTXQ+KjVo6RXOqM8shm3E9QK16Q+IMZTR2Gvv21am/AEv4G5iDbdrgG7pIKiUhZGRFetirydjjraColgqw8gpZYRcLDBPk+nSIj9xXUUUNnpbrhDtx3MqpqWSXMWweH2XL0GXHW7631S8N1uh1uirTRRDILBm/J6QMdVlf49wFypCPta5LSJSyvNrulQpGPVFO9SY08VWCD8xmYlzD7lJzbw2JflDAgHD2e1Jg9yGFa5xcRyKIV8SNmDC9OByADc1hgb9cwgUP20h/r0PKtA2Kgb1fDkzfUGK/V2I+FahSTFt20OLaxhzLCKxeI//xdWusvcrv7w8Pw7SIE3NLwc0XBVaip6ZfpqdImqWrqKT6p+M5AiyzYtqeQnTl2NV8sNgGgvW52tGKn85fgOjbDdYr4WqsNEBD51SKpYyNeL6Eb9xEcY0zgpISsmTGIXOzwBGhy9MSOZQ6qM+zx3EIm+Ekc20/FOrm7gNtaZOUO9wXuBiXOnt69Ffn7/+RDD79ZfoYYu7DXqsJ97frUUhyQ6nFf04k1nG9opeynhMZDDAed9JoiA6f1L002urhpE/xilcF7IBVKtM2dkWJuZylVxXwRX2zYKa8OKAU6/SSoVevKQtMLvJIs94QDniuY3YiV2rhW2XGZVwIQiT1dhL4ELVt5yZY872Vt/Yras1QBOFnlOupsci+FzEw1L9KgoR5k9T1yK84KmiIMOKTYz+DDU2l22lvqo+aF4IstgGYyj50p5GdKsM3QQy+LgGPQcNFt0MxELWplEVjzqkXHBCeibx+D1zbE3lmfK+c+8hF/+43BpEZ71Wk81ybqslyHllRi7bm4WuowRe5MXSrf5Z2SCLwZX5JOsMlEjqSRK4sl3LBtx5GiNo0rC89ZMFXhZzcf93iBhAWACLvAJ/UuIaH/qAt7ShqnckGOrUTvZ5QB5i54N1oAY/+EP318Icc6KRYXj9jTHKEXwAT8rpNaCUnYyR5iRJ6S9KDPOFXdTSgPC5KX9c7Za699LSXEKe1SyldSm8x1hMT0czRItwT14yWH3QEoE4lwuyvm9XHqTkR95N6puwDimOaZNRZ0Y8rwDgGxcsgwyFh6IMkc6/MevJa6Hp8o5bVX+0N9ZeRetLK9XT/cHrf1iXBAtODAuzKwTzkmg9EPXnSdpNth6Wh66U4zCN6rc/VKfbucIA3oBWDjoPuo6hB2XyoNKfmJsvntej7wNfIBDH6srpweEB+AIQv1sYXHrb7c3LMEzF+5Fj7lrkCLsLuZH0tNJj1i48GoJ2Iwk+aSenFp0SnObEa1ZmeKl9FR7CxTBUmB6lpZTEk77dNFpPzZbWnYqClQIa4w42UM0GAA2BIZQ9P3eDaDlZn0xUkOMBVAguzyEKkQ72MkAnnRn0ckI35bfqVaZAZZZoMKm4POoha2HlHKYR6+BJog6mNLIFpy2V+XNdRnIZelW8EDUM48iRzjQZRrmIXzaVKp/C+qyB1+Lf/43gqBeYLOwAioY2pG1iqWnCKkGdfnlcgQxwUKB4yMU8aJbIhtAOLAuxufpvBJSFh75cctIidQtkTFGsocVt2mI6Zf/IfLQyZaHejSZdpReKOdphkxEJzjBmaJ46vLy8vvuZbtWLBFxjp0ZWWW7IAZiVPlE1sEJUlzk6rTOi9awM3UwJug9ubk60QT7brNl1Z5f91pjX683u+dXwkCKIMWNPoUvLrV4dB4JJLQQVqtZZ38lIOmk6JBFrqwaIBaecgtKmOmzlW15EeEfXQpVrWB2mMWJ+NFw4kTYbErhd/sQzxIuKJjOrpg7IYuEkVhzt6VVuxlDMKjfAyDALQFoAaTmD1I8QXmHf9pmkkdWDbcHWT4L7cse1lrqr+v3bYJhEgbjHOCylMUnfQLesQOEFnDbrTh+463yhM3zID/3Rr8zlwCaXeOYFr/NJ+19eUMlkXytHbGzdCvPF9+BJVHsPkDshK14k8ptxeCPtRPdcSYuV/DHglIJ/Cs2kHvhIsxapxEtSjLSDKAtyKurO5R60ceIrjFPFTkDSoIMQ6RCIhkQkiK3Jt+ua/BW2IzNHsMMPAvupF2RdRjiKbH3tteSWaUqXK0vN5mtbnqdDJU+NbMhaq6jEd6EvHPRPFH0bgjfp0N5SZuWwLDJQU7VQKpHilbkrdEMgR2TKvvCNI5+XUZ6PIAkVKrvhHE0qFYTCgKRAGaMM+3DvBDMxdOZMr6Eh+FpeJ7RMKh12unexDACjMYe0Zv397Lp3IiKnRbrNYmzn60wL5msCjow5E1uJB/RxqAJuyJGzV8gaYJYkJBzr7x1ceOUrxDg06tufQQ12ZXdOF84B2C/1zcbS00iPZ8uYEX+X3Tkx8O9ttisCmPgcpbzy2vHU1TBa2GvNutKkTRgFIT4rGeGBJktqZrWk7TGut0SHtIh4rAtCGEdXcm5N0NXr1hjnlb28u31lg25Xnnji6ufThd/93f/6F6bZ2c7aaultur80a7EeNPuxuvsu4L9EEj5XEm4uqWFpBO3kJASEcqJK+QoOElY0jwqYRdIvTiY9QE6Ax2lTK7X/OMv50NJAlvmVxKFCAb5Ej2KagjiiUQRjpMqmjr8dCXN6dAPyJ5bnWAcMyMO5sQVPZzKda5sdHvQchzEsFz0m7tYMW6hVjcd1aeIOUkfsWlkYsADpC3+439hI5CRNE7pqISk5aDS2RJYI2BfJ7/cNpKKdHO3owzvRbW8gbykw2mNsrBOG/ETvYUZIVXSvHnLFRp2aB62EDsz9V1rHuHEXKa8jx8B1mwbhYkHTQPFohkT4cjHlMzBlHdPUU5ydISgAi0MdcHubNecMBzJrk2j4LiAgR+aDgjBaHzJPtm87mJW+OVqHl1Q3My2vvn59vI//K//4LayP91e/bLdcsXV56oEIJSCl6FXxTx8zK4Rzn6BQL0wu8+WZSSOMiyguWie7XmavlT8NgMVSSMbcQSQfXJWLy0L8vWpTQ0M8QciIrtgITNIT2Jox54czGPsySL5f8oIgrNAA2g9reJ01z6LtrtA1J7YJo+NvCWb4ISmxov92W2lMAJMdqL6+BwvASo2Jy5o6GlAcZs+XiirEhGh+6AF3e4sbr6R3zYWcx8zhuxQuHZkAgWz6QKOGQd1gUYLJSvKQR0Mp8UQ5i4f42c2iOT4gC22xHk/6JyacSUQMztbg1sS9uQkrtDNdD7TNE1WqvbzVKDDLxwUUgHosaDQGZFcKamslcOTCIOfqRUidXzwtw4zMwctJoJiJqeCOISxwGMSlMyTFqNylbI/q4vzIdhq7tXMe5m9G48k3jW40cC8p3toLd1iLxuBOamUp3Ja9H9VFlFTmlmAiR15DMRUrCLy7jSM/uzqHUrI32wBbMma3Y8d5dzcONx5DinJP+yrU5PCjjMpwSK1ZSaIZzHgTvBpnYKL4DnjchWFVFUE/dXFFR7J59CAMwe2GOTJwvnPgKNFVsxm05zm0VPR/rJwJtZ5AkE5xr9aoLrqd+Ri4uDV1Er0skM/JN8/3nkYCITaeIcK7745yHX9vH9E9HHSFj+K6pxH8Rbr4OilTD6jOSTq9OJ69bD7iTvq+xjdqEYj60SpgcOV68xOrvvSs8t4325vtoK00PPp/ol522j0BigYk9eXb7e3jBuzWrwR42TzOhkHdiScwe+7ugDrMQYR5oqeQwDHtQo8cTv0kbLaAm5xpOnmhDnx8qW5cAvCWQYCLRb1jepwPbnIA1jBWiwCthbsyaume+9AJaGaM0a+aI8QDNXF11f8zs51x4AQMdJHnKyM3cxDcc6A76JbrfZtO462FgwOV17r2eJIFwmMYk7cpzBGnJhb6oeyak7nUSWvJrQSGRjXNZdO5Bzv8boAjOQW6KLwDkJcLnPdFv/Rn5bCBzBxhelOXEnwwzabFeueoiZg5Q/BhyHn1SFOwhpQ3pAwd+DijCskABiS2MJl1rsSb/9JdfFD6O+b5MwbqM3sscUixQak90CXqNC+AhW0PcGkLn7WRrJ6qFM6IOj81gbbV35D1Kr4k1U+HrMKmCdPFrUxYWDsfQbAvJm7Fo8CNOOTVE7gM2SUpMuxHD5O//+//f4//bbXTOFfvD3907et1A8eYHf4xWQFPWcKBV7N3T77szHABExfTILzYrQAiK3zc0f9kHgSUdKmBsVQ3rZZipi+f4iyqYvckDf+csIQWuYt2kDmwQrSGmGrTrWElfSxcIeHiMBR+aM5yYfKEpaz9WCCzvzaZ1PzMnrhqRgiGCjVNtWfPo4LAHxCeyyjFZdL58Nl8Ng/2X/hblKtZZDrD5dV3JMa/MhnwKc6DkV4CjRQuRqLalOjehVS0Bw2R2gBuAb+NZqRhSiU61JAHVd5gLxx3L8FgZfVcviTKIKV7Ee2p18Uqrb1fphlP5pPrjI5nOElgUmPaG8xVmvFk6ug10UCKqw6vvAmt+PDZJxm/byX49fGUiN45mIsoHHmeeDIJ0/xZEO2kP5J7vbif6zyaqFWzFJwBK1G0NjQSmna5j6bM9vmlwIyGrbKpU4HbXlsED9p8KyvnawF+vqw8tNOfuIfmnUZiUH4lDgiDbm8Pt+stGC5Rp8lqFFTF8naJasaAeo8p9xurpA92rtHQSqYYTfgkoLTYJmTMZUqFBbztM2MKKCjLOZj2H5um3JV3z/+9IdbkPzw8Fg6Y7Qa8JUyiefK2XZSz622Xjz9cEROLEYxbqEVLTbZ7sfd019+7H68CNp57Ylfu5T6T7/cRApJ77Wjc89CnClMfZbOyCetzTlmvwXoO4ThGhSxONbhbKefVHGccCObcyr4rIGNIno51Ve3/Qq5CgUZKrtOj5xVsIAlAcTloi9AOksv51snqbki2HZIZQv87mo59yJZdAxIsj1GUVEWykl9+MpQS1yJHg0VMnc7zwNIhWxcQhhV5RMCJL9sr8HHFL2wcK6aZE8AOKVBf9EBX/BzXMT+eCinnGn3xo5hkkIfNDYXjkKJLM5iDZ0EutmaIkgF+TknXm4LJMBTDpfteB8GwUhT0NJXyRgJAB1FYoLl/jE1cC1YGuAwF4WFsVz1SBQlTCxlBcBqJ/e1+M/+yW237npq+9Khb0xhu11D+2pQIPoQOkLilUVABN9dciRc1Yvz35mKNPoiMtVnaqaBw6WrfHerNi5LJ6FRNCOoqI7ZnE2jPySCHUkERZYxHHNEfqbamlhbQnrmG+WOarNTet9a3Iv/dXzUWUZLkINK8/0/TAwoNUWqR8rq/vEB9phaqwDH8/cdnXBWpXbkRsYPe3RPqOCPpsoxu6/v2+WfVBlXkq95QI1FaQ7Bsiuj8pXr6gRb/p2k12cT74N2C6ee+4SZhBcZ16COmTENpgKaSd4W4Yn1Wwx7SGwJe6Jb+D4uDFaJA4Hy12PElrY38wrlouY5ioZOUumV/j9qV6wVzLYEuZuVAKfq5/bxWTQ4QtQ9HW0xTMV6fdEWR9w7XqtUNCvFKntLbmOndmMl1tN3mHf7tVqe8QX/evTUAr4wTiuYbW30D4gZMMk1bBBnQroEeYo4kXxaQbgQtAxbdZMFTQyc3GMPhNoU6ilkTC6Z6lS9l/dYFpBgsNLRAWAck7C6nllOkmvMnUcoMrKfbvZQyff+6JoaExxHYw/DIuR5NCIG9qRQDtlgRWjXxF5SdXksFN1pKDMQgqZJndr9EP2+XLPuGhvJ9Z3crWcZ+xi8soKGQ5SZGH8QV3FNVkrlUu8wJYJY8YJYilnNpPLezDGa5m6cxyfH1r5/v2NW5TsopxvnrTw3wl6U78mipwtjQiqKZ6gkKajf/a8/6HfRQn7S2Ep5CHtvqWkW3aF/5M7zrZ+1icI7VnJ99fJE9ozM0YcL+M+WEjDXhohFhRfylo/62QnxKcKx+fmAhI4cOHygvFKowKY/3j9Kh1yu5BLcTbZSfa+Yr41qPf3GdJ1R4x8Q0Cxruh+j15a1/5gs2IsYqd1i/IrcaXFS7AdGCnrYXpEIu2bTwQEBAYsYhOshpTUUUNCcQucDmWiV50TNBp3JijSD/DDQvL25cdVEUaDt2o5Iwv1eRAlZtvKuCSOjqgC6WYiQkA47SCYVqXqYMaANIkV0ClgL1HDjFEbS54fdkyw9K0lAZYW4U7h28hB9a72dshbnxoj0HMoGF5gtJeDhGj+ca2rlvao4hp2q6ULmrMGaATw7vVXQioK73VUbQRmLjWDY+vX5y530v//4+Ze//Nl0RLMLfdsznFE06bk9XW6WDBhsNx177XUCPqwLgdFHGEBRIB71CH+1CYVgTWaOKRQy/r/+0++pjRo+F5jNEW0kgAEUNIkZ+AZZD0BDHEtn1SkpD9Df/B4fwRWYSLxUPATdgGMcRmpDEBkAnNa42A07h71YMLIusG7Z4DwxxOTVQoEuH6ghgzfyGDWlcUrApDGa6rIZR6Cgaa2/TZXeHCzybLYDzTEU3RCHTQO3nL71DQxXKvAsvzoV5HVvyOs84d1bvugp7DYKYsqEwErZVGLtRZJbSgz4Vk6js8kP3FyrUZ8Ra4wZTLHNJG8DfAPusEZhQuSa7LdW6sbocNQOTH3dOMGtU1Ohia4ytwkZh6Qv2onHW70jMEUtWtkKzGfNgzELXh4yz6HYMQskIpqTkJvMPyj4wWFHcCSm2GxKbr0Bpm3GwSuHtdgJiEUWFGf2KmujB5Yd14txeTHpS+vaAr/U+OXVTQDLa1m50CtYV6nSqRmg5gvMsjiJxOvRDVGmbnoJVz5q5SgMtR2hleG7kiq1sH5uyej9BfRBSSqEx/yYX+9n/G10rlAxAIKmMoSo06MCa8EMK1fgvqhCYQypIwSBfSVt/mPdNGWrCF1K6WgVsDC/N28pmBlY0uv9fG16xFbE35Fi5MBTWgHyLnDnK67VLuz8QkslhwE6ky+yhEX4Do2SoiSRFoqPqMf74dEhoy85pdiMR9g8eywtDMet7+Jzt3OSyPIuNJLHA0XwzdHkPLxAqJivA9L6WQKXpH0YUmKQsJES4sDmKVUSLZToA1uiwHgJs+oEhc+LuNpGi0XzWBr0C0cmAo6CtVwYuioRAu/c7POLVUITLKhgZ1jrLc9zabBLyUFJZ+NF23TAdwXQkpb9/MuPw2MF8pWp8y2E7skhJi4U8tfDtC6OdcvKs+5mYFRV1VP3ShYWls4gD2ZACQJDLcSvpRk0yOx0Hp5oel5XAv5yVZodLtCo1iGfiYbZLPOlucTYVIiuYwCWnRvKXbjbbAqgrTc1k5+YgOn8/f/8Z51ibbKKT7pOCrSTlVaV0uCpsfkiOcp1wTbaRnRAE+yiQ3xKuXiwNl3cIQQ6au01bKlXFdFmUZSXoMN2WOjSygr1mKBr0PScmM8aMKZGKJUJVMxGB8GpbAKZsHxeauXrQpa9JOvtAJlsGaTlEdfLs7tvm9vbLRPz+qRsvftpUBwhupJJ9LCCmOMh3BzHwGR6nouFelq0u9CWAaaNkVwY1ZmnWKsdMGFRfQ9kixb/4d+tmHDdcFbrerMgMpSVK2DhQw3KIrClngQumAD/IPXTy7fuoUd2NiEs3lDd2wtNdLIuB57YeYIRWESQQIyRC1UxUKd9y9vizPHKz1DLYr10OSJTVbDfW2j3HnF6aYi/ZlQqsRHDGY84T6F7IotCIEdzEBwk2vLoq2X2p/LnNo8cUD38O6ZKGqwRU2w0ymNM1XN4F43SOS89UOdiMm2RLIs4MpEKgHy5UIzPxCnbAZsWvHIvbHDFyGTcVnSW9Xht/URU2HZ/JS4ErhP6xlzJUEYH5SHeGDqzwwCQWmOWOWQnJuiYXwbiqz6aAgjfkSYhTm2s1oPIOhLRgRGVTb7bPzwoY6YtVgCagESEww4UNzBaniUEIPFMuIFN1SDLAf1tNVuCtRaeytRph2BOalEcreS8A7t3JkEhsznw0/PT2IOL03vxZaubbSFyKrJqy2XYjmi2xdZ3gvuOX5uyaJG98XpGaBSvlSSXOc6lMbWo1WlVByoXbdcGJ9NKOMNSJ6slOS6Qb+CyF/KXL1EGVjlsbdzzMK4oax0dgK/By8snPfRZ7K5bZbKoRJcxkBDKKZhC76SCaWNFCSX5Fqpitccs8zU1Gy+eJ/5Zp08xMOYy96vNMuA3VzfRsMiBamlOD9yDcOhmnVmJDhNvOz2ts5ews+Ae10oQD/4y4erNHC2U6YRTfCyK4ocUkBxKOMwVx6hVOY821BQYcxj9+qJ43yL6pG3kh8wcEgB7YYjg38fIVD/XKU8oxog9nZp0Ir6DrNoyE+x827CsE45kW9yJnNsDg6eYdCmq8fF5s9naGslaJFqo5K81S559diZm5U3sPY20MnoR4JfoiWskumilU11fOlbJdGc/WgnMMiak5JH6s7z9PddEDoBpy/pjwWg1CMk2vNrPAA+ZJDv+3TZ9va8Vr0Be1vXkzAEfJs/0CPPf/O0vSvJ9w/eJms5oEjSebBr+z9bJz1mTidHZH5uD0APYjlPwtVFDg7EwLi3Y3qzojj8rfhUxZ1Hhm0yM1TVncs7dsawGYZtZZBAHIHAlAm71AEUcVyndhEdN2S5g9oPXlVnkKfHOg2Z5DueQHK3Qy+KtTkeguM4/EOaUWYJaNN3Owo68WCBAA6uDoBTyxqXYPL/4QL0J5EWYAUXAyrt5GFwPPabSU4IEb0BQ/jFmSXT/nX+05FhR3PWVkji3LPFTaE7OhXXuDFBxjawwA5yt9McmnQIQx7BI39BXma6a9PL7FPT7SnP190huPh1M8R9LSFHdP4NZwyBmP+45HVEKzo+xiKqh8/ya88v7PZ/kGUTK7JmDp0bJxByj/In0M4oS5l7piXqwZ+K82t0eJBVObm9QtdKD31Y3z2KPhabJVFaLoBecLoFZ+R0pSqI4+AoZmdC0jVSM8eiDRQCxXGsrHmaG6CzSbJ0IAUbFucId7b8fTtgkSxclyO8RlITeYYxVqsImWeaSI3OUoVITLJ0+WTdIZ2p9brgpLpntga3F0zwu6WOoRJDZy+gSMpkxC0d8TJ6rkCtO3R3aTTSE+1KS/Fx+kz7rG6EQpnulsu5grhp8YRwJQkmaitICDlAfkceFjZT0qxh81+091s/2oMN4XDrDMuFT6ytD1p8WOyOUYMtNtjv5unEshFfxNpqWqIoQOgbyDv7qH5ySQDM9Hqht8hTxl3W3aqRTKRn5hm+O523W4ucdcB+qrid2h4eIZjY06+DkxDOAw6DtC0ChBsWRXP5MY5RXl8LJQ7K2HWlK+SJ03uKHLuBGFTwEbfabilOKIX0CL3yskGH9f4oqqHq3IPU7PdYO0XpmKnKf58SAkxk2XK7O83U2Niq0wOvu7rY4Cfm7uVthOILcXuDnwj6GCiNsia3qcRbEO4a6FP/xOUhSultus0oPy6iyNsXheTi7cHhS1UMSEWX0KCUdPY16og4ZWlPJbHgffbQpooUWLc0+Pb370zfHrZ7fclygDXxfXaz0FIXKpUZr0fPm+VuVPestP3pP7T4+dgc3uDAnmAQFdaiihgyEXwSJJAhr+LnzFGaTDZVjk3ny4oqAky8IAUVa6q6VFk2FbBkmx7y0ZLAAXQMllyDmaMzkWGwFT88dJA+1RTGpXGEj/hA9kw83MUrAay9OikrdbG9BqjfaGqlUs1eRKdlADhm2J2eap4M6eRi8UiHNXNXIj754GL3x2PbBxaVg6qTLGo8CwhrBCiySOQSAz7Kz9HbAQhXIxLxVRi0rNKptUcZMOdV6eZmYWnSQ0WYVM/ePR2EUKLAFtvFt3qeirKW+0yZFV+wvaZT4wW5ZRZ/k/kT6OOpnX2iDptF5MKnY0C/uBXWwsKxpGV5N5mlZyV5yDyDQYKYosMjkV+sl5LL4N2/jv3bFMfLClsraVKO5jpXN4d2X4GrHcKkJnwrZFRq2f3bCT8wfTAAj9hyb40vbXY51TjVjaA0igRlIomH+Qv623E9oKu2izIC3re3z1fwUiKZCjswBOI+nCOQ3gRYiygG0lB4qhwaDHgFmtYwnj6l+WxHmtU96paHqsIdi56Z58ojKZ2Wdc1bNCEpwiOHGl+L03FKutCtTmXTiwkQ4msrmE1FUwRYhmKDTvOJiCz4v16RwrYf34rbQ6TbhDJsdfPp/IzB6EkDVGU00zzApmw+g1XaLwsf58tI8M3Hi8BSOF8CqwuevEklj/ZMTVjFvVpJ5YTeogr6AuIifeAZl3j1mFu05pmCgJsta+I0xe7UWtW6DvruVX2SFXGatn+CSSjw96lC7Bx0gRijbYu9+Pnqw7zMAmNUf/3j353/4YR2j/ypS3t705KAvdreTtpWNnYvCXZycP7xJynXG3Wp4LxWhWPJe/t9PwDJYZSPzFyyNcBMJ8LmSUNKb/EKrXFCHWQVD8n6gQ8zVbvq4NSa7bUEn+MRAmD/+kqO3CXbaS5orhgnI/c5SkD3Abed8V2YyYy8DQQHEl1S+4RYKVIzYqQv9Qsbhs280R5aJQXC3yc7BHkNfaKz2ZCNsoePh3sUfgzTfvnU7kPLB7+vNnX6/rMr5+e332/3jjhlwosqLx3EUMSAwDKrbNssTZEJzdmFDMtfiOP6WSXPfr49xxc7ZbPMS08Qhti4THEg6uO1Mt+fuP7iE/r6d+FpHCtgx4SLykxot/FW9InaswrPY9Dv/iXWzZb/+9kP01YY6Cw2JKJVykW+1r1z/2D0cdjozW/OsPjizw9Y3jcAmnbTSeRhJt605VWwMW+VdY6e1j8QCst+mYolU/jiR12Gd43YLghgGlaIFHmnrnX3kE/3UxLgx8s/0SHffhodynP1b1j26F0WGBfXeV91XuUFbe3ElKsoAkHWmkegwwDQSoxAvZ3r2u53Q8JWMEQypA4qAwTUBQBjMAlR3bWlWdLh/u4p9eEJK7cdsISlpOie6pR0sJDpqIeyesQvKk8MwOmFfIDdF2xBEt+tMDMQaECT/ESAl0AQDq8kzqHNMviEYCb5jQ0UaTBcnxs3uvq1ZMQJscD5FPQ4uTiMJ72JTT8gyg+q4tXIbPKNiy0xI3mGp0ecPcgKTcaIsWtrWk81B1sdGkj/RWSM2iMW/+0epO/NPly+1+27o1kchJngX561XQQBYiidxcQZPbVoKaxFIBN765l5ZB/Q7sUWdfFhjUv0Us9XteedDCr9FBPyyLDm5jFeKRF+cOw9i2wausb8qHNTrElBBRsjrV1mEuHC14+OQgsqTJ56r6v56eNl9GWhZsosK0epjcfLsKLPGc2oA7ZvX2VRz4bCen6mkJswGAM0hDkpHcVj4ozSzMCsZ8+srGVTiyM205fBLGaMlJgsErPrr0C28t2feELZUVyDBTQTrihFzsVd2NMMTpBkD1kIzzzQpnX4+iuH8St2fUZED9m/C6fq7ZfR4tn6S8CTT8hZibwbCUfWf2meO4SE0Pd4A7BZMRYOAMWh1O7cXw0mlFKIihmnBlJTBMmci6bAf5gKcvG+3Gwfm4AKJZDI2lG/l5IQpk4+CZ+b0cP/kYjtEe7frqrnCzPa0LHkn4+ttXHVNkYrCeNQvC1pwwLq4qEt1R6snAHhx5vI9YSWiaaPBn9qLKcFknAAuiyuJrXDW5CDSh/P+mtVbAmUkAJrH2mRyGnnYRFFMFOupcXzmuVCSKikKLfuSawEZSQmmQ3boifkWrQlxK0VHuW2ZSvAsimYyzFLxt4BJRB295dea/Mhex/BQOYEpCgl57TsBJgYmggxZ8+woDa0g7WIrKu2ox/uL7LR9F3y2Xh5L/C25MAXFZgcNklgW0a4iIGKEFZIs68w+Hg77MLVGj6bwKnZqK1NmnBrJsIAsHeYGh17d5OyYdoKkWpAYZoBDi85CN97YByVKDR+f35wJcI/In3/97ef9qN5a3VRLsUaOonSWChN/vbnbokG8DZmesZ1yTHDSI8WpXvhxRoLs0yp/JXwatEUyhHD51hYfIyl6LNkwTzdB1mJiiZhgJQiXicT4vl/b25v73YO9qQ8pcSZDkCXy6P5qEU7xH9701yNDXNGwipc00Pow3haRtaEkgIsiGLlYogS4H5EVaMA0KDPKjwMKCGggWysDJMwo7XGwlAEtOEyPQpDyw0KvOdINtmof195pOUe0eMAnv//8QWe7M5UZqW92p9vIFRX3b6sNr/zbulPVZK6jTtWYsOw1L+Ca72G3SmtzeffdsYVVZ/AIseHjZ/Abk73d3hIMUu8BKJ+tb0cWyL7GSjuggDzTZhTQsMm8pYHMFpgzcXldLUkdp7Xz03a4S1vfHx92VRV4tbX+f/4b/wQuMB6AhqOXLbBefDwG4bl8i7FD+882pqOnTlRz1agFXW9TODlw1uIXOqR1bapf+joLYdvaRH9Va8F19W/4kVdS+ELEho8pMupMAcyz4uQsqovDQBliKCBgy4Lbrvck+6FdSiBNkB/tfUAAd9KOxBK3j8MRBMTGlWYbK+eqsgbcM+nEJPNBmZegzbC0cyAq5txuKfj3aoMoNiXoY+vTB0LrDwVL692WvSC35AJq6Mpobbybqno362t9YRlGDBRsrS3Xe6C6TbOiimzutdoGyRiGlbqu03lzV6hq20JEMmfBWnLe0rSwy5cwbAOzatDMKdkWuNuOOm9BQyyMlkucVs6rofhDFs46k1zZGje9FN3oJV5DQYANWzSbbPCUjXHShkVFiR4sPFbngE9c6uuQNHHw4l9u/9DBlDnIxrCJAni1RFP9kj9PfsJ4naquXY/+/qCxeDvMTNJLVk0LvGcwZ61UYbKKnX4gqbPTFogVsS6QC15lRsfVK0pAl0k6oSoC3nYyKlSaCDGNCptQThB0W4lqDYCup5RwZNLK1w9AcMTzOq1qb8ecOMdkQqCfJrDfsh1X67pZqGJATwrHlpoumKNbXO9KhJUYvE7VY1jqVwDHRjBavAIlJVBFa30PIbhi67//9nP5bQOZyRAGg6T6TQL1yXITk2weB5uk2ifCabPyZUrKOdLRSZfIa2DmCM5hlCxt4puWIq0vbDF6O3bkED5K5nM2sQ6OzvQCY5rCUTL6AYwgU7ynmgEzcepeNqf6UeunZSzPwNlpmoyrubhLTWW3ObHitBbHtDWi41SHB2M3L1TLRH1faQBjoB1WkpqF1Ij4pZNJPG8G2IC7LxccFeWQ7bKMZlTlkmdXACaSgwJ+iiYBYkcBAp/SjyDifS26cX75wMtxGK2MdGcgilhTXANLdthUm/zmJgNwAY/Sk+TndD1VTOJV8OcYU84u8PV9jgmsQJN7SvVQCMlnQlUyhtjRfjIlZOL/zaBR2rhkLsSxwMJEEWVFEPoiW8jXZyYX8rLNOFBubXmdsKo5Yrtf1XHRZgZJgYkJsm7Aww5SRHrJ3zKqWpKQvYtLVZuAAn7aAl5s0WqwdnXp8TgruejbJ6IOYuP4pRS9NnYPggg5uVxMRIFcODRxoUxIj4Nqu2mpgCigl4/wageSfCJqUmit80k0dfH//mf/0gOn8ESdogxwtWVNI2z84H0DHrkpL+A92WdT9ieKiBuRPENP9Bba9eDaIm66XVYvD6002UysuhLE06J7IBV7E13lYhGbNCEMryySdA2l4hlEluEsoLegBJCRB6YdM5xtMAobKd/gvdhuzpP/KgwYRfN1kmqvtXHzLpsBrfuip9lJovD2ZjVdf1E6Qf1S0enKh7zHPvh9O9Bbv/7y248At/PZHAWQSiJSNWsXynCpinKKrvtMuIAKOsPlmUiTF4Icd1zExK8VzJUwP5oTK586TUycDlMw8ifYR8PJNLPqCdbGF02OeDFHdh3Yc+jMzNzEWot/0FpgVIuLpbc/HkThM7IDB5IN54/3T8UT+J7MFi9biFMOoAQ1e4PdsOVYFarkVKeu5XFNJw9ZQnUnQJ2ZF2aibPzf25vV9mqlNEJgZithGamy/JZanWA2xo67k7UPuFJjJx4viN/ZDh4eS6EKC/LduLrzc/Hj/tE38SZOhqOSdMuSMU5EDFSWHR13yPjKSouEqmxp9wAedapsSUReCsFBBBfkEhJnYe7Wy8p4xU/vpNe+zMKSMbR8RgYJJiVk1Li+8E1qrrLPEfBhZpEIFQuzGf4ktsCXtAFEwQkP6uDzmocNVRT7giJAzeoBSQ6UkDdFtWtj/ctFy8hDjaGBQuEWAAcqOwrYlHLwKkrABC3Ur4BnN6+ddGF1fEPtVkZdYU/HqeVqlaxgV0WU1b9lyC1etxfA05yHybGDEmNlCZTB0ODQ3LphXlbkyyFH4JNxHZxS940gqyOpRQS5Vf4ITRiUcNwX314VS3q4QqosMbexui94x3nSV8HXU/7oUXaHjdc5uTYvgmiAW2qCz0FtGdrpYRTvtuwMADS2dONVlzAV9fc63rBoWKDa3T7vjpQVklJ5oeqjGhMbqLlF8Rh7osMFwcrvOT/78fAY5nliAcPOr/Rqu9IkDDv+wbZhmdBThtdqG7YVjo4qjvU9d2eiL7iD18xlduxrsZ6QT4w+W+X5nsbqWn7ftSWcIWElo+dieCPMrSaAR89dsEafiHzstZVHoTIchbdDpkTQUFtPD6WnngM8ww0xDBIlMFCILG3CXLHA570IBBNbRkH4xowEFDzch0xd1rOYuYiLS1XpLyIlnDV1tLVktlXFw0D12/6wk3rRIuD75s7Q+B90yr7DDTivyd7in/+tyuXFDlZnK5iH7BhkcoLQhyw/YKJehc9kiurTjddm2+2jXceavcDv53CMqzgZjuoQsGezz70F7uJoZVxRtliMfBW5J3LiVEI6dufs6vRuuxblgILZWAvgUt9CN9XF2yOqXt8NkI+slhQt012Sj7RaWnDo6olSffwGqOVHnP20z6PIhw3P5s1FIpbOb3MJGr8j9QKJNsIHBRm7Gc2fiQ4EaBu629q3q7dD1myJYRMOgqVgkuIJAlIBWk7E+EhWVikKLkelmTwiwMz7x9P82TYTJhzEf/lzltWvgEGsP+XmMCJuJIYsl66ZHj9mp3LRZGyCAPppvL07A9DtDychlcLZFw8vy9p9JvkdwkoKkYAveHp4PLB+5mvRPMdwyosqx8Ybx4STeQkAKPDjQQzGCe+5fI4SeMXb+/3+7erGFwvBKdBbL5VbqD/p3j6YLVV3u1rdAe5OVhK0xdpdtbk/GS7mTSmtrUSxDIahZ4z3Wvj5Q/eMtxrpZl0Sa7GomFIUlZzgdcScWTBtRThC1DrijkKFVPTKK0RKbdvP3x/u7tx1eK3uG7zKFzDYZZMsJr3yabf0lGOpa7SnehnpTTI6iyeJ8jIRBnqeAyfqUmw9A+DZObg0JtnOXlYSE975B5HHsrWetk+OYmjULvfR/ULqRtZ+H/+d0t6UJT8p64tIEUUbzf8gDTaChnCfgRJpsVrADnCTNjcyTvHllEgQK3LWHaVXMhomEaszHnpaOgzXDGvsiHOO0ETQCfahcM0TZynUMbYgfOSBxixsDg2J+TYzcJEuWDcWF7cpmPBVO0Vf4+xSQJ+nv6KoUfh6jWnPhynBIh0vLUglyKytxBvkK/NP6+GTwEw329Cst0JdnVR6R2F4xqYgS2DFeZPEyYfttHgLA+A4mMyLnvhsAKxiCgT6/MH6sLNNmdDGXzuOY5egHFZkx6S0/B3EsWyUAM4ADIJoN1ntCt6AFRxoa9joc5H0ohS+mlExafdHSs4o466tBatr1ZEChUx0h5X3XcIM12gl2ymWxWAxuk0syMJEW0j6Tm4KUTr6g3kZH++K3aFLNXkGVQqZJBUA2FSvFUkP1s0X2uApQkwoJPfaAF4+HKE4fzEtp2XnFmg7bqOgNNqNuZpd54qBkhvE7n9KrFPE6nFOHEq4dkmcgB9JuPza3t38EeC58hfXMUg82CrG+P/tGxmJU3afwONAf7jVEOpk48QzYCrRyq7MNXidF0Sb5xqHKtOjVJQ3UpAQKG94X6cHHUbn4RFMKJtPmOdVve0YkQWaywoI3AJzjcXdSKBtYyfiqPzb10EGiWwW8YA4BXBMG757Duz3p4oA8gLt2pwUNxtml6TziSg8EZh6O3uQBcz0O3/UZ/yNnwMryaOErp2hQhTJ8jP8MQQGgHCY/OJcMVbvUwTN5Aq+QmvC7jczcVhN8ygqxRONCVthUv4axpP1yr7gTa/vupk49k4FGY9w0KoROQbsUmDOBdYlx2wznfR5IN7cRCGprIwRakfVeicwrEClcoB6DtEEJ7eLwAYZrawDcXkItMNSz5yqdCGQ9ozsin/0sNJkBIUV5/cr2ZrPagAQ23K2SwNhmRXM4695iwd+CXNV+ezn4+v77uX14flkver0HHPnUZR97aiOqiQKcXpyI+az6BgdLwP4eH/cW/A8NCYer24l5e21XJoyrlZFao2d4IFhDTDM1pDpi39K4aa0UkHsisAUXfVZmAXqRGm/f7tRgQJeMWc6b9CX6yu40LCiUcKgHcWiZiTBONuxLDn8hQgFHyC3UqbYAJ/VynoyEz6ZcyTUk31mJiBQCWqoSo1Z1NzClJGgU5eykG1izYp7/uO+DFlZQR6qihoTZE1xytcOKNlBnqd1Iikeb2g0uMhY0+ZqIFlSvqJ5inyQkTI9UZkOfvN84tcAZexRJwM4iEIW+R6Q0WXOsndXV8KPxIY85GznwOm/tidtvktghLYgELTHBZwEISVUBogeiQdjn/ft2IFF7zhxUV9/90GG1NpUnGhYr89Ga7b63GAeVsVYCCuBNw2Qb/+iIcUrIs2E0WL5O/nADvwBlFl5y9xe55BX1hxQXiyKs32dii66x1yxqZEEFJ23t5s+Y3Nl5tPZ1EmFT5mVhMUqWwEeUkhNvHPR4hisWnsqDaZjBAYzG0o7mjsIiN97ucrdA6nDoIzVAFmu5CRgkMvtWJwryVghD58KIicJxOtLfDIY5IEeaWQtO+V1jgXQNX42hWY5dHYzjrZLC+SkpiYNYlfueuR2o0Xw8pgbpzkQxagkDQmZ7xDZaMu7m1ZyMDOuDKRKNsfFHesrUpftIk6cCdECIEwAHqEwvU7cP75/u/UWC0KaGbKrq40YFT/BFx08Yx7YmGJC/86f1oKDfiraAUjvbgSijnfySVCcCVGdo3l8orG9ajDhac5LDUDj+Tg7wkeL8FhVQ/RNew69NczNqM2EUdPFwir7q3WhJa2E8KuCCFLBJsOAll6SGn5VVelj6f9pR6XtEbUkOySPcWWR7BxIszhtUjbfYgGFai2yN/Q/ElphuI9YCgwSnCAvSUBePnAsiRSFRPZzXwRGD1aISgACksTq+ZXRw2LsxkiEFXzXqBLrjiu0+F7vKEoMI98DRzjZ3T94RowmH1ANdZ63P5AvG+zLNmO73aoqRbM7RTV0pqdac4pEXHyy841MDqxDkyePngiJsH1UKTJZx2IlVntCRvvnQ4/maaH6Zho1GgXviFPKA4nklFgLS7QUMWjouXC+lVBWMI6NUZkLJd3q2PSi4W/K+doQu7N/eP6hKp+inp0I8XPwZ8fbrKJgKiwFAGlToMm6lI23I8QRIJB+0AOOWK397nm9uVIk08LSshKg5IYloZ2uKygoIe6BlWw3W9baLDLS7pqYu+MtHRPw8igSdXK3VeUGLo1c31ZRXRMpeM71thdHBWZmzEIQ0sxzgo3VX4Ya2PqsLX17xUALsILdbKSq51IyWQsshJx7cqGGFND/mevpk3K3lvj9+fFV84kglXoAS3I4BWmjgbn2UT61QR5CQc6l9YS8SBZr7dmJVj7fiNXv9w+hdmekP58fnyRjBNBkzj3aB3wsxtFaZi1RBzpoTTzYbymnfwbHwzOfBdX+LafnAG2BckGeTiQ5MPxh8AgkATAqcGJbCAL0FpvoSDoFq+Gdx9BCVsbAi72yBUTI/wFTEsvGpMpCtSUOCh0QeIyEXtpK+0IzrWWISP001Oz6Uh3GLLa3CnA5+rChF8PEPZ0FB25vknnFgj47H0CUSLCHOpdggmKDhCfXXluXGo0cQ1IcstpCRJytfvtjLqcaUxAsK8HyebhzUgTL3BsplElS7S89Q8xJi8shFOm+yuH7haMlwHZOC8elyIYd6QCwmMn1tfYbDrp5kHkYIO/E/kyIidUrGO7JREQ+vnWCwYXxncqEo/7qGt2llbEONEm4KXlnvOlFVyXjblxeF4fd0A7uBan1QHUE9l2n4iEOsRRBC1lN7KUl1aT5cOBIEnltf+z9z91eRZR1Np4/fL8zTPrk5yTTrkvsIK2WiT2GY5JGClMW/9Yf10LNQhcKCnrNAKgV4WUHKxdm1uKiG2Q6Owz3pObWuHmdPS2qq9LjNvX/iKBBWC/AOuytb1FLfoEvf7ujADIECrBcKtZ1fSIHpieGRLJbfSX/Sh2WkaDwyFvtNDhvnBRNt2GK2SdNiU6SOS4/skkhxZEj/ThJqJ/6WVMiS46QE/ibkXFyWstD96f7PpfP0UquZjYodu9lDDgVFNjVKVKlCqxoRqC1xhrPmnLgE5gdtXt21qBMl1KcFNRaYn94i4IJanrenMArKRTgswxiHYQmTuVhDjVI33dQ8lW1lFcwrr09Z9w4579zkW3s0rv4WGQ7/qWSpeXVco6+qolm9jKTHi2UwuKYxXLtvWSb+GZ2rIX86atJlB0h8RwLf5Ad+np6EbDCefsi94N2bZdnv3xf31hWpyfkBFgMHa9UTNFFCKHX482ywqJCZ+zxietuuoRaiQfcX8mdluJTCJOwvbvoTaFCZV12E1z4N+BI8dRyPCkiPMcMAD8osa39OwbH2dGhBRYqafDthTzBUvyKY5T2tr0kk9yiYPQChubFRAKRgHAHLFGwiUuTO/bAClh2pi0ShTAjsWZjv1kObzt5lS3I1rrmm0LyuAln6fE0sr0g6gQ9WxtRpa3pI3br317L4WZj8Fd4bdD8CGF/5f1QzDiZQApjhzSSgHq0yF9tlWodcgh3IK+HwGJTpbREmwfQZJQmX17sibTJvdW8AT89jgqVzBiFtKZOOoTciSCIwFsgqLBk0CkQw1fUTOb16YXJJTXCmBbEA5N7UYocIB+koQXEWqOClmmOlecBmKWXcF8oAfIBF0wQlBFhHpHpjxXxntyQzfJmvdwYrJhrhN3zyCm0ok0q0/DNJXftnXoAa98FnVi/bm7WjWzDzeIhhQnImT+y1nQFJzUU9UhdOnJcZ7f7OjBXPyKfyr/NgSAX4pUCPuDW7jFDHGKmgGAwy8Uh2Kqj+J063S0K0JFGuyQnYSMtHOcsQzzJdpaDG2fvYKuANY0z8aRRMByda0kYcXdc19ZHmi2HeAix/chQB08THoioaQ9tr6pSmyipHWYVmKuP7VrrP9lacXXp+MJujwjX/nClI6uRy8S5mKRSRkduSctlE3UJCkL/6UYzxCJtn+xuJNq2Gh6+aJdVpOqQuMczhMiLllhYs081pMiEGjNTHZKw/JZmsfj3fikdDQ/lN5Gx4WVSW7kdFoUZEwfCJ2yuQVBZegGbh+6QP1pRjkUALvESipP3E9Lp5pAWHQoJ6TkSigUweB7QWlFsPWYlpKrehbeFjwPsj7rNlIHnTIULlfe0+jQ17xe5SPIJje5d4tsRWX0KK0IvhsDckm2rb6oelnaV5OlRJLvwRCcM+wjAtUs+4P94o62gZU3462ZTUIv2e4SVeq/xUfPQw1ZJlqPZlcAHOjd3N3/60/d7ac3HJ1LBQlkigTxDBMomzC/TTVeIwvDwFF/SpgqQWT74wioL+CtrgztUli4yHjvqodpMZeSx99yT+gFuEPEQtTkXSKC7Xu3SbXMkx8WcukIaddAGvSsVSyHG9H2MEgtqQ8PqXmgT3qgnVu8que24VjdlAW57XRCsrjgeaxUyGzCHJZMbBTZ3m+VdrQhPN74WcE5HB10otOpz1U9XfuKAaBpKUacH7XX7sid2zoySFo/qBxPv2juyIVZB2YCQ8s78HDtTu90os6MYkXk/raK/a6GACDowBCsD2p6T4vJAAIYFsmLqbRxPyYsUGhZWl6xTTxpjre8NekDUyRgZQPqsAOn3COB+62TQziIzP7IYQA2cEQ072EkXEgJZBinJu/RQSk9MGVzz8o9xZd/yEQri2eQMSDMuYCVyXfSLx71ZMbt8ZURdEKBgMU3BovIU8ybsL++eJPhPsb5a7OicUXd+y1Vi6fx6//QoRA0KhOxsPUBuETlhTLx9xJ9hQPHSzwsB9EsB5Y5rMVomfHQT5vNwI1tugU0OygN8to4NoYdRPFkx6oPZG2P7QuHFTKAsMPCPtcl7IN2WSYDLJkB6PXAEQ1I6VhmmVzOD6fm6g758uGhc+zyNoM1URYCl6xRkXi/ohfVM5KKTKCen7n3q9MDzQTY4Q8LEyp9zfECCgDgRnxgaU+xWFmJFwfMlI+DmUvNPoBEStxFtuPEYG47vVU5ggA0LSilNjQxwfPis+eK9SYefiBe+SGIVf0sU397dYM3yQ/bFR1t8A5YhAwhdMpHGETCzzUMShjJEbQuQ7PLz5TuJR0bloG/KmdOeBEmf0eiPf+Z4vHiJWw9xFsuLJ3m9FyRzC0dDkmT5J1skmBkqteg+e9yNdNkuK8TjNurnoOvbn3/8+J9//bWaFXUf7rcoKvAq6ABIbl0x/fX18HiPWsL33LZ//n+4KUxAdfIbbK5qa7/Feq/U0BAdhQG5emx7mQ01LfQ/7y/MkjUPMDpdZZKo2lwyEZjzzu14TMkOCX9M0gkGkDG0W3Ge+ID+o0KJll61kwGgaR6OSVYlbjmpEvbBZgiweUvaWU2ydUHlBeYYWDtnSGRD6K1RM2j0KrWrkwQwg+6ELHIEjIomsQ2fjqCzjHTMsq43G8DnM064ULDXw8EkYxYfX/Vi7HCTMV3U/lHNiyPI7nGWKcT0G+2SLVaX8vr0jNqLisayqZxZf8l21IBbhpCeODOsSw+v5bBDEQGQ7wo1iCQqw18RCJJnPgLxOuR0E0iRijopoZNlkWDxehmgIuYmYn2cTBXYcbWFyNilwJquT11AaqUJqG+Qy1C3NQnsiAJHxW7WyEFfkVQ+mjunXIlht2kSOfVIqKtwP3wyCEtE+GgL3aUdeH20gX8w6+35sJsRwr/hMlUHl2mC0EoBEUIMLLMF0THeGNcwcHH8EDcpxGkOvkghp0B0YlcVkmgidCbDQ3TioZCSavmjAcLRDhCV2XRCQ0bBPjNN2Q/W6+L8sH9UYtG+TidzIAQzTA/IyrtYGf/1NfpmTTzZNhkJRInYT5dQWxOUwHaYB7G6Gyna7zkUbCy1ZSD7WRJDtE/e5YEG6TmW1a9IL6LBdjIyBNu6twqelvPRo32MlxN1LVhhlThRn9u7Df1GPJFlI62Nq3kwY921aRT+VcwwnMuDZZwnvUtq8YY2+Iy2WCbLv3Y3gtvPmA5+osO3UtAaGzGErb/7lDoegdNUh6Pyy9KMiIAfpNhzmlLC5kUeGHuD1yFqm2hsnU4yAxtjVg6V4SEWA9+yBUU2UJCMccF6vJhR9xfjdryGJBJtbXzohZxqvka2s6M2Zo0UU0qr1RgYywiTpxaG8iJDEchyc0AvJ9R0XsrQ/lKH4pBucaGlYdgsdZLfFKzpWDAWBhxbef1BRU2ptSebpJ3ii3uah9jqEOOkawqt/48fP2VnNJz03p22knr2FfItskoCRT6JGuNjgbAHC8JvA882ymQJAWyKnirAMyqOnUSg5oC10Sz1EkFM8HJnrQkAwT5Q6xgOnOpPOfzmjy4ANwwOCnYOTfgeFeCEqfF0ubqYfKes3re3Amtvy/Xtj/3hf/rzX37yRr8+bruwU+CnNPFagFevsNgJk8r0uqBNOk7JReXCuIlq6G5nlKaAxbn9dYS/cn3d7Xb5xRiKGpDD5KDbIgWAVe+zGmqhYD0hLiF30akE08u02ig1f6yrf0/KlCdIv6gGkbIukKv4vnYr6MviXIht//j6oA7jtRMr9Al19fn4iwWdDKcqQTzgD8vrX3iRiW03GPNVIBEJpRLWnUS2BTkrCOn7yuHJ+Lsjf5fXbmR0AYCW3IvzuReCGumkVdzffC/uLokl7W02c/23XXn0eWjvKh/PgsiuoX9HXs4q+v7pwI6fFWVS8s4cSiZQ/zyjQGPxJnD5HF08deoAlitczEs6UT6nF6HwigKXj8d9VrQw8el3gTyqro0N756JPDvXqFwS8n63e/j9ntNDLbJluU2d35a8YUJ4yqIU+4eXw4PznlWOCr8Eq2hMBfKVJKXRXyfaEtT/8V3HCGE6ilqwf3EtRrEmoEwRWPZ+tUAVaog6vX78w49HtIMLca8JwNuz5SKm2qtQl+CUgThVKR802FPbXWMhBS1xBXy5U2mAkObnbZIKvSQBV0cyP7l6U9hIcU43p2RmyVeNdgILQla1nD3thJTNj66HN7Fs882vcEmI+IpPW+rcVUBCN0Rg6MDWoYXlFQJOTufki8u25srJz2tnko/AsHh3lxRiRS+TmlQxK9AJjRyXMhmiKC4JUqAkBlOm3MsooxcG9GPi0lH6zwVhE/yBWUjsBAhYi6kxiz0wfdmLsQrexi5TIT8cilNu2YHbUiimGGvKtBiNP5k4SpN9KC40iAD8hK3ZHiId/U/FtAPzVKU7PvfpZsanPLPLJTgAF7pMGpzwOle7UjC570iK95uFvFrZZpPxTGOis3THWo7Njgmn7VoG2L6CQqMcDcUqKMpQExiFZ1b9qqz+G9F1pYMobrfdElQ6QePsF7QCi6K29lapj+iUUmCgzNhZLnVo6bKoxcX5UydD6Xu5cWP1ZtpaCAgB/hI+6joma0KiLREdMRyc5/jkXlnT98wXNQ4FKitX61zZIW33HehJ/JiYzcU1wuOPjIRi5bxYCTApE5MCOUIOAXG+PHhySg5v9SuTtejMTDUvsE1awuu5MuyTVbPd+L+B1jLk3Wp+v9lk6jgBm2s02rjMhH+muEGuHSNDT9RRkBrRS29vs/rH2c+D2VDVTg5wslEG5blBHCtSzZikijiCWqBnH+Mnf72Jbz/sfwIBidx70a6Xk6ezg74VVTK56AWDIJPDdoycjnv+4j/9uwg0Uy40CcIsmrADtWEODbcokks2uJ062WKk1I8cRKVR5PfxBmzcAoKQahZVltgxyAyaU1rdTxT7IGRGbyY2nDnwedBhePe7vX7iyntplCqLyH3FEtZEZFOJK2nVW6PIIzxgN1hyK6Y9vURlnfu0hxUq+XwSySw6x8YK9hpoOffOZ4caxIidj0CeinAhLSSAz0dbqRtv0DTstR0mwbHgUn8OIVuIomF9nKiz9AJWREYcDIahWJPKEU0k816EPKJbrih9PYgCBUVcZBK0utps5ZaTpA/G71EjJ5JZzgy+8OliLqWTR58CHqPggTWOk9s/3NEy86aoVp/Z8Zf9/SN8bckm57GvGQl/SKaBtDs69O4AMAYmptGC0e2ku520HXQlQ6kTn6qmzrrYMLEOMkvBTm/kPSQ/4PckZrh2AcPXqfMQzhoYp+4AVtjxFf4SFwp5HQPALgPlYJtc4rJOGQsSAW5GwL2b9jLSDXjEaLiobb8xVI3s5zSPrQyVorNZEVY2vhbWwXWVvlJzX09PDrwU8uMcm7mP87qwfDExJohZ8A2a45hSCq+X7fVVYWKtmUBMZ6+omI4O8AT5hYTCEXWhAYLk+vgqPIOFIH8EWzKT0BpDVsh9INXO2BjyEj0TACOYTkyJM1hjiGu3yTbrpWYQfeJDmabh2DLeu0c1FwaGRAQ1doQsJ9J21vjRdEOl8d5oDUmLgZms4td+YNGVrld46rx1Q4JufAJeLpJEteKaFq1kSKBA5r1cZYYtALl+ncU9K3BEMCCizzwd9j5NTapCCupcncS0n801wt3RCDSJnv1FTu0k757FMULj5eX0PeJV/zB4WkVsSmXExjrHfX0dvZW5JIKmLO3hJa2dJSAak7Xizqv6NabSmOO2aHtPFIu+GbO6L8fEdDMew+WxFmhQBeDGIL2Lp0hfmHNfoMlHZeklca9GaD1N3AAEQPyQxctlL3qWrEI9v4MBnkfy/MTYYi3dKmNfOoFMrkzfyL2URHKFfY7k5pqY67tWEHZcrD8+bl6gYRStMgSr7dXAQdWDcEUGxZdzqFxtcLCP682NpSOWvnN3e0P6cK68dYtUviqTD26sQ0JOLmxe+29eHzeqkmq0I770qj6YOfEoqHW92Th90oG6k7PfnbeX9tcGsRLEBmYZxUuMyr/zgrup7dml8N80fODDm3QJawtQ4BJQ6V8d3SQGtCiQ0LABjchV4XRZHLui5E61uAP617mjdSyin7oVuqjUEnRkJmVW24C+vn8JVBHujGCB8sW37dp8FDaZlCCc4J31Af2mao+NO/WZBgMg2i4wq+Joc/iNzbVhJFsBw7u+kWW443BlqBKF9Cm1RAypLQTwTp8XQfJGb8lnUlhlGAWoq7q1xvXpSWEFzT8OH7VzMnJUQ79tZ6PsHxCA0p5EXITzLAk54LtaHKhKzwm0wVsQoxIR0Ubr1v+W1y/ui7CyxfMwdHz/+WZzLrZoe56fdqJV3mMAWsVbaKYMhgtgi4XVdZXSChldXekYvn95NFJZJIQFPv/cVewkYmZrizVw6xsGPyE3tb1Gye2CdQRSopycq0Pncqtq5uqSihzzCuD4PL4A3Ai/Va3JF0eX47PsdJJ9NCTYhOpaL/slekDViF2GJYENzuyNqJbRwIuPczcEvB/0N/RTfRfgTUWXLEiIZLtJEgpDqCOvTsmEjZaYOtMlMUi5OOVqSto/OFFHFY32EQrAWkDClvMUOy9CGoyEGbYGBLeR8GKzKwVfWlkz6IxVZ+69O5NLD9rRQImkeaA/0WmYlhVJ9Uy0mINR7R6edMjwRiEQY9P3+Genb/rMGGuvaOyMnPeZPrSSVkCXCmZ4PrOBnx1LKlmeotAa520bNuialKsHhOo144tQGqchET9jNkb7UCwE+No7N/Exl8MEexBUqLdaPhmB9KLtzS010TGVXySCYHkqNzEKgj/BE2TaBNlkm0AN2Xzvoc9cedmgIUUGRqAIjn9qtqi1hxJDj4KWBfSN6UMEWRlIlrIl1AVSrcTLiwrHtNdeFN+3WvE5X4Er5pXrUQ9pl8zICHqnnkUv4N5ZQy0FH1QPqgjvigVCeWxi2jNyLqe6iXa0L2104mcDzC9sHTwKHD2xns+VGIK8WcJpwzVF975r5NXa9oVKvHCS9fbGDZT2T6TQSqJ0ZANiklPDpuC0ZEwdWKcl+SsebYa2zEYzk+TbwHgPnG8YEYkjVUShJSdswYUv+gt8kKMK7i6ue5z4WBxTHIwZE9u8Yg+SP7lfuXQmp5TDJ5eIHeDeAndipLrA6SMGnkquVhsSf//zd8NebvzZu2gon4W3cC2y+g9/+dmyT1Xej/t7TM1f5cDRTStgHRb/xT/7F8m8LUlpvPhKgqK79DA2Ny6Qw40Uz3qlUeivv/3FVthcqyekCB9E1dgG0UWQYOpCUanlpOYyIM6CVr1bQMAKqFuiQKIBViEbDmxZErGwKyRLL4ras5A2S2ub8lYKZE+qMHZJWDr/TV0YZ0sWzgwmQ+1Hb2nJKwD3Qx6Y047lwO2qXBOFzOugIIkp29R50CQhCSIiq2u13sTGS8tzO0Ml+uXzbRhH5+PkbmkdhJpSbrsqrsvzUwdVyA+Tol1M0XG3Hb/mBQ2Dg3rMkBVgyEiRglG+jPUDH3axnseWqPgwuuQY5yVDIgTLJzVrEOYtMuIWU8oRetgFFtuS2D85uPS+CA8dj+QIPI3mFnnDlW0Tt8o6mKWIWdI+MJGCaJPSGU73rDKXdj4k5Az6jSyZ3IbpMOrcYS8I7kurWBuSkyqnieyVQxJMZaUgHcjSbiRIt6pglFp81pV+0rt444lClAhI9RLCRimISaFRqbRq3fkGdt0YqxeqUE2JxE23IUJ/eXIGDiv2ZbsWCxFEhXTXos2yEYtOZvqqPLsNDWyD95DFmDuopXGK07bQWcU3oeEwdWCyPgeEjU9LHsYttFQcAqs5Ngn4Mfb5UtyFyh9YYhhWzXgOWqoDGMibV5oXmG45re/YM1Ce+FgNrpz/p9yQeiEPqckSlpTka+hq7kS4GLfoi10NjZ3U7UbApMLCF0CvdOoYu8gvyZixN2CvIrTOLTpnazUc3KmSvTMoilqMUbYzPguz5KkqXbEjcMcJx5qpaRU37ZLK59nhbFv0sONTVMAEGRszQpKLM3TmaEVSXaHronMQYBJNxLN4n4w5uMUG/JVUdV5Jc02xszesRYGNx42YZZ1i1p0SUhou701RZDCU2ydwnuevy/VqmJshOrNsFOXebcbYOZLZwQ7CCYQbcp5cx1ZmhTPhZMCi2jjzOo7QG7EZ4pqhDRen8ur1VUGRjSEfIvUaCHu1FyG4bXe6VdJLEkVDjs12a+X9tWCYOviunEvWWFPqVjDj6UWUVcraSMjdeHBO1cA2cXuC5rYG+P+ap0sfG0Ss2uDtC20yTthkCqv12p8FjogrqmFsvmIFvIuQuOFKIIXrbD/JGldGdSxvwdIVD3XVCusCHN5eNNCFoqq/WHzxaCQmI/EomftoSB249nz+vyCM3Jhj3NA/F9Wh9ze3+VxZOa7rhMFPfv64d5bfbWxqGDSSLJwCBOrxlHtH5y2WBoLkzPi+1rbKjFYFB6q9VHoCI2IpBDDJf/24lz7lJec1Wo2vJ07K25vehippOGdcEcxWcLPuuxGgYTgUsKmMotLdzobI9Qbv5A7Z9CHPI6y2icKtuNSW2Cr6elGFDnPRHc5BrKqTAS7nTBYpHJWvRYwVBaN60soYE0FWXHpRX32gV/1SbgHGX4lSoeQQRGecavksYRnIV78NTaCvkUL8IK3DQUTFSxWm/P7wRE/8oLScyhAKhZ7XB4MXq/5SO/b6Tqw2N2rLheYlxvkKMsmuQjJ4UuXqVTb1YmnkLgJzA63MpOuHqrvHzd4hZwsiYPKGuK4qsnLTCIPafyMsYFiZ0+la7gpZNFAx9ClpT4hhzcOj77DQSMqBEsL3apdQBodHEpAyDWxdrkwLYqVNJGU7X0iqk4gj/NmFiiIQILFpdb6YCMqGtXKdiKeKPurJSJHv5XY162cdKha0g3xLTXZ//fH4D78ruLaGdRyyiFdLJ05CtHk5dOWKxdDgbWCZuXYozBXYpLOolDfxMQtZ2ghkwh0PUNKVFZlWspJXJEQq7CWjTRa9yHPwKnjHbJgfxfKyzxqtqxQoKUgwxwkwz6/6ppXYcAirkK6CKusCI6wnbUKnvRRVb+FLK2eUvINZtFqa3tgRRfoBJy3qNwUjhU/DXhZMJhakXlUU7zEMlXQMSy9LTZ4LWbagDJUB6/jIMrMWuTaPb5xhX4hnx7uDj27j0gHKQHJSsTfVAR0SqCy1HLMohNtSK0lsFYCLx+khIYhEJayD5xAskkAeTcXCSCxX/tkx8zBRNB/s2Qg0WeIqZtDh/xDXf8GCHXh+ZHZ5JG0f2JWwNcujpyfia64OyXuY4RV7LLGTOcjYj6tKo6yEdTASA7K9cqveAu6yXclh1boWndnGxwxkGB7nQJAgZ0ClfOFjSt7/TN2SlnE1H+gfHmJDvudATMs58uALmAWHXs2+FnsTy4LyGJgXNfcaRahZkdIDtULqrQZB9Bl6J4BUAgzhW6/cLbW8ruczLt5wCquIwrFq/uHEGHb3umgQwFLANeLBXDkQbo0YZRpF5ezI3fI7eOQuM95fl44vgIdnHZmsgZUnuVx8rNRDCKUUAom6dK3T5RU3nEB+v91gU6tbj2SVu+soXxVonTy5NkhywLZ6rW4zWseZAPJTt5DXernoUXx1+e2clxGXISuojeX5KqJiVhDk3vGN+Tk6jx+4T1LZAW4TWqFI7n+Y1NavPx8rrtLI0z312gKVB+yUmbTidRXfQfaQWnDe9nsL8mZbkISjYIFMW0jTAag/RThJtMEyKUSOf/D5udk4T52jLLlmOzkgvuIHbAgWKN5tiZEgW85syqN6rQJcSNeO1vH00hk60R6AMhVK5heHondeuwJbL6/rLxVpkmx2szqEyhht/4Rd+Ixflydqv7GT9ujj3Z2CRNW5bG2vZALcuvzrfcFgDV8fbfLJyc3lD7nq2+ur25vrG2OXA1B7wFbpK4lEOcPntBe9gpevSP3y5vtmKg+I1JeTUPgOhX562dtN4O4OVMEHc0PooD2x5nYpqlQjU1dRQW5W6+xUAZZDkiyrR1Wt1N3bxR/sCMeFu2STuaijtAUzzBQZpYcCp7AqAiHof934SRNlwVyJAlOQbh/3oxCHmpXyDMZuLbj26DFRsVdYMBrIrZUygZ5jbsuJ0vZUFTp9SRi+7TUlfPLIkws5Ww2s1ECVFdunPhHlBkClqRZS6lsoQTRVFBAGNTY/ABooRdkgE/cvDfrZAvYSKtJAkuvX5BZ+qozi+HLYfKmmWqSX7eEajO9C/T0aeQfugy5QMlIUOxvPklwixg6dGJLREUKzF/6B8xlAGJ4DLd3KlyrFXRysZ4F024LsqyMEQhgug8p9Zj803WQhZFktSrHTQFZkRsLHJCVIuyP38+VTkQ/EdlCrnhMS10dPyIZzrCqCMmFTUPJwcXl7y5hDTGQr/wCAsmiIqJXwkZJ5FE+CyQlhDaB0epD4kaSr+uxtiW9WPwatgrNsm9iKHwImlwHkYIl1cE0qBWxOg2gdVxJpKDDiR4rF9dKgtW7vSUWFM0qEQ1vIb/1sGhs2JX5WMapeU+ipcnJngFCDXWBDyq7FX40XPvbB2RifNCOWnij6QBgUhUL27SnrqSYt58u3GO/1dktPuCxW5+mp3EIbZx2tMkthRspSFO/bAGzMjYFYapTc40kNjU8KdKYyemZTFxkMkaWLBiGCLx+7zz2uYCQlft0eKoymz9mk5NknfkzBHo+a90G9/mO+er7aahbzVHz4U6yfk+Et8gReZ72I9hAgMVgyI99QKLoviuld2i838jx7JGPu/iR9LxxP+/nbrwzo9WrtOAagY/+cuT4HNnbh24WeLpecQw8iA7TJlZRiUmTcRH0hJTp3nWANXcVqyAakQNn7CzNENtFwrczV478TAn7j6aPki662BScBinRa9MZyuXOV5CpKxU+4nzgYGGU9dZ/Fgg1Rx2D2FUxwIImLYnlNqwQHwRkkp06OUnuDjCVttxlF6MCDrBTB/5Jp0GY54QcJFh7qIyA2U+WlG3zJrm2jX5fbHKXoXHRRZubRjIUNGANgr3ATJIp2XK9u3DRTLQ/n6qloREbj4qzDqLfiwh5lyfC32LQNMKDS2uiBHi+X1/YHW6G/LtaAPR6j7R+B/PXHTpshSYffNXLTeuLj8y+PJ3//8+Xj71+uz++Zqrvt2ff19c36YDVEn/RZACA89mqiT1/EeXV34qkBZKGlqsC/Pr+fftv/+F3VqRPULiCU9FT8YPVYXz7QQJ9A2CdPM3BFtGhjUq72dHNzd6s9Fd+Nm1CFXLzTZz9EtPx1XHDmp9Jcc5R8dYOSSAYMqDUky8ffoHQvuXpTExlMQyEyQoin7yL8Ii+EiDIj9imntE9OBVGsIJJdARZnL4s3vq3fukcTtt/mnhumTHJRWQK6Q43kckgTpirZkmrnP3ploEkO3QZlTAiRDK14VxBRLpdCQtaGGTMxv1rcsCUGCUnJfLTGI4YA9lioho676/jL4eesiL0Wd0JFIE0ZaPEZn645F7so7UJIVyBP6E4w31cCLw1c69/XHetUCbxaBNG20rjFZ2CsqtZ5zIm+G1FOAsoI5c8qY9XdUbxYpZU4nFUu4w2cs7WtRrVYSLNAFhID7ISYsvfKgi3XgTkbmZR9sZVsOVNnKeUym0n20y7E6P/qynj8KFTZH7rCL7Pdmc4IlzTeKHIO1thT3y0CabQ+Q6RNjdAlWJ39vNQMgRCxeHgpww+khKdMPsIrxlAYOLpAkSMWvBmVDkkj8lpYX8meURmhh7du2ejg/ai9vC8fNoxYXaS9ZCTzZkkJLgwiDNECE8paC2Qpl8QR8QKtB2ql5R8muPOe1QUF5yx0iGZ97VzV/YZ47mIzB8X8TDoXLcDlSSaaxBc3EX+wFr4JbkCW2VWecAx3L041WvcrD2cAGAyVF1bdmrULlU3nqwi8q9MzC2dDrYXSeI6s7eMfoK0uC6QTnitHAdZZXG/ECfz8SS3fl966ahjRjW4sYCGInRnn/5070r94ut8RE+0qvILcyMupJndLRepUarwCwsX/79//P/L73hcqEqFnVwsJilKM8N4ZT0AvUfOspTtpr9ygE4651iZWWRaBO8j8dYIvncox0nr+vej5wRFTvOmjmAygzF6efv3iljUBkMtzf5DJTskdGMCzzrQ2ixEYAPrG4jHXeMFYF93N5iqmDl84q0UiMRei3AErG0e17BvyhBi1fYVLWOnT35Uw0m2bQM6wyHE/yS+LGmqnacx03DMSUeJFXOJDU1a+mG0geYawXm7xOLXW5kKiYBcZlT6WSa+/QljgvyDMs8pr1XCYAFqCtBRjPru93SgDgIzZwzINqhNGcHWtosw1VdfEozsONVz49fFFv8/dE5pZdGtiySUt7tYqNvSRLl5qh4XIb643X+f6pmFVyo1YJXzi1FkD78GKlJXZPzQNE9lpCuP9sYda2sFaXGG1uWKWnKZgtxC4SJjRlqcStOwGxKwjbSA3Tts5qe/3WsYCRUodr2PLI34EVAcn/oXFcXmpyxDpG/eRWOZCEynekT9Zj6xlARcLWFDP3bCwisdaq6LFH2/WQmiPh0+23aAQOOUAFtlS+QZVJvb43LjL3S//jd+tuEDHiIWvPDuPBwyZi8gAAOYySURBVB0wBispwEjfhIwetNgtgK5+rN5THLt8k8CW0o6JqrSsh4vEkKvKPGCi+hl55sIfItFyy9CbC9qw82w77Fa5hG8lCyGTBA+9NSj8rqSXQKBx5q3FzdnZeLxjFrRj4gTdCJThmP/5v4aCr8Q8nE7KH0jGnHd5elltVLIWZ4AxVMxv/YpWUgIb5bOsEGMc8/C9ei45ONbBfBIPlaaQxtY6YNyk00PPga8lgUn00UJxU+wsmR0YnNrE4zD6I0ZabMY5xLCSkfFdfzW5mM/krm1tngUE5I7Hnd0tUw1BK2YiMF8nJRuQVBZRJIcWcI71Otfb0TOqRRT821OggBdRLabNmJn14bxEzZLkzEBPu0E0wamRezzR8karh07YFEJpSX3Y0Ryfh2/8A4JdyAgVqLTU6ApCZkMs5efnw/29+q7Vcj1DpqTFeWyaGYlxKfuxrbbLDw3GZ/ykaGKWTFtHl7RUdea/+8c9KcosAaPSpVxMyl581iflW0yVNvmtpcj2fwqer02DZ4A8eoOn0n3mECLZ3939E8kX+JfZFsbfP+26T/p6fTjsHEG6csbQlewmhm1IfKBcToRVOeIPYn0kTzmCUM7n/udPytRF3CT0Mg+ISbvcLhf/9f/lX5HrE+VVXSNuJEyI38vbAUobhbRpLuEIMrjsz0M/wH10qK2xF0G8S91RqHYxOwvfTpV4EY66yV6cfJMesSdv0oyXN5tLB58KWYc1Z2j9xXVJAcxMyEOkoO4ptKcOdJVhGQLBR3aqZuDk0A3YKgcLYui44KtCePtkELUAYtcq9bPM1otOpVrvsiLktULGau5PTp0vNTbhUqEPi1YsQNAMi8Q9HP2/WNwsL+8fnNEN4gXMgPytxptOO9LFpoki4p4vlJ/M0VT+O/3k8MOK5HXScfSpbCnUvIYylO/E8j7qh0weOMh8+c5SdFV3TJXttKIfC23X3Bb1YAN00Hx9/b3LBYs+mcewQyJY/AT+eDZkqZeFVRZOUH4qHPJxIsDF6KuV/8PNmpjKCDEyXBiH2B5+Kg4WgH12HA3BIjSIGkyQBCauKJ4/O/NBH70CnEz4lzhegE5Xb7Pcr09N2x+k9L2O5CAEXs9XdNUMGugho6LJw+7h8ffDU/ExaAACaQXhs/QEvdMGb6UfaVXJt7oW0wp2mxK4dpC5MoeSlq41l05HbbIERYARs82lmuzr8/eFW8nUilfHTp+t0dxJ6yBdJsM3szEdcPV27wFNlTbUktH21ZQfO7N+JlLVkChxHbOZLt8LRqxDiMYeDNNn98CWAZcYvFkzCwxVcD8Nsmisl4qhhK++ycqygYST9OCYJCwnHQx7mpALLoqF9z/bZoTMhQVf3ywDQe8uBIejhMpADQ8GL21HGFHGj+Ix2FlpH/NDEl+xJqL6wjCoDfcMVy2ZSzBHbay+RSRijmsFeuaB/7H5ai6o8vQXKFZkgDaHaIjNKBQr2epRXpd0FTwLE9OYqaxjRVgjRtQqeibtg4YWQPqX0fXCsFAlXivaU3zVxyyLHeGXIObMLLNGxEMUFHrSkqA06+g7Wcb2xeaCJoBubRXygwJzENcEx6S31ZiSJMNqSb3ITGvX000P/lRSSk4LbTTyGo93F6N3mRGERM1YOZ8s30shOFyutCKxMvw8LFEXL4sOeRWxJLYgicNBUDoU5rvKjgvI1Gi9hxu2XwmWWEzI4t0g3kwMw4aUOi28WnUms1ScxxMTP9RuImNVozJXEI/U2KoJVJwITiqaPYjgL6+WhRV4pVa96S5e3eZbRkvVieVxYHC221GDQoXciFomEQwnf/JMqzelHwXwF//J367JmWWzr3i3/S85zxOt5ql+Df7xBoO2MAJHBJqvZ3qCpE5NxIBlDp2h0B0XP2rZynRvN5zXVpC1ovecAgLA1QHVJXAWX0+7muvxOrAnupwHxUjKTVlm/aLnJjJhBpAq6ADI+YXYvn1gHqwK9cgTQAVazPx0WxJAiPwa0VepGGh+922FjcswEcTnw4fYEWwEi/FxG649A1DQUOzN3q9iJs8vf/nL7mZLD9Mrc/YOPgU2fLWiAdhHETq/qeU1e5X8sed0M7AwQU0l6IPT+GxDgSHcEWZTUSjJrYim1X6SFOZnNlLFwhaUQWgtDZtLnPZbPA6D1fIf4zoI5kCFUtA/Hl4sJEI/gEK7BZ0sEqsE6Im+oIe+ni5TlC2fXYOktiTRXqx5S87gH8SdofzX7a2D9/oL6iQFMdnCF/QG8OC6JC9I/cTd1PVW586TsGqyDfEn7s6RnSkdda1bCauMGcnQ+sZdiHRSjuDn42G1Prvd6P1xAZ3FEE266NzXwtOwP3kn2UncBDSUftCJhWtsQQlkVf8CppAN6FhU+WR9xJb1LAHcQZht6cIDK3m1KRtTCapZwaki/kSV2H7Va5bPRqpMJnTntZN2EqdjqErnq2fRkZAWl2APCPqgo3/ZBOicLawnKiBn0aGG5aEC/t8D+V8eR1dh7vyBqca4y92x61TDqgvfeLQTDGYhWUdeodCRSErKMSoqxOy0wWqWZ6d425oi6AThvEt+d7GrUMnawhf/TldTEOtUY03jTB3kohAnlQ4CwrFw8ptlFAmk3sUf7DBSqUois+qj2QWLWKzJL7pLoBXmpnP7Ta5HE1Dv8iDEHHhAq+SGJlYPY0TGwIL2rqF9oBYvsfJmgZzlTGJ4lCKIz1m36HwDy2U61p96Ld3hdz6ty+1+CkcEWLhcHnLlUVY7mxGy1/PRNE2sPa/M33Yh8ukIpW5skxUg6x5SHruHLZ52e/PSgAhKPOlrUNevXMunXW0EDXEwx/lQTnQpDXFQuKZRGPvDG4UqJkKFRVS90H/whgLJMBLcgDRjaR1HaWu9DphMIfNjH5QDGbApgCVCZqG6pOHy0tm3Knovar9oYEhlbJSYJ2/wt6AiNHZ41YCFo6sxVttiASVOGcVqsQpg+hCnijJf5w0oQFDi9UTCraFiU6uEtJMZh73YDXvqXbIRPiCzaMSL/7O7G+G0Igoz6OY7+xdeGCNHSOK+ChoeGW3S/Xh7TZjJN9Hj9RP68pNVkcPusnwtikWsSwHqpbqDP3flOZm2j8/NzQ1bpJECf2dFtfzsoBykmJ0aZHhZJofk1ZXtQkgM6iE8ZQLccQOsV+7HiDuQCZFtS27jle4ZP5psG8g9EcEFABzrymyJ+8vjccQLxuQPxoXEjr25+yKeD4E6r9CAtxvEByHeK3Z71HQF6AQirjpSl8KXsFrQo++rO7QgglJzea8FA2oiT1gLYT+V13AM8tIlRzYP6AemnhO3Ilu6UGj4jicjILH54wmJ8IrQwMCL1RWNz5cIsp17hDk5kd5qsykzdIGbZiMQJ75f3XqtcbmD9RihCv/LX3a32yv9dflt1sSOuEWAVir3IrsSXiaGBdilHKuC7XqFaq55end3x1s6PDzSeRuw2WzqH4BCnJ0+6ALKOHRUjofkrkegeu0UGkXGkB3o++3HA+28vd0aJHH3PmTQHvX2lj3ez7VTczUCtbiW2r6SjNpbQyIEcCQs7kW+KnJz0EaNTe0wETG/svL1H6LrdVpWqZPMMB+QqVYi1Q3r9tG5myxxvBi7YccyeNZ0IntdWcWGgWzCjglGEsBjltzUQ0y5LYrtT2aFgBE8uVBLr5MPmgxYGT3jMQCv0ByQDRePo8+m5jVWGKn1QI8kfITTM9vCFBUV8Y+AVdeMpGGcNoGOdKLoPu2wv4pfqSrxhiTaddApS01Dq9bXhjqvOCvlu7bSY03PCpBl8gl1FLNZKDImgjxhOuCd02PkKZGDoHCuyjdcrSNmFgOxwC+NzMpkXnw6D5aUB9DSFkgArGUCZBCGZfOfBCsKniAKRuQQj+mkFlGlTs3J31hhvxN2hbmm4xMF4o//cMBxJWjHyMAt8fHyPWX2/cQXo1MC+uh8IQdW5xyA0h3fnhUFo5IfuGYhRSMLal8P18qareV0AIuh+2leOrMxwZZe7Xs+b8j14PNzNsCHIjQto+B5ZM7eHZ0OuuI/Epz01M+dIKOFnaGh4HX7SRGMMC4YQQxhFZv6JIPBlHomhffGmJ8d7FSOz5pkAM8JMyNeseyd2JEV8xDdgVUjCNZbBIkHZN5gBK5zUHX7qUg3b8wX60Ftqtaf/ChI225Mh+x6G2cd2bXFvkW0AD2bjm3EJesARmTaO4riof4a8fwPvtXVdLu+EOHISrCDokb+yxeW9b6+2NxcKeXGFxhf+UABf6NhDbrpcILvDi9wYEkiu2ishFjbSFN/+O0+4UNjD6+gMOJ9c2P7JaaIR9eRdwtPQkm0Q0is2TmLEe0ksQWzmKUHJSRoyUR1IFpQqYYPEDMAUI2b1YYwgubjQScLgQ5U2mxBSAwKLMjOC9Dz4wiK0Ifi3NKH7zIR+11XHAokMf7HAKPPkBKf0yZF45w4ZfHgeKvslr9SRfEZsQTAb4MhHkbI6c8NjOQ6z3zFioIe3G7obNfhFtcTz5mEmJl6KLJrzwrVYTrpWVyGLiIdFpkSWqgwtO7/SQ+wCJCRMXSvImi3MTs44zg0xakaCnsRQ1e/zGSwsk6A8PndD0OLQZr3lMbNIZFdNBXmT0pHk3cSSyROf/njjfpPAv2y52B0jLDCi/mt9aVU3ivgY9hoGYLfmYXF6W8Pj3/+dSfND6vYnPuHvciSTw9GxT1J3PNbTRwpZ0BpbxdOHV/rwSkdKsBhHekRxDdkK4ETkU7wxR59iIiR8o3yO834OE/cQ0CYx8Y/MBPjk5ggeLSoAkeryWMkHN6UuNNG+GUIDENZGeTr8fHg8BGzXZa4vBmlDLJop82S7LRQpq9jjGWnAvYjqIFAU6XT0wqb8B1lFCU568di/H4NxexjnMAFKR2dpXg+5trFhTb3HuB0TFvpeUdW76Ejz+LdIIbakTp6YQo2JnsWwy5mM6MVTku9GSFEewRGnMHAq5rKvNsaiI+dJyeIVLIFI8alCxXF6/IypRn7mtFYEz82vfxaiEmwSUc+egkYFiWylUubDTiGv5N2w6veqGU3DP7Y8dqllNsOO79CleypUcImhjVhq5VbnpyfDKEe61Xw03oEuwCJOJK9kDPqLyZ5kAWU8WcHFXrw01jN3PY2o7YFvgrHMI1u7DDN2KetO4MnRCGxTYOkfzwxDbUXvt2xAByohEGsy6eQFbtsQ/zBxD0+258nVRAPquQwsOdMiKoDz/KWIl55Yj4f2VSTUzgEUXhVecHoITHMKqwXmKHjmDLy53WekvLQgjnMxZb09RwXlt1hKRySFTGeK4TL27vkOU2X2KvNnNHaiSpxkT5lplcXynu811/QZQEQ8/LJDoWJtgIZMMfQKk9yHIAmziayYiCtjkyHw+L/9a/+nRnwVpQqHvZ7zjp3KS/AA0yauVdoxtE+aMOZ96rci/DvH3ZiOxadVhs/EfIp3wgoPz+/fd8KkDzsHvxd/pFeYApUTRV8Z/RJjwaHtbHElQqb0l6muACOpnIFwgrPZV36J/lIUUeUrSxNs33emFkoxd82TvAHs7NSXPVO7q1vNu56fXoqD2wffuhl8/bMWDMwMs++TSj+cLtlE/2WTr1gEjqRawbn+wL31SMtgPqNxjLVxzkn3OHAm/Xd4+5BulL5CV01HvOFSoZH8YSbilZ1AVQoLpyAJBYt5dHppltxaod07K7UJUeZ0ckuaMBQdgtSUFfyBuAkZp1lu9JzAa5CRtec6vGa516FLjqR9ykWwK9QpOgUBfsa9Si6fuKAoFpeisRQ7H7ushYFYBGiYqAI15w/iJmZGCn3SyrEIfMjv8rQfpzIH7pRyBM5fLMhOd3qiXOuoIe/FDRKxwgZ01qk/jK7xW3AYq0eTmqrUK0BWPpZ5WhQKYiQGSuRELLkcS/00bCjdvtW76k7fasuFL2JaKx0OGQkIqqURAZWfaNFL6fCMjxqH+WbnVTglpEWOplvalwWSNzGt1hOOggz7cjQi8462RFKZUMrYOwj6sc7nEg/8Tk/8H0Jyh4FRCCke+LNjdPuV3Y23aD7lj81Acr2zLsyBpV+L302OLNKlSA0ch/yWqU44YIUHHIa+iy40VSXilIobw+hsDA5ORevZzAN2+frnQcIWgViz8LREBTAMEFJx9O5yEixt9VP26/819dZURaR+gxf9lOhA0O16M00siy64qNhUAleYE1+/Le/IWHG7KOlQ4JlwhMD4BawmFZPRMI34wCMSuLkrSInmaKgoHIvtsQ6+xanjDnqh1OaBX0SoQyF9znRrXRt1URNiVZUXtitOEyNnb/Z3qBWqcAz2udJ1kDdUbdb0wUpmSNL8VoOs+G+PIscWBcFsuRNesyBSuH7IuPMjBcCcLwYs2vnoLwyEGtok2kY+XE3Z94DU1TexKBw7YYeCyRRk4liS6wiI1dcm3Ux7HEGpejZQ5flbVZWA75vV6oN0wulMv7gwzY1kOkcJfBhYwoewApDOcp5q0I2YjHlOdhxdYK+xETQSqPpkBdLbaGK1DInJlYtbAU7T8/moue8EVEuvzL42PxkX/L5RO/HbzMEgrD4//xr/1jo5VToQWyX/Km3uxRnbw6zpyJf4sKiZvxTAUQXyX+K6jM4MA7BT3ToclE/FZbSiVarcHms+eTkD7/cpZ8OEF6fPty//a6k9oktytfwCqJlPdPFkpdku6Y7VNev6BaZY+R9yJ/9CtXySU5HwZ8OZxWIskLmQzbtt6cCK8GNKuc0Q7o4vfa5RPCdQg2//3x7PPjWWjM471RZLKLi1WIOm/XnOY7M8J7/zZ++k9H/4X/8B7aZb6O4ihS2xy49/qjRBWmjWupbMAuoxg/DinlA1EsfPHgBT5N9Z+vdm1hHUnWHEjpAlaXSdgohuha18G8t8vQdp7d+F1uzl0V/iljX1De2KHhX9t432fEmxUqouqvfrBp/JMHqKrKocFsWH+5ZwRDRi6N+dDmEs3jWVrnRcNVGjmRZBIXvVgDiAynzKniZ44G9ffGZyOdvDw8cetsAyzLRc6bCtpPgq7XrMqpyc6xfHB9kOzAFGZQzTaFGUEmurZ69U+1jk45pMW6YBby5W8v5ujtIdPxqRb1rWi0gsBcCr1DvQnExB9zXt3q8dZFpxSGKPRDkb5tNoFzNle3L+c3g6ISaw0kW2Bc8gVfURYMop2gxVthNLhxKI4rlVc+j2wmSRd/815L4aoQQMrqV288ojaA2ioefoiMnSiGdibH8FcKz6GRvxdn6ax0BIBXIIt5qMLDvwiBe6jCG0i86ya6+fZlTlUsUj6tEcsRIJMDUmdAm5kS5nTkW1yooW5ugMT8pmc30IswmxSEw1tRHjYVO0lv1P2bCfyKL9OCj9osNNGTqnmefMxlvYedMtsIhIoDmWAQT4UHmrSDINrkAgVdYSoDUAxjsngV6IXnhCBP3Sa43dCfVeYZWnyqhF3ksRJYdnJI88lEcBh6n0t5lp4yZ7wdi8GXrMwE7Gl2pTM7MbIGRtzWnX9/u7v7mb3/xnl//4deCgvsnwFppV41USy2Ae2dRDa99LDHgPUVrTcqj6CHfI/qfYHPLFeOG8qZnTD5lTVJaumlHqkCtpVLQlC+iRj4UBhF+73pKAF2cIZ+RdPSkHpre9BwPLVTotAGPzyCsPMZdtL10QspFuHLlvaXcp121i1bSlkmpijc9a2+uPJhqY5FmzwOy5JXDoqRiRvlD8MXGWR9vsP42vTrxksyVCpB/Usqv9fkMQGW+Cao9kkNmAKmelUlxNIyCOf+3f6ylmsYGmrCduMzLwWGzwEHFhY1J3MkAVSYCDmbTuW3M4cZJfX0ldah/2ZNNgp9PUxFbF6NjgjAXJpm213KmkJ7ivANz+icwd4x7fsmrkxeRE3yOhDhnT7Yjjw2XTDkxMJVg8xONgsSqtFbFx9kWgOef8v+yPQETc9hPfFfSwcF1glvZYGz+y7Wx3/R2ngIMjuiWQbSEHUbTNtNa8Cm0+Tysl2e33zTUu/Z8hK3GOfvOZCrrebh3fhb5DEdoTnW4SkC6pE1kO8rGXDnGpmoIrBiDlDj1IDe27vv3W6PkXbGNuF6Bf7USMFELvY3P01j1AB78rt8DLVQsRPRzq1OYLCtVZT4wBstibQhEgtTx9FIvXHKeOj3Dblgpu1DHJXFk+WInDJ3drQ53wSf4ef9kF4iUeZURAmQ56M7CqC91Id9Ci1YFP4ytowxSVZXsq/mxY1IyT9qfVZNO6OtsTjUkz7E53m73EIiihpt0W3afkYTdEOR44IV76FtILnnjPJMOzHR1s7rd3FJpBQxmd7PdQk9xfSeZ//zbb1xSdT7hVtaOQDqPaF14nzppywnB4tqCiknaHxaIOZt8KCNaUofiJLoEwgMdPLGs3QnKAJhOwWXKUzDSlnDgvH/OtRgn8IiJpVquOlCFLH7K8mEVNYHztDZMfIZFzONBNsZuQ2F7VG/ADCrgJ3nxYXSeImmXGx+IxPR/KMvcBECEQIc9jTRNWDbqq6hUIBGhtFTiOVGk4CQAApEDRhgPFCIGwdz4ywk+2D+afEGbD1ZErbU4slQ8+/J+xpQgQxUstUSA1j+IkSiWcvWC8bngZs8HVbNEcjOlbP9kBcr3W800IbbbutI24GtkzBFSJRyBLxqwOfstPz3FKbrCtllPq5Lh8RkiEirpyK91/x6jj2dv6yZwsXcdGoLopH7MFwPIIbm50ywMQ3Z2un2UhimMIvMckZeVafNqr2+bYkG+ytzC+avHh0d2qbhHm2wPPcOuI+NHO5/vZlYWk5mwPyTEYHwg6lrE668mNndjFgNsiCh6lndkb3Ly/cPsHW2xtSox4xmw2Aq2a/wJMVDBLVsT4fLgsRvF3/wNRKg7dw2MaErs4enxkZi1jvmjXhV9A1mkm0WdTSvSCBl8xIMpeuSM/IDg6qqUHhDyauhRba6GCBLRyIPPA2BFikAyvIYU7pr6f+QSYDnVz5cKQWx+kisUdX53u8EsioUlFXoGw1II1IX0CJ9wEOsIfEg2bieyWrazlRRJAZUwPxQlI8DDV806D5cPi9kgILPkOdS5k15qbmU5jIl84EEK70Ak9GZOAC60sv3CY4iKUBbkfXg6WAO/BYra3wKGeKPQjHNYgoNszCsP0RqePItvVNFGXLqZ4HbN2SmI7ahwfKi2JJEPiE827vTkQaIxUPum5VIaIKWpIlsjBkVdVN9aG21rKvECpt3kAwSspaPNYEvxooGBf66HW36kIly/iaDjewidAW3kx9Vcvqnl3xZK+hTHL/yp+Epik7eLj4j/5lmlcnnovs5kkGMrwyKQHZJm1RwtxrWSu9p3F1lKhmmUEGH9e5jJ5x8/dt2XZZU96/TM1eG+6TnGTPK552hWklOnpvEYRPM62XT5dSbg48i+SM71arN5vN9RgJDXBTiu+S7K73xfzeVFfnw1RlQtbXGXHg2hoHDS7Evc3ykQLKJIRoMb3qeBkxsPUqhSVPD64sfuoAO26VrIkfpz9rgTI4Ty813OgECyGT/u3Rr7LDOtMSpAwAkIL/lOHdkDBQj6OmTkzde/fPvLURLnnTRBIr2FuZWfC2XV1aPbuGAsEU0ECBImZrShCVYuRlQtHpgJCmk6Dfd4Ym1JU2MgEGwUDWPwmM+oa0KbNCfbGWwryTzzywS7ikgAka9ThbOAh6SbVI68bbA6WJfKyJLr5eugsH/82yjQdcvFYnp4ITCjCIeLYDCdwG3yhIkNsqj8whIL1iUVVljLoK9Ptw82fLOi/76ZG9RlvGefp4oIDJcnZzIcVt20KKBVmQO8AaLUSzw/Q1D1tPkhP8SDo9wYaVi37mjf5J85Ex2vxse/NtulpQVlTjzaVgE9CzTkmCrlXdAaYmyDgRdKRuDtESfOd7wWSGGsnLA8lpARQ687QiDLpk7LirZ/NuIo6bYEW4a5pNvDiWucNxxDpzjlpWoAmP31EmJrem0V604a8TbICzisvxVsbgWJgr3JHtsUGB06GxIhZZKjF7NPEEamtGjMG6zEzQ2s8w2mYS+mxDN9KHEN+mRl67TojRuno4R31DXozCFmzinXBnmyzaCrIFO3yQar5km1rKHhGpfnz0Y1FRMkQtYQXmipxJlgWX2eoxy71NvqirvpigK3qri/BEqUpFz8P/7p31pL3gF/iE3FU83I78gobKDh9sjjPagxe7nR0dz+YNa2VRBTusTStb58JLlwgRRCn17aLa/RMy8rRXsKij2LYVVd92aX7TycHYPWBetiHyQS+kE1EEYj2DdpHdQYIjP+mcE5YKk2B8Xr2EzeKAvYJZR1gUbqYrsv55ern046H14e8ArMR09HTSjcXiBpWlKBdaIAHM8p6DQh9uHk/dtmxekTVYh3sxvOzdZYUPynM99lXRIH2kkG0LC4CF++0vTT059pV7E8lXvBmjBO+dLpxW8ZlfQoeGAVZ+0EeSpGfnP46zEnSzkTh6nIwKcUKIk0U3lOXhQNRCegAhII29uLqVQ5tEFuAsLahZFezRSUk1uklDB4EDigfpR/BEvSPjeFzHWuQMkQY8ikVHFx3ecBYfRWsueZdbQXaFSS2lyDDItQ0IOgO6Ig1KeUK/XJfR9/AyLGR2JVqWPMtjX1kpxmsqvOXZMP9B9YqCF2uhXGspu556qR9s5dX1/9tnv8+98eDEB1KitChW4cwOCLTIx4e7PxRtBJ0iHg7vFZZ/kHaSWxIuf4nYGGc9TXiXRSr6N3ZNRyBQ6uHFfXdsPXAV8dg/9U/maX7Yuhgz3maramSEA4PwEraIskmHnszGWzLdqJ8mfq7g+mCg6sO78aaJovtxkkM3BZkmYP1gzC8gYqJMpv6QN2Qf/RB59k6ulkpU7WQvqqI7DCFNPjrP2rWsZSR1a/PuGCt7BP/DYjl0i3mOJjUuwZ8dok0DDu1A22BL5EyS1jbjcH/XBgZE2XbiJlNknwpGMcaqiMaKHngadWu2pFCDzfPLbKuaGfgaBYhV8LA3H+YNzkhLXYKy6PXLTbFtCyWBVTZ3v8uBAG2J47AzLpZl+PPxvWBQAYq3kR22zZEEjmMA/FupWY7DDww+4xzLTtnPsGcrpZb6m/BfQTM5EbMefZSu4vQeCRiz0cTXPJEtNog32SGRG9FJNtZwbWeuyXcCuIn3hjHN5yGgFbwfXL9HpSCZWSvOZlI8Epby49xCF9Ehgq9aSm5IYeJVMWTdY6gARitKX6CwajPHf9MBAgOxXpFKUo4pQt9F9/FyWUJYAbSCbwtrZmFHxWmcItcCl0hJGE5B22WeVmmt742ZkacqKRVA09Fba6113TlLZ97CeTbD0KtZtdrP7/+6//k9Ra7KfTg1Il8LX6MweylOHTVkssA8z/puqPMu5GM3aI404IeOetJQKAZXiPswIFal1OaziRfeqPL3h40i9i7mL7q5o92GIfRhG/OVOjUc8lQmZStCiVramIGkfoaZUpYIfZxuvkznYGn0GWqMGjOLP22GEraTPPF9Q40blpQDnrp1YyB5/oUsH3z58/HmVXOVEQE3rqxW0VqZf/suI0NVGrPKjpwP+i+VTeX0r+dy6XOlM5JBkUi/gn6Dh8iGaWvFTUuL4XomjE7PutziHtyNodBgklsXlzareNiablFGMi3+42dSsDhuJ0zmR5vuZq9IzUlxMgFSREoYWFYaUL7ArFmYVJTRLGRHgGQiVNVkFsJMV3Mxiu9pyU76lbd67smEWwbW1ZVSQKb/J1wHHJm7I7HVtArhmPv0JiAUTcjPB5ZFxabMEyZAAEoNSNUQlGl9gD9xhKJg4+1rqEMAmeOg1usMjDkbNayPqSoavVU8E+qy7TtZ/Y4W/4f6Fhlp1fglOWKEcONboEFuYVAQIp+Vne7Ex89tzGA+8QCBXQzIcqv3/sgNvb584Z6A5qorfS+NNmytzja20oDe2mooJImjwvIRTd4nEr+7M1VirYZoVGJr0dfpJOG5S0pIIdwTVUEogWR5NIB1rdyC3VmJF+XfWOI1qqdxwBUer37e4bUKFBUNVQe6BnNPiIqgF4ip9bKeLHXogIsqGpkBIRfYZsobs6um1Yur6SAcUjm+u1x+jpb/0Rjyh2vqHEVZ6qlUREoeofvn+j1MBd3sYKvrw+AYKu9L12Fd3Tg5isOZjPnL+h8sbAc2qLw3/CnpiaLazD9y2iWBx5zl9n+UqxJLTA0k/oFveR1bB0cRkq4DEcWb7Dy7PJ2L4MG73gtQtJadIFeAV/RLpRLkjBzT04v3nY3q7NRxInlHQvCqrIa+y8hZnyA95ubjYA0UEf4Fg+uQ36K2LOHpm7/wieeGslJ0PhiXbVO+OUcI8O4JjzQWWzuIE1JyA7Z6/HJ8hNtFn+a3dziP3ZZ4dhkEzT9DGGso9kAwq9eiOzg2zREJLn4YDO+vScsabsZsEL6RDuST4Ol7qroUHT6EikudfWBacz7daXcBFP22c7PNYQ8ufL9vcgA+P3PPx4ZBQ1LrOtlCXnxkhUCmT8cr9DNkvkXBAvAmd3BIQ4fPu2ZtY8d/n9Ft+3jmjUpNISTapFaJELm4eyBapUW0S+SEyO/8OTLndFDDuWBtvAVRRRo91cciAC/sSXEuzUBgn61EqR+CKOkvzppjISkI1Ez/kRPkjRCaVmroxfy6d1uIZEpp+fn/c/npRpi7FSRSxfj0wUb//z2QNf911lzFgK73+/Qa874vLxd1qlg6QIB3isR94L4ylrWlBY0hD68CtxSutdNs8MAUdtP9tYK24bmH7yodEBPyezH30XTi20/LH50kwbljJ7Dw/i6Yje56NWTFySjnQWPX+AlDXaLNku8wIU/pfffsvv52dQszlAO+dGCid2dkhnOdeqiHJV90IjOkFtJKwFybCWhiQ6rdWwuByRqgAuAOKds/bydqZqr/ljyZQfi/eDG2ug9GTtreyradUGI62h1ZvzNfNIrs20raKKOvPHBAM1n/WjFBvFk3NjKy5qPuxIF5kClwyPfwDCZuPoTZxU6IIIizr0FQxLiwS+WluozZSymdMfTzs5H7Azg4kpcERwM2KNcdFA2IGNMedmgrT5pm6aPCqlURYicVREXMaK9gj71Ib4T0Ry0iTTl6Eh2QPeDwPyIIKkpOrU5aAgy963y7/dP1klUT/uqSXiPhEzt3agxI/7fRo9JifaAUpix+In/H2g4Aegu3wm2ELucBYqAgwYimHQ8IJJbNdWXHv+RM21asaAQ6ID0klshtyQoxUhU3auVA1D5ryFV8sjuJIQIFE/BgCNhchd3iolR5C5PKV/WP0sItbGKfSPODpdZhoZgP3b28bRaZ9/3Pu3hhmJiYEuJG/Ml7sQ2SkMkAkzAPtcWMsiEosQjQM3xaem2moyIwOUZgHL/N4/Zk11gZ1/x96STX5eBVGRGg4f+HaSxX84w6x7PmVoO74+CTIeH1N0kI5qUsfDZA+20+DBYI/1kZEJ5WrKvHEHF9KJEpeVwSRSUPAIfAU5mUuoYJRx3rxjb8+7sHn5wV5G3Dl6+pWB/sVEWrANMYbFgH5bNyUZnP7+6eKzYNgTTQYEAStunMK2Mq7IIAflHI4YBctHrLzO+Hy579NG9nnixlmFa9m+Dzkp7mx2odaNhdQtfOFcR447AMjWdpKcEFlvkkMjUDt/tvnMK+rGe7BrAqs4EdUgE97GmSWXlyux/TI6uTH2/VMwMw8Fk7CzZqa2Yv9+WPw3/+zvwITcFTAnURTb4sKpa3/IRpTapVf+6tnHeNPyvLCIueThWHBnwWBQJ6SNqxQL0Nd+2oTpZJDAm8M3whASVj22OCWHj4ZoemVvkOcEfxiKPxeMhSwdKSB2Qn4Xc6KBr9QRM49frdZ//u2nbU2UKqLsyEYPsD/YKky/qKA4PVTRVKf7/GMezdEMksgCow6gPXeMQChG0sHEKSkMVGYodF54Ubp3AhnE08BBH4XkNzCJEl+g0U6w2Fmyao0r4RRjdWQJbloGN3KQRnurAqjZ4lJthx3omIIbXc6W4npoaamtcH7gD4hyth6e8T4rqz9EF5kxtVofMY9EBWOtSCQv+0RHBHe/3rgIuoiebRQlY1G5q5bErudmem31u1YtJzTPmphy82gCnbAlQlvZYoNHk4MAq5YU+wayz2bwSUGyy0VNhmUFaiJIvo5l4KQmSBHtE8H1Lc83VwJDLpmhadxI1EMFn2Of0iHcmQRQpPcTR8ysCMok9OvCUi/lw++03DqVmxEsDgodGfHAgkiOwHRjWm7iCHcukI3wNk+LSSaOBaxyHdANY7Iw7KWii2nrUQRBU+77PbrQKZ/4+AWQoNukwjOpWpa+c/zuLHS0uXPyPx/2rHwKWZlcqaYingkpOEAIijXnYueXnDoWI1eet4aZJq5QOZensK9G3VbwzI3WqNRrZ8qwPRhtVTuyy+yyC2gAcYh64PjcRQFgxmcQl/catfRrxsxnKJTtggCZLht5fnmzvN29PCh6tgJ6cWnTZGVub9TRam9AgihXiHA4dMuTQbIe5OYffb8jJEiyOurRYJs6yVLwY5r2LpvkJQQ4OLRE5BnKGCpsy/rBpJOuvfP9aqU4AUWkIqp0hliJPvd91SVO2PJf00FhADXTmID52TQ106hI9oB5s0/WQqRE7D0mqcBPl1Q2pjZE9j3SY/1JrnkwQr5irymo5bDncaVzlReRVDVm6hiSUsBQDUxl/lk7qBwxF0Jx2quWivmstsl8k97LYeW8nJRW+UMtqkbaLaDlLeA+p3w90OK0DZHS0J4W2GgPsaR+6x9fJF2kpWdyx7ubVxlOWAz6DWX0V7lgB8KnMoMxpE8RruktFNesqyDMv7ra6/LxeOC15SP6D8Woh65qr7wB6uZjvD/MACoy/zy3zXaNygv6QbzhDeWBNBM7//03YkEKrJm0XzaEyhuqglOWKmPlULsOBGDVgxduk5HSfLHh5kwOCDgeY5T+6z/H3N1rrOH9t58POkPjdTSyI1HqizqdLDdQiPDoRBu91aFGZCXrJFk3kQ4EWWzVQ5kBIiXll4N/+NpueJKX7pF0jpZeWWI6TMadaLUYMq6CDffnexvBDVx2LeoBIxantWQ8UVljPhZb/6znThlLRwDEyHa04uVQhpl2uKdOygoeAx81SoxcST/GfLbw9ZA02/H+a2oC7lwgvtGzYmRr8Lm/16Mjom5evm0/rgA2IMpkXD783N9s1nfL5fNGvndrnYlsmb2aaedxCVmA3R1ZZ1tdeKcx54kO0qU9lamS9B+PjxLIjy/6BTFUb4vV6Z0mqlXGW/7qA9pX8wcUQWHQ71esL1EghtZzygYQgyKY5Nj3SLW9J4IWJTPfKRi/4QhWDCY+o65BCgHSygDjTdmI7A3V8FQDDeutMi0CVqG5+hYF9S/OfATOxWeLg7CoF+r6KTTdrfn528fd9ZKt9URVVroMhYCQHM/R+A9VCXW+3BgRtOfziiyt3LJ5//OnI3nAT/9Lb8j/S/9jlJFxTdDq9Z1mBJEsE4b4rMcEc53eexpuNRdsyLpioV+bjixUyWPXQJEmkU9z7n8qqbRtRe6K93EYwgK3osi0dMZSTafe6V0GGTJQXe2F1cISOQVqUO0qFINlxKjoOeBDLnRP1JN5moMCrqTGWwu2WEbR5OKfFoAVQkcYNT7cBHkmpeNJPI85XJTGgR1Gt+oebKz9eHh2GfgrFS8kDNrKIPlgN37rADAcNgq+1pLrMsZzv3uQB+K/eVdBbuf7XBMBPfJs2AZNiaV/yWASbzDWPDsL5QZ5bD4x869Snm9f7tEzSvJC12ZSaRGAIzCEwR56/tu5HK9SQyTJMy1LQeaC0QIas3cGKeOado11hWE4QUnwrvHpCBshYSoKgjmd+ypVAAesnbVVEMwDi3ht1htfFzCWh2PnPNO7WFs2oO3QyU6wWhOKWiE5MXs40ZCY6HUUjvkNfIWrhWTYafsunmnweFUL6ENoihqwlKx8j4gNkQOKJCd5HRtAemmXj+VJJZKRRUuht6+J5Me9vypqyCTEkAcpWuJCOkgB511MFDMo3aBcuI5hcrcvV1bws4KW7d1GqEfORq0U2B3TovjF91QwyyiVPSIVNgsgVPqpCa7jESQ/9cVnrES231XsdEEY6NW5KQqneIj9EU9oOVrxczcuuoYXMacVUBYPtt5K99hkLqRmLzVxU3U0bRtIxey3gzcaqN2cbbqB1k602Yk630aWWCQhSAKKFjP7B4rQY0CUT3ymWkBPBhFYX6kfex2Hi2iazI/7w+d9GlTE6nPx+KAFzeLu2y2odEff7v3ADWea2A9s1skvMwwXlmutFUUsFTdAWQdlWWY8YG1HBSOmk1Qbaa9eT1SH2AfsEHDTEDIv5io4hFNIUDOIDJIBszTw2mPIV65aVK/bcv644q2/4IK1M4EKwfGJIhiiycm4/eW7AZPtzli+6Q++/H73HVfCbrzDkV15a1bol6tbQvHwILO31ACD0dWfwtLrWaZOToO4n7vDn3/uqAh7qsndky1VqlhqYSqkrS9lo5X5vExsrTIMlQWHO6Jq4JUY5Bk6wDxAwpbjrUsNYMwwj0QBCRmpoTlijh6WIlNZoqLp8O7uBNbCNpk19kSoPbnU2AlR4StePVvJLhLIN0ZS/AvBJ+hRmmhRLFTLoHfwBy3JgUtr39T4Pu0EL3Y7FTtsscAwBBe0ceEG0AoEBiMesM3uC6QegI4HE4/zOr/FD8Fh/gUjHfGo5LR3Xpw79HjYqbqpRjACex5l9o3dz3trXO2aWE812Drja7PxinOgqF7qe8Tb2noP1JA9Ot/kU5DqAh1lT2vLHE7BeG/3Wgm2Dug5xUdJ9LmySDwLrLaTsQm/ZAJjTeFJmEiU6m7EIpJawxJmDCNRvWpSWUeuOf7uFAg7ykcRcFJmk3nL6bCDTrAwUdbKajCzaJwXMMnyP6uNiqPe/7jbJ6sMX3FZo8515dyDVPYwkXZhzoQgKAxcq5couZ574IEVvfSVJDpgkbnAT5o09OSWGomuQSVg3BBCW7USyeR3es5gLXKWmMRUdqza8eNFjWB+qhYI+aA4KWsN/YWtQt9ClaICfKzT7lrxiqmFM/cjn2CnSnKyA2WMoXlmdaJweQPk2dLEHMTHWSNIkr9iEGhOYzZ6j7JWiT8Uz6A63KCor5w8jQGYQNM4cmd7jqIQLon9q/DKxJNYQSr/9stLcvsShiQf6bjd2e/ElHwPrhke9kBCfbbTiHxX1gArb32Ep9p1hJK68RXw8DaYklIQDofhrmW8n3Zorm+p1xIqNEoMKZht7hXoC8LOKtkZTpypSH6U5LcqJiKETl9Igb32OyUhJJz3YLd4PIv/6t/4py+7R2aL0bX3L4eqPuwXqZE+8hYUxrC4ok+vT5beajZRn8JTlEhkG+UIXhxyRUSV5SrYZ7iEEaC5Yz6KgmKGDBO32PI/C25m/D08K1uELuoEHxVXxFlyJV3atbTAFydVLzBywjrI16SgbAqvX5sd96TXlZ7zw3uxvCAuSb2q+c/vP3+WahXcxL79P37Qaz7eTH6O+ZWg/6hADZE1bWZDmSMnh8xlI0+/6D/txRGIimJziEJwBX8IISxgyhhtlNJf6V3UnSZXu/q5cbCzu6tKE2PPQnFOq2IlPuc6AVON61FYuaYQgVK0T2QzY+7A9+Wl27H1YvBGoEDTWN/8BxyqeMjpH//0nbSwMb7tcDLKoHKOf2bh2ykCpRQEW5iHCxKatgR1ljYhUG7hB56pjKwa0PSAuo9sGovNBTcESBgH32HGPQH3VxIk+oZrmg8XzBPsEtZNyEXhVK3QIiSRrxHkgR4BbkH5q6USoISPt+vJLFHYwa8QAwJsFMCESBpbiaP1M30spDHllbq9Gfqp3PisGqSAsnd0TSYOJH4UFvUTh5wMgLnt7p3Oh/NTKK4EsGsPbDE/kMZbJqOVuzJROgwdKIagHQLhKfSdtRtjGbn1E9JIVtkq+wpnrGns0pZl8d/V8Nh3miZ+STaMhC7YqaIzX46buc/FzDqG5mFAxkLyLZw0oThMvgX0ZHycKEPFvo75qESegXK5PMeJmDxU9qT1i3VaxIJCEax2MyXNxwlZCBBLYl5TiGBj0dhO0s7Gxrs4ycpaQQIrVmRkxrfoxgFyaMxe9MZNQt4tqn/aCrvGRKlF6QDHszMW2Am4NyQDgIfUEA2y3VXHtmZADHj6byMmuPSyn3HoxOu1Cy5o0wFGTzZ4M7u9vSUOeG4cgVW2bdpnhgkOBtcLgVhGvSkWOkAWyG6N2BJ6SZ6By6hkbNZHEdI80NEvr8yRdUpI1dGZajlQYLnA1GQoGELFF5BJdrdlJolZo3rvULJ3MkNSRVOzA+I87+oyq7SxwzC3mB0lAly13iKCdCFyAw3aE88E/Cx2hctZfbNmn45CYk1Yaspu2D5rZuSpbWoBI9S+54mG7/NqGDxNesur/dU6eOZ8GwH1ri7ss8D+wFPp+2OZvBRYShh4qjjz1P7FDvTV8E5IwV7rMT4eU3GdxX/5f/qXxCqnG28XDfJQiIE4Zh5kiRpj89ICNZbJ9oJd+2HfKZD1xAEUaxkPAcYcWynld0oPPyzgm+wKofR685RTMyY83+xCH3V7L9Lu1Nlj+0cEi1TVLUDhCo7p/1pse4lvAxi50AsZX8NBv6yQ1WtLyFl9DG08GdPhTGGSkno1BhkAn6mtksQHSew+aHLw9ap51teZLmScANyWV0cymBx2mV9kmsZwvRYafqeuYpgOuSbHzNTnx36n0x7q5nCBy0GvbFhJVyOMrzhWVV6uRUku6VoVGSL+Nhzp0pF59+OJqbhcXctY0xy4ltVinYmIdjGaN+nV1vHaV/YrVlPYxrcltV7X2xWzG3OjQbiGdpGdHX11ZTMC+Vf7RznfKwUx2/QwyasNkbMIzKCoBVMEEWLBWofKwLy8gDC0nRAZNg4kQkGUZJThlM9nN2NBxBkHYdhIynU2IpyJm3ENCaOdpdLJtPPGLwfQtHYCvtJhtFgIIXfIY82IWqY0MgV+7YvVWJPv3C5WWlCoigcpL0B/LrDm2lu0ixQG2bAjejsXq3q5dfNQH5udr5C8r4pCWKO+k/tMenzBG4m0uIGzUWXtCh8pfCiqM8A6UQiXBUX1kD7lsJ5Ww30jYVcAT8POtoUswwAUy0M5tozMylmBwi7RY28Rt3HqI+nhg2VRVnu99EPWtuCv/WNZ8EpmWoJ0XFJvrDSQS9Wvy84SOEpNg4CUVYr1I7YWfGKk1p9mUEu7YItAANtrTSZcnsUzKpfAJp8UxcsEynBMNF8oz4AmauOr2VIUKwkE2V2AkWnpzoOw3b88lH2Ag8d1Nl+T9amjDJAlAxaHMaPQ1+B7oV3gYNEadah1SZm2zUmJTyPjfk6hHf6yf24899MLRl7xj+ZjSuwLVnQO0RyHZERo4IP5jnzWMBHnXd+szUOyV8SCPhgTtyMIIA8uuqg5efyXtEw1Nm2SxZmhdXxSRLoj3AyZ2mFhH2lFmmb7DR1vi4YWuuPZF8xq6PHSlNHy+HlcJsiu69HLq7Yr2F5r5FsN3gUsIQCLhkOzF2GEr4g4cfWzYz6raEdNPLqQ88qudx2FFSRP9CNT4LDkaiXccRS5dBy+MGb+UROR5BWqTR7sK6lpXAhTJVWWE9IhEaxR6ub1Zsu6MMw4XKd9yYN8Zu0PBLUhJO8DewLrOQuqjGxn9bXizScKci5ub7bOMelDDWmKOxn4/uBnht4CPslb1jFiebOER16+PZfmTlboIRmK1htxkTKQZLhCFrUtNDeOgvWwgTqBsagV75mZftHMm7gauxYgzEFTA1f9jmtNQaFhYF/ovxANCl8WmjA6dq9MbHWLw2LMrjl2qMp3qlmiwE/lUiyN3/FfcFSpz1L9esPun0yzlWJeri8etVeVFLKq52tHwApwuL7KcC8uKxejV++f9+K8MMueRyzTKoOXJACK/mZ4Lw7cagAnvor+OJFUz32Q+3ImjlO7kqwCVYS8RpRt93iWpJhyDdRcVsfL3z0++rCzx9zZ77db4W9aCnswSlRBHMPlb9bVEhIsL9HBcnWxJVDUiaWw+5QNkDsBNej29fuPe+tUUCPfO29azLeDd2r14PuqyqBk9/mjc0Pxu+Se6uit4BVnZw852CxuOTrky3U0Vs4lbkXG6JsBE7Wry4fBhegrrDSMJisIWPhFcwjogjoA4pK6Ol3bQPUGnoyyhQ9QnS4XdjD87H245ItVnWu4V5qKWIQ+MtIM8JV0jIgS+DYpkthwkZ0YRA3grp4uquIQMfsQ6tT4/OFZyEsgnAtoSEsY5YoFJw1zdOq2xqGC5N5AbIsV5ZtXmdNiTNaHshlm8Uvj0apWHyuqeOp6In+qsKeC96+vh90edhGRIbmBUBiaWRCwjbR6XYpatIGHMXhda1tXzg9w6BYl/FUUiCtG3Sq1NBGCyv4ZFJ+jh1N+mjhxtkEbq3G5mP6UQjSomBTk00K5AboixZLVYyQL5LCjbK465+qpBSzik/wLnM6+xPlSj+p8qJ3ZWU36zq4GNgClo/ue5P0AzkMqGImeUHBS59NUygvZuQkAJEjT1YCDMa06BOhCUlawzSRqsh+BeVkEk+OHQU4tA329xflaLLdX7vOwO3CSzAfZrCOfVRsC0chJ+4c0QM/DarMagDh7lHLlAraVBiHoCpX5GQ5Kh0KeHVYqNADuiZZp0JeUJ0Ln/m1OAuNUCYARE300IkM4PpjPyBFYzvmDWMD4vVbDxKxGjN5ouGLJqhVg0kiatxI5J+0tZopgLshEKR9+Q+CQYfPSUuJRGSeEJKASC9xCfEVV/vgWRzNgzAibtQCnraWVzMDP/spv0XZ7e+Fm+Vqz6L9ilb30/E9/WLXTpIZcVLZUt13nMv2Z4vH7MCB0Atx3PHWpvj3fnh8Ot+yKKjbuhkKXG+3DHl88XfCC6mhJo34BhaTF81/VNTTunPdrZIgeBLlWL5MrETW1cDxGLsXX2XOkaypS4CprNK0uE+0T96q3fLaQjMAaEceYfXfsEk5YZacSPtc0SzSkWvFO3pwAu3KC5TUqatGLznyduvXGsUvfF7IQnqLpfZeMt/lZYAyR/8U2HksRmA+g6JnWGW+c/gKQibBQH0GVc1bRcEAXYUDV0EuBD8zPQpM8Z5aIjo6o4oUOsBJrx1Xw1zF2xFLIJXpZNTzZtEZUTmnaWZ1MdLuy5Er1VUz98fu3v/z9rze3a3Hp5KlaJhkZvh5i/rnXmacOCkyu+9/TJLhOvFBfVIKfobxSjJ40mkYBrFNXUb8ddi/3VcKd/M0fv6P9ZKGEJWdv5B2hpRVzLFy8+sMlIQbPk8MEeFbEFF4wfNREYzZL5/uGLzDVmQygbynm60IMGtT4JOxj3/Jwib+Ss6/XqQchEYkhKsglENJS72RnaKvgWybGqnNyx020vD5DIU0ABMOCEpjkghXByMp9nTw/vIp16lFUgphaFGp784HVzfWL5V8X6Yb2esQYOy8RP6eB/ez566XuNzITJlp9F+JvaNQy7Cv0z/5ngkGGgRXdrPRtWhmfduoFDlpkcTQPlOEnEzwhy+XzhffFcP1JZq8UCVJ1pYTNpjvLQFg1F1KxnLhjoQvkrIBJEOIqCfJhCRCzyesQGp4BjLDano4LwVQ6a8myPXkmE4QmBe60ZOnPzrVp8p3OSimsmBa6xSohCs+gW4QKAQH6QFNhDLxQnsQLwWo6fjxLnOxTvIkc+vCgYTHrLyHKwlbC7coGgEQvEvkspk2jumsEtkBOs1FWl+GKYDKxRYjJcP7We2fL8/eHdFNfCEOW60oiKvjigLEqgBxTBzJhiFiHXpMC4Ov1mgUFsVSHkqAjQIJ0T4M566BTLaqAhsJPZbVRCkhRTWN1CjaQNpQgsYlmIBtgzS1GHwSlAAUryHYwo4DZLByCSLtM7K+GCt/PhzUlG+vX1scaS/bSXzvkTGsXbZlzEdi2KZFMrdSS24Kh8DBRPECwE3bZ6Hm4Q09Qscw3FKHahmQckQCfLPSNQ1Rf4+PWp0IMijoNX42cfWBwIxmyuWC2YoTgLMfoTLv1Z7bwnENuzhDUsovld0Q3M1zuOBGMDFwewKaoz4cOwM9zpssH/N0Rgs+3J0eI4SyYd7XjhejKO035QqvhTsPzK4tdEgU99BiKWAQQCYl0tFi5GhLofpis+AE/IA/UPJCa0V1ydlwUQjP1ZBJObFxHYa/KqF0i6fy7JJnOfn3tnkUqCwISP0pC9whBlJudINAyy6/d2aJU/0w2zqZpn+RwY/VErbAxUAw3ElBuB3jsIw8Rh7BClp8wGZiDDUbLeezuUteUacsWT11o8GCH9g9Vwa6+bahCOktWaKhXWfETGRgX+mDqkkiUjLYWN+RFS6iU8ZP6OBHrpDB+7dsnrn/Um8GiEXgo1lElA2OnYisGdHIjajmzDyMKW7vDulSS46s+0MHsk4V8xAsecZ7VMe7MZdf11LPw7KnrlBkhz9rtHmxCUnN2oavlxeWGRAtVgFGCDFCL0hTZrBDGgo5cXbjHDFFc3dQThiKMH0NnIjg8fguFalnZetcNowfN3zZbCVWuJP3vqCp3fv9MtfibKUYniQy0AALZMDIzozdGGP1ol4s5FIOhPMyCf9FeYV8uZTcg1i9apMDz1FCpSMJ2rJj1Y2GzRxLIyTmUnhyPwx8asQEjkRwHtgs1BUy0P7qCE1llo+j8Gq1zDarjC2SrkdojHyBpbFQ0Oc2rNsB/FHOYjaHbODSqFYdCupGPoEJvQku7aXI3tI9MYxvC6E0G0nq33QGXTSr4NwLbQQBACei3+Zbi7StsNRTxS8/wHOCP5pCZnBi4dOqYhwAJ0MwqkGNLyhehRfAqE56WpDi001sRMO6+YUMQa1+KivnENoWYtAYZDLUUwainv+kKjeynXL5rUkJOYqoylp5MSUedCUGz9vsLdX6nF5LSAHm7ERlDHTIkUFvtifHbCAgKBZCqYBxFYFjmDIEmj3BReMeLaT1OQ726gcAgUKjr5XOZFbeYpP5V1NMGWzk0xQS95WXBFXD8CKepbxrlsnS0ewhHpdpAI1BywbgF57kRM6sPxo5FvUaW3Y2QWUmKYtptQo7M4Etl+PhBiO6H9sOKgnUbaTeBaRpqZ9GFRuLPnaP0qzZyDsaCA1+xUYP7WmcrYW132pGqSMvcgoPbm7UviUYEaQaMn8KE6cVJMCICHeGK04wsGVJvt4cYp/31DeIK+8//u//+BwRUy8aIG4E3cU8gTBtKIKacFkMGcyxBO47gfkmdZcXMUFzK3GLVon5ShVZQvLSygkL5GHneqqW3seP+V9/WUFJpv8DjjN5a93d73SLg59XdE1XCXAwGs6uYi/+ScYMwaiU17CFuOWtVPp1s7jYZcAo8KkHdANzVKQxH83POpc1ds1idYP4cjsQqf35bbew/4ar2i/O+XsrjmpZnwAZvYC39RhqduNtHjjFLzhS/7l7M42a9wexcusjIu339vuse87KsHRAxOzsLmhhnVbtnaMxaz8KydZvVRuVA9oYAxajMvcYAgonei0CBtSVD4hFZSrQFAYZgom0Y3IkYJVUUECRcijJgpaVQhaUayiqpMQ8MGHl+iTigRfrsgIKtQWDLS19cviw6OeyYRmosSHehm8cdLuMCgGmJfSAf8uddH6/FZmf12NAu6fQ0tlm8iLzLV9snFYoOpsbsWDaQtwSHGW9Tq6290LP5R/P7TFbteDRENyG1N0x/C2Bm5qGPefbOk0m8R6EFSEdxCbFapN73RSToqK+Zt+UlOkTcYpGxbp9uMD6U3zOFK+vLK2hC7q2B8CO19zZjyqs0YK/BKK+EgGQ+SkSFm3E6VYZWCXykv3RL2gM6Im3CdPoTEmWsjUQSvT4DcTzK5wBylknfIW5H2H2z2uRIKBO60AucUtQPw6hBIEywQaAfyAYEbYT172oNZN9eeJqnS+nJ1/kofPBFBQ7CWTSIz0Ays9IqYTWXKuefeU+/gLqnIzJXGWB4gSMbjHUuGi63Rii74zectZVRBm8KmFgKz7zYAFcRfDAknc4YyO9dnCv9TkkhgIM1Oa95VEu/8i2goPWIuPxVh2Pjt4qg8rusDS22LRIzYpS4jxEZkkR60YOe1V2uIgNcFrm86nNC2KBY4X91qDhOS1XdC1PKYSWLnmmj7Avmi66Ym91D+ILLORHEPCTcBqs4ov3qYo8XKGZGYNS7PBwmrtZSXwUtc5HFM1sjgcp8097JUbYqKl84f4XluBldICH249u5HGTVB0KvWWGLiBaEkjgilmF/YnC01V8NI/KTaYBVfhKNMwg/81pSTHbMImswSOwT4gE2Yrtd22tfs5xDp0jil4iw9WctKIBiZbiCApH8PjPxUy+b95i70TFleVceYhTSHsLk5CQP4OYf3TnrelMJQWvtBJEF9aisY9nInFNwzEVQd9utwM5M6d5Dy6rNO13CAsEgz/oSqei2Hd7F4pXDIHcHr7UwrO5Thqdz+Q9PuJ4gqdH0uvLyTJKFZi+KZnXeSaibiNDFzpqH+0aMv/tKqAwC5qxrpwRzJf8aZLPiBNj+aIxsdSm4+dNi/6Uo6WXRpNCTO286xIYISOqxqyu1sF2enkkkYaATVz88Sv2TV/CHDGqowsSlYpaMAAIMfMXKGxO6owLWsjvbo4M/Hc5yKlSoo0t0JsQKLQycqALBsD+t5pnaSSJR2gLJoNfO+00ixt2KgT/iCTHKRhYOdJZCeESO18HUzsdKjukn7B/+RMaAEAvXWFZqSqekpImj0o6svqq76/PtzTbKbLMISxopJqAykgmRocftUgnp21++ba1NqHemEol0vZT28ECsBEH1v+IM/QdKpeAiG8TZskeAAziagn5aLzGneI6kBa0tv2oHQDEeKC5uXcJz0i6w2mpkDmK46C0Ay+ImxPk2PsQ2ETgaHuFQwujF7AMC25mjTvcUBgmOqU8IC8c1NrC8moNDSWR3+aX8twofIykKMZSXIVHtVHTcbhl5G1Lsi7WERDgsK+EPtJxC2EqOhIAI4WL/DMyG+hExMDN8ypStud23IMvleoLyVqs2qHotyGR7ugkW+jFM/kX/yT8m5EwX4iVw2oJUjUBMiz8YllXwLYMzu1fN20soRHGL3dBhkk9ajpawPjC2trKLxubMdn1MhX2tcVlQxmmp3Degc+GB4yYBBakHWUKe4nKNw3SFj9vaXEEDJcy2y6jSbp9Pi+LRIMwwCEaalZBzuTB9xfipjXCWteN3PLqyCGcsr20JjvbeStsyMVLPZj5jDAxDhghiyP1QV25WkYtzEclBzFrMEtqzIkJJkvoSY3DT0PV6zV2DVKCRaJMqX8sXEbWYS9uPG4HoyHzJ9tnWVHHaVFki7q1V4NWFTeabDBNqUk2o231i7v5koSdjZhctmm8XI5lOZVagwAJuBKaOaF4FKKLoAcr50xrxTOsj5DUSaP1MNG4Ye+l8TAHnAkighXDW95so+E+PsfWA23getTPDzNy3Xk/AgoTwryBLfmonPnqm5FDegxhMjZLE5w0YyTBblEu8j9SNOggWZS2EgGpcNcYjeXs5+XQxqVnk0DjEwa12GNXfhXgcjo/MOVUY9BPBCiMWqh1UiEs9g2kw5CFF8FaXKzvpIQQpYijiv3L37OVxhd6FSqd20FVrYtHOoOZmmxCwO13ks5+dKFfA3O1zZk83GaDJJPhaZ8qu9KkkitaFRMIzL02D4WLK4yrI866xtdjdU8VFKrLnGkPZpJKwpPxr4ZwY6+y/hEklDCWqmTFfwWp2pYEV++Dt2uSd+nGf6ShjOCGgsv6+sfcF/ET7RJREnJ/fb1ybLmbVPYpvxklEzBpksLcGRsm8DBY7jqO4Tqat0BwRsE/RSdqcwWYprR9tZc1VXDJUDAavLXdIbmO7Nlf0xIdTreIVig90ro52KP4JEkCsxXjLbHgF8SogGTzN7eqX9d4KBInJ1I3ZemLjHlHkyHcF0+9uFfSWMUYiue3G5Wya5WgQJ84FJ47PLuqSw4/xpOFuB4WoBTkblanRx9CSSBJNDc9CEzleMuPa4QZ/SSfz/0yQl5aijUHIFYjaR2NMhW5Rgok/kDlKgT8IpUAtwkDOvIXZ8zbyYx1aTC2C1VBdw+6cdDMH0gbsWfSTwlgow4hHowAWwShiwYU+nTLjI3usX2SXzCX+d/rVobD6XDK6BoCISgzYMUqI8BisJSKNMMUrpG4anhr2jVPE5aj1VioqMFXeUuu0sbIr5i/PxsEU6nHWdRwRAIS6wHH/8OpymDq41/hRDSLlf7Uza/r2LWNzKhDL6Cn6J2ITSPKHwmo1BCYqgCFKQOVUAcoukYaTM231mAG9gs0Ux6um4/XtyVaJbtmgN1GXNyw8DEirLHWoxx7QZK5hBsIWEvIR9VwK5rpUh16tnf7zJETBnnaY3JdghGJNLkoOt1nYXAamow+0yO8JA+sAFL3DGIgxKaIXQW+hehY9d82UKUzzriduFDX3QPmh/3cS0Q8i+wXv7SPjSB5YEPtogoQnDKFqzKyowEIrKrCSg2k8ZIa+tAFdVUJsbBeqQqgKp4D3KTypjtH+GRJZ9GobYk0YjHh1/5/k+z8STvSsWiAS5/tEqzP1bWTmtj+HPX3dZBqwdBEfSs1IdbHWqWEPSGpFLFivMEFzhxWJlUgDbU7hLLdbh1Qsjv3myIa71SP4gcAJe5sjYXkJNvPvFSy3vxIMAm9sCoUcDjl/Eq2udTGrwzrHnTWy9Mci1mZCbfIUyFTXm/AtsU6LErL7hF2xymX5hXGs6+XzvpTA3c3WtR0jBG+Pu939y4vCaZsH0Xhz+jY7fyFFZCNJBoYJe8S2CUGr40617vE5W6tKEl3Uifr84g/f7nxMSSGjUkEbsD67fHp1OVX5HU4dCcSOLVj+gnlKtny9H2qDhyJ5y1ktl8nNl+6S+mcBEfHX+WVxKpf/KYQFRkX5PQCq3NzdVBvBrtpQ57qVzk5fWT9kz2mGNwqY2L142ZyitKkWO2uvdTsRYy/ZlbOTJ12JgPnKYdEqJh2XF90A6aTBC9xJMgVwHe4ATPj749M+mYAOCqHyMQ2s6s3E/vQAj6i9TcZzapdkMxFeTlX0NbPq02MtInJ0mOckiEZrzIuaiteYL3giYrTX6/yH3R5wBBvn7JxSiFgr8q9GDbCYiB6aqdvnKefgHUYIVnBP+dQ++eW8vb+qfvJJ2OffLBNyYP0sc+TYZcvKnYq6CiU7u0cF6Foak7BOWSpFMWcyQ66gKjsECZh4wYiwx8couWeKyDnrN7prE0fl/Ty4pBvSEkicSLFf+AektTJ+xQgrwNI4vpuS0kDfRQP9ou2Gmx1YpdR+dq19Qp62XUVkLKrS5Dl3Y4S4qkSXrWFSyuzIJIwNZLBpy+pm0+ktPYsQ6Y/3n7uHq6Ur4JwUOgBfZiBr2bIISpd6M1PeWPCsUdvAFnwggEmdd3SHD5DVN8Z1C7E0SAlfaLLnS3wNRYZC1uZ4UKu8lem0+2weW+i2zYUj1hJzapPgxpkF+XgGAWrMRel59VX4DBjZeSgpmGcHCA5qE4ohsSNLCwERCGVHYvdF85EqBazy8LVvQ9dKU5pVAX1fr64uoIyPWe1zTVTgkm18HufMjoMnGi8m9fHC7ZfpEf5z2AWnksI17JFmG2CxMg15WfTaPrbgiilJT05MAVd/YOUTNlKQO9upFO54imISIx1k0uoYkMJLmtN7GlvBxux/5Sev0XIk3SuZpdC7V4CEFCpNsScD3wwG3LZMvl9cTkV4nx6OkSHwNWSFjJFZ4grNpfX9PDgtuxtY+WbPNfAsGlFtmGIgtNJL/AecknEeD4ykfeTTP1uF4NV2dxzn5m83irv8g4JmPBSJLDAaaoecC/1dLbrr3nK2VIblFFgqlgnyujxUXdEi/r/cqXh7u9h26hUXs9gMQPvYiTV8qh1DQRhDNR7ccFh46yTtFGJWLfB18qh/JXBVtCTlpRPnx/v+9fW3fH6rRDwDaK16zepDFwOUznKgj2fd8OkqZPz4sWNCNN+6G0OER7Dpx8MTo7Y+P90WD+GBaNkmcsLTmOW3gihVwpf7Q4bUBtpPwJovQGPHXaBbzIxiBkUOzltm0XSMoCfduyS27wqcUyWZ/i20hAU/6hH29nW9vbzdrNECl5C448aKUrauo+DjJCvMup0rS0GwbWH2Wgjc6V5Xq7tRBgjxU0iBizCh6WJLb1GyPNxsNUT7EF+QeSeFWrFTByNOdUWTJfQmthPa0nZF4uMH4SVWxiJEd8mMyHISVjravhgMKsgeOAHvWc9XpfiFmiq4aZPZ1Yiirh4lMugMUjNiKMJLieLFzujnhTkJSVxqXemGHJqM0ssmOJDpsTIKVE6LN7u1+HYmoMe+yKlyWCRd5NU/BVqcX2MigCeNCVwKQ0uExqoELpjWsy1jb7a2yxgNnPJDeaTGOpgU0M/hoYKcrbl2lGFYrzeSc2gHTaal9h5Tg/heJH/hm06RSJ0LClJAO04KmC6nkqmNBZy7cL2KRKaTlA3wWZ/YIh/3RexY6bcVqhiCll44QyCVaNg8tsdnu2l3IvvdV3Mw4lfVWUJytOMTOzDtsV6KLsQCqov3/CJvYhY/f97LhSiFtwNwhJBnYsVJPJ05UGbW/ROkwZLgrQTNTehUpBjI+voWTrI6DL5x366Xu70qF0MkcGhv08F0xkjJ06CQJfLEFCi5JxBKiwiNsIcp8HQgS5s77VKQb6W3HIw563R2enPb7czWH7jQQ2JMQS0gwocbAR/vslEA1cbIJ1lhEE7nsxt4BFIoMr5y05528yI5uH+dRThv1dL6tgSSHsiCVPRbdLQTGMUz/YRZsunE2hRFhEgM3TTA8c4Ye7FoZUXSeafSU56ECFtYn2wXBIR8vjMxVwYcFzLj4NVfWh7bwyTYJiPxRmIPnczMxK2a9WELfC4yPl4LHKcECBQrAnG4oiVjisGasX8p6VZlYO4Alw93Zo3stUd5uId5F7ALZP01U338c9tHn0SRWWiDzBbGT0JiQkU94bvP+km8jx2aymC7V80F/t7R34avPQ9FhqycFUsBB6gu9M93Iff+O5SosG9OBD2mlC6pZQFKt9LNwhthNtTK5IQnf/Onm9wPkcMzloCr9OqSJXYIUpsAYXDoTmsa0G+Yz1/fYz2UR7uVD/dGabb78Vzw/uTjficAj5/KU3nTkQCaA0MHfwijkzg+qdFupZcN6qRDkyW1jTPVJFvWBmLT47FU50D3/uTz9nXPHil7tJ1+QkBBQLsSqpZpsej2tcj6+55wUy1Zh7J4wlXU4GRuF/p4OXu/dHiEJ+jVwNl/vO5SOc/p2e12y1I5GsZv/vHjkbru4kim51LhAwhTaGcnIgRJDGZKSZGPpNcUKR6GRciCM5AnQie6mh3FrfNem10kCG34cvDDogDEbHt3+jj7pp2GvQZr1W/E3TieFMIcdb6FOlPXBSXzv3gYpIZAaqRuIfXwmeouMEqjmjM/Ts7m2UrGriGpT4Lu8qZ6BOibrS2GI3+v+kO51u2iOztfXzc3G0JgDTE5+EOaePpmY3mJi1HVHmeMhpu6AGIzRoXL2Jx8LS/QNrLC2Fojds39jgDDwNPk2vTVl5H+GLXIli6RIF6jDsomaKTlWgzr63SzXAt2hYMs6+sr2ByVhiNu9xWElTXAUtKHcb0TeHSJ40jWCZ4xSR4XE9ZYyZnh6sXXaCaFv1lu9JcRfRJWTfEkrEpp2Ag9SpnpDoVir/ePTxQ/4bDTsCOFLGguTe7hFBz1toXOCSpE5H1ZSSLLrIuuViwLUyIDCz1EXTxTRhdZhiVzkNERsGTQngCMpNSQF6qweBMwFmQA7kB8uRTQh1uGQUQRudgMIMjKyj3wo2YVPj9vNjf710cDwrQUsfgOV8vrcEsSTlonyJZOQRLQC5FM33uFd9gJAfrtelliMEb1fv/4w+7wTCZqLAiT3BlGRtyAVBVpqKeQTLZuMkGQ9/xKoQTxTmAz6WlHY/ZJOALdZN+rMgXBugsr0ekUFEwl5B+rdZWLLk9Q+k4XIFfiM/KWhfZ6SjpwWLcncV6QRNnZaaOlgOi6rcL40u/8NG/3yeYec3abG1OU+KGJgZ1IhhdD184Si69Wz4Q/mAqooW5JZVGTFtHT2GPqFs/3XFLIoq+qNqb7hN4kLYd3EqTEXjLCI0hD/rJ/+00VVjEtctTG1CdV+grUSNCS+iwP0GuJfal8MivPFCArZMYI6zvgfLJOBKTEZ9AbKEQPxfAQGddn0TVvsv5IDLUqNZNLxHASGApVYHJyUdbHcN6fTV8MBolBVyxSdZ4mqkqkwzMciR+7nZ28Xq8eDceRGTGT5SXmQEBM8PpyXVPo54/ftZB//bg/PP94ff1dtP5NqufUJYs5fHOqw+AkRbebBjUqSpK/NhiN6L1T+ypYWmU9Xt4LGzhzwNKI1zhGoDYJep4ZWeWu1ePwQDtde4KYu5CIDcBrSLfl84E0uaKF0xXi/8QalYq1ESuli+I1yIP2OyVSMo/Wdxo7lCFA/dTE17BhlQVh0ckeTgS2EoTpnYnewgtE4aqjC4pFO0wMLGyzw7qdUy2OHK3DVxCQnHdkmX3myI3Z8wfYWsUe38UlD9ObmsEg67GSqDEfi4EIx2UeLCtZceetoXLbaFGCqPmMU9mnp9u1FMaSiDgPSPrJnj9kbtRC6EQGB7vHETFylxa1mjMj77X+oFz+42nkAhF2NQNVGo8rQYQsp88Wkoy9Q/TdTqIVEh0YCw+3g6yyQRAniO/v1IskCVM+L7LEHkv0f7m7laao0oPVDWFPD2uKzqV1kLBjlvTIbMSUiDZTZ/vJPVrnuLUBCjV9PQswKovP3/RXi4rVQ8jS8YXLIhVk3Qlq+V4jit1RD/FxoHk4kH3+nH33IQbGbytdQJW0D7TdnS5R+1xE234HFZYk+amPvJMAXEGnl+2CgzZCIAZHqPRQ8UXRUxM0LZ4DssLCX8p3UUdqb+dZoFhfJqH8pGL72g6d65UkNsLqIjjilIeHw/c/3nbU/OTUnYdGwZZYUkRd4ZWBUEkufByyCohzpWs03Su0i1BWYCOYLHMMwrLn7CAbZY0CRtwQZ73eokeKJoJDKElMwSxPrFKKXPlKm3hfxI/oq5W3tnJgmRLcrdtPtYODw9B1oXs5GTcYj7XK/pzQIpu52uQWnPZAnI435xv4OED0kw+tq0iPK8aW3XEoUE7IcUpbYNdg7pl4htnmbdPmpJcmtHxvn4VBW/PqwJywYT+y0hEX3yi1ILrQgiCIkan6YxtZfmLdNOvBZyK+XkY1a8oJsEpglzuQHnXEOkEuIOBVlJXji+O9n+oAVoSAsIM1U456zj8ZMILaytMYxoFfkjaUgXWq38yn0N44W4E2Kc8ji2OTmKhONUR0kapstn+MyQd8JCtCGvPCM2zWTMQsa1VvBipkiY8XrQjYGj/LDf3Ryo7DxiJPsNK3i9qPe6iRWGc21iDRq3hqYll6yXwQPgtpneOquK/313VGKELn6lyW8//2v/tffUJwRBtoKHy1RJ8ceKqBMxlylhSZBhiG6YG757dcwg/Vn4E1ms8FFN29FzvKHY46+We5kQ4o+8cj4ad4vtI5SOzuZpJ7eHygJvT58Kix2Mf12gyLLXAknG3zQARPQBFnfHs+uZF87jrK7va7dRIyh8+wPbtJyijghgi5yL4gD76Ob615/nOAZfiZkg+bKlXYxRE2w3O9yJhYV2z36Y1H37Fk2x8tIiCVzF9Loy/7koRz7W/IkX8w2jiEmHjkPxeDZonGt+/VPrcKnD8rQx5TItSx6wBfcXIqMWoVO9OMHpA0RbCt8gpmRLTap/CxA6uzIB8FSUhbHpo1mSvsHR/L1Unr6jzFzBoRrsMD8s/7/n1/vocaXcCplD5OU29FT4BFpdp8RqWpMj4bSFjhgspRpXKuhVpeff/DTQ2Nok6SauAV5yACMFRXXjavRNPhcdcKgI8he4DDA/2nZH1QkFeeu5ZKXLvuMn9X+vGdv3e4f9j5fGqBLGID9WPUmymnvf3j6PAugNSzyIxag5gOmuNBtaWgMxRnttKv9WtLmoHs4YkSwfyAAauaK5oZKvv8QiVO9GzgN/HZsdcU2FJzdJmu/qHkNqwiGZbtVWiCVWJR0lhqxEyBlYqdaGaehLgTUXUs1QJhluIpz++H+90eg2Fd9o9uWp5SaXUMr3urZKM8SDY4XxWTLjhLbSuddElEZK1wjf+EHqDYVRkw6mrhIDFL5jumXO0bQbRmqKpzoN4Mp7gVL68735ehFb8jj6FSbY2LX2WdcGo1DTpl8resvqm7MLRLUXkJ0ECvdfGBYlZCyKBXpm3pz+KeXd0swhioixcYHCIl2DPWIC2w3zWiqnEiCchV9V647Ft8AEy3emCGCUWmn+PNeEiYaEqeqTeRCzmLXhangvhzrKGwD42mNI2SUAbWvmWyxFYI3jrUBYFwNqQmW1xFaEjYNH9HpsceCXZ3f6g7rDAQgGhmpCOvnXpbHIuCH9A4okouCw8P+hMMHw+R+xeWkOD5X3YX+yHPGS+OUflg+2JtB4q5Ck4RIj3ZWs8YK15mHpRz/el0d96AHqWWwqS9NN8u3VM4G6fyGdzfauuM/eq4nBfhOYyoHRuKS9KECjm+Zly1JD0FpJXiDQ1yMA7y9EfCPry+GaczXYgMKLBAiETuCJS+CMZgGc2SeYCh5//jP1T9zWHTr8n7iluWBemmdbfyBN8ne7/xz+CmvWUT6u6k7qNnIT0MI47ltdZXD4ALE6hkYjx1kQBhNQdWvTHDy4rmFFFJhNreXxfjZlPUSlngO4E1BxHEZFXg1AvhVM/TG50NLi++Oew09k9Uw7KSYzbPKoBRfyEjxEDg3g5avkgBrBdfciZZ1kFMo2yt9WO63h8fD8qE7C0ltJcOXsKOloTsUj8Hu7Qdrmbfmz6cJuoQk1AKRZS2wr4JMUz8qDurVxMgQqk9N3EZf2KYu7PjV1cbEEBZu/V3RT/deAPneRKkls1vk8xELK9TvrUhnLPBOHt1vGOl8+hZNX04+CXOzljizWZrI2EaXbFoZAmPIVQ+SQaDEYgrZFJ3M7A1KSAyRsp4veqXKdPbqzYs2HXUuINT6HOXQIAMAWUfo5BCGaSB8NC1bIDF8t8MW1UEAheiK8CLYOVrfnZPE3XqB+r+PwEv6DEi2gKHuvnPIm9WlwxDGzGHHkAzyZQkXd7eoI0TysttgN1klZrZFNsneytAb14eYrMYskBiVBAi+P+tawNsidT08yEO6NDZ1dl2u3VKaJgCNRMXMms2wg0wslwMaAUI/A3rYHv5HIaEvlAURDW9pU9sXf/tTBMb4A+MqOiGMEnsv5SA2mgrVEQbflPAQqf0ZLGo9zj7gOoTqe5hXnKZOCWZFZ+ED9Y3+hBnL+Pgx5XVdxghA6gq11mQskcKNhDbwgumqKEb8ulUFBNFmwwMPLHcfB9j406bKTwRXfMt8IG5stRgrOADdTqhZRo2l67ofacX0QqRq6LBUMnNkUthdX8m0h70VtVMNrUF8W2+q/1mQeGHZl/caChe9YfL5ihxiSUem8BBsWJ1X9CQPpQo6Ovkx1jtrKUDADwATqMzlr2LA+3TxlLRA1bH+mSXwZgZCHLgPvGVtH7IXBH6ctFstNFKYR7/wL54u7WFPcFjl0px1wimzwVuHscOeRTfpOg+xxTi8avaUKYWz/chW/RXnAKDFoZnzHRReWvA5Ig4G5jsF+nos9y4epe2pETKM7zFfhEK4JthZ4advJkTPD7c1ww3ffQ7gmbeQtjtVV/2c31Hx6Inf5U8sJGdUTXgrGDLh6ZfcdAKrewDQKCXb9GTPqXN4CPVztpBF7UGOT3EoFcJwaVNzr5dLp9P9JL5Ov+X/zH2vK1nBGp06Uz2yV/u98vV5e+Pz39//6C3T6PEm8Vn4gFV3Zm0/d7U9aGU0afKe1XCE4wj98odkQOVvXP+pp21jqij79Ft4+xEojRLt43Tp2oqcGP9OCrwJCQ14YleEjKRFuVqNskXxSpiGk6SzhlXAmy6tjKFcYpd/VNVHsroerPAb+/M9KhyZdKe9y5zSCLTCdfL2Wzk3QvBLX2zeTimaA+zbL38NUM9sGcbFLzIAVCpQckMp183EySqg/Kca90SL4RUXOo8F1BH2WxCUmf0kMYlCMfAiVapnKP4flWDdms51jp3HXqevkJH+sk/JcRDcL8cLWYz4i6iNRo5rPoGwSsoIQPDmUYldM4S6mxyp5e6sFUPwAZU1wcsiQEaAASgOQ3nN3hy/kekJfJomENtrGENUqgF73AOPHUnl5CCXIT1CKx072rkn5pPtLURTdviElrJFXlLFwbHOZANgunWBqvMJxB2IN7WRLEj4INuXcG2OHl4fKyuzGVVNUZnrTmEH0Jegmt03PoMKV7ZS5qBaDJQRs2rtWvOIiGQpMVDST+LGGtLirOsoIOhwvRt1dJ55tReotLNQgfFdrrxNUwvS0MkxmS9Xy2sAIi+Ut5NN5GaU4LdqRkHwj2Ma6k/YJXaFHh1vQIYruK1vwIuxF4G1FyIjAU3krhD+NI/RZT1gTm8MIqJjoIThRa+38oXacn6NPbCTMEcRZc+5RxvrsXKjdknBS6msuFLiS5+4Iu65YE5FotSxMWuWK94cZSZjAKAXh5dteKoIzagRtPsvAH1nFPAgtEonZ5FnTf2UpRAICtKHtrqVlLspoQWRtgNOSuev+puNotla+TXpLvDZSo1UK4B184J4oUUykPAqyEFdS1Gpa7YMW/48kO1br0r8HLsWt7ZCIE8t9VOMckimSEWdK4zoGKN3MFUA8SLLM3pOYbNuwjSEYekJ4HJMUplv6qqLHtnH6IToTx2UUwC1cRUA/z2l7/LKnJ5SVTM3JizRbFy0GCDJ66RC4FSr66FpwzMWllckkNxPJMOeBQQBQjcID8U77YIyJlpyYoxfh42mSecTOLEKfmj1RsSQUBO35WH1LzWowjPImTjUIwK49zEyPdY39Kb7L42n1bJBBBcPzRU+4txGRUJyMetIoAsFWjLUsFPHKiEhlPQSjGVeDAKZl3V8ftyewvZIcKNp3+7FNK8XcmdrqEMOLZ4mc0kU+BxyA6p4RcLd8N7KX0CUkHVpxL4gabiXxASP8Itx00wNuFeMZiKltwvENh3Gkk9tZKDCso664z1I86bLRML7W0YNge4FYBgPTLABpEfSXhLETSzyhzfFNU3BpLvXAAulHE2Km8QXGtTuhrCHsRPvQodjWHYXcBNUUs8Dqvz4KrD5f3AjaA8uoplW+USJf5S+WAuYWwmV9ojqYHKujQktkBYqjTHm+rolLsjm8RzAY4c2Ej6lzaZtstOkIziKso5iNxIodCe+lH2kkEh3NbPwMAlXXdPAHBnPHzRwAESLkyMCkHRVAV2U5HNVx1agrAUPZwMxNfLfrrsImLjw3oIIWBgSDkJYYQRdLl8Y89uhSm0wz819sKpLbiPEoCxSQ6UXTdlTSYyb7BtToA7N2Aagv46jSBB0gwCTi9vEhceK6ZnRqDez0MoRydwQNl/BiPxU9eoO49lzsugOOMgFdrCkJkri2NlJeU5SZgVVzqo0h0FoitD5NR3rTybmt8gg83W8tV8xaMAigcTX+1ixNCWFzLNCihV9qtYdn6Sy8uF8g1lOdU3S/dZdmh4BCPqa4+8whZGtdA9/cynULr0Op/PrqTc1FOaJ/ODVd1sN2yApQCIspHGabmtgPcaITbX2RjWGturBpLM+XtXpVIN8GnprSXxtREfl1hbAdEqlOAOIkAPQSm50RKSMr2+3N1uWDE/J7TOkmt/W0MZaw2HToCFhUVa/ftjRe+crUliiByq2drI7MFNoUAuoTdSEpgYzwzfkuC0fw7J6kWpizK0cxTGViD9AuKkqDgkdjX8fEoTE0rZUPJWzjZqWgSgEJEtFq5mJ6mP3Hj8wlHNoifGAEERTavtH18ZDc9aAAnmE98oWJ8UR2fbEslJD6KMphTtZqW0/WBXDMnvQJ+1JI2DDrCsTAZ+Sn875JAl8GKPqxy21ijRcLBY3TzjX2sNVhrR9EMj9hlyYonInS8m/kcvvie3ks2g9/ka3oAXltXwSSNj5A1S+6K2NVRuKfqSmJWamva0ibRNZEy3D6o1Z9oxBSpjkHChh/ikosqDksTBgWO7Fz2pxPoIAMCNp2qGtvLwnJaxjggB9taAbZXUrMeRXpvsRjAEMjbydLj8EHt9Fgz0m2dwc+kY7MnrwaVYr8vrtb/6UlsEggnb64lDYQVLzFsmyhQVvOfRsKiV9ZA2CypI6sSknWQp/QMgsOstx8xddzm2FLlktj+IWc9J/vel7nhETKPNeprr4onJqka3ZahsBk65pS0ylvh4sFEaipEhQryb7lCdn1ArcILOGJO9Pl2xjNkp6+tpihytNSX0BGoj8Y6y8Em9x/cA1s1q+2gV6plHjBiRagDqCnZ6rleYBbXWXG/SAIksaq6FPFKH1zsgw3FTJ69eyg0qpIC6algXKVBIUZHoWWff3BMjYVU41c4nTNEmtzeIUMwxVFfJ2yOSLEXJU/ZqxkJIRMTFy1qTERp00obaAeifNc39JZDM1Xjc9iulPh7TVVbPVZ8iipgEx6lHJTWBlQaWxg47SIjP0Cu/pRLko8gVARWCMPnMZXbOT3ILWEYVL0xEXAOodXBEMh0v8WTiXld33KcKM4kWNnsUE9TS57vbDouWDHl9bW1CXjDn3zaig6bwx+JiE5Xoy4M1KM4inkhOg2DowsFBrokglSoq7ghPfedrFxMGCocyPzIxQKcIhXO/b2h57ZUm8NokdcU1Q4GQSHQWwUMxvoyRRWjNUDE+hFO12JxmD1iRI2dJi4IPu2cclgoaxg3td/8X4DcqU5Bg65TyFAlKj+XN5AIaBkXpDZd9SeoTLjjvsz/syNXXo3doNA+DPpXCGdTsBjm4Qo6AJTRLwnXhrbbY3N8Bx5TWVuABU62MYPSGyysVdL6ig9bNPhgSYSR5nH5xNvuI7wLmCFnKTLhsXatr3kU/7Kyto90doiwv4iM2guSZhXWzzccP202jsmwtPP2iPCVo/RDMUAE6F9EkYeaO0vhA+ZVwkMV3wWtNJlgmLxpB6g/+iMAxW2yR71sipUg+3SLbYLLN60+wcl7lhAAOQaaJaSn5j1EIKNiFSrkM2KCaYrJUMYxHGHZhT38wMlEy33WQvsNlgu9KZFoLIt1yDCefHac8OYuZKl83DhsJ5k3gr+FQ1wyQZefLrBH+ZAU7nWunTW1WzhB8VtKxkkYfs+mARFDRW4SoOt1ARcntws2103ysaPM+Xsv3lS+UgaxS48vxZituLhxqilmyikw3+6wwwfBnr4C3tIkaEn5WhCoCiYJ0AODFHbZIiUx0t7RbNJ06uiXq7fFFUxihfydgoLkloUvCB4BUVbz1sCdl9lvOIoAWCWedhYLIqoP0/NNvqygaLP3lRnmQSNwbLxDr8U0mgsZNpZxw5mWVQTzVJ/UbWdqi/HV28211ftBG/ITmvIsOmVgnT+2h7AJZcArB+b5yV/ErMi4K0t0EkgqfnxvMJQVvnwmU/WuTOgfIBRWplfMu3krsRUIxxt3zExHFMJlVQR4RCKXo3qbZnaj1lRs9yRZtncwVCXJsOX/2vUsLSIIlVU+VmPsEt6tDtrIdfIFquR6BNBE4+XzcPRmHpT6ShVFj3K98GsUwNYBAJfWENz9dIjhQDwwAbMKoFqeiJT5UH9DiDf0DcUhinpDdIGqEhwEB1h1ISSM9lutgp7IWPoQE8SILrfCuiIgB0/CUhLYTqU+RmLD0mP4yxcAuQFS5YaZptTUtS8wngymU0ozzGyoRcRbPuzRjqMZuKFPXXAzN5F8WuoFTQM23kM7UJ2lnX4pI+IFESJ8IgdJ3bSnINVg3VP9mYmNQk5TLiJfgnbYHT6LhHaLOap7XMp4aIy5y94SipqyT1tNNhUUSLJHkd2LCudm51D2pAJemhqwc+yy2iw2VUJNQ4ZPWAGYUQ3936tjb+HZDTZypzrmKQvuGYcMgfyIBZuUfPwOooeYrH79JRURtzXQMy5AjgHIbzN1yWSHH+8lhLuZESzuvsFlBG3FNjyW3LPiZjucq4vOql0buvUj6jJaG8sME/YWVlrwf3m3yJJDkHdbJ3hXq+fQiCgSWWPaSPBbUh0iUo3uYODkYYEx64v1VxvgqHsaHYyXYWpVfyf5EU4kAgCUofl4oU1oCD8IEGIbBbutImUPhjjvZ5bIl9p3HrOTVOEBVJjlkr76WEujtz2Bm/nNGOmFmnlIiHuND/gxIKDUxMcIwqqBSHpoFwUyAvwnSAsucMk703P+bJzm1Mf3BHuR64yJ2ORuIiw3uN24giqKBU7tuK33LSMhAozAWRpywJt9Wp8kkvR2gKTJnq+BJO8Pex0LLH1rgZtrks69FE1RwKrB900PF4DtE4rehuIdU8FZQrhWRZHUbGq+LlazDRJ5VID/uAu1JdtUdCR/ZBU83KoNgns2j7vG9DCElDIqhJOgkcBhsYZ3uO90g/Vyj5QfiTirjau44/5bv4UlhjPZD7eml2Cqi6ckMUesFErieOa1CPiCrk5ZWf7New6rnZVlQMsETwvKa3sezekeDAlXCZFTeFerQqROgp9Sb5FwLC47FPnNgHcqQPlvYyuot6uBwJS1xj9LnupGQEJIa27WjloMGE8qMoe+SB/ud1kHdQUWmhIrZFQ+MOfkDA+AJdNW+lth8aS9lHbMWlN0TrAHvuCM89fzRSC+GA5iXpZvzY7y/HUVzCrYyVm6bKUklolZTaGdUNYwWEowJf3zcbrZO67EjBCXPpGBiJJFxVe/oLUnml/aceyX5xz/bXc+xwhbcwtvUsoUCuEw4oVDeF6k3iSSeyFHRENWe0w0iPL/ywYBAGdxLnSBxczJitHK9Hkl+EmOSMVWq2MDArzUgOuSmEkm/L4ptKa1dlUS9hSkliEG1lFeZ+VaeZCh2aRuY6YmBmKsBw24xSv+xDpNLRtOQpepYOGdwkXTQR2aPTrbVjP2JJohuMurWUrw1UsNY8SMA/fGOtvLeyuQK1OTJlEWw/EcaLimNI2NzRX8zjYSac51EdITQAL0KXzNkAGKZKbg/z3ZAlniT/0Ht/tgZgk54+hnLxwO1O3JZZmSevI3KbWkVhgQiMjf4ZhzbP2MvOxmDdnmQDQF0GQbzEr4YQ/iyf0KFq66woOBGbJZMSEWwah2CRSQT8Wz9hy7fe+Fyy02D2hdidnHZhd6dtJJesxC5zG+KjmyKEhk7PtAzyhsRJvwW32LyOw0DIYzS5ruwGLbXlUpWPUUbpgdueJmNAY7IsbGyLBH0sOZ166y1zl/jVETGallhmkyASI4FTybISCnTSg6sLbRiz9SxCU8xV3RPlwK635WaNkkPgstK+3rU4KhlY9At2rHCsobgAX5ptgxzL0NALZEfiwl0QsUik+fEmpDmvbAZBJFoybzRqXPmSoWU1bO0GInfA4Qgg3Wx73DPiOAvo8kOmTy88SROm4Bk3lPq5uN+SGhDFSskggpkVNiWuEJOWBTfTB+xYmvqfGUglpvSTY3+1LcKS7LupCIiZpetsH8fnCRH0GpfCLJrroW1Km8jGcNw7Ke5CXxx7bMWdtMDE/TYJHQ651CESCxYTs4HBCZFcATmeXMlDRgAXZU6ceuj301IwUpHnwJPxg7iL0RLZiXKKzjOG1EMZKKeOeHkTayG6BYEGIPp1g2uOMEtCfb5+fC0w5eNFRmU1WS6LbrlBMpMurVUDCmvAFKecB3yAnzVQaHi/p/1T40NE0nEuWya8SWwiVARKvcRALwkxzMtD1XyN98LeHx0ZECIiclqezj8Ha4Y812DEfyluCqrCwCS2nNFMnwCC2rhkA45gLd9EahW3Mz1euGAuLmJgrPj8U0pz/PTPSiTeMyAW6fEyJQRIzLnpRbo/uHBvfCD4BDByslAk+HQ2Sf9yYfRGYJgZZxJKwsyWIqxsms+BomMJ3+5GJ8gYQ4cHQij/XoiuEZCXNJgqp4fcKYXBSVjd6kxedtekImu+XTOZ+qOMZdOsximvTDUmJPJEhMLyLCaZ5jVI616rCCf+igtjBM3j9UkfsEZDcd/xlbNuiv/V2PrvOhFmi7kSFkIp3Jt/qy5JG1tXiqdhjUJyKpPuh/5SbIIMAyHwiXw9oA0W0/ABx+NiyizJDTZfhuhS7jqol4lTIcSxg8nPAZGxatdsYyq2vBWEb5CT7E/wmOlLa/Tiz7bmy142d2CJ8SLAj3kqyYSbzLzbiqi+zG8s58PO7ZsYZKABA1ingkXLsygGm5BI+pjbYwQTqFxSv679cWnOGXebrTHyIZ29p2c0NgK2aJZTu392FE7mWE00A6jE55EN+E6Yy8GgKm9fL6orDHfMlwK7J2uPTu9VVEHZ+cog0GUeumZ6QiJ1CECwk+RqM1yFDlyapfTmEYshQNFeJayYoCMw6oxrUuzKwa240hPCBg7r/cWlSGKhRDinyzchel5PEyxIZ7pVw4OF9lJqJQ5FYhwt04s5OPz59MTh4o8v+r8C5ocM566hKygJ/p8ERIPAty9i2egWtqTAI69trlFoal7iAT3KaBZelAJQvKMHJICkGrCpgUHaVr2Uj4DkvjAiJUVsdNFLUbMxoYT91DOpqrqsr3mQcGzEBbQmXOXQAwboLt0MJiWS0BkRP5sN7Nk3BYaUBwDuuoyoluJRda+dBrWIjrnMpVL02gAoWsT8MzJ7yFqjTY6OrAuGOgjxa8oGsRHKywfh5FHlbLQ/VEXE7ZBlBq+5nacPVtra5hggqYy10Oig06U2JctUALsoQZgNAnIufN7Pc7re41mB7AxOYa9U5AD8Z1BzahCRWfQhe9jtf7LFaROwqtgWzIYnrIUOjAjOOZGi3kmaThJq8q7Sm3etGhTFkqkJaqQWfbb5kTwG+o40PoDllU3HfsMDsQ2Wzc7d64nefefZErJdJ6beeLK8PXlRf27mEXkqttZp5NAPXOgYtew4G6GDuolL22zbYC319drq+iwqVFhRrgCCYv1+yPH2qIIMT+74SRspmYUHJ66EdDIzV49gA9aVl+n3oCUwAWgpu4/DoJVJK40rfA6DTFluEbCVjerH7//PFIAKkFI488oWP4wkcrmGUlSUUyuUI7vWs9ITRl1JIbwOY1W38H3vSP1Ihc41Gu1BDSBEw3ricsstHWEyAEr95f1hfn2hU/Nf0ks7ZON4kblpkA9r8PIhbDInykd/YOin+TBU8hl565JW9rEQuezg5V00NdxjjavuIEKIpmcOh7b9P5HuNprjk1/NYlYjKUuPdeBeE+V5y/AGqx0PInYJp8Wcwxb2sVjo89AU2aWSfVUwRVDlp/LPvrA1XWSlr2haSkY+DbJ1kg+PeHvndTdd9lOMC1TY3GMWLg1QXBG7/nVlR3KiH1eorklslBER5LGKI7QDgOKHY4KloA1bRYB7PpI1dE20uBR5SUneKJYAy5Wu7AqYcgZHmcRHgJnCkx9OqxljvDJoFNzOCQ21BXKpm/71JJZajeYH+GM6PSWZPbs7breudifVkBtnEVAzQ3prenYYzpAcjyEaDEbljlom9MMRiujh72KJpHi8N62WlK1oD4q8JVYswYhu1Hb65S5U35d+fm831+4g9bMT6rNJ8vcpZu1gxZsPAIvVgZMFNo6M99bsjRToWAvijvYaxUElU5R+YQJdzN3f7TcVpUCwnbqTLBN/LlS7XFh57bUiIzuWlqeuDmOvtIgY7oScVIDnxhAEYbZyI3Pq7tkY/JSebMDRsR+2FUHvOmjPxsehfbaTGKb7kGV7niFfSQzxIkzYTAWVQiNRDOyNpeL454I0tBTWFPqNPapA8JRsOTeE2S4GVRqYgC2AxLYdAtFm2OHzlWrxbILJ5+yqpGVsi+lgrwxcFBkOycwRhpl/jVk1lpIbK7w/Z2tJyS+nax2ik9o3iL4Cz0E/OVMsyORMpqm+s2nO+1NXhB/tIuKWpuWH8vIveKkS0PVtvTE9exQh6pHsIh+4fs9IuHcDTHsUFWqEUDItuVN54WO8wCCNahgNH2GBLGl4EZWjMWCA6Gz16UURmahcWLbXmEQq8asEbu5e+zrpKsk7C3hFgOTCSSPnQfwvIBA2JUNJ0zkpqBRuahgNEPIXJE2o1PsYTsYH4tLlE2/kBhxhQCYmp8WljEPaphQ5hbgrNYIqJfs3Gxqu6RtBl5Pu1SY+g2bZOM9xIc8z1SIcoIQ1p7+9utv6s/gYnImgfzkwJFSzoqEoEALVo40KTRny+MhuWxZrExCCwktu224ZaL5Huqn9Mr3fLlbsFmqKbOxiXac2BiSjxEJKwD9vcIbwwsdmFmaTqL1fEfRUhi9REQb8jRoSlLZ2/FggOjr73YBYad7xSwpuSF5TbtlinbAIAZebI0NJVSMYu5555zTzLBgVNewvQMYcWBGzT09A4igEJvCQlXT6rymdJJQlMTzCqQYpTKNze0NSabSWut6q8Ggn9DSldk5VyMqAyW1QPBF6xWzccuBR3k1uK2Ctj6dlj27a/QnyOOqL1+CmANCWpAlNUkzi660jILLyL6z8kZd0IhSCsTLtSln0yOpNRtUykj3/blaBzt3VN6DbKaCOocWrtXxhu5UD7tQOHfpuL4+LYN6yjAVTsBsm37YPfJvRM/U6HPExc0aKkYsfygInsR3ia54xc167Y2Oae8dUMztqyeYc+YkyHdtJcyyJTZdIHevT/oTHA3qe8LFFTkx417pYUluCGLIABSAsFbFr22DsiP5BhFgGmztJilSViwOkdCEffEsgqpQ3TQxp1oR0zkSizh6MoJcdZkIeHBb9aEzzBDQRBSVGBOhUVxXFEahqlWkuSbrm9LCYoCdc0qYxGNk2oOCOXxbWDrvhGg1sSMG2jXy5IcEVQOvhkH3zZlHXoIKGKZ4FJBEJyrFBqBaZsOzYsxen8LZ5+aICOGCIGJSfipETdG0MzWC875oIvDhdNWTM8bMz5x/jNMxwBFe0l5kkBD5ng+bC0CnVX5kM/kCNrmHspSOOpWaEfxgEkgGAmTsnZxFCZhtNsb4XUmnGD6zk475Zhka+wHdPJcwWIdKxXGLvJzof6ckSrMg3e+K6yldV5HRGNP3evBoNH3SR106YrrF2wnh6etTDb4lk42bNPnOp8BXnlTvsZQ4GJXlqyovYzPoMeQ1oBh+yui5rE56B29FDElMWNe1D6bpASOsFQsVgp7VLcVq2dK70lin7o+EMv4CRMgEBoa8lRPjQLavZFsUxdcrNYF9ofyxkCtBNDMHZ87ZMfTSrBpSV+hwkqh1MuRh1NINMIZlCfgf3kSSxET4HemI/eWWuA9TjqJVTF5wfoOk/ExWkselcDKzRvb4QJ6fClHsVkG9N/oRcDQ4HSYM2pdYr3TOJo8chMHpY/9QPSUPWMmgUsEiSUFMK9kXLwKKmJMpJv3WNekhFoGdmR3/IQFUFPtJRQ0zXmtDB73JjL9VQ9J3RLqE5GpIkD8BAvTSg/QWXGTGRCit5fIbY7K81MZjMeQO40SGqmwjh9nRGQV1FB0xCiMjoKQ0/ptGOpUkpuikO9fK8RyHxRJhDxmzEtNknAue1gqCZXzf8pa0vsi+SY+cCL6xxradiirhf9Le+sSZQSMiJbQo1tZmC3r0SpuitbrboctoCHZnp5TDF35cQF/BTIvnNUN+KFm4QN5MFnoSAPMIOzx5Okc5/mHJySC8dAZXZMpEemlXQavmIlmi4SplFSFHPMUOfNmWSdsIGYmIiK6DsLzwrLuYW+UApTxgHY+DlmluGIcATzo9ZDWsiD87DiKV68Qp6XYoqHNjF+/O9Rm/NoZLI58kUImLizMNxo3YMIvmRn+euF/+jyhn30r5xF55bHtXL4xIGYy3RKpYsu0ayOaxuSPvaW+kkECWECjb9wwDq1/ZIvonpa/pulNX+WptLlk+dd6iYjKSKI+GPq9dIbbaPNw/WN/1txs+nM4kNJzUFdGGum72k+kfifUqKI17WPvIYOEgZAHvTu24yeRPjrwaiq4XtpJeGw6bkU2ngwWtoqmSKOVyE85GDBzLoxbNiH59nStJbXMrWPIAitnOtrJ58GZnSRJh360OLR/Oslg/vwXxDs/RV6PyV5Ml/BTEcKOzcddcBwLceiZBiRPoczZDjXL5kzkzHIWiwgZIdRqDAZ6/Oi1X9VH32/R3OjvDsDxQBEj6eEk20GyN+PcecqRkHhdO+VGEUuCzSJ7BjHXEm5I6MRQfwTzoKv04oiT1F3wT//NijazyrxWjVxVjeyxCqw8xZMqEVLARFY3X2zXFE7gpePy/1XRfyZEmWXqGoRGICACZWa1GcGi8GPKGZuQeuCIuguviUmi0nu5pWZkAQkHzeU8Us6szgYhfuB/xHenuhHjSI7ttnjGxBdOFpR+13wDR6IAPGojraG4RGaHPd7IuRguZTYTM7YhkNDsYGEaWETRB3nq9j9hFIsM9BlzFKi6RcgZMPKKOhOgIlM+OY4mJ4QG/qm8QUN6ujeB9CYm9iyazs3Wk8IjhCzsnoWRrFyOhhTUasvSmUobY93pkgTtb4S3ZcFA4SVMdR7YbASLAEPHhKWpjObIRIpCgVgE0yYdeTMTjCjGNI7j5yxKIM1/uUeBdXiPiJKx0iQ2EkESXN4ALIMYT4LyREC6VGPKIfJ4zboEZmV6BrgcQv8g40ZlTfZOKyQfaWplOTePaCc+OnMnpq21RcqpbOV/4lewiXRJp2MTai1AejBFks6M2OgwQEhnd5nq40j1yINIv1IhotTwYq23AUIDFUBg64qRsEta0Br0iFGekIj/ura0/yJoyNnziHLPtVuMvj2Kn8uYHE5TfkJmnWrTKKoF4jusSet6eopgo9ncKG+dd/XkfxpXRIT3rW3WFkELhnavlDdDCagaLzgRqvFJv9xiz4ZDJ82I2S02y6LbAFa+9qBe4isZA5JdnfvT4SQhYE6dBIiBnnHyK/+xuYuI7ff6jQBRW3p8AxzDzpoccX6kAywChWyErD7396kiluoASY5sIAVeES6FqrSFwfn14fVxsq+RqHxILXrRKLDeOz6e5kPSSf7pmMOivMdrjEUEUS7x7vPOfd4oIhTBYzU05Cgmpozmei+ehJJvtx7xMIgD/wkS/hld8UjyQMn3mBjnuF9rmoigFE4/8aOO5OL29WlExnrWu4lbapCCdyQxhS+w26Y42RgsOicnQZqRRDCmOoYEvr7Yz7kXWlABGW0hZ3gG5R8Hdz9R5Ea3lD5EnAqV2kZMEJaSMaxjx6QWv3eD8bfzuAFD4aJdpE2S9VJEM2cjwVxiFPyZCLMGgaUOGhQV7WnVpPh9/cXFzt8RB+4Ub4bW9GS+u25P7UCo/jJpORcqAHoaZn9zaIn8l58aDtv7VWgKaUN+Myl2XhDuzaDlQRiO606a5ddyyrUgfRlBD7OM1eYkPC/R4vlQzPB/3Gdt8R45LRs16d2rTcqpEKPgy4RoiCCtmRro4jYqYNl1Z6kEMAPoxFxUDuofzrFxmt+GXVxulXVh3ShxmVTf1K3jMNEoy0DkIiFm459EcAsZ5xDKj5hZqaVvacJoJXCxsAtGCA95lEuIBZWkNSY9L/pch1KJZ6QaoS9GwJjhTjkEyYXc4yjTHyabC+6ct6q9XNEK474KmSzy1NimAiCfyA8hBhlrjLh9A+vJqebvEDXxFdwyyZlK1E/UUbve7JzBIkO/XN/6Nxp5pRBzY1sPRBBCPv4SwQwHNSFBk7vSKyhUemUvJHRAj8VJOAKXhFw/EkzkizKHbcYUHwoZijbdAIg6rHyRy8Zi+kFYQoBQ6JLXADcvKfpoUHCcthLoZW4aWOWyJuWnnzBbw5T3N80si0WM8WraWG4/luBPK45JR21kWH8RPIWh/zN3qB9wlA0H7SGowMKI8epQUslSRQKA7CUbaCShtykSyWfelrT07zafWTT1QmCuA85VCqHdRgxJLpjjbfcP+5Kr91lqTCRD4+0qWEIqv6pNsmvladVEEaxu41erW4l7mwAM1R/GZSkIiJR05ZptwLXPVnjYmzd0rTiEMjL1hGFa6kFA5zh4mMXshoLFlBgKCQBA7+BmEoMg5VUq5aKKkhMkZM1P6roSpFsI9ZKbbD7KCKv5KG6kwnN7Yat9d/Az7tpIfiPVuKywSba4mHg91HxQtMZ4nT+Zb66z1PaAgHQSM0kSYK75hOIHJ4+NudbuAcZVPi8WxHuCXHTq/a4rDNdDaaYBHdaCnBJm75o0yF+BHEpKThC+FQp4C+RDEaMD5ZPNgceszRp9cNQkSUbhVyoFmZYGWIeZytaSEfSXrIWBCznEgA3YmLk91qIJFQ4gsaINhZpFTUnR1ofmJWjCg2luf+BnF5LVGmRQhydUTvMk7OTPZylOzEK5wwxQ4eVcoYN8A+kUQCW1daq3IyzG0kIJwZhuQpnQrcCjbV/qxafED0zBe/OxHQtJTn0opaODxkTRdDmYJCAzxvT08XNZeEZ5zRJp8dWfx5Z91IxFqPO6h4qxyOazgg/gZQkwsns6V6IK64EILDBjnsmZTRouAcViJ4qwcpP7ZJPyR+rqoZpuqgpwCNwSBa+Q2d2JcfdxhmMxKJAYCiTAbZJpjLrnyGUmPw2gjQgMGlN4LVSmbshiO4oeIC7893nxUZZgxpJoaoRxVMawpgPh5QseHIkY5XJWFQUr52wLGHo6bimzOWdoXvAiLqi3Z6/+wFajwhUrc2UOmnyU/uSl8hCibMo94nbv4g1OeS0K8Rjsff2zxUh8P4eDrkS7qN12kiU8cB6+hXCRCWRMBEESA08OYQROQ4Y9JYAZ/JIKDY+2QUm74qKbaoRDFy4QTWGCv64Ee4mASZ9Qz2RTc5bbvrNrIeyK1zSWgQYGWPgCMnCKTZOV4KclcnGjLawc3yjgbKTr6D1AaHpgTV5Ml9XS3E0G2yb0obHCeU5k+H41sl8iaTFi5QvrgG4Srizo3SU27lzLFngYL2WCXGbNBklGZlqSAYy49bzdoIk7ZAkA0O2tHft4RVQ42EZ5AMxo8KY5jYup13MwySBUkp1jRwjovsdgkg+PlHk6VDCYMUX7sCKfqOpJJrY/Ljvay9CM/qdavnnp9JUl3fXWDTJeW2lOR8Wk8OjWoXDR9Wp5Iz4YQyHNeKyK41yF0rXbvMtBMUZg9Q4qnSHqwM5dtKdla4FbkR5JLnkh2481gcVGUQwUsuoEzYva84DksJVDIBiEsYvJPIIvPKmfLO/Eiobl/0JCxVaDBLOmy5nOl8cMsCRWpNwpOHJBGaUHgzrLvKo1ttsqDURYUYXkGKqR6wYDn2HWjXbjbe6rtLdlRHRnpJqRLhwuzRKufThs2DZbZE6iYryAvfYM+7WCEo+NWcjk0W/AHLUIzhfKT0uYrb8y/hLxAiUqkSdkdAivTWJ4GLcIN7OYs2lOoHh5fxsEOncFOUbstZTo0IyAjJ/TufJUJVj44e35/3B0QxNhN3zc8h4Z1cs7smYaDz3pPweLzrChm7bJbh+1+YWuxivNlM8AlrG88JLUtuGGaDFVkSSBwWq1MNglhHRavFCdwN1AiJoCYXSaBhnkp8moCZK7VNrDR0xgtPetmkhWd2u+LDXjK2Wo6Zkrl9PjDth6wRq++ftvZwEWxsuvpJkJ4NaTi4Jt72pcG4RTClOpodBOJ4EjqI6wMf+IzOHU7545OmoCLiayZHsloCRUhwQlbdC3aSrilCAQlQwHuTG0UyWrvK+YJP5KBsisGZyPJvFcQLeZVRPEl1fG+3PTqjHQb0MVH7Ka6HkzGq8cReUsup2KOZLb4MFg2MMqffbAy/LnyKESyNnbqxwvQIdb2hgG1UMvxRrb8vLr89u1ObtH0cPRpIzPL/IhO+ByOAVn8dH/PJNsRED/VA12DLmDXgPCPsXl63EImDUiGxFckWSNsoRX8J6keRZMZV6ETxGDeURtvXK9XUgduST9cuejgXAlK3uvFqvo+WkmwoLj+/UBV8ytYy3rmy9ioy5Cgg6gke8qq4a7EFjeaRW7RrAbJoiaTK+hRhuE1UM+OTgtwMd9EPNnccYTcSJORikIRusRHs/Mds37Y0ihskAug5AbiD1/fRyk6ddHqd10WJSjLHegyVEjjS2f3p+CaxOR+Eg2aBXRSb796i3vqILKzWye44UmVmxRdooPvPau+TdybgXvLB/cHuuHVaNjjxod1cbLSxDJVbkdt8uCWMsydenalaIRv2QYf0LTxg/I8seP8xKYoRjKa4jKney5JIXNBwPHX67Qn5OMUwsukaZcEaFGUSITRk7QtLMiuuKz8GPfZOXxIkRor2QgZbo7luyxuG6YlUXPcbjTJiRMHnBwKC9b2/S9JVdCDZK5jACgf2iIpQ260LMfRzeSbYlwxYMHm2IB+oNM2gj3AuXRW+WGZe67hW/1yTg+bTHGUglFy/oJCB9mvbHWHe9vXjaSRp5gcTmlG0IFe6FLeJHcc0HCmi3bKJuXG7pRD0FN+HsV4E8lqpho5uDsMm8EH3LSUlMpq5WHLKeeTeQ7owT63uCzfAjNp7yc71xYgoY6Lxm1QioczzbDr5TryftovBDVL3JBMr9WNBlGUwYUjxS03646hZQOlnT3APmuk1be87LWTrIyXL4yrHTfzos0/RwfyWE+gdHHdSQ/4Q4TJGfrDaIcaob9DMTA8b7yuOWtTKuKRAnICBLm8hNMm83SKh8GPPHbpFP5pD5u0vocgCxGyO6LBb8o+5XLJceC1HT0ZJOUShgmJwurxFA2DVBu24j0wjB2FFSDC7AtoRhNymJs+StHWvIDBbo3mqphooml7Fr6AKxfl83ElT5UGs5xkjDAnpYyNyrNN0RQNmLHJxKKwKaps8BNP/9d/+y1uN10+fWPkRxTZwSc6CtGYej4NHnJMStHE8mzPccTpXn/S4ax66J+9E2QhIjJ6axpSOR5feJilmhzCBcW0dZiJ6XkJdUxdXWrGzAUccXcPji6gcPxoHjcvjzvjtIcjdAE1G2RiNEi2cLQY1oIaP3iCnRwIr7cin888qSipV1kvrsEJyzysbVOZDb9x7jytdEpuuJrElQFL0dIIgpXQirEtZH95cS6zyx62W06Z8QcZBStACXFwKiCWOga+3mHjdRF3jG7M2VbDkPrCM2CBYEjhtW7nu6FbTwskTTYoYAlQn9QaempBCyuajcp4X/lHBr9cp/YImAiRkE39MH+KJot8s1stg8gy9V5ULE0Es/HTqPhoDT4sY5E7h8hvol4y4VE8aPhuMKyv/l2UGUVSRrjgCJkph4gOMpy8bGJTWJQH3n5/QwoywpGu6C1JilKTUJ1sT82s2DGakesQy/m47KLpHD8N2U3ZsGpv0K1LvE5FXSZBDIwZYV1gnK7wLC8iooylXByfPVWeVebSHTLppjjoRFZTBiMtTmr8GiV5i+/77UFSgifZWvSMXjEi5Wfbqtn7BEZZ/LG1l5bjXNgehGoLXzWPZGGyGSjB1EPJOVyl5XiZMtqdRiJFa/Rx1rOOA0i0RtFRj0ymMcE1u8Kvsg7rSnlW8HuYOupyuf5Ft9NFRtqtqM0YQA7QbEYCQZBi2wx2j0YF2VQA3xMDUCJjTJJydjshhDeCC7AGEdoVOkf15NnutvI5wdyJOB6P7EKcvrABXeoZsJsQwZpJjo8fI49IkXCCQlFjB+xBIW0mPA8TMXmCmpOliXba8ACF0fvLQgpfmLgpEZ5GNkk2oOGH8j2n55rNbPy5vDVibCz1YaNctoHKbH5s17d3cM0bFdKYmenfLVAmT3ZG8yKuVWgAGS6drrHWSofzXmc/8iZjSRVeTjcdCabOzmIyXjv4khAOH+7hu7ETJOT1B/voiGEEzZYgLNtIvHvJlQfK2rf4o8jeW0gxRpQhjwixnptvtlReSOkTDjHPlWZ1MRKgeIY0c8wHhpCBc4rOQkDugeCWGZtOGAvQjCMlcIL3auVCA8uC0vqbhQc5y9tdJMoF6egrc8JB4Ct6VpGC2I3QaU0IBJJyAz87sa/565NQIogErSGSzJ6Nq+zrCEzVJThYsS3PUUaJREyapEdRGNBC/MCAbzqC8gLytiyQi20lCDEylMbjD03OTfIADQMRxXwE24gAlFE+R6gTIT6mp7tVVuaaJ+4QeWeif34nqUY+iGMHgn0s8h7N+uIEHt3J+Wq9lD4hUmamZyC3rh3qmnFy175ZpZ5a2xnDeGvWUS44w6Yxv+cgG2tZS+g2rbCb71slKTELdZq1YGdfvt45B0aGqiSZ/G3rmXM/AWLmxkIKQY92FKxId4NjUMD2GI2NjArV8ZXteOEvdxGikOyyYaTG61Nm3bw1e6Py2Np2MnCNPyYVtWwOIxMfmgct3lMEdqKEULWA6iFsSXm5z+kgct6NZyKEz12e053f+q5XpNWGqc047ZMqsc5QuGAv4Feb8LENDGrOjVG1w91Eh+ELDQ4C8/4GiApdYAd1ASN5727JfjV7bMM3zM8wSYaAFjCG2FBjJqy9z5N7Ws2cECq7xdLaDj43hnxbXmbW6MMD5VKZo1kDbp9J6nslCEdNzpn3epbB8G2RWnjqiPb8jMaTKKY2KF0Cqo1/ybkx2PwuLKZdt04R6ni7kf9Qwaio7KlYKNekoiRLjBJGBbFQYgxk5WXX5oGwUgu5qBt4BJtUaKJaGo8G3CAxe7lFI4icklDCYb/IfS1bTZI9SzWlvyRPZmPRsi5WNgR8zvWLcrKr0kE7bRq84zfFD/LZCmkp2w6hjJYxlxIHH95SFos2hoJAnIqfnq/u1qH/XodVzhkQp2B5D672BsdUFPW3mEMWVxkw0NO10Vaptf0Qz+l4mSgkTJfHyPWQviI/TJo95T0WaNojcl98ZkuYsSutLlKEMkXYUmMheNG7YPR6gr2jw3skyNQMMgwl/aSLiSAu2xYXBRgov9raVgxkFSpbCMTVL02BrcN/O6sHL0Kbt89rGbwuKe8Bd3kDKjCzUjniI/divQLauENGgLiXdARTNY45JFX5QfLhmXy0uzBBMnSJVDv28K5iaZ7KOAReBmknZ55l/hQi2DPdunGphB7eVChC+JSb4oomrWtykvg8GsPLooRWbS6b1klMSxtIDl8r0NokPKkmnLQvthphVQqeZXMDrUlO8O+mfshroIAklzkUcpsxtAAl9Jr9f91bX+qIijwsgfft3S1/uAYG2TG89KDZhNJYA5ha2mkfJ/SyGiV/lnAQa5UTSGUn8haInThxmwXmFNslwn4+viaOWw6bo7QdOLPfFQOU8OFcnshn27SIQ5a3QlgoK1BkV+2/AIU52u/vf/jz3/76/fGhWMo5JEqaTXh9dXP3ZQ0UwIm14LgodcQBhCebrbUH2poSsCkrTFZXP6WElI2F4aqdVlg5tK9XRdYvgqfbxeNAbSrbrIK+TJ0Jtze+nPMtROhvLMZmsyXfebqgfyXyoBSyceyrPyU62QLaRBFT6tIalKpsufGTb1JuDGS00mUdFvNYkX8eqBxju9ygbWSluhquALAIEydiXYvaZMJrE5onSyPQWGAHKLHClXzn4/JNbx+cDqmxi5YFOgZtJCXn2/DLN9wZf/tVtG4AUM/U2l3GEB1SaM99GIojnH3jdpmilltsI0i5hAsnDp8ZIwEjJeU7WahFHiTV1NzAzpNiks9AwSZ3MmyOUkRwFi159FV8gvOYEky+722CVu8PHThx0sSHYxSvX/RAq+C0fF355pyWV/RDmPbLniPbmPbZyDdBYaAyk23CpUkHJisXWUFjQNE585GvdM2JlvRnXeXwapIpjY7LnEfBDqmAcXS1GgPjp1TObBBxFKSWygCWUKFBsSNT9fax5SfyzhZCVBR7jtVdKstHx/E8vEBMjkBumtw+giFKewnit5RULNflYodhnILQmNA8cJc/lTISOqLVejeeLxqlenrO2kdM34eKsXvkxkScTAXNE9bQWGS0FJilpzqlFm3PRdUC0tLHwup0UB9Be0W8OCKGL0GhHXOWWycZaMDVHrGzZXXmxP4RxawwvEiWci39V2f+yTT4cxNZAuvgEI28xV2HmOqyZ+yqvQOqPAP+OBswYQ1h04rjSGOs8V6ClifKuWtbNO5zU2a5lQBpPQ5TuljA2h6zWPaeNpcFoa3zCQWJ4tVK1xPg94YaT8Y5p1uJGYF0pYdrcaeChCGiszSGpiTeQl+0rXkpR1I9+do+XXukMCkDUZV09K2pYaQ+XkIQV2uyJcw2FZ+tU1pgyHK6oya6rDNDTmj5tUr9o4OG7AJxBmbyeEYf+QAXu92WsEFwYxo/hgTIGGeecSrVLsjDKNemD23PCVQmjIAWqJxZ9ZHl7swmW5Ylly5LrOX8rSuri07KxxNvFreT+QmmOG4Box0ZzLK1DoECgaPPmxznT7s2qWBy7f1n9uqH+uLULyUuf/qtEMkx4igb92Aws0lfMGTLzRCQ5XIeHABwt3Ja6Tf7QnOnKn4ijm6UkVDcXX1xSMxCjfnx8eH//N9/YxWR6db+bFeX91IqIW+NySt7qTs8yw7Ai+vt044iM6+IxSk77DfLmztoyhnh9nEEBEA8er4FouK9NxLceTL+ULajntVYIkYml4AJuTFpNqhCqEoQ7cXA7rbtsx8zbOTmQ6MiT02jG+XtYJmWmYmaaA63OtWvmTWDSaQETNIFHsGtsjIwFXFz3tOUT6WDqGY+r7a+UlhkLyROLCmmQX0oe0SuiTlw0BgSKw7lsLisj7Sk6PXsw9YiHs83IQ9ZmanhhsQ8V3hy4UyVFeoB9OCmBtZyXB6FT8kWYNIpVHqhdmav5i7RHX9TESs3SR8DrQQU7tRHoZ5bKjxED+1KaLqUJSNCPs8Hz7ctdcClIp+Qbhp2iWceXavVxnNvG2d1MAOwHxxbFEB8mo4HIqMxcAxz5XKTahI1AETkCgGEw+N2ssFwll5wOa1Dqb5XOFEgyKtBnxbcGVFtq5Wa85FNwTjBKKvNzcZUcAORDUx/EjHdvFtFJU55E1tOAdCacnNvIW6w6BGeyWCDeMZGf8TDvnSPVZo6SMydIWCuUJbEKeNT9wyejk37tcsF5S4ABq+LVp7Gvcpgs5ORTP4A3OcRSqSR0/G9sFoq17tDPRQtzSniDxmPM5IlYzFulst6XdUS+Bkmf7YAQ+55ed7SbxRAWId5gk08Yk4Is/F7sqwChOXwWgCufqD6b8TEpfRc1LOZQS5CHlvRj6mRLQkfOF+MC1yMx6dgig/KO8cL5CLAI668piykJxezgh/5Rjgui01o9VbBrGln7HEEmg1hX+T7+YI52ggtJGVjJr3Jf3iloSimxlYicblYM65gjKNpQoS+MXDDQsSkHXmtWyJdvjkiFQ6gIKcIjNSxXfY7fuWric9qyKHywCJsNm46nlNIvfO9CZLCAWl/u1hp2SoqlSMR/VSNyp3CePW2QMYDYvg4jbxHU3DxerVsm5my3MdadCulkCWpLRfQRtMGRlaWt+vnkwOB475jNEq78mIF9PNmeJlE3YgcBuUtDplTNDm9vVsBIV/37ssrhgkz6CqEFKNR0H4PSvwH5U40emC19fLArl0KLUZ8FWma4cXDXv5w/2TJ0unncnFxd7M8fVUuu7y/vcPoUgeorolQoF3o6m82gaVrF61f3X+1FwLX/evdrbyktSGyKU4gwRnnRZJbsnj/eM+lkGTZfH90VvDz5nB/cfWr3/wE1UrptGevGP95a5+H3ZO+bLqw2W9+fH9EZjOmV5qqiQdTBvQiNcqRJ8hlcqGoVrx2dxhnDpod06PZW5pHl1I/vD9rXSJmuw5BjBFo+se95XxR6fzMCMCB89ucw0hnWA5/MWkAO/FpDXfA2QMgo+m1RlSWhsrwAwi8xuFEsMxtORMXkbDLOnY9jAxBIhOeHDRgMgxRgWEQUgOrJDCBuwcfVcvr9cOlL+3BCQ+EI2UPoaWKxdgnCIQXZdJ9ZWAGUO0CkwKCyk2GmFFyXfazipnZ0+wpG0VhyLQRjeaZ0kBDTTeNPz+BipJIws7Flg0nEPW4el3FYbIHhnyHy73dRCrPQIfcFTN2AffXU0RQ6Z7lP4slm+57S5N8CwQZ455UykS4X3GSMyK3wHdX2SIPJuVDZsFUYIa6Ii9ZwpKOuheTLVnhdBAJz2hUQbPL2lJHVAE/K2Qq7rWkou4MtLXHK+GXLAdV9QhZy1ITausbikhHgkC3qBomiohk0j3f+BFRrz2Pz3Psf7WwOxiC1wWKwJDAqm9LlFsfN2BYWMMnEBRbI0gg0Op502ZByhMElOebg8xcBmsVTsyA2650c2HfHn1BWMiHQ8uScWDWcOS7TNqCTa6TMFfwCIOcRG0RrwkdzFSCIU9b3F3/tERO+9YRDpZVcIAbHmcknqdbiownp0VjbylImQdJkwqqXmvOSOoq/h7315FQoxTWG3FeWvv6WdCSOrkS11gdgXjoqekOHM2aDhqUVJTSlNXx1AAdpvjbvsXwitKBT6JN+iwvw8lSVsDJEegUrMI+T1z3VxwBSOYvBZRRS695uQVwOI5ERMpIGrxJpZMjPJ1aajWZyYFj9gZFQQLoIFDghBIYj0fxsTEEQbDCrdk/v5ohG+YVcgTvFyUwiQF3x4WX6873xhcfet9AShWFqFFnYBG5GZCeMr61V4wiUMrZ/s80kd5yRdrKvLHfZsEp0atG2ZkxPlPvF9RmCA3XeQBzcAEdN+OGAnS4yGqk3kVfO9i6rRFINMfK9z5yHuQFfanEhzUtF9zRy4+T7w8P3Ctia9FW53C/SiBq+X/C/JePBQuR2S9jS+mTgtyezkuzIxClaAWHNK3OolrMmad6fmApLrN40Zq7wy/+OPk3FgdTLIS5urn+3a/uc9+z+Z9Pmx+xYViAmobPDLg3R6YEYluc5oAIhaiKDWYXl2onrC6uegOMLZGYjogiCAUMj9yGjVihTls1JY5lXcppGlQ5UJSif1YYZdJmqRXhmWXsk1/hPtscot3+HJXFGcoH4SW5neJ4+PGFRMUYxA5YBatctjls4bA4k6sj4kh1zeUV6LusjBjokSagScQCXBmy+N8/gIwLE4em325eUT0qaYC800Dma5ew6NIYaGwq3m6C2araRwOIkfy4HN8rzpaeBgq5xz5KSf1tGqSOOrBSfZJg8lPqS7GYWb2k4kyh8k1dzPVqEnc2JSeSjoxy5jVH5v5UpCHrhulghuKRPCwfs6HloNu/BPTA/XFSejM6dlJ62eYaHjb7tnjyLWVbk5jLG04UyUugxU8amcBoONUiVbhfIse3Mh7XVWUx0QQAHoJ7BpWVQR1TIMM3XfCTtRD5I3kI2hJWh7jq0L/a8Rn1NgBnBPSUBFUvphTDWEw1DICiuuAUQ3U/R4zBzAhPGPFZGoEiCUw/ZDh1pNsnR2RT+7LlbK8OrxeTtFlNvI5yNa5IVEnhcOw9AikQpy3grK30B87lc3yc3ZyzcXTpuKMkZEn6B5t6CqmrpFk6Cp1kQLPqZSNPFss7lDEdzEkNgZ3VwLbUaTuQ675qQ9MTvRYIRY+W520syN+wdyFwIeQTbmayS5iAhpykJIQMqj8Hl0QGxXXmBrHNYpSdEyBw5OUTEnecMWD0rloLdC1iIr6gslOX5wLX9JjEg9XNszEizONoV0EBO6IXczU2Pgf9goaayQ2PqucSnUkV5qywNGwAUoALgSSi5VgdJR2xvKfxFIB6IJnHPlODFGiGAyZACjErX1khKncj3STHrD/XqUEInril7Y7F5W+ngNiZyex2lgAXQXcRaTGdSqcEF5zJiyJGpL9qeBnCsgte56l0ilGxaNCKq4joILylvB8fJfLwATi7MI3n4AOzd4Rtm6zALkv70LE+Si3DpmRtp9Ve8olkTSwgoLi7YTmYWaBDGUp71RHFP6mDU5WYx5Ii61c1C7m897efVovfKLaIU3VcrZRMqoHwtJCBSxRNUIVaV6aDKvXNmoaC2LPyhfBiXxdjpGYk7DYzUaeIyg+cFKJiWzxEI+6wsECrQgqd4s47BhfRQwjvID058KbH0XNoJYEyJphovFhYs5RkpY/JXSykQL4owCasno6yQp9SZcEfbDUiCpuQWYKfH9OKNaQgDGVjRkqAr/dKTvrPFLIHrYKphABQsjTzbh5M9h8ylidtqgBI9YY0KAg4nNJyLaLG1GjcM96G2f8CasTs+dc3+/2WoBtvsDubQxQ2qQLZXqlEUHLl/5MpPSapyauo3IQKr9lCHGfeMch0ammwa5MNSOTB2Uz+V76M4ynKPKCPt9ARqqdVxJDliQn/mOckEIVijyKqQ8idLCT9Ma5yYuDJnsc/9AdqZKoAdxQmNiaVYtPshnS1edlxEFBU7gWGEhZOMU7QHFiWVpzz2va0kjuIPV6UrSvz62i28JQCMD9kFdQcLmSWJ76egphpBYiFJuXTUFsxSpO+OBnFJPmtM0JmrCW62l1RiCZHx/kr+JURGjBl3CjLs9z1ztaFTqGrdZVHbJgEeuvAaFOVR60FiGEM3jzFrD151NK8WrSjfYjwCBqoW5gUdJL/Jmh7iOXNSirl4cfPyOt/6vPoiRTeVRpWMd96BetFJGDrYJbSu2FCkpOWQdRewnCyqXI6rk4kOsrR+5nDM/EQPTA6isy80o+xvhxtuCkhLFjM+ahVhRs4CQQCjCNjOGU+3zUmyYAhOBY3u5E4sVb5usrpxSu6WwxP82I+icAvaxvLwYi/05o88YAMmU2OT2RQz9a9R5h+xRF4l5K2uuuG8Lgy98vd+nV8DQnYSKT2B3omBFw8X3iGxGCOOQayO+lgI5g4IImq1Q0mP4t4GDzlHCzpPUTK2hObDsUzr05ckcxQCdA8B904Mm4fyS1u+MXBhx9FybWZ9YdXPl3abUJTJTkZALbOluZaZZZCnOQrrjRqABVYIQJEjJEdwFDVBNM5QRjqSvpSkRUgl18MH2osRZqRLbqAIyjsLmJGBPBfd4dMLOPtKu4LeTSRC5GHEqJM/UrHC4vH25Ee6+BZ4VJ+Mr02ybr/VFnk9xj7jcpDIIL+OXQfn2u8Jf1f1qZGBUsIBq9Odj+35yVyrKwSWa8MGoPTT+lSB5lCn/rwyj+YejDa+g4tz4QVZZlynXaOU8ehNtAkCofN5u6nlSfAShSnqD8/bmAPAiEZB299tyi/lLbbN+9TBBKpcmJDAayDv9UWPFTCIU3yjefVVdaMTCDJIjHEWBASJ+UsuKGGjgLkU06I6S2Wqe7nPBbOWryrIlregMf6/50K94wVhKGE1MXsAWzlEMnFZwqQQ5etZA4jl9cpDjUYSuYb+co8L5Y26s6eoyP6YX0BExeggAxS8lqsWg8ZzBUVNCCX3k30EznEAXZsPO2lUq6jJtkHgE4CSKhu9PE4zJ9FZi9L75hpRnZqHg77TLsEzqF2Dp1h+lY97tRW2Nt6ws0oifYtF8ESypKPWvXApWERYnLeS+fV5pf4oHWNqnlecJx1YCWZjZAgzwf2e+An5US9llaTw/aeeGMDTQ1NKLMBeSy69YbApQxSfvY1jeI4d2Vvy5kocOY8rdYreZDtZmePTKOQQH+3tNOUiENZG0DVvXiVuBZvZc7wOLlVpqNsnSHjfPmWBflVoyROwQxiZTY8YrOgaaQhwaTeHj2RaEJb2BZ4ibVyEiWn8Pjsiu3xqF5YMtYCro9raxoIsBQTHjJW7aCu/lGGyu7t5qKIaZD8NLNgnMxy91wnIsJ7c0pv/lyQD5059iDwmMvDq6XuW17CYmVJgQ5IEj25qZYdDVvJnz5a0sIwpO02XLqW+kcROxcQWdwUoNiViFHOVWriowWiSVEZGauTrUPZjM067Lj7qlrmqL5T3d+hgu3n0Dgc5AWXSRtpJE/J7yvLaDdGTLSdoFVgpiUTxPH6eDELskuwitGKI/xWpA43G5eR1bZr7rgX/COCB9JBkX2QTARdWqpQgUZmP4OBjpCQmy6YAw8EgFRWXACVvjJM404g8bEIIlIzD6KlWaIoMUb7ClVeazTPKgxkexp5sDtWbhMJmH0tURYgG7aR5opAI+HBi8A97YWJ3mImZi0MSWxr1AlqXO+r49FpoYP35J6P4e+3pmpkzV+q4wbQS6CVaBomVm9wP4qaDByCHryjUIwfoC1BOlOw5C9sMzs1rnxOVd6SCW+bLUE1cRCQMEWMIo4TuQoGk8Yqt3JOMmat4nnfoUvA9t5+r1fXtk42a55JB3kVb3qqQapL8CMQrBH7RH8Wd888YqM1FPqEipaRTwDeSMyOD1LNrn0KP7YWcXx8aCZTlU3BCJPtcTQ/QHtUU9V0irvYzDvhJJgVGdZN0et4BcTFY3HCjMmQufMj82Njd7Dq7STGfNGsXSAqBDHgWUQMptUxBYe8wubHjkDxbm2yNTV3gg2ZbhHrHE13facI6XBNpigsLgaBRKyLXAFiOgYNcaxPyQ99X67ruZC3BREMA7TLtrEPY+3cY7wyev4JgJA5iGYn9HXYRas8dYDDVXSlZlAKU2GN0AV/SIvsVuCBMX3HXpGL8ervqevSQtTXNtjE9bZTkySnHHob5kiOcwUqJZkYn85msj06EwKJEzEOaZ0A5DFzXFmFJ0IQCYIEEtmwwlrWjvaSfbQl2scH5mrQE5bfabkiwxqcy454P43FfNyZ8JQ2t/kHnngGruQTAbijs1mlumPjxuQU40gToKCLjSZ/E0ny06VBDSE+2k0EAwhEnmcZhxxh+WqUQMhUlPkJCgkJWQXFtszUZX9muRmyWH5M+LN26s1P1iZ0sARpMDneqIiZ1WG3sP1lb68YlXFLskue8rRjpREwkxZCVtRErjdo5yxSMxf+eRTHh67yunLVoQhRzkrZPAnutvUF1hsrqbtcahJDbqio1aozfv2vsJXOtwod1a9vv3z1+X6/gtyPPx4OG04dGkIAG3qABy6IQtyYo8mO0i+ZPK4/eUhPebra8wI6DS8tOKqDoPP1auyOQeIhOE2wS4uW//GJW/qk86qARq7DwJXsLtxHQqMrj1+JPtnLvx52mGlYFFp4Xn2nHlRkaOoGQHj4RDwf8bYAmxhKrrqWHfZxUE9ipfvahKZWchP3M6VLc4FCS77qfVehIYrMeu5JwUfdMsSKdBJG2Qouf55QuMwZJM3GMi4V8Sg/kAIbUVggs+S7NDQ9p2xJv938zrR1JFEgKGd0zqOZALXsBGPjhpxP9zJ+44dJ9LR1EJBuWQwryPNQMFR8uqpLNQySoblOPI1K0yZ7vH22ti/qFYWZF7IxJRUCE5myfO2U4yBsOGjAgxA1xVQEbqO52swLr4mZVVrOuPDTWJ7GRFJdZpPWO2uu9MbbMQoj6QMtNLA8hTZoxZZSMc6XoCQVl4u2QBdbJVHgZaY5YVFL6k0G28JPuCCZUDyEeuxOe5xaboKz5JmQIFmZP4qqFfX2Fo+RQI+KpxevIbQgtbCWwHhGknP0byG+zX1cYIcgAgdwcQXiuDJ8QRqefxlFzzGLzA3MmzgKmonoWKTiEhQ1UQMdV4UeQ9UGhvmmNojXvjQwEYJ7uFsQLatZFr29JfzMFwuILE4wR8mVnILxWx1Oe11tymB5kB6JXzodvJGgJxWg7OhxGy1r4IIc6jfLYvRXhpvQ6+O9k0KxRLV/vXTaZ14JgE/O8yw07hI9Nx9dLbII2v3nYBaxAh2yzIcQGIYEIEFEDTfnFvDBP9vftES3eWE42nHRdKAb6JjDDBiMpMwn72r2cnoaRgAxi5PG9WAOX+6Ad7oDPcOakgA1UIqYET1ykSVXEItUotoMrUbm69s24zWLfd2NiUExmffRvW5j496FB341PH2uaWAp4oSDGnM/hUiwGV7peFFTGm4G6wjHj0sF21vJEYx6NmiJXcV8QoFDPpl6o+opfpuFx6zm02bLDKMg2XNLMj4bEyHW5PHbXjfPw0SIigRuSwRYXFn7gI2EQGYUQkEDdjvddY0I+GZxc3u+/AEOVZ5f3p4+txkrMzoWEmFJO2vatQSoiRSEmywr0SKiJe3osOl79amKBvBAzty0kl2Sf5rSNBz+7qdv6+uFZZIP2yedd3VIQj3VO+7OrFNphFKeFxebA3s8pmSWX9Q0AQouBeU1V7kmmzeudvuVmEwgn2+rKw44uMA8WbJOiFMyI/kuMAGG5vLaBmoBRBOhv2C6/ZyZ2XCfSeH7K1fFolycXIFpI1ZvLwx7frYeDH/AmXhlyjjGjJBlC8KV9vh6VrVGxyBGFwDhH3Ei5N7B5xKpk2UvABnpBf81SIU1cCKfndRxIvtEziglS3Q9xgduEY4AVyDJT/OJjTUMMQhl+YhEA0s4Iwuu2ZH7cOgfD5egm0fRERejVLgR1gM6G6e3kUbOR7aGRk+Mm5NHFcV5chX0L+8wfSwGSls5Z5ayQTXEQ9Mk6gphkuFBAchbcOl2FtEdhMVc2ljn3/++KdqwNb9uhLyvS0liOVQuw5JQePBNbuZ6yV23HroqVK2ckkr8Ss4GteRFNrT22bDqz4ZrLK2qdFDu2DO+/LGhGKoNw80KQ1OnNIHEc2PUkcA6hOwj1IXyY13e6ohhOJGUK665W/gvngj4Um9swFwGi+AMdAz3RbUtf6kqZcIIqRZUjybexQo8cUV4w0H2IQ7q0MTJ0CyM4qCxkYg87BvHAeNQ6fpydqZDfSxrh0sLesix/vI2ugm2xLwLu+9y9tNgEAggtD/6yl6lHoE5YNObJtGBjPVS+5bvjSvIgpjiRoOzcYLxsNtQJolJGPBVz6j0jByXKlzGNLip8BUA+yGv7Znj6RX5nkQ7nRSIFotoTE5CGSovcnHuDdc4b4FbrWqSZ2nSJCOQlb45vGAfOgh/uDvGhpayB6Tv6KcbMx85fcGKanxlkAaZOuI2w1WbIqvPLfGOnAyASyFDJkzMq06+mbCuuNZUXsumEQ770s8cGveSQXNMYPs310jUGAT00h5QNiKm+MUzaULBgTl+ZL2obeb55PxYscJrFABnQfsU3CpQOXLyWpM+hC4+9iiBuYdkJ+WgxZRi/AllcCHL2srqTHtGMta0QVheETzg3l6xCsVAJFle3hMUCuCSh9MfHTnA2cWIgAUE/PzGMyfnpi/0xS0TXfGcKr4m81wTbMrHYzuhoRkLQeoWzb30GtIGlhNTwHmufquUIyKSbGyLNcqoMKN+5obV3f0///a32+fDt939X7//XepJvQJH7u5viYGTWrhG5lXmKMvdW9oXk1pbrqj3ALQhKChhPXlFBdxZ7irESZjsRGYaxBNeAkn9Lm9q8SDClJpo7k6c/qj8EBeZD3mhAD6vBotKiusU0WGLoS4m+eSH86WMhgmQ+RpHr6xz9LSOfOELFz/W0Bl51OfQLAjkXhDj7GfH2HqbyfT+bEkenL+oqVExVGNgdE9VVH3WgcIjLOnrNzKaawwYW2qtBq4LmdyLiCTe7H5k9FOleLHyqxkRWC+QrmiA5R74dfT08MxPNzy4ko+KTJNjR0m3iJYgISmquNIrq3kkZil9wxinMMMTDWsHb//BgCwUKp1gCt4+IufD1g3g11EjcvEzvvCBrCb5xXTulYGw19Mf/rpFHDy9X+t9O1VyUtXRvKENnzfqESRKiQsfaYDsy8umgh7j5H3Q1HQ819xKCete79YSTPTtx+OjwSYMdvr1WuLQJAFS3WxIrwzNjeABshVWCuAZkWVIfIumsF5Kta3iONHtV06XWeST8MPUvRwdo0eygusuMIAohjsWbuSCBJs+5J4bLYLpA4OYc3YYT4m1zGhBc6V4uYKsw5vzZECJJKM7IuiAKSGmVBxDvkOlJL9OaGYbgCKP/quWlmKboDOGMEkOC6Aw++jV99ja0jLfELhoxVjlktQ45LGfjubK+Ci7iaQiqzeSWI7s7N85wmFehVKTNY+OVWfH9noQvWy1oxfKRVKnPCb39kTYXqa7to0oUkpW+W43CxfG48oekJFICgggUgNQ0GedrIey1H67IfSe6vH45WWrZf2ULnYXXhEpsd4U9NUMaEx/MKIEqyH2WnKem+Mh8sSGN+E0QSR7zESnpxmpETdyyFgFOBOMxaEDAgUM9JUCMzmUoZWbJNHLKAZCTTndVV5rV2R5Hko1i6y0XaWN1qUNVJUHE9uDKax81Vmggo+W+WbejU30DzPRv8yQNcAtZ3M5F8UNn1c1Jng9WLR3lxqst3MwcaVNBRAnx6o96YE4IyWMC27e2NDiK2+gIGBp+lZx3R1ko5J9z3mv1ppN4MAWo77LY9zYWXzsdPCGAhkAeaZOERB7o57BMh0y9SaOlIiGL+TBe6Obu95aJ0Fs/cogK5Lq2afpog1S8cc//7sNElWt2WaqlcEL4uuYOHoVbCBdiqoOHZrsKv+f/0RFEEuqNc8MuRG8PrFYZL1O2faJ26I0C+5xWTDBt2gDaMh8tbJ6pCW49mVXQfAxICgtUEQHsAHgSr917kqBjhcggBEqeDBrnCTzUI4W3rUHlSHm/6F3so2UXiH/4tW2GGARU1+HaJZ0yuXMxzXQ4s5SBEexZGlklHxXLAXmyaWUNeeYTzbb2vJEKBqOZwxE9diYJpKcNM5npZk4Brkms+LHQi3jwiwQNjKPcYPuwQCsTbwlilsIVuhIp4qP0Wd8DSMhdGjoajEfKZjlhHkn+TpXZZOOiO8CMy8/Y0Lpnq9y7vxKeoyNhBQhBn5J+sV//o9fXc13sJLkFs7Wt6DjkGNox/DHH48bcmbahsLbv121wYD/KIvb0ZpKfbu7k010AWIH3W3YIvTzNCtk6mq0QKT0ZSTByVyDJp/E6CFp338vhSk66TlwnpBeA0pyxCk4eb9dr/ic4B9vGCGkSMb8Ge+AiAyVzSd2+78sTtUu7iEFKPEt4JrkaWjD0ymXaUIsHXKgaLGV9vx+DmIysUhVzb2WDz+MLKq3WxJh+bVcVQCnJw95qbGJUSPswbxAL/+iapi2bi/0qycQOOnNgk5BAVhBQWPL0szDTj403sLX7F4ui/uq+4UJfE8RpcfCY/SaRJkIxMV+Ld2RC0PmYqt0V0ObWisaylTgEQyUjrAuMZdNA6eY0gnDbYUknwMVZ8Y+JZhYR+fHBzdsfgSA8EAmBYkjCLYtV5xD4Yu0MnGE0QOaFZOt+Rp1ThMCESCbvY5vI4fsXJbJM9jOWJDbWFUqfEeLI7ghFNkzhrAdv2czhvCBTv2CZl4gdTsQibF536BYiwcO8l8NlX2crO6nLA0mkl9P88xCHy5IAejoW+M8z/BQL/3UH23tAnWFnIYnvtjvps5u9IjmtrghF5Q4mWooQjxrrWmR9ggO+oMOg6kdqMH5P1YiXUkkQYZmFdsKmrhghQXJoVYCPlngK74hEffmhWTyi6QU3i4YABCVHMy/Gj+LNxnVVpO0jdoNpIjgMng4iQt2lrXiXf3pxf6jKk+yf7XYakGxbQ55NkRJAG6QKTtDQMb9n0QDh4NebjtT4vVjpzad36wUOWrNwixt36P+z8hF6vCPhy7tivKU3odeTfPsZGISpA55EYkTbBWPTLLkfBreNExVIpCbbLr5kdwCkiLoILMsC61mM4voiAMiB3m6Yq0Xa1V8ikwz0NGpZzYExW8GgsuIKi+HehRxCHS2E3DbXgWB5XDMnsI4vzMxmuoqLihzsskuBBh0zR/JZ8WMAm5muLgSPNbnSTGM1KMtsYTITdlGYOaDD+ohBAYFCr5MhbSP4Bb74xjyCXt5jkDZhUS6hGE7fhuID8k8m4t6zPyIVsUTspRwTztAE2ZAXDruL0lUmpAyIH6a5Fw2aRTT9NZAJXUfZ85tpgNu/BknSXNm+RgklABNH53/c31tE8CLL5cKSGh3rjeANww/TMtT5GSk9GXzMcrMkW8az5nCjhKc/QWJTxltr1frsBubqXqxlEL4Lt3IriiIEZNCN7nmdksh8Uy8mo+ZkxqDc4v8h2z7m60nUqq3hfKyvETcRhrHrO84PORD4g+BkA31UissksD3Jh81iEiVqexvfyFlWZG4ZwzuClIxKyw75qPTXzapuKEwsPfR6QZRe48PhcZJOerjvwcxUC6G7RqyS/QTYZdzTWJA88n76YbV+sZg8kbChvbHjz1u9UpMqHxSxHoMCFgutBsJwZhebclFqeoWgkolR8YGwd78Em8qip7z4pkuQCNPYahF45N8nGnWWqNaE1mkY+0q8F6neP8QTPAp12TpnglUaOjAoLANLMIQaURGNBcytacDolbzmyVUMssdiJG6NE1SVtndH2R1b9Z/mnMIACCmWkyC/1ENM2dyCR4ie34WuOOfvCJkLCyfxVYAzg9RSI3k7FQXP8Pu3obEretj+HsiO5E+G16ELTz3ZLJBAr1M0x0EUuokwdxNNEBqKiZHwjXxpJ5BnrDA6sVJDsDlNLfd8DM8LmT8iCsSd6VOmzPbaOvJ6fAc06HCbeYzWVdlYoQiXLiQSWCqLUxLsJMOr3avWaO8z9tAGDVKoLbsNshwBMXuudTKPNOOMo1ELddmUPIvMkaNv7PPPI+mtImTcEOWQ9+nBJ3UR7mZNtP1vs447SyB6/3zDiVF82bBCF/d+T49wTGi4hV8183TI0N+Jlt0fnH76984PUKP/8PlRtkZqPOD8Ah3pHgRLTGQ4617LgOJL1lZybQa7QvRSAzNAXeGv98m+3RFjp10QYRjjlO+0GOBMmQnaULgIg6qStgZG3l/zpPx23V6sdALi53eAju9UnmRYKBTuRQcub5UEdyqfgJu+MzXCPq1iJd2vl2vZd7Iy8PT9++PT9vNRiQB6sKAgqT6DlNlFPN78RwcaGIoP5/4VHI49eFWQXEfZp7DmVbxZpB1nyvWE8rw+hcBSLDlhcYXJJmRXtfG1Tp83W/UmXCNZBBA8ksfRfNNP2PqqUgbaMMf9Ym4D3ngC1vK0VEbTYRKtebjZszy0vB3zISRGUltKhDQPUcZM6yWdrDDPErZFkyZPiVvnyq9wOTw+mgv9bNLadcOimur0lKroAEJvt45E+KGK25PPLPabbfsP/oSgIBAIOp53n9SWqq5GWx+qBS7MVuzbEgiIJBhMy5pzUogeiSi6CwZRR0fjAKfbQ+7v/14oLP/+Ouvv/52T5k6kEiT6P5ldILkBMfQPKNSNgl9UZ6dBZTkGSiRkDSYrqNH8JULTuehg8vTT9cgTgScQ3N4SoiIrDEc2v6CNeAQCLWiygxmsDCNEJTn8SfsS839kl4TZyux2WA4bmSUCg9kcgzWoM3IIKTGQLTwCEIYZqnQRmNMfF72dDZnKUtfw6uPjc8DUZ24u12dI/4REUNXCAIK4xO5DBOQHaFcxqs2OwSBa4HhUErco9LnrVRpbLkW2yvGSydu3nSri4jahBEDJZ5sjPzQNP6jBWjR2DhzT9owlXRQmCEaKoCCLiCiSBzwC3sjdQbJTM3G12oGcJdMAFMTQeeSx17ELawP+GAMxABR3ME5xmQ5bMELOvGj3XXmGJh5Uemf1oQXRvCEsNvrJmFoINDfRDmWpPCNT0smjR1smburoQNqJLDJEQMsKKnw4BWyecbKrJEPFULqYfrwnyQkOrVdxT13elepcgcMRJL+Rw64y8hiAEn0nCR+nH687n0fi6slJ8EYwA3hlDAzd566DyxIpFwu9MZIWdSaLxlalzFugyPGpgUDEzNdPsspH5RDyvaenD7tqtDwGlgaUs9NMXB9SuEmp5VvSwEvT//y57+SCVRWvdStZZn6er2Gt9vHJ5MRFdg54Dff7r/e33nr3vIGZ2bFKA4EnRLd8tD8G4WdOzaMlQ1D04CTMBkSuRVkTMEolWTSdIqZCD1CVku7ABq2cxqCPV4MS8pgIwt3dVG5j/vBjdGHQqpdrVIN5vzMepm8eRUuUtT2eiI1e25Q7LEmtg1n2qKOe37/5V6/GRUnvxe39+446JPhzh42nFvpoNEe42n38hTRGMmhNEYRLe/XnEAO2PUxXJHvISYUfGTadT7XsUXrS7Dgf/ThSYFzRoaow6lWexANPcTSgZ8bss8Ooj5Jkfb4fJaSdYSA0FxI2oljAWXC42J358smxyOp6TP94szI1bdkz1cu4phwvwFGwukCcpMbOi41DGfW5fB9mDK2O5lf4RdglIJzjzeVv7GcpBXY1NUefmvrfq3PdlD1S13kYoyyVedXPGmqJwpQJEVis4JiTJRjlDzcOSSl+XLl7CSo4J7au16iYBSjGiC6KPnV2Qd3OmrK4y/s0kajPCp0ONUppHZRWgnISpDMuPWAvtyul4XmVj9hcLdMniXf8ugPyriaVZQwd05cCih2J7jtXoJ7vzA5TeB4TKcX2Dt5LfmDbNgkIjbadC8+RGDqDyvYYWYc/YMP8eovQFkhXn2yrTYYCd04ebJ6uCaWZCOInq2S29GSUiF2ubxCjdnpARwEqXIs0p14VlLPGl0KxnRfYaxpydiIAJwMx/YHZ2R+HEzeOp1EH4PmoKCH9wYSWaRYBZc8nHyYQqauiMa5XSuQAK/Br9iB4G6e7KDKR2t/ZkOJI5k3l6CtRzWGZpRXbt/Qi1a08mRdYzBguAx1HDluF47doZ4HuxNehe5Jbr8nzJM06AN+D2MgsSYzTqLKgJXvzGbw/gye16OlUgHGxrHGNULfcCICHGX6UMIAjSY58dIAlfRqpS0MMksY2iUuemtbHnktE2u/Attl55V6MyWXiJft9BTcofV8BakvnJeBqIaJfBkSVLD5TR6PpAoULZngs+zER9skGD3xgNV1sEqLIlo2CqKx3zXLGraCGqqnnjjVjjpSB3oWYqhvTd3hAJdXa11Y26eNcVu2UMWyxhizRNkgFd3afG1JoHMPAtEeWH+L37hoRgFEFrf8fSv+AJStFbNSsuRAAV1Z2W9f7omOSUwDMchS9L5UFZ8CKZ7Sppenh81yfQtklK6+2ve0Gk82ngMBKKgJQ+V0p/dXiYEKywYrsT16xBtVy3MWuU25rkSquDlA8HHYOiCP29D6WEhUdgWJyh7IE+gN8mNm2FsoLWDAOyEKWgsCVe+85yhVKIbaVm94lAABtfFThbNyVN0HfFywrduqPJetorEMx69ubr4ub6xG272+/vz0s1LHwSapwY/cUZlAlDQR08IguoP7R1lzSSpAEo6Nl4I5Ay9iE6ZgCFzQmpJMegZ8iCEa/yvzVqSZWDOJil3ByWy5L/NhYMVGslvhO74LcWiWFBYrLjIxGKOAs6MviVdFEeo9xTnE4QfgBeWFZeacgrVul+W5bEkXNUkPQ4PSU5RYtqBwF0qk4GwpWSUbsJreSZW05Ani6D8Se1NJ/hfbTJBNhyh9tyO2vB7Vox0FJcfmB0x/+/Hz3wEGD8wwy4W3bZ4Sc/zy4rDIcgl2ytBxS2dw3Vm1o/CozNOqvnJ+6J1/+WY/iH/86RsGUaLnQ6e4WN0L2THH9exNLUrW+jMNWJ1BrheQsy+44X0EUWghBWEJkE32LcfhS5ic7UTIR+iiuwa0qfoqutdMTbG9Bf3oYgEVMDEaQFjVw7DqPPJS+o4mFcx48frViEWQxayJfhXK82aCMEyQR+Ikvr9t39qJN4nE2XYMS0Tkd8O6o39XNF3pJ8dmNnEdeE3hYzd3iQ3JIhlA6SPJC0rl62S0k3gt8hQXy5yKQ1vhKRdEyjjf6AFo0YNXDPvAsEIFwHG97elJMC3yhxsTBoUk/Z1iV5wl8FSCZ5Bvnk4GNuUWSV5+KE+QkKAImyMILU2UWNF8HzURGQnAWlhc+riHyDaEEcVSnoILSWEKLyrLzw2yJc3a67g+VBsIarPvsW3NW6JjMqZhpeeTk0LPSuU1VyrLjvjgb70uVD59husdG1c2qfbWkv744GZvSwdyHIhUaRn1t4WpK2RTeBMXXniT7jbwjdK1UNKrzDKDpmryfBuYtZ4+F4DAj1FM0jw1ta+cjn0eYrzmL9moCZN7KJuKC3DDVcPDeoSC+I/WBlOKOrvzUAJJFDbMWG9wsnkrYyvLJ3FRD36rCMsHwyhaiu8xLUUq53AQ8rcH6vupFf3FJXb4CCcYAJMiDNvN42p9Z8Wl1AiMXN9zUE6s/dQHqOdiv3HUwQg36HGWzmzLYbnDxf70rsJA83xyLiOXlw3hO9hqyD74pGhMRduAFgldaIjwLv9bORrMsTPqn9aXsRDMi8GGFSyA1JyFUNocDpbG6/mrKFWZQQbf9fluFWCd01Awx99odWBeQC18KwWrL99sGoYatnEtutv92IJz2U1OvKCEaFleDbo9kB7/h9/9w/eHRy28Ts6xLgf5sSdxr6pUBtjPUCzGVhiobY2dpLYFXggO6zNe0Jnnl4xzVax3o/i8R9+kIK8vjmTj1avEJJkW2blR7o3rQ+ZFZymVwLWkqO2ygZjLqIl/XJRAaM4WOo9RJHfoevyWRo1LZK8DA9E8ycrqR6pcTK5orjGbSgLOBrA5SuhGmEGqTkZdjS0Br7CSlXIlklatAgb0QQ5BDQJOipVoBVjxoY1/iMHtii//qRXEXFraJwASZtKBkrLpNLnPjrcNQ76+77zQJ0TBi0ArwJHR8zmTDjeKnvx/sjT+wWA4vZ6mBKgBR9uOAo5N8A6uKW2I2zaNHC2efsGPT3kBMhhRmIQXrQvvOPfcDfbtEO0mvU7hiKdXYiqKwoK+MqVcG7aNuIL6unG8yM84rCd5nHIgWDLasD20QdO76Niqca/lv+AlmvPkjaTYASVZW4hAHKQeBAf6glIEloH55Xvq8S8QQX3hYjyzbvmmD8GKl0fYlsnAE8OeORTxqbGW/PEBxjdOyy8lQ8fnRWvkMo88A7NAT8TBjLxjQtpSLDoAFo3v4fsjd9iwzd18Xh3WTeYmw5MfSI0SUiahzJVBZvT7AaczkWOP03cdNgaJO1ODySIQL0CLO9TKbFALI9IrX0yNPicih8Xzk75RBWzw1Ero8sHu5QFiT4mPXFf+2BgaAVPrM82tP6yLNL25o0O/NrCQjhwaebmtTugVCZlBGdJ03D/4DVf6HHgWQAhGOPuqJIZei6Fc4lmRhGu918EazLY5ug/Eu4AkZ8GuHcb0PKfMz+YqFE/LkixES2yoBKRWyFXHmpYKWaZ258cTOdXLjhNoS+TXNjkEiwIRuYaxhaQQVQ0Xx1ycQbi8cQqBz2kAd4PPRLnDjmNIylYlBchLVtg4nTljeJQuFE5dk1dbpI+VerE3T1I9Cz9DKvLB5NwpA252qY/Ga9t9Ww5pTUd1/nZmECpIPOpCldKcUKODq1TV06Cb1dm6gMe3LMD+SpOejfwESO/FHEHR58/ff07LjWQp837Z6bDpaUltWgEmeETey4wQBMNnDi2mk0HnaImqAQejnjVlKqzGIPmv795+9+0uQilKLdfozBS8Ohuc/PMc2snHUP2tS1IgVUuyXUPG0sMPdaFXGwbb5ftpu/vxtEHktmlqpbethuFirh4JRLC8aN1mmrVaR4bOfvQ/eQl8CLFD1JYVqUdChiss8SGLw8R6KggaKQJ/pamzK4CY55LMy5YHUMiO0fYuJZzJbczzEUFGg1wztKMDll2FM3VO8xRtlWZXY+6I3lJKlW3wYQCsY1gz+jyKk2hSHuf1HudPCmXMIybMHsJwn8xDUHCx28r2NFDb7nseflQT8lSzxlSg+fH5qDPnXdwkTL5i36xhJ8/7ciARmazDtqrMDdTgaB1iE+sOK/DiuCjNkk7y/duztFiRtwMBz6scsLJsK7NbhvJMv/mWp09JZMb9/uqk0RCezTTkWhM8j0IHLhUb/ECcWknoU0jqyqDctEM52ZX2OwQcHH+G/Oi0ciqBCVl0PcTkkGI7ouFru7OGZuy5nTQKWHkq8l98HUklqokOoujTFWoTgODBvUcT0gdqNHNeeZiNpQW2Vcbakx3RayQPBMswHSslZQnLA+atIJeX1dFVLo8YUJVCpXaYAfKZDbPOES6PZNJa0nAdCjRkD8mffi3jIRdqsu4QTAN/f7wllGI5bJKRaWYhwGWtSM86lCoQISuOyK+WUkfb3CiJFNGogZmNvxo19C8sJVou8kQThQlAuNdlSPKe0j4U8oKQ6JXGJ8fEkn3xqIDSl5zTC0nbNEdLU0qqD6H5kEAjAYho6I04z0qMw3xMxGUZKQGOmYhX5ydj1kCDyRI2OT0CO25G4sJZCXqcA845AoIZB8KgLVgaWfN4dG54tCa31COhMhNxtt1uWGKP2j8D0BsmjjXX68KE8KzdFRbkSlaCIqiIkKlE8woDBNbHZEAHtl9Ob9CEdeQ/1SzHFDFahbY+jjdhBdHOKIha7VSIwoSHSavBgufVYZyKhMyYMGjejFYUoAR4VjPTb9/7JVddK9oen1EYR3lU6CNpl3/j5vAmBwg1cYOeaAHCEFQm1finLRTpZeVo81uHSpSkFhgVkVBxJyVbH9SKnE4j+bKyPeSCV/1cVUWQ4YqTh4eHdv59fdt9WKJYfwHRVzoitUhT76rJGq+0PqevRgYTZ2g/FB+CEQr++WGyBnB3u76/XfMwQfPSzgUWVVjRevpmF4MfP36wb1IFwiFDX16f3a2+/dj+DChzBmslgGXEIjl5+v6gBYJo3dsi8Mppva/b/VZxtrRZAD+RoqEWYwwf2uardisDq5jYVyFqwuZylRW8YW7xRdLC5vbVurw0ZXQ2WXifD60cVVLYxzwT10uvyJI5hRQAYTCAYkTovtsbrPRFTIGfZCAfKN8DMuXk4XT8MpfDh10RHNxifdNRCHNZjAQghl56vcoM1x+hiEhcR64lbzTEZ9ySQtaCQXtx9Nvb6+79ZfVhIySg4Q2Ci/Y3KGd3wS/eeay+JjdY4+Vz52SiRENxDJb3+JpVYj1E9wSQwFd30tJcimqo3z4b65tVJbvUkDnJG5CmcOL4Un/DbLTCKDBivin+PtWc19pBiECVzMcNiJgyEVIDT4SZR73PEYXfm9KliaBcQa8mJuBe0lxkyv9P7bMZTCH6UkaXMoyNHbKE1EBsliZFvRzG0UWz9tR6B5G7q0K0ShEu8GOvLLjwGyZlivwxRiTACnrMSZnfwgJ3ucAEs30Rpk41COWrchee5ltmCt8NoUHQ8MSLtxCwlqQv80AUXeE7zhcHg8y5kRil6ZFnBCAbjEl6zhBJGgTXwVBIJP1cpY0PVXm5pWcGr8CZDUAWAjUyJyZKGBVdZp7N0E8wLiH1ZU6PwZt19o8jRinKV0o7EMBm6mpfhRcuHPmm1V7Mh0rSfTJtdpphzE0JDeHgAevlfi8CiEgd2ImxjLgMVZYyTfQtbhwTYsiSf42OUjoF2qlyptw4hcAewGMR5ZQHoI9GVKyGhDGTsiOx90GKcQ6gc/1Xlsz+4lA37/pSTqRNTD4QklzUJ+70cITNfUkPsbVoXgynWzyj74mpQnlwT+D39aeeHEP1+zS6tFcX34Yyol43T8LDXZk7myx1mC1otobIjSTUOxwkqy6q4smSlfnBBGdqMb7wjogSLWpvQwfPHVAMMzT50MfsZIKVB4Y2Fmz60MKjsRpGhDCJ5vuevnNQMkYLynVqg7xNGJ3jIh6yV2NnUSAvEBDH8GyW65u3V8cFwuvzVcsRIPz14f6r6ECzGWfGqd3sO7s2bsb7ZrMxWVflCPFbmXrJrDkblZ6jNmt6v9Zrcn23WmPnWobaRq02bJI6t/S0GoSjRIgRFZOms+HYSkRGd14v3h92Pwg5S8VQJClN71MvKdHCiHosoMjZx53CgMbms7Pbxcp2S5jC/o9iCt1bu4XgBjnvBeTpXU6xnCgLmKuUdni2N5AxMkAR9PfSiHR/qkIlbTBGwoAk6ma0V1i+ETuS6AfETEhi6rJkRPfpL/suCvI6QkMjrCM3PL1olaZ5WPiizM4jKBw0EQ8UULauhNrybxMdulkCVldU+dg8BP7NMzxcEGBU8sTY1/LGj4v/+t//6fFxK/phSDc/JMiSC8luQ64qpi5vvXmNPYTTeGzM+4rNw77qNnnW9dAlyVxXMUGGov6KErtEPZqWA8yhkm7S6gO/NZISNiprIk51ciJoOMI7yBLKYqtCS+PWsOhRPUR8DRdqWbnmnAr0eBvGeb6U+W2dGmtnyT7oqfg+cJk+WrWjNpibOsglzCzD2Erosa7m3xgwApEMGptcZ2D03yyglRuDNPpJM+i8EmWOsx7tAmFUPn+X7+JZF7kdISUlLqPSfjrH4flCIM3aCuG4RAjFAinxibilgEAwDafRuGnk7uvBULhdSgzGPLCZN2c2oYVxpoHqYCqc4gjjyXuoVTQPqGNFwziGx+S4S0oQGE4U3rZSnqe2+vQMb/GHDJjRM0mTsII3086KEl6K0cSOYuJkL+ahOzd8ijfG4KO8onqcoixx9rSUo7pOHhMCY1wiQ9B87UrwTRe9NMzKFhJ6v3vXk71LM6mVy7y33gHGU68Y8fWgxMhNwVnuQCSqSS4DgzRluvJavGyYHMXogDyb8ZlCgQEp5Zuen9v0RkfZMD9/0Bfen0YJQZzGPK+m3H4wRGqad1qBh8HPa5aTIaWyczxO1q/1qGKjHMHcuq5t8WwrUc3YVXhKoqgbOjfuyjO5fy3Ds0lMDLIqRVKpw7CUhw1H2gi3TUuxS/ee2wb2i9nZHUpIbFDKi/kJOTTOlimz0pIOQ/JqNS+ksKXokfTTaiKON0j2layGg+QZAdHRuy3KaeciFVdY4nCCqn2FCXTOlnmIZLsk+rW8qLy82zyLLQxgv2t/G3/kXHVPWUevE4RqxK7zs6/ffgJIQGx5a/+Altn97ttPBmyCUJNvbnWR3CEMc8sP+55O3ImsRPPLt3urAWHr1293kqfFVbT/WUASsmKBgx6xnsVA5M3Dk0CifAtXgDlBbX9Ct3Nd6WK7+7vbzeMPpCJfeCHhJkARzesxkYaQP7Yim4DdK7C9vD4YlS/kDKQDsZJ+aN2xKUu6wj/IvsI33pe+AvJOJskt24Av0gOmTssYVLwGXA5RS3J4aVJXsE0bnkBT09PkdoQLAji678mLmzUPnWnwbPdlCeJ7/inQCU/0XNisRUTb6kiPh5MpC0MzuiW/pBxajwEt4ytknLhCL+/2qVSbKgoDUuDh2N4pGw9wdEnBSSb5n/7l279e/87KFQbgb3/66x//9iB5R9kUy42fBlIpKTqSo1FD3R0gmDohLYbH0uq6WRPxuaQwhvkIiJU39C+NaTe3nDtaCkkMO70jJplFhi7onORY+/UPjKAJroNgpOyPm8qZGXHe+rmdSNpzsNBKi6s2DnsxktoONqic6mlOUOr54+2ETXPqlmcCUSkUsyh6CeuHi3maaO5nbmHOFz1sSol3cFO0xd1zqLRdvbinBlE4AnCRARM8iUEU0/Xu/mvMnNjLhS1TFujWu94d8BsN2UoLRIkLZ45BxirJVw8k0gDPhpr0n9yBTkMMIa2uHOKbDLJZWufIyWRIq9VidtBkt2JH1hevuKIuU/9lhLq3egiTKVTYBbGaEFCn9FhW1nPMbrxN4yZyHpJh7nlYxN/AvXC6DYJokYQiBhNrPPFIf1vnF7AbM0NpttDEY0YekI/r5RWeiYx+Fqgg6UAdb5OWS0GkPMYjNWj4MdrCJ/PnMNiaEJoZqQGExRLBCTbc9OQW+GZfmJM+NYeEiZS7x3OM/hhEFIx5r/PES8R7VuG4b9kKs6NIE1CTAOwDK0ydgeEFMGIF5DbFVEXP2OyUSYQh/O+2zl0QP5kMAKC6S40BafX49rzUU1szsXtAhte7DEVdD7ABLcsOo+CZs1ZLCufTJE4I4WVFkUxLe5zpN7LAsMoQXScVAML7DbO9S/OKqsaZpvW85iLQKL3QJAkoJiSMSXA9KlQWy8/31oKprlt16HRJq09bbWdkcS9Zi+z+VDMEcswEHIJvaI8gCKpqavDEwNWXN8cFXxcqtSIAZgMr8WG3eVnfLZ8eHnsj6aqZQrJeST+WyOTAxSVLLElvj+jXl83T/p+//gSbuDqIXz65bI2BC7kuQNeP7z+cdWaJbz2pehYhey1taid6dzVFXtlA3nzLWr7b5dAz+Yevyo13X+40erUCLEdhhEQNH3k/Th6fno3EhaQc5+iI0y+ltPg41naEoqKu4Rbx36uRICPYAc0xX48ZMChpzuYwYYDCzwW0iSq+YFWSGRbM4jKSz58uyuf3+GiKAlEexafL2R3kdvKBZctRO0nLjZO1I+E8aFwuVZ4Jodf4SyXSi95ErNyCZl7kV75jgF/XN86GS3zl3FwqwioExvVhUwAZSTec/u//+T+WZaV0FuTO4OXuaffHP/3p93/481/+8j2rdLE4rYpr5Da8K1NMaFLdhhiWlN7KIZVd4qKlYGbX7OvpdMnAMYljIwCH5l2eHVHySZsdUhDD0mKV8pY9yNdro4gR3zKjSHPsEETSX0R2MhVeR1GZ6N2zlq+8RSSQ9skvr+DWorPIlDL6Bg0a9+Al7pR7yQUxEw+d6msEre+VX4zOTCrYSpchuKccwdGvUZAf14K4q9FY0w3MkFU+zJMD0xBmRKDV0fXPmRChor139+vHxycSo0GFE0dbgN7qdi2tSVAAkPXGJSHRh0LjS1tWvW+2tvhvYLOGZnJ/008JCtg/jBM7aTzwd3MVC3fYmbZo4zy1zBjkoaceOE9AhnwTiJm97E953RIsXlcZwi9kKGpZT9BZQme7zd4jsy42L2tJQdGDPyjjRe6ljViKjG4ck4IImljYSx9OGzjjed05SgwQombn3EDM+UHDa9SaZ8Yj7zV3FPZwAuGHEDTfIq6hiblgjOtjXcriAx/1x3P8bYT+MjLz9a3W5GJBm47oLnOdVhyZmo6fbRsDjBuYPoaDvi7QH3epqAVHigPn/BO9PB6iODzDI0BNRULSAQAjTjXA+NBLPdBYSB2nZ6SQZOVqEOw0tmA03XDONpuRoeUS5zlAA1wHJMdoWNqdA8caKWfOzAia00XPT5yI59kl97ASLp7yKiwdg9Fjcs9t3nULQY7CiCmRdwwb5xZTRKP+kApzFqPrQfT40ZTWexs0KpqCdDzs0NL3/fsP7U83V0uMgQMBULkzrnIlELzgXJB6tTFwD7hsR0hYtQf1HHUshajrKXXw+LSzSwXI7neYoCroQR2QtCzXK2K831azZY05xeQL11gj/jdLwDJBIL6MILF0nN4KGfynDSf5frXGNLH6wuoHTVCGbN+umyubxh82e/UJSQ7BMhkh9tXQxA0CAkIrhXJuGMkRcSJuHBTOOwLJVSC902oVLEsDEEiLsaQBSPWIvYnfrtamhuPQKxzRTQaCFlf1gZQyQvI8Ub7Wy0Gyv2Adjk5gTRgiP/nHglRJxrvwrJqQ+6LIrGe2Nrs4Qw4gP57Ph861syfubict1LaUiZAl80AkUIBGe4Dn+3XwOC3xn8GmNV5ZFtjai9OLH/uHi6v7zZ9+Ni34t/qy/M1vb5lplWoZ14fHLX9JQ/qazTQA0Dyo5Erylt6Fc/3FQTT/XpM1yptz4sFYSCMk9o3Gi9Hf3iakmkJMENSq151NU8VENXFwhFMFUOQGFyCh+zw/LAiHo20sHIthhI/bHwLDpWCHJAccwFd8AC9CEJ+5fdw6NGzdr0IuzyzgxvTyLYVaCfEsprVYHsv6gkoSffAyFp15LAdk2AMuHTTG0taf0Oy5bPiEoLBPx1jSxJAs2qnYzwg16ETUmEAPKV7B5ryVZwXeTjYCVWSISORBeDOR4qAzVmFR6SdrhTzVzYGLs5RmeyniaYSALIgpafj8vIt0xNUzFUKJQXOQJhLYvyiaOWHKgv4yacGN2Q1xDNtPflQCjmK42Faox8o2xCh4F37SBw+LVMUHRI/zjo5QMg+gSbuykD1Pjaa5q+42/3PPRWch+IT66PiiNLy+IKHsEBFTCQ9XPNn0ERVto/38jsTpEcp7E89FjhgVdAGhtztq7iIQBcW+BWsFWuCVhRZxB6b67SQEalQWELk4FWpBLNWqbdxLMAImevrolYpf23fn2ddWYcBaOOp2QeimPWVt9PGcrj+x/d8W0czUr7IEbvEzCPOjwfuZQWCnU2BwmF/tje8SAjTY52bnZsOmYhgXZVQvLNPjqUEoKReJ8pRBrNMy14ftxvhR94AjaO+yHLQ60/abrZIXMYYK7eDUEjA0KNuOhElCcttOv+RoKBrQV4AfN9/g0Q0O8XMdyWKu7hJb3y7XBoDreOIQYxcvVlqJXCYnNjlVXMtDPH983NFx2wwRfm46Muqrur25tWhrv20ziTZk7Zy/WhgLQz8/tz92on8C6Fea+7w9MPKeR/Cggc0RVWtjrs1/HrdEVNL5+cePdJv9CbxPt6cHJ7IhBS7Jb6D2QtHe+jja1OofAyY3WJkXjAi029zzsP1zA20lTF4cKIP/47DotNSWckJf5Ii08MUmjaNw8LhwtyxeHQcWZJNVLrnEGxjxOpLP28BIckYYEqiam+G1kfwiZqDdZFMd0J+nMV5wC5kLNokAQSXH7ERSrGcavHj5HPwXyuFOsV4ZhVa8tkTRPlo6MxMCW3WTSPRDGXrscv5NHh7VTCkJi1gwDp6nuGcXf/vTzgAcoydDBpA+9q9P19/vf/3tP/2Xf7z/5/uHP3z/9z99VyPg9+XiGVZoQxYF1DScXKWVFAklJwksFxkN+dB+9zI09l+IGF606Z08tZZLIhu7nY49wgeVyqlDP+GrxYrXyxJq7SOH1BpdLz/5JWWBda2AD7nIhdSgu/LyHc6+0LLreKZADYRbWlYxQMSS6+8ZHoxPhVrRRTzsTi0H/PpKf/Pzi+P6iqn57kYF/6CJQbrMn2lxys55HmPzxI4Qq7byn90r98/XZ7qes0xiINTQiQlOCYf8qpmZv9wcDMJeEOJBGqHyQN8PfoWYpC+6frYLm/hY+lLbTBqVw0hh0VN2SKWmMXSjgTFyDZMRAyUqb1h+7EqmoyPT7YRhGA5jKNUY79vCwImyeHhRJT9QnV2ljJvg8grMWvjN1aIS2CqGE8Wne/nriOsZIFcWdURbO0RrLNKBaU8yKpJBNGmvR/qVbKA+s8sRdzvi0gp4ZBtVMMO9o8EZKylv4DhjMCh6D3p4a8TUBM2dw0ETPKfKR+WlOvYQZ+C44MyE+808MTfr1PvcS1yauNX5L3UNJb1B5tTkJ6PC36KJ469RIeazCVWDU8bl3ysdy7dqYPNKtUH8lYtpcBC4gaGhliA1LcllfLbmrqR8iuFSES2Gv6/XN0G/VFimlGzCiFdn0dBuYA0GSCQZANm+hXYSjXXo1INoBLMM4lyV4p1T5l2X8rp6ksL9wkE1UXF5hhH20Jw2SgjZeQMuAQ3ri5sq0pM0KP710/g9eSf2klS3s/LTrkOz+Z2pR3PjjyYlKvVNSYoDTkO3uliCQOpEZHlw3NisxCCfSKGNQx8yOQmeZhUuRCBwMJ0Wiox4qBqmZIasgCZl++37ly+rqXad3N3+xDmgi/563lmzurSTMzkJCg+aUNnCJNDIrhzDcMbc1n/P55bjnra+65zFJKsUCqLygO0uSD1QHxGI0d3Xu/3m2QZHYJwlt69b7BPaHvarcjsqg5ykIPz2fsWPtQcBT/bwuG3FoArqycf9ly+2neCGAhbk6dC9UUnSMmsXcr/ZFcUnAnTQONWxfcTbIhgKBQTxvYZ7qkd0qTa1yjATFn4RQGQMSDXRs55U7pFfrwfPnYKSXO2cCkNSTTxrJ+vaB8rZldkGr0Qfz9MBvPNHLo3oDtLQJxCn5FOwS1/wsRVItPHmcmfr6debn37/88P9hWP2DuTw5cfm+tsS8y1cWd3e/su/rvF2tXn+/e//An6dcmfjKmNBAjLheTGcL2Ia/ihWEFiWVm+SmMVK2rI6Xpr/XvX57XO5urCByMXagqnMUnaIrJUBMrszPgKPGCI/f76c34BjoJ+JrJUn7kArXklodPhEFBp+roGxyheh4tZz0Ji9ibNYqErDUE13M/PgmsBSbJirGHxQmLGiPsYzi81IihlFzNzSlJLoTI+vjUa6CepSa0DvGO66cyIIHvHB0dh34N5Srxr5xQet4xQUZ4I+v3xZjmtaBKrfGgAyZc5u9q6WiZWdYi0vSA1nGBB4r1Veg8+yFnk5zk8teibZ1w4yy/vJsumM1vh2ea6CCEoUZ7jNTWFMLlNjmvSSIwQYEMRliIPOvlL/gg6e4AaQ6MQz6CGFRZKIDuGytxeGklyvMgAljk+Hl41pZ0fMO8PKdXU3D7T9PeKw4F0dOCe8l1NIfUrVfowpTz9BzgWjY5J1dTkmuBW0VUUVwnIU0LkmVwa/zJLZzTUFCpklRBFQZ54BzPAKrTIs7XagOFlqgg+hy4AfSfzwcowxysB64E0cEYQVMlMUMwlwIHVlTxneMZ7KQ5NefoRJ4FfV9wn5watjIBXHxJFUVrz6fGJHHeb3UwQbBBuzgvWVwJdK+qFIFxF4Zrmybuls415o9Q2fVtWE9h6syFPSkDBBH8MTq7WBTFutZOXRUQ6BAIsdT94sGX7cPbfHne4BYcrJx2//4Vd//+t3hCttikW3LSYnwltn0BQ0W1DkQbySS49lWrgPdaSTWnKP1Vdn+4/n8+tzfj0bpO1HUCZ1aDGVkVQvUu5aX78d3q/XS6d1ojng71gdT5LryEt3Y1u7YZQ+Fu0SVnoxSroPYJhmReEyCgtEdCup8WhOhbQn1+/7z2eL55kr5r9ewkDMzShI2vJ7+NtM/3ZfH51dV0NSOqvLqNS5nQ25pJe3v7plv2VrMNS6t6KoSl9nlrShD7xCpuW1bcA/rr/ds5HvL9ut0ETJ5tKu1PakObHQVVdLa5qtChS1y9vhnrJE1c+Pv/38N6mi1f2tsepJ+tXqVqrWa2RxHx/3rkQ2DHIX9fMuu0lEf/NFYACV3EdqGlZmZ6UjA/8RNS0jqjDU0wooQvGTy+Xl614tMNi0s0T+EtmDtoItkt3GzgXoELdskkpZfoL3E5LyHhNOlwNwFKd9IyAImUrkklJf9mrYMhovQXOyPbn4f/vwwtIL0vCDAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=512x512>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from diffusers import AutoPipelineForText2Image\\n\",\n    \"import torch\\n\",\n    \"\\n\",\n    \"pipeline = AutoPipelineForText2Image.from_pretrained(\\n\",\n    \"\\t\\\"runwayml/stable-diffusion-v1-5\\\", torch_dtype=torch.float16, variant=\\\"fp16\\\"\\n\",\n    \")\\n\",\n    \"pipeline = pipeline.to(\\\"cuda\\\")\\n\",\n    \"\\n\",\n    \"generator = [torch.Generator(device=\\\"cuda\\\").manual_seed(42)]\\n\",\n    \"image = pipeline(\\n\",\n    \"\\t\\\"Fictional photograph of Taj mahal on Mars\\\", generator=generator\\n\",\n    \").images[0]\\n\",\n    \"image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"7b6b7ebc\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"UNet2DConditionModel(\\n\",\n       \"  (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"  (time_proj): Timesteps()\\n\",\n       \"  (time_embedding): TimestepEmbedding(\\n\",\n       \"    (linear_1): Linear(in_features=320, out_features=1280, bias=True)\\n\",\n       \"    (act): SiLU()\\n\",\n       \"    (linear_2): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"  )\\n\",\n       \"  (down_blocks): ModuleList(\\n\",\n       \"    (0): CrossAttnDownBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-1): 2 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=320, out_features=320, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=320, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=320, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=320, out_features=320, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=320, out_features=2560, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=1280, out_features=320, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (1): CrossAttnDownBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-1): 2 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=640, out_features=640, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=640, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=640, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=640, out_features=640, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=640, out_features=5120, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=2560, out_features=640, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (2): CrossAttnDownBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-1): 2 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=5120, out_features=1280, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (downsamplers): ModuleList(\\n\",\n       \"        (0): Downsample2D(\\n\",\n       \"          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (3): DownBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (up_blocks): ModuleList(\\n\",\n       \"    (0): UpBlock2D(\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-2): 3 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (1): CrossAttnUpBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-2): 3 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=5120, out_features=1280, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0-1): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (2): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (2): CrossAttnUpBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-2): 3 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=640, out_features=640, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=640, out_features=640, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=640, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=640, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=640, out_features=640, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=640, out_features=5120, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=2560, out_features=640, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (2): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (upsamplers): ModuleList(\\n\",\n       \"        (0): Upsample2D(\\n\",\n       \"          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (3): CrossAttnUpBlock2D(\\n\",\n       \"      (attentions): ModuleList(\\n\",\n       \"        (0-2): 3 x Transformer2DModel(\\n\",\n       \"          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\\n\",\n       \"          (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"          (transformer_blocks): ModuleList(\\n\",\n       \"            (0): BasicTransformerBlock(\\n\",\n       \"              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn1): Attention(\\n\",\n       \"                (to_q): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=320, out_features=320, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (attn2): Attention(\\n\",\n       \"                (to_q): Linear(in_features=320, out_features=320, bias=False)\\n\",\n       \"                (to_k): Linear(in_features=768, out_features=320, bias=False)\\n\",\n       \"                (to_v): Linear(in_features=768, out_features=320, bias=False)\\n\",\n       \"                (to_out): ModuleList(\\n\",\n       \"                  (0): Linear(in_features=320, out_features=320, bias=True)\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"              (ff): FeedForward(\\n\",\n       \"                (net): ModuleList(\\n\",\n       \"                  (0): GEGLU(\\n\",\n       \"                    (proj): Linear(in_features=320, out_features=2560, bias=True)\\n\",\n       \"                  )\\n\",\n       \"                  (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                  (2): Linear(in_features=1280, out_features=320, bias=True)\\n\",\n       \"                )\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"          (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"      (resnets): ModuleList(\\n\",\n       \"        (0): ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"        (1-2): 2 x ResnetBlock2D(\\n\",\n       \"          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\\n\",\n       \"          (conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\\n\",\n       \"          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"          (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"          (nonlinearity): SiLU()\\n\",\n       \"          (conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (mid_block): UNetMidBlock2DCrossAttn(\\n\",\n       \"    (attentions): ModuleList(\\n\",\n       \"      (0): Transformer2DModel(\\n\",\n       \"        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\\n\",\n       \"        (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"        (transformer_blocks): ModuleList(\\n\",\n       \"          (0): BasicTransformerBlock(\\n\",\n       \"            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"            (attn1): Attention(\\n\",\n       \"              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"              (to_k): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"              (to_v): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"              (to_out): ModuleList(\\n\",\n       \"                (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"            (attn2): Attention(\\n\",\n       \"              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\\n\",\n       \"              (to_k): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"              (to_v): Linear(in_features=768, out_features=1280, bias=False)\\n\",\n       \"              (to_out): ModuleList(\\n\",\n       \"                (0): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"                (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\\n\",\n       \"            (ff): FeedForward(\\n\",\n       \"              (net): ModuleList(\\n\",\n       \"                (0): GEGLU(\\n\",\n       \"                  (proj): Linear(in_features=1280, out_features=10240, bias=True)\\n\",\n       \"                )\\n\",\n       \"                (1): Dropout(p=0.0, inplace=False)\\n\",\n       \"                (2): Linear(in_features=5120, out_features=1280, bias=True)\\n\",\n       \"              )\\n\",\n       \"            )\\n\",\n       \"          )\\n\",\n       \"        )\\n\",\n       \"        (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"    (resnets): ModuleList(\\n\",\n       \"      (0-1): 2 x ResnetBlock2D(\\n\",\n       \"        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"        (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\\n\",\n       \"        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\\n\",\n       \"        (dropout): Dropout(p=0.0, inplace=False)\\n\",\n       \"        (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \"        (nonlinearity): SiLU()\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True)\\n\",\n       \"  (conv_act): SiLU()\\n\",\n       \"  (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pipeline.unet\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"99afd5ed\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter11/pong.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[31mERROR: Could not find a version that satisfies the requirement torch==2.2 (from versions: 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0, 1.1.0.post2, 1.2.0, 1.3.0, 1.3.0.post2, 1.3.1, 1.4.0, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.1, 1.10.2, 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1)\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0m\\u001b[31mERROR: No matching distribution found for torch==2.2\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mRequirement already satisfied: matplotlib==3.5.2 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (23.2)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (3.1.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (4.38.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (1.21.6)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (0.11.0)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: typing-extensions in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib==3.5.2) (4.7.1)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (2021.11.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (23.2)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (2.31.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (1.7.3)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (2.6.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (1.21.6)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (9.3.0)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from scikit-image==0.19.3) (1.3.0)\\n\",\n      \"Requirement already satisfied: atari-py==0.2.9 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (0.2.9)\\n\",\n      \"Requirement already satisfied: six in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from atari-py==0.2.9) (1.16.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from atari-py==0.2.9) (1.21.6)\\n\",\n      \"Requirement already satisfied: opencv-python==4.6.0.66 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (4.6.0.66)\\n\",\n      \"Requirement already satisfied: numpy>=1.14.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from opencv-python==4.6.0.66) (1.21.6)\\n\",\n      \"Found existing installation: gym 0.24.0\\n\",\n      \"Uninstalling gym-0.24.0:\\n\",\n      \"  Successfully uninstalled gym-0.24.0\\n\",\n      \"Collecting gym[accept-rom-license,atari]==0.24.0\\n\",\n      \"  Using cached gym-0.24.0-py3-none-any.whl\\n\",\n      \"Requirement already satisfied: gym-notices>=0.0.4 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from gym[accept-rom-license,atari]==0.24.0) (0.0.8)\\n\",\n      \"Requirement already satisfied: numpy>=1.18.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from gym[accept-rom-license,atari]==0.24.0) (1.21.6)\\n\",\n      \"Requirement already satisfied: cloudpickle>=1.2.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from gym[accept-rom-license,atari]==0.24.0) (1.6.0)\\n\",\n      \"Requirement already satisfied: importlib-metadata>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from gym[accept-rom-license,atari]==0.24.0) (4.13.0)\\n\",\n      \"Requirement already satisfied: autorom[accept-rom-license]~=0.4.2 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from gym[accept-rom-license,atari]==0.24.0) (0.4.2)\\n\",\n      \"Collecting ale-py~=0.7.5\\n\",\n      \"  Using cached ale_py-0.7.5-cp37-cp37m-macosx_10_15_x86_64.whl (1.1 MB)\\n\",\n      \"Requirement already satisfied: importlib-resources in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from ale-py~=0.7.5->gym[accept-rom-license,atari]==0.24.0) (5.12.0)\\n\",\n      \"Requirement already satisfied: tqdm in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (4.66.2)\\n\",\n      \"Requirement already satisfied: click in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (8.1.7)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (2.31.0)\\n\",\n      \"Requirement already satisfied: AutoROM.accept-rom-license in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (0.6.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.6.4 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata>=4.8.0->gym[accept-rom-license,atari]==0.24.0) (4.7.1)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata>=4.8.0->gym[accept-rom-license,atari]==0.24.0) (3.15.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (3.3.2)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (2022.12.7)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from requests->autorom[accept-rom-license]~=0.4.2->gym[accept-rom-license,atari]==0.24.0) (2.0.7)\\n\",\n      \"Installing collected packages: gym, ale-py\\n\",\n      \"  Attempting uninstall: ale-py\\n\",\n      \"    Found existing installation: ale-py 0.7.3\\n\",\n      \"    Uninstalling ale-py-0.7.3:\\n\",\n      \"      Successfully uninstalled ale-py-0.7.3\\n\",\n      \"Successfully installed ale-py-0.7.5 gym-0.24.0\\n\",\n      \"Collecting ale-py==0.7.3\\n\",\n      \"  Using cached ale_py-0.7.3-cp37-cp37m-macosx_10_15_x86_64.whl (1.1 MB)\\n\",\n      \"Requirement already satisfied: importlib-resources in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from ale-py==0.7.3) (5.12.0)\\n\",\n      \"Requirement already satisfied: importlib-metadata in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from ale-py==0.7.3) (4.13.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from ale-py==0.7.3) (1.21.6)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.6.4 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata->ale-py==0.7.3) (4.7.1)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata->ale-py==0.7.3) (3.15.0)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Installing collected packages: ale-py\\n\",\n      \"  Attempting uninstall: ale-py\\n\",\n      \"    Found existing installation: ale-py 0.7.5\\n\",\n      \"    Uninstalling ale-py-0.7.5:\\n\",\n      \"      Successfully uninstalled ale-py-0.7.5\\n\",\n      \"Successfully installed ale-py-0.7.3\\n\",\n      \"Requirement already satisfied: importlib-metadata==4.13.0 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (4.13.0)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata==4.13.0) (3.15.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.6.4 in /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages (from importlib-metadata==4.13.0) (4.7.1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# requires python3.7\\n\",\n    \"!pip install torch==2.2\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\\n\",\n    \"!pip install atari-py==0.2.9\\n\",\n    \"!pip install opencv-python==4.6.0.66\\n\",\n    \"!pip uninstall -y 'gym[atari,accept-rom-license]'\\n\",\n    \"!pip install 'gym[atari,accept-rom-license]==0.24.0'\\n\",\n    \"!pip install ale-py==0.7.3\\n\",\n    \"!pip install importlib-metadata==4.13.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--2024-02-19 15:36:15--  http://www.atarimania.com/roms/Roms.rar\\n\",\n      \"Resolving www.atarimania.com (www.atarimania.com)... 195.154.81.199\\n\",\n      \"Connecting to www.atarimania.com (www.atarimania.com)|195.154.81.199|:80... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 19612325 (19M) [application/x-rar-compressed]\\n\",\n      \"Saving to: ‘Roms.rar.3’\\n\",\n      \"\\n\",\n      \"Roms.rar.3          100%[===================>]  18.70M  4.64MB/s    in 4.1s    \\n\",\n      \"\\n\",\n      \"2024-02-19 15:36:19 (4.51 MB/s) - ‘Roms.rar.3’ saved [19612325/19612325]\\n\",\n      \"\\n\",\n      \"--2024-02-19 15:36:19--  http://./\\n\",\n      \"Resolving . (.)... failed: nodename nor servname provided, or not known.\\n\",\n      \"wget: unable to resolve host address ‘.’\\n\",\n      \"FINISHED --2024-02-19 15:36:19--\\n\",\n      \"Total wall clock time: 4.3s\\n\",\n      \"Downloaded: 1 files, 19M in 4.1s (4.51 MB/s)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!wget http://www.atarimania.com/roms/Roms.rar ./\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[34m==>\\u001b[0m \\u001b[1mDownloading https://formulae.brew.sh/api/formula.jws.json\\u001b[0m\\n\",\n      \"\\n\",\n      \"\\u001b[34m==>\\u001b[0m \\u001b[1mDownloading https://formulae.brew.sh/api/cask.jws.json\\u001b[0m\\n\",\n      \"\\n\",\n      \"\\u001b[33mWarning:\\u001b[0m unar 1.10.8 is already installed and up-to-date.\\n\",\n      \"To reinstall 1.10.8, run:\\n\",\n      \"  brew reinstall unar\\n\",\n      \"./Roms.rar: RAR\\n\",\n      \"\\\"Roms\\\" already exists.\\n\",\n      \"(r)ename to \\\"Roms-1\\\", (R)ename all, (o)verwrite, (O)verwrite all, (s)kip, (S)kip all, (q)uit? ^C\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!brew install unar\\n\",\n    \"!unar ./Roms.rar\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"copying koolaid.bin from ./Roms/ROMS/Kool-Aid Man (Kool Aid Pitcher Man) (1983) (M Network, Stephen Tatsumi, Jane Terjung - Kool Aid) (MT4648) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/koolaid.bin\\n\",\n      \"copying phoenix.bin from ./Roms/ROMS/Phoenix (1983) (Atari - GCC, Michael Feinstein, Patricia Goodson, John Mracek) (CX2673) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/phoenix.bin\\n\",\n      \"copying alien.bin from ./Roms/ROMS/Alien (1982) (20th Century Fox Video Games, Douglas 'Dallas North' Neubauer) (11006) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/alien.bin\\n\",\n      \"copying demon_attack.bin from ./Roms/ROMS/Demon Attack (Death from Above) (1982) (Imagic, Rob Fulop) (720000-200, 720101-1B, 720101-1C, IA3200, IA3200C, IX-006-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/demon_attack.bin\\n\",\n      \"copying crazy_climber.bin from ./Roms/ROMS/Crazy Climber (1983) (Atari - Roklan, Joe Gaucher, Alex Leavens) (CX2683) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/crazy_climber.bin\\n\",\n      \"copying video_pinball.bin from ./Roms/ROMS/Pinball (AKA Video Pinball) (Zellers).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/video_pinball.bin\\n\",\n      \"copying yars_revenge.bin from ./Roms/ROMS/Yars' Revenge (Time Freeze) (1982) (Atari, Howard Scott Warshaw - Sears) (CX2655 - 49-75167) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/yars_revenge.bin\\n\",\n      \"copying donkey_kong.bin from ./Roms/ROMS/Donkey Kong (1982) (Coleco - Woodside Design Associates - Imaginative Systems Software, Garry Kitchen) (2451) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/donkey_kong.bin\\n\",\n      \"copying carnival.bin from ./Roms/ROMS/Carnival (1982) (Coleco - Woodside Design Associates, Steve 'Jessica Stevens' Kitchen) (2468) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/carnival.bin\\n\",\n      \"copying defender.bin from ./Roms/ROMS/Defender (1982) (Atari, Robert C. Polaro, Alan J. Murphy - Sears) (CX2609 - 49-75186) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/defender.bin\\n\",\n      \"copying gopher.bin from ./Roms/ROMS/Gopher (Gopher Attack) (1982) (U.S. Games Corporation - JWDA, Sylvia Day, Todd Marshall, Robin McDaniel, Henry Will IV) (VC2001) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/gopher.bin\\n\",\n      \"copying krull.bin from ./Roms/ROMS/Krull (1983) (Atari, Jerome Domurat, Dave Staugas) (CX2682) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/krull.bin\\n\",\n      \"copying pacman.bin from ./Roms/ROMS/Pac-Man (1982) (Atari, Tod Frye) (CX2646) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/pacman.bin\\n\",\n      \"copying surround.bin from ./Roms/ROMS/Surround (32 in 1) (Bit Corporation) (R320).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/surround.bin\\n\",\n      \"copying name_this_game.bin from ./Roms/ROMS/Name This Game (Guardians of Treasure, Octopussy) (1983) (U.S. Games Corporation - JWDA, Roger Booth, Sylvia Day, Ron Dubren, Todd Marshall, Robin McDaniel, Wes Trager, Henry Will IV) (VC1007) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/name_this_game.bin\\n\",\n      \"copying lost_luggage.bin from ./Roms/ROMS/Lost Luggage (Airport Mayhem) (1982) (Apollo - Games by Apollo, Larry Minor, Ernie Runyon, Ed Salvo) (AP-2004) [no opening scene] ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/lost_luggage.bin\\n\",\n      \"copying pong.bin from ./Roms/ROMS/Video Olympics - Pong Sports (Paddle) (1977) (Atari, Joe Decuir - Sears) (CX2621 - 99806, 6-99806, 49-75104) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/pong.bin\\n\",\n      \"copying keystone_kapers.bin from ./Roms/ROMS/Keystone Kapers - Raueber und Gendarm (1983) (Activision, Garry Kitchen - Ariola) (EAX-025, EAX-025-04I - 711 025-725) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/keystone_kapers.bin\\n\",\n      \"copying kung_fu_master.bin from ./Roms/ROMS/Kung-Fu Master (1987) (Activision - Imagineering, Dan Kitchen, Garry Kitchen) (AG-039-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/kung_fu_master.bin\\n\",\n      \"copying trondead.bin from ./Roms/ROMS/TRON - Deadly Discs (TRON Joystick) (1983) (M Network - INTV - APh Technological Consulting, Jeff Ronne, Brett Stutz) (MT5662) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/trondead.bin\\n\",\n      \"copying air_raid.bin from ./Roms/ROMS/Air Raid (1982) (Men-A-Vision) (PAL) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/air_raid.bin\\n\",\n      \"copying king_kong.bin from ./Roms/ROMS/King Kong (1982) (Tigervision - Software Electronics Corporation, Karl T. Olinger - Teldec) (7-001 - 3.60001 VE) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/king_kong.bin\\n\",\n      \"copying qbert.bin from ./Roms/ROMS/Q-bert (1983) (Parker Brothers - Western Technologies, Dave Hampton, Tom Sloper) (PB5360) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/qbert.bin\\n\",\n      \"copying robotank.bin from ./Roms/ROMS/Robot Tank (Robotank) (1983) (Activision, Alan Miller) (AZ-028, AG-028-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/robotank.bin\\n\",\n      \"copying skiing.bin from ./Roms/ROMS/Skiing - Le Ski (1980) (Activision, Bob Whitehead) (AG-005, CAG-005, AG-005-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/skiing.bin\\n\",\n      \"copying hero.bin from ./Roms/ROMS/H.E.R.O. (1984) (Activision, John Van Ryzin) (AZ-036-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/hero.bin\\n\",\n      \"copying star_gunner.bin from ./Roms/ROMS/Stargunner (1983) (Telesys, Alex Leavens) (1005) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/star_gunner.bin\\n\",\n      \"copying tutankham.bin from ./Roms/ROMS/Tutankham (1983) (Parker Brothers, Dave Engman, Dawn Stockbridge) (PB5340) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/tutankham.bin\\n\",\n      \"copying gravitar.bin from ./Roms/ROMS/Gravitar (1983) (Atari, Dan Hitchens, Mimi Nyden) (CX2685) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/gravitar.bin\\n\",\n      \"copying montezuma_revenge.bin from ./Roms/ROMS/Montezuma's Revenge - Featuring Panama Joe (1984) (Parker Brothers - JWDA, Henry Will IV) (PB5760) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/montezuma_revenge.bin\\n\",\n      \"copying venture.bin from ./Roms/ROMS/Venture (1982) (Coleco, Joseph Biel) (2457) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/venture.bin\\n\",\n      \"copying pitfall.bin from ./Roms/ROMS/Pitfall! - Pitfall Harry's Jungle Adventure (Jungle Runner) (1982) (Activision, David Crane) (AX-018, AX-018-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/pitfall.bin\\n\",\n      \"copying mr_do.bin from ./Roms/ROMS/Mr. Do! (1983) (CBS Electronics - Individeo, Ed English) (4L4478) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/mr_do.bin\\n\",\n      \"copying laser_gates.bin from ./Roms/ROMS/Laser Gates (AKA Innerspace) (1983) (Imagic, Dan Oliver) (720118-2A, 13208, EIX-007-04I) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/laser_gates.bin\\n\",\n      \"copying tennis.bin from ./Roms/ROMS/Tennis - Le Tennis (1981) (Activision, Alan Miller) (AG-007, CAG-007) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/tennis.bin\\n\",\n      \"copying space_invaders.bin from ./Roms/ROMS/Space Invaders (1980) (Atari, Richard Maurer - Sears) (CX2632 - 49-75153) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/space_invaders.bin\\n\",\n      \"copying up_n_down.bin from ./Roms/ROMS/Up 'n Down (1984) (SEGA - Beck-Tech, Steve Beck, Phat Ho) (009-01) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/up_n_down.bin\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"copying riverraid.bin from ./Roms/ROMS/River Raid (1982) (Activision, Carol Shaw) (AX-020, AX-020-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/riverraid.bin\\n\",\n      \"copying enduro.bin from ./Roms/ROMS/Enduro (1983) (Activision, Larry Miller) (AX-026, AX-026-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/enduro.bin\\n\",\n      \"copying amidar.bin from ./Roms/ROMS/Amidar (1982) (Parker Brothers, Ed Temple) (PB5310) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/amidar.bin\\n\",\n      \"copying bowling.bin from ./Roms/ROMS/Bowling (1979) (Atari, Larry Kaplan - Sears) (CX2628 - 6-99842, 49-75117) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/bowling.bin\\n\",\n      \"copying kangaroo.bin from ./Roms/ROMS/Kangaroo (1983) (Atari - GCC, Patricia Goodson, Josh Littlefield, Kevin Osborn) (CX2689) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/kangaroo.bin\\n\",\n      \"copying atlantis.bin from ./Roms/ROMS/Atlantis (Lost City of Atlantis) (1982) (Imagic, Dennis Koble) (720103-1A, 720103-1B, IA3203, IX-010-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/atlantis.bin\\n\",\n      \"copying battle_zone.bin from ./Roms/ROMS/Battlezone (1983) (Atari - GCC, Michael Feinstein, Patricia Goodson, Brad Rice) (CX2681) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/battle_zone.bin\\n\",\n      \"copying breakout.bin from ./Roms/ROMS/Breakout - Breakaway IV (Paddle) (1978) (Atari, Brad Stewart - Sears) (CX2622 - 6-99813, 49-75107) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/breakout.bin\\n\",\n      \"copying jamesbond.bin from ./Roms/ROMS/James Bond 007 (James Bond Agent 007) (1984) (Parker Brothers - On-Time Software, Joe Gaucher, Dan Kurczewski, Louis Marbel, Kathy Von) (PB5110) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/jamesbond.bin\\n\",\n      \"copying seaquest.bin from ./Roms/ROMS/Seaquest (1983) (Activision, Steve Cartwright) (AX-022) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/seaquest.bin\\n\",\n      \"copying road_runner.bin from patched version of ./Roms/ROMS/Road Runner (1989) (Atari - Bobco, Robert C. Polaro) (CX2663) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/road_runner.bin\\n\",\n      \"copying berzerk.bin from ./Roms/ROMS/Berzerk (1982) (Atari, Dan Hitchens - Sears) (CX2650 - 49-75168) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/berzerk.bin\\n\",\n      \"copying double_dunk.bin from ./Roms/ROMS/Double Dunk (Super Basketball) (1989) (Atari, Matthew L. Hubbard) (CX26159) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/double_dunk.bin\\n\",\n      \"copying centipede.bin from ./Roms/ROMS/Centipede (1983) (Atari - GCC, Patricia Goodson, Josh Littlefield, Douglas B. Macrae) (CX2676) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/centipede.bin\\n\",\n      \"copying frostbite.bin from ./Roms/ROMS/Frostbite (Iceman) (1983) (Activision, Steve Cartwright) (AX-031) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/frostbite.bin\\n\",\n      \"copying assault.bin from ./Roms/ROMS/Assault (AKA Sky Alien) (1983) (Bomb - Onbase) (CA281).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/assault.bin\\n\",\n      \"copying ice_hockey.bin from ./Roms/ROMS/Ice Hockey - Le Hockey Sur Glace (1981) (Activision, Alan Miller) (AX-012, CAX-012, AX-012-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/ice_hockey.bin\\n\",\n      \"copying zaxxon.bin from ./Roms/ROMS/Zaxxon (1983) (Coleco) (2454) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/zaxxon.bin\\n\",\n      \"copying elevator_action.bin from ./Roms/ROMS/Elevator Action (1983) (Atari, Dan Hitchens, Dave Staugas) (CX26126) (Prototype) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/elevator_action.bin\\n\",\n      \"copying beam_rider.bin from ./Roms/ROMS/Beamrider (1984) (Activision - Cheshire Engineering, David Rolfe, Larry Zwick) (AZ-037-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/beam_rider.bin\\n\",\n      \"copying sir_lancelot.bin from ./Roms/ROMS/Sir Lancelot (1983) (Xonox - K-Tel Software - Product Guild, Anthony R. Henderson) (99006, 6220) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/sir_lancelot.bin\\n\",\n      \"copying wizard_of_wor.bin from ./Roms/ROMS/Wizard of Wor (1982) (CBS Electronics - Roklan, Joe Hellesen, Joe Wagner) (M8774, M8794) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/wizard_of_wor.bin\\n\",\n      \"copying bank_heist.bin from ./Roms/ROMS/Bank Heist (Bonnie & Clyde, Cops 'n' Robbers, Hold-Up, Roaring 20's) (1983) (20th Century Fox Video Games, Bill Aspromonte) (11012) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/bank_heist.bin\\n\",\n      \"copying ms_pacman.bin from ./Roms/ROMS/Ms. Pac-Man (1983) (Atari - GCC, Mark S. Ackerman, Patricia Goodson, Josh Littlefield, Douglas B. Macrae, Glenn Parker) (CX2675) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/ms_pacman.bin\\n\",\n      \"copying private_eye.bin from ./Roms/ROMS/Private Eye (1984) (Activision, Bob Whitehead) (AG-034-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/private_eye.bin\\n\",\n      \"copying journey_escape.bin from ./Roms/ROMS/Journey Escape (1983) (Data Age, J. Ray Dettling) (112-006) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/journey_escape.bin\\n\",\n      \"copying asterix.bin from ./Roms/ROMS/Asterix (AKA Taz) (1984) (Atari, Jerome Domurat, Steve Woita) (CX2696).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/asterix.bin\\n\",\n      \"copying boxing.bin from ./Roms/ROMS/Boxing - La Boxe (1980) (Activision, Bob Whitehead) (AG-002, CAG-002, AG-002-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/boxing.bin\\n\",\n      \"copying fishing_derby.bin from ./Roms/ROMS/Fishing Derby (1980) (Activision, David Crane) (AG-004) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/fishing_derby.bin\\n\",\n      \"copying kaboom.bin from ./Roms/ROMS/Kaboom! (Paddle) (1981) (Activision, Larry Kaplan, David Crane) (AG-010, CAG-010, AG-010-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/kaboom.bin\\n\",\n      \"copying freeway.bin from ./Roms/ROMS/Freeway (1981) (Activision, David Crane) (AG-009, AG-009-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/freeway.bin\\n\",\n      \"copying pooyan.bin from ./Roms/ROMS/Pooyan (1983) (Konami) (RC 100-X 02) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/pooyan.bin\\n\",\n      \"copying asteroids.bin from ./Roms/ROMS/Asteroids (1981) (Atari, Brad Stewart - Sears) (CX2649 - 49-75163) [no copyright] ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/asteroids.bin\\n\",\n      \"copying frogger.bin from ./Roms/ROMS/Frogger (1982) (Parker Brothers, Ed English, David Lamkins) (PB5300) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/frogger.bin\\n\",\n      \"copying chopper_command.bin from ./Roms/ROMS/Chopper Command (1982) (Activision, Bob Whitehead) (AX-015, AX-015-04) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/chopper_command.bin\\n\",\n      \"copying time_pilot.bin from ./Roms/ROMS/Time Pilot (1983) (Coleco - Woodside Design Associates, Harley H. Puthuff Jr.) (2663) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/time_pilot.bin\\n\",\n      \"copying galaxian.bin from ./Roms/ROMS/Galaxian (1983) (Atari - GCC, Mark S. Ackerman, Tom Calderwood, Patricia Goodson, Glenn Parker) (CX2684) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/galaxian.bin\\n\",\n      \"copying adventure.bin from ./Roms/ROMS/Adventure (1980) (Atari, Warren Robinett) (CX2613, CX2613P) (PAL).bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/adventure.bin\\n\",\n      \"copying solaris.bin from ./Roms/ROMS/Solaris (The Last Starfighter, Star Raiders II, Universe) (1986) (Atari, Douglas Neubauer, Mimi Nyden) (CX26136) ~.bin to /Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/atari_py/atari_roms/solaris.bin\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!python -m atari_py.import_roms ./Roms/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/ale_py/roms/utils.py:90: DeprecationWarning: SelectableGroups dict interface is deprecated. Use select.\\n\",\n      \"  for external in metadata.entry_points().get(self.group, []):\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/gym/envs/registration.py:424: UserWarning: \\u001b[33mWARN: Custom namespace `ALE` is being overridden by namespace `ALE`. If you are developing a plugin you shouldn't specify a namespace in `register` calls. The namespace is specified through the entry point package metadata.\\u001b[0m\\n\",\n      \"  f\\\"Custom namespace `{spec.namespace}` is being overridden \\\"\\n\",\n      \"Warning: Gym version v0.24.0 has a number of critical issues with `gym.make` such that the `reset` and `step` functions are called before returning the environment. It is recommend to downgrading to v0.23.1 or upgrading to v0.25.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# general imports\\n\",\n    \"import cv2\\n\",\n    \"import math\\n\",\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"# reinforcement learning related imports\\n\",\n    \"import re\\n\",\n    \"import atari_py as ap\\n\",\n    \"from collections import deque\\n\",\n    \"from gym import make, ObservationWrapper, Wrapper\\n\",\n    \"from gym.spaces import Box\\n\",\n    \"\\n\",\n    \"# pytorch imports \\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch import save\\n\",\n    \"from torch.optim import Adam\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvDQN(nn.Module):\\n\",\n    \"    def __init__(self, ip_sz, tot_num_acts):\\n\",\n    \"        super(ConvDQN, self).__init__()\\n\",\n    \"        self._ip_sz = ip_sz\\n\",\n    \"        self._tot_num_acts = tot_num_acts\\n\",\n    \"\\n\",\n    \"        self.cnv1 = nn.Conv2d(ip_sz[0], 32, kernel_size=8, stride=4)\\n\",\n    \"        self.activation = nn.ReLU()\\n\",\n    \"        self.cnv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)\\n\",\n    \"        self.cnv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)\\n\",\n    \"        self.fc1 = nn.Linear(self.feat_sz, 512)\\n\",\n    \"        self.fc2 = nn.Linear(512, tot_num_acts)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        op = self.cnv1(x)\\n\",\n    \"        op = self.activation(op)\\n\",\n    \"        op = self.cnv2(op)\\n\",\n    \"        op = self.activation(op)\\n\",\n    \"        op = self.cnv3(op)\\n\",\n    \"        op = self.activation(op).view(x.size()[0], -1)\\n\",\n    \"        op = self.fc1(op)\\n\",\n    \"        op = self.activation(op)\\n\",\n    \"        op = self.fc2(op)\\n\",\n    \"        return op\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def feat_sz(self):\\n\",\n    \"        x = torch.zeros(1, *self._ip_sz)\\n\",\n    \"        x = self.cnv1(x)\\n\",\n    \"        x = self.activation(x)\\n\",\n    \"        x = self.cnv2(x)\\n\",\n    \"        x = self.activation(x)\\n\",\n    \"        x = self.cnv3(x)\\n\",\n    \"        x = self.activation(x)\\n\",\n    \"        return x.view(1, -1).size(1)\\n\",\n    \"\\n\",\n    \"    def perf_action(self, stt, eps, dvc):\\n\",\n    \"        if random.random() > eps:\\n\",\n    \"            stt = torch.from_numpy(np.float32(stt)).unsqueeze(0).to(dvc)\\n\",\n    \"            q_val = self.forward(stt)\\n\",\n    \"            act = q_val.max(1)[1].item()\\n\",\n    \"        else:\\n\",\n    \"            act = random.randrange(self._tot_num_acts)\\n\",\n    \"        return act\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def calc_temp_diff_loss(mdl, tgt_mdl, bch, gm, dvc):\\n\",\n    \"    st, act, rwd, nxt_st, fin = bch\\n\",\n    \"\\n\",\n    \"    st = torch.from_numpy(np.float32(st)).to(dvc)\\n\",\n    \"    nxt_st = torch.from_numpy(np.float32(nxt_st)).to(dvc)\\n\",\n    \"    act = torch.from_numpy(act).to(dvc)\\n\",\n    \"    rwd = torch.from_numpy(rwd).to(dvc)\\n\",\n    \"    fin = torch.from_numpy(fin).to(dvc)\\n\",\n    \"\\n\",\n    \"    q_vals = mdl(st)\\n\",\n    \"    nxt_q_vals = tgt_mdl(nxt_st)\\n\",\n    \"\\n\",\n    \"    q_val = q_vals.gather(1, act.unsqueeze(-1)).squeeze(-1)\\n\",\n    \"    nxt_q_val = nxt_q_vals.max(1)[0]\\n\",\n    \"    exp_q_val = rwd + gm * nxt_q_val * (1 - fin)\\n\",\n    \"\\n\",\n    \"    loss = (q_val - exp_q_val.data.to(dvc)).pow(2).mean()\\n\",\n    \"    loss.backward()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def upd_eps(epd):\\n\",\n    \"    last_eps = EPS_FINL\\n\",\n    \"    first_eps = EPS_STRT\\n\",\n    \"    eps_decay = EPS_DECAY\\n\",\n    \"    eps = last_eps + (first_eps - last_eps) * math.exp(-1 * ((epd + 1) / eps_decay))\\n\",\n    \"    return eps\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def models_init(env, dvc):\\n\",\n    \"    mdl = ConvDQN(env.observation_space.shape, env.action_space.n).to(dvc)\\n\",\n    \"    tgt_mdl = ConvDQN(env.observation_space.shape, env.action_space.n).to(dvc)\\n\",\n    \"    return mdl, tgt_mdl\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def gym_to_atari_format(gym_env):\\n\",\n    \"    return re.sub(r\\\"(?<!^)(?=[A-Z])\\\", \\\"_\\\", gym_env).lower()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def check_atari_env(env):\\n\",\n    \"    for f in [\\\"Deterministic\\\", \\\"ramDeterministic\\\", \\\"ram\\\", \\\"NoFrameskip\\\", \\\"ramNoFrameSkip\\\"]:\\n\",\n    \"        env = env.replace(f, \\\"\\\")\\n\",\n    \"    env = re.sub(r\\\"-v\\\\d+\\\", \\\"\\\", env)\\n\",\n    \"    env = gym_to_atari_format(env) \\n\",\n    \"    return True if env in ap.list_games() else False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RepBfr:\\n\",\n    \"    def __init__(self, cap_max):\\n\",\n    \"        self._bfr = deque(maxlen=cap_max)\\n\",\n    \"\\n\",\n    \"    def push(self, st, act, rwd, nxt_st, fin):\\n\",\n    \"        self._bfr.append((st, act, rwd, nxt_st, fin))\\n\",\n    \"\\n\",\n    \"    def smpl(self, bch_sz):\\n\",\n    \"        idxs = np.random.choice(len(self._bfr), bch_sz, False)\\n\",\n    \"        bch = zip(*[self._bfr[i] for i in idxs])\\n\",\n    \"        st, act, rwd, nxt_st, fin = bch\\n\",\n    \"        return (np.array(st), np.array(act), np.array(rwd, dtype=np.float32),\\n\",\n    \"                np.array(nxt_st), np.array(fin, dtype=np.uint8))\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return len(self._bfr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class TrMetadata:\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self._avg = 0.0\\n\",\n    \"        self._bst_rwd = -float(\\\"inf\\\")\\n\",\n    \"        self._bst_avg = -float(\\\"inf\\\")\\n\",\n    \"        self._rwds = []\\n\",\n    \"        self._avg_rng = 100\\n\",\n    \"        self._idx = 0\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def bst_rwd(self):\\n\",\n    \"        return self._bst_rwd\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def bst_avg(self):\\n\",\n    \"        return self._bst_avg\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def avg(self):\\n\",\n    \"        avg_rng = self._avg_rng * -1\\n\",\n    \"        return sum(self._rwds[avg_rng:]) / len(self._rwds[avg_rng:])\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def idx(self):\\n\",\n    \"        return self._idx\\n\",\n    \"\\n\",\n    \"    def _upd_bst_rwd(self, epd_rwd):\\n\",\n    \"        if epd_rwd > self.bst_rwd:\\n\",\n    \"            self._bst_rwd = epd_rwd\\n\",\n    \"\\n\",\n    \"    def _upd_bst_avg(self):\\n\",\n    \"        if self.avg > self.bst_avg:\\n\",\n    \"            self._bst_avg = self.avg\\n\",\n    \"            return True\\n\",\n    \"        return False\\n\",\n    \"\\n\",\n    \"    def upd_rwds(self, epd_rwd):\\n\",\n    \"        self._rwds.append(epd_rwd)\\n\",\n    \"        self._upd_bst_rwd(epd_rwd)\\n\",\n    \"        return self._upd_bst_avg()\\n\",\n    \"\\n\",\n    \"    def upd_idx(self):\\n\",\n    \"        self._idx += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ClassicControl(Wrapper):\\n\",\n    \"    def __init__(self, env, is_atari):\\n\",\n    \"        super(ClassicControl, self).__init__(env)\\n\",\n    \"        self._is_atari = is_atari\\n\",\n    \"\\n\",\n    \"    def reset(self):\\n\",\n    \"        if self._is_atari:\\n\",\n    \"            return self.env.reset()\\n\",\n    \"        else:\\n\",\n    \"            self.env.reset()\\n\",\n    \"            return self.env.render(mode=\\\"rgb_array\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class FrameDownSample(ObservationWrapper):\\n\",\n    \"    def __init__(self, env):\\n\",\n    \"        super(FrameDownSample, self).__init__(env)\\n\",\n    \"        self.observation_space = Box(low=0, high=255, shape=(84, 84, 1), dtype=np.uint8)\\n\",\n    \"        self._width = 84\\n\",\n    \"        self._height = 84\\n\",\n    \"\\n\",\n    \"    def observation(self, observation):\\n\",\n    \"        frame = cv2.cvtColor(observation, cv2.COLOR_RGB2GRAY)\\n\",\n    \"        frame = cv2.resize(frame, (self._width, self._height), interpolation=cv2.INTER_AREA)\\n\",\n    \"        return frame[:, :, None]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MaxAndSkipEnv(Wrapper):\\n\",\n    \"    def __init__(self, env, atari, skip=4):\\n\",\n    \"        super(MaxAndSkipEnv, self).__init__(env)\\n\",\n    \"        self._obs_buffer = deque(maxlen=2)\\n\",\n    \"        self._skip = skip\\n\",\n    \"        self._atari = atari\\n\",\n    \"\\n\",\n    \"    def step(self, act):\\n\",\n    \"        total_rwd = 0.0\\n\",\n    \"        fin = None\\n\",\n    \"        for _ in range(self._skip):\\n\",\n    \"            obs, rwd, fin, log = self.env.step(act)\\n\",\n    \"            if not self._atari:\\n\",\n    \"                obs = self.env.render(mode=\\\"rgb_array\\\")\\n\",\n    \"            self._obs_buffer.append(obs)\\n\",\n    \"            total_rwd += rwd\\n\",\n    \"            if fin:\\n\",\n    \"                break\\n\",\n    \"        max_frame = np.max(np.stack(self._obs_buffer), axis=0)\\n\",\n    \"        return max_frame, total_rwd, fin, log\\n\",\n    \"\\n\",\n    \"    def reset(self):\\n\",\n    \"        self._obs_buffer.clear()\\n\",\n    \"        obs = self.env.reset()\\n\",\n    \"        self._obs_buffer.append(obs)\\n\",\n    \"        return obs\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class FrameResetEnv(Wrapper):\\n\",\n    \"    def __init__(self, env):\\n\",\n    \"        Wrapper.__init__(self, env)\\n\",\n    \"        if len(env.unwrapped.get_action_meanings()) < 3:\\n\",\n    \"            raise ValueError(\\\"min required action space of 3!\\\")\\n\",\n    \"\\n\",\n    \"    def reset(self, **kwargs):\\n\",\n    \"        self.env.reset(**kwargs)\\n\",\n    \"        obs, _, fin, _ = self.env.step(1)\\n\",\n    \"        if fin:\\n\",\n    \"            self.env.reset(**kwargs)\\n\",\n    \"        obs, _, fin, _ = self.env.step(2)\\n\",\n    \"        if fin:\\n\",\n    \"            self.env.reset(**kwargs)\\n\",\n    \"        return obs\\n\",\n    \"\\n\",\n    \"    def step(self, act):\\n\",\n    \"        return self.env.step(act)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class FrameBuffer(ObservationWrapper):\\n\",\n    \"    def __init__(self, env, num_steps, dtype=np.float32):\\n\",\n    \"        super(FrameBuffer, self).__init__(env)\\n\",\n    \"        obs_space = env.observation_space\\n\",\n    \"        self._dtype = dtype\\n\",\n    \"        self.observation_space = Box(obs_space.low.repeat(num_steps, axis=0),\\n\",\n    \"                                     obs_space.high.repeat(num_steps, axis=0), dtype=self._dtype)\\n\",\n    \"\\n\",\n    \"    def reset(self):\\n\",\n    \"        self.buffer = np.zeros_like(self.observation_space.low, dtype=self._dtype)\\n\",\n    \"        return self.observation(self.env.reset())\\n\",\n    \"\\n\",\n    \"    def observation(self, observation):\\n\",\n    \"        self.buffer[:-1] = self.buffer[1:]\\n\",\n    \"        self.buffer[-1] = observation\\n\",\n    \"        return self.buffer\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Image2PyTorch(ObservationWrapper):\\n\",\n    \"    def __init__(self, env):\\n\",\n    \"        super(Image2PyTorch, self).__init__(env)\\n\",\n    \"        obs_shape = self.observation_space.shape\\n\",\n    \"        self.observation_space = Box(low=0.0, high=1.0, shape=(obs_shape[::-1]), dtype=np.float32)\\n\",\n    \"\\n\",\n    \"    def observation(self, observation):\\n\",\n    \"        return np.moveaxis(observation, 2, 0)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class NormalizeFloats(ObservationWrapper):\\n\",\n    \"    def observation(self, obs):\\n\",\n    \"        return np.array(obs).astype(np.float32) / 255.0\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"def wrap_env(env_ip):\\n\",\n    \"    env = make(env_ip)\\n\",\n    \"    is_atari = check_atari_env(env_ip)\\n\",\n    \"    env = ClassicControl(env, is_atari)\\n\",\n    \"    env = MaxAndSkipEnv(env, is_atari)\\n\",\n    \"    try:\\n\",\n    \"        env_acts = env.unwrapped.get_action_meanings()\\n\",\n    \"        if \\\"FIRE\\\" in env_acts:\\n\",\n    \"            env = FrameResetEnv(env)\\n\",\n    \"    except AttributeError:\\n\",\n    \"        pass\\n\",\n    \"    env = FrameDownSample(env)\\n\",\n    \"    env = Image2PyTorch(env)\\n\",\n    \"    env = FrameBuffer(env, 4)\\n\",\n    \"    env = NormalizeFloats(env)\\n\",\n    \"    return env\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def upd_grph(mdl, tgt_mdl, opt, rpl_bfr, dvc, log):\\n\",\n    \"    if len(rpl_bfr) > INIT_LEARN:\\n\",\n    \"        if not log.idx % TGT_UPD_FRQ:\\n\",\n    \"            tgt_mdl.load_state_dict(mdl.state_dict())\\n\",\n    \"        opt.zero_grad()\\n\",\n    \"        bch = rpl_bfr.smpl(B_S)\\n\",\n    \"        calc_temp_diff_loss(mdl, tgt_mdl, bch, G, dvc)\\n\",\n    \"        opt.step()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def fin_epsd(mdl, env, log, epd_rwd, epd, eps):\\n\",\n    \"    bst_so_fat = log.upd_rwds(epd_rwd)\\n\",\n    \"    if bst_so_fat:\\n\",\n    \"        print(f\\\"checkpointing current model weights. highest running_average_reward of\\\\\\n\",\n    \" {round(log.bst_avg, 3)} achieved!\\\")\\n\",\n    \"        save(mdl.state_dict(), f\\\"{env}.dat\\\")\\n\",\n    \"    print(f\\\"episode_num {epd}, curr_reward: {epd_rwd}, best_reward: {log.bst_rwd},\\\\\\n\",\n    \" running_avg_reward: {round(log.avg, 3)}, curr_epsilon: {round(eps, 4)}\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def run_epsd(env, mdl, tgt_mdl, opt, rpl_bfr, dvc, log, epd):\\n\",\n    \"    epd_rwd = 0.0\\n\",\n    \"    st = env.reset()\\n\",\n    \"\\n\",\n    \"    while True:\\n\",\n    \"        eps = upd_eps(log.idx)\\n\",\n    \"        act = mdl.perf_action(st, eps, dvc)\\n\",\n    \"        if True:\\n\",\n    \"            env.render(mode=\\\"rgb_array\\\")\\n\",\n    \"        nxt_st, rwd, fin, _ = env.step(act)\\n\",\n    \"        rpl_bfr.push(st, act, rwd, nxt_st, fin)\\n\",\n    \"        st = nxt_st\\n\",\n    \"        epd_rwd += rwd\\n\",\n    \"        log.upd_idx()\\n\",\n    \"        upd_grph(mdl, tgt_mdl, opt, rpl_bfr, dvc, log)\\n\",\n    \"        if fin:\\n\",\n    \"            fin_epsd(mdl, ENV, log, epd_rwd, epd, eps)\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def train(env, mdl, tgt_mdl, opt, rpl_bfr, dvc):\\n\",\n    \"    log = TrMetadata()\\n\",\n    \"\\n\",\n    \"    for epd in range(N_EPDS):\\n\",\n    \"        run_epsd(env, mdl, tgt_mdl, opt, rpl_bfr, dvc, log, epd)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"B_S = 64\\n\",\n    \"ENV = \\\"Pong-v4\\\"\\n\",\n    \"EPS_STRT = 1.0\\n\",\n    \"EPS_FINL = 0.005\\n\",\n    \"EPS_DECAY = 100000\\n\",\n    \"G = 0.99\\n\",\n    \"INIT_LEARN = 10000\\n\",\n    \"LR = 1e-4\\n\",\n    \"MEM_CAP = 20000\\n\",\n    \"N_EPDS = 50000\\n\",\n    \"TGT_UPD_FRQ = 1000\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A.L.E: Arcade Learning Environment (version +978d2ce)\\n\",\n      \"[Powered by Stella]\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/gym/utils/seeding.py:160: DeprecationWarning: \\u001b[33mWARN: Function `hash_seed(seed, max_bytes)` is marked as deprecated and will be removed in the future. \\u001b[0m\\n\",\n      \"  \\\"Function `hash_seed(seed, max_bytes)` is marked as deprecated and will be removed in the future. \\\"\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/gym/utils/seeding.py:204: DeprecationWarning: \\u001b[33mWARN: Function `_bigint_from_bytes(bytes)` is marked as deprecated and will be removed in the future. \\u001b[0m\\n\",\n      \"  \\\"Function `_bigint_from_bytes(bytes)` is marked as deprecated and will be removed in the future. \\\"\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/gym/utils/seeding.py:64: DeprecationWarning: \\u001b[33mWARN: Function `rng.randint(low, [high, size, dtype])` is marked as deprecated and will be removed in the future. Please use `rng.integers(low, [high, size, dtype])` instead.\\u001b[0m\\n\",\n      \"  \\\"Function `rng.randint(low, [high, size, dtype])` is marked as deprecated \\\"\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python37/lib/python3.7/site-packages/gym/envs/registration.py:620: UserWarning: \\u001b[33mWARN: Env check failed with the following message: The environment cannot be reset with a random seed, even though `seed` or `kwargs` appear in the signature. This should never happen, please report this issue. The error was: reset() got an unexpected keyword argument 'seed'\\n\",\n      \"You can set `disable_env_checker=True` to disable this check.\\u001b[0m\\n\",\n      \"  f\\\"Env check failed with the following message: {e}\\\\n\\\"\\n\",\n      \"[W NNPACK.cpp:51] Could not initialize NNPACK! Reason: Unsupported hardware.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -21.0 achieved!\\n\",\n      \"episode_num 0, curr_reward: -21.0, best_reward: -21.0, running_avg_reward: -21.0, curr_epsilon: 0.9974\\n\",\n      \"episode_num 1, curr_reward: -21.0, best_reward: -21.0, running_avg_reward: -21.0, curr_epsilon: 0.9948\\n\",\n      \"episode_num 2, curr_reward: -21.0, best_reward: -21.0, running_avg_reward: -21.0, curr_epsilon: 0.9919\\n\",\n      \"episode_num 3, curr_reward: -21.0, best_reward: -21.0, running_avg_reward: -21.0, curr_epsilon: 0.9888\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.8 achieved!\\n\",\n      \"episode_num 4, curr_reward: -20.0, best_reward: -20.0, running_avg_reward: -20.8, curr_epsilon: 0.9855\\n\",\n      \"episode_num 5, curr_reward: -21.0, best_reward: -20.0, running_avg_reward: -20.833, curr_epsilon: 0.983\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.714 achieved!\\n\",\n      \"episode_num 6, curr_reward: -20.0, best_reward: -20.0, running_avg_reward: -20.714, curr_epsilon: 0.98\\n\",\n      \"episode_num 7, curr_reward: -21.0, best_reward: -20.0, running_avg_reward: -20.75, curr_epsilon: 0.977\\n\",\n      \"episode_num 8, curr_reward: -21.0, best_reward: -20.0, running_avg_reward: -20.778, curr_epsilon: 0.9744\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.6 achieved!\\n\",\n      \"episode_num 9, curr_reward: -19.0, best_reward: -19.0, running_avg_reward: -20.6, curr_epsilon: 0.9712\\n\",\n      \"episode_num 10, curr_reward: -21.0, best_reward: -19.0, running_avg_reward: -20.636, curr_epsilon: 0.9678\\n\",\n      \"episode_num 11, curr_reward: -21.0, best_reward: -19.0, running_avg_reward: -20.667, curr_epsilon: 0.9654\\n\",\n      \"episode_num 12, curr_reward: -21.0, best_reward: -19.0, running_avg_reward: -20.692, curr_epsilon: 0.9628\\n\",\n      \"episode_num 13, curr_reward: -21.0, best_reward: -19.0, running_avg_reward: -20.714, curr_epsilon: 0.9603\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.467 achieved!\\n\",\n      \"episode_num 14, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -20.467, curr_epsilon: 0.9566\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.438 achieved!\\n\",\n      \"episode_num 15, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.438, curr_epsilon: 0.9538\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.412 achieved!\\n\",\n      \"episode_num 16, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.412, curr_epsilon: 0.9505\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.278 achieved!\\n\",\n      \"episode_num 17, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.278, curr_epsilon: 0.947\\n\",\n      \"episode_num 18, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.316, curr_epsilon: 0.9444\\n\",\n      \"episode_num 19, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.3, curr_epsilon: 0.9414\\n\",\n      \"episode_num 20, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.286, curr_epsilon: 0.9388\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.273 achieved!\\n\",\n      \"episode_num 21, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.273, curr_epsilon: 0.9362\\n\",\n      \"episode_num 22, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.304, curr_epsilon: 0.9335\\n\",\n      \"episode_num 23, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.333, curr_epsilon: 0.931\\n\",\n      \"episode_num 24, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.32, curr_epsilon: 0.9284\\n\",\n      \"episode_num 25, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.308, curr_epsilon: 0.9253\\n\",\n      \"episode_num 26, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.333, curr_epsilon: 0.9229\\n\",\n      \"episode_num 27, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.357, curr_epsilon: 0.9204\\n\",\n      \"episode_num 28, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.379, curr_epsilon: 0.9181\\n\",\n      \"episode_num 29, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.9153\\n\",\n      \"episode_num 30, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.419, curr_epsilon: 0.9127\\n\",\n      \"episode_num 31, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.438, curr_epsilon: 0.9103\\n\",\n      \"episode_num 32, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.455, curr_epsilon: 0.9076\\n\",\n      \"episode_num 33, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.441, curr_epsilon: 0.9049\\n\",\n      \"episode_num 34, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.457, curr_epsilon: 0.9023\\n\",\n      \"episode_num 35, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.472, curr_epsilon: 0.8998\\n\",\n      \"episode_num 36, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.486, curr_epsilon: 0.8975\\n\",\n      \"episode_num 37, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.895\\n\",\n      \"episode_num 38, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.487, curr_epsilon: 0.8925\\n\",\n      \"episode_num 39, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.8898\\n\",\n      \"episode_num 40, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.512, curr_epsilon: 0.8875\\n\",\n      \"episode_num 41, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.524, curr_epsilon: 0.8851\\n\",\n      \"episode_num 42, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.535, curr_epsilon: 0.8827\\n\",\n      \"episode_num 43, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.545, curr_epsilon: 0.8803\\n\",\n      \"episode_num 44, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.556, curr_epsilon: 0.8779\\n\",\n      \"episode_num 45, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.522, curr_epsilon: 0.8752\\n\",\n      \"episode_num 46, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.511, curr_epsilon: 0.8724\\n\",\n      \"episode_num 47, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.521, curr_epsilon: 0.8697\\n\",\n      \"episode_num 48, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.51, curr_epsilon: 0.8674\\n\",\n      \"episode_num 49, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.52, curr_epsilon: 0.8649\\n\",\n      \"episode_num 50, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.51, curr_epsilon: 0.8625\\n\",\n      \"episode_num 51, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.519, curr_epsilon: 0.8599\\n\",\n      \"episode_num 52, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.528, curr_epsilon: 0.8573\\n\",\n      \"episode_num 53, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.519, curr_epsilon: 0.8545\\n\",\n      \"episode_num 54, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.527, curr_epsilon: 0.8519\\n\",\n      \"episode_num 55, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.518, curr_epsilon: 0.8491\\n\",\n      \"episode_num 56, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.526, curr_epsilon: 0.8467\\n\",\n      \"episode_num 57, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.517, curr_epsilon: 0.8439\\n\",\n      \"episode_num 58, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.508, curr_epsilon: 0.8413\\n\",\n      \"episode_num 59, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.517, curr_epsilon: 0.8387\\n\",\n      \"episode_num 60, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.492, curr_epsilon: 0.8358\\n\",\n      \"episode_num 61, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.8333\\n\",\n      \"episode_num 62, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.492, curr_epsilon: 0.8306\\n\",\n      \"episode_num 63, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.8282\\n\",\n      \"episode_num 64, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.508, curr_epsilon: 0.8254\\n\",\n      \"episode_num 65, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.8229\\n\",\n      \"episode_num 66, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.493, curr_epsilon: 0.8202\\n\",\n      \"episode_num 67, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.8179\\n\",\n      \"episode_num 68, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.507, curr_epsilon: 0.815\\n\",\n      \"episode_num 69, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.514, curr_epsilon: 0.8122\\n\",\n      \"episode_num 70, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.521, curr_epsilon: 0.8096\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 71, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.528, curr_epsilon: 0.8067\\n\",\n      \"episode_num 72, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.534, curr_epsilon: 0.8043\\n\",\n      \"episode_num 73, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.514, curr_epsilon: 0.8006\\n\",\n      \"episode_num 74, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.507, curr_epsilon: 0.7983\\n\",\n      \"episode_num 75, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.513, curr_epsilon: 0.7959\\n\",\n      \"episode_num 76, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.519, curr_epsilon: 0.7931\\n\",\n      \"episode_num 77, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7905\\n\",\n      \"episode_num 78, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.506, curr_epsilon: 0.7881\\n\",\n      \"episode_num 79, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7855\\n\",\n      \"episode_num 80, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.494, curr_epsilon: 0.7833\\n\",\n      \"episode_num 81, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7805\\n\",\n      \"episode_num 82, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.494, curr_epsilon: 0.7781\\n\",\n      \"episode_num 83, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7753\\n\",\n      \"episode_num 84, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.506, curr_epsilon: 0.7731\\n\",\n      \"episode_num 85, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7705\\n\",\n      \"episode_num 86, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.506, curr_epsilon: 0.7684\\n\",\n      \"episode_num 87, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.511, curr_epsilon: 0.7661\\n\",\n      \"episode_num 88, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.517, curr_epsilon: 0.7639\\n\",\n      \"episode_num 89, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.522, curr_epsilon: 0.762\\n\",\n      \"episode_num 90, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.527, curr_epsilon: 0.7597\\n\",\n      \"episode_num 91, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.533, curr_epsilon: 0.7571\\n\",\n      \"episode_num 92, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.527, curr_epsilon: 0.7542\\n\",\n      \"episode_num 93, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.521, curr_epsilon: 0.7516\\n\",\n      \"episode_num 94, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.526, curr_epsilon: 0.7494\\n\",\n      \"episode_num 95, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.531, curr_epsilon: 0.7471\\n\",\n      \"episode_num 96, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.536, curr_epsilon: 0.745\\n\",\n      \"episode_num 97, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.52, curr_epsilon: 0.7423\\n\",\n      \"episode_num 98, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.515, curr_epsilon: 0.7396\\n\",\n      \"episode_num 99, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7368\\n\",\n      \"episode_num 100, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.5, curr_epsilon: 0.7343\\n\",\n      \"episode_num 101, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.47, curr_epsilon: 0.7314\\n\",\n      \"episode_num 102, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.47, curr_epsilon: 0.7287\\n\",\n      \"episode_num 103, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.47, curr_epsilon: 0.7266\\n\",\n      \"episode_num 104, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.47, curr_epsilon: 0.7243\\n\",\n      \"episode_num 105, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.7215\\n\",\n      \"episode_num 106, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -20.42, curr_epsilon: 0.7184\\n\",\n      \"episode_num 107, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.42, curr_epsilon: 0.7161\\n\",\n      \"episode_num 108, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.7137\\n\",\n      \"episode_num 109, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.41, curr_epsilon: 0.7112\\n\",\n      \"episode_num 110, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.41, curr_epsilon: 0.7092\\n\",\n      \"episode_num 111, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.7071\\n\",\n      \"episode_num 112, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.7046\\n\",\n      \"episode_num 113, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.39, curr_epsilon: 0.7023\\n\",\n      \"episode_num 114, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.42, curr_epsilon: 0.7002\\n\",\n      \"episode_num 115, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.41, curr_epsilon: 0.6979\\n\",\n      \"episode_num 116, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.42, curr_epsilon: 0.6953\\n\",\n      \"episode_num 117, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.6929\\n\",\n      \"episode_num 118, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.43, curr_epsilon: 0.6904\\n\",\n      \"episode_num 119, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.44, curr_epsilon: 0.6885\\n\",\n      \"episode_num 120, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.44, curr_epsilon: 0.6864\\n\",\n      \"episode_num 121, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.6845\\n\",\n      \"episode_num 122, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.6824\\n\",\n      \"episode_num 123, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.44, curr_epsilon: 0.6798\\n\",\n      \"episode_num 124, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.44, curr_epsilon: 0.6775\\n\",\n      \"episode_num 125, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.6753\\n\",\n      \"episode_num 126, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.673\\n\",\n      \"episode_num 127, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.45, curr_epsilon: 0.6712\\n\",\n      \"episode_num 128, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.44, curr_epsilon: 0.669\\n\",\n      \"episode_num 129, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.42, curr_epsilon: 0.6665\\n\",\n      \"episode_num 130, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.41, curr_epsilon: 0.6643\\n\",\n      \"episode_num 131, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.39, curr_epsilon: 0.6616\\n\",\n      \"episode_num 132, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.39, curr_epsilon: 0.6593\\n\",\n      \"episode_num 133, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.6574\\n\",\n      \"episode_num 134, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.6554\\n\",\n      \"episode_num 135, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.6536\\n\",\n      \"episode_num 136, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.4, curr_epsilon: 0.6517\\n\",\n      \"episode_num 137, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.39, curr_epsilon: 0.6493\\n\",\n      \"episode_num 138, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.37, curr_epsilon: 0.6461\\n\",\n      \"episode_num 139, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.35, curr_epsilon: 0.6437\\n\",\n      \"episode_num 140, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.32, curr_epsilon: 0.6413\\n\",\n      \"episode_num 141, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -20.28, curr_epsilon: 0.6386\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.25 achieved!\\n\",\n      \"episode_num 142, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.25, curr_epsilon: 0.6361\\n\",\n      \"episode_num 143, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.25, curr_epsilon: 0.6338\\n\",\n      \"episode_num 144, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.25, curr_epsilon: 0.6321\\n\",\n      \"episode_num 145, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.26, curr_epsilon: 0.63\\n\",\n      \"episode_num 146, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.26, curr_epsilon: 0.6276\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.23 achieved!\\n\",\n      \"episode_num 147, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.625\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 148, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.24, curr_epsilon: 0.6227\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.22 achieved!\\n\",\n      \"episode_num 149, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.22, curr_epsilon: 0.6205\\n\",\n      \"episode_num 150, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.6187\\n\",\n      \"episode_num 151, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.6166\\n\",\n      \"episode_num 152, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.22, curr_epsilon: 0.6142\\n\",\n      \"episode_num 153, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.6122\\n\",\n      \"episode_num 154, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.6101\\n\",\n      \"episode_num 155, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.23, curr_epsilon: 0.6079\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.19 achieved!\\n\",\n      \"episode_num 156, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -20.19, curr_epsilon: 0.6048\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.17 achieved!\\n\",\n      \"episode_num 157, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.17, curr_epsilon: 0.6022\\n\",\n      \"episode_num 158, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.18, curr_epsilon: 0.5999\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.15 achieved!\\n\",\n      \"episode_num 159, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5973\\n\",\n      \"episode_num 160, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.17, curr_epsilon: 0.5956\\n\",\n      \"episode_num 161, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5929\\n\",\n      \"episode_num 162, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.16, curr_epsilon: 0.5908\\n\",\n      \"episode_num 163, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5883\\n\",\n      \"episode_num 164, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5859\\n\",\n      \"episode_num 165, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.16, curr_epsilon: 0.5839\\n\",\n      \"episode_num 166, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.16, curr_epsilon: 0.5815\\n\",\n      \"episode_num 167, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5791\\n\",\n      \"episode_num 168, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.15, curr_epsilon: 0.5767\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.14 achieved!\\n\",\n      \"episode_num 169, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.14, curr_epsilon: 0.5747\\n\",\n      \"episode_num 170, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.14, curr_epsilon: 0.5729\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.12 achieved!\\n\",\n      \"episode_num 171, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.12, curr_epsilon: 0.5704\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.11 achieved!\\n\",\n      \"episode_num 172, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.11, curr_epsilon: 0.568\\n\",\n      \"episode_num 173, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.11, curr_epsilon: 0.5658\\n\",\n      \"episode_num 174, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.11, curr_epsilon: 0.5634\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.08 achieved!\\n\",\n      \"episode_num 175, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.08, curr_epsilon: 0.5609\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.05 achieved!\\n\",\n      \"episode_num 176, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.05, curr_epsilon: 0.5587\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.04 achieved!\\n\",\n      \"episode_num 177, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.04, curr_epsilon: 0.5562\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.02 achieved!\\n\",\n      \"episode_num 178, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -20.02, curr_epsilon: 0.5537\\n\",\n      \"episode_num 179, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -20.03, curr_epsilon: 0.5516\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.01 achieved!\\n\",\n      \"episode_num 180, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -20.01, curr_epsilon: 0.5497\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -20.0 achieved!\\n\",\n      \"episode_num 181, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -20.0, curr_epsilon: 0.5476\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.99 achieved!\\n\",\n      \"episode_num 182, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.99, curr_epsilon: 0.5453\\n\",\n      \"episode_num 183, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.99, curr_epsilon: 0.5433\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.97 achieved!\\n\",\n      \"episode_num 184, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.97, curr_epsilon: 0.5415\\n\",\n      \"episode_num 185, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.97, curr_epsilon: 0.5393\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.93 achieved!\\n\",\n      \"episode_num 186, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -19.93, curr_epsilon: 0.5368\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.89 achieved!\\n\",\n      \"episode_num 187, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -19.89, curr_epsilon: 0.5344\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.86 achieved!\\n\",\n      \"episode_num 188, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.86, curr_epsilon: 0.5323\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.84 achieved!\\n\",\n      \"episode_num 189, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.84, curr_epsilon: 0.5298\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.81 achieved!\\n\",\n      \"episode_num 190, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.81, curr_epsilon: 0.5269\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.8 achieved!\\n\",\n      \"episode_num 191, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.8, curr_epsilon: 0.5252\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.78 achieved!\\n\",\n      \"episode_num 192, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.78, curr_epsilon: 0.5229\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.77 achieved!\\n\",\n      \"episode_num 193, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.77, curr_epsilon: 0.5208\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.74 achieved!\\n\",\n      \"episode_num 194, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5186\\n\",\n      \"episode_num 195, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5165\\n\",\n      \"episode_num 196, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5148\\n\",\n      \"episode_num 197, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5127\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.72 achieved!\\n\",\n      \"episode_num 198, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.72, curr_epsilon: 0.5103\\n\",\n      \"episode_num 199, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5081\\n\",\n      \"episode_num 200, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.72, curr_epsilon: 0.5058\\n\",\n      \"episode_num 201, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.75, curr_epsilon: 0.5039\\n\",\n      \"episode_num 202, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.74, curr_epsilon: 0.5022\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.7 achieved!\\n\",\n      \"episode_num 203, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -19.7, curr_epsilon: 0.5\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 204, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.71, curr_epsilon: 0.4986\\n\",\n      \"episode_num 205, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.72, curr_epsilon: 0.4968\\n\",\n      \"episode_num 206, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.75, curr_epsilon: 0.4952\\n\",\n      \"episode_num 207, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.72, curr_epsilon: 0.4931\\n\",\n      \"episode_num 208, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -19.7, curr_epsilon: 0.4905\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.69 achieved!\\n\",\n      \"episode_num 209, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.69, curr_epsilon: 0.4884\\n\",\n      \"episode_num 210, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.69, curr_epsilon: 0.4865\\n\",\n      \"episode_num 211, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.69, curr_epsilon: 0.4848\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.68 achieved!\\n\",\n      \"episode_num 212, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.68, curr_epsilon: 0.4829\\n\",\n      \"episode_num 213, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.69, curr_epsilon: 0.481\\n\",\n      \"episode_num 214, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.68, curr_epsilon: 0.479\\n\",\n      \"episode_num 215, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.69, curr_epsilon: 0.4771\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.67 achieved!\\n\",\n      \"episode_num 216, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.67, curr_epsilon: 0.4753\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.66 achieved!\\n\",\n      \"episode_num 217, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.66, curr_epsilon: 0.4735\\n\",\n      \"episode_num 218, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.67, curr_epsilon: 0.4716\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.64 achieved!\\n\",\n      \"episode_num 219, curr_reward: -18.0, best_reward: -17.0, running_avg_reward: -19.64, curr_epsilon: 0.4692\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.63 achieved!\\n\",\n      \"episode_num 220, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.63, curr_epsilon: 0.4672\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.62 achieved!\\n\",\n      \"episode_num 221, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.62, curr_epsilon: 0.4654\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.61 achieved!\\n\",\n      \"episode_num 222, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.61, curr_epsilon: 0.4635\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.6 achieved!\\n\",\n      \"episode_num 223, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.6, curr_epsilon: 0.4617\\n\",\n      \"episode_num 224, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.6, curr_epsilon: 0.4599\\n\",\n      \"episode_num 225, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.6, curr_epsilon: 0.4583\\n\",\n      \"episode_num 226, curr_reward: -21.0, best_reward: -17.0, running_avg_reward: -19.6, curr_epsilon: 0.4565\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.56 achieved!\\n\",\n      \"episode_num 227, curr_reward: -17.0, best_reward: -17.0, running_avg_reward: -19.56, curr_epsilon: 0.4545\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.55 achieved!\\n\",\n      \"episode_num 228, curr_reward: -19.0, best_reward: -17.0, running_avg_reward: -19.55, curr_epsilon: 0.4521\\n\",\n      \"episode_num 229, curr_reward: -20.0, best_reward: -17.0, running_avg_reward: -19.56, curr_epsilon: 0.4497\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.52 achieved!\\n\",\n      \"episode_num 230, curr_reward: -16.0, best_reward: -16.0, running_avg_reward: -19.52, curr_epsilon: 0.4469\\n\",\n      \"episode_num 231, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.53, curr_epsilon: 0.445\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.51 achieved!\\n\",\n      \"episode_num 232, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.51, curr_epsilon: 0.4434\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.49 achieved!\\n\",\n      \"episode_num 233, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.49, curr_epsilon: 0.4414\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.47 achieved!\\n\",\n      \"episode_num 234, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.47, curr_epsilon: 0.4393\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.44 achieved!\\n\",\n      \"episode_num 235, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.44, curr_epsilon: 0.4372\\n\",\n      \"episode_num 236, curr_reward: -21.0, best_reward: -16.0, running_avg_reward: -19.44, curr_epsilon: 0.4357\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.42 achieved!\\n\",\n      \"episode_num 237, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.42, curr_epsilon: 0.4337\\n\",\n      \"episode_num 238, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.42, curr_epsilon: 0.4314\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.41 achieved!\\n\",\n      \"episode_num 239, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.41, curr_epsilon: 0.4293\\n\",\n      \"episode_num 240, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.42, curr_epsilon: 0.4277\\n\",\n      \"episode_num 241, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.44, curr_epsilon: 0.4257\\n\",\n      \"episode_num 242, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.45, curr_epsilon: 0.4236\\n\",\n      \"episode_num 243, curr_reward: -17.0, best_reward: -16.0, running_avg_reward: -19.41, curr_epsilon: 0.4213\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.39 achieved!\\n\",\n      \"episode_num 244, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.4196\\n\",\n      \"episode_num 245, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.418\\n\",\n      \"episode_num 246, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.4161\\n\",\n      \"episode_num 247, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.414\\n\",\n      \"episode_num 248, curr_reward: -21.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.4123\\n\",\n      \"episode_num 249, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.39, curr_epsilon: 0.4103\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.36 achieved!\\n\",\n      \"episode_num 250, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.36, curr_epsilon: 0.408\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.35 achieved!\\n\",\n      \"episode_num 251, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.35, curr_epsilon: 0.4061\\n\",\n      \"episode_num 252, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.35, curr_epsilon: 0.4043\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.33 achieved!\\n\",\n      \"episode_num 253, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.33, curr_epsilon: 0.4022\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.31 achieved!\\n\",\n      \"episode_num 254, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.31, curr_epsilon: 0.4001\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.28 achieved!\\n\",\n      \"episode_num 255, curr_reward: -17.0, best_reward: -16.0, running_avg_reward: -19.28, curr_epsilon: 0.398\\n\",\n      \"episode_num 256, curr_reward: -17.0, best_reward: -16.0, running_avg_reward: -19.28, curr_epsilon: 0.3957\\n\",\n      \"episode_num 257, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.3, curr_epsilon: 0.3938\\n\",\n      \"episode_num 258, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.28, curr_epsilon: 0.3918\\n\",\n      \"episode_num 259, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.28, curr_epsilon: 0.3895\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.27 achieved!\\n\",\n      \"episode_num 260, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.27, curr_epsilon: 0.388\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 261, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.27, curr_epsilon: 0.3859\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.25 achieved!\\n\",\n      \"episode_num 262, curr_reward: -19.0, best_reward: -16.0, running_avg_reward: -19.25, curr_epsilon: 0.3839\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.22 achieved!\\n\",\n      \"episode_num 263, curr_reward: -17.0, best_reward: -16.0, running_avg_reward: -19.22, curr_epsilon: 0.3817\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.19 achieved!\\n\",\n      \"episode_num 264, curr_reward: -18.0, best_reward: -16.0, running_avg_reward: -19.19, curr_epsilon: 0.3798\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.18 achieved!\\n\",\n      \"episode_num 265, curr_reward: -20.0, best_reward: -16.0, running_avg_reward: -19.18, curr_epsilon: 0.3781\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.12 achieved!\\n\",\n      \"episode_num 266, curr_reward: -14.0, best_reward: -14.0, running_avg_reward: -19.12, curr_epsilon: 0.3758\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.1 achieved!\\n\",\n      \"episode_num 267, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.1, curr_epsilon: 0.3742\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.09 achieved!\\n\",\n      \"episode_num 268, curr_reward: -20.0, best_reward: -14.0, running_avg_reward: -19.09, curr_epsilon: 0.3722\\n\",\n      \"episode_num 269, curr_reward: -20.0, best_reward: -14.0, running_avg_reward: -19.09, curr_epsilon: 0.3703\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.07 achieved!\\n\",\n      \"episode_num 270, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.07, curr_epsilon: 0.3686\\n\",\n      \"episode_num 271, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.07, curr_epsilon: 0.367\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.05 achieved!\\n\",\n      \"episode_num 272, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.05, curr_epsilon: 0.365\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.04 achieved!\\n\",\n      \"episode_num 273, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3631\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.02 achieved!\\n\",\n      \"episode_num 274, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.02, curr_epsilon: 0.3616\\n\",\n      \"episode_num 275, curr_reward: -20.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.36\\n\",\n      \"episode_num 276, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.05, curr_epsilon: 0.3584\\n\",\n      \"episode_num 277, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.05, curr_epsilon: 0.356\\n\",\n      \"episode_num 278, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3544\\n\",\n      \"episode_num 279, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.02, curr_epsilon: 0.353\\n\",\n      \"episode_num 280, curr_reward: -21.0, best_reward: -14.0, running_avg_reward: -19.05, curr_epsilon: 0.3517\\n\",\n      \"episode_num 281, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.03, curr_epsilon: 0.35\\n\",\n      \"episode_num 282, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.03, curr_epsilon: 0.3481\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.01 achieved!\\n\",\n      \"episode_num 283, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.01, curr_epsilon: 0.3463\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -19.0 achieved!\\n\",\n      \"episode_num 284, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.0, curr_epsilon: 0.3446\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.97 achieved!\\n\",\n      \"episode_num 285, curr_reward: -17.0, best_reward: -14.0, running_avg_reward: -18.97, curr_epsilon: 0.3427\\n\",\n      \"episode_num 286, curr_reward: -20.0, best_reward: -14.0, running_avg_reward: -19.0, curr_epsilon: 0.3411\\n\",\n      \"episode_num 287, curr_reward: -21.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3398\\n\",\n      \"episode_num 288, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3379\\n\",\n      \"episode_num 289, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3366\\n\",\n      \"episode_num 290, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.05, curr_epsilon: 0.335\\n\",\n      \"episode_num 291, curr_reward: -19.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3336\\n\",\n      \"episode_num 292, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.04, curr_epsilon: 0.3317\\n\",\n      \"episode_num 293, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.03, curr_epsilon: 0.3301\\n\",\n      \"episode_num 294, curr_reward: -18.0, best_reward: -14.0, running_avg_reward: -19.03, curr_epsilon: 0.3283\\n\",\n      \"episode_num 295, curr_reward: -15.0, best_reward: -14.0, running_avg_reward: -18.97, curr_epsilon: 0.3263\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.93 achieved!\\n\",\n      \"episode_num 296, curr_reward: -17.0, best_reward: -14.0, running_avg_reward: -18.93, curr_epsilon: 0.3249\\n\",\n      \"episode_num 297, curr_reward: -20.0, best_reward: -14.0, running_avg_reward: -18.94, curr_epsilon: 0.3232\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.92 achieved!\\n\",\n      \"episode_num 298, curr_reward: -16.0, best_reward: -14.0, running_avg_reward: -18.92, curr_epsilon: 0.3214\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.84 achieved!\\n\",\n      \"episode_num 299, curr_reward: -13.0, best_reward: -13.0, running_avg_reward: -18.84, curr_epsilon: 0.319\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.81 achieved!\\n\",\n      \"episode_num 300, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.81, curr_epsilon: 0.3165\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.79 achieved!\\n\",\n      \"episode_num 301, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.79, curr_epsilon: 0.3149\\n\",\n      \"episode_num 302, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.79, curr_epsilon: 0.3136\\n\",\n      \"episode_num 303, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.82, curr_epsilon: 0.3124\\n\",\n      \"episode_num 304, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.79, curr_epsilon: 0.3111\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.77 achieved!\\n\",\n      \"episode_num 305, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.77, curr_epsilon: 0.3094\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.76 achieved!\\n\",\n      \"episode_num 306, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.76, curr_epsilon: 0.308\\n\",\n      \"episode_num 307, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.78, curr_epsilon: 0.3067\\n\",\n      \"episode_num 308, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.77, curr_epsilon: 0.3051\\n\",\n      \"episode_num 309, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.78, curr_epsilon: 0.3039\\n\",\n      \"episode_num 310, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.76, curr_epsilon: 0.3025\\n\",\n      \"episode_num 311, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.76, curr_epsilon: 0.3012\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.72 achieved!\\n\",\n      \"episode_num 312, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.72, curr_epsilon: 0.2995\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.67 achieved!\\n\",\n      \"episode_num 313, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.67, curr_epsilon: 0.2979\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.64 achieved!\\n\",\n      \"episode_num 314, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.64, curr_epsilon: 0.2961\\n\",\n      \"episode_num 315, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.64, curr_epsilon: 0.2948\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.6 achieved!\\n\",\n      \"episode_num 316, curr_reward: -15.0, best_reward: -13.0, running_avg_reward: -18.6, curr_epsilon: 0.293\\n\",\n      \"episode_num 317, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.6, curr_epsilon: 0.2914\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -18.59 achieved!\\n\",\n      \"episode_num 318, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.59, curr_epsilon: 0.2896\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.57 achieved!\\n\",\n      \"episode_num 319, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.57, curr_epsilon: 0.2877\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.55 achieved!\\n\",\n      \"episode_num 320, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.55, curr_epsilon: 0.2861\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.54 achieved!\\n\",\n      \"episode_num 321, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.54, curr_epsilon: 0.2847\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.52 achieved!\\n\",\n      \"episode_num 322, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.52, curr_epsilon: 0.2831\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.51 achieved!\\n\",\n      \"episode_num 323, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.51, curr_epsilon: 0.2817\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.49 achieved!\\n\",\n      \"episode_num 324, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.49, curr_epsilon: 0.2801\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.45 achieved!\\n\",\n      \"episode_num 325, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.45, curr_epsilon: 0.2785\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.4 achieved!\\n\",\n      \"episode_num 326, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.4, curr_epsilon: 0.277\\n\",\n      \"episode_num 327, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.42, curr_epsilon: 0.2758\\n\",\n      \"episode_num 328, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.43, curr_epsilon: 0.2747\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.38 achieved!\\n\",\n      \"episode_num 329, curr_reward: -15.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.273\\n\",\n      \"episode_num 330, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.42, curr_epsilon: 0.272\\n\",\n      \"episode_num 331, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.4, curr_epsilon: 0.2706\\n\",\n      \"episode_num 332, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.41, curr_epsilon: 0.2694\\n\",\n      \"episode_num 333, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.4, curr_epsilon: 0.2679\\n\",\n      \"episode_num 334, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.39, curr_epsilon: 0.2666\\n\",\n      \"episode_num 335, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.4, curr_epsilon: 0.2653\\n\",\n      \"episode_num 336, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.2637\\n\",\n      \"episode_num 337, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.2624\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.36 achieved!\\n\",\n      \"episode_num 338, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.36, curr_epsilon: 0.2609\\n\",\n      \"episode_num 339, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.2598\\n\",\n      \"episode_num 340, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.2586\\n\",\n      \"episode_num 341, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.39, curr_epsilon: 0.2572\\n\",\n      \"episode_num 342, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.37, curr_epsilon: 0.2558\\n\",\n      \"episode_num 343, curr_reward: -16.0, best_reward: -13.0, running_avg_reward: -18.36, curr_epsilon: 0.2544\\n\",\n      \"episode_num 344, curr_reward: -21.0, best_reward: -13.0, running_avg_reward: -18.38, curr_epsilon: 0.2533\\n\",\n      \"episode_num 345, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.36, curr_epsilon: 0.2519\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.33 achieved!\\n\",\n      \"episode_num 346, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.33, curr_epsilon: 0.2502\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.32 achieved!\\n\",\n      \"episode_num 347, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.32, curr_epsilon: 0.2487\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.3 achieved!\\n\",\n      \"episode_num 348, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.3, curr_epsilon: 0.2476\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.29 achieved!\\n\",\n      \"episode_num 349, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.29, curr_epsilon: 0.2462\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.28 achieved!\\n\",\n      \"episode_num 350, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.28, curr_epsilon: 0.2448\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.27 achieved!\\n\",\n      \"episode_num 351, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.27, curr_epsilon: 0.2437\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.25 achieved!\\n\",\n      \"episode_num 352, curr_reward: -18.0, best_reward: -13.0, running_avg_reward: -18.25, curr_epsilon: 0.2425\\n\",\n      \"episode_num 353, curr_reward: -20.0, best_reward: -13.0, running_avg_reward: -18.26, curr_epsilon: 0.2413\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.24 achieved!\\n\",\n      \"episode_num 354, curr_reward: -17.0, best_reward: -13.0, running_avg_reward: -18.24, curr_epsilon: 0.2401\\n\",\n      \"episode_num 355, curr_reward: -19.0, best_reward: -13.0, running_avg_reward: -18.26, curr_epsilon: 0.239\\n\",\n      \"episode_num 356, curr_reward: -21.0, best_reward: -13.0, running_avg_reward: -18.3, curr_epsilon: 0.2381\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.22 achieved!\\n\",\n      \"episode_num 357, curr_reward: -12.0, best_reward: -12.0, running_avg_reward: -18.22, curr_epsilon: 0.2363\\n\",\n      \"episode_num 358, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.22, curr_epsilon: 0.2353\\n\",\n      \"episode_num 359, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.23, curr_epsilon: 0.2336\\n\",\n      \"episode_num 360, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.22, curr_epsilon: 0.2324\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.16 achieved!\\n\",\n      \"episode_num 361, curr_reward: -13.0, best_reward: -12.0, running_avg_reward: -18.16, curr_epsilon: 0.2307\\n\",\n      \"episode_num 362, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.16, curr_epsilon: 0.2296\\n\",\n      \"episode_num 363, curr_reward: -21.0, best_reward: -12.0, running_avg_reward: -18.2, curr_epsilon: 0.2287\\n\",\n      \"episode_num 364, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.19, curr_epsilon: 0.2272\\n\",\n      \"episode_num 365, curr_reward: -18.0, best_reward: -12.0, running_avg_reward: -18.17, curr_epsilon: 0.2261\\n\",\n      \"episode_num 366, curr_reward: -16.0, best_reward: -12.0, running_avg_reward: -18.19, curr_epsilon: 0.2248\\n\",\n      \"episode_num 367, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.2, curr_epsilon: 0.2237\\n\",\n      \"episode_num 368, curr_reward: -18.0, best_reward: -12.0, running_avg_reward: -18.18, curr_epsilon: 0.2224\\n\",\n      \"episode_num 369, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.17, curr_epsilon: 0.2213\\n\",\n      \"episode_num 370, curr_reward: -18.0, best_reward: -12.0, running_avg_reward: -18.16, curr_epsilon: 0.22\\n\",\n      \"episode_num 371, curr_reward: -21.0, best_reward: -12.0, running_avg_reward: -18.18, curr_epsilon: 0.2191\\n\",\n      \"episode_num 372, curr_reward: -20.0, best_reward: -12.0, running_avg_reward: -18.2, curr_epsilon: 0.2181\\n\",\n      \"episode_num 373, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.21, curr_epsilon: 0.2166\\n\",\n      \"episode_num 374, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.22, curr_epsilon: 0.2152\\n\",\n      \"episode_num 375, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.19, curr_epsilon: 0.2141\\n\",\n      \"episode_num 376, curr_reward: -20.0, best_reward: -12.0, running_avg_reward: -18.2, curr_epsilon: 0.213\\n\",\n      \"episode_num 377, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.21, curr_epsilon: 0.2121\\n\",\n      \"episode_num 378, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.2, curr_epsilon: 0.2109\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 379, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.18, curr_epsilon: 0.2095\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.14 achieved!\\n\",\n      \"episode_num 380, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.14, curr_epsilon: 0.2082\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.12 achieved!\\n\",\n      \"episode_num 381, curr_reward: -16.0, best_reward: -12.0, running_avg_reward: -18.12, curr_epsilon: 0.2068\\n\",\n      \"episode_num 382, curr_reward: -19.0, best_reward: -12.0, running_avg_reward: -18.12, curr_epsilon: 0.2059\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.1 achieved!\\n\",\n      \"episode_num 383, curr_reward: -17.0, best_reward: -12.0, running_avg_reward: -18.1, curr_epsilon: 0.2046\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.08 achieved!\\n\",\n      \"episode_num 384, curr_reward: -16.0, best_reward: -12.0, running_avg_reward: -18.08, curr_epsilon: 0.2036\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.02 achieved!\\n\",\n      \"episode_num 385, curr_reward: -11.0, best_reward: -11.0, running_avg_reward: -18.02, curr_epsilon: 0.2021\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -18.0 achieved!\\n\",\n      \"episode_num 386, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -18.0, curr_epsilon: 0.201\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.97 achieved!\\n\",\n      \"episode_num 387, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.97, curr_epsilon: 0.1999\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.95 achieved!\\n\",\n      \"episode_num 388, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.95, curr_epsilon: 0.1988\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.94 achieved!\\n\",\n      \"episode_num 389, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.94, curr_epsilon: 0.1979\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.92 achieved!\\n\",\n      \"episode_num 390, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.92, curr_epsilon: 0.1967\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.9 achieved!\\n\",\n      \"episode_num 391, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.1956\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.88 achieved!\\n\",\n      \"episode_num 392, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.1944\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.87 achieved!\\n\",\n      \"episode_num 393, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1932\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.84 achieved!\\n\",\n      \"episode_num 394, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.84, curr_epsilon: 0.1918\\n\",\n      \"episode_num 395, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1908\\n\",\n      \"episode_num 396, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.19\\n\",\n      \"episode_num 397, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.86, curr_epsilon: 0.189\\n\",\n      \"episode_num 398, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.84, curr_epsilon: 0.1878\\n\",\n      \"episode_num 399, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1868\\n\",\n      \"episode_num 400, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1857\\n\",\n      \"episode_num 401, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1848\\n\",\n      \"episode_num 402, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1841\\n\",\n      \"episode_num 403, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.1831\\n\",\n      \"episode_num 404, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1822\\n\",\n      \"episode_num 405, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1815\\n\",\n      \"episode_num 406, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.1805\\n\",\n      \"episode_num 407, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1794\\n\",\n      \"episode_num 408, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1782\\n\",\n      \"episode_num 409, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.1773\\n\",\n      \"episode_num 410, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1763\\n\",\n      \"episode_num 411, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.84, curr_epsilon: 0.1753\\n\",\n      \"episode_num 412, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.1745\\n\",\n      \"episode_num 413, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.87, curr_epsilon: 0.1732\\n\",\n      \"episode_num 414, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.1719\\n\",\n      \"episode_num 415, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.88, curr_epsilon: 0.1708\\n\",\n      \"episode_num 416, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.93, curr_epsilon: 0.17\\n\",\n      \"episode_num 417, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.169\\n\",\n      \"episode_num 418, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.1682\\n\",\n      \"episode_num 419, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.94, curr_epsilon: 0.1673\\n\",\n      \"episode_num 420, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.96, curr_epsilon: 0.1664\\n\",\n      \"episode_num 421, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.94, curr_epsilon: 0.1654\\n\",\n      \"episode_num 422, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.95, curr_epsilon: 0.1646\\n\",\n      \"episode_num 423, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.97, curr_epsilon: 0.1639\\n\",\n      \"episode_num 424, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.97, curr_epsilon: 0.1632\\n\",\n      \"episode_num 425, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.97, curr_epsilon: 0.1623\\n\",\n      \"episode_num 426, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.98, curr_epsilon: 0.1613\\n\",\n      \"episode_num 427, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.95, curr_epsilon: 0.16\\n\",\n      \"episode_num 428, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.92, curr_epsilon: 0.1589\\n\",\n      \"episode_num 429, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.91, curr_epsilon: 0.1577\\n\",\n      \"episode_num 430, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.89, curr_epsilon: 0.1568\\n\",\n      \"episode_num 431, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.9, curr_epsilon: 0.1559\\n\",\n      \"episode_num 432, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.85, curr_epsilon: 0.1548\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.81 achieved!\\n\",\n      \"episode_num 433, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.81, curr_epsilon: 0.1537\\n\",\n      \"episode_num 434, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.83, curr_epsilon: 0.1531\\n\",\n      \"episode_num 435, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -17.84, curr_epsilon: 0.1521\\n\",\n      \"episode_num 436, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.84, curr_epsilon: 0.1513\\n\",\n      \"episode_num 437, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.83, curr_epsilon: 0.1504\\n\",\n      \"episode_num 438, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.83, curr_epsilon: 0.1492\\n\",\n      \"episode_num 439, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.82, curr_epsilon: 0.1484\\n\",\n      \"episode_num 440, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.82, curr_epsilon: 0.1475\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.79 achieved!\\n\",\n      \"episode_num 441, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.79, curr_epsilon: 0.1467\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.78 achieved!\\n\",\n      \"episode_num 442, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.78, curr_epsilon: 0.1456\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 443, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.81, curr_epsilon: 0.1448\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.76 achieved!\\n\",\n      \"episode_num 444, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.76, curr_epsilon: 0.1438\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.71 achieved!\\n\",\n      \"episode_num 445, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -17.71, curr_epsilon: 0.1426\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.69 achieved!\\n\",\n      \"episode_num 446, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.69, curr_epsilon: 0.1416\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.68 achieved!\\n\",\n      \"episode_num 447, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.68, curr_epsilon: 0.1407\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.66 achieved!\\n\",\n      \"episode_num 448, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.66, curr_epsilon: 0.1398\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.64 achieved!\\n\",\n      \"episode_num 449, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.64, curr_epsilon: 0.139\\n\",\n      \"episode_num 450, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.66, curr_epsilon: 0.138\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.6 achieved!\\n\",\n      \"episode_num 451, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -17.6, curr_epsilon: 0.1368\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.57 achieved!\\n\",\n      \"episode_num 452, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.57, curr_epsilon: 0.1359\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.52 achieved!\\n\",\n      \"episode_num 453, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.52, curr_epsilon: 0.1348\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.48 achieved!\\n\",\n      \"episode_num 454, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -17.48, curr_epsilon: 0.1337\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.42 achieved!\\n\",\n      \"episode_num 455, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -17.42, curr_epsilon: 0.1327\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.37 achieved!\\n\",\n      \"episode_num 456, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.37, curr_epsilon: 0.132\\n\",\n      \"episode_num 457, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.44, curr_epsilon: 0.1313\\n\",\n      \"episode_num 458, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.42, curr_epsilon: 0.1304\\n\",\n      \"episode_num 459, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.41, curr_epsilon: 0.1296\\n\",\n      \"episode_num 460, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.37, curr_epsilon: 0.1288\\n\",\n      \"episode_num 461, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.4, curr_epsilon: 0.1279\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.35 achieved!\\n\",\n      \"episode_num 462, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.35, curr_epsilon: 0.127\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.27 achieved!\\n\",\n      \"episode_num 463, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -17.27, curr_epsilon: 0.1261\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.25 achieved!\\n\",\n      \"episode_num 464, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.25, curr_epsilon: 0.1251\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.23 achieved!\\n\",\n      \"episode_num 465, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.23, curr_epsilon: 0.1243\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.19 achieved!\\n\",\n      \"episode_num 466, curr_reward: -12.0, best_reward: -11.0, running_avg_reward: -17.19, curr_epsilon: 0.1232\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.16 achieved!\\n\",\n      \"episode_num 467, curr_reward: -16.0, best_reward: -11.0, running_avg_reward: -17.16, curr_epsilon: 0.1224\\n\",\n      \"episode_num 468, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.16, curr_epsilon: 0.1215\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.11 achieved!\\n\",\n      \"episode_num 469, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.11, curr_epsilon: 0.1207\\n\",\n      \"episode_num 470, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -17.12, curr_epsilon: 0.12\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.09 achieved!\\n\",\n      \"episode_num 471, curr_reward: -18.0, best_reward: -11.0, running_avg_reward: -17.09, curr_epsilon: 0.1193\\n\",\n      \"episode_num 472, curr_reward: -21.0, best_reward: -11.0, running_avg_reward: -17.1, curr_epsilon: 0.1185\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.05 achieved!\\n\",\n      \"episode_num 473, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -17.05, curr_epsilon: 0.1176\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -17.01 achieved!\\n\",\n      \"episode_num 474, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -17.01, curr_epsilon: 0.1167\\n\",\n      \"episode_num 475, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -17.01, curr_epsilon: 0.116\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.94 achieved!\\n\",\n      \"episode_num 476, curr_reward: -13.0, best_reward: -11.0, running_avg_reward: -16.94, curr_epsilon: 0.115\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.92 achieved!\\n\",\n      \"episode_num 477, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -16.92, curr_epsilon: 0.1143\\n\",\n      \"episode_num 478, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -16.94, curr_epsilon: 0.1136\\n\",\n      \"episode_num 479, curr_reward: -20.0, best_reward: -11.0, running_avg_reward: -16.97, curr_epsilon: 0.1131\\n\",\n      \"episode_num 480, curr_reward: -14.0, best_reward: -11.0, running_avg_reward: -16.94, curr_epsilon: 0.1123\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.9 achieved!\\n\",\n      \"episode_num 481, curr_reward: -12.0, best_reward: -11.0, running_avg_reward: -16.9, curr_epsilon: 0.1113\\n\",\n      \"episode_num 482, curr_reward: -19.0, best_reward: -11.0, running_avg_reward: -16.9, curr_epsilon: 0.1105\\n\",\n      \"episode_num 483, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -16.9, curr_epsilon: 0.1096\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.89 achieved!\\n\",\n      \"episode_num 484, curr_reward: -15.0, best_reward: -11.0, running_avg_reward: -16.89, curr_epsilon: 0.1086\\n\",\n      \"episode_num 485, curr_reward: -17.0, best_reward: -11.0, running_avg_reward: -16.95, curr_epsilon: 0.1079\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.85 achieved!\\n\",\n      \"episode_num 486, curr_reward: -8.0, best_reward: -8.0, running_avg_reward: -16.85, curr_epsilon: 0.1068\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.82 achieved!\\n\",\n      \"episode_num 487, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.82, curr_epsilon: 0.1061\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.8 achieved!\\n\",\n      \"episode_num 488, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.8, curr_epsilon: 0.1052\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.78 achieved!\\n\",\n      \"episode_num 489, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.78, curr_epsilon: 0.1045\\n\",\n      \"episode_num 490, curr_reward: -21.0, best_reward: -8.0, running_avg_reward: -16.82, curr_epsilon: 0.104\\n\",\n      \"episode_num 491, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.81, curr_epsilon: 0.1034\\n\",\n      \"episode_num 492, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.78, curr_epsilon: 0.1025\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.74 achieved!\\n\",\n      \"episode_num 493, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.74, curr_epsilon: 0.1015\\n\",\n      \"episode_num 494, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.76, curr_epsilon: 0.1008\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 495, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.75, curr_epsilon: 0.1001\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.73 achieved!\\n\",\n      \"episode_num 496, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.73, curr_epsilon: 0.0995\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.7 achieved!\\n\",\n      \"episode_num 497, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.7, curr_epsilon: 0.0986\\n\",\n      \"episode_num 498, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.7, curr_epsilon: 0.0979\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.69 achieved!\\n\",\n      \"episode_num 499, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.69, curr_epsilon: 0.0971\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.65 achieved!\\n\",\n      \"episode_num 500, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.65, curr_epsilon: 0.0963\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.63 achieved!\\n\",\n      \"episode_num 501, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.63, curr_epsilon: 0.0957\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.6 achieved!\\n\",\n      \"episode_num 502, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.6, curr_epsilon: 0.095\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.59 achieved!\\n\",\n      \"episode_num 503, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.59, curr_epsilon: 0.0943\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.57 achieved!\\n\",\n      \"episode_num 504, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.57, curr_epsilon: 0.0936\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.5 achieved!\\n\",\n      \"episode_num 505, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.5, curr_epsilon: 0.0928\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.48 achieved!\\n\",\n      \"episode_num 506, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.48, curr_epsilon: 0.0922\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.47 achieved!\\n\",\n      \"episode_num 507, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.47, curr_epsilon: 0.0914\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.46 achieved!\\n\",\n      \"episode_num 508, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.46, curr_epsilon: 0.0908\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.44 achieved!\\n\",\n      \"episode_num 509, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.44, curr_epsilon: 0.0902\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.42 achieved!\\n\",\n      \"episode_num 510, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.42, curr_epsilon: 0.0897\\n\",\n      \"episode_num 511, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -16.44, curr_epsilon: 0.0892\\n\",\n      \"episode_num 512, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -16.43, curr_epsilon: 0.0886\\n\",\n      \"episode_num 513, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.46, curr_epsilon: 0.0881\\n\",\n      \"episode_num 514, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.42, curr_epsilon: 0.0874\\n\",\n      \"episode_num 515, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.42, curr_epsilon: 0.0869\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.4 achieved!\\n\",\n      \"episode_num 516, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.4, curr_epsilon: 0.0863\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.38 achieved!\\n\",\n      \"episode_num 517, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.38, curr_epsilon: 0.0856\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.33 achieved!\\n\",\n      \"episode_num 518, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.33, curr_epsilon: 0.0851\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.3 achieved!\\n\",\n      \"episode_num 519, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.3, curr_epsilon: 0.0846\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.27 achieved!\\n\",\n      \"episode_num 520, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.27, curr_epsilon: 0.084\\n\",\n      \"episode_num 521, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.27, curr_epsilon: 0.0835\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.26 achieved!\\n\",\n      \"episode_num 522, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.26, curr_epsilon: 0.0829\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.22 achieved!\\n\",\n      \"episode_num 523, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.22, curr_epsilon: 0.0823\\n\",\n      \"episode_num 524, curr_reward: -18.0, best_reward: -8.0, running_avg_reward: -16.22, curr_epsilon: 0.0817\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.2 achieved!\\n\",\n      \"episode_num 525, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.2, curr_epsilon: 0.0811\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.18 achieved!\\n\",\n      \"episode_num 526, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.18, curr_epsilon: 0.0806\\n\",\n      \"episode_num 527, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.18, curr_epsilon: 0.0799\\n\",\n      \"episode_num 528, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.18, curr_epsilon: 0.0792\\n\",\n      \"episode_num 529, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.18, curr_epsilon: 0.0786\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.16 achieved!\\n\",\n      \"episode_num 530, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.16, curr_epsilon: 0.078\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.13 achieved!\\n\",\n      \"episode_num 531, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.13, curr_epsilon: 0.0775\\n\",\n      \"episode_num 532, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.14, curr_epsilon: 0.0769\\n\",\n      \"episode_num 533, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.15, curr_epsilon: 0.0762\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.1 achieved!\\n\",\n      \"episode_num 534, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.1, curr_epsilon: 0.0757\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.05 achieved!\\n\",\n      \"episode_num 535, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.05, curr_epsilon: 0.0751\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.02 achieved!\\n\",\n      \"episode_num 536, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.0745\\n\",\n      \"episode_num 537, curr_reward: -20.0, best_reward: -8.0, running_avg_reward: -16.05, curr_epsilon: 0.0741\\n\",\n      \"episode_num 538, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.05, curr_epsilon: 0.0735\\n\",\n      \"episode_num 539, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.0731\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -16.0 achieved!\\n\",\n      \"episode_num 540, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.0, curr_epsilon: 0.0727\\n\",\n      \"episode_num 541, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.0723\\n\",\n      \"episode_num 542, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -16.05, curr_epsilon: 0.0718\\n\",\n      \"episode_num 543, curr_reward: -21.0, best_reward: -8.0, running_avg_reward: -16.07, curr_epsilon: 0.0713\\n\",\n      \"episode_num 544, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.08, curr_epsilon: 0.0708\\n\",\n      \"episode_num 545, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.08, curr_epsilon: 0.0702\\n\",\n      \"episode_num 546, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.08, curr_epsilon: 0.0697\\n\",\n      \"episode_num 547, curr_reward: -8.0, best_reward: -8.0, running_avg_reward: -16.0, curr_epsilon: 0.069\\n\",\n      \"episode_num 548, curr_reward: -20.0, best_reward: -8.0, running_avg_reward: -16.03, curr_epsilon: 0.0686\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 549, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.068\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.95 achieved!\\n\",\n      \"episode_num 550, curr_reward: -12.0, best_reward: -8.0, running_avg_reward: -15.95, curr_epsilon: 0.0674\\n\",\n      \"episode_num 551, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -15.96, curr_epsilon: 0.0669\\n\",\n      \"episode_num 552, curr_reward: -20.0, best_reward: -8.0, running_avg_reward: -16.01, curr_epsilon: 0.0664\\n\",\n      \"episode_num 553, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.03, curr_epsilon: 0.066\\n\",\n      \"episode_num 554, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.03, curr_epsilon: 0.0653\\n\",\n      \"episode_num 555, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.07, curr_epsilon: 0.0649\\n\",\n      \"episode_num 556, curr_reward: -11.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.0643\\n\",\n      \"episode_num 557, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -15.96, curr_epsilon: 0.0636\\n\",\n      \"episode_num 558, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -15.98, curr_epsilon: 0.0632\\n\",\n      \"episode_num 559, curr_reward: -15.0, best_reward: -8.0, running_avg_reward: -15.95, curr_epsilon: 0.0628\\n\",\n      \"episode_num 560, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -15.99, curr_epsilon: 0.0624\\n\",\n      \"episode_num 561, curr_reward: -12.0, best_reward: -8.0, running_avg_reward: -15.95, curr_epsilon: 0.0618\\n\",\n      \"episode_num 562, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -15.98, curr_epsilon: 0.0615\\n\",\n      \"episode_num 563, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -16.04, curr_epsilon: 0.0611\\n\",\n      \"episode_num 564, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -16.02, curr_epsilon: 0.0606\\n\",\n      \"episode_num 565, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -16.03, curr_epsilon: 0.0601\\n\",\n      \"episode_num 566, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.05, curr_epsilon: 0.0596\\n\",\n      \"episode_num 567, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -16.03, curr_epsilon: 0.0591\\n\",\n      \"episode_num 568, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -15.99, curr_epsilon: 0.0587\\n\",\n      \"episode_num 569, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -16.01, curr_epsilon: 0.0582\\n\",\n      \"episode_num 570, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -15.95, curr_epsilon: 0.0578\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.91 achieved!\\n\",\n      \"episode_num 571, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -15.91, curr_epsilon: 0.0573\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.82 achieved!\\n\",\n      \"episode_num 572, curr_reward: -12.0, best_reward: -8.0, running_avg_reward: -15.82, curr_epsilon: 0.0568\\n\",\n      \"episode_num 573, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -15.84, curr_epsilon: 0.0563\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.81 achieved!\\n\",\n      \"episode_num 574, curr_reward: -12.0, best_reward: -8.0, running_avg_reward: -15.81, curr_epsilon: 0.0558\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.8 achieved!\\n\",\n      \"episode_num 575, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -15.8, curr_epsilon: 0.0554\\n\",\n      \"episode_num 576, curr_reward: -14.0, best_reward: -8.0, running_avg_reward: -15.81, curr_epsilon: 0.0548\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.77 achieved!\\n\",\n      \"episode_num 577, curr_reward: -13.0, best_reward: -8.0, running_avg_reward: -15.77, curr_epsilon: 0.0544\\n\",\n      \"episode_num 578, curr_reward: -19.0, best_reward: -8.0, running_avg_reward: -15.77, curr_epsilon: 0.0541\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.74 achieved!\\n\",\n      \"episode_num 579, curr_reward: -17.0, best_reward: -8.0, running_avg_reward: -15.74, curr_epsilon: 0.0538\\n\",\n      \"episode_num 580, curr_reward: -16.0, best_reward: -8.0, running_avg_reward: -15.76, curr_epsilon: 0.0533\\n\",\n      \"episode_num 581, curr_reward: -21.0, best_reward: -8.0, running_avg_reward: -15.85, curr_epsilon: 0.053\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.73 achieved!\\n\",\n      \"episode_num 582, curr_reward: -7.0, best_reward: -7.0, running_avg_reward: -15.73, curr_epsilon: 0.0525\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.7 achieved!\\n\",\n      \"episode_num 583, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -15.7, curr_epsilon: 0.052\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.64 achieved!\\n\",\n      \"episode_num 584, curr_reward: -9.0, best_reward: -7.0, running_avg_reward: -15.64, curr_epsilon: 0.0515\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.57 achieved!\\n\",\n      \"episode_num 585, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -15.57, curr_epsilon: 0.051\\n\",\n      \"episode_num 586, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -15.62, curr_epsilon: 0.0505\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.55 achieved!\\n\",\n      \"episode_num 587, curr_reward: -8.0, best_reward: -7.0, running_avg_reward: -15.55, curr_epsilon: 0.05\\n\",\n      \"episode_num 588, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -15.58, curr_epsilon: 0.0496\\n\",\n      \"episode_num 589, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -15.56, curr_epsilon: 0.0492\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.49 achieved!\\n\",\n      \"episode_num 590, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -15.49, curr_epsilon: 0.0488\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.41 achieved!\\n\",\n      \"episode_num 591, curr_reward: -8.0, best_reward: -7.0, running_avg_reward: -15.41, curr_epsilon: 0.0483\\n\",\n      \"episode_num 592, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -15.45, curr_epsilon: 0.0479\\n\",\n      \"episode_num 593, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -15.44, curr_epsilon: 0.0475\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.36 achieved!\\n\",\n      \"episode_num 594, curr_reward: -9.0, best_reward: -7.0, running_avg_reward: -15.36, curr_epsilon: 0.0471\\n\",\n      \"episode_num 595, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -15.36, curr_epsilon: 0.0467\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.29 achieved!\\n\",\n      \"episode_num 596, curr_reward: -9.0, best_reward: -7.0, running_avg_reward: -15.29, curr_epsilon: 0.0463\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.27 achieved!\\n\",\n      \"episode_num 597, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -15.27, curr_epsilon: 0.046\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.26 achieved!\\n\",\n      \"episode_num 598, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -15.26, curr_epsilon: 0.0457\\n\",\n      \"episode_num 599, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -15.26, curr_epsilon: 0.0454\\n\",\n      \"episode_num 600, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -15.27, curr_epsilon: 0.0451\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.24 achieved!\\n\",\n      \"episode_num 601, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -15.24, curr_epsilon: 0.0447\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.19 achieved!\\n\",\n      \"episode_num 602, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -15.19, curr_epsilon: 0.0444\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.12 achieved!\\n\",\n      \"episode_num 603, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -15.12, curr_epsilon: 0.044\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.11 achieved!\\n\",\n      \"episode_num 604, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -15.11, curr_epsilon: 0.0437\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.09 achieved!\\n\",\n      \"episode_num 605, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -15.09, curr_epsilon: 0.0432\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.06 achieved!\\n\",\n      \"episode_num 606, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -15.06, curr_epsilon: 0.0429\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -15.03 achieved!\\n\",\n      \"episode_num 607, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -15.03, curr_epsilon: 0.0426\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -14.99 achieved!\\n\",\n      \"episode_num 608, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -14.99, curr_epsilon: 0.0423\\n\",\n      \"episode_num 609, curr_reward: -18.0, best_reward: -7.0, running_avg_reward: -15.0, curr_epsilon: 0.042\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.95 achieved!\\n\",\n      \"episode_num 610, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -14.95, curr_epsilon: 0.0416\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.87 achieved!\\n\",\n      \"episode_num 611, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -14.87, curr_epsilon: 0.0413\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.82 achieved!\\n\",\n      \"episode_num 612, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.82, curr_epsilon: 0.041\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.76 achieved!\\n\",\n      \"episode_num 613, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -14.76, curr_epsilon: 0.0406\\n\",\n      \"episode_num 614, curr_reward: -16.0, best_reward: -7.0, running_avg_reward: -14.77, curr_epsilon: 0.0403\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.73 achieved!\\n\",\n      \"episode_num 615, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.73, curr_epsilon: 0.0399\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.69 achieved!\\n\",\n      \"episode_num 616, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.69, curr_epsilon: 0.0396\\n\",\n      \"episode_num 617, curr_reward: -18.0, best_reward: -7.0, running_avg_reward: -14.72, curr_epsilon: 0.0393\\n\",\n      \"episode_num 618, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -14.73, curr_epsilon: 0.0391\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.67 achieved!\\n\",\n      \"episode_num 619, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -14.67, curr_epsilon: 0.0388\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.63 achieved!\\n\",\n      \"episode_num 620, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -14.63, curr_epsilon: 0.0384\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.55 achieved!\\n\",\n      \"episode_num 621, curr_reward: -9.0, best_reward: -7.0, running_avg_reward: -14.55, curr_epsilon: 0.038\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.54 achieved!\\n\",\n      \"episode_num 622, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -14.54, curr_epsilon: 0.0378\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.5 achieved!\\n\",\n      \"episode_num 623, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -14.5, curr_epsilon: 0.0374\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.43 achieved!\\n\",\n      \"episode_num 624, curr_reward: -11.0, best_reward: -7.0, running_avg_reward: -14.43, curr_epsilon: 0.0371\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.38 achieved!\\n\",\n      \"episode_num 625, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -14.38, curr_epsilon: 0.0368\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.36 achieved!\\n\",\n      \"episode_num 626, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -14.36, curr_epsilon: 0.0365\\n\",\n      \"episode_num 627, curr_reward: -18.0, best_reward: -7.0, running_avg_reward: -14.38, curr_epsilon: 0.0363\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.35 achieved!\\n\",\n      \"episode_num 628, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.35, curr_epsilon: 0.036\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.34 achieved!\\n\",\n      \"episode_num 629, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -14.34, curr_epsilon: 0.0357\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.28 achieved!\\n\",\n      \"episode_num 630, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -14.28, curr_epsilon: 0.0354\\n\",\n      \"episode_num 631, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -14.29, curr_epsilon: 0.0351\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.25 achieved!\\n\",\n      \"episode_num 632, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -14.25, curr_epsilon: 0.0349\\n\",\n      \"episode_num 633, curr_reward: -17.0, best_reward: -7.0, running_avg_reward: -14.27, curr_epsilon: 0.0346\\n\",\n      \"episode_num 634, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -14.27, curr_epsilon: 0.0344\\n\",\n      \"episode_num 635, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.26, curr_epsilon: 0.0341\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.24 achieved!\\n\",\n      \"episode_num 636, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.24, curr_epsilon: 0.0338\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.18 achieved!\\n\",\n      \"episode_num 637, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.18, curr_epsilon: 0.0336\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.12 achieved!\\n\",\n      \"episode_num 638, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -14.12, curr_epsilon: 0.0333\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.1 achieved!\\n\",\n      \"episode_num 639, curr_reward: -14.0, best_reward: -7.0, running_avg_reward: -14.1, curr_epsilon: 0.0331\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -14.03 achieved!\\n\",\n      \"episode_num 640, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -14.03, curr_epsilon: 0.0327\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.94 achieved!\\n\",\n      \"episode_num 641, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -13.94, curr_epsilon: 0.0325\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.88 achieved!\\n\",\n      \"episode_num 642, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -13.88, curr_epsilon: 0.0322\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.79 achieved!\\n\",\n      \"episode_num 643, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -13.79, curr_epsilon: 0.0319\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.77 achieved!\\n\",\n      \"episode_num 644, curr_reward: -15.0, best_reward: -7.0, running_avg_reward: -13.77, curr_epsilon: 0.0317\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.76 achieved!\\n\",\n      \"episode_num 645, curr_reward: -12.0, best_reward: -7.0, running_avg_reward: -13.76, curr_epsilon: 0.0315\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.74 achieved!\\n\",\n      \"episode_num 646, curr_reward: -13.0, best_reward: -7.0, running_avg_reward: -13.74, curr_epsilon: 0.0313\\n\",\n      \"episode_num 647, curr_reward: -10.0, best_reward: -7.0, running_avg_reward: -13.76, curr_epsilon: 0.0309\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.58 achieved!\\n\",\n      \"episode_num 648, curr_reward: -2.0, best_reward: -2.0, running_avg_reward: -13.58, curr_epsilon: 0.0306\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.54 achieved!\\n\",\n      \"episode_num 649, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -13.54, curr_epsilon: 0.0304\\n\",\n      \"episode_num 650, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -13.58, curr_epsilon: 0.0302\\n\",\n      \"episode_num 651, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -13.6, curr_epsilon: 0.03\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.46 achieved!\\n\",\n      \"episode_num 652, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -13.46, curr_epsilon: 0.0297\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.38 achieved!\\n\",\n      \"episode_num 653, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -13.38, curr_epsilon: 0.0294\\n\",\n      \"episode_num 654, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -13.43, curr_epsilon: 0.0292\\n\",\n      \"episode_num 655, curr_reward: -13.0, best_reward: -2.0, running_avg_reward: -13.39, curr_epsilon: 0.029\\n\",\n      \"episode_num 656, curr_reward: -13.0, best_reward: -2.0, running_avg_reward: -13.41, curr_epsilon: 0.0287\\n\",\n      \"episode_num 657, curr_reward: -20.0, best_reward: -2.0, running_avg_reward: -13.48, curr_epsilon: 0.0286\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -13.31 achieved!\\n\",\n      \"episode_num 658, curr_reward: -2.0, best_reward: -2.0, running_avg_reward: -13.31, curr_epsilon: 0.0282\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.22 achieved!\\n\",\n      \"episode_num 659, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -13.22, curr_epsilon: 0.0279\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.13 achieved!\\n\",\n      \"episode_num 660, curr_reward: -10.0, best_reward: -2.0, running_avg_reward: -13.13, curr_epsilon: 0.0277\\n\",\n      \"episode_num 661, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -13.19, curr_epsilon: 0.0276\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -13.1 achieved!\\n\",\n      \"episode_num 662, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -13.1, curr_epsilon: 0.0273\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.98 achieved!\\n\",\n      \"episode_num 663, curr_reward: -7.0, best_reward: -2.0, running_avg_reward: -12.98, curr_epsilon: 0.0271\\n\",\n      \"episode_num 664, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -13.03, curr_epsilon: 0.0269\\n\",\n      \"episode_num 665, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -12.98, curr_epsilon: 0.0268\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.93 achieved!\\n\",\n      \"episode_num 666, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -12.93, curr_epsilon: 0.0265\\n\",\n      \"episode_num 667, curr_reward: -15.0, best_reward: -2.0, running_avg_reward: -12.94, curr_epsilon: 0.0263\\n\",\n      \"episode_num 668, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -12.96, curr_epsilon: 0.0261\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.88 achieved!\\n\",\n      \"episode_num 669, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -12.88, curr_epsilon: 0.0259\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.82 achieved!\\n\",\n      \"episode_num 670, curr_reward: -7.0, best_reward: -2.0, running_avg_reward: -12.82, curr_epsilon: 0.0257\\n\",\n      \"episode_num 671, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -12.84, curr_epsilon: 0.0255\\n\",\n      \"episode_num 672, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -12.83, curr_epsilon: 0.0253\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.77 achieved!\\n\",\n      \"episode_num 673, curr_reward: -10.0, best_reward: -2.0, running_avg_reward: -12.77, curr_epsilon: 0.0251\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.76 achieved!\\n\",\n      \"episode_num 674, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -12.76, curr_epsilon: 0.0248\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.73 achieved!\\n\",\n      \"episode_num 675, curr_reward: -13.0, best_reward: -2.0, running_avg_reward: -12.73, curr_epsilon: 0.0246\\n\",\n      \"episode_num 676, curr_reward: -15.0, best_reward: -2.0, running_avg_reward: -12.74, curr_epsilon: 0.0244\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.64 achieved!\\n\",\n      \"episode_num 677, curr_reward: -3.0, best_reward: -2.0, running_avg_reward: -12.64, curr_epsilon: 0.0241\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.51 achieved!\\n\",\n      \"episode_num 678, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -12.51, curr_epsilon: 0.0239\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.42 achieved!\\n\",\n      \"episode_num 679, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -12.42, curr_epsilon: 0.0237\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.4 achieved!\\n\",\n      \"episode_num 680, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -12.4, curr_epsilon: 0.0235\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.29 achieved!\\n\",\n      \"episode_num 681, curr_reward: -10.0, best_reward: -2.0, running_avg_reward: -12.29, curr_epsilon: 0.0233\\n\",\n      \"episode_num 682, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -12.31, curr_epsilon: 0.0232\\n\",\n      \"episode_num 683, curr_reward: -20.0, best_reward: -2.0, running_avg_reward: -12.37, curr_epsilon: 0.023\\n\",\n      \"episode_num 684, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -12.42, curr_epsilon: 0.0229\\n\",\n      \"episode_num 685, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -12.44, curr_epsilon: 0.0227\\n\",\n      \"episode_num 686, curr_reward: -3.0, best_reward: -2.0, running_avg_reward: -12.34, curr_epsilon: 0.0224\\n\",\n      \"episode_num 687, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -12.38, curr_epsilon: 0.0222\\n\",\n      \"episode_num 688, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -12.39, curr_epsilon: 0.0221\\n\",\n      \"episode_num 689, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -12.39, curr_epsilon: 0.022\\n\",\n      \"episode_num 690, curr_reward: -10.0, best_reward: -2.0, running_avg_reward: -12.35, curr_epsilon: 0.0218\\n\",\n      \"episode_num 691, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -12.38, curr_epsilon: 0.0216\\n\",\n      \"episode_num 692, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -12.3, curr_epsilon: 0.0215\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.26 achieved!\\n\",\n      \"episode_num 693, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -12.26, curr_epsilon: 0.0213\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.23 achieved!\\n\",\n      \"episode_num 694, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -12.23, curr_epsilon: 0.0211\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.16 achieved!\\n\",\n      \"episode_num 695, curr_reward: -10.0, best_reward: -2.0, running_avg_reward: -12.16, curr_epsilon: 0.0209\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.13 achieved!\\n\",\n      \"episode_num 696, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -12.13, curr_epsilon: 0.0208\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.06 achieved!\\n\",\n      \"episode_num 697, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -12.06, curr_epsilon: 0.0206\\n\",\n      \"episode_num 698, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -12.07, curr_epsilon: 0.0205\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -12.04 achieved!\\n\",\n      \"episode_num 699, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -12.04, curr_epsilon: 0.0203\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.96 achieved!\\n\",\n      \"episode_num 700, curr_reward: -7.0, best_reward: -2.0, running_avg_reward: -11.96, curr_epsilon: 0.0201\\n\",\n      \"episode_num 701, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.96, curr_epsilon: 0.02\\n\",\n      \"episode_num 702, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -12.0, curr_epsilon: 0.0199\\n\",\n      \"episode_num 703, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -11.98, curr_epsilon: 0.0197\\n\",\n      \"episode_num 704, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.98, curr_epsilon: 0.0196\\n\",\n      \"episode_num 705, curr_reward: -16.0, best_reward: -2.0, running_avg_reward: -12.03, curr_epsilon: 0.0195\\n\",\n      \"episode_num 706, curr_reward: -15.0, best_reward: -2.0, running_avg_reward: -12.03, curr_epsilon: 0.0194\\n\",\n      \"episode_num 707, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -12.02, curr_epsilon: 0.0192\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.92 achieved!\\n\",\n      \"episode_num 708, curr_reward: -3.0, best_reward: -2.0, running_avg_reward: -11.92, curr_epsilon: 0.0191\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.88 achieved!\\n\",\n      \"episode_num 709, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.88, curr_epsilon: 0.019\\n\",\n      \"episode_num 710, curr_reward: -17.0, best_reward: -2.0, running_avg_reward: -11.94, curr_epsilon: 0.0189\\n\",\n      \"episode_num 711, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -11.94, curr_epsilon: 0.0187\\n\",\n      \"episode_num 712, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -11.92, curr_epsilon: 0.0186\\n\",\n      \"episode_num 713, curr_reward: -13.0, best_reward: -2.0, running_avg_reward: -11.93, curr_epsilon: 0.0185\\n\",\n      \"episode_num 714, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -11.89, curr_epsilon: 0.0183\\n\",\n      \"episode_num 715, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.89, curr_epsilon: 0.0182\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -11.83 achieved!\\n\",\n      \"episode_num 716, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -11.83, curr_epsilon: 0.0181\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.76 achieved!\\n\",\n      \"episode_num 717, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -11.76, curr_epsilon: 0.0179\\n\",\n      \"episode_num 718, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -11.79, curr_epsilon: 0.0178\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.7 achieved!\\n\",\n      \"episode_num 719, curr_reward: -2.0, best_reward: -2.0, running_avg_reward: -11.7, curr_epsilon: 0.0176\\n\",\n      \"episode_num 720, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.72, curr_epsilon: 0.0175\\n\",\n      \"episode_num 721, curr_reward: -7.0, best_reward: -2.0, running_avg_reward: -11.7, curr_epsilon: 0.0174\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.66 achieved!\\n\",\n      \"episode_num 722, curr_reward: -13.0, best_reward: -2.0, running_avg_reward: -11.66, curr_epsilon: 0.0172\\n\",\n      \"episode_num 723, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -11.66, curr_epsilon: 0.0171\\n\",\n      \"episode_num 724, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.69, curr_epsilon: 0.017\\n\",\n      \"episode_num 725, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -11.77, curr_epsilon: 0.0169\\n\",\n      \"episode_num 726, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.78, curr_epsilon: 0.0168\\n\",\n      \"episode_num 727, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -11.72, curr_epsilon: 0.0167\\n\",\n      \"episode_num 728, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.72, curr_epsilon: 0.0166\\n\",\n      \"episode_num 729, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -11.68, curr_epsilon: 0.0165\\n\",\n      \"episode_num 730, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -11.76, curr_epsilon: 0.0164\\n\",\n      \"episode_num 731, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.73, curr_epsilon: 0.0163\\n\",\n      \"episode_num 732, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.75, curr_epsilon: 0.0162\\n\",\n      \"episode_num 733, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -11.66, curr_epsilon: 0.0161\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.55 achieved!\\n\",\n      \"episode_num 734, curr_reward: -4.0, best_reward: -2.0, running_avg_reward: -11.55, curr_epsilon: 0.016\\n\",\n      \"episode_num 735, curr_reward: -14.0, best_reward: -2.0, running_avg_reward: -11.55, curr_epsilon: 0.0159\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.52 achieved!\\n\",\n      \"episode_num 736, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -11.52, curr_epsilon: 0.0157\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.49 achieved!\\n\",\n      \"episode_num 737, curr_reward: -11.0, best_reward: -2.0, running_avg_reward: -11.49, curr_epsilon: 0.0157\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.48 achieved!\\n\",\n      \"episode_num 738, curr_reward: -9.0, best_reward: -2.0, running_avg_reward: -11.48, curr_epsilon: 0.0155\\n\",\n      \"episode_num 739, curr_reward: -19.0, best_reward: -2.0, running_avg_reward: -11.53, curr_epsilon: 0.0155\\n\",\n      \"episode_num 740, curr_reward: -6.0, best_reward: -2.0, running_avg_reward: -11.49, curr_epsilon: 0.0154\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.47 achieved!\\n\",\n      \"episode_num 741, curr_reward: -8.0, best_reward: -2.0, running_avg_reward: -11.47, curr_epsilon: 0.0153\\n\",\n      \"episode_num 742, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -11.52, curr_epsilon: 0.0152\\n\",\n      \"episode_num 743, curr_reward: -18.0, best_reward: -2.0, running_avg_reward: -11.58, curr_epsilon: 0.0151\\n\",\n      \"episode_num 744, curr_reward: -12.0, best_reward: -2.0, running_avg_reward: -11.55, curr_epsilon: 0.015\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.44 achieved!\\n\",\n      \"episode_num 745, curr_reward: -1.0, best_reward: -1.0, running_avg_reward: -11.44, curr_epsilon: 0.0149\\n\",\n      \"episode_num 746, curr_reward: -14.0, best_reward: -1.0, running_avg_reward: -11.45, curr_epsilon: 0.0148\\n\",\n      \"episode_num 747, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.47, curr_epsilon: 0.0147\\n\",\n      \"episode_num 748, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.57, curr_epsilon: 0.0146\\n\",\n      \"episode_num 749, curr_reward: -6.0, best_reward: -1.0, running_avg_reward: -11.52, curr_epsilon: 0.0145\\n\",\n      \"episode_num 750, curr_reward: -11.0, best_reward: -1.0, running_avg_reward: -11.47, curr_epsilon: 0.0144\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.4 achieved!\\n\",\n      \"episode_num 751, curr_reward: -9.0, best_reward: -1.0, running_avg_reward: -11.4, curr_epsilon: 0.0143\\n\",\n      \"episode_num 752, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.46, curr_epsilon: 0.0142\\n\",\n      \"episode_num 753, curr_reward: -9.0, best_reward: -1.0, running_avg_reward: -11.46, curr_epsilon: 0.0141\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.37 achieved!\\n\",\n      \"episode_num 754, curr_reward: -9.0, best_reward: -1.0, running_avg_reward: -11.37, curr_epsilon: 0.014\\n\",\n      \"episode_num 755, curr_reward: -13.0, best_reward: -1.0, running_avg_reward: -11.37, curr_epsilon: 0.0139\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.33 achieved!\\n\",\n      \"episode_num 756, curr_reward: -9.0, best_reward: -1.0, running_avg_reward: -11.33, curr_epsilon: 0.0139\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.18 achieved!\\n\",\n      \"episode_num 757, curr_reward: -5.0, best_reward: -1.0, running_avg_reward: -11.18, curr_epsilon: 0.0137\\n\",\n      \"episode_num 758, curr_reward: -15.0, best_reward: -1.0, running_avg_reward: -11.31, curr_epsilon: 0.0137\\n\",\n      \"episode_num 759, curr_reward: -8.0, best_reward: -1.0, running_avg_reward: -11.33, curr_epsilon: 0.0136\\n\",\n      \"episode_num 760, curr_reward: -16.0, best_reward: -1.0, running_avg_reward: -11.39, curr_epsilon: 0.0135\\n\",\n      \"episode_num 761, curr_reward: -15.0, best_reward: -1.0, running_avg_reward: -11.36, curr_epsilon: 0.0134\\n\",\n      \"episode_num 762, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.4, curr_epsilon: 0.0134\\n\",\n      \"episode_num 763, curr_reward: -17.0, best_reward: -1.0, running_avg_reward: -11.5, curr_epsilon: 0.0133\\n\",\n      \"episode_num 764, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.44, curr_epsilon: 0.0132\\n\",\n      \"episode_num 765, curr_reward: -9.0, best_reward: -1.0, running_avg_reward: -11.41, curr_epsilon: 0.0131\\n\",\n      \"episode_num 766, curr_reward: -14.0, best_reward: -1.0, running_avg_reward: -11.46, curr_epsilon: 0.0131\\n\",\n      \"episode_num 767, curr_reward: -4.0, best_reward: -1.0, running_avg_reward: -11.35, curr_epsilon: 0.013\\n\",\n      \"episode_num 768, curr_reward: -16.0, best_reward: -1.0, running_avg_reward: -11.35, curr_epsilon: 0.0129\\n\",\n      \"episode_num 769, curr_reward: -14.0, best_reward: -1.0, running_avg_reward: -11.41, curr_epsilon: 0.0128\\n\",\n      \"episode_num 770, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.46, curr_epsilon: 0.0127\\n\",\n      \"episode_num 771, curr_reward: -6.0, best_reward: -1.0, running_avg_reward: -11.36, curr_epsilon: 0.0126\\n\",\n      \"episode_num 772, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.37, curr_epsilon: 0.0126\\n\",\n      \"episode_num 773, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.39, curr_epsilon: 0.0125\\n\",\n      \"episode_num 774, curr_reward: -12.0, best_reward: -1.0, running_avg_reward: -11.4, curr_epsilon: 0.0124\\n\",\n      \"episode_num 775, curr_reward: 5.0, best_reward: 5.0, running_avg_reward: -11.22, curr_epsilon: 0.0123\\n\",\n      \"episode_num 776, curr_reward: -16.0, best_reward: 5.0, running_avg_reward: -11.23, curr_epsilon: 0.0122\\n\",\n      \"episode_num 777, curr_reward: -9.0, best_reward: 5.0, running_avg_reward: -11.29, curr_epsilon: 0.0122\\n\",\n      \"episode_num 778, curr_reward: -6.0, best_reward: 5.0, running_avg_reward: -11.29, curr_epsilon: 0.0121\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.15 achieved!\\n\",\n      \"episode_num 779, curr_reward: 6.0, best_reward: 6.0, running_avg_reward: -11.15, curr_epsilon: 0.012\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.13 achieved!\\n\",\n      \"episode_num 780, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -11.13, curr_epsilon: 0.0119\\n\",\n      \"episode_num 781, curr_reward: -18.0, best_reward: 6.0, running_avg_reward: -11.21, curr_epsilon: 0.0119\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 782, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -11.18, curr_epsilon: 0.0118\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -11.03 achieved!\\n\",\n      \"episode_num 783, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -11.03, curr_epsilon: 0.0117\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.97 achieved!\\n\",\n      \"episode_num 784, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.97, curr_epsilon: 0.0116\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.93 achieved!\\n\",\n      \"episode_num 785, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.93, curr_epsilon: 0.0115\\n\",\n      \"episode_num 786, curr_reward: -18.0, best_reward: 6.0, running_avg_reward: -11.08, curr_epsilon: 0.0115\\n\",\n      \"episode_num 787, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -11.04, curr_epsilon: 0.0114\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.92 achieved!\\n\",\n      \"episode_num 788, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -10.92, curr_epsilon: 0.0113\\n\",\n      \"episode_num 789, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -10.93, curr_epsilon: 0.0113\\n\",\n      \"episode_num 790, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -10.99, curr_epsilon: 0.0112\\n\",\n      \"episode_num 791, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -10.95, curr_epsilon: 0.0111\\n\",\n      \"episode_num 792, curr_reward: -17.0, best_reward: 6.0, running_avg_reward: -11.03, curr_epsilon: 0.0111\\n\",\n      \"episode_num 793, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -11.02, curr_epsilon: 0.011\\n\",\n      \"episode_num 794, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -11.02, curr_epsilon: 0.0109\\n\",\n      \"episode_num 795, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -11.01, curr_epsilon: 0.0109\\n\",\n      \"episode_num 796, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -11.04, curr_epsilon: 0.0108\\n\",\n      \"episode_num 797, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -11.13, curr_epsilon: 0.0107\\n\",\n      \"episode_num 798, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -11.14, curr_epsilon: 0.0107\\n\",\n      \"episode_num 799, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -11.12, curr_epsilon: 0.0106\\n\",\n      \"episode_num 800, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -11.16, curr_epsilon: 0.0106\\n\",\n      \"episode_num 801, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -11.16, curr_epsilon: 0.0105\\n\",\n      \"episode_num 802, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -11.07, curr_epsilon: 0.0104\\n\",\n      \"episode_num 803, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -11.12, curr_epsilon: 0.0104\\n\",\n      \"episode_num 804, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -11.1, curr_epsilon: 0.0104\\n\",\n      \"episode_num 805, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -11.06, curr_epsilon: 0.0103\\n\",\n      \"episode_num 806, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -11.06, curr_epsilon: 0.0103\\n\",\n      \"episode_num 807, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -11.04, curr_epsilon: 0.0102\\n\",\n      \"episode_num 808, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -11.15, curr_epsilon: 0.0102\\n\",\n      \"episode_num 809, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -11.16, curr_epsilon: 0.0101\\n\",\n      \"episode_num 810, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -11.14, curr_epsilon: 0.0101\\n\",\n      \"episode_num 811, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -11.08, curr_epsilon: 0.01\\n\",\n      \"episode_num 812, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -11.07, curr_epsilon: 0.0099\\n\",\n      \"episode_num 813, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -11.0, curr_epsilon: 0.0099\\n\",\n      \"episode_num 814, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -10.98, curr_epsilon: 0.0098\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.89 achieved!\\n\",\n      \"episode_num 815, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -10.89, curr_epsilon: 0.0098\\n\",\n      \"episode_num 816, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.9, curr_epsilon: 0.0097\\n\",\n      \"episode_num 817, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.92, curr_epsilon: 0.0097\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.87 achieved!\\n\",\n      \"episode_num 818, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.87, curr_epsilon: 0.0096\\n\",\n      \"episode_num 819, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -10.97, curr_epsilon: 0.0096\\n\",\n      \"episode_num 820, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.96, curr_epsilon: 0.0095\\n\",\n      \"episode_num 821, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -10.93, curr_epsilon: 0.0095\\n\",\n      \"episode_num 822, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -10.92, curr_epsilon: 0.0094\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.84 achieved!\\n\",\n      \"episode_num 823, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -10.84, curr_epsilon: 0.0094\\n\",\n      \"episode_num 824, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.84, curr_epsilon: 0.0093\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.75 achieved!\\n\",\n      \"episode_num 825, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.75, curr_epsilon: 0.0093\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.68 achieved!\\n\",\n      \"episode_num 826, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -10.68, curr_epsilon: 0.0092\\n\",\n      \"episode_num 827, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.7, curr_epsilon: 0.0092\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.6 achieved!\\n\",\n      \"episode_num 828, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -10.6, curr_epsilon: 0.0091\\n\",\n      \"episode_num 829, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.65, curr_epsilon: 0.0091\\n\",\n      \"episode_num 830, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -10.62, curr_epsilon: 0.0091\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.57 achieved!\\n\",\n      \"episode_num 831, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.57, curr_epsilon: 0.009\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.46 achieved!\\n\",\n      \"episode_num 832, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -10.46, curr_epsilon: 0.009\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.41 achieved!\\n\",\n      \"episode_num 833, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -10.41, curr_epsilon: 0.0089\\n\",\n      \"episode_num 834, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -10.48, curr_epsilon: 0.0089\\n\",\n      \"episode_num 835, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.48, curr_epsilon: 0.0088\\n\",\n      \"episode_num 836, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.45, curr_epsilon: 0.0088\\n\",\n      \"episode_num 837, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.42, curr_epsilon: 0.0088\\n\",\n      \"episode_num 838, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -10.48, curr_epsilon: 0.0087\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.27 achieved!\\n\",\n      \"episode_num 839, curr_reward: 2.0, best_reward: 6.0, running_avg_reward: -10.27, curr_epsilon: 0.0087\\n\",\n      \"episode_num 840, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -10.33, curr_epsilon: 0.0086\\n\",\n      \"episode_num 841, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.33, curr_epsilon: 0.0086\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.2 achieved!\\n\",\n      \"episode_num 842, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -10.2, curr_epsilon: 0.0085\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.16 achieved!\\n\",\n      \"episode_num 843, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.16, curr_epsilon: 0.0085\\n\",\n      \"episode_num 844, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.18, curr_epsilon: 0.0085\\n\",\n      \"episode_num 845, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -10.29, curr_epsilon: 0.0085\\n\",\n      \"episode_num 846, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.24, curr_epsilon: 0.0084\\n\",\n      \"episode_num 847, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.25, curr_epsilon: 0.0084\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -10.14 achieved!\\n\",\n      \"episode_num 848, curr_reward: -1.0, best_reward: 6.0, running_avg_reward: -10.14, curr_epsilon: 0.0084\\n\",\n      \"episode_num 849, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.17, curr_epsilon: 0.0083\\n\",\n      \"episode_num 850, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -10.14, curr_epsilon: 0.0083\\n\",\n      \"episode_num 851, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -10.16, curr_epsilon: 0.0082\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.07 achieved!\\n\",\n      \"episode_num 852, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -10.07, curr_epsilon: 0.0082\\n\",\n      \"episode_num 853, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -10.09, curr_epsilon: 0.0082\\n\",\n      \"episode_num 854, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -10.11, curr_epsilon: 0.0082\\n\",\n      \"episode_num 855, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.11, curr_epsilon: 0.0081\\n\",\n      \"episode_num 856, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -10.13, curr_epsilon: 0.0081\\n\",\n      \"episode_num 857, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.22, curr_epsilon: 0.0081\\n\",\n      \"episode_num 858, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -10.14, curr_epsilon: 0.008\\n\",\n      \"episode_num 859, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.2, curr_epsilon: 0.008\\n\",\n      \"episode_num 860, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -10.13, curr_epsilon: 0.008\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.02 achieved!\\n\",\n      \"episode_num 861, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -10.02, curr_epsilon: 0.0079\\n\",\n      \"episode_num 862, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -10.04, curr_epsilon: 0.0079\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -10.0 achieved!\\n\",\n      \"episode_num 863, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -10.0, curr_epsilon: 0.0079\\n\",\n      \"episode_num 864, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -10.0, curr_epsilon: 0.0079\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.95 achieved!\\n\",\n      \"episode_num 865, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.95, curr_epsilon: 0.0078\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.9 achieved!\\n\",\n      \"episode_num 866, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.9, curr_epsilon: 0.0078\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.85 achieved!\\n\",\n      \"episode_num 867, curr_reward: 1.0, best_reward: 6.0, running_avg_reward: -9.85, curr_epsilon: 0.0078\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.73 achieved!\\n\",\n      \"episode_num 868, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.73, curr_epsilon: 0.0077\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.71 achieved!\\n\",\n      \"episode_num 869, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -9.71, curr_epsilon: 0.0077\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.68 achieved!\\n\",\n      \"episode_num 870, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.68, curr_epsilon: 0.0077\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.65 achieved!\\n\",\n      \"episode_num 871, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -9.65, curr_epsilon: 0.0076\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.64 achieved!\\n\",\n      \"episode_num 872, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.64, curr_epsilon: 0.0076\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.49 achieved!\\n\",\n      \"episode_num 873, curr_reward: 3.0, best_reward: 6.0, running_avg_reward: -9.49, curr_epsilon: 0.0076\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.45 achieved!\\n\",\n      \"episode_num 874, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -9.45, curr_epsilon: 0.0075\\n\",\n      \"episode_num 875, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -9.55, curr_epsilon: 0.0075\\n\",\n      \"episode_num 876, curr_reward: -20.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.0075\\n\",\n      \"episode_num 877, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0075\\n\",\n      \"episode_num 878, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.6, curr_epsilon: 0.0075\\n\",\n      \"episode_num 879, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.76, curr_epsilon: 0.0074\\n\",\n      \"episode_num 880, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.68, curr_epsilon: 0.0074\\n\",\n      \"episode_num 881, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.0074\\n\",\n      \"episode_num 882, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.62, curr_epsilon: 0.0073\\n\",\n      \"episode_num 883, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.67, curr_epsilon: 0.0073\\n\",\n      \"episode_num 884, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.68, curr_epsilon: 0.0073\\n\",\n      \"episode_num 885, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -9.75, curr_epsilon: 0.0073\\n\",\n      \"episode_num 886, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.67, curr_epsilon: 0.0073\\n\",\n      \"episode_num 887, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.68, curr_epsilon: 0.0072\\n\",\n      \"episode_num 888, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.75, curr_epsilon: 0.0072\\n\",\n      \"episode_num 889, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -9.63, curr_epsilon: 0.0072\\n\",\n      \"episode_num 890, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.61, curr_epsilon: 0.0072\\n\",\n      \"episode_num 891, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -9.6, curr_epsilon: 0.0071\\n\",\n      \"episode_num 892, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -9.49, curr_epsilon: 0.0071\\n\",\n      \"episode_num 893, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0071\\n\",\n      \"episode_num 894, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.6, curr_epsilon: 0.0071\\n\",\n      \"episode_num 895, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.55, curr_epsilon: 0.0071\\n\",\n      \"episode_num 896, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.007\\n\",\n      \"episode_num 897, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.57, curr_epsilon: 0.007\\n\",\n      \"episode_num 898, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.51, curr_epsilon: 0.007\\n\",\n      \"episode_num 899, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.52, curr_epsilon: 0.007\\n\",\n      \"episode_num 900, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.51, curr_epsilon: 0.007\\n\",\n      \"episode_num 901, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.5, curr_epsilon: 0.0069\\n\",\n      \"episode_num 902, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.57, curr_epsilon: 0.0069\\n\",\n      \"episode_num 903, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.56, curr_epsilon: 0.0069\\n\",\n      \"episode_num 904, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.54, curr_epsilon: 0.0069\\n\",\n      \"episode_num 905, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -9.58, curr_epsilon: 0.0069\\n\",\n      \"episode_num 906, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -9.5, curr_epsilon: 0.0069\\n\",\n      \"episode_num 907, curr_reward: -13.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0068\\n\",\n      \"episode_num 908, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -9.54, curr_epsilon: 0.0068\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.43 achieved!\\n\",\n      \"episode_num 909, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.43, curr_epsilon: 0.0068\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.38 achieved!\\n\",\n      \"episode_num 910, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.38, curr_epsilon: 0.0068\\n\",\n      \"episode_num 911, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.44, curr_epsilon: 0.0068\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.37 achieved!\\n\",\n      \"episode_num 912, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.37, curr_epsilon: 0.0067\\n\",\n      \"episode_num 913, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -9.39, curr_epsilon: 0.0067\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 914, curr_reward: -17.0, best_reward: 6.0, running_avg_reward: -9.46, curr_epsilon: 0.0067\\n\",\n      \"episode_num 915, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.55, curr_epsilon: 0.0067\\n\",\n      \"episode_num 916, curr_reward: 5.0, best_reward: 6.0, running_avg_reward: -9.41, curr_epsilon: 0.0067\\n\",\n      \"episode_num 917, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.38, curr_epsilon: 0.0067\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.34 achieved!\\n\",\n      \"episode_num 918, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.34, curr_epsilon: 0.0066\\n\",\n      \"episode_num 919, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -9.37, curr_epsilon: 0.0066\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.33 achieved!\\n\",\n      \"episode_num 920, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.33, curr_epsilon: 0.0066\\n\",\n      \"episode_num 921, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -9.35, curr_epsilon: 0.0066\\n\",\n      \"episode_num 922, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.37, curr_epsilon: 0.0066\\n\",\n      \"episode_num 923, curr_reward: -17.0, best_reward: 6.0, running_avg_reward: -9.5, curr_epsilon: 0.0066\\n\",\n      \"episode_num 924, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -9.44, curr_epsilon: 0.0065\\n\",\n      \"episode_num 925, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -9.5, curr_epsilon: 0.0065\\n\",\n      \"episode_num 926, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.57, curr_epsilon: 0.0065\\n\",\n      \"episode_num 927, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -9.55, curr_epsilon: 0.0065\\n\",\n      \"episode_num 928, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.65, curr_epsilon: 0.0065\\n\",\n      \"episode_num 929, curr_reward: -7.0, best_reward: 6.0, running_avg_reward: -9.58, curr_epsilon: 0.0065\\n\",\n      \"episode_num 930, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -9.48, curr_epsilon: 0.0065\\n\",\n      \"episode_num 931, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.5, curr_epsilon: 0.0064\\n\",\n      \"episode_num 932, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.58, curr_epsilon: 0.0064\\n\",\n      \"episode_num 933, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.65, curr_epsilon: 0.0064\\n\",\n      \"episode_num 934, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.0064\\n\",\n      \"episode_num 935, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -9.61, curr_epsilon: 0.0064\\n\",\n      \"episode_num 936, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -9.58, curr_epsilon: 0.0064\\n\",\n      \"episode_num 937, curr_reward: -2.0, best_reward: 6.0, running_avg_reward: -9.52, curr_epsilon: 0.0064\\n\",\n      \"episode_num 938, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0063\\n\",\n      \"episode_num 939, curr_reward: -9.0, best_reward: 6.0, running_avg_reward: -9.64, curr_epsilon: 0.0063\\n\",\n      \"episode_num 940, curr_reward: -14.0, best_reward: 6.0, running_avg_reward: -9.66, curr_epsilon: 0.0063\\n\",\n      \"episode_num 941, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -9.66, curr_epsilon: 0.0063\\n\",\n      \"episode_num 942, curr_reward: -17.0, best_reward: 6.0, running_avg_reward: -9.78, curr_epsilon: 0.0063\\n\",\n      \"episode_num 943, curr_reward: -18.0, best_reward: 6.0, running_avg_reward: -9.82, curr_epsilon: 0.0063\\n\",\n      \"episode_num 944, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -9.84, curr_epsilon: 0.0063\\n\",\n      \"episode_num 945, curr_reward: 3.0, best_reward: 6.0, running_avg_reward: -9.69, curr_epsilon: 0.0063\\n\",\n      \"episode_num 946, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -9.66, curr_epsilon: 0.0062\\n\",\n      \"episode_num 947, curr_reward: -11.0, best_reward: 6.0, running_avg_reward: -9.64, curr_epsilon: 0.0062\\n\",\n      \"episode_num 948, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.73, curr_epsilon: 0.0062\\n\",\n      \"episode_num 949, curr_reward: -4.0, best_reward: 6.0, running_avg_reward: -9.68, curr_epsilon: 0.0062\\n\",\n      \"episode_num 950, curr_reward: -1.0, best_reward: 6.0, running_avg_reward: -9.61, curr_epsilon: 0.0062\\n\",\n      \"episode_num 951, curr_reward: -5.0, best_reward: 6.0, running_avg_reward: -9.55, curr_epsilon: 0.0062\\n\",\n      \"episode_num 952, curr_reward: -10.0, best_reward: 6.0, running_avg_reward: -9.62, curr_epsilon: 0.0062\\n\",\n      \"episode_num 953, curr_reward: -2.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0062\\n\",\n      \"episode_num 954, curr_reward: -16.0, best_reward: 6.0, running_avg_reward: -9.58, curr_epsilon: 0.0061\\n\",\n      \"episode_num 955, curr_reward: -8.0, best_reward: 6.0, running_avg_reward: -9.53, curr_epsilon: 0.0061\\n\",\n      \"episode_num 956, curr_reward: -6.0, best_reward: 6.0, running_avg_reward: -9.48, curr_epsilon: 0.0061\\n\",\n      \"episode_num 957, curr_reward: -17.0, best_reward: 6.0, running_avg_reward: -9.51, curr_epsilon: 0.0061\\n\",\n      \"episode_num 958, curr_reward: -15.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.0061\\n\",\n      \"episode_num 959, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -9.57, curr_epsilon: 0.0061\\n\",\n      \"episode_num 960, curr_reward: -3.0, best_reward: 6.0, running_avg_reward: -9.51, curr_epsilon: 0.0061\\n\",\n      \"episode_num 961, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -9.59, curr_epsilon: 0.0061\\n\",\n      \"episode_num 962, curr_reward: -1.0, best_reward: 6.0, running_avg_reward: -9.46, curr_epsilon: 0.0061\\n\",\n      \"episode_num 963, curr_reward: -1.0, best_reward: 6.0, running_avg_reward: -9.34, curr_epsilon: 0.006\\n\",\n      \"episode_num 964, curr_reward: -12.0, best_reward: 6.0, running_avg_reward: -9.34, curr_epsilon: 0.006\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.21 achieved!\\n\",\n      \"episode_num 965, curr_reward: 9.0, best_reward: 9.0, running_avg_reward: -9.21, curr_epsilon: 0.006\\n\",\n      \"episode_num 966, curr_reward: -14.0, best_reward: 9.0, running_avg_reward: -9.26, curr_epsilon: 0.006\\n\",\n      \"episode_num 967, curr_reward: -17.0, best_reward: 9.0, running_avg_reward: -9.44, curr_epsilon: 0.006\\n\",\n      \"episode_num 968, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -9.53, curr_epsilon: 0.006\\n\",\n      \"episode_num 969, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -9.51, curr_epsilon: 0.006\\n\",\n      \"episode_num 970, curr_reward: 7.0, best_reward: 9.0, running_avg_reward: -9.35, curr_epsilon: 0.006\\n\",\n      \"episode_num 971, curr_reward: -16.0, best_reward: 9.0, running_avg_reward: -9.48, curr_epsilon: 0.006\\n\",\n      \"episode_num 972, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -9.43, curr_epsilon: 0.006\\n\",\n      \"episode_num 973, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -9.58, curr_epsilon: 0.006\\n\",\n      \"episode_num 974, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -9.6, curr_epsilon: 0.0059\\n\",\n      \"episode_num 975, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -9.64, curr_epsilon: 0.0059\\n\",\n      \"episode_num 976, curr_reward: 6.0, best_reward: 9.0, running_avg_reward: -9.38, curr_epsilon: 0.0059\\n\",\n      \"episode_num 977, curr_reward: 2.0, best_reward: 9.0, running_avg_reward: -9.33, curr_epsilon: 0.0059\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.19 achieved!\\n\",\n      \"episode_num 978, curr_reward: 1.0, best_reward: 9.0, running_avg_reward: -9.19, curr_epsilon: 0.0059\\n\",\n      \"episode_num 979, curr_reward: -14.0, best_reward: 9.0, running_avg_reward: -9.23, curr_epsilon: 0.0059\\n\",\n      \"episode_num 980, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -9.32, curr_epsilon: 0.0059\\n\",\n      \"episode_num 981, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -9.34, curr_epsilon: 0.0059\\n\",\n      \"episode_num 982, curr_reward: -14.0, best_reward: 9.0, running_avg_reward: -9.39, curr_epsilon: 0.0059\\n\",\n      \"episode_num 983, curr_reward: 5.0, best_reward: 9.0, running_avg_reward: -9.24, curr_epsilon: 0.0059\\n\",\n      \"episode_num 984, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -9.19, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.12 achieved!\\n\",\n      \"episode_num 985, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -9.12, curr_epsilon: 0.0058\\n\",\n      \"episode_num 986, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -9.14, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.09 achieved!\\n\",\n      \"episode_num 987, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -9.09, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -9.08 achieved!\\n\",\n      \"episode_num 988, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -9.08, curr_epsilon: 0.0058\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 989, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -9.16, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.97 achieved!\\n\",\n      \"episode_num 990, curr_reward: 5.0, best_reward: 9.0, running_avg_reward: -8.97, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.95 achieved!\\n\",\n      \"episode_num 991, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -8.95, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.9 achieved!\\n\",\n      \"episode_num 992, curr_reward: -1.0, best_reward: 9.0, running_avg_reward: -8.9, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.89 achieved!\\n\",\n      \"episode_num 993, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.89, curr_epsilon: 0.0058\\n\",\n      \"episode_num 994, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.89, curr_epsilon: 0.0058\\n\",\n      \"episode_num 995, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -8.89, curr_epsilon: 0.0058\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.85 achieved!\\n\",\n      \"episode_num 996, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -8.85, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.76 achieved!\\n\",\n      \"episode_num 997, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -8.76, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.69 achieved!\\n\",\n      \"episode_num 998, curr_reward: -2.0, best_reward: 9.0, running_avg_reward: -8.69, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.63 achieved!\\n\",\n      \"episode_num 999, curr_reward: -5.0, best_reward: 9.0, running_avg_reward: -8.63, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1000, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.63, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1001, curr_reward: -16.0, best_reward: 9.0, running_avg_reward: -8.66, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1002, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.63, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.52 achieved!\\n\",\n      \"episode_num 1003, curr_reward: -2.0, best_reward: 9.0, running_avg_reward: -8.52, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.36 achieved!\\n\",\n      \"episode_num 1004, curr_reward: 6.0, best_reward: 9.0, running_avg_reward: -8.36, curr_epsilon: 0.0057\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.3 achieved!\\n\",\n      \"episode_num 1005, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.3, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1006, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -8.31, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1007, curr_reward: -15.0, best_reward: 9.0, running_avg_reward: -8.33, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1008, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.31, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1009, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.38, curr_epsilon: 0.0057\\n\",\n      \"episode_num 1010, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.41, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1011, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -8.37, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1012, curr_reward: -1.0, best_reward: 9.0, running_avg_reward: -8.34, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1013, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.37, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1014, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.31, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.14 achieved!\\n\",\n      \"episode_num 1015, curr_reward: 3.0, best_reward: 9.0, running_avg_reward: -8.14, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1016, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -8.26, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1017, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -8.22, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.1 achieved!\\n\",\n      \"episode_num 1018, curr_reward: 3.0, best_reward: 9.0, running_avg_reward: -8.1, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.04 achieved!\\n\",\n      \"episode_num 1019, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -8.04, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -8.03 achieved!\\n\",\n      \"episode_num 1020, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -8.03, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1021, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.07, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.97 achieved!\\n\",\n      \"episode_num 1022, curr_reward: -4.0, best_reward: 9.0, running_avg_reward: -7.97, curr_epsilon: 0.0056\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.88 achieved!\\n\",\n      \"episode_num 1023, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -7.88, curr_epsilon: 0.0056\\n\",\n      \"episode_num 1024, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -7.92, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1025, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -7.88, curr_epsilon: 0.0055\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.85 achieved!\\n\",\n      \"episode_num 1026, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -7.85, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1027, curr_reward: -14.0, best_reward: 9.0, running_avg_reward: -7.87, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1028, curr_reward: -15.0, best_reward: 9.0, running_avg_reward: -7.88, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1029, curr_reward: -15.0, best_reward: 9.0, running_avg_reward: -7.96, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1030, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -8.0, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1031, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -7.96, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1032, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -7.92, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1033, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -7.94, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1034, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -7.98, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1035, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -7.93, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1036, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -7.99, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1037, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.1, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1038, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -8.06, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1039, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -8.03, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1040, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -7.99, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1041, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.01, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1042, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -7.95, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1043, curr_reward: -14.0, best_reward: 9.0, running_avg_reward: -7.91, curr_epsilon: 0.0055\\n\",\n      \"episode_num 1044, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -7.87, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1045, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -8.02, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1046, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -8.02, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1047, curr_reward: -18.0, best_reward: 9.0, running_avg_reward: -8.09, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1048, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -8.05, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1049, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -8.09, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1050, curr_reward: -6.0, best_reward: 9.0, running_avg_reward: -8.14, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1051, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.22, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1052, curr_reward: -19.0, best_reward: 9.0, running_avg_reward: -8.31, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1053, curr_reward: 8.0, best_reward: 9.0, running_avg_reward: -8.21, curr_epsilon: 0.0054\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1054, curr_reward: 5.0, best_reward: 9.0, running_avg_reward: -8.0, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1055, curr_reward: -1.0, best_reward: 9.0, running_avg_reward: -7.93, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1056, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -7.95, curr_epsilon: 0.0054\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.8 achieved!\\n\",\n      \"episode_num 1057, curr_reward: -2.0, best_reward: 9.0, running_avg_reward: -7.8, curr_epsilon: 0.0054\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.77 achieved!\\n\",\n      \"episode_num 1058, curr_reward: -12.0, best_reward: 9.0, running_avg_reward: -7.77, curr_epsilon: 0.0054\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.72 achieved!\\n\",\n      \"episode_num 1059, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -7.72, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1060, curr_reward: -7.0, best_reward: 9.0, running_avg_reward: -7.76, curr_epsilon: 0.0054\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.69 achieved!\\n\",\n      \"episode_num 1061, curr_reward: -5.0, best_reward: 9.0, running_avg_reward: -7.69, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1062, curr_reward: -15.0, best_reward: 9.0, running_avg_reward: -7.83, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1063, curr_reward: -5.0, best_reward: 9.0, running_avg_reward: -7.87, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1064, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -7.85, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1065, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.05, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1066, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.01, curr_epsilon: 0.0054\\n\",\n      \"episode_num 1067, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -7.92, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1068, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -7.92, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1069, curr_reward: -8.0, best_reward: 9.0, running_avg_reward: -7.9, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1070, curr_reward: -9.0, best_reward: 9.0, running_avg_reward: -8.06, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1071, curr_reward: -11.0, best_reward: 9.0, running_avg_reward: -8.01, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1072, curr_reward: -10.0, best_reward: 9.0, running_avg_reward: -8.05, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1073, curr_reward: -13.0, best_reward: 9.0, running_avg_reward: -8.06, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1074, curr_reward: 10.0, best_reward: 10.0, running_avg_reward: -7.86, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1075, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -7.8, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1076, curr_reward: -19.0, best_reward: 10.0, running_avg_reward: -8.05, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1077, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1078, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1079, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.2, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1080, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1081, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1082, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.14, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1083, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1084, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.32, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1085, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.41, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1086, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1087, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1088, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.26, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1089, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.2, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1090, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1091, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.39, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1092, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.49, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1093, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1094, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1095, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.5, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1096, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.52, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1097, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.57, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1098, curr_reward: -19.0, best_reward: 10.0, running_avg_reward: -8.74, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1099, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1100, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1101, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.0053\\n\",\n      \"episode_num 1102, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1103, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1104, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1105, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1106, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1107, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1108, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1109, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.87, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1110, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.83, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1111, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1112, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1113, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1114, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1115, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.0, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1116, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1117, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1118, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.15, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1119, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.15, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1120, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.08, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1121, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.11, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1122, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.21, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1123, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1124, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.3, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1125, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.28, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1126, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1127, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.3, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1128, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.25, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1129, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -9.18, curr_epsilon: 0.0052\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1130, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.15, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1131, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.09, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1132, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1133, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1134, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1135, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1136, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.02, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1137, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.03, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1138, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.99, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1139, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1140, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1141, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.08, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1142, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.07, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1143, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.98, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1144, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -9.02, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1145, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.98, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1146, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1147, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1148, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1149, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.05, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1150, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.13, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1151, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.0052\\n\",\n      \"episode_num 1152, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.0, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1153, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.17, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1154, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.32, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1155, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.45, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1156, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.44, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1157, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.53, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1158, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -9.38, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1159, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.37, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1160, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.37, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1161, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.41, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1162, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.39, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1163, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.48, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1164, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.42, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1165, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.41, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1166, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.32, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1167, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -9.4, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1168, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.36, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1169, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -9.27, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1170, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1171, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1172, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.3, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1173, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.24, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1174, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.4, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1175, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.44, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1176, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.36, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1177, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.31, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1178, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.35, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1179, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.33, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1180, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.31, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1181, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.3, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1182, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1183, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.22, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1184, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.24, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1185, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.08, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1186, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.14, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1187, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.14, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1188, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.22, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1189, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.29, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1190, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.21, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1191, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.3, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1192, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.28, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1193, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.23, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1194, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.23, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1195, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -9.21, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1196, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.2, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1197, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.18, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1198, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1199, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.78, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1200, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -8.69, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1201, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1202, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.48, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1203, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.56, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1204, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.42, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1205, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1206, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1207, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.42, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1208, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.0051\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1209, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1210, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1211, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1212, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.26, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1213, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1214, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1215, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1216, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1217, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1218, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.21, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1219, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.21, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1220, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.3, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1221, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.25, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1222, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.23, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1223, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.19, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1224, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1225, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1226, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1227, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.98, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1228, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.99, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1229, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.98, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1230, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1231, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.19, curr_epsilon: 0.0051\\n\",\n      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best_reward: 10.0, running_avg_reward: -8.3, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1241, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1242, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1243, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.39, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1244, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1245, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1246, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1247, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.2, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1248, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1249, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.14, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1250, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1251, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.21, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1252, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1253, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1254, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1255, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.0051\\n\",\n      \"episode_num 1256, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      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10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1266, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1267, curr_reward: 8.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1268, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1269, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1270, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1271, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1272, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1273, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1274, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1275, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1276, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -7.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 1277, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1278, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1279, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1280, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 1281, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1282, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1283, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 1284, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 1285, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 1286, curr_reward: 5.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1287, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1288, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1289, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1290, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1291, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1292, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1293, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1294, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1295, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 1296, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1297, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1298, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 1299, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1300, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1301, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 1302, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.61, curr_epsilon: 0.005\\n\",\n      \"episode_num 1303, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1304, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1305, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1306, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1307, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1308, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1309, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1310, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1311, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1312, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1313, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1314, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1315, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1316, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1317, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1318, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1319, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1320, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 1321, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1322, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1323, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1324, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1325, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 1326, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1327, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1328, curr_reward: 8.0, best_reward: 10.0, running_avg_reward: -8.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1329, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1330, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1331, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1332, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1333, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1334, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1335, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1336, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1337, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1338, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1339, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -8.35, curr_epsilon: 0.005\\n\",\n      \"episode_num 1340, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 1341, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1342, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.2, curr_epsilon: 0.005\\n\",\n      \"episode_num 1343, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.005\\n\",\n      \"episode_num 1344, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1345, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1346, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1347, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -8.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 1348, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 1349, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1350, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1351, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1352, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1353, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1354, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1355, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1356, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1357, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1358, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1359, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1360, curr_reward: 8.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1361, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1362, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.22, curr_epsilon: 0.005\\n\",\n      \"episode_num 1363, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1364, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 1365, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1366, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1367, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.65, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1368, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 1369, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.75, curr_epsilon: 0.005\\n\",\n      \"episode_num 1370, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1371, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1372, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1373, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1374, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1375, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1376, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1377, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1378, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1379, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1380, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1381, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1382, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -8.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 1383, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1384, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1385, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 1386, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1387, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 1388, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1389, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1390, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1391, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 1392, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1393, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1394, curr_reward: 8.0, best_reward: 10.0, running_avg_reward: -8.3, curr_epsilon: 0.005\\n\",\n      \"episode_num 1395, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.26, curr_epsilon: 0.005\\n\",\n      \"episode_num 1396, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 1397, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 1398, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 1399, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1400, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 1401, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.26, curr_epsilon: 0.005\\n\",\n      \"episode_num 1402, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1403, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1404, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1405, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1406, curr_reward: 9.0, best_reward: 10.0, running_avg_reward: -8.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1407, curr_reward: -19.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1408, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1409, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1410, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1411, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1412, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1413, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1414, curr_reward: 8.0, best_reward: 10.0, running_avg_reward: -7.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1415, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1416, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.7, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.62 achieved!\\n\",\n      \"episode_num 1417, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.61 achieved!\\n\",\n      \"episode_num 1418, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.61, curr_epsilon: 0.005\\n\",\n      \"episode_num 1419, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1420, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1421, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.54 achieved!\\n\",\n      \"episode_num 1422, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -7.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1423, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.54, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.44 achieved!\\n\",\n      \"episode_num 1424, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1425, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 1426, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.33 achieved!\\n\",\n      \"episode_num 1427, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1428, curr_reward: 5.0, best_reward: 10.0, running_avg_reward: -7.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1429, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1430, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1431, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1432, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.33, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.3 achieved!\\n\",\n      \"episode_num 1433, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.3, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.27 achieved!\\n\",\n      \"episode_num 1434, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.27, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.25 achieved!\\n\",\n      \"episode_num 1435, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.25, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.12 achieved!\\n\",\n      \"episode_num 1436, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -7.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1437, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.2, curr_epsilon: 0.005\\n\",\n      \"episode_num 1438, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1439, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.2, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1440, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.22, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.11 achieved!\\n\",\n      \"episode_num 1441, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.11, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.09 achieved!\\n\",\n      \"episode_num 1442, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.09, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.07 achieved!\\n\",\n      \"episode_num 1443, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -7.07, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -7.01 achieved!\\n\",\n      \"episode_num 1444, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1445, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1446, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1447, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -7.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1448, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1449, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.05, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.88 achieved!\\n\",\n      \"episode_num 1450, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -6.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1451, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -6.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1452, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1453, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -6.91, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.82 achieved!\\n\",\n      \"episode_num 1454, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.82, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.72 achieved!\\n\",\n      \"episode_num 1455, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -6.72, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.7 achieved!\\n\",\n      \"episode_num 1456, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -6.7, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.69 achieved!\\n\",\n      \"episode_num 1457, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1458, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1459, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.74, curr_epsilon: 0.005\\n\",\n      \"episode_num 1460, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -6.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 1461, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1462, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1463, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1464, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1465, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1466, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1467, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.82, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.67 achieved!\\n\",\n      \"episode_num 1468, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -6.67, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.64 achieved!\\n\",\n      \"episode_num 1469, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.64, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.63 achieved!\\n\",\n      \"episode_num 1470, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.63, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.57 achieved!\\n\",\n      \"episode_num 1471, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1472, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -6.67, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.47 achieved!\\n\",\n      \"episode_num 1473, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -6.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1474, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.49, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.33 achieved!\\n\",\n      \"episode_num 1475, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -6.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1476, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1477, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1478, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 1479, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1480, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -6.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1481, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1482, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -6.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1483, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.45, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.27 achieved!\\n\",\n      \"episode_num 1484, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -6.27, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.12 achieved!\\n\",\n      \"episode_num 1485, curr_reward: 5.0, best_reward: 10.0, running_avg_reward: -6.12, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.09 achieved!\\n\",\n      \"episode_num 1486, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1487, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1488, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 1489, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.15, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.03 achieved!\\n\",\n      \"episode_num 1490, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.03, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -6.0 achieved!\\n\",\n      \"episode_num 1491, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -6.0, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -5.93 achieved!\\n\",\n      \"episode_num 1492, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -5.93, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -5.86 achieved!\\n\",\n      \"episode_num 1493, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -5.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1494, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1495, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -5.95, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -5.82 achieved!\\n\",\n      \"episode_num 1496, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -5.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1497, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -5.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1498, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -5.88, curr_epsilon: 0.005\\n\",\n      \"checkpointing current model weights. highest running_average_reward of -5.77 achieved!\\n\",\n      \"episode_num 1499, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -5.77, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"checkpointing current model weights. highest running_average_reward of -5.7 achieved!\\n\",\n      \"episode_num 1500, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -5.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 1501, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -5.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1502, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -5.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1503, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -5.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1504, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -5.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1505, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -5.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1506, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -6.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1507, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -6.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1508, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1509, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -5.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 1510, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1511, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1512, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1513, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1514, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1515, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1516, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1517, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1518, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1519, curr_reward: 7.0, best_reward: 10.0, running_avg_reward: -5.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 1520, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -5.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1521, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -5.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 1522, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1523, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 1524, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -6.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 1525, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1526, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1527, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1528, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.22, curr_epsilon: 0.005\\n\",\n      \"episode_num 1529, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.22, curr_epsilon: 0.005\\n\",\n      \"episode_num 1530, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 1531, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 1532, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1533, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.23, curr_epsilon: 0.005\\n\",\n      \"episode_num 1534, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1535, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.23, curr_epsilon: 0.005\\n\",\n      \"episode_num 1536, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 1537, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.26, curr_epsilon: 0.005\\n\",\n      \"episode_num 1538, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 1539, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -6.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1540, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1541, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1542, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -6.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1543, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.5, curr_epsilon: 0.005\\n\",\n      \"episode_num 1544, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -6.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1545, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1546, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -6.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1547, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1548, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1549, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1550, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -6.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1551, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1552, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -6.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 1553, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1554, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.74, curr_epsilon: 0.005\\n\",\n      \"episode_num 1555, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1556, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1557, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -6.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1558, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -6.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1559, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -6.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1560, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -6.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1561, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1562, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1563, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1564, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -6.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1565, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -6.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1566, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1567, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1568, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -6.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1569, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -6.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1570, curr_reward: 6.0, best_reward: 10.0, running_avg_reward: -6.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1571, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -6.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1572, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -6.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1573, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1574, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -6.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 1575, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -6.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1576, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -6.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1577, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -6.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1578, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -6.84, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1579, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -6.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1580, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -6.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1581, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1582, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 1583, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.22, curr_epsilon: 0.005\\n\",\n      \"episode_num 1584, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1585, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.5, curr_epsilon: 0.005\\n\",\n      \"episode_num 1586, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -7.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1587, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1588, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1589, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1590, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1591, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 1592, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1593, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1594, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1595, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1596, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1597, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1598, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1599, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1600, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.3, curr_epsilon: 0.005\\n\",\n      \"episode_num 1601, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1602, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1603, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1604, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1605, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1606, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1607, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1608, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1609, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.005\\n\",\n      \"episode_num 1610, curr_reward: 7.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1611, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 1612, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1613, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1614, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1615, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1616, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1617, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.27, curr_epsilon: 0.005\\n\",\n      \"episode_num 1618, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1619, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1620, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -8.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1621, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1622, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.3, curr_epsilon: 0.005\\n\",\n      \"episode_num 1623, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 1624, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1625, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 1626, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1627, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1628, curr_reward: -19.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1629, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1630, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1631, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.75, curr_epsilon: 0.005\\n\",\n      \"episode_num 1632, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1633, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1634, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1635, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1636, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1637, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1638, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1639, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1640, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1641, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1642, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1643, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -9.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1644, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1645, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1646, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1647, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1648, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1649, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1650, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1651, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1652, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1653, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1654, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1655, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1656, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1657, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1658, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1659, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1660, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1661, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1662, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1663, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 1664, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 1665, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1666, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 1667, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1668, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1669, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -9.17, curr_epsilon: 0.005\\n\",\n      \"episode_num 1670, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1671, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1672, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -9.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1673, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1674, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.35, curr_epsilon: 0.005\\n\",\n      \"episode_num 1675, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 1676, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1677, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 1678, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 1679, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1680, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1681, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1682, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1683, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1684, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1685, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1686, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1687, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1688, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1689, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1690, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1691, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1692, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1693, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1694, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1695, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1696, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1697, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1698, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1699, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1700, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1701, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1702, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1703, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1704, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -8.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1705, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1706, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1707, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1708, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -8.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1709, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1710, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1711, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1712, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1713, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1714, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 1715, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1716, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1717, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1718, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1719, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1720, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1721, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1722, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1723, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1724, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1725, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.93, curr_epsilon: 0.005\\n\",\n      \"episode_num 1726, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1727, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 1728, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 1729, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1730, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1731, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1732, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1733, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1734, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1735, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 1736, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1737, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1738, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.75, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1739, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1740, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1741, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1742, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1743, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1744, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1745, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1746, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1747, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1748, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1749, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1750, curr_reward: 7.0, best_reward: 10.0, running_avg_reward: -8.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1751, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1752, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1753, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1754, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1755, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1756, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1757, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -8.2, curr_epsilon: 0.005\\n\",\n      \"episode_num 1758, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -8.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 1759, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -8.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1760, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1761, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1762, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1763, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1764, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.21, curr_epsilon: 0.005\\n\",\n      \"episode_num 1765, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1766, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1767, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 1768, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 1769, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1770, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1771, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 1772, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1773, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1774, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1775, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1776, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.94, curr_epsilon: 0.005\\n\",\n      \"episode_num 1777, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1778, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1779, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1780, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1781, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 1782, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1783, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1784, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1785, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1786, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 1787, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1788, curr_reward: 1.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1789, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1790, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 1791, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1792, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1793, curr_reward: 5.0, best_reward: 10.0, running_avg_reward: -7.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 1794, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1795, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1796, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1797, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1798, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1799, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 1800, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.61, curr_epsilon: 0.005\\n\",\n      \"episode_num 1801, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1802, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1803, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 1804, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1805, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1806, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 1807, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1808, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 1809, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1810, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1811, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1812, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -7.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 1813, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -7.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1814, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 1815, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.89, curr_epsilon: 0.005\\n\",\n      \"episode_num 1816, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -7.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 1817, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1818, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.69, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1819, curr_reward: 4.0, best_reward: 10.0, running_avg_reward: -7.5, curr_epsilon: 0.005\\n\",\n      \"episode_num 1820, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1821, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1822, curr_reward: 2.0, best_reward: 10.0, running_avg_reward: -7.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1823, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 1824, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.3, curr_epsilon: 0.005\\n\",\n      \"episode_num 1825, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1826, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 1827, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1828, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1829, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1830, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1831, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1832, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1833, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1834, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1835, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 1836, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1837, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1838, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1839, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1840, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1841, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.35, curr_epsilon: 0.005\\n\",\n      \"episode_num 1842, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1843, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 1844, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 1845, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1846, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 1847, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 1848, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1849, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 1850, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 1851, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.35, curr_epsilon: 0.005\\n\",\n      \"episode_num 1852, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1853, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1854, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1855, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1856, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1857, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1858, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1859, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1860, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1861, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1862, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 1863, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1864, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -7.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 1865, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 1866, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.35, curr_epsilon: 0.005\\n\",\n      \"episode_num 1867, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1868, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1869, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 1870, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1871, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1872, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 1873, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1874, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1875, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.5, curr_epsilon: 0.005\\n\",\n      \"episode_num 1876, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1877, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 1878, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 1879, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 1880, curr_reward: 5.0, best_reward: 10.0, running_avg_reward: -7.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 1881, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -7.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 1882, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1883, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 1884, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1885, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 1886, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1887, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.66, curr_epsilon: 0.005\\n\",\n      \"episode_num 1888, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 1889, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1890, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1891, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1892, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1893, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 1894, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1895, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1896, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1897, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.76, curr_epsilon: 0.005\\n\",\n      \"episode_num 1898, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1899, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -7.72, curr_epsilon: 0.005\\n\",\n      \"episode_num 1900, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 1901, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1902, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1903, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1904, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 1905, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 1906, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -7.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1907, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 1908, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1909, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 1910, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1911, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1912, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1913, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1914, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 1915, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1916, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 1917, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 1918, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 1919, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1920, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -7.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1921, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 1922, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1923, curr_reward: -19.0, best_reward: 10.0, running_avg_reward: -8.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1924, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1925, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1926, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -7.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 1927, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -7.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 1928, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 1929, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1930, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1931, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 1932, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 1933, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -7.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 1934, curr_reward: 10.0, best_reward: 10.0, running_avg_reward: -7.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1935, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1936, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 1937, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1938, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 1939, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -7.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1940, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 1941, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1942, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 1943, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -7.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 1944, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1945, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1946, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -8.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1947, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 1948, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -7.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 1949, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -7.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 1950, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.04, curr_epsilon: 0.005\\n\",\n      \"episode_num 1951, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1952, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 1953, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1954, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 1955, curr_reward: 3.0, best_reward: 10.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 1956, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -7.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 1957, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 1958, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 1959, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 1960, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1961, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.23, curr_epsilon: 0.005\\n\",\n      \"episode_num 1962, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1963, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.22, curr_epsilon: 0.005\\n\",\n      \"episode_num 1964, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 1965, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.23, curr_epsilon: 0.005\\n\",\n      \"episode_num 1966, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 1967, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1968, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1969, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -8.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 1970, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 1971, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1972, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.36, curr_epsilon: 0.005\\n\",\n      \"episode_num 1973, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 1974, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 1975, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -8.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 1976, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 1977, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 1978, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.47, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 1979, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -8.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 1980, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 1981, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -8.71, curr_epsilon: 0.005\\n\",\n      \"episode_num 1982, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -8.75, curr_epsilon: 0.005\\n\",\n      \"episode_num 1983, curr_reward: -6.0, best_reward: 10.0, running_avg_reward: -8.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 1984, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 1985, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.66, curr_epsilon: 0.005\\n\",\n      \"episode_num 1986, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 1987, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -8.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 1988, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.74, curr_epsilon: 0.005\\n\",\n      \"episode_num 1989, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -8.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 1990, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 1991, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 1992, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -8.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 1993, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 1994, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -8.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 1995, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1996, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -8.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 1997, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -8.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 1998, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 1999, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.16, curr_epsilon: 0.005\\n\",\n      \"episode_num 2000, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.21, curr_epsilon: 0.005\\n\",\n      \"episode_num 2001, curr_reward: -14.0, best_reward: 10.0, running_avg_reward: -9.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 2002, curr_reward: -16.0, best_reward: 10.0, running_avg_reward: -9.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 2003, curr_reward: -12.0, best_reward: 10.0, running_avg_reward: -9.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 2004, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 2005, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 2006, curr_reward: -15.0, best_reward: 10.0, running_avg_reward: -9.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 2007, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -9.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 2008, curr_reward: -1.0, best_reward: 10.0, running_avg_reward: -9.69, curr_epsilon: 0.005\\n\",\n      \"episode_num 2009, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2010, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.66, curr_epsilon: 0.005\\n\",\n      \"episode_num 2011, curr_reward: -11.0, best_reward: 10.0, running_avg_reward: -9.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 2012, curr_reward: -2.0, best_reward: 10.0, running_avg_reward: -9.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 2013, curr_reward: -4.0, best_reward: 10.0, running_avg_reward: -9.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 2014, curr_reward: -18.0, best_reward: 10.0, running_avg_reward: -9.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 2015, curr_reward: -13.0, best_reward: 10.0, running_avg_reward: -9.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 2016, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 2017, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -9.64, curr_epsilon: 0.005\\n\",\n      \"episode_num 2018, curr_reward: -10.0, best_reward: 10.0, running_avg_reward: -9.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 2019, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 2020, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.61, curr_epsilon: 0.005\\n\",\n      \"episode_num 2021, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -9.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 2022, curr_reward: -3.0, best_reward: 10.0, running_avg_reward: -9.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 2023, curr_reward: -9.0, best_reward: 10.0, running_avg_reward: -9.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2024, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2025, curr_reward: -17.0, best_reward: 10.0, running_avg_reward: -9.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 2026, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 2027, curr_reward: -7.0, best_reward: 10.0, running_avg_reward: -9.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 2028, curr_reward: -5.0, best_reward: 10.0, running_avg_reward: -9.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 2029, curr_reward: -8.0, best_reward: 10.0, running_avg_reward: -9.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 2030, curr_reward: 11.0, best_reward: 11.0, running_avg_reward: -9.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 2031, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 2032, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -9.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 2033, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 2034, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 2035, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -9.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 2036, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -9.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 2037, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 2038, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -9.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 2039, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 2040, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -9.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 2041, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2042, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2043, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -9.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 2044, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.39, curr_epsilon: 0.005\\n\",\n      \"episode_num 2045, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -9.47, curr_epsilon: 0.005\\n\",\n      \"episode_num 2046, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 2047, curr_reward: 1.0, best_reward: 11.0, running_avg_reward: -9.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 2048, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -9.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2049, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.5, curr_epsilon: 0.005\\n\",\n      \"episode_num 2050, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 2051, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -9.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2052, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 2053, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.59, curr_epsilon: 0.005\\n\",\n      \"episode_num 2054, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 2055, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.66, curr_epsilon: 0.005\\n\",\n      \"episode_num 2056, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -9.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 2057, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -9.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 2058, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -9.43, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 2059, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -9.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 2060, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -9.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 2061, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -9.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 2062, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.37, curr_epsilon: 0.005\\n\",\n      \"episode_num 2063, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -9.33, curr_epsilon: 0.005\\n\",\n      \"episode_num 2064, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -9.26, curr_epsilon: 0.005\\n\",\n      \"episode_num 2065, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -9.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 2066, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.21, curr_epsilon: 0.005\\n\",\n      \"episode_num 2067, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.12, curr_epsilon: 0.005\\n\",\n      \"episode_num 2068, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -9.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 2069, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -9.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 2070, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -9.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 2071, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -9.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 2072, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -9.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 2073, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -9.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 2074, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -9.06, curr_epsilon: 0.005\\n\",\n      \"episode_num 2075, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -9.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 2076, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.13, curr_epsilon: 0.005\\n\",\n      \"episode_num 2077, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -9.02, curr_epsilon: 0.005\\n\",\n      \"episode_num 2078, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -9.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 2079, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -9.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 2080, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -9.14, curr_epsilon: 0.005\\n\",\n      \"episode_num 2081, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -9.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 2082, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -8.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 2083, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -8.99, curr_epsilon: 0.005\\n\",\n      \"episode_num 2084, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -8.97, curr_epsilon: 0.005\\n\",\n      \"episode_num 2085, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -8.98, curr_epsilon: 0.005\\n\",\n      \"episode_num 2086, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 2087, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -9.01, curr_epsilon: 0.005\\n\",\n      \"episode_num 2088, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -9.0, curr_epsilon: 0.005\\n\",\n      \"episode_num 2089, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -8.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 2090, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -8.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2091, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -8.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 2092, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -8.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 2093, curr_reward: -16.0, best_reward: 11.0, running_avg_reward: -8.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 2094, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -8.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 2095, curr_reward: -16.0, best_reward: 11.0, running_avg_reward: -8.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 2096, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -8.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 2097, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -8.8, curr_epsilon: 0.005\\n\",\n      \"episode_num 2098, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -8.67, curr_epsilon: 0.005\\n\",\n      \"episode_num 2099, curr_reward: 3.0, best_reward: 11.0, running_avg_reward: -8.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 2100, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -8.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2101, curr_reward: -2.0, best_reward: 11.0, running_avg_reward: -8.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 2102, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -8.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 2103, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -8.2, curr_epsilon: 0.005\\n\",\n      \"episode_num 2104, curr_reward: 3.0, best_reward: 11.0, running_avg_reward: -8.08, curr_epsilon: 0.005\\n\",\n      \"episode_num 2105, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -8.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 2106, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -8.07, curr_epsilon: 0.005\\n\",\n      \"episode_num 2107, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 2108, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -8.05, curr_epsilon: 0.005\\n\",\n      \"episode_num 2109, curr_reward: 10.0, best_reward: 11.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 2110, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2111, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 2112, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 2113, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 2114, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -7.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 2115, curr_reward: -2.0, best_reward: 11.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 2116, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.85, curr_epsilon: 0.005\\n\",\n      \"episode_num 2117, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 2118, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.7, curr_epsilon: 0.005\\n\",\n      \"episode_num 2119, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 2120, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.66, curr_epsilon: 0.005\\n\",\n      \"episode_num 2121, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.73, curr_epsilon: 0.005\\n\",\n      \"episode_num 2122, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.81, curr_epsilon: 0.005\\n\",\n      \"episode_num 2123, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 2124, curr_reward: -17.0, best_reward: 11.0, running_avg_reward: -7.9, curr_epsilon: 0.005\\n\",\n      \"episode_num 2125, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.88, curr_epsilon: 0.005\\n\",\n      \"episode_num 2126, curr_reward: 6.0, best_reward: 11.0, running_avg_reward: -7.77, curr_epsilon: 0.005\\n\",\n      \"episode_num 2127, curr_reward: 2.0, best_reward: 11.0, running_avg_reward: -7.68, curr_epsilon: 0.005\\n\",\n      \"episode_num 2128, curr_reward: 1.0, best_reward: 11.0, running_avg_reward: -7.62, curr_epsilon: 0.005\\n\",\n      \"episode_num 2129, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 2130, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.82, curr_epsilon: 0.005\\n\",\n      \"episode_num 2131, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 2132, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -7.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 2133, curr_reward: -17.0, best_reward: 11.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\",\n      \"episode_num 2134, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.84, curr_epsilon: 0.005\\n\",\n      \"episode_num 2135, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2136, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 2137, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 2138, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -7.87, curr_epsilon: 0.005\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"episode_num 2139, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.79, curr_epsilon: 0.005\\n\",\n      \"episode_num 2140, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.75, curr_epsilon: 0.005\\n\",\n      \"episode_num 2141, curr_reward: 10.0, best_reward: 11.0, running_avg_reward: -7.54, curr_epsilon: 0.005\\n\",\n      \"episode_num 2142, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -7.42, curr_epsilon: 0.005\\n\",\n      \"episode_num 2143, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2144, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 2145, curr_reward: 3.0, best_reward: 11.0, running_avg_reward: -7.21, curr_epsilon: 0.005\\n\",\n      \"episode_num 2146, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 2147, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.28, curr_epsilon: 0.005\\n\",\n      \"episode_num 2148, curr_reward: -2.0, best_reward: 11.0, running_avg_reward: -7.23, curr_epsilon: 0.005\\n\",\n      \"episode_num 2149, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.15, curr_epsilon: 0.005\\n\",\n      \"episode_num 2150, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -7.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 2151, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.19, curr_epsilon: 0.005\\n\",\n      \"episode_num 2152, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 2153, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.09, curr_epsilon: 0.005\\n\",\n      \"episode_num 2154, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.1, curr_epsilon: 0.005\\n\",\n      \"episode_num 2155, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.11, curr_epsilon: 0.005\\n\",\n      \"episode_num 2156, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.18, curr_epsilon: 0.005\\n\",\n      \"episode_num 2157, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -7.24, curr_epsilon: 0.005\\n\",\n      \"episode_num 2158, curr_reward: -17.0, best_reward: 11.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 2159, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.32, curr_epsilon: 0.005\\n\",\n      \"episode_num 2160, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2161, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2162, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -7.46, curr_epsilon: 0.005\\n\",\n      \"episode_num 2163, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 2164, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 2165, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 2166, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 2167, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.53, curr_epsilon: 0.005\\n\",\n      \"episode_num 2168, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 2169, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 2170, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -7.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 2171, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.48, curr_epsilon: 0.005\\n\",\n      \"episode_num 2172, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 2173, curr_reward: 1.0, best_reward: 11.0, running_avg_reward: -7.45, curr_epsilon: 0.005\\n\",\n      \"episode_num 2174, curr_reward: -16.0, best_reward: 11.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2175, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -7.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 2176, curr_reward: -12.0, best_reward: 11.0, running_avg_reward: -7.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 2177, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2178, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2179, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2180, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 2181, curr_reward: -2.0, best_reward: 11.0, running_avg_reward: -7.52, curr_epsilon: 0.005\\n\",\n      \"episode_num 2182, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.56, curr_epsilon: 0.005\\n\",\n      \"episode_num 2183, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.6, curr_epsilon: 0.005\\n\",\n      \"episode_num 2184, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.63, curr_epsilon: 0.005\\n\",\n      \"episode_num 2185, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 2186, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -7.57, curr_epsilon: 0.005\\n\",\n      \"episode_num 2187, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.55, curr_epsilon: 0.005\\n\",\n      \"episode_num 2188, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -7.44, curr_epsilon: 0.005\\n\",\n      \"episode_num 2189, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.38, curr_epsilon: 0.005\\n\",\n      \"episode_num 2190, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 2191, curr_reward: -5.0, best_reward: 11.0, running_avg_reward: -7.41, curr_epsilon: 0.005\\n\",\n      \"episode_num 2192, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.49, curr_epsilon: 0.005\\n\",\n      \"episode_num 2193, curr_reward: -1.0, best_reward: 11.0, running_avg_reward: -7.34, curr_epsilon: 0.005\\n\",\n      \"episode_num 2194, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -7.31, curr_epsilon: 0.005\\n\",\n      \"episode_num 2195, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 2196, curr_reward: -11.0, best_reward: 11.0, running_avg_reward: -7.29, curr_epsilon: 0.005\\n\",\n      \"episode_num 2197, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.25, curr_epsilon: 0.005\\n\",\n      \"episode_num 2198, curr_reward: -3.0, best_reward: 11.0, running_avg_reward: -7.27, curr_epsilon: 0.005\\n\",\n      \"episode_num 2199, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.4, curr_epsilon: 0.005\\n\",\n      \"episode_num 2200, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.43, curr_epsilon: 0.005\\n\",\n      \"episode_num 2201, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.51, curr_epsilon: 0.005\\n\",\n      \"episode_num 2202, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.58, curr_epsilon: 0.005\\n\",\n      \"episode_num 2203, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.65, curr_epsilon: 0.005\\n\",\n      \"episode_num 2204, curr_reward: -15.0, best_reward: 11.0, running_avg_reward: -7.83, curr_epsilon: 0.005\\n\",\n      \"episode_num 2205, curr_reward: -9.0, best_reward: 11.0, running_avg_reward: -7.78, curr_epsilon: 0.005\\n\",\n      \"episode_num 2206, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.74, curr_epsilon: 0.005\\n\",\n      \"episode_num 2207, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.75, curr_epsilon: 0.005\\n\",\n      \"episode_num 2208, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.74, curr_epsilon: 0.005\\n\",\n      \"episode_num 2209, curr_reward: -8.0, best_reward: 11.0, running_avg_reward: -7.92, curr_epsilon: 0.005\\n\",\n      \"episode_num 2210, curr_reward: -4.0, best_reward: 11.0, running_avg_reward: -7.86, curr_epsilon: 0.005\\n\",\n      \"episode_num 2211, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2212, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2213, curr_reward: -6.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2214, curr_reward: -14.0, best_reward: 11.0, running_avg_reward: -7.91, curr_epsilon: 0.005\\n\",\n      \"episode_num 2215, curr_reward: -7.0, best_reward: 11.0, running_avg_reward: -7.96, curr_epsilon: 0.005\\n\",\n      \"episode_num 2216, curr_reward: -10.0, best_reward: 11.0, running_avg_reward: -7.95, curr_epsilon: 0.005\\n\",\n      \"episode_num 2217, curr_reward: -13.0, best_reward: 11.0, running_avg_reward: -8.03, curr_epsilon: 0.005\\n\",\n      \"episode_num 2218, curr_reward: -2.0, best_reward: 11.0, running_avg_reward: -7.98, curr_epsilon: 0.005\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"env = wrap_env(ENV)\\n\",\n    \"dvc = torch.device(\\\"cuda\\\") if torch.cuda.is_available() else torch.device(\\\"cpu\\\")\\n\",\n    \"mdl, tgt_mdl = models_init(env, dvc)\\n\",\n    \"opt = Adam(mdl.parameters(), lr=LR)\\n\",\n    \"rpl_bfr = RepBfr(MEM_CAP)\\n\",\n    \"train(env, mdl, tgt_mdl, opt, rpl_bfr, dvc)\\n\",\n    \"env.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.16\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter12/convnet_distributed.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\n\nimport os\nimport time\nimport argparse\n\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n     \n\ndef train(cpu_num, args):\n    rank = args.machine_id * args.num_processes + cpu_num                        \n    dist.init_process_group(                                   \n    backend='gloo',                                         \n    init_method='env://',                                   \n    world_size=args.world_size,                              \n    rank=rank                                               \n    ) \n    torch.manual_seed(0)\n    device = torch.device(\"cpu\")\n    train_dataset = datasets.MNIST('../data', train=True, download=True,\n                                   transform=transforms.Compose([\n                                       transforms.ToTensor(),\n                                       transforms.Normalize((0.1302,), (0.3069,))]))  \n    train_sampler = torch.utils.data.distributed.DistributedSampler(\n        train_dataset,\n        num_replicas=args.world_size,\n        rank=rank\n    )\n    train_dataloader = torch.utils.data.DataLoader(\n       dataset=train_dataset,\n       batch_size=args.batch_size,\n       shuffle=False,            \n       num_workers=0,\n       sampler=train_sampler)\n    model = ConvNet()\n    optimizer = optim.Adadelta(model.parameters(), lr=0.5)\n    model = nn.parallel.DistributedDataParallel(model)\n    model.train()\n    for epoch in range(args.epochs):\n        for b_i, (X, y) in enumerate(train_dataloader):\n            X, y = X.to(device), y.to(device)\n            pred_prob = model(X)\n            loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            if b_i % 10 == 0 and cpu_num==0:\n                print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n                    epoch, b_i, len(train_dataloader),\n                    100. * b_i / len(train_dataloader), loss.item()))\n         \n            \ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--num-machines', default=1, type=int,)\n    parser.add_argument('--num-processes', default=1, type=int)\n    parser.add_argument('--machine-id', default=0, type=int)\n    parser.add_argument('--epochs', default=1, type=int)\n    parser.add_argument('--batch-size', default=128, type=int)\n    args = parser.parse_args()\n    \n    args.world_size = args.num_processes * args.num_machines                \n    os.environ['MASTER_ADDR'] = '127.0.0.1'              \n    os.environ['MASTER_PORT'] = '8892'      \n    start = time.time()\n    mp.spawn(train, nprocs=args.num_processes, args=(args,))\n    print(f\"Finished training in {time.time()-start} secs\")\n    \nif __name__ == '__main__':\n    main()\n    "
  },
  {
    "path": "Chapter12/convnet_distributed_cuda.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\n\nimport os\nimport time\nimport argparse\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n\ndef train(gpu_num, args):\n    rank = args.machine_id * args.num_gpu_processes + gpu_num                        \n    dist.init_process_group(                                   \n    backend='nccl',                                         \n    init_method='env://',                                   \n    world_size=args.world_size,                              \n    rank=rank                                               \n    ) \n    torch.manual_seed(0)\n    model = ConvNet()\n    torch.cuda.set_device(gpu_num)\n    model.cuda(gpu_num)\n    criterion = nn.NLLLoss().cuda(gpu_num) # nll is the negative likelihood loss\n    train_dataset = datasets.MNIST('../data', train=True, download=True,\n                                   transform=transforms.Compose([\n                                       transforms.ToTensor(),\n                                       transforms.Normalize((0.1302,), (0.3069,))]))  \n    train_sampler = torch.utils.data.distributed.DistributedSampler(\n        train_dataset,\n        num_replicas=args.world_size,\n        rank=rank\n    )\n    train_dataloader = torch.utils.data.DataLoader(\n       dataset=train_dataset,\n       batch_size=args.batch_size,\n       shuffle=False,            \n       num_workers=0,\n       pin_memory=True,\n       sampler=train_sampler)\n    optimizer = optim.Adadelta(model.parameters(), lr=0.5)\n    model = nn.parallel.DistributedDataParallel(model,\n                                                device_ids=[gpu_num])\n    model.train()\n    for epoch in range(args.epochs):\n        for b_i, (X, y) in enumerate(train_dataloader):\n            X, y = X.cuda(non_blocking=True), y.cuda(non_blocking=True)\n            pred_prob = model(X)\n            loss = criterion(pred_prob, y) \n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            if b_i % 10 == 0 and gpu_num==0:\n                print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n                    epoch, b_i, len(train_dataloader),\n                    100. * b_i / len(train_dataloader), loss.item()))\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--num-machines', default=1, type=int,)\n    parser.add_argument('--num-gpu-processes', default=1, type=int)\n    parser.add_argument('--machine-id', default=0, type=int)\n    parser.add_argument('--epochs', default=1, type=int)\n    parser.add_argument('--batch-size', default=128, type=int)\n    args = parser.parse_args()\n    \n    args.world_size = args.num_gpu_processes * args.num_machines                \n    os.environ['MASTER_ADDR'] = '127.0.0.1'              \n    os.environ['MASTER_PORT'] = '8892'      \n    start = time.time()\n    mp.spawn(train, nprocs=args.num_gpu_processes, args=(args,))\n    print(f\"Finished training in {time.time()-start} secs\")\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "Chapter12/convnet_undistributed.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\nimport time\nimport argparse\n\ntorch.manual_seed(0)\ndevice = torch.device(\"cpu\")\n\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n    \n    \ndef train(args):\n    train_dataloader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=True, download=True,\n                       transform=transforms.Compose([\n                           transforms.ToTensor(),\n                           transforms.Normalize((0.1302,), (0.3069,))])),\n        batch_size=128, shuffle=True)  \n    model = ConvNet()\n    optimizer = optim.Adadelta(model.parameters(), lr=0.5)\n    model.train()\n    for epoch in range(args.epochs):\n        for b_i, (X, y) in enumerate(train_dataloader):\n            X, y = X.to(device), y.to(device)\n            pred_prob = model(X)\n            loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            if b_i % 10 == 0:\n                print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n                    epoch, b_i, len(train_dataloader),\n                    100. * b_i / len(train_dataloader), loss.item()))\n         \n            \ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--epochs', default=1, type=int)\n    args = parser.parse_args()\n    start = time.time()\n    train(args)\n    print(f\"Finished training in {time.time()-start} secs\")\n    \nif __name__ == '__main__':\n    main()\n    "
  },
  {
    "path": "Chapter12/convnet_undistributed_cuda.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\nimport time\nimport argparse\n\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n    \n    \ndef train(args):\n    torch.manual_seed(0)\n    device = torch.device(\"cuda\")\n    train_dataloader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=True, download=True,\n                       transform=transforms.Compose([\n                           transforms.ToTensor(),\n                           transforms.Normalize((0.1302,), (0.3069,))])),\n        batch_size=128, shuffle=True)  \n    model = ConvNet()\n    model.to(device)\n    optimizer = optim.Adadelta(model.parameters(), lr=0.5)\n    model.train()\n    for epoch in range(args.epochs):\n        for b_i, (X, y) in enumerate(train_dataloader):\n            X, y = X.to(device), y.to(device)\n            pred_prob = model(X)\n            loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\n            loss.backward()\n            optimizer.step()\n            optimizer.zero_grad()\n            if b_i % 10 == 0:\n                print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n                    epoch, b_i, len(train_dataloader),\n                    100. * b_i / len(train_dataloader), loss.item()))\n         \n            \ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--epochs', default=1, type=int)\n    args = parser.parse_args()\n    start = time.time()\n    train(args)\n    print(f\"Finished training in {time.time()-start} secs\")\n    \nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "Chapter12/convnet_undistributed_cuda_amp.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\nimport time\nimport argparse\n\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n    \n    \ndef train(args):\n    torch.manual_seed(0)\n    device = torch.device(\"cuda\")\n    train_dataloader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=True, download=True,\n                       transform=transforms.Compose([\n                           transforms.ToTensor(),\n                           transforms.Normalize((0.1302,), (0.3069,))])),\n        batch_size=128, shuffle=True)  \n    model = ConvNet()\n    model.to(device)\n    optimizer = optim.Adadelta(model.parameters(), lr=0.5)\n    model.train()\n    for epoch in range(args.epochs):\n        for b_i, (X, y) in enumerate(train_dataloader):\n            X, y = X.to(device), y.to(device)\n            with torch.cuda.amp.autocast():\n                pred_prob = model(X)\n                loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\n            loss.backward()\n            optimizer.step()\n            optimizer.zero_grad()\n            if b_i % 10 == 0:\n                print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n                    epoch, b_i, len(train_dataloader),\n                    100. * b_i / len(train_dataloader), loss.item()))\n         \n            \ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--epochs', default=1, type=int)\n    args = parser.parse_args()\n    start = time.time()\n    train(args)\n    print(f\"Finished training in {time.time()-start} secs\")\n    \nif __name__ == '__main__':\n    main()\n\n"
  },
  {
    "path": "Chapter13/Dockerfile",
    "content": "FROM python:3.9-slim\n\nRUN apt-get -q update && apt-get -q install -y wget\n\nCOPY ./server.py ./\nCOPY ./requirements.txt ./\n\nRUN wget -q https://github.com/PacktPublishing/Mastering-PyTorch/raw/master/Chapter10/convnet.pth\nRUN wget -q https://github.com/PacktPublishing/Mastering-PyTorch/raw/master/Chapter10/digit_image.jpg\n\nRUN pip install -r requirements.txt\n\n\nUSER root\nENTRYPOINT [\"python\", \"server.py\"]\n"
  },
  {
    "path": "Chapter13/convnet.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op"
  },
  {
    "path": "Chapter13/convnet_handler.py",
    "content": "from torchvision import transforms\nfrom ts.torch_handler.image_classifier import ImageClassifier\n\nclass ConvNetClassifier(ImageClassifier):\n    \"\"\"\n    Extends the ImageClassifier handler meant to handle image data for image classification. We have adpated it to convert color (RGB) images to grayscale images resized to 28x28 pixels as well as normalizing pixel values. The postprocess method ensures to return the digit with the highest prediction probability.\n    \"\"\"\n\n    image_processing = transforms.Compose([\n        transforms.Grayscale(),\n        transforms.Resize((28, 28)),\n        transforms.ToTensor(),\n        transforms.Normalize((0.1302,), (0.3069,))\n    ])\n\n    def postprocess(self, output):\n        return output.argmax(1).tolist()"
  },
  {
    "path": "Chapter13/convnet_tf/fingerprint.pb",
    "content": "\bš\u0001\u0010Ū\u0001\u0018\u0001 ݐ\u0001(艬ڕ\u00012\u0002\b\u0001"
  },
  {
    "path": "Chapter13/cpp_convnet/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.0 FATAL_ERROR)\nproject(cpp_convnet)\nfind_package(Torch REQUIRED)\nfind_package(OpenCV REQUIRED)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}\")\nadd_executable(cpp_convnet cpp_convnet.cpp)\ntarget_link_libraries(cpp_convnet pthread)\ntarget_link_libraries(cpp_convnet ${TORCH_LIBRARIES} ${OpenCV_LIBS})\nset_property(TARGET cpp_convnet PROPERTY CXX_STANDARD 14)\n"
  },
  {
    "path": "Chapter13/cpp_convnet/cpp_convnet.cpp",
    "content": "#include <torch/script.h>\n#include <opencv2/core.hpp>\n#include <opencv2/imgcodecs.hpp>\n#include <opencv2/highgui.hpp>\n#include <opencv2/imgproc.hpp>\n#include <iostream>\n\nusing namespace cv;\nusing namespace std;\n\nint main(int argc, char **argv) {\n    Mat img = imread(argv[2], IMREAD_GRAYSCALE);\n    resize(img, img, Size(28, 28));\n    auto input_ = torch::from_blob(img.data, { img.rows, img.cols, img.channels() }, at::kByte);\n    auto input = input_.permute({2,0,1}).unsqueeze_(0).reshape({1, 1, img.rows, img.cols}).toType(c10::kFloat).div(255);\n    input = (input - 0.1302) / 0.3069;\n    auto module = torch::jit::load(argv[1]);\n    std::vector<torch::jit::IValue> inputs;\n    inputs.push_back(input);\n    auto output_ = module.forward(inputs).toTensor();\n    auto output = output_.argmax(1);\n    cout << output << '\\n';\n    return 0;\n}"
  },
  {
    "path": "Chapter13/example.py",
    "content": "from flask import Flask\napp = Flask(__name__)\n \n@app.route('/')\ndef hello_world():\n    return 'Hello, World!'\n \nif __name__ == '__main__': \n    app.run(host='localhost', port=8890)\n\n"
  },
  {
    "path": "Chapter13/make_request.py",
    "content": "import io\nimport json\nimport requests\nfrom PIL import Image\n\nfrom torchvision import transforms\n\n\nimage = Image.open(\"./digit_image.jpg\")\n\ndef image_to_tensor(image):\n    gray_image = transforms.functional.to_grayscale(image)\n    resized_image = transforms.functional.resize(gray_image, (28, 28))\n    input_image_tensor = transforms.functional.to_tensor(resized_image)\n    input_image_tensor_norm = transforms.functional.normalize(input_image_tensor, (0.1302,), (0.3069,))\n    return input_image_tensor_norm\n\nimage_tensor = image_to_tensor(image)\n\ndimensions = io.StringIO(json.dumps({'dims': list(image_tensor.shape)}))\ndata = io.BytesIO(bytearray(image_tensor.numpy()))\n\nr = requests.post('http://localhost:8890/test',\n                  files={'metadata': dimensions, 'data' : data})\n\nresponse = json.loads(r.content)\n\nprint(\"Predicted digit :\", response)"
  },
  {
    "path": "Chapter13/mnist_pytorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 10 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset),\\n\",\n    \"        100. * success / len(test_dataloader.dataset)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\\n\",\n    \"    batch_size=32, shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,)) \\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=500, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(0)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = optim.Adadelta(model.parameters(), lr=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.310609\\n\",\n      \"epoch: 1 [320/60000 (1%)]\\t training loss: 1.924133\\n\",\n      \"epoch: 1 [640/60000 (1%)]\\t training loss: 1.313337\\n\",\n      \"epoch: 1 [960/60000 (2%)]\\t training loss: 0.796470\\n\",\n      \"epoch: 1 [1280/60000 (2%)]\\t training loss: 0.819801\\n\",\n      \"epoch: 1 [1600/60000 (3%)]\\t training loss: 0.678459\\n\",\n      \"epoch: 1 [1920/60000 (3%)]\\t training loss: 0.477066\\n\",\n      \"epoch: 1 [2240/60000 (4%)]\\t training loss: 0.527125\\n\",\n      \"epoch: 1 [2560/60000 (4%)]\\t training loss: 0.469366\\n\",\n      \"epoch: 1 [2880/60000 (5%)]\\t training loss: 0.239070\\n\",\n      \"epoch: 1 [3200/60000 (5%)]\\t training loss: 0.523908\\n\",\n      \"epoch: 1 [3520/60000 (6%)]\\t training loss: 0.267831\\n\",\n      \"epoch: 1 [3840/60000 (6%)]\\t training loss: 0.464627\\n\",\n      \"epoch: 1 [4160/60000 (7%)]\\t training loss: 0.413977\\n\",\n      \"epoch: 1 [4480/60000 (7%)]\\t training loss: 0.324858\\n\",\n      \"epoch: 1 [4800/60000 (8%)]\\t training loss: 0.495965\\n\",\n      \"epoch: 1 [5120/60000 (9%)]\\t training loss: 0.153463\\n\",\n      \"epoch: 1 [5440/60000 (9%)]\\t training loss: 0.377620\\n\",\n      \"epoch: 1 [5760/60000 (10%)]\\t training loss: 0.097269\\n\",\n      \"epoch: 1 [6080/60000 (10%)]\\t training loss: 0.181864\\n\",\n      \"epoch: 1 [6400/60000 (11%)]\\t training loss: 0.295800\\n\",\n      \"epoch: 1 [6720/60000 (11%)]\\t training loss: 0.082916\\n\",\n      \"epoch: 1 [7040/60000 (12%)]\\t training loss: 0.353389\\n\",\n      \"epoch: 1 [7360/60000 (12%)]\\t training loss: 0.040657\\n\",\n      \"epoch: 1 [7680/60000 (13%)]\\t training loss: 0.365705\\n\",\n      \"epoch: 1 [8000/60000 (13%)]\\t training loss: 0.231380\\n\",\n      \"epoch: 1 [8320/60000 (14%)]\\t training loss: 0.321043\\n\",\n      \"epoch: 1 [8640/60000 (14%)]\\t training loss: 0.211378\\n\",\n      \"epoch: 1 [8960/60000 (15%)]\\t training loss: 0.054053\\n\",\n      \"epoch: 1 [9280/60000 (15%)]\\t training loss: 0.337620\\n\",\n      \"epoch: 1 [9600/60000 (16%)]\\t training loss: 0.216428\\n\",\n      \"epoch: 1 [9920/60000 (17%)]\\t training loss: 0.174468\\n\",\n      \"epoch: 1 [10240/60000 (17%)]\\t training loss: 0.213010\\n\",\n      \"epoch: 1 [10560/60000 (18%)]\\t training loss: 0.357055\\n\",\n      \"epoch: 1 [10880/60000 (18%)]\\t training loss: 0.070653\\n\",\n      \"epoch: 1 [11200/60000 (19%)]\\t training loss: 0.055425\\n\",\n      \"epoch: 1 [11520/60000 (19%)]\\t training loss: 0.230391\\n\",\n      \"epoch: 1 [11840/60000 (20%)]\\t training loss: 0.048202\\n\",\n      \"epoch: 1 [12160/60000 (20%)]\\t training loss: 0.403469\\n\",\n      \"epoch: 1 [12480/60000 (21%)]\\t training loss: 0.025848\\n\",\n      \"epoch: 1 [12800/60000 (21%)]\\t training loss: 0.104852\\n\",\n      \"epoch: 1 [13120/60000 (22%)]\\t training loss: 0.101376\\n\",\n      \"epoch: 1 [13440/60000 (22%)]\\t training loss: 0.178021\\n\",\n      \"epoch: 1 [13760/60000 (23%)]\\t training loss: 0.070927\\n\",\n      \"epoch: 1 [14080/60000 (23%)]\\t training loss: 0.048668\\n\",\n      \"epoch: 1 [14400/60000 (24%)]\\t training loss: 0.090581\\n\",\n      \"epoch: 1 [14720/60000 (25%)]\\t training loss: 0.165846\\n\",\n      \"epoch: 1 [15040/60000 (25%)]\\t training loss: 0.324705\\n\",\n      \"epoch: 1 [15360/60000 (26%)]\\t training loss: 0.077008\\n\",\n      \"epoch: 1 [15680/60000 (26%)]\\t training loss: 0.128724\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 0.084200\\n\",\n      \"epoch: 1 [16320/60000 (27%)]\\t training loss: 0.158558\\n\",\n      \"epoch: 1 [16640/60000 (28%)]\\t training loss: 0.040566\\n\",\n      \"epoch: 1 [16960/60000 (28%)]\\t training loss: 0.287925\\n\",\n      \"epoch: 1 [17280/60000 (29%)]\\t training loss: 0.057604\\n\",\n      \"epoch: 1 [17600/60000 (29%)]\\t training loss: 0.031712\\n\",\n      \"epoch: 1 [17920/60000 (30%)]\\t training loss: 0.046777\\n\",\n      \"epoch: 1 [18240/60000 (30%)]\\t training loss: 0.025740\\n\",\n      \"epoch: 1 [18560/60000 (31%)]\\t training loss: 0.065141\\n\",\n      \"epoch: 1 [18880/60000 (31%)]\\t training loss: 0.220227\\n\",\n      \"epoch: 1 [19200/60000 (32%)]\\t training loss: 0.102033\\n\",\n      \"epoch: 1 [19520/60000 (33%)]\\t training loss: 0.068922\\n\",\n      \"epoch: 1 [19840/60000 (33%)]\\t training loss: 0.075009\\n\",\n      \"epoch: 1 [20160/60000 (34%)]\\t training loss: 0.111961\\n\",\n      \"epoch: 1 [20480/60000 (34%)]\\t training loss: 0.056665\\n\",\n      \"epoch: 1 [20800/60000 (35%)]\\t training loss: 0.234118\\n\",\n      \"epoch: 1 [21120/60000 (35%)]\\t training loss: 0.303591\\n\",\n      \"epoch: 1 [21440/60000 (36%)]\\t training loss: 0.217894\\n\",\n      \"epoch: 1 [21760/60000 (36%)]\\t training loss: 0.091958\\n\",\n      \"epoch: 1 [22080/60000 (37%)]\\t training loss: 0.079766\\n\",\n      \"epoch: 1 [22400/60000 (37%)]\\t training loss: 0.151057\\n\",\n      \"epoch: 1 [22720/60000 (38%)]\\t training loss: 0.262819\\n\",\n      \"epoch: 1 [23040/60000 (38%)]\\t training loss: 0.061579\\n\",\n      \"epoch: 1 [23360/60000 (39%)]\\t training loss: 0.135419\\n\",\n      \"epoch: 1 [23680/60000 (39%)]\\t training loss: 0.285402\\n\",\n      \"epoch: 1 [24000/60000 (40%)]\\t training loss: 0.148586\\n\",\n      \"epoch: 1 [24320/60000 (41%)]\\t training loss: 0.066807\\n\",\n      \"epoch: 1 [24640/60000 (41%)]\\t training loss: 0.126747\\n\",\n      \"epoch: 1 [24960/60000 (42%)]\\t training loss: 0.026213\\n\",\n      \"epoch: 1 [25280/60000 (42%)]\\t training loss: 0.024073\\n\",\n      \"epoch: 1 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training loss: 0.080851\\n\",\n      \"epoch: 2 [32640/60000 (54%)]\\t training loss: 0.040496\\n\",\n      \"epoch: 2 [32960/60000 (55%)]\\t training loss: 0.013496\\n\",\n      \"epoch: 2 [33280/60000 (55%)]\\t training loss: 0.092531\\n\",\n      \"epoch: 2 [33600/60000 (56%)]\\t training loss: 0.017307\\n\",\n      \"epoch: 2 [33920/60000 (57%)]\\t training loss: 0.134077\\n\",\n      \"epoch: 2 [34240/60000 (57%)]\\t training loss: 0.175124\\n\",\n      \"epoch: 2 [34560/60000 (58%)]\\t training loss: 0.220506\\n\",\n      \"epoch: 2 [34880/60000 (58%)]\\t training loss: 0.018582\\n\",\n      \"epoch: 2 [35200/60000 (59%)]\\t training loss: 0.073093\\n\",\n      \"epoch: 2 [35520/60000 (59%)]\\t training loss: 0.030787\\n\",\n      \"epoch: 2 [35840/60000 (60%)]\\t training loss: 0.010614\\n\",\n      \"epoch: 2 [36160/60000 (60%)]\\t training loss: 0.034699\\n\",\n      \"epoch: 2 [36480/60000 (61%)]\\t training loss: 0.034723\\n\",\n      \"epoch: 2 [36800/60000 (61%)]\\t training loss: 0.009217\\n\",\n      \"epoch: 2 [37120/60000 (62%)]\\t training loss: 0.005739\\n\",\n      \"epoch: 2 [37440/60000 (62%)]\\t training loss: 0.040345\\n\",\n      \"epoch: 2 [37760/60000 (63%)]\\t training loss: 0.218793\\n\",\n      \"epoch: 2 [38080/60000 (63%)]\\t training loss: 0.198174\\n\",\n      \"epoch: 2 [38400/60000 (64%)]\\t training loss: 0.035814\\n\",\n      \"epoch: 2 [38720/60000 (65%)]\\t training loss: 0.068463\\n\",\n      \"epoch: 2 [39040/60000 (65%)]\\t training loss: 0.007639\\n\",\n      \"epoch: 2 [39360/60000 (66%)]\\t training loss: 0.026163\\n\",\n      \"epoch: 2 [39680/60000 (66%)]\\t training loss: 0.148529\\n\",\n      \"epoch: 2 [40000/60000 (67%)]\\t training loss: 0.075740\\n\",\n      \"epoch: 2 [40320/60000 (67%)]\\t training loss: 0.047596\\n\",\n      \"epoch: 2 [40640/60000 (68%)]\\t training loss: 0.013017\\n\",\n      \"epoch: 2 [40960/60000 (68%)]\\t training loss: 0.063155\\n\",\n      \"epoch: 2 [41280/60000 (69%)]\\t training loss: 0.022223\\n\",\n      \"epoch: 2 [41600/60000 (69%)]\\t training loss: 0.024700\\n\",\n      \"epoch: 2 [41920/60000 (70%)]\\t training loss: 0.001801\\n\",\n      \"epoch: 2 [42240/60000 (70%)]\\t training loss: 0.002220\\n\",\n      \"epoch: 2 [42560/60000 (71%)]\\t training loss: 0.048338\\n\",\n      \"epoch: 2 [42880/60000 (71%)]\\t training loss: 0.046190\\n\",\n      \"epoch: 2 [43200/60000 (72%)]\\t training loss: 0.004485\\n\",\n      \"epoch: 2 [43520/60000 (73%)]\\t training loss: 0.107624\\n\",\n      \"epoch: 2 [43840/60000 (73%)]\\t training loss: 0.078155\\n\",\n      \"epoch: 2 [44160/60000 (74%)]\\t training loss: 0.156051\\n\",\n      \"epoch: 2 [44480/60000 (74%)]\\t training loss: 0.001538\\n\",\n      \"epoch: 2 [44800/60000 (75%)]\\t training loss: 0.300366\\n\",\n      \"epoch: 2 [45120/60000 (75%)]\\t training loss: 0.041359\\n\",\n      \"epoch: 2 [45440/60000 (76%)]\\t training loss: 0.390815\\n\",\n      \"epoch: 2 [45760/60000 (76%)]\\t training loss: 0.024482\\n\",\n      \"epoch: 2 [46080/60000 (77%)]\\t training loss: 0.017786\\n\",\n      \"epoch: 2 [46400/60000 (77%)]\\t training loss: 0.028327\\n\",\n      \"epoch: 2 [46720/60000 (78%)]\\t training loss: 0.001570\\n\",\n      \"epoch: 2 [47040/60000 (78%)]\\t training loss: 0.066162\\n\",\n      \"epoch: 2 [47360/60000 (79%)]\\t training loss: 0.154574\\n\",\n      \"epoch: 2 [47680/60000 (79%)]\\t training loss: 0.091009\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 0.042673\\n\",\n      \"epoch: 2 [48320/60000 (81%)]\\t training loss: 0.106071\\n\",\n      \"epoch: 2 [48640/60000 (81%)]\\t training loss: 0.004252\\n\",\n      \"epoch: 2 [48960/60000 (82%)]\\t training loss: 0.014724\\n\",\n      \"epoch: 2 [49280/60000 (82%)]\\t training loss: 0.010253\\n\",\n      \"epoch: 2 [49600/60000 (83%)]\\t training loss: 0.010249\\n\",\n      \"epoch: 2 [49920/60000 (83%)]\\t training loss: 0.362407\\n\",\n      \"epoch: 2 [50240/60000 (84%)]\\t training loss: 0.115282\\n\",\n      \"epoch: 2 [50560/60000 (84%)]\\t training loss: 0.106830\\n\",\n      \"epoch: 2 [50880/60000 (85%)]\\t training loss: 0.105281\\n\",\n      \"epoch: 2 [51200/60000 (85%)]\\t training loss: 0.119776\\n\",\n      \"epoch: 2 [51520/60000 (86%)]\\t training loss: 0.067086\\n\",\n      \"epoch: 2 [51840/60000 (86%)]\\t training loss: 0.023813\\n\",\n      \"epoch: 2 [52160/60000 (87%)]\\t training loss: 0.047860\\n\",\n      \"epoch: 2 [52480/60000 (87%)]\\t training loss: 0.005951\\n\",\n      \"epoch: 2 [52800/60000 (88%)]\\t training loss: 0.003520\\n\",\n      \"epoch: 2 [53120/60000 (89%)]\\t training loss: 0.018581\\n\",\n      \"epoch: 2 [53440/60000 (89%)]\\t training loss: 0.055058\\n\",\n      \"epoch: 2 [53760/60000 (90%)]\\t training loss: 0.060451\\n\",\n      \"epoch: 2 [54080/60000 (90%)]\\t training loss: 0.047163\\n\",\n      \"epoch: 2 [54400/60000 (91%)]\\t training loss: 0.003440\\n\",\n      \"epoch: 2 [54720/60000 (91%)]\\t training loss: 0.153727\\n\",\n      \"epoch: 2 [55040/60000 (92%)]\\t training loss: 0.126677\\n\",\n      \"epoch: 2 [55360/60000 (92%)]\\t training loss: 0.031780\\n\",\n      \"epoch: 2 [55680/60000 (93%)]\\t training loss: 0.010447\\n\",\n      \"epoch: 2 [56000/60000 (93%)]\\t training loss: 0.036427\\n\",\n      \"epoch: 2 [56320/60000 (94%)]\\t training loss: 0.006692\\n\",\n      \"epoch: 2 [56640/60000 (94%)]\\t training loss: 0.001685\\n\",\n      \"epoch: 2 [56960/60000 (95%)]\\t training loss: 0.124056\\n\",\n      \"epoch: 2 [57280/60000 (95%)]\\t training loss: 0.169876\\n\",\n      \"epoch: 2 [57600/60000 (96%)]\\t training loss: 0.228522\\n\",\n      \"epoch: 2 [57920/60000 (97%)]\\t training loss: 0.115276\\n\",\n      \"epoch: 2 [58240/60000 (97%)]\\t training loss: 0.042843\\n\",\n      \"epoch: 2 [58560/60000 (98%)]\\t training loss: 0.003510\\n\",\n      \"epoch: 2 [58880/60000 (98%)]\\t training loss: 0.008058\\n\",\n      \"epoch: 2 [59200/60000 (99%)]\\t training loss: 0.006398\\n\",\n      \"epoch: 2 [59520/60000 (99%)]\\t training loss: 0.016740\\n\",\n      \"epoch: 2 [59840/60000 (100%)]\\t training loss: 0.039168\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0385, Overall Accuracy: 9872/10000 (99%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(1, 3):\\n\",\n    \"    train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"    test(model, device, test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"\\n\",\n    \"plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 0\\n\",\n      \"Ground truth is : 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"PATH_TO_MODEL = \\\"./convnet.pth\\\"\\n\",\n    \"torch.save(model.state_dict(), PATH_TO_MODEL)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter13/model_scripting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (10.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\",\n      \"Collecting Pillow==9.3.0\\n\",\n      \"  Downloading Pillow-9.3.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.3 MB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m3.3/3.3 MB\\u001b[0m \\u001b[31m36.8 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0ma \\u001b[36m0:00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hInstalling collected packages: Pillow\\n\",\n      \"  Attempting uninstall: Pillow\\n\",\n      \"    Found existing installation: pillow 10.2.0\\n\",\n      \"    Uninstalling pillow-10.2.0:\\n\",\n      \"      Successfully uninstalled pillow-10.2.0\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"ydata-profiling 4.6.0 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires attrs==21.4.0, but you have attrs 23.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires filelock==3.7.1, but you have filelock 3.13.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires fsspec==2022.5.0, but you have fsspec 2023.12.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires huggingface-hub==0.8.1, but you have huggingface-hub 0.20.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires numpy==1.23.1, but you have numpy 1.26.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires Pillow==9.2.0, but you have pillow 9.3.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tokenizers==0.12.1, but you have tokenizers 0.15.2 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torch==1.12.0, but you have torch 2.2.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires torchvision==0.13.0, but you have torchvision 0.17.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires tqdm==4.64.0, but you have tqdm 4.66.1 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires transformers==4.20.1, but you have transformers 4.36.0 which is incompatible.\\n\",\n      \"minimagen 0.0.9 requires typing_extensions==4.3.0, but you have typing-extensions 4.9.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed Pillow-9.3.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\\n\",\n    \"!pip install Pillow==9.3.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from PIL import Image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\\n\",\n    \"    \\n\",\n    \"model = ConvNet()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<All keys matched successfully>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"PATH_TO_MODEL = \\\"./convnet.pth\\\"\\n\",\n    \"model.load_state_dict(torch.load(PATH_TO_MODEL, map_location=\\\"cpu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.eval()\\n\",\n    \"for p in model.parameters():\\n\",\n    \"    p.requires_grad_(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"scripted_model = torch.jit.script(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"graph(%self : __torch__.ConvNet,\\n\",\n       \"      %x.1 : Tensor):\\n\",\n       \"  %51 : Function = prim::Constant[name=\\\"log_softmax\\\"]()\\n\",\n       \"  %49 : int = prim::Constant[value=3]()\\n\",\n       \"  %33 : int = prim::Constant[value=-1]()\\n\",\n       \"  %26 : Function = prim::Constant[name=\\\"_max_pool2d\\\"]()\\n\",\n       \"  %20 : int = prim::Constant[value=0]()\\n\",\n       \"  %19 : NoneType = prim::Constant()\\n\",\n       \"  %7 : Function = prim::Constant[name=\\\"relu\\\"]()\\n\",\n       \"  %6 : bool = prim::Constant[value=0]()\\n\",\n       \"  %17 : int = prim::Constant[value=2]() # /var/tmp/ipykernel_2981568/2721400238.py:16:28\\n\",\n       \"  %32 : int = prim::Constant[value=1]() # /var/tmp/ipykernel_2981568/2721400238.py:18:29\\n\",\n       \"  %cn1 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name=\\\"cn1\\\"](%self)\\n\",\n       \"  %x.5 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%cn1, %x.1) # /var/tmp/ipykernel_2981568/2721400238.py:12:12\\n\",\n       \"  %x.9 : Tensor = prim::CallFunction(%7, %x.5, %6) # /var/tmp/ipykernel_2981568/2721400238.py:13:12\\n\",\n       \"  %cn2 : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d = prim::GetAttr[name=\\\"cn2\\\"](%self)\\n\",\n       \"  %x.13 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%cn2, %x.9) # /var/tmp/ipykernel_2981568/2721400238.py:14:12\\n\",\n       \"  %x.17 : Tensor = prim::CallFunction(%7, %x.13, %6) # /var/tmp/ipykernel_2981568/2721400238.py:15:12\\n\",\n       \"  %18 : int[] = prim::ListConstruct(%17, %17)\\n\",\n       \"  %21 : int[] = prim::ListConstruct(%20, %20)\\n\",\n       \"  %23 : int[] = prim::ListConstruct(%32, %32)\\n\",\n       \"  %x.21 : Tensor = prim::CallFunction(%26, %x.17, %18, %19, %21, %23, %6, %6) # /var/tmp/ipykernel_2981568/2721400238.py:16:12\\n\",\n       \"  %dp1 : __torch__.torch.nn.modules.dropout.Dropout2d = prim::GetAttr[name=\\\"dp1\\\"](%self)\\n\",\n       \"  %x.25 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%dp1, %x.21) # /var/tmp/ipykernel_2981568/2721400238.py:17:12\\n\",\n       \"  %x.29 : Tensor = aten::flatten(%x.25, %32, %33) # /var/tmp/ipykernel_2981568/2721400238.py:18:12\\n\",\n       \"  %fc1 : __torch__.torch.nn.modules.linear.Linear = prim::GetAttr[name=\\\"fc1\\\"](%self)\\n\",\n       \"  %x.33 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%fc1, %x.29) # /var/tmp/ipykernel_2981568/2721400238.py:19:12\\n\",\n       \"  %x.37 : Tensor = prim::CallFunction(%7, %x.33, %6) # /var/tmp/ipykernel_2981568/2721400238.py:20:12\\n\",\n       \"  %dp2 : __torch__.torch.nn.modules.dropout.___torch_mangle_1.Dropout2d = prim::GetAttr[name=\\\"dp2\\\"](%self)\\n\",\n       \"  %x.41 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%dp2, %x.37) # /var/tmp/ipykernel_2981568/2721400238.py:21:12\\n\",\n       \"  %fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_2.Linear = prim::GetAttr[name=\\\"fc2\\\"](%self)\\n\",\n       \"  %x.45 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%fc2, %x.41) # /var/tmp/ipykernel_2981568/2721400238.py:22:12\\n\",\n       \"  %op.1 : Tensor = prim::CallFunction(%51, %x.45, %32, %49, %19) # /var/tmp/ipykernel_2981568/2721400238.py:23:13\\n\",\n       \"  return (%op.1)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"scripted_model.graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"def forward(self,\\n\",\n      \"    x: Tensor) -> Tensor:\\n\",\n      \"  _0 = __torch__.torch.nn.functional._max_pool2d\\n\",\n      \"  _1 = __torch__.torch.nn.functional.log_softmax\\n\",\n      \"  cn1 = self.cn1\\n\",\n      \"  x0 = (cn1).forward(x, )\\n\",\n      \"  x1 = __torch__.torch.nn.functional.relu(x0, False, )\\n\",\n      \"  cn2 = self.cn2\\n\",\n      \"  x2 = (cn2).forward(x1, )\\n\",\n      \"  x3 = __torch__.torch.nn.functional.relu(x2, False, )\\n\",\n      \"  x4 = _0(x3, [2, 2], None, [0, 0], [1, 1], False, False, )\\n\",\n      \"  dp1 = self.dp1\\n\",\n      \"  x5 = (dp1).forward(x4, )\\n\",\n      \"  x6 = torch.flatten(x5, 1)\\n\",\n      \"  fc1 = self.fc1\\n\",\n      \"  x7 = (fc1).forward(x6, )\\n\",\n      \"  x8 = __torch__.torch.nn.functional.relu(x7, False, )\\n\",\n      \"  dp2 = self.dp2\\n\",\n      \"  x9 = (dp2).forward(x8, )\\n\",\n      \"  fc2 = self.fc2\\n\",\n      \"  x10 = (fc2).forward(x9, )\\n\",\n      \"  return _1(x10, 1, 3, None, )\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(scripted_model.code)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.jit.save(scripted_model, 'scripted_convnet.pt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"loaded_scripted_model = torch.jit.load('scripted_convnet.pt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = Image.open(\\\"./digit_image.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def image_to_tensor(image):\\n\",\n    \"    gray_image = transforms.functional.to_grayscale(image)\\n\",\n    \"    resized_image = transforms.functional.resize(gray_image, (28, 28))\\n\",\n    \"    input_image_tensor = transforms.functional.to_tensor(resized_image)\\n\",\n    \"    input_image_tensor_norm = transforms.functional.normalize(input_image_tensor, (0.1302,), (0.3069,))\\n\",\n    \"    return input_image_tensor_norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_tensor = image_to_tensor(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"code/__torch__/torch/nn/functional.py:57: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[-1.0458e+01, -1.3929e+01, -2.5734e-03, -8.8133e+00, -1.0267e+01,\\n\",\n       \"         -1.5833e+01, -1.2593e+01, -1.3940e+01, -6.0533e+00, -1.2960e+01]])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"loaded_scripted_model(input_tensor.unsqueeze(0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[-1.0458e+01, -1.3929e+01, -2.5734e-03, -8.8133e+00, -1.0267e+01,\\n\",\n       \"         -1.5833e+01, -1.2593e+01, -1.3940e+01, -6.0533e+00, -1.2960e+01]])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model(input_tensor.unsqueeze(0))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter13/model_tracing.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: Pillow==9.3.0 in /opt/conda/lib/python3.10/site-packages (9.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\\n\",\n    \"!pip install Pillow==9.3.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from PIL import Image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\\n\",\n    \"    \\n\",\n    \"model = ConvNet()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<All keys matched successfully>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"PATH_TO_MODEL = \\\"./convnet.pth\\\"\\n\",\n    \"model.load_state_dict(torch.load(PATH_TO_MODEL, map_location=\\\"cpu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.eval()\\n\",\n    \"for p in model.parameters():\\n\",\n    \"    p.requires_grad_(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"demo_input = torch.ones(1, 1, 28, 28)\\n\",\n    \"traced_model = torch.jit.trace(model, demo_input)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"graph(%self.1 : __torch__.ConvNet,\\n\",\n       \"      %x.1 : Float(1, 1, 28, 28, strides=[784, 784, 28, 1], requires_grad=0, device=cpu)):\\n\",\n       \"  %fc2 : __torch__.torch.nn.modules.linear.___torch_mangle_2.Linear = prim::GetAttr[name=\\\"fc2\\\"](%self.1)\\n\",\n       \"  %dp2 : __torch__.torch.nn.modules.dropout.___torch_mangle_1.Dropout2d = prim::GetAttr[name=\\\"dp2\\\"](%self.1)\\n\",\n       \"  %fc1 : __torch__.torch.nn.modules.linear.Linear = prim::GetAttr[name=\\\"fc1\\\"](%self.1)\\n\",\n       \"  %dp1 : __torch__.torch.nn.modules.dropout.Dropout2d = prim::GetAttr[name=\\\"dp1\\\"](%self.1)\\n\",\n       \"  %cn2 : __torch__.torch.nn.modules.conv.___torch_mangle_0.Conv2d = prim::GetAttr[name=\\\"cn2\\\"](%self.1)\\n\",\n       \"  %cn1 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name=\\\"cn1\\\"](%self.1)\\n\",\n       \"  %123 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%cn1, %x.1)\\n\",\n       \"  %input.3 : Float(1, 16, 26, 26, strides=[10816, 676, 26, 1], requires_grad=0, device=cpu) = aten::relu(%123) # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1473:0\\n\",\n       \"  %124 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%cn2, %input.3)\\n\",\n       \"  %input.7 : Float(1, 32, 24, 24, strides=[18432, 576, 24, 1], requires_grad=0, device=cpu) = aten::relu(%124) # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1473:0\\n\",\n       \"  %70 : int = prim::Constant[value=2]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %71 : int = prim::Constant[value=2]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %72 : int[] = prim::ListConstruct(%70, %71)\\n\",\n       \"  %73 : int[] = prim::ListConstruct()\\n\",\n       \"  %74 : int = prim::Constant[value=0]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %75 : int = prim::Constant[value=0]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %76 : int[] = prim::ListConstruct(%74, %75)\\n\",\n       \"  %77 : int = prim::Constant[value=1]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %78 : int = prim::Constant[value=1]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %79 : int[] = prim::ListConstruct(%77, %78)\\n\",\n       \"  %80 : bool = prim::Constant[value=0]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %input.9 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], requires_grad=0, device=cpu) = aten::max_pool2d(%input.7, %72, %73, %76, %79, %80) # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:796:0\\n\",\n       \"  %125 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%dp1, %input.9)\\n\",\n       \"  %85 : int = prim::Constant[value=1]() # /var/tmp/ipykernel_2981674/2721400238.py:18:0\\n\",\n       \"  %86 : int = prim::Constant[value=-1]() # /var/tmp/ipykernel_2981674/2721400238.py:18:0\\n\",\n       \"  %input.11 : Float(1, 4608, strides=[4608, 1], requires_grad=0, device=cpu) = aten::flatten(%125, %85, %86) # /var/tmp/ipykernel_2981674/2721400238.py:18:0\\n\",\n       \"  %126 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%fc1, %input.11)\\n\",\n       \"  %input.15 : Float(1, 64, strides=[64, 1], requires_grad=0, device=cpu) = aten::relu(%126) # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1473:0\\n\",\n       \"  %127 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%dp2, %input.15)\\n\",\n       \"  %128 : Tensor = prim::CallMethod[name=\\\"forward\\\"](%fc2, %127)\\n\",\n       \"  %94 : int = prim::Constant[value=1]() # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1947:0\\n\",\n       \"  %95 : NoneType = prim::Constant()\\n\",\n       \"  %96 : Float(1, 10, strides=[10, 1], requires_grad=0, device=cpu) = aten::log_softmax(%128, %94, %95) # /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1947:0\\n\",\n       \"  return (%96)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"traced_model.graph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"def forward(self,\\n\",\n      \"    x: Tensor) -> Tensor:\\n\",\n      \"  fc2 = self.fc2\\n\",\n      \"  dp2 = self.dp2\\n\",\n      \"  fc1 = self.fc1\\n\",\n      \"  dp1 = self.dp1\\n\",\n      \"  cn2 = self.cn2\\n\",\n      \"  cn1 = self.cn1\\n\",\n      \"  input = torch.relu((cn1).forward(x, ))\\n\",\n      \"  input0 = torch.relu((cn2).forward(input, ))\\n\",\n      \"  input1 = torch.max_pool2d(input0, [2, 2], annotate(List[int], []), [0, 0], [1, 1])\\n\",\n      \"  input2 = torch.flatten((dp1).forward(input1, ), 1)\\n\",\n      \"  input3 = torch.relu((fc1).forward(input2, ))\\n\",\n      \"  _0 = (fc2).forward((dp2).forward(input3, ), )\\n\",\n      \"  return torch.log_softmax(_0, 1)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(traced_model.code)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.jit.save(traced_model, 'traced_convnet.pt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"loaded_traced_model = torch.jit.load('traced_convnet.pt')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = Image.open(\\\"./digit_image.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def image_to_tensor(image):\\n\",\n    \"    gray_image = transforms.functional.to_grayscale(image)\\n\",\n    \"    resized_image = transforms.functional.resize(gray_image, (28, 28))\\n\",\n    \"    input_image_tensor = transforms.functional.to_tensor(resized_image)\\n\",\n    \"    input_image_tensor_norm = transforms.functional.normalize(input_image_tensor, (0.1302,), (0.3069,))\\n\",\n    \"    return input_image_tensor_norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_tensor = image_to_tensor(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[-1.0458e+01, -1.3929e+01, -2.5734e-03, -8.8133e+00, -1.0267e+01,\\n\",\n       \"         -1.5833e+01, -1.2593e+01, -1.3940e+01, -6.0533e+00, -1.2960e+01]])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"loaded_traced_model(input_tensor.unsqueeze(0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[-1.0458e+01, -1.3929e+01, -2.5734e-03, -8.8133e+00, -1.0267e+01,\\n\",\n       \"         -1.5833e+01, -1.2593e+01, -1.3940e+01, -6.0533e+00, -1.2960e+01]])\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model(input_tensor.unsqueeze(0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.mobile_optimizer import optimize_for_mobile\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimized_traced_model = optimize_for_mobile(traced_model)\\n\",\n    \"optimized_traced_model._save_for_lite_interpreter(\\\"./optimized_for_mobile_traced_model.pt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.jit.mobile import (\\n\",\n    \"    _backport_for_mobile,\\n\",\n    \"    _get_model_bytecode_version,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"model version 8\\n\",\n      \"new model version 5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"MODEL_INPUT_FILE = \\\"optimized_for_mobile_traced_model.pt\\\"\\n\",\n    \"MODEL_OUTPUT_FILE = \\\"optimized_for_mobile_traced_model_v5.ptl\\\"\\n\",\n    \"\\n\",\n    \"print(\\\"model version\\\", _get_model_bytecode_version(f_input=MODEL_INPUT_FILE))\\n\",\n    \"\\n\",\n    \"_backport_for_mobile(f_input=MODEL_INPUT_FILE, f_output=MODEL_OUTPUT_FILE, to_version=5)\\n\",\n    \"\\n\",\n    \"print(\\\"new model version\\\", _get_model_bytecode_version(MODEL_OUTPUT_FILE))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter13/onnx.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: torch==2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (1.26.4)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (2.31.0)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (2024.2.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: tensorflow==2.15.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.15.0)\\n\",\n      \"Requirement already satisfied: tensorflow-macos==2.15.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow==2.15.0) (2.15.0)\\n\",\n      \"Requirement already satisfied: absl-py>=1.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: astunparse>=1.6.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.6.3)\\n\",\n      \"Requirement already satisfied: flatbuffers>=23.5.26 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (23.5.26)\\n\",\n      \"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.5.4)\\n\",\n      \"Requirement already satisfied: google-pasta>=0.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.2.0)\\n\",\n      \"Requirement already satisfied: h5py>=2.9.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.10.0)\\n\",\n      \"Requirement already satisfied: libclang>=13.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (16.0.6)\\n\",\n      \"Requirement already satisfied: ml-dtypes~=0.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.2.0)\\n\",\n      \"Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.26.4)\\n\",\n      \"Requirement already satisfied: opt-einsum>=2.3.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.3.0)\\n\",\n      \"Requirement already satisfied: packaging in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (23.2)\\n\",\n      \"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.20.3)\\n\",\n      \"Requirement already satisfied: setuptools in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (68.2.2)\\n\",\n      \"Requirement already satisfied: six>=1.12.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.16.0)\\n\",\n      \"Requirement already satisfied: termcolor>=1.1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.4.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.6.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.14.1)\\n\",\n      \"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.36.0)\\n\",\n      \"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.60.1)\\n\",\n      \"Requirement already satisfied: tensorboard<2.16,>=2.15 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.15.2)\\n\",\n      \"Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.15.0)\\n\",\n      \"Requirement already satisfied: keras<2.16,>=2.15.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.15.0)\\n\",\n      \"Requirement already satisfied: wheel<1.0,>=0.23.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from astunparse>=1.6.0->tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.41.2)\\n\",\n      \"Requirement already satisfied: google-auth<3,>=1.6.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.28.0)\\n\",\n      \"Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.2.0)\\n\",\n      \"Requirement already satisfied: markdown>=2.6.8 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.5.2)\\n\",\n      \"Requirement already satisfied: requests<3,>=2.21.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.31.0)\\n\",\n      \"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.7.2)\\n\",\n      \"Requirement already satisfied: werkzeug>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.0.1)\\n\",\n      \"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (5.3.2)\\n\",\n      \"Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.3.0)\\n\",\n      \"Requirement already satisfied: rsa<5,>=3.1.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (4.9)\\n\",\n      \"Requirement already satisfied: requests-oauthlib>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (1.3.1)\\n\",\n      \"Requirement already satisfied: importlib-metadata>=4.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (7.0.1)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (2.1.5)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.17.0)\\n\",\n      \"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (0.5.1)\\n\",\n      \"Requirement already satisfied: oauthlib>=3.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-macos==2.15.0->tensorflow==2.15.0) (3.2.2)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: onnx2tf==1.19.11 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (1.19.11)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: onnx==1.15.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (1.15.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from onnx==1.15.0) (1.26.4)\\n\",\n      \"Requirement already satisfied: protobuf>=3.20.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from onnx==1.15.0) (3.20.3)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: Pillow==9.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (9.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: nvidia-pyindex==1.0.9 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (1.0.9)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: onnx-graphsurgeon==0.3.27 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.3.27)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from onnx-graphsurgeon==0.3.27) (1.26.4)\\n\",\n      \"Requirement already satisfied: onnx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from onnx-graphsurgeon==0.3.27) (1.15.0)\\n\",\n      \"Requirement already satisfied: protobuf>=3.20.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from onnx->onnx-graphsurgeon==0.3.27) (3.20.3)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\\n\",\n      \"Requirement already satisfied: sng4onnx==1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (1.0.1)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0m\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install tensorflow==2.15.0\\n\",\n    \"!pip install onnx2tf==1.19.11\\n\",\n    \"!pip install onnx==1.15.0\\n\",\n    \"!pip install Pillow==9.3.0\\n\",\n    \"!pip install nvidia-pyindex==1.0.9\\n\",\n    \"!pip install onnx-graphsurgeon==0.3.27\\n\",\n    \"!pip install sng4onnx==1.0.1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import onnx2tf\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\\n\",\n    \"    \\n\",\n    \"model = ConvNet()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<All keys matched successfully>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"PATH_TO_MODEL = \\\"./convnet.pth\\\"\\n\",\n    \"model.load_state_dict(torch.load(PATH_TO_MODEL, map_location=\\\"cpu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = Image.open(\\\"./digit_image.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def image_to_tensor(image):\\n\",\n    \"    gray_image = transforms.functional.to_grayscale(image)\\n\",\n    \"    resized_image = transforms.functional.resize(gray_image, (28, 28))\\n\",\n    \"    input_image_tensor = transforms.functional.to_tensor(resized_image)\\n\",\n    \"    input_image_tensor_norm = transforms.functional.normalize(input_image_tensor, (0.1302,), (0.3069,))\\n\",\n    \"    return input_image_tensor_norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_tensor = image_to_tensor(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.eval()\\n\",\n    \"for p in model.parameters():\\n\",\n    \"    p.requires_grad_(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"demo_input = torch.ones(1, 1, 28, 28)\\n\",\n    \"torch.onnx.export(model, demo_input, \\\"convnet.onnx\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Summary on the non-converted ops:\\n\",\n      \"---------------------------------\\n\",\n      \" * Accepted dialects: tfl, builtin, func\\n\",\n      \" * Non-Converted Ops: 10, Total Ops 21, % non-converted = 47.62 %\\n\",\n      \" * 10 ARITH ops\\n\",\n      \"\\n\",\n      \"- arith.constant:   10 occurrences  (f32: 8, i32: 2)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"  (f32: 2)\\n\",\n      \"  (f32: 2)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\",\n      \"Summary on the non-converted ops:\\n\",\n      \"---------------------------------\\n\",\n      \" * Accepted dialects: tfl, builtin, func\\n\",\n      \" * Non-Converted Ops: 10, Total Ops 29, % non-converted = 34.48 %\\n\",\n      \" * 10 ARITH ops\\n\",\n      \"\\n\",\n      \"- arith.constant:   10 occurrences  (f16: 8, i32: 2)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"  (f32: 2)\\n\",\n      \"  (f32: 8)\\n\",\n      \"  (f32: 2)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\",\n      \"  (f32: 1)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.src.engine.functional.Functional at 0x2f6c5d460>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Now, let's convert the ONNX model to TF\\n\",\n    \"onnx2tf.convert(\\n\",\n    \"    input_onnx_file_path=\\\"convnet.onnx\\\",\\n\",\n    \"    output_folder_path=\\\"convnet_tf\\\",\\n\",\n    \"    non_verbose=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<ConcreteFunction (inputs_0: TensorSpec(shape=(1, 28, 28, 1), dtype=tf.float32, name='inputs_0')) -> TensorSpec(shape=(1, 10), dtype=tf.float32, name='unknown') at 0x2F71EEBB0>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Load the saved model\\n\",\n    \"model = tf.saved_model.load(\\\"./convnet_tf/\\\")\\n\",\n    \"\\n\",\n    \"model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model Output: tf.Tensor(\\n\",\n      \"[[-1.04575987e+01 -1.39286604e+01 -2.57339468e-03 -8.81325722e+00\\n\",\n      \"  -1.02665386e+01 -1.58330278e+01 -1.25931921e+01 -1.39404545e+01\\n\",\n      \"  -6.05328608e+00 -1.29597530e+01]], shape=(1, 10), dtype=float32)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Perform inference\\n\",\n    \"output = model(input_tensor.unsqueeze(-1))\\n\",\n    \"\\n\",\n    \"# Print the output\\n\",\n    \"print(\\\"Model Output:\\\", output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter13/requirements.txt",
    "content": "torch==1.12.1\ntorchvision==0.13.1\nPillow==9.3.0\nFlask==2.2.2\ncaptum==0.5.0\n"
  },
  {
    "path": "Chapter13/run_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"from PIL import Image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\\n\",\n    \"    \\n\",\n    \"model = ConvNet()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<All keys matched successfully>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"PATH_TO_MODEL = \\\"./convnet.pth\\\"\\n\",\n    \"model.load_state_dict(torch.load(PATH_TO_MODEL, map_location=\\\"cpu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"ConvNet(\\n\",\n       \"  (cn1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1))\\n\",\n       \"  (cn2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\\n\",\n       \"  (dp1): Dropout2d(p=0.1, inplace=False)\\n\",\n       \"  (dp2): Dropout2d(p=0.25, inplace=False)\\n\",\n       \"  (fc1): Linear(in_features=4608, out_features=64, bias=True)\\n\",\n       \"  (fc2): Linear(in_features=64, out_features=10, bias=True)\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"image = Image.open(\\\"./digit_image.jpg\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=251x248>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def image_to_tensor(image):\\n\",\n    \"    gray_image = transforms.functional.to_grayscale(image)\\n\",\n    \"    resized_image = transforms.functional.resize(gray_image, (28, 28))\\n\",\n    \"    input_image_tensor = transforms.functional.to_tensor(resized_image)\\n\",\n    \"    input_image_tensor_norm = transforms.functional.normalize(input_image_tensor, (0.1302,), (0.3069,))\\n\",\n    \"    return input_image_tensor_norm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_tensor = image_to_tensor(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_model(input_tensor):\\n\",\n    \"    model_input = input_tensor.unsqueeze(0)\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        model_output = model(model_input)[0]\\n\",\n    \"    model_prediction = model_output.detach().numpy().argmax()\\n\",\n    \"    return model_prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"<class 'numpy.int64'>\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output = run_model(input_tensor)\\n\",\n    \"print(output)\\n\",\n    \"print(type(output))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def debug_model(input_tensor):\\n\",\n    \"    model_input = input_tensor.unsqueeze(0)\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        model_output = model(model_input)[0]\\n\",\n    \"    model_prediction = model_output.detach().numpy()\\n\",\n    \"    return np.exp(model_prediction)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2.6426747e-05 5.4441585e-07 9.9744761e-01 7.3382223e-05 4.2181364e-05\\n\",\n      \" 1.5351664e-07 5.4828524e-06 7.8664209e-07 2.4013412e-03 2.2161457e-06]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(debug_model(input_tensor))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def post_process(output):\\n\",\n    \"    return str(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"<class 'str'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"final_output = post_process(output)\\n\",\n    \"print(final_output)\\n\",\n    \"print(type(final_output))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter13/server.py",
    "content": "import os\nimport json\nimport numpy as np\nfrom flask import Flask, request\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ConvNet(nn.Module):\n    def __init__(self):\n        super(ConvNet, self).__init__()\n        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n        self.dp1 = nn.Dropout2d(0.10)\n        self.dp2 = nn.Dropout2d(0.25)\n        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n        self.fc2 = nn.Linear(64, 10)\n \n    def forward(self, x):\n        x = self.cn1(x)\n        x = F.relu(x)\n        x = self.cn2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dp1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dp2(x)\n        x = self.fc2(x)\n        op = F.log_softmax(x, dim=1)\n        return op\n    \nmodel = ConvNet()\nPATH_TO_MODEL = \"./convnet.pth\"\nmodel.load_state_dict(torch.load(PATH_TO_MODEL, map_location=\"cpu\"))\nmodel.eval()\n\ndef run_model(input_tensor):\n    model_input = input_tensor.unsqueeze(0)\n    with torch.no_grad():\n        model_output = model(model_input)[0]\n    model_prediction = model_output.detach().numpy().argmax()\n    return model_prediction\n\ndef post_process(output):\n    return str(output)\n\napp = Flask(__name__)\n\n@app.route(\"/test\", methods=[\"POST\"])\ndef test():\n    data = request.files['data'].read()\n    md = json.load(request.files['metadata'])\n    input_array = np.frombuffer(data, dtype=np.float32)\n    input_image_tensor = torch.from_numpy(input_array).view(md[\"dims\"])\n    output = run_model(input_image_tensor)\n    final_output = post_process(output)\n    return final_output\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=8890)"
  },
  {
    "path": "Chapter14/Android/app/.gitignore",
    "content": "/build\n"
  },
  {
    "path": "Chapter14/Android/app/build.gradle",
    "content": "apply plugin: 'com.android.application'\n\nandroid {\n    compileSdkVersion 28\n    buildToolsVersion \"29.0.2\"\n    defaultConfig {\n        applicationId \"org.pytorch.mastering_pytorch_v2_mnist\"\n        minSdkVersion 21\n        targetSdkVersion 28\n        versionCode 1\n        versionName \"1.0\"\n    }\n    buildTypes {\n        release {\n            minifyEnabled false\n        }\n    }\n}\n\ndependencies {\n    implementation 'androidx.appcompat:appcompat:1.1.0'\n    implementation 'org.pytorch:pytorch_android_lite:1.12.2'\n    implementation 'org.pytorch:pytorch_android_torchvision_lite:1.12.2'\n}\n"
  },
  {
    "path": "Chapter14/Android/app/src/main/AndroidManifest.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<manifest xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    package=\"org.pytorch.mastering_pytorch_v2_mnist\">\n\n    <application\n        android:allowBackup=\"true\"\n        android:icon=\"@mipmap/ic_launcher\"\n        android:label=\"@string/app_name\"\n        android:roundIcon=\"@mipmap/ic_launcher_round\"\n        android:supportsRtl=\"true\"\n        android:theme=\"@style/AppTheme\">\n        <activity android:name=\".MainActivity\">\n            <intent-filter>\n                <action android:name=\"android.intent.action.MAIN\" />\n\n                <category android:name=\"android.intent.category.LAUNCHER\" />\n            </intent-filter>\n        </activity>\n    </application>\n\n    <uses-permission android:name=\"android.permission.CAMERA\" />\n    <uses-feature android:name=\"android.hardware.camera\" />\n    <uses-feature android:name=\"android.hardware.camera.autofocus\" />\n</manifest>"
  },
  {
    "path": "Chapter14/Android/app/src/main/java/org/pytorch/mastering_pytorch_v2_mnist/MainActivity.java",
    "content": "package org.pytorch.mastering_pytorch_v2_mnist;\n\nimport android.content.Context;\nimport android.Manifest;\nimport android.content.Intent;\nimport android.content.pm.PackageManager;\nimport android.graphics.Bitmap;\nimport android.os.Bundle;\nimport android.provider.MediaStore;\nimport android.util.Log;\nimport android.widget.ImageView;\nimport android.widget.TextView;\n\nimport androidx.annotation.NonNull;\nimport androidx.appcompat.app.AppCompatActivity;\nimport androidx.core.app.ActivityCompat;\nimport androidx.core.content.ContextCompat;\n\nimport org.pytorch.IValue;\nimport org.pytorch.LiteModuleLoader;\nimport org.pytorch.Module;\nimport org.pytorch.Tensor;\nimport org.pytorch.torchvision.TensorImageUtils;\nimport org.pytorch.MemoryFormat;\n\nimport java.io.File;\nimport java.io.FileOutputStream;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.OutputStream;\n\nimport android.widget.Button;\nimport android.view.View;\nimport android.widget.Toast;\n\n\npublic class MainActivity extends AppCompatActivity {\n\n    private static final int CAMERA_PERMISSION_CODE = 101;\n    private static final int CAMERA_REQUEST_CODE = 102;\n\n    private Module module;\n\n    @Override\n    protected void onCreate(Bundle savedInstanceState) {\n        super.onCreate(savedInstanceState);\n        setContentView(R.layout.activity_main);\n\n        // Check for camera permission\n        if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {\n            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.CAMERA}, CAMERA_PERMISSION_CODE);\n        } else {\n            openCamera();\n        }\n\n        try {\n            module = LiteModuleLoader.load(assetFilePath(this, \"optimized_for_mobile_traced_model.pt\"));\n        } catch (IOException e) {\n            Log.e(\"MasteringPyTorchV2MNIST\", \"Error reading assets\", e);\n            finish();\n        }\n    }\n\n    @Override\n    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {\n        super.onRequestPermissionsResult(requestCode, permissions, grantResults);\n        if (requestCode == CAMERA_PERMISSION_CODE) {\n            if (grantResults.length > 0 && grantResults[0] == PackageManager.PERMISSION_GRANTED) {\n                openCamera();\n            } else {\n                // Permission denied, handle accordingly\n                Toast.makeText(this, \"Camera permission denied. Cannot open the camera.\", Toast.LENGTH_SHORT).show();\n            }\n        }\n    }\n\n    @Override\n    protected void onActivityResult(int requestCode, int resultCode, Intent data) {\n        super.onActivityResult(requestCode, resultCode, data);\n        if (requestCode == CAMERA_REQUEST_CODE && resultCode == RESULT_OK) {\n            if (data != null && data.getExtras() != null) {\n                Bitmap capturedBitmap = (Bitmap) data.getExtras().get(\"data\");\n                if (capturedBitmap != null) {\n                    processImage(capturedBitmap);\n                }\n            }\n        }\n    }\n\n    private void openCamera() {\n        Intent cameraIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);\n        if (cameraIntent.resolveActivity(getPackageManager()) != null) {\n            startActivityForResult(cameraIntent, CAMERA_REQUEST_CODE);\n        }\n    }\n\n    private void processImage(Bitmap bitmap) {\n        // Resize the input image to 28x28 pixels\n        Bitmap resizedBitmap = Bitmap.createScaledBitmap(bitmap, 28, 28, true);\n\n        ImageView imageView = findViewById(R.id.image);\n        imageView.setImageBitmap(resizedBitmap);\n\n        final float[] mean = {0.1302f, 0.1302f, 0.1302f};\n        final float[] std = {0.3069f, 0.3069f, 0.3069f};\n        final Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(resizedBitmap, mean, std,\n                MemoryFormat.CHANNELS_LAST);\n\n        final Tensor outputTensor = module.forward(IValue.from(inputTensor)).toTensor();\n        final float[] scores = outputTensor.getDataAsFloatArray();\n\n        // Log the raw scores\n        Log.d(\"Raw Scores\", \"Scores:\");\n        for (int i = 0; i < scores.length; i++) {\n            Log.d(\"Raw Scores\", \"Score[\" + i + \"]: \" + scores[i]);\n        }\n\n        float maxScore = -Float.MAX_VALUE;\n        int maxScoreIdx = -1;\n        for (int i = 0; i < scores.length; i++) {\n            if (scores[i] > maxScore) {\n                maxScore = scores[i];\n                maxScoreIdx = i;\n            }\n        }\n\n        String className = String.valueOf(maxScoreIdx);\n\n        TextView textView = findViewById(R.id.text);\n        textView.setText(className);\n\n        // Add \"Retake Photo\" button logic here\n        Button retakeButton = findViewById(R.id.retake_button);\n        retakeButton.setVisibility(View.VISIBLE); // Show the retake button\n        retakeButton.setOnClickListener(new View.OnClickListener() {\n            @Override\n            public void onClick(View v) {\n                openCamera(); // Call the openCamera method again to capture a new image\n            }\n        });\n    }\n\n    // Helper method to get asset file path\n    private String assetFilePath(Context context, String assetName) throws IOException {\n        File file = new File(context.getFilesDir(), assetName);\n        if (!file.exists()) {\n            try (InputStream is = context.getAssets().open(assetName)) {\n                try (OutputStream os = new FileOutputStream(file)) {\n                    byte[] buffer = new byte[4 * 1024];\n                    int read;\n                    while ((read = is.read(buffer)) != -1) {\n                        os.write(buffer, 0, read);\n                    }\n                    os.flush();\n                }\n            }\n        }\n        return file.getAbsolutePath();\n    }\n}\n"
  },
  {
    "path": "Chapter14/Android/app/src/main/res/drawable/ic_launcher_background.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    android:width=\"108dp\"\n    android:height=\"108dp\"\n    android:viewportWidth=\"108\"\n    android:viewportHeight=\"108\">\n    <path\n        android:fillColor=\"#008577\"\n        android:pathData=\"M0,0h108v108h-108z\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M9,0L9,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M19,0L19,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M29,0L29,108\"\n        android:strokeWidth=\"0.8\"\n        android:strokeColor=\"#33FFFFFF\" />\n    <path\n        android:fillColor=\"#00000000\"\n        android:pathData=\"M39,0L39,108\"\n        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  },
  {
    "path": "Chapter14/Android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml",
    "content": "<vector xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:aapt=\"http://schemas.android.com/aapt\"\n    android:width=\"108dp\"\n    android:height=\"108dp\"\n    android:viewportWidth=\"108\"\n    android:viewportHeight=\"108\">\n    <path\n        android:fillType=\"evenOdd\"\n        android:pathData=\"M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z\"\n        android:strokeWidth=\"1\"\n        android:strokeColor=\"#00000000\">\n        <aapt:attr name=\"android:fillColor\">\n            <gradient\n                android:endX=\"78.5885\"\n                android:endY=\"90.9159\"\n                android:startX=\"48.7653\"\n                android:startY=\"61.0927\"\n                android:type=\"linear\">\n                <item\n                    android:color=\"#44000000\"\n                    android:offset=\"0.0\" />\n                <item\n                    android:color=\"#00000000\"\n                    android:offset=\"1.0\" />\n            </gradient>\n        </aapt:attr>\n    </path>\n    <path\n        android:fillColor=\"#FFFFFF\"\n        android:fillType=\"nonZero\"\n        android:pathData=\"M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z\"\n        android:strokeWidth=\"1\"\n        android:strokeColor=\"#00000000\" />\n</vector>\n"
  },
  {
    "path": "Chapter14/Android/app/src/main/res/layout/activity_main.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<FrameLayout xmlns:android=\"http://schemas.android.com/apk/res/android\"\n    xmlns:tools=\"http://schemas.android.com/tools\"\n    android:layout_width=\"match_parent\"\n    android:layout_height=\"match_parent\"\n    tools:context=\".MainActivity\">\n\n    <ImageView\n        android:id=\"@+id/image\"\n        android:layout_width=\"match_parent\"\n        android:layout_height=\"match_parent\"\n        android:scaleType=\"fitCenter\" />\n\n    <TextView\n        android:id=\"@+id/text\"\n        android:layout_width=\"match_parent\"\n        android:layout_height=\"wrap_content\"\n        android:layout_gravity=\"top\"\n        android:textSize=\"18sp\"\n        android:background=\"#80000000\"\n        android:textColor=\"@android:color/white\" />\n\n    <Button\n        android:id=\"@+id/retake_button\"\n        android:layout_width=\"wrap_content\"\n        android:layout_height=\"wrap_content\"\n        android:text=\"Retake Photo\"\n        android:layout_gravity=\"center_horizontal|bottom\"\n        android:layout_marginBottom=\"16dp\"\n        android:visibility=\"gone\" />\n\n</FrameLayout>"
  },
  {
    "path": "Chapter14/Android/app/src/main/res/values/colors.xml",
    "content": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<resources>\n    <color name=\"colorPrimary\">#008577</color>\n    <color name=\"colorPrimaryDark\">#00574B</color>\n    <color name=\"colorAccent\">#D81B60</color>\n</resources>\n"
  },
  {
    "path": "Chapter14/Android/app/src/main/res/values/strings.xml",
    "content": "<resources>\n    <string name=\"app_name\">MasteringPyTorchV2MNIST</string>\n</resources>\n"
  },
  {
    "path": "Chapter14/Android/app/src/main/res/values/styles.xml",
    "content": "<resources>\n\n    <!-- Base application theme. -->\n    <style name=\"AppTheme\" parent=\"Theme.AppCompat.Light.DarkActionBar\">\n        <!-- Customize your theme here. -->\n        <item name=\"colorPrimary\">@color/colorPrimary</item>\n        <item name=\"colorPrimaryDark\">@color/colorPrimaryDark</item>\n        <item name=\"colorAccent\">@color/colorAccent</item>\n    </style>\n\n</resources>\n"
  },
  {
    "path": "Chapter14/Android/build.gradle",
    "content": "buildscript {\n    repositories {\n        google()\n        jcenter()\n        \n    }\n    dependencies {\n        classpath 'com.android.tools.build:gradle:3.5.0'\n    }\n}\n\nallprojects {\n    repositories {\n        google()\n        jcenter()\n        \n    }\n}\n\ntask clean(type: Delete) {\n    delete rootProject.buildDir\n}\n"
  },
  {
    "path": "Chapter14/Android/gradle/wrapper/gradle-wrapper.properties",
    "content": "#Tue Oct 01 13:22:43 PDT 2019\ndistributionBase=GRADLE_USER_HOME\ndistributionPath=wrapper/dists\nzipStoreBase=GRADLE_USER_HOME\nzipStorePath=wrapper/dists\ndistributionUrl=https\\://services.gradle.org/distributions/gradle-5.4.1-all.zip\n"
  },
  {
    "path": "Chapter14/Android/gradle.properties",
    "content": "android.useAndroidX=true\nandroid.enableJetifier=true\n\n"
  },
  {
    "path": "Chapter14/Android/gradlew",
    "content": "#!/usr/bin/env sh\n\n##############################################################################\n##\n##  Gradle start up script for UN*X\n##\n##############################################################################\n\n# Attempt to set APP_HOME\n# Resolve links: $0 may be a link\nPRG=\"$0\"\n# Need this for relative symlinks.\nwhile [ -h \"$PRG\" ] ; do\n    ls=`ls -ld \"$PRG\"`\n    link=`expr \"$ls\" : '.*-> \\(.*\\)$'`\n    if expr \"$link\" : '/.*' > /dev/null; then\n        PRG=\"$link\"\n    else\n        PRG=`dirname \"$PRG\"`\"/$link\"\n    fi\ndone\nSAVED=\"`pwd`\"\ncd \"`dirname \\\"$PRG\\\"`/\" >/dev/null\nAPP_HOME=\"`pwd -P`\"\ncd \"$SAVED\" >/dev/null\n\nAPP_NAME=\"Gradle\"\nAPP_BASE_NAME=`basename \"$0\"`\n\n# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\nDEFAULT_JVM_OPTS=\"\"\n\n# Use the maximum available, or set MAX_FD != -1 to use that value.\nMAX_FD=\"maximum\"\n\nwarn () {\n    echo \"$*\"\n}\n\ndie () {\n    echo\n    echo \"$*\"\n    echo\n    exit 1\n}\n\n# OS specific support (must be 'true' or 'false').\ncygwin=false\nmsys=false\ndarwin=false\nnonstop=false\ncase \"`uname`\" in\n  CYGWIN* )\n    cygwin=true\n    ;;\n  Darwin* )\n    darwin=true\n    ;;\n  MINGW* )\n    msys=true\n    ;;\n  NONSTOP* )\n    nonstop=true\n    ;;\nesac\n\nCLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar\n\n# Determine the Java command to use to start the JVM.\nif [ -n \"$JAVA_HOME\" ] ; then\n    if [ -x \"$JAVA_HOME/jre/sh/java\" ] ; then\n        # IBM's JDK on AIX uses strange locations for the executables\n        JAVACMD=\"$JAVA_HOME/jre/sh/java\"\n    else\n        JAVACMD=\"$JAVA_HOME/bin/java\"\n    fi\n    if [ ! -x \"$JAVACMD\" ] ; then\n        die \"ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\n    fi\nelse\n    JAVACMD=\"java\"\n    which java >/dev/null 2>&1 || die \"ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\nfi\n\n# Increase the maximum file descriptors if we can.\nif [ \"$cygwin\" = \"false\" -a \"$darwin\" = \"false\" -a \"$nonstop\" = \"false\" ] ; then\n    MAX_FD_LIMIT=`ulimit -H -n`\n    if [ $? -eq 0 ] ; then\n        if [ \"$MAX_FD\" = \"maximum\" -o \"$MAX_FD\" = \"max\" ] ; then\n            MAX_FD=\"$MAX_FD_LIMIT\"\n        fi\n        ulimit -n $MAX_FD\n        if [ $? -ne 0 ] ; then\n            warn \"Could not set maximum file descriptor limit: $MAX_FD\"\n        fi\n    else\n        warn \"Could not query maximum file descriptor limit: $MAX_FD_LIMIT\"\n    fi\nfi\n\n# For Darwin, add options to specify how the application appears in the dock\nif $darwin; then\n    GRADLE_OPTS=\"$GRADLE_OPTS \\\"-Xdock:name=$APP_NAME\\\" \\\"-Xdock:icon=$APP_HOME/media/gradle.icns\\\"\"\nfi\n\n# For Cygwin, switch paths to Windows format before running java\nif $cygwin ; then\n    APP_HOME=`cygpath --path --mixed \"$APP_HOME\"`\n    CLASSPATH=`cygpath --path --mixed \"$CLASSPATH\"`\n    JAVACMD=`cygpath --unix \"$JAVACMD\"`\n\n    # We build the pattern for arguments to be converted via cygpath\n    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`\n    SEP=\"\"\n    for dir in $ROOTDIRSRAW ; do\n        ROOTDIRS=\"$ROOTDIRS$SEP$dir\"\n        SEP=\"|\"\n    done\n    OURCYGPATTERN=\"(^($ROOTDIRS))\"\n    # Add a user-defined pattern to the cygpath arguments\n    if [ \"$GRADLE_CYGPATTERN\" != \"\" ] ; then\n        OURCYGPATTERN=\"$OURCYGPATTERN|($GRADLE_CYGPATTERN)\"\n    fi\n    # Now convert the arguments - kludge to limit ourselves to /bin/sh\n    i=0\n    for arg in \"$@\" ; do\n        CHECK=`echo \"$arg\"|egrep -c \"$OURCYGPATTERN\" -`\n        CHECK2=`echo \"$arg\"|egrep -c \"^-\"`                                 ### Determine if an option\n\n        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition\n            eval `echo args$i`=`cygpath --path --ignore --mixed \"$arg\"`\n        else\n            eval `echo args$i`=\"\\\"$arg\\\"\"\n        fi\n        i=$((i+1))\n    done\n    case $i in\n        (0) set -- ;;\n        (1) set -- \"$args0\" ;;\n        (2) set -- \"$args0\" \"$args1\" ;;\n        (3) set -- \"$args0\" \"$args1\" \"$args2\" ;;\n        (4) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" ;;\n        (5) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" ;;\n        (6) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" ;;\n        (7) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" ;;\n        (8) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" ;;\n        (9) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" \"$args8\" ;;\n    esac\nfi\n\n# Escape application args\nsave () {\n    for i do printf %s\\\\n \"$i\" | sed \"s/'/'\\\\\\\\''/g;1s/^/'/;\\$s/\\$/' \\\\\\\\/\" ; done\n    echo \" \"\n}\nAPP_ARGS=$(save \"$@\")\n\n# Collect all arguments for the java command, following the shell quoting and substitution rules\neval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS \"\\\"-Dorg.gradle.appname=$APP_BASE_NAME\\\"\" -classpath \"\\\"$CLASSPATH\\\"\" org.gradle.wrapper.GradleWrapperMain \"$APP_ARGS\"\n\n# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong\nif [ \"$(uname)\" = \"Darwin\" ] && [ \"$HOME\" = \"$PWD\" ]; then\n  cd \"$(dirname \"$0\")\"\nfi\n\nexec \"$JAVACMD\" \"$@\"\n"
  },
  {
    "path": "Chapter14/Android/gradlew.bat",
    "content": "@if \"%DEBUG%\" == \"\" @echo off\r\n@rem ##########################################################################\r\n@rem\r\n@rem  Gradle startup script for Windows\r\n@rem\r\n@rem ##########################################################################\r\n\r\n@rem Set local scope for the variables with windows NT shell\r\nif \"%OS%\"==\"Windows_NT\" setlocal\r\n\r\nset DIRNAME=%~dp0\r\nif \"%DIRNAME%\" == \"\" set DIRNAME=.\r\nset APP_BASE_NAME=%~n0\r\nset APP_HOME=%DIRNAME%\r\n\r\n@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\r\nset DEFAULT_JVM_OPTS=\r\n\r\n@rem Find java.exe\r\nif defined JAVA_HOME goto findJavaFromJavaHome\r\n\r\nset JAVA_EXE=java.exe\r\n%JAVA_EXE% -version >NUL 2>&1\r\nif \"%ERRORLEVEL%\" == \"0\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:findJavaFromJavaHome\r\nset JAVA_HOME=%JAVA_HOME:\"=%\r\nset JAVA_EXE=%JAVA_HOME%/bin/java.exe\r\n\r\nif exist \"%JAVA_EXE%\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:init\r\n@rem Get command-line arguments, handling Windows variants\r\n\r\nif not \"%OS%\" == \"Windows_NT\" goto win9xME_args\r\n\r\n:win9xME_args\r\n@rem Slurp the command line arguments.\r\nset CMD_LINE_ARGS=\r\nset _SKIP=2\r\n\r\n:win9xME_args_slurp\r\nif \"x%~1\" == \"x\" goto execute\r\n\r\nset CMD_LINE_ARGS=%*\r\n\r\n:execute\r\n@rem Setup the command line\r\n\r\nset CLASSPATH=%APP_HOME%\\gradle\\wrapper\\gradle-wrapper.jar\r\n\r\n@rem Execute Gradle\r\n\"%JAVA_EXE%\" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% \"-Dorg.gradle.appname=%APP_BASE_NAME%\" -classpath \"%CLASSPATH%\" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%\r\n\r\n:end\r\n@rem End local scope for the variables with windows NT shell\r\nif \"%ERRORLEVEL%\"==\"0\" goto mainEnd\r\n\r\n:fail\r\nrem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of\r\nrem the _cmd.exe /c_ return code!\r\nif  not \"\" == \"%GRADLE_EXIT_CONSOLE%\" exit 1\r\nexit /b 1\r\n\r\n:mainEnd\r\nif \"%OS%\"==\"Windows_NT\" endlocal\r\n\r\n:omega\r\n"
  },
  {
    "path": "Chapter14/Android/mobile_optimized_model.py",
    "content": "import torch\nfrom torch.utils.mobile_optimizer import optimize_for_mobile\n\ntraced_model = torch.jit.load('../../Chapter13/traced_convnet.pt')\noptimized_traced_model = optimize_for_mobile(traced_model)\noptimized_traced_model._save_for_lite_interpreter(\"./app/src/main/assets/optimized_for_mobile_traced_model.pt\")\n"
  },
  {
    "path": "Chapter14/Android/settings.gradle",
    "content": "include ':app'\nrootProject.name='MasteringPyTorchV2MNISTApp'\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/AppDelegate.swift",
    "content": "import UIKit\n\n@UIApplicationMain\nclass AppDelegate: UIResponder, UIApplicationDelegate {\n    var window: UIWindow?\n    func application(_: UIApplication, didFinishLaunchingWithOptions _: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {\n        // Override point for customization after application launch.\n        return true\n    }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/Assets.xcassets/AppIcon.appiconset/Contents.json",
    "content": "{\n  \"images\" : [\n    {\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"2x\",\n      \"size\" : \"20x20\"\n    },\n    {\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"3x\",\n      \"size\" : \"20x20\"\n    },\n    {\n      \"filename\" : \"Icon-Small@2x-1.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"2x\",\n      \"size\" : \"29x29\"\n    },\n    {\n      \"filename\" : \"Icon-Small@3x.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"3x\",\n      \"size\" : \"29x29\"\n    },\n    {\n      \"filename\" : \"Icon-Small-40@2x.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"2x\",\n      \"size\" : \"40x40\"\n    },\n    {\n      \"filename\" : \"Icon-Small-60@2x.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"3x\",\n      \"size\" : \"40x40\"\n    },\n    {\n      \"filename\" : \"Icon-60@2x.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"2x\",\n      \"size\" : \"60x60\"\n    },\n    {\n      \"filename\" : \"Icon-60@3x.png\",\n      \"idiom\" : \"iphone\",\n      \"scale\" : \"3x\",\n      \"size\" : \"60x60\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"1x\",\n      \"size\" : \"20x20\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"2x\",\n      \"size\" : \"20x20\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"1x\",\n      \"size\" : \"29x29\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"2x\",\n      \"size\" : \"29x29\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"1x\",\n      \"size\" : \"40x40\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"2x\",\n      \"size\" : \"40x40\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"1x\",\n      \"size\" : \"76x76\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"2x\",\n      \"size\" : \"76x76\"\n    },\n    {\n      \"idiom\" : \"ipad\",\n      \"scale\" : \"2x\",\n      \"size\" : \"83.5x83.5\"\n    },\n    {\n      \"idiom\" : \"ios-marketing\",\n      \"scale\" : \"1x\",\n      \"size\" : \"1024x1024\"\n    }\n  ],\n  \"info\" : {\n    \"author\" : \"xcode\",\n    \"version\" : 1\n  }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/Assets.xcassets/Contents.json",
    "content": "{\n  \"info\" : {\n    \"author\" : \"xcode\",\n    \"version\" : 1\n  }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/Base.lproj/LaunchScreen.storyboard",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<document type=\"com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB\" version=\"3.0\" toolsVersion=\"13122.16\" targetRuntime=\"iOS.CocoaTouch\" propertyAccessControl=\"none\" useAutolayout=\"YES\" launchScreen=\"YES\" useTraitCollections=\"YES\" useSafeAreas=\"YES\" colorMatched=\"YES\" initialViewController=\"01J-lp-oVM\">\n    <dependencies>\n        <plugIn identifier=\"com.apple.InterfaceBuilder.IBCocoaTouchPlugin\" version=\"13104.12\"/>\n        <capability name=\"Safe area layout guides\" minToolsVersion=\"9.0\"/>\n        <capability name=\"documents saved in the Xcode 8 format\" minToolsVersion=\"8.0\"/>\n    </dependencies>\n    <scenes>\n        <!--View Controller-->\n        <scene sceneID=\"EHf-IW-A2E\">\n            <objects>\n                <viewController id=\"01J-lp-oVM\" sceneMemberID=\"viewController\">\n                    <view key=\"view\" contentMode=\"scaleToFill\" id=\"Ze5-6b-2t3\">\n                        <rect key=\"frame\" x=\"0.0\" y=\"0.0\" width=\"375\" height=\"667\"/>\n                        <autoresizingMask key=\"autoresizingMask\" widthSizable=\"YES\" heightSizable=\"YES\"/>\n                        <color key=\"backgroundColor\" xcode11CocoaTouchSystemColor=\"systemBackgroundColor\" cocoaTouchSystemColor=\"whiteColor\"/>\n                        <viewLayoutGuide key=\"safeArea\" id=\"6Tk-OE-BBY\"/>\n                    </view>\n                </viewController>\n                <placeholder placeholderIdentifier=\"IBFirstResponder\" id=\"iYj-Kq-Ea1\" userLabel=\"First Responder\" sceneMemberID=\"firstResponder\"/>\n            </objects>\n            <point key=\"canvasLocation\" x=\"53\" y=\"375\"/>\n        </scene>\n    </scenes>\n</document>\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/Base.lproj/Main.storyboard",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<document type=\"com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB\" version=\"3.0\" toolsVersion=\"21701\" targetRuntime=\"iOS.CocoaTouch\" propertyAccessControl=\"none\" useAutolayout=\"YES\" useTraitCollections=\"YES\" useSafeAreas=\"YES\" colorMatched=\"YES\" initialViewController=\"FxE-BR-UmL\">\n    <device id=\"retina6_1\" orientation=\"portrait\" appearance=\"light\"/>\n    <dependencies>\n        <deployment identifier=\"iOS\"/>\n        <plugIn identifier=\"com.apple.InterfaceBuilder.IBCocoaTouchPlugin\" version=\"21679\"/>\n        <capability name=\"Safe area layout guides\" minToolsVersion=\"9.0\"/>\n        <capability name=\"System colors in document resources\" minToolsVersion=\"11.0\"/>\n        <capability name=\"documents saved in the Xcode 8 format\" minToolsVersion=\"8.0\"/>\n    </dependencies>\n    <scenes>\n        <!--Navigation Controller-->\n        <scene sceneID=\"JQc-Lc-3UY\">\n            <objects>\n                <navigationController automaticallyAdjustsScrollViewInsets=\"NO\" id=\"FxE-BR-UmL\" sceneMemberID=\"viewController\">\n                    <toolbarItems/>\n                    <navigationBar key=\"navigationBar\" contentMode=\"scaleToFill\" insetsLayoutMarginsFromSafeArea=\"NO\" id=\"7IF-Ml-aLx\">\n                        <rect key=\"frame\" x=\"0.0\" y=\"48\" width=\"414\" height=\"44\"/>\n                        <autoresizingMask key=\"autoresizingMask\"/>\n                    </navigationBar>\n                    <nil name=\"viewControllers\"/>\n                    <connections>\n                        <segue destination=\"bcn-4C-CCs\" kind=\"relationship\" relationship=\"rootViewController\" id=\"Gmk-bd-yxX\"/>\n                    </connections>\n                </navigationController>\n                <placeholder placeholderIdentifier=\"IBFirstResponder\" id=\"EbU-uZ-XIZ\" userLabel=\"First Responder\" customClass=\"UIResponder\" sceneMemberID=\"firstResponder\"/>\n            </objects>\n            <point key=\"canvasLocation\" x=\"131.8840579710145\" y=\"137.94642857142856\"/>\n        </scene>\n        <!--Capture View Controller-->\n        <scene sceneID=\"wx0-6S-tDO\">\n            <objects>\n                <viewController id=\"bcn-4C-CCs\" customClass=\"CaptureViewController\" customModule=\"HelloWorld\" customModuleProvider=\"target\" sceneMemberID=\"viewController\">\n                    <view key=\"view\" contentMode=\"scaleToFill\" id=\"di7-LO-rOm\">\n                        <rect key=\"frame\" x=\"0.0\" y=\"0.0\" width=\"414\" height=\"896\"/>\n                        <autoresizingMask key=\"autoresizingMask\" widthSizable=\"YES\" heightSizable=\"YES\"/>\n                        <subviews>\n                            <imageView clipsSubviews=\"YES\" userInteractionEnabled=\"NO\" contentMode=\"scaleAspectFit\" horizontalHuggingPriority=\"251\" verticalHuggingPriority=\"251\" fixedFrame=\"YES\" translatesAutoresizingMaskIntoConstraints=\"NO\" id=\"Jg4-i5-fAv\">\n                                <rect key=\"frame\" x=\"31\" y=\"71\" width=\"353\" height=\"316\"/>\n                                <autoresizingMask key=\"autoresizingMask\" flexibleMaxX=\"YES\" flexibleMaxY=\"YES\"/>\n                            </imageView>\n                            <button opaque=\"NO\" contentMode=\"scaleToFill\" fixedFrame=\"YES\" contentHorizontalAlignment=\"center\" contentVerticalAlignment=\"center\" buttonType=\"system\" lineBreakMode=\"middleTruncation\" translatesAutoresizingMaskIntoConstraints=\"NO\" id=\"9nI-t6-b6S\">\n                                <rect key=\"frame\" x=\"152\" y=\"559\" width=\"110\" height=\"35\"/>\n                                <autoresizingMask key=\"autoresizingMask\" flexibleMaxX=\"YES\" flexibleMaxY=\"YES\"/>\n                                <state key=\"normal\" title=\"Button\"/>\n                                <buttonConfiguration key=\"configuration\" style=\"plain\" title=\"Capture\"/>\n                                <connections>\n                                    <action selector=\"captureButtonTapped:\" destination=\"bcn-4C-CCs\" eventType=\"touchUpInside\" id=\"k6c-qy-mSa\"/>\n                                    <segue destination=\"2WL-LC-Hwy\" kind=\"showDetail\" identifier=\"showImagePreview\" id=\"bzw-RY-vzP\"/>\n                                </connections>\n                            </button>\n                        </subviews>\n                        <viewLayoutGuide key=\"safeArea\" id=\"iIj-bp-C6C\"/>\n                        <color key=\"backgroundColor\" systemColor=\"systemBackgroundColor\"/>\n                    </view>\n                    <navigationItem key=\"navigationItem\" id=\"lvD-5b-Wcp\"/>\n                    <connections>\n                        <outlet property=\"captureButton\" destination=\"9nI-t6-b6S\" id=\"5ra-K9-228\"/>\n                        <outlet property=\"imageView\" destination=\"Jg4-i5-fAv\" id=\"Adv-ha-vaV\"/>\n                    </connections>\n                </viewController>\n                <placeholder placeholderIdentifier=\"IBFirstResponder\" id=\"fJ4-yf-Ou8\" userLabel=\"First Responder\" customClass=\"UIResponder\" sceneMemberID=\"firstResponder\"/>\n            </objects>\n            <point key=\"canvasLocation\" x=\"1089.8550724637682\" y=\"137.94642857142856\"/>\n        </scene>\n        <!--Preview View Controller-->\n        <scene sceneID=\"9Cj-H6-4Rs\">\n            <objects>\n                <viewController id=\"2WL-LC-Hwy\" customClass=\"PreviewViewController\" customModule=\"HelloWorld\" customModuleProvider=\"target\" sceneMemberID=\"viewController\">\n                    <view key=\"view\" contentMode=\"scaleToFill\" id=\"oyU-tZ-ILm\">\n                        <rect key=\"frame\" x=\"0.0\" y=\"0.0\" width=\"414\" height=\"886\"/>\n                        <autoresizingMask key=\"autoresizingMask\" widthSizable=\"YES\" heightSizable=\"YES\"/>\n                        <subviews>\n                            <imageView clipsSubviews=\"YES\" userInteractionEnabled=\"NO\" contentMode=\"scaleAspectFit\" horizontalHuggingPriority=\"251\" verticalHuggingPriority=\"251\" fixedFrame=\"YES\" translatesAutoresizingMaskIntoConstraints=\"NO\" id=\"k3X-sv-FHS\">\n                                <rect key=\"frame\" x=\"31\" y=\"72\" width=\"352\" height=\"318\"/>\n                                <autoresizingMask key=\"autoresizingMask\" flexibleMaxX=\"YES\" flexibleMaxY=\"YES\"/>\n                            </imageView>\n                            <textView clipsSubviews=\"YES\" multipleTouchEnabled=\"YES\" contentMode=\"scaleToFill\" fixedFrame=\"YES\" textAlignment=\"natural\" translatesAutoresizingMaskIntoConstraints=\"NO\" id=\"NhW-bQ-YEU\">\n                                <rect key=\"frame\" x=\"87\" y=\"524\" width=\"240\" height=\"128\"/>\n                                <autoresizingMask key=\"autoresizingMask\" flexibleMaxX=\"YES\" flexibleMaxY=\"YES\"/>\n                                <color key=\"backgroundColor\" systemColor=\"systemBackgroundColor\"/>\n                                <color key=\"textColor\" systemColor=\"labelColor\"/>\n                                <fontDescription key=\"fontDescription\" type=\"system\" pointSize=\"14\"/>\n                                <textInputTraits key=\"textInputTraits\" autocapitalizationType=\"sentences\"/>\n                            </textView>\n                        </subviews>\n                        <viewLayoutGuide key=\"safeArea\" id=\"RUp-k4-Rgv\"/>\n                        <color key=\"backgroundColor\" systemColor=\"systemBackgroundColor\"/>\n                    </view>\n                    <navigationItem key=\"navigationItem\" id=\"MkU-Wy-YWP\"/>\n                    <connections>\n                        <outlet property=\"imageView\" destination=\"k3X-sv-FHS\" id=\"KZj-kk-MRM\"/>\n                        <outlet property=\"resultView\" destination=\"NhW-bQ-YEU\" id=\"yQt-ju-LK3\"/>\n                    </connections>\n                </viewController>\n                <placeholder placeholderIdentifier=\"IBFirstResponder\" id=\"nWP-YQ-v8i\" userLabel=\"First Responder\" customClass=\"UIResponder\" sceneMemberID=\"firstResponder\"/>\n            </objects>\n            <point key=\"canvasLocation\" x=\"1918.840579710145\" y=\"137.94642857142856\"/>\n        </scene>\n    </scenes>\n    <resources>\n        <systemColor name=\"labelColor\">\n            <color red=\"0.0\" green=\"0.0\" blue=\"0.0\" alpha=\"1\" colorSpace=\"custom\" customColorSpace=\"sRGB\"/>\n        </systemColor>\n        <systemColor name=\"systemBackgroundColor\">\n            <color white=\"1\" alpha=\"1\" colorSpace=\"custom\" customColorSpace=\"genericGamma22GrayColorSpace\"/>\n        </systemColor>\n    </resources>\n</document>\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/CaptureViewController.swift",
    "content": "import UIKit\nimport AVFoundation\n\nclass CaptureViewController: UIViewController, AVCapturePhotoCaptureDelegate {\n    @IBOutlet var captureButton: UIButton!\n    @IBOutlet var imageView: UIImageView!\n    \n    private var captureSession: AVCaptureSession!\n    private var photoOutput: AVCapturePhotoOutput!\n    private var previewLayer: AVCaptureVideoPreviewLayer!\n    private var capturedImage: UIImage?\n\n    override func viewDidLoad() {\n        super.viewDidLoad()\n        setupCamera()\n    }\n    \n    func setupCamera() {\n        captureSession = AVCaptureSession()\n        guard let captureDevice = AVCaptureDevice.default(for: .video) else {\n            fatalError(\"Cannot access camera.\")\n        }\n        \n        do {\n            let input = try AVCaptureDeviceInput(device: captureDevice)\n            captureSession.addInput(input)\n            \n            photoOutput = AVCapturePhotoOutput()\n            captureSession.addOutput(photoOutput)\n            \n            previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)\n            previewLayer.videoGravity = .resizeAspectFill // Maintain aspect ratio\n            // Calculate the square frame that fits within the screen bounds\n            let minSideLength = min(view.bounds.width, view.bounds.height)\n            let previewFrame = CGRect(\n                x: (view.bounds.width - minSideLength) / 2,\n                y: (view.bounds.height - minSideLength) / 2,\n                width: minSideLength,\n                height: minSideLength\n            )\n            previewLayer.frame = previewFrame\n            view.layer.addSublayer(previewLayer)\n            \n            captureSession.startRunning()\n        } catch {\n            fatalError(\"Cannot set up camera.\")\n        }\n    }\n    \n    @IBAction func captureButtonTapped(_ sender: UIButton) {\n        let settings = AVCapturePhotoSettings()\n        photoOutput.capturePhoto(with: settings, delegate: self)\n    }\n        \n    // Inside photoOutput(_:didFinishProcessingPhoto:error:) function\n    func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) {\n        if let imageData = photo.fileDataRepresentation(), let image = UIImage(data: imageData) {\n            capturedImage = cropImage(image, to: previewLayer.frame)\n            performSegue(withIdentifier: \"showImagePreview\", sender: self)\n        }\n    }\n\n    func cropImage(_ image: UIImage, to frame: CGRect) -> UIImage? {\n        let scale = UIScreen.main.scale\n        let imageSize = image.size\n        let previewSize = frame.size\n\n        // Calculate scaling factors to map preview frame to image size\n        let xScale = imageSize.width / previewSize.width\n        let yScale = imageSize.height / previewSize.height\n        \n//        print(\"Image Size: \\(imageSize)\")\n//        print(\"Preview Size: \\(previewSize)\")\n//        print(\"Preview Size width: \\(previewSize.width)\")\n//        print(\"Preview Size height: \\(previewSize.height)\")\n//        print(\"Preview X: \\(frame.origin.x)\")\n//        print(\"Preview Y: \\(frame.origin.y)\")\n//        print(\"X Scale: \\(xScale)\")\n//        print(\"Y Scale: \\(yScale)\")\n//        print(\"scale: \\(scale)\")\n        \n\n        // Calculate the crop rect based on the visible portion of the preview layer\n        let cropRect = CGRect(\n            x: -frame.origin.x * xScale,\n            y: 0,\n            width: previewSize.width * xScale,\n            height: previewSize.height * xScale\n        )\n\n        if let cgImage = image.cgImage?.cropping(to: cropRect) {\n            return UIImage(cgImage: cgImage, scale: 1, orientation: image.imageOrientation)\n        }\n\n        return nil\n    }\n    \n    override func prepare(for segue: UIStoryboardSegue, sender: Any?) {\n        if segue.identifier == \"showImagePreview\", let previewVC = segue.destination as? PreviewViewController {\n            previewVC.capturedImage = capturedImage\n            capturedImage = nil // Reset the capturedImage property\n        }\n    }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/Info.plist",
    "content": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<!DOCTYPE plist PUBLIC \"-//Apple//DTD PLIST 1.0//EN\" \"http://www.apple.com/DTDs/PropertyList-1.0.dtd\">\n<plist version=\"1.0\">\n<dict>\n\t<key>CFBundleDevelopmentRegion</key>\n\t<string>$(DEVELOPMENT_LANGUAGE)</string>\n\t<key>CFBundleExecutable</key>\n\t<string>$(EXECUTABLE_NAME)</string>\n\t<key>CFBundleIdentifier</key>\n\t<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>\n\t<key>CFBundleInfoDictionaryVersion</key>\n\t<string>6.0</string>\n\t<key>CFBundleName</key>\n\t<string>$(PRODUCT_NAME)</string>\n\t<key>NSCameraUsageDescription</key>\n\t<string>We need access to your camera to capture images for analysis.</string>\n\t<key>CFBundlePackageType</key>\n\t<string>$(PRODUCT_BUNDLE_PACKAGE_TYPE)</string>\n\t<key>CFBundleShortVersionString</key>\n\t<string>1.0</string>\n\t<key>CFBundleVersion</key>\n\t<string>1</string>\n\t<key>LSRequiresIPhoneOS</key>\n\t<true/>\n\t<key>UILaunchStoryboardName</key>\n\t<string>LaunchScreen</string>\n\t<key>UIMainStoryboardFile</key>\n\t<string>Main</string>\n\t<key>UIRequiredDeviceCapabilities</key>\n\t<array>\n\t\t<string>armv7</string>\n\t</array>\n\t<key>UISupportedInterfaceOrientations</key>\n\t<array>\n\t\t<string>UIInterfaceOrientationPortrait</string>\n\t</array>\n\t<key>UISupportedInterfaceOrientations~ipad</key>\n\t<array>\n\t\t<string>UIInterfaceOrientationPortrait</string>\n\t\t<string>UIInterfaceOrientationPortraitUpsideDown</string>\n\t\t<string>UIInterfaceOrientationLandscapeLeft</string>\n\t\t<string>UIInterfaceOrientationLandscapeRight</string>\n\t</array>\n</dict>\n</plist>\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/PreviewViewController.swift",
    "content": "import UIKit\n\nclass PreviewViewController: UIViewController {\n    @IBOutlet var imageView: UIImageView!\n    @IBOutlet var resultView: UITextView!\n    \n    var capturedImage: UIImage?\n    \n    private lazy var module: TorchModule = {\n        if let filePath = Bundle.main.path(forResource: \"model\", ofType: \"pt\"),\n            let module = TorchModule(fileAtPath: filePath) {\n            return module\n        } else {\n            fatalError(\"Can't find the model file!\")\n        }\n    }()\n    \n    private lazy var labels: [String] = {\n        if let filePath = Bundle.main.path(forResource: \"digits\", ofType: \"txt\"),\n            let labels = try? String(contentsOfFile: filePath) {\n            return labels.components(separatedBy: .newlines)\n        } else {\n            fatalError(\"Can't find the text file!\")\n        }\n    }()\n    \n    override func viewDidLoad() {\n        super.viewDidLoad()\n        imageView.image = capturedImage\n        guard let resizedImage = capturedImage?.resized(to: CGSize(width: 28, height: 28)),\n              var pixelBuffer = resizedImage.grayscaleNormalized() else {\n            return\n        }\n//        imageView.image = resizedImage\n        \n        guard let outputs = module.predict(image: UnsafeMutableRawPointer(&pixelBuffer)) else {\n            return\n        }\n\n        print(\"Raw Predictions: \\(outputs)\") // Print the raw predictions array\n        \n        // Find the index of the maximum value in the outputs array\n        if let maxIndex = outputs.indices.max(by: { outputs[$0].floatValue < outputs[$1].floatValue }) {\n            let predictedDigit = maxIndex // This is the predicted digit\n            print(\"Predicted Digit: \\(predictedDigit)\")\n            resultView.text = \"Predicted Digit: \\(predictedDigit)\"\n        } else {\n            print(\"Unable to determine predicted digit\")\n            resultView.text = \"Unable to determine predicted digit\"\n        }\n    }\n\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/SceneDelegate.swift",
    "content": "import UIKit\n\nclass SceneDelegate: UIResponder, UIWindowSceneDelegate {\n    var window: UIWindow?\n\n    func scene(_ scene: UIScene, willConnectTo _: UISceneSession, options _: UIScene.ConnectionOptions) {\n        // Use this method to optionally configure and attach the UIWindow `window` to the provided UIWindowScene `scene`.\n        // If using a storyboard, the `window` property will automatically be initialized and attached to the scene.\n        // This delegate does not imply the connecting scene or session are new (see `application:configurationForConnectingSceneSession` instead).\n        guard let _ = (scene as? UIWindowScene) else { return }\n    }\n\n    func sceneDidDisconnect(_: UIScene) {\n        // Called as the scene is being released by the system.\n        // This occurs shortly after the scene enters the background, or when its session is discarded.\n        // Release any resources associated with this scene that can be re-created the next time the scene connects.\n        // The scene may re-connect later, as its session was not neccessarily discarded (see `application:didDiscardSceneSessions` instead).\n    }\n\n    func sceneDidBecomeActive(_: UIScene) {\n        // Called when the scene has moved from an inactive state to an active state.\n        // Use this method to restart any tasks that were paused (or not yet started) when the scene was inactive.\n    }\n\n    func sceneWillResignActive(_: UIScene) {\n        // Called when the scene will move from an active state to an inactive state.\n        // This may occur due to temporary interruptions (ex. an incoming phone call).\n    }\n\n    func sceneWillEnterForeground(_: UIScene) {\n        // Called as the scene transitions from the background to the foreground.\n        // Use this method to undo the changes made on entering the background.\n    }\n\n    func sceneDidEnterBackground(_: UIScene) {\n        // Called as the scene transitions from the foreground to the background.\n        // Use this method to save data, release shared resources, and store enough scene-specific state information\n        // to restore the scene back to its current state.\n    }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/TorchBridge/HelloWorld-Bridging-Header.h",
    "content": "#import \"TorchModule.h\"\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/TorchBridge/TorchModule.h",
    "content": "#import <Foundation/Foundation.h>\n\nNS_ASSUME_NONNULL_BEGIN\n\n@interface TorchModule : NSObject\n\n- (nullable instancetype)initWithFileAtPath:(NSString*)filePath\n    NS_SWIFT_NAME(init(fileAtPath:))NS_DESIGNATED_INITIALIZER;\n+ (instancetype)new NS_UNAVAILABLE;\n- (instancetype)init NS_UNAVAILABLE;\n- (nullable NSArray<NSNumber*>*)predictImage:(void*)imageBuffer NS_SWIFT_NAME(predict(image:));\n\n@end\n\nNS_ASSUME_NONNULL_END\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/TorchBridge/TorchModule.mm",
    "content": "#import \"TorchModule.h\"\n#import <Libtorch-Lite/Libtorch-Lite.h>\n\n@implementation TorchModule {\n @protected\n  torch::jit::mobile::Module _impl;\n}\n\n- (nullable instancetype)initWithFileAtPath:(NSString*)filePath {\n  self = [super init];\n  if (self) {\n    try {\n      _impl = torch::jit::_load_for_mobile(filePath.UTF8String);\n    } catch (const std::exception& exception) {\n      NSLog(@\"%s\", exception.what());\n      return nil;\n    }\n  }\n  return self;\n}\n\n- (NSArray<NSNumber*>*)predictImage:(void*)imageBuffer {\n  try {\n    at::Tensor tensor = torch::from_blob(imageBuffer, {1, 1, 28, 28}, at::kFloat);\n    c10::InferenceMode guard;\n    auto outputTensor = _impl.forward({tensor}).toTensor();\n    float* floatBuffer = outputTensor.data_ptr<float>();\n    if (!floatBuffer) {\n      return nil;\n    }\n    NSMutableArray* results = [[NSMutableArray alloc] init];\n    for (int i = 0; i < 10; i++) {\n      [results addObject:@(floatBuffer[i])];\n    }\n    return [results copy];\n  } catch (const std::exception& exception) {\n    NSLog(@\"%s\", exception.what());\n  }\n  return nil;\n}\n\n@end\n\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/UIImage+Helper.swift",
    "content": "import UIKit\n\nextension UIImage {\n    func resized(to newSize: CGSize, scale: CGFloat = 1) -> UIImage {\n        let format = UIGraphicsImageRendererFormat.default()\n        format.scale = scale\n        let renderer = UIGraphicsImageRenderer(size: newSize, format: format)\n        let image = renderer.image { _ in\n            draw(in: CGRect(origin: .zero, size: newSize))\n        }\n        return image\n    }\n\n    func grayscaleNormalized() -> [Float32]? {\n            guard let cgImage = self.cgImage else {\n                return nil\n            }\n            let w = cgImage.width\n            let h = cgImage.height\n            let bytesPerPixel = 4\n            let bytesPerRow = bytesPerPixel * w\n            let bitsPerComponent = 8\n            var rawBytes: [UInt8] = [UInt8](repeating: 0, count: w * h * 4)\n            rawBytes.withUnsafeMutableBytes { ptr in\n                if let cgImage = self.cgImage,\n                    let context = CGContext(data: ptr.baseAddress,\n                                            width: w,\n                                            height: h,\n                                            bitsPerComponent: bitsPerComponent,\n                                            bytesPerRow: bytesPerRow,\n                                            space: CGColorSpaceCreateDeviceRGB(),\n                                            bitmapInfo: CGImageAlphaInfo.premultipliedLast.rawValue) {\n                    let rect = CGRect(x: 0, y: 0, width: w, height: h)\n                    context.draw(cgImage, in: rect)\n                }\n            }\n            var normalizedBuffer: [Float32] = [Float32](repeating: 0, count: w * h)\n            // convert RGB to grayscale and normalize\n            for i in 0 ..< w * h {\n                let red = Float32(rawBytes[i * 4 + 0])\n                let green = Float32(rawBytes[i * 4 + 1])\n                let blue = Float32(rawBytes[i * 4 + 2])\n                let grayscaleValue = (0.2989 * red + 0.5870 * green + 0.1140 * blue) / 255.0\n                normalizedBuffer[i] = (grayscaleValue - 0.1302) / 0.3069\n            }\n            return normalizedBuffer\n        }\n}\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld/model/digits.txt",
    "content": "0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n"
  },
  {
    "path": "Chapter14/iOS/HelloWorld/HelloWorld.xcodeproj/project.pbxproj",
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  },
  {
    "path": "Chapter14/iOS/HelloWorld/Podfile",
    "content": "\nplatform :ios, '12.0'\ntarget 'HelloWorld' do\n    pod 'LibTorch-Lite', '~> 1.13.0.1'\nend\n"
  },
  {
    "path": "Chapter15/fastai.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Collecting torch==2.2\\n\",\n      \"  Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl.metadata (25 kB)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl (59.7 MB)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mInstalling collected packages: torch\\n\",\n      \"  Attempting uninstall: torch\\n\",\n      \"    Found existing installation: torch 1.11.0\\n\",\n      \"    Uninstalling torch-1.11.0:\\n\",\n      \"      Successfully uninstalled torch-1.11.0\\n\",\n      \"\\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\\n\",\n      \"torchdata 0.4.1 requires torch==1.12.1, but you have torch 2.2.0 which is incompatible.\\u001b[0m\\u001b[31m\\n\",\n      \"\\u001b[0mSuccessfully installed torch-2.2.0\\n\",\n      \"Requirement already satisfied: fastai==2.7.14 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.7.14)\\n\",\n      \"Requirement already satisfied: pip in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (23.3.1)\\n\",\n      \"Requirement already satisfied: packaging in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (23.2)\\n\",\n      \"Requirement already satisfied: fastdownload<2,>=0.0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (0.0.7)\\n\",\n      \"Requirement already satisfied: fastcore<1.6,>=1.5.29 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.5.29)\\n\",\n      \"Requirement already satisfied: torchvision>=0.11 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (0.14.1)\\n\",\n      \"Requirement already satisfied: matplotlib in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (3.5.2)\\n\",\n      \"Requirement already satisfied: pandas in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.2.0)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.31.0)\\n\",\n      \"Requirement already satisfied: pyyaml in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (6.0.1)\\n\",\n      \"Requirement already satisfied: fastprogress>=0.2.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.0.3)\\n\",\n      \"Requirement already satisfied: pillow>=9.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (9.3.0)\\n\",\n      \"Requirement already satisfied: scikit-learn in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.4.1.post1)\\n\",\n      \"Requirement already satisfied: scipy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.12.0)\\n\",\n      \"Requirement already satisfied: spacy<4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (3.7.4)\\n\",\n      \"Requirement already satisfied: torch<2.3,>=1.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.2.0)\\n\",\n      \"Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.0.12)\\n\",\n      \"Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.0.5)\\n\",\n      \"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.0.10)\\n\",\n      \"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.0.8)\\n\",\n      \"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.0.9)\\n\",\n      \"Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (8.2.3)\\n\",\n      \"Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.1.2)\\n\",\n      \"Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.4.8)\\n\",\n      \"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.0.10)\\n\",\n      \"Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (0.3.4)\\n\",\n      \"Requirement already satisfied: typer<0.10.0,>=0.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (0.9.0)\\n\",\n      \"Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (6.4.0)\\n\",\n      \"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (4.66.2)\\n\",\n      \"Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.6.1)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.1.3)\\n\",\n      \"Requirement already satisfied: setuptools in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (68.2.2)\\n\",\n      \"Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.3.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.19.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.26.4)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (2024.2.2)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (2.8.5)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (2024.2.0)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (4.49.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (1.4.5)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (3.1.1)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pandas->fastai==2.7.14) (2024.1)\\n\",\n      \"Requirement already satisfied: tzdata>=2022.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pandas->fastai==2.7.14) (2024.1)\\n\",\n      \"Requirement already satisfied: joblib>=1.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-learn->fastai==2.7.14) (1.3.2)\\n\",\n      \"Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-learn->fastai==2.7.14) (3.3.0)\\n\",\n      \"Requirement already satisfied: annotated-types>=0.4.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai==2.7.14) (0.6.0)\\n\",\n      \"Requirement already satisfied: pydantic-core==2.16.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai==2.7.14) (2.16.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->fastai==2.7.14) (1.16.0)\\n\",\n      \"Requirement already satisfied: blis<0.8.0,>=0.7.8 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from thinc<8.3.0,>=8.2.2->spacy<4->fastai==2.7.14) (0.7.11)\\n\",\n      \"Requirement already satisfied: confection<1.0.0,>=0.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from thinc<8.3.0,>=8.2.2->spacy<4->fastai==2.7.14) (0.1.4)\\n\",\n      \"Requirement already satisfied: click<9.0.0,>=7.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from typer<0.10.0,>=0.3.0->spacy<4->fastai==2.7.14) (8.1.7)\\n\",\n      \"Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from weasel<0.4.0,>=0.1.0->spacy<4->fastai==2.7.14) (0.16.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->spacy<4->fastai==2.7.14) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch<2.3,>=1.10->fastai==2.7.14) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0m\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install fastai==2.7.14\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: dlopen(/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/image.so, 0x0006): Symbol not found: __ZN3c1017RegisterOperatorsD1Ev\\n\",\n      \"  Referenced from: <3F789787-FE38-3CE7-8599-064BDD0416EE> /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/image.so\\n\",\n      \"  Expected in:     <ADC0A61A-5B83-3A02-975F-EE5DFF441305> /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/lib/libtorch_cpu.dylib\\n\",\n      \"  warn(f\\\"Failed to load image Python extension: {e}\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"from fastai.vision.all import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/.fastai/data/mnist_png\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = untar_data(URLs.MNIST)\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"60000\\n\",\n      \"/Users/Ashish.Jha/.fastai/data/mnist_png/training/9/36655.png\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"files = get_image_files(path/\\\"training\\\")\\n\",\n    \"print(len(files))\\n\",\n    \"print(files[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 900x900 with 9 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def label_func(f): return f.parent.name\\n\",\n    \"dls = ImageDataLoaders.from_path_func(path, fnames=files, label_func=label_func, num_workers=0)\\n\",\n    \"dls.show_batch()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learn = vision_learner(dls, arch=resnet18, metrics=accuracy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"<style>\\n\",\n       \"    /* Turns off some styling */\\n\",\n       \"    progress {\\n\",\n       \"        /* gets rid of default border in Firefox and Opera. */\\n\",\n       \"        border: none;\\n\",\n       \"        /* Needs to be in here for Safari polyfill so background images work as expected. */\\n\",\n       \"        background-size: auto;\\n\",\n       \"    }\\n\",\n       \"    progress:not([value]), progress:not([value])::-webkit-progress-bar {\\n\",\n       \"        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\\n\",\n       \"    }\\n\",\n       \"    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\\n\",\n       \"        background: #F44336;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SuggestedLRs(valley=0.002511886414140463)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n 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\",\n      \"text/plain\": [\n       \"<Figure size 1500x1000 with 9 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"interp = Interpretation.from_learner(learn)\\n\",\n    \"interp.plot_top_losses(9, figsize=(15,10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter15/poutyne.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Collecting poutyne==1.12.0\\n\",\n      \"  Downloading Poutyne-1.12-py3-none-any.whl (211 kB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m211.2/211.2 kB\\u001b[0m \\u001b[31m12.7 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n      \"\\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from poutyne==1.12.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from poutyne==1.12.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: torchmetrics in /opt/conda/lib/python3.10/site-packages (from poutyne==1.12.0) (1.2.1)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch->poutyne==1.12.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->poutyne==1.12.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: packaging>17.1 in /opt/conda/lib/python3.10/site-packages (from torchmetrics->poutyne==1.12.0) (21.3)\\n\",\n      \"Requirement already satisfied: lightning-utilities>=0.8.0 in /opt/conda/lib/python3.10/site-packages (from torchmetrics->poutyne==1.12.0) (0.10.0)\\n\",\n      \"Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from lightning-utilities>=0.8.0->torchmetrics->poutyne==1.12.0) (68.2.2)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>17.1->torchmetrics->poutyne==1.12.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->poutyne==1.12.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->poutyne==1.12.0) (1.3.0)\\n\",\n      \"Installing collected packages: poutyne\\n\",\n      \"Successfully installed poutyne-1.12\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install poutyne==1.12.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"2024-02-16 02:04:56.133404: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\",\n      \"2024-02-16 02:04:56.133460: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\",\n      \"2024-02-16 02:04:56.135131: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import math\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import Subset, DataLoader\\n\",\n    \"from torchvision import transforms, utils\\n\",\n    \"from torchvision.datasets.mnist import MNIST\\n\",\n    \"\\n\",\n    \"from poutyne import set_seeds, Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"set_seeds(42)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cuda_device = 0\\n\",\n    \"device = torch.device(\\\"cuda:%d\\\" % cuda_device if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"train_split_percent = 0.8\\n\",\n    \"\\n\",\n    \"num_classes = 10\\n\",\n    \"\\n\",\n    \"batch_size = 32\\n\",\n    \"learning_rate = 0.1\\n\",\n    \"num_epochs = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./mnist/MNIST/raw/train-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:00<00:00, 257662891.88it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./mnist/MNIST/raw/train-images-idx3-ubyte.gz to ./mnist/MNIST/raw\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./mnist/MNIST/raw/train-labels-idx1-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28881/28881 [00:00<00:00, 98324426.81it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./mnist/MNIST/raw/train-labels-idx1-ubyte.gz to ./mnist/MNIST/raw\\n\",\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1648877/1648877 [00:00<00:00, 70367117.37it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to ./mnist/MNIST/raw\\n\",\n      \"\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4542/4542 [00:00<00:00, 9675230.46it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting ./mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist/MNIST/raw\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"full_train_dataset = MNIST('./mnist/', train=True, download=True, transform=transforms.ToTensor())\\n\",\n    \"test_dataset = MNIST('./mnist/', train=False, download=True, transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"num_data = len(full_train_dataset)\\n\",\n    \"indices = list(range(num_data))\\n\",\n    \"np.random.shuffle(indices)\\n\",\n    \"\\n\",\n    \"split = math.floor(train_split_percent * num_data)\\n\",\n    \"\\n\",\n    \"train_indices = indices[:split]\\n\",\n    \"train_dataset = Subset(full_train_dataset, train_indices)\\n\",\n    \"\\n\",\n    \"valid_indices = indices[split:]\\n\",\n    \"valid_dataset = Subset(full_train_dataset, valid_indices)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=2, shuffle=True)\\n\",\n    \"valid_loader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=2)\\n\",\n    \"test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=2)\\n\",\n    \"\\n\",\n    \"loaders = train_loader, valid_loader, test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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3ved27d9d88803ax45cqTmFStWZPr5swtnfgAAAAB4ApMfAAAAAJ4Q47O1JFEiNTVV4uLisv15R4wYoblnz55+9+nYsaNje+LEiWEdk4hIhw4dNNuuG27nnnuu5k2bNoVzSBFTpkwZx/YjjzyiuUePHprz5s0b1OPZUh/7q2K7aH3yySeOYyZNmhTUY8M/W8YWzIKp7nK6q666KsQjil62i5Qt1bTc71m2VMSWOsyZM0ezXfAuN3VUysjjjz+ueciQISF9bFvqdTqLNE6fPl2zLYexpXrRwi66u2XLFr/73HrrrY5tdyernOiXX37RfM455zju69atm2ZbQulFtmzN/p13/fXXa7799tsdx9SsWdPvY02ePFnzHXfcEaoh5mi2K9xDDz2kefv27Y797PcmGMWLF3dsL1++XLNdGNt+Htv3ouzsPJmSkvKfMj03zvwAAAAA8AQmPwAAAAA8gckPAAAAAE/gmp9MCOaan5kzZzq2W7RoEZaxFC1aVPOiRYs016lTx7Hfhx9+qPmee+7RfPjw4bCMKxLstQwPPvig475nn31Ws/3ZLFu2THOnTp0cx5x55pmav/jiC80lS5bU3KBBA822Xl/E2dK0cePGmrdt25bBvwKnYlti29Wl3WzNMdf/ZN6jjz6qediwYX73sat4e+UaN9ue134WiDjb6NvrD37++WfNtm22W5EiRTRffPHFWRpn2bJlNf/5559ZeqxIsK+tCRMmaLbvq9WqVXMck5aWFvZxnQ47zh9//FGzvS5DRKRevXp+94N/toW8iMhPP/2k2bbH3r17t+ZatWpp3rNnT/gGFwH2val3796a7TXPLVu2dBzz7bffZuk5u3btqvm1117zu4/9e3TdunVZer7M4JofAAAAAPgfJj8AAAAAPCHfqXfBSbaNdKCyt+xiT23aU4uHDh1y7GfbBuemUjfr5Zdf1mzbfos421DbcgpbHmfL3NzsaskbNmzQbEuDbDtJEZGzzz7b734259QyjZzMlrBlVAKXUUkcTs2WauJfR48e1WxXNxdxltrY95MdO3ZotiunuxUoUECzXZLAltDZ9/y6des6jrdtlHPr+7wtVY6W988bb7xRsy11c5cA2bItnNrff//t2D5+/Ljf/ezSF4UKFQrrmCLJXl7Rt29fzfb9w5aNhoJ9PwzEluBmZ9lbMEJ+5mfo0KFy4YUXSvHixaVMmTJy4403Ov5oFBE5cuSIdOvWTUqWLCnFihWT1q1by65du0I9FAAAAABQIZ/8zJ8/X7p16ybfffedzJ49W9LS0qRp06aOMxI9e/aU6dOny5QpU2T+/Pmyc+dOufnmm0M9FAAAAABQIS97mzVrlmN7woQJUqZMGVm5cqVcccUVkpKSIm+++aa89957cvXVV4uIyPjx46V69ery3XffZbnTTTj9/vvvmn/44QfNtgThwgsvdBxjT7vaziOnw5ZT3HbbbX73Wb9+vWPbfdYtt2jVqpVmW87mPrVqO9xVqVJFs13F3s2WkAT6/r3wwguav/rqK8d9q1at0mxX7p4yZYrmhQsXBnx+nNr8+fM1U+aGSLOrl5/OSubHjh3TvHbtWr/5nHPO0ewue7Olcra81pY9Rwt398yTbAmNe5/09PSwjulUYmJiNNvv/+DBg/3u//HHHzu2o6WML6do1KiRY7tUqVJ+97N/s7kvCchN7KUP+fPn9/v1UJe9BcOe1Pj666+z/fkzEvaGBykpKSIiEh8fLyIiK1eulLS0NGnSpInuU61aNalYsaIsWbLE72McPXpUUlNTHTcAAAAAyIywTn7S09OlR48ecumll2qP9eTkZClQoIDjAlGRf/q2Jycn+32coUOHSlxcnN4qVKgQzmEDAAAAyIXC2u2tW7dusnbt2iyX+PTt21d69eql26mpqRGZANnTpi+99JJmuxCb+/TrNddco/l0FgO05QxTp07VfN5552m2JRODBg3K9HNEI9vRxJZA2M5vIv801zhpxowZmu3rZ+PGjY5j7FnJYNjSFBGRJ554QrP9edhSu2gpe7NlMwMGDPC7zzPPPOPYtouM2nw6bEmbzYHG4hV2kWP3fyRZttQ22NKaYM6sFy9ePKjHQui88cYbmt2/c1bevHmzYzhhE2hxwiuuuELzdddd57jPvrdnB1vmJhLcwsC2VH7o0KHhGVgY2UVC3R1O7c/syy+/1Dx+/PiQPb8tYc/o/d92frMdQjPqthjt7MK49u+hihUrRmI4KlA1V04QtslP9+7dZcaMGbJgwQLHtSqJiYly7Ngx2b9/v+NDe9euXZKYmOj3sQoWLOhYwRYAAAAAMivkZW8+n0+6d+8uU6dOlblz50rlypUd99evX1/y588vc+bM0a9t2LBBtm7dKklJSaEeDgAAAACISBjO/HTr1k3ee+89+eyzz6R48eJ6HU9cXJwULlxY4uLipFOnTtKrVy+Jj4+X2NhYefDBByUpKSlHd3oDAAAAEN1CPvkZPXq0iPy3/ez48eOlQ4cOIvLP9TJ58uSR1q1by9GjR6VZs2byf//3f6EeSljZFoq2lt62GRQRGTNmjGZ7titQc4f27ds7tt966y3N7jrjk2wt+PTp0zMYde5RuHBhv19v27ZtwG17nY+tkX388ccdx+zcuTNTY3G3WR0+fLhmu8J348aNNffr1y9TzxEpga7ZsTXX7vrrQPXYgR4r1K2qbZ13bmJf8++9957m66+/PuAx9erV07xmzRrNBQoU0Oy+xuK3337z+1gnTpzQfNZZZ51yvIiM22+/XXO0vM9YY8eO1dyzZ0/NlSpV0nzJJZc4jsmOa35Kly6t+dVXX3XcF2jpCXud7sMPP6z56NGjIR5deNj3hokTJ2o+//zzAx5jq32CvebHXqfWuXNnzbZVsr3my/13lr3ueeTIkZq3bNkS1PPnVvbawH379jnus0tv2O9fKNllP3KakE9+fD7fKfcpVKiQjBo1SkaNGhXqpwcAAAAAv8K+zg8AAAAA5ARhbXWdmy1YsECzPQVuW3KLOFvSfv/995ptqZotTXG3WQ5U6mZbZQ4ZMiTIUecetjSnZs2amjMqobItrW2p2+zZs0M7OMOO89Zbb9XcvHlzzTNnzgzb82dVMG2rg207HcryNjuW+fPnB7wvN7ElnC1atAjqmN69e2ves2ePZlsCetNNNwU83pZ0jhgxQrNtNY/sYZdaWL9+veO+GjVqaLY/2wsvvFDz8uXLwzi60LElYePGjdM8ePBgzbaETERk+/btmrNaQm+Xl2jVqpVm+9mekJDgOMa2V/7www812xKuw4cPZ2lckWBLau3fKRnZtGmTZnd52knuVtn2s/Giiy465XO4S81tqZt9z/OKr7/+WvO8efM02xLwt99+23GMLeN0LxHiT7t27RzbGbXbjwac+QEAAADgCUx+AAAAAHhCjC+YDgU5TGpqqsTFxUV6GKpIkSKaX3/9dcd9rVu31mw7LAXLniofOHCg5pdeeklzuDp15GR2hfkvvvhCs3tdqUGDBmm25QjZtdpzsWLFNNtVwG0J0YEDB7JlLNnFlrfZkjj79YzK1gJ5+umnsziy3MO+z3Ts2NFxX6BS2WDZjwTbrSeYchRkj06dOjm2bYc0a+3atZqDLVvKSWx5mX2fd3cbs5+TtvObLYeyHcXcZaO2PKhOnTqaA31m79q1y7HdrVs3zZ988onfY6Kd7XBbsWLFbH/+xYsXa3Z3MbSlXl5n/+bYsWOH36+LOEsHbZ48ebJm+7eJLeEUcX7O2E6g/fv31zxs2DDN2TnVSElJ+U8XUzfO/AAAAADwBCY/AAAAADyBsrcwu/POOzXbhdBuuOEGzbYE6Oeff3YcbzvJ/fTTT2EYIYBo5l6U1C4GeTqee+45zU8++WSWHgvh4f78W7RokeZq1appjvayN8uWwLk7ZIbr32YXhrSlpnYha5HsK6OOJNv5zZYTimR90eOUlBTNdvFNW0I4d+5czV4s9T8ddevW1Txp0iTHfdWrVw/Z89hLCuzfvJFC2RsAAAAA/A+THwAAAACeQNkbAABRrFy5cponTJigeefOnZo7dOiQjSMKL3cXtieeeEKzXYzRlmNt2bJFs+1iKOIsPbclhLbUPBoXKQ0Xd7c32y3PLiBuSzCt6667zrFtFyD/9ddfQzFEuNjFj0WcHQrt74ldcNb68ssvHdvTpk3TbBcjdi9AGwmUvQEAAADA/zD5AQAAAOAJTH4AAAAAeALX/AAAAACIelzzAwAAAAD/w+QHAAAAgCcw+QEAAADgCUx+AAAAAHgCkx8AAAAAnsDkBwAAAIAnMPkBAAAA4AlMfgAAAAB4ApMfAAAAAJ7A5AcAAACAJzD5AQAAAOAJTH4AAAAAeAKTHwAAAACewOQHAAAAgCcw+QEAAADgCUx+AAAAAHgCkx8AAAAAnsDkBwAAAIAnMPkBAAAA4AlMfgAAAAB4ApMfAAAAAJ6QL9IDAAAAAMaPH6+5Q4cOmgsUKKA5LS0tO4eEXIgzPwAAAAA8gckPAAAAAE+g7A1AVHv44Ycd2wMHDtQ8b948zTfeeGN2DQkAEKQrr7xS8w033KA5PT09AqOBF3DmBwAAAIAnMPkBAAAA4AmUvZ1CyZIlNe/Zs+eU+/t8Psd2TExMwPv82bRpk2P7gw8+0Dx69GjNf/zxxykfC8itGjVqpPnJJ5903Fe0aFHN559/fraNCQBwasWLF3dsv/TSS5pLlCihedeuXZqD+fvJS/bu3av5999/19y4cWPN+/fvz8YRRRfO/AAAAADwBCY/AAAAADyByQ8AAAAAT+Can1OwdaYHDhzQbK8rCPb4YFSpUsWxba9naN26tWbbzvfDDz/M1HMA0SghIUHzlClTNMfHx0diOLlWUlKS5iVLlkRwJHjkkUc0t2rVSvO6desc+/Xp00dzSkpK+AcGZFKxYsU0v/HGG4776tSp4/eYIUOGaD5+/Hh4BhalZs+erfm2227TvHv3bs1r1qzRPHny5ICP9e6772resWNHqIaYo3HmBwAAAIAnMPkBAAAA4AmUvZ3Cvn37NJctW1bzvffe63f/m2++2bFdo0aNTD1f/vz5Hdu2JWS1atU09+/fX/NXX32l+a+//srU8wE5Wf369TVPmzZNc7Clbg888EDIxxStbDlbz549NV988cWO/SpUqHDKx7Jlhx999JHjvozKK7zGtlp/9dVXNQ8dOtSx3+eff+73+EKFCmlu2LCh5ssuu8yxn11SwT72li1bMjni6Gc/M215uv1sPHr0aFDH33LLLZr79eunuVKlSgGPb9mypeYvvvgiiBF7Q8WKFTXb76vb9OnTNY8dOzasY4pm3333nWZb9pYv379/1tv3n4yWfbB/T6anp2u2JXCDBg1yHGNL5aIRZ34AAAAAeAKTHwAAAACeEOOLwmVzU1NTJS4uLtLDCIsWLVo4tm2pTyCVK1fWvHXr1pCPKTt16tTJsW3LCP/880/NP//8s+bLL79cs/vUrO2QV716dc22TCTYX4EePXpotisqp6WlBXU8gmO7unXt2lXzgAEDNNtT84cPH3YcP2zYMM3PPvtsOIYYNWw5hO0KuW3bNs22fELkv2VsJ9myq169egV8TlsSZ7uV2efMzWxJlH2fKliwoGb3e87333+v+dZbb9X80EMPaX744YeDen57/McffxzUMbnJ+PHjNd99992a586dq/mPP/4IeLwttbWl5sGy3bbKlSuX6eNzE1ueaUurGjdu7NjPfp5ee+21mjdu3Bi+wUU5WxJryzvte3OJEiU033777Y7jM9sl1d0F7sorr9T866+/Zuqxwi0lJUViY2Mz3CfsZ36ee+45iYmJcfzheOTIEenWrZuULFlSihUrJq1bt5Zdu3aFeygAAAAAPCysk5/ly5fL2LFj/9PDvWfPnjJ9+nSZMmWKzJ8/X3bu3PmfRgEAAAAAEEphK3s7ePCgXHDBBfJ///d/MmjQIKlXr568/PLLkpKSIqVLl5b33ntPO378/PPPUr16dVmyZMl/Og/5k9vK3hITEzUPHjzYcV+HDh1OeXxuKns7ceKEYztcVZmnU/Zmj3niiSc0Dx8+3LFfFFaSRpTtriQisnDhQs01a9bUHOhn1rFjR8fx77zzTqiHmKO5u7PZ8jbb4c2y38usGjFihGPbll3YUjdbdpHbFk+17+EzZ87UXLdu3Uw/1t9//6352LFjmm0Ji5stm2vevLlmW4KVm9lSvw8++EDz6bwXn85ng+X1sre8efNqXrBggWb7t537+3r11Vf7PQahU7p0ace27Qpn2UsPunTporl8+fKO/ez7uS11zgkiWvbWrVs3adGihTRp0sTx9ZUrV0paWprj69WqVZOKFSsG/EA8evSopKamOm4AAAAAkBlhWefngw8+kFWrVsny5cv/c19ycrIUKFDgP/+LlZCQIMnJyX4fb+jQofLMM8+EY6gAAAAAPCLkk59t27bJww8/LLNnz3Z0o8iKvn37OkopUlNTg1qIL6cpXLiw5scff1yzPbVYpkyZTD+uLXvbuXOn477jx49n+vFwarY8cfXq1Y77vvzyy2weTfSxHd3sIr0izlI3y3Z1GzdunOZPP/00tIOLAvb9L9hS1xdffDEsY7Ed3USc3eIWL17sN4ey7C4nsF2tTqfULZCMSt2sGTNmaPZKqZv1zTffhOyxjhw5otmW89gOZXbBc7c+ffqEbCzR6KabbtJsS93s3yJPP/204xhK3cLPdsvNiP1steWk7rK3aBfysreVK1fK7t275YILLpB8+fJJvnz5ZP78+TJy5EjJly+fJCQkyLFjx2T//v2O43bt2uWom7YKFiwosbGxjhsAAAAAZEbIz/w0btxY1qxZ4/hax44dpVq1avL4449LhQoVJH/+/DJnzhxdg2XDhg2ydevWgBfnAgAAAEBWhXzyU7x4calVq5bja0WLFpWSJUvq1zt16iS9evWS+Ph4iY2NlQcffFCSkpKC6vQGAAAAAKcjLA0PTuWll16SPHnySOvWreXo0aPSrFkz+b//+79IDCVb2aYN7jr5rLArV7tXTv722281u9tI50TnnntuUPudbJMu4myb6V7R3LZ3fPLJJzXbaw7cxxQpUkTzK6+8EtR4kDn2PzoCXePjNnv2bM32GkAvWrRoUcD77HUKt912W3YMx8F27axYsaJme22SHdfkyZOzZ2AhdP755zu2X331Vc2n0yrZvrZHjhypefr06UEdb1uKe5G9nuGcc87RfP/99wd1/OjRozUfOnRIs/1Zzp8/P6jHevvtt4PaLzeJj4/XPGHCBL/72PeFoUOHhntIyAR7DW7nzp01165dW/OGDRscx8yaNSv8AwujbJn8uC9GLFSokIwaNUpGjRqVHU8PAAAAAOFb5wcAAAAAcpKIlL151RlnnBH255gzZ45j+4EHHtA8duzYsD9/Vv36669B7Tds2LBMP17Lli2DOuaFF1445T4rVqzQvHLlyqAe1+uuv/56zVOnTtWcUWnQs88+q9ndHtVrbKmYbXXtLnmKRKlbIHZsttT3ww8/1BwtZW8FChTQPGjQIMd9tlNpMKVu7hJk+3h79+7N9Nh+//33TB+TW23evFmzXVIiWHnz5tVsX7MZlWQHWxKXWz366KOa7ZIeaWlpmp977rlsHROCZy8jCLSm5g8//ODYPnDgQFjHFG6c+QEAAADgCUx+AAAAAHgCZW/ZyHYOq1GjhmZ7mv69994L6rGuuuoqzRl1vurdu7dmWxK3adOmoJ7HC/r16+fYtitUB2K7C+3ZsyfkY8ot7Npd77//vmZbGuQuE/ruu+80B1vemFvZ8rZA5ZiXXnppdg0nS2wXuhEjRmh2v3+9+OKL2TamzLCdw5o3b+64L5hSN7uPLRMScXblfPjhhzM9tvXr12f6GPhnP5uHDBmi2f78/vrrL8cx/fv3D//AcpBrrrnGsd2zZ0+/+9nOhVntDpYv379/rrpLoG23RK+XIAZiO7qJiIwbN07z1Vdf7feYzz77THOwnROjBWd+AAAAAHgCkx8AAAAAnkDZWzZau3at5qyWqnz//fead+3apdl2xxIROeusszR36tRJc9++fbP0/NHOluDcfPPNQR3z2muvaXZ/n+Gf7ZZkuwBlZNq0aZoPHz4c8jFFE7swqHX77bdrjpYFLgON03YaEslZZW92kctWrVoFtV+gEjjbFTKjxZOrVKmSmSEixNxl0P6sWrXKsW0Xxr7yyis1u9c4zC3cXfRsJ8T09HTNX3/9dcie0y5Ee8899zjuu+uuuzR36dJF85dffhmy549GttTNdtgUEbniiis02/evTz/9VLP9Pu/fvz/0A4wgzvwAAAAA8AQmPwAAAAA8IcYXTJuaHCY1NVXi4uIiPYwcaefOnY5te9pz+/btmm1pxfHjx8M/sBzAljPYbjEZ/QrYLkp16tQJy7hym/r162u2XaxsaURGZUK21G3fvn2aDx06pHngwIGaP/jggyyOOGexi5TaUgVbNlaxYsVsHVOoBSrnE8lZ/7aqVatq3rBhQ1DH2AVMbXmyLVW0HT7dbFel//u//wvqOc8880zN7s8AL7vssss0V65cWXO7du0c+5UqVUqzfZ8/nT+Pdu/erblcuXKZPj6nsh1m3Z3bbCe2xYsXa7788ssz/Tw1a9bU3LRpU83Dhw/XnCdP4P+3t51sGzVqpDk5OTnTY4l29rMk2M9JW3besWNHze4OhzlZSkqKxMbGZrgPZ34AAAAAeAKTHwAAAACewOQHAAAAgCfQ6joXsDXyBQsWDLifbbtp21HmZuXLl9fcoUOHoI754YcfNAfbBtvLKlSo4NieNGmS5kCvR3vNj5ttG2uzPcY+R3x8vOP4YK+TyKleeOEFv19/9NFHs3kk4WNfM7btfE7jbsMdjIMHD2q+6KKLMn18jRo1Mn1MtWrVNHv9mp8BAwZofuyxxzQXKlQoW57fLimRmzzxxBOa7TU+IiJpaWmar7vuukw/tn28yZMna7av62Cdc845mu37jBev+TkdN9xwg+avvvpK87p16xz7jRgxQvOaNWvCP7AQ48wPAAAAAE9g8gMAAADAEyh7ywUefPBBzSVKlAi4ny2H8ErZmy2bqlSpUlDH2NP7W7ZsCfmYchvbwlfEWXYQTKtY9z579+7VbEuIzjrrrEw9brRylxGe1KNHD822NCRaJCUl+f36Rx99lM0jCZ59LwiWbcN7OiVs7jLOYNiWvnPnzs308dFu+vTpmq+//nrNp/M5Z39+Gzdu1Dxu3DjNdgkEEZEvvvgi088TDWzZsv0sdXv99dc1HzhwIFOPKyIycuRIzYFK3ez7X+vWrR33nU5LbS9YuHCh5htvvDHgfvfdd59m+/23y1Y0aNDAcYz9GXz88ceabUty9+9JTsKZHwAAAACewOQHAAAAgCdQ9iYiefPm1ew+NWhP5+UktiOHLXvLSE4+BRkur7zyimbbLcyWNvTp08dxzJdffhn+gUW5Nm3aaB42bJjjvmBK0g4fPqzZlpOIiIwePVqzLWEJ1AWtatWqp3y+aLVkyRLNl1xySQRHknW2O5C1bdu2bB5J8DJaST6Q4sWLa167dm0oh4MAbHmOLXU7nfLY/fv3a27WrJnmzZs3n97goph9b61Vq1aWHst223vppZcc9917771+j5k5c6bmpUuXau7evXvA57F/5/z222+ZHmduYi91mDZtWsD97H2lSpXS/NZbb2l2//xtGXq7du00N2nSRPOZZ56ZuQFnI878AAAAAPAEJj8AAAAAPIGyN3EusPXBBx847rNlU5FeZNCWug0dOlRz/vz5Ax4zYcIEzXZhyNyqdOnSju2SJUtqtiUQtjQiJ3ebyqkSExOD2u/ZZ5/V/Oabb2o+ceKE5j/++MNxjC2psx1+Avn888+DGks0ysllA8Ho1auXZtvtzZbz2ZzT2Pd/d3lsTmJLgrzIduVMSEjI0mMNGjRIsxdL3SxbtrlixQrNV155pWO/li1barYlVLNnz9Z89tlna+7cuXNQz2+7JS5evFhzRotkv/baa5pt51AEZ8+ePZrt35xnnHGGY7/27dtrtp/zsbGxmu0iz8uWLQvpOLOKMz8AAAAAPIHJDwAAAABPoOzNxd3dx56etWU7P/30U1iev1WrVo7tyy67TLPt6hao1M09rn79+mn2ysKmmWUXaBNxdiKzApVguUvt7CJht9xyi+ZgOw+1aNEiqP2ym13kzpaAZlSCYDvnpaSkaLav86eeespxTKDubampqZq7du2q+euvv85o2Agz96Ks9vfElr3Z8jb3wrg51eDBgzXHxcVp7tSpk2O/AgUKZOpxbUcxEWeHw4svvljzVVddFdTjlS9fPlPPn9vY8hxbgmXfZzJ6X504caLmQF0JvW7Dhg2a3WVv9j3AdmhbuXKl5sKFC2f6OYNdmHzGjBma3ZcuIDTsguMiIvPmzdO8a9cuzbarX04rdbM48wMAAADAE5j8AAAAAPAEJj8AAAAAPIFrfsTZdnf16tWO++rVq6fZtlqcPHmy5hdffFGzu0b1nHPO8fucbdu21Vy5cmXN8fHxjv0yamN9kr3Op2nTpo777Aq/XvDnn386tseOHat53Lhxfo+54oorgnrsn3/+WXOw1+/Y62HsMXac9rqCnMzWzNt2su7vhd22LTBt2/G6desGdbxtg21/frm5rnvKlCmab731Vs32/eeRRx7RHM5W0bfddpvfr9tr2ewY3ew47ftktDh06JDmbt26af74448d+9n3hmAcO3bMsW3by5533nmaf/zxR80ZfRZEe0v0rLLvp8nJyZobN24c1PGzZs0K+ZhyG3udZ4kSJRz32Wv47GdegwYNQvb8x48f12z/fhJx/vzc16bg1Ow1i7ZVtW1nbdtWiwR+39+0aVOIRxcenPkBAAAA4AlMfgAAAAB4Qowv2PqdHCQ1NdXRdjSUbr75Zse2LUEJxJZGuEsTMtsCNVgTJkzQbNtZe63MLTNsqY79uQbbAty2QT+dY+zPbPjw4ZrD1TY91JKSkjTbVbRtaahIcCWBgcoBRUTGjBmj+fnnn9dsV3H3iq1bt2p2t5Q+adu2bY7t77777pSPm1GpWjDs789HH33kuM+W4bnHhsyZNGmS5jZt2gTcz7aUta2yvaJo0aKabQve+vXr+93/22+/dWzbkl77eQ7/3O/5tuzNfuZdfvnlmhs2bJjp51mxYoXm6667TvPevXsz/Vi5jf3b0raXtssLuMsTA7GfLTfddFOmx2LL0+2lF+vWrcv0Y4VCSkqKo3zPH878AAAAAPAEJj8AAAAAPIGyNxf3avV33nmn5rfffjssz5kRu/L0wIEDNdtymGBLsPCvKlWqaH7qqacc95UqVcrvMfa1sX79es22o1xG7M8sLS0tqGNyqnfeeUez/R0RCVz2tmrVKs1ffPGF5jfeeMOx3+7duzW7u2J5WaDOa9nFdrhE9ihevLjmHTt2aC5WrJhjP/sZcP3112v2ShezOXPmaL7yyis12/ci2wXswgsvdBy/cePG8A0OCIMHHnhAsy1DPx0ZlaGfZLvtiYi89957mm0Zv/3bKFIoewMAAACA/2HyAwAAAMATKHs7hSJFimh+/PHHNdvSAnfnk8x65plnNA8dOtRxnz3VGIU/KgBACNx3332aO3To4LjPLiZpO/HZkt7ff/89bGOLNNvlNNACzK+//rrmrl27Zs/AgDCxnQybN2+u+XS6vW3evFnz+PHj/e7jLnv+5ZdfgnrsSKDsDQAAAAD+h8kPAAAAAE+g7A0AgCg2bdo0zTVr1tS8YcMGzXaRyNwmmLI3Wxo0e/bs7BkYgGxH2RsAAAAA/A+THwAAAACewOQHAAAAgCfki/QAAADA6bvhhhsiPYSI6tSpk+bHHntM8/LlyzXn5lbfADKHMz8AAAAAPIHJDwAAAABPoNU1AAAAgKhHq2sAAAAA+B8mPwAAAAA8gckPAAAAAE8Iy+Rnx44dctddd0nJkiWlcOHCUrt2bVmxYoXe7/P5pH///lK2bFkpXLiwNGnSRDZu3BiOoQAAAACAiIRh8vPXX3/JpZdeKvnz55eZM2fK+vXrZcSIEXLGGWfoPsOHD5eRI0fKmDFjZOnSpVK0aFFp1qyZHDlyJNTDAQAAAAARCUO3tz59+siiRYvk22+/9Xu/z+eTcuXKySOPPCKPPvqoiPzTmSEhIUEmTJggd9xxxymfg25vAAAAAKyIdHubNm2aNGjQQG699VYpU6aMnH/++fL666/r/Zs3b5bk5GRp0qSJfi0uLk4aNmwoS5Ys8fuYR48eldTUVMcNAAAAADIj5JOf3377TUaPHi1Vq1aVL7/8Urp27SoPPfSQTJw4UUREkpOTRUQkISHBcVxCQoLe5zZ06FCJi4vTW4UKFUI9bAAAAAC5XMgnP+np6XLBBRfIkCFD5Pzzz5fOnTvLfffdJ2PGjDntx+zbt6+kpKTobdu2bSEcMQAAAAAvCPnkp2zZslKjRg3H16pXry5bt24VEZHExEQREdm1a5djn127dul9bgULFpTY2FjHDQAAAAAyI+STn0svvVQ2bNjg+Novv/wilSpVEhGRypUrS2JiosyZM0fvT01NlaVLl0pSUlKohwMAAAAA//CF2LJly3z58uXzDR482Ldx40bfpEmTfEWKFPG9++67us9zzz3nK1GihO+zzz7z/fjjj75WrVr5Kleu7Dt8+HBQz5GSkuITEW7cuHHjxo0bN27cuHHziYgvJSXllPOIkE9+fD6fb/r06b5atWr5ChYs6KtWrZpv3LhxjvvT09N9/fr18yUkJPgKFizoa9y4sW/Dhg1BPz6TH27cuHHjxo0bN27cuNlbMJOfkK/zkx1Y5wcAAACAFZF1fgAAAAAgJ2LyAwAAAMATmPwAAAAA8AQmPwAAAAA8gckPAAAAAE9g8gMAAADAE5j8AAAAAPAEJj8AAAAAPIHJDwAAAABPYPIDAAAAwBOY/AAAAADwhHyRHoCXFCxYUPOSJUs0f/7555r79euXrWPyqssuu0xz+/btHffdc889mj/99FO/+x08eDB8gwMAAIhiJUqU0FysWDHHfdu3b8/m0Thx5gcAAACAJzD5AQAAAOAJlL1lo3z5/v12V65cWXN8fHwkhuM59erV0/zBBx9oLlu2rGO/9PR0zTfccIPmkiVLaqbsLThFihTR/N5772lu1aqVZp/P5zjm559/1nzeeedp/vXXXzUPGTJE8yeffOI4PjU1NQsjRiBFixbV3KFDB81JSUmO/WzZ6LFjx8I+rtzs0Ucf1WxLordt2+bY75prrtH8xx9/hH9g8Jw8ef79v/IpU6Y47rvxxhs1//bbb5qvuOIKzeF8XZ577rma27Vrp/n111/XvGXLlrA9P/6VmJioecaMGZqrVKni2K9Ro0aaf/zxx/APzIUzPwAAAAA8gckPAAAAAE+g7C0b2bK3uLg4ze6yEYSH7daWkJAQwZF4R+3atTW3bNlS84kTJzS/++67AY/fsGGDZluC+Oabb2q2v0siIq+88srpDdZDSpcurdmWH4iI1KhRQ/NVV12l+ZxzztFcvnz5gI9tS7WSk5OzNE4vatCggeaBAwdqtt1Cq1ev7jjmzDPP1OzFsrfmzZtrtqU2Vs+ePTVPnDjRcV9KSkp4BpaL2LIlW7Ys4ixdtiX99nM2lK/LatWqOba/+OILzRUrVtR8yy23aL788ss1//nnnyEbC5yOHDmiuVKlSpqLFy/u2K9JkyaaKXsDAAAAgDBh8gMAAADAEyh7y0a2C9WyZcs0X3TRRZrPPvtsxzG2cwoy76yzztJ82223Zemxxo0bp3no0KGa3aU9tluZF9mSHLuArz0dbl/z69atC/hYtsNQrVq1NM+aNUvzCy+84DjGdsJyd4Lzsr59+2ru1auXZtvF8HQsX77csf3XX39l6fG8znZus6Vu1tatWx3be/bsCeuYcjpbxjR//nzNttvYSy+9pLlHjx6O49PS0vw+7rfffqv5448/1tylSxfHfvY9zz62HdeKFSsCDT8qtGjRIqj91qxZo9n9Og2VZ555xrFtS90s+1509OjRsIwlp6lbt67mW2+9NeB+48eP12w7qWaV/cy1i5yuX7/esV+ky9M58wMAAADAE5j8AAAAAPAEyt6yke2IEqjzSYECBbJrOLlSqVKlHNu2VKFMmTJZeuyrr77ab968ebNjP9vVzHYr8wr7vTnjjDM09+7dW3NGpW6WXXDWdoR5+umnNY8ePdpxjD3t78WyN/s7MHv2bM32+2JLM9955x3H8fZ3ZunSpZqHDRum+eGHH9b85JNPOo73SnlJuNhue5YtIbrpppsc97nfg7zGlpTZ919b6ma7GrrLpPLnz+/3cW2HQ7uwb0Zst7ndu3dr7tixo2ZbtpuT2ffvBx98MKhjVq5cqXnfvn0hG4stAbXlVBmx719eWfzadjW86667Au43d+5czVkte7Od3OznT0xMjOadO3c6jrEdXyOBMz8AAAAAPIHJDwAAAABPYPIDAAAAwBO45idC7PULN954o2a7oroIbZODYeuy3StP16tX75TH23bKp6Nq1aqO7QULFmhu1qyZ5tWrV2fpeXKq2NhYx7atDbftrUNZ5z558mTNjzzyiOO+Cy+8MGTPEw2KFSvm2J45c6Zme53bxRdfrNm2HT148GBQz1OoUCHN+/fv1/z1118HPVacWtu2bTXb60SLFi2qmXbigR06dEhz586d/e5z9913O7arVKmi+amnngrZWEqXLq3ZXn8ULdf8tGvXTnOlSpWCOmbChAlhGcujjz6quXHjxhEdS05jl4FwXw94kv1cEMl663X7eWDbZttrFu3nv30t5QSc+QEAAADgCUx+AAAAAHgCZW85gC1tQHBsO8Vp06Zpdpeg2VbJ9hSsbbNo2zS6jzkd8fHxmm15xdq1azUfP348S8+Rk7hXkbY/g5SUFM3BtrcOhi27crfprF+/fsieJxq4X7/232/bu+7atUtzsKVutlTu3nvv1exub43MyZs3r2P7iSee0Gzbw9rPBj4nQsfd3t2ybfRtCa8tWwvW77//rvndd9/N9PGRdvbZZwe1ny3DDGVJZsmSJTV369YtZI+bG5QrV07zN998o9mWx6alpWl+5plnHMdntfV3nz59NNtLN6yuXbtqtm3fcwLO/AAAAADwBCY/AAAAADyBsrcI+emnn/x+/ZZbbnFsu1evxz+GDx+u+bzzztOcUcmaXe3Zrpbevn37EI/uXw899JDmkSNHat6yZUvYnjO7uUsNrbfeeisbR/IP24XGlm389ttv2T6W7GBLG0ScZYC2i9WkSZM029XmN27c6Di+cOHCmt9++23NmzZt0vziiy9mYcTo0qWLY9uWWlm2hOj222/XnJveP3KC5s2ba3755Zc1lyhRwu/+tqOciLPD58KFCzXb358//vgji6PMHkWKFNFsX3MZsSXNoSxvLlCggGbbuTJYTZo00Ww/p+x7mYjI999/fxqjy16XX365Y/uVV17RHOh1aju8ZbW7my0BFXGWvVnLli3THOjv3JyAMz8AAAAAPIHJDwAAAABPoOwtQrZv3+7360lJSY5tu4BkVrtzRDt7ajchIcHvPrajm4izi5JdGNN2W3N3QQnEltfZcq6yZcsGdXxudc899wS879VXX83GkfzDvgZya6mbtWfPHse27b737bffar7kkks0f/zxx5ovu+wyx/ENGjTQbBcQvvPOOzVntSOiF9kukN27dw+4n+3qNmbMGM22oxMyz35+2JIhEedi1LaTqO28t3nzZs227FpEZNy4caEaZsTZToT2e5Fd8ufPr9mWIJ6O5557zu/XbRdSEZEKFSpo/vvvv7P0nKFkF8kdPHiw4746der4PcaWXfbv3z9Lz3/RRRdpdnd1zZfP//Th6NGjfp/fllO72c9pd0lwuHDmBwAAAIAnMPkBAAAA4AmUveUwtlOViEjt2rU1L1q0KLuHk6PYbmlNmzb1u49dyFEkuLKrvXv3BvX8tsPSV199pTmc3eJyqjx5Av+/if0+HThwIDuGA2P16tWaH3nkEc3PP/+85lq1aml2v6/YEpApU6Zo/uSTT0I5TM+xnbNsCa3bsWPHNAcq20HmTZ06VbO7c1YgtoubXcjRXTbldQ0bNtS8Y8cOv/uMHTtW81133eW4zy7MaT9bbNlXKMXFxTm2bXljTjJixAjNtmzZLTk5WfOQIUM0N27c2G92q1u3ruZGjRppth32Mipbs4L93bJlpJ07dw7qmFDizA8AAAAAT2DyAwAAAMATmPwAAAAA8ASu+YkQWsVmnq0ltXXBNnfq1ClbxmLbO9uVo0Uyrs3NLW6++WbNJUuWdNy3fPlyzfv27QvL8xcsWFCzbQcvQktgy7bgta9Tey1QzZo1HcfY67RsbX5aWlo4hpir2Zr5rl27BnXMtddeq/ngwYMhH5NXXXnllZrdn79vvvmm5khcf5CT2GvOVq5cqbl+/foBj7HtqQMtQxFs22V7/Y1t+x5K9vphkZz13lavXj3Nbdu2DeoYu9zGzJkzs/T89u+pjP5OPXHihOatW7dq/uWXXzQPGjRI8+LFi7M0rlDjzA8AAAAAT2DyAwAAAMATKHuLEHsK8PDhw5qDbSfoFR06dNBs217a07GzZs3S/MMPP2TLuCxbMiEicvHFF2f7GLzmqquu0pyUlOS4z55qx7/69u2r+YwzztBsSzhFnCUsLVu21Pzpp59qpmw3OLYk1LYXzwhlm+Gxdu1azdWrV3fc16BBA83NmzfXnNUSomh09OhRzbY9sv2ctUtwiDj/bsmbN2+Wnj+Ysiv318ePH6/52Wef1bxt27YsjSUS7HtuVsv+fv/9d82//fab475SpUpprlOnjmb7vbXPv3PnTsfxtnX54MGDszTOSODMDwAAAABPYPIDAAAAwBMoe8sBMip7s6cmvaBEiRKO7bvvvluz7fBl2Y5W+/fvD8ewMnTDDTdk+3NGWkYd7RYtWhT25580aVLA+/7666+wP380KlKkiObrrrsu4H6FChXS/NFHH2m2Zad79+4N8ehypwEDBmjOqITlmWeeyY7heNrzzz+v2ZbsiDjLfqZPn675q6++0mx/fu7OZbYrWm5iuw1edtllAfezn4EVK1bM9PMMHz5cs/2cD/Q7Y3+WIiJPPPFEpp8zp7Ld9jIyceJEzdu3b9c8ZcoUzX/++afmXbt2OY63ZdD29R/IiBEjHNuvvPJKUOPMqTjzAwAAAMATmPwAAAAA8ATK3nKAb7/9VnOrVq0c99lSEy9wd0q74oorTnnMZ599Fq7hBGRPu7t/Zl7ohOXu9mPZrkrhYjsC7dixw3GfLQfAv1577TXNRYsW1dykSRPHfnZhU9tt8ZxzztFM2Vtg559/vmbbuclyL6q4atWqsI4JIm+//bZmdzlVlSpVND/11FOamzVr5vcY93uO1xdGnTZtWqb2d7/n2PfzYOTm3xf7t8Wrr74acD97uUQwXeFuuukmx/a99957ymPeffddze+9994p948mIT/zc+LECenXr59UrlxZChcuLFWqVJFnn33W8cPx+XzSv39/KVu2rBQuXFiaNGkiGzduDPVQAAAAAECFfPIzbNgwGT16tLz22mvy008/ybBhw2T48OGOGezw4cNl5MiRMmbMGFm6dKkULVpUmjVrJkeOHAn1cAAAAABARMJQ9rZ48WJp1aqVtGjRQkREzjrrLHn//fdl2bJlIvLPWZ+XX35ZnnrqKS0XevvttyUhIUE+/fRTueOOO0I9pBwvJSUl4H32VOUbb7yRHcOJqN69ewe1n3th0exmFy+EU7FixcLyuB07dtQcGxuruX379o796Pb2L9vF55ZbbtFsO1rNnTvXccyWLVs027I3uzDk0qVLQznMXMW+h9nOedbQoUMd2zNmzAjrmOD0zjvvBLzv6aef1mwrVmw5c6NGjcIyLq+wncZERPLlO/WfosnJyZq/++67kI8pp7Cvs7///jtLj1W+fHnN7dq1c9xXqVIlv8fYRW7t35y2c1xuEPIzP5dcconMmTNHfvnlFxER+eGHH2ThwoW6avLmzZslOTnZUfMZFxcnDRs2lCVLlvh9zKNHj0pqaqrjBgAAAACZEfIzP3369JHU1FSpVq2a5M2bV06cOCGDBw+Wtm3bisi/s/eEhATHcQkJCY6ZvTV06FDWQQAAAACQJSE/8zN58mSZNGmSvPfee7Jq1SqZOHGivPDCC1nqwNS3b19JSUnR27Zt20I4YgAAAABeEPIzP4899pj06dNHr92pXbu2bNmyRYYOHSrt27eXxMREEflntdmyZcvqcbt27ZJ69er5fcyCBQs6Vv3NbU5eDyXy37rMM844I7uHE1ExMTGObdsC054ZzKhmO5Tsa9Ku9m2v+XG36bSrYtufp72WIjc7eZZXJOvtMe1q4bYF6O7duzUHKpeFSJcuXTTnzZtX84svvhjwmD179vj9Ote5BadUqVKn3CfQ9xiRYVu/2+UV7PUX9vqfdevWZc/AcpGmTZtqTkpKCuqYY8eOabbXyW3fvj10A8vFPv/8c821atUK6pixY8dqXrhwYcjHlFOE/MzP33///Z8/BvPmzatvIpUrV5bExESZM2eO3p+amipLly4N+hcCAAAAADIr5Gd+WrZsKYMHD5aKFStKzZo15fvvv5cXX3xR7rnnHhH553/2e/ToIYMGDZKqVatK5cqVpV+/flKuXDm58cYbQz0cAAAAABCRMEx+Xn31VenXr5888MADsnv3bilXrpx06dJF+vfvr/v07t1bDh06JJ07d5b9+/fLZZddJrNmzQrYFhTe4V6p2JYd/PTTT5oXLVoUsud0t2auVq2a5g8//FCzLUG043J79NFHNX/22WehGGKOs3LlSs1XX3112J7n2muv1Wy//3YV9tzWgjOU6tevr3nevHmabatq9/vuQw89pNmWodrfBWSeLeHZtWtXBEeCk91nT7LtyS+//HK/x8ycOVNz586dwzOwXMy2XS5QoEBQx+zbt0/zqFGjQj6m3MJ+b+3SD5UrVw54zIkTJzTb1uFvv/12iEeXM4V88lO8eHF5+eWX5eWXXw64T0xMjAwcOFAGDhwY6qcHAAAAAL9Cfs0PAAAAAOREIT/zg9CypzNtxzu7Cq9XnHXWWZptF7bVq1dn+rHs9WXuDnstW7bM9ONZGzZsyNLx0eDVV1/VfP/99zvus13BChcurPnw4cNBPbY9bf9///d/fvcJ9HWIVKlSRfP555+veeTIkX73r1mzpmPbrqlmV/imVCtrbHnmRx99FMGReNMrr7yi+a677nLcFxcX5/eYBQsW+D0mJSUlxKPL/Tp27BjpIeRa9u+hQBVV7r8ZbdMx+9pOTU0N7eByKM78AAAAAPAEJj8AAAAAPIGytxyuQoUKmr1e9lapUiXNt956q+YjR45o/vnnnx3H2FI52znsueee01y8eHHHMRl1cvNn0KBBjm27aG1utWPHDs3Lly933Ge7v7311lua7733Xs12LbBJkyY5jm/SpInmv/76S3O/fv0024Vk4dSrVy/N+fPn97tPq1atNNvfBRHn79B9990X4tF5l11k1t1hktdz1tjSZVv2abtC2veczZs3O463DZqeffbZMIzQm/r06aP5wgsvzPTxP/74YyiHk2vZv4GmT5+uuVmzZpqHDx/uOMaWN3sRZ34AAAAAeAKTHwAAAACeQNlbDvD9999rdnfEst2y8C+7KJ0tgVu4cKFjv7p162quU6dOyJ7/zTff1DxgwICQPW40evrppx3b5cqV03zbbbf5zcEaPHiw5jFjxmR+cB5UunRpzXaR0i5dumju1q2b5vXr1zuOP52fE04tMTFRs+1oKMICjoHYUqlSpUppdq8jWLFiRc221NMumv35559r7tu3r+P4NWvWZHms+C+7yGagEly3+fPna77llltCPqbcyHZus9n+XtgScnDmBwAAAIBHMPkBAAAA4AlMfgAAAAB4QozPFsVGidTU1IArMgOIrJo1a2ru2bOn5kArfH/zzTeO7SFDhmieO3eu5ih8q4qIhg0barbXwNkW1h9//LFmd6v248ePh3F0uV+bNm00P/HEE5pr1Kih+aGHHnIc4/VrfkqUKKH5lVde0Wxb9dprfvbv3+84fsGCBZrHjh3r9zm+/PLLLI4SmVW7dm3N9r38jDPOCHiMXerA/dkABCMlJUViY2Mz3IczPwAAAAA8gckPAAAAAE+g7A0AAESMXZLAlqfZP09s2/sff/zRcbwtewPgbZS9AQAAAMD/MPkBAAAA4AmUvQEAAACIepS9AQAAAMD/MPkBAAAA4AlMfgAAAAB4ApMfAAAAAJ7A5AcAAACAJzD5AQAAAOAJTH4AAAAAeAKTHwAAAACewOQHAAAAgCcw+QEAAADgCUx+AAAAAHgCkx8AAAAAnsDkBwAAAIAnMPkBAAAA4AlMfgAAAAB4ApMfAAAAAJ7A5AcAAACAJzD5AQAAAOAJTH4AAAAAeAKTHwAAAACewOQHAAAAgCfki/QAAAAAQq1kyZKaH3vsMc233HKLY7+zzz5b87vvvqt5/PjxmufNmxeOIQKIAM78AAAAAPAEJj8AAAAAPIHJDwAAAABPiPH5fL5IDyKzUlNTJS4uLtLDyJKLL75Y8+233665R48ejv3sj6dx48aaqT/OHuXKldPct29fzaVKldI8ZMgQxzFr1qwJ/8AA5BpNmzbVPHPmTM0PPPCA5rFjx2brmHKDQN/XjMTExGhOS0vT3KdPH81vvPGG45gDBw6c7hAhIhdccIHmyZMna/7mm280L1u2TPPSpUsdx//www/hGxyiTkpKisTGxma4D2d+AAAAAHgCkx8AAAAAnkDZWzbKnz+/5jfffFNz27Ztgzq+SZMmmil7C58vv/xS85VXXqk5Xz7/neFPnDjh2E5PT/e739GjRzVfc801mu3p/NygRIkSmv/66y/NI0eO1Pziiy8GPH7Lli1hGReC07BhQ801atTQfPPNN2s+cuSI5sWLFzuOf+mll8I4utzpww8/1Ny6dWu/+wR6/0FgxYsX1/zoo49qLlSokGO/zZs3ay5cuLDmZs2aabbv2b169XIc/8orr2R9sB5Ss2ZNx/b06dM1n3XWWZo//vhjzfZz1f6MRERuuOGGEI8QoWY//0VE7r//fs3XX3+95q+++irLz0XZGwAAAAD8D5MfAAAAAJ5A2Vs2Gjp0qObevXtn+vhXX31V85QpUzQvWrQoawPziCpVqmju2bOn5rvvvtuxnz2lnjdv3rCM5dixY5oHDBjguG/48OFhec7scumll2pesGCB5uTkZM179uzRbEseRESeeuqpMI7Ou2ypT3x8vOaJEyc69rNdJY8fP67ZvmatOXPmOLZbtWql2ZZqderUSbMtbZw1a9Ypx57bUfaWM9myue+//16zu2zuiiuu0Pzbb7+Ff2BRKDExUfPq1asd95UpU8bvMW3atNF81VVXaZ46dapjP1uqjpzDljCuXLnScZ/9PClbtmxIn5eyNwAAAAD4HyY/AAAAADyB8+hhVrJkSc333HNPlh7rwQcf1GxLI5YsWaK5c+fOjmP279+fpeeMdva0+TPPPKPZlsC5T6E/99xzmu2p2S5dumi2ZSrr1q0LaiwFCxbU/OSTT2ouXbp0UMdHixUrVmi25Q316tXTbEsg3GwZoLuTHk7NLsBrX8vXXXed5oy+/7bDki3BXL58eVDPb1/njz/+uOann35asy17O++88xzHByqv87rRo0dr7tq1awRH4h128VJbHu0uNb/11ls1Dxs2LPwDi0L2My9QmZubfS+x3cGQdc2bN9eckJCgecKECVl6XHvZgC11c1+qMnDgwCw9T1Zl+szPggULpGXLllKuXDmJiYmRTz/91HG/z+eT/v37S9myZaVw4cLSpEkT2bhxo2Offfv2Sdu2bSU2NlZKlCghnTp1koMHD2bpHwIAAAAAGcn05OfQoUNSt25dGTVqlN/7hw8fLiNHjpQxY8bI0qVLpWjRotKsWTPHuhBt27aVdevWyezZs2XGjBmyYMGC/5yxAAAAAIBQylK3t5iYGJk6darceOONIvLPWZ9y5crJI488oguKpaSkSEJCgkyYMEHuuOMO+emnn6RGjRqyfPlyadCggYj80+3nuuuuk+3bt0u5cuVO+bzR1O3t+eef1+xeGO0ke/bMdlcSEfn55581FytWTHOPHj38PtbXX3/t2L799ts159YSuHPPPVdzt27dHPfZU+W2W9KIESM0u7uLUXYTOnfeeafmd955J6hjbrnlFs3ukkT8o0CBApqbNm3quG/cuHGabXlbamqqZvv6f++99xzH225VwXw82IVQRUSeffZZzS1atNC8Y8cOze3bt9fsXiQ10CLBuYm7c5t9ndvyROuiiy7S7O6chPCzf3O4S53tZ0bdunU127I5rzt06JBm9yKlll302y4sy/cya5KSkhzbtkunvXTCdvs8Hfbv2ZYtW2revXu3Y7/q1atrDvXfptne7W3z5s2SnJwsTZo00a/FxcVJw4YN9Zu7ZMkSKVGihE58RESaNGkiefLkkaVLl/p93KNHj0pqaqrjBgAAAACZEdLJz8l1POzFUye3T96XnJz8n4vd8uXLJ/Hx8Y51QKyhQ4dKXFyc3ipUqBDKYQMAAADwgKhodd23b19JSUnR27Zt2yI9JAAAAABRJqStrk/Wl+/atcuxYuuuXbu0zW1iYuJ/av+OHz8u+/btC9h+tWDBgo6WhzmZu/69Q4cOfvezrV5ff/11zUePHnXsN2/ePM22TjwtLU3zY489ptmWHIqIlChRQnNuuubHXuczc+ZMzXZFYTdbS//NN99o5hqf8JkyZYpm+1revn17wGNefvllzXZV9d9//z2kY4s2efPm1Wyv62nXrl3AY2x7/S+++EKz+z04sx566CHNvXv3dtxna/vvuusuzR999FGWnjM3ueCCCxzbtu1soOusuM4nsv7++2/N9vNbxHk9RZEiRTR7/ToVe81HRn/D2Wuo7Oe0179/oXTyOvyT7HWja9euzdJj288Z9zWoJ7nb80f679GQnvmpXLmyJCYmOi6kSk1NlaVLl+qbQ1JSkuzfv9/xRj537lxJT0+Xhg0bhnI4AAAAAKAyfebn4MGDsmnTJt3evHmzrF69WuLj46VixYrSo0cPGTRokFStWlUqV64s/fr1k3LlymlHuOrVq8u1114r9913n4wZM0bS0tKke/fucscddwTV6Q0AAAAATkemJz8rVqyQq666SrdPtm9u3769TJgwQXr37i2HDh2Szp07y/79++Wyyy6TWbNmSaFChfSYSZMmSffu3aVx48aSJ08ead26tYwcOTIE/5zIs6f/RETi4+P97jdo0CDNs2bNCuqxbRtsW0LStm1bzbl5Ank6pW5WrVq1NNvV6t2lhgid8847T/OQIUOCOuaMM87QbNu7e519b7Glbvv27XPsV7t2bc22vO3EiROZfk7b3te27betqt0r2tv9KFvxzy5BgOhg34uqVq0awZFED7t+Y548/xYa2RJCEWfpvvv9DKfPLmNw8gTESRs2bND88MMPZ/qxX3nlFc3du3f3u8+iRYs02xbYOUGmJz9XXnllhms/xMTEyMCBA2XgwIEB94mPj//P2hIAAAAAEE5R0e0NAAAAALIqpN3evKpnz56aH3zwwYD7/fXXX5p37tyZpedcsWKFZlvakpvL3mwJVfny5TN9/PDhwzV36dJF86uvvurYb/Xq1Zq//fbbTD8P/mXLG+wK9T///LPmatWqOY7ZunWr5qx2oclNbCMZ2+7fdnQUcZaXnE6pmy07/OyzzzTbn9Mdd9yheerUqZl+Dq+79957g9rPdgJFZNluZaVKlXLcZ0u1vN49tGTJkpqvvfZav/scPHjQsZ2SkqK5Zs2amm0XOATnnHPO0WzLa90VW4MHD87U41555ZWObVt6HagarE2bNpl6juzEmR8AAAAAnsDkBwAAAIAnUPYWAkWLFtVsFyJ1sws2BtvhLRB7OrlixYpZeqxoMX36dM12kUW7KGawi+Gmp6f7PV7EeUrelvQEWrAWgf3222+a77vvPs0ZdX75+uuvwzmkqGW/ly+++KLml156ybHfL7/8onnUqFGan3nmGc22c6S77NOWKuzatUuzXbzOloYi82JjYx3b9v3Iymxpilflz59fs11o/NJLL9Vsy0PdvzOHDx8+5XPYn4W7zMcuOmzL272oTp06mu3CzFaZMmUc21999ZVmW7ZrP/P79++v2b7HeZX9u/Omm27SHKiTpP3+iYi8//77p3yOSpUqaX7zzTcd9xUvXvyUx9tSbTtGEZGffvrplMeHE2d+AAAAAHgCkx8AAAAAnsDkBwAAAIAncM1PNurYsWPIHqtWrVqa4+PjQ/a40cLWWK9atUrzhRde6NjPtseePHmyZttO2d3C0a52bOtU7QrfDzzwgGbbahyBXXbZZUHtd9ttt2n+4YcfNI8fPz7kY4pWEydO1OxuB/v4449rfvTRRzU3b95cs23He8EFFziOt3X2d955p2Z3e1qcPvc1PhktHI5/xMXFabbXfIo4r3OoXr265piYGM32e3z33Xc7jv/8888122t77HUNzZo1Czg2+xnkda1btz7lPu72+GeddZbmNWvWaK5Xr57mL7/8UvNVV13lOP7333/P3CBzAXvdZ6dOnU65/6FDhxzbV1xxhd/97N+W9vfMXv8TrKpVq2p2t/d/5JFHMv14ocSZHwAAAACewOQHAAAAgCdQ9naaEhISNNvVzt1secqePXuy9Jz2FH5GLbW9ZsWKFX5zsN59913Hti37sSus29P5ttTQ3Vryzz//zPQYvKBhw4ZB7Wd/t2wLW/xr//79mt2twefOnavZlnCOGDHC72O5S67S0tI029c5ZW/Ibk2aNNFsP0sTExMDHmNbwi9evFizfZ3bdtgiIj179tTcvn17zQcOHNBctmxZzbbsWkTko48+CjgerwlU3pyamqrZvi+JiGzfvt3vMbbU0X62u9vzt2zZMtPjjDbnnHOOYzujvzv9CfT+n5FAZaMZWbt2reYhQ4ZotqWlOQFnfgAAAAB4ApMfAAAAAJ5A7dRpKlKkiGbbXcbNngI8cuRIlp7TnuYMtPL3ypUrHduUqmReSkqK5vvuu0/zmWeeqblRo0aa3V38hg8fHsbRRa9PPvlE8+WXXx7UMT///HO4hpNr2U5ittuhLVtYtGiR5qNHjzqOb9WqleZLL71Us+1w+Nlnn2k+ceJEFkcMy65eb8uuvMJ+ntpSN1t2Zr9HIiLdunXTbFeVD6Rw4cKObbva/Q033KC5ZMmSmpOTkzV37dr1lM/hJTVr1tQc6O8h25UyUJmbm/0sHjNmjOaBAwc69hs1apRm+1rITc4//3zHtu0++/fff2ueMWOG5oULF2o+++yzHce3bdtWc+nSpf0+Z548/54fcXeo/OKLLzT36NFD86+//ur3sXIazvwAAAAA8AQmPwAAAAA8gbK3KOJeJOqk48ePa3Z3Lstqhzmvs6fdAy1m2qVLF8c2ZW+IJLswYLt27TTbBetsmYjbxRdfrLlPnz6abUer9evXa77nnnscxy9btixzA/YIWypry0lEnCUlP/74o2b7/uMVLVq00Gy7utlFHQcMGOA4xr2Aoz+21M3dKSvQAqa2VNSWGZ177rmO/dxleF5ju3IG6tC5YcOGLD2HXZTTXbaYlJSUpceORva1ad9z27RpE9Txtvtev379/O5j35dsR0QRkWnTpmmOxkVmOfMDAAAAwBOY/AAAAADwBMrecoGNGzdqHjlyZARH4k3uTilXXXWV5nnz5mX3cHIs2yHspZdeCrifPR3PgrGnZjtPioi89tprmu2CpbNnzw7q8b777jvNN910k2ZbdmI797kXHLzmmms025+l111wwQWa3Z2Tgl1A0Mt++OEHzRmVudmSqGrVqmm2pT22o6GIyOHDhzU/8sgjmletWqX5ww8/1NyrVy/H8ffff3+GY4fI0qVLM33MRRddpPmSSy4J5XCiji01FnF2vNuyZcspj+/QoYNjO5iueLaLm+22J+L8bIlGnPkBAAAA4AlMfgAAAAB4ApMfAAAAAJ7ANT+nydY72usS3Nd/lClTRnO+fP9+u217asvdAtXWEjdo0MDvMU8++WQQI0a4FC1a1LF91llnRWYgOdwff/wR1H72d8u2bf7pp59CPaRcwd1219bGDxo0SPPptOO116LMnz9f83333afZtsAWEWndurXm8ePHZ/o5AX/s6/qMM85w3HfddddpLlu2rObatWtrtq/llStXOo637drXrl3r9/nt+5d9/Ys4lzf47bff/P8DcjF7zZTN9vor+/fL2LFjAz5W8eLFNQ8dOlRzyZIlAx7z2GOPBT/YKLVu3boMt/25+eabNT/33HOO++Lj4/0es3z5cs3u6zlzE878AAAAAPAEJj8AAAAAPIGyt9O0fft2zba17DPPPOPYz56OffnllzUnJyf7fdy77rrLsR3Macdg2hxGi/Llyzu2d+7cqTlc7WDdpYoFChTQ3K5dO822BMvas2ePY3vhwoWhG5wH2fKG66+/XvP7778fieHkeH379g143+jRo8PynEePHg14H2Wf/tlW1xmx7ZW9aNGiRZpjYmI02xLwjD4L7GfG22+/rdmWZ86YMSPT41q8eLHmunXrOu679NJLNXux7G3Dhg2a16xZo9m2qq5Ro0ZQjzVs2DDNdtkIa8GCBY5tW5KLf02ZMkVzRr8ztjy5e/fuYR1TTsGZHwAAAACewOQHAAAAgCdQ9paN3nrrLc0dO3bUfPvtt2sePHhwwOOPHTum2a7u6175N5rZckIRkbvvvlvzpEmTNJ9OCVzBggU19+7dW3PXrl0d+yUkJJzysU6cOKHZ3dFq48aNmR4b/KtZs6bmQoUKaT5y5EgkhpMj1a9f37Ftu/X89ddfYXnOF154IeB9uakMN5QaNWqk2d3VMz093W8eMWKEZltq9cADDziOP51OfjnVsmXLNNsOb5b7NW9LzWx5WkpKSsjGZX+X3J8/559/vuZ33nknZM8ZjSZPnqzZlr1Vq1ZNs7u8/dprr9VsP48Dfc7/+OOPju1A3XO9wnb8HDdunGZbNuq2detWzbYTXEYlzbkJZ34AAAAAeAKTHwAAAACeQNlbNmrWrJlmW8J1xRVXaM6bN2/A420JhF38Kzdxn3KdOHGi5osvvlizXQjz+eef11ylShXH8XaRr7PPPluz7SIWLLt4m+3Cl1G3LfzLlvPYjkzlypULeIxdJDij3w0v++yzzxzbtmwoq+WBdjHJF198UXPVqlU1uxeMdC966mVJSUma7ffS/i6IOMt73IsRnmQ7ieamMjc3W1L83Xff+d0n0NfDqUWLFgHvC7aTmRdMmDBBs12A3b7+3aXh+fPn1xyo1G3u3Lmae/bsmdVh5ir2M/Syyy7TbL+X7u+rLdf/9ddfwzi6nIkzPwAAAAA8gckPAAAAAE+g7C0EbOejWrVqOe679dZb/R4TaPEut23btmm2XVRyK/f3xZ42d3dlO+mhhx4K6Rjs6eHff/9ds+1Is2nTppA+pxfYzjPFixcP6ph58+ZpPnToUMjHlBu433OsadOmabZlmxl58MEHNdsF72ypm+225C4HSk1NDep5vODMM8/UXLhw4aCOsR0vbXnXs88+G7qBIdPmzJmjuU6dOo77bEmW1+3bt09zy5YtNdv3ctu5MyP275+HH35Ysy2N9KpSpUppHj58+Cn3d5cnjxo1KuRjiiac+QEAAADgCUx+AAAAAHgCkx8AAAAAnhDjC9RXMAdLTU2VuLi4SA/Dr/j4eMe2bdV83XXXnfL4l19+2bFtr/NZunRp1gYXhewK3/Z6INteOtha+kBef/11x/aSJUs0258fssauav/ee+9pDnRdnIjIBx98oLlt27bhGViUu/LKKx3b9vqDPXv2aH7//ff9Hm9XBxdxtuS3XnvtNc32+pM///wz6LF6TZEiRTTb17L7Oim7KvtTTz2lee/evWEcHZB97Pu8+zM3NjZW86JFizR36dJF8/r168M4uugzYMAAzf369fO7j73O07bAFhH54YcfwjOwHCAlJcXxmvKHMz8AAAAAPIHJDwAAAABPoOwNQLarVKmS5t69ezvuq1mzpuY+ffpojsSq7tHAro4uItKqVSvNTZs21dy8eXPN5cuX1+xebf3jjz/W/Omnn2petmxZlscKAMi6ihUran7ppZc02/f/1q1ba/7ss8+yZ2A5AGVvAAAAAPA/TH4AAAAAeAJlbwAAAACiHmVvAAAAAPA/TH4AAAAAeAKTHwAAAACewOQHAAAAgCcw+QEAAADgCUx+AAAAAHhCpic/CxYskJYtW0q5cuUkJibGsQJ4WlqaPP7441K7dm0pWrSolCtXTtq1ayc7d+50PMa+ffukbdu2EhsbKyVKlJBOnTrJwYMHs/yPAQAAAIBAMj35OXTokNStW1dGjRr1n/v+/vtvWbVqlfTr109WrVoln3zyiWzYsEFuuOEGx35t27aVdevWyezZs2XGjBmyYMEC6dy58+n/KwAAAADgVHxZICK+qVOnZrjPsmXLfCLi27Jli8/n8/nWr1/vExHf8uXLdZ+ZM2f6YmJifDt27AjqeVNSUnwiwo0bN27cuHHjxo0bN24+EfGlpKScch4R9mt+UlJSJCYmRkqUKCEiIkuWLJESJUpIgwYNdJ8mTZpInjx5ZOnSpX4f4+jRo5Kamuq4AQAAAEBmhHXyc+TIEXn88celTZs2EhsbKyIiycnJUqZMGcd++fLlk/j4eElOTvb7OEOHDpW4uDi9VahQIZzDBgAAAJALhW3yk5aWJrfddpv4fD4ZPXp0lh6rb9++kpKSordt27aFaJQAAAAAvCJfOB705MRny5YtMnfuXD3rIyKSmJgou3fvdux//Phx2bdvnyQmJvp9vIIFC0rBggXDMVQAAAAAHhHyMz8nJz4bN26Ur7/+WkqWLOm4PykpSfbv3y8rV67Ur82dO1fS09OlYcOGoR4OAAAAAIjIaZz5OXjwoGzatEm3N2/eLKtXr5b4+HgpW7as3HLLLbJq1SqZMWOGnDhxQq/jiY+PlwIFCkj16tXl2muvlfvuu0/GjBkjaWlp0r17d7njjjukXLlyQY3hn0ZzAAAAAPCPoOYIQfWWNubNm+e3tVz79u19mzdvDth6bt68efoYe/fu9bVp08ZXrFgxX2xsrK9jx46+AwcOBD2Gbdu2RbyVHjdu3Lhx48aNGzdu3HLObdu2baecR8T4ovA0Snp6uuzcuVN8Pp9UrFhRtm3b5riuCMgOqampUqFCBV5/iAhef4gkXn+IJF5/cPP5fHLgwAEpV66c5MmT8VU9YWl4EG558uSRM888U9f7iY2N5cWPiOH1h0ji9YdI4vWHSOL1BysuLi6o/cK+yCkAAAAA5ARMfgAAAAB4QlRPfgoWLCgDBgxgDSBEBK8/RBKvP0QSrz9EEq8/ZEVUNjwAAAAAgMyK6jM/AAAAABAsJj8AAAAAPIHJDwAAAABPYPIDAAAAwBOidvIzatQoOeuss6RQoULSsGFDWbZsWaSHhFzo6aeflpiYGMetWrVqev+RI0ekW7duUrJkSSlWrJi0bt1adu3aFcERI5otWLBAWrZsKeXKlZOYmBj59NNPHff7fD7p37+/lC1bVgoXLixNmjSRjRs3OvbZt2+ftG3bVmJjY6VEiRLSqVMnOXjwYDb+KxCtTvX669Chw3/eD6+99lrHPrz+cLqGDh0qF154oRQvXlzKlCkjN954o2zYsMGxTzCfuVu3bpUWLVpIkSJFpEyZMvLYY4/J8ePHs/OfghwuKic/H374ofTq1UsGDBggq1atkrp160qzZs1k9+7dkR4acqGaNWvKH3/8obeFCxfqfT179pTp06fLlClTZP78+bJz5065+eabIzhaRLNDhw5J3bp1ZdSoUX7vHz58uIwcOVLGjBkjS5culaJFi0qzZs3kyJEjuk/btm1l3bp1Mnv2bJkxY4YsWLBAOnfunF3/BESxU73+RESuvfZax/vh+++/77if1x9O1/z586Vbt27y3XffyezZsyUtLU2aNm0qhw4d0n1O9Zl74sQJadGihRw7dkwWL14sEydOlAkTJkj//v0j8U9CTuWLQhdddJGvW7duun3ixAlfuXLlfEOHDo3gqJAbDRgwwFe3bl2/9+3fv9+XP39+35QpU/RrP/30k09EfEuWLMmmESK3EhHf1KlTdTs9Pd2XmJjoe/755/Vr+/fv9xUsWND3/vvv+3w+n2/9+vU+EfEtX75c95k5c6YvJibGt2PHjmwbO6Kf+/Xn8/l87du397Vq1SrgMbz+EEq7d+/2iYhv/vz5Pp8vuM/cL774wpcnTx5fcnKy7jN69GhfbGys7+jRo9n7D0COFXVnfo4dOyYrV66UJk2a6Nfy5MkjTZo0kSVLlkRwZMitNm7cKOXKlZOzzz5b2rZtK1u3bhURkZUrV0paWprjtVitWjWpWLEir0WE3ObNmyU5OdnxeouLi5OGDRvq623JkiVSokQJadCgge7TpEkTyZMnjyxdujTbx4zc55tvvpEyZcrIeeedJ127dpW9e/fqfbz+EEopKSkiIhIfHy8iwX3mLlmyRGrXri0JCQm6T7NmzSQ1NVXWrVuXjaNHThZ1k589e/bIiRMnHC9sEZGEhARJTk6O0KiQWzVs2FAmTJggs2bNktGjR8vmzZvl8ssvlwMHDkhycrIUKFBASpQo4TiG1yLC4eRrKqP3vuTkZClTpozj/nz58kl8fDyvSWTZtddeK2+//bbMmTNHhg0bJvPnz5fmzZvLiRMnRITXH0InPT1devToIZdeeqnUqlVLRCSoz9zk5GS/75En7wNERPJFegBATta8eXPNderUkYYNG0qlSpVk8uTJUrhw4QiODACy1x133KG5du3aUqdOHalSpYp888030rhx4wiODLlNt27dZO3atY5rbIFQibozP6VKlZK8efP+p7vHrl27JDExMUKjgleUKFFCzj33XNm0aZMkJibKsWPHZP/+/Y59eC0iHE6+pjJ670tMTPxP45fjx4/Lvn37eE0i5M4++2wpVaqUbNq0SUR4/SE0unfvLjNmzJB58+bJmWeeqV8P5jM3MTHR73vkyfsAkSic/BQoUEDq168vc+bM0a+lp6fLnDlzJCkpKYIjgxccPHhQfv31VylbtqzUr19f8ufP73gtbtiwQbZu3cprESFXuXJlSUxMdLzeUlNTZenSpfp6S0pKkv3798vKlSt1n7lz50p6ero0bNgw28eM3G379u2yd+9eKVu2rIjw+kPW+Hw+6d69u0ydOlXmzp0rlStXdtwfzGduUlKSrFmzxjEJnz17tsTGxkqNGjWy5x+CnC/SHRdOxwcffOArWLCgb8KECb7169f7Onfu7CtRooSjuwcQCo888ojvm2++8W3evNm3aNEiX5MmTXylSpXy7d692+fz+Xz333+/r2LFir65c+f6VqxY4UtKSvIlJSVFeNSIVgcOHPB9//33vu+//94nIr4XX3zR9/333/u2bNni8/l8vueee85XokQJ32effeb78ccffa1atfJVrlzZd/jwYX2Ma6+91nf++ef7li5d6lu4cKGvatWqvjZt2kTqn4QoktHr78CBA75HH33Ut2TJEt/mzZt9X3/9te+CCy7wVa1a1XfkyBF9DF5/OF1du3b1xcXF+b755hvfH3/8obe///5b9znVZ+7x48d9tWrV8jVt2tS3evVq36xZs3ylS5f29e3bNxL/JORQUTn58fl8vldffdVXsWJFX4ECBXwXXXSR77vvvov0kJAL3X777b6yZcv6ChQo4Ctfvrzv9ttv923atEnvP3z4sO+BBx7wnXHGGb4iRYr4brrpJt8ff/wRwREjms2bN88nIv+5tW/f3ufz/dPuul+/fr6EhARfwYIFfY0bN/Zt2LDB8Rh79+71tWnTxlesWDFfbGysr2PHjr4DBw5E4F+DaJPR6+/vv//2NW3a1Fe6dGlf/vz5fZUqVfLdd999//lPR15/OF3+Xnsi4hs/frzuE8xn7u+//+5r3ry5r3Dhwr5SpUr5HnnkEV9aWlo2/2uQk8X4fD5fdp9tAgAAAIDsFnXX/AAAAADA6WDyAwAAAMATmPwAAAAA8AQmPwAAAAA8gckPAAAAAE9g8gMAAADAE5j8AAAAAPAEJj8AAAAAPIHJDwAAAABPYPIDAAAAwBOY/AAAAADwBCY/AAAAADzh/wHLhoQ6QyjhygAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 1000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"inputs = next(iter(train_loader))[0]\\n\",\n    \"input_grid = utils.make_grid(inputs)\\n\",\n    \"\\n\",\n    \"fig = plt.figure(figsize=(10, 10))\\n\",\n    \"inp = input_grid.numpy().transpose((1, 2, 0))\\n\",\n    \"plt.imshow(inp)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_convolutional_network():\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function returns the convolutional network layed out above.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    return nn.Sequential(\\n\",\n    \"        nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, padding=1),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.MaxPool2d(2),\\n\",\n    \"        nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.MaxPool2d(2),\\n\",\n    \"        nn.Dropout(0.25),\\n\",\n    \"        nn.Flatten(),\\n\",\n    \"        nn.Linear(32*7*7, 128),\\n\",\n    \"        nn.ReLU(),\\n\",\n    \"        nn.Dropout(0.5),\\n\",\n    \"        nn.Linear(128, num_classes)\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def poutyne_train(pytorch_network):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function creates a Poutyne Model (see https://poutyne.org/model.html), sends the\\n\",\n    \"    Model on the specified device, and uses the `fit_generator` method to train the\\n\",\n    \"    neural network. At the end, the `evaluate_generator` is used on  the test set.\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        pytorch_network (torch.nn.Module): The neural network to train.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    print(pytorch_network)\\n\",\n    \"\\n\",\n    \"    optimizer = optim.SGD(pytorch_network.parameters(), lr=learning_rate)\\n\",\n    \"    loss_function = nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"    # Poutyne Model\\n\",\n    \"    model = Model(pytorch_network, optimizer, loss_function, batch_metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"    # Send model on GPU\\n\",\n    \"    model.to(device)\\n\",\n    \"\\n\",\n    \"    # Train\\n\",\n    \"    model.fit_generator(train_loader, valid_loader, epochs=num_epochs)\\n\",\n    \"\\n\",\n    \"    # Test\\n\",\n    \"    test_loss, test_acc = model.evaluate_generator(test_loader)\\n\",\n    \"    print('Test:\\\\n\\\\tLoss: {}\\\\n\\\\tAccuracy: {}'.format(test_loss, test_acc))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential(\\n\",\n      \"  (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (1): ReLU()\\n\",\n      \"  (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\\n\",\n      \"  (4): ReLU()\\n\",\n      \"  (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  (6): Dropout(p=0.25, inplace=False)\\n\",\n      \"  (7): Flatten(start_dim=1, end_dim=-1)\\n\",\n      \"  (8): Linear(in_features=1568, out_features=128, bias=True)\\n\",\n      \"  (9): ReLU()\\n\",\n      \"  (10): Dropout(p=0.5, inplace=False)\\n\",\n      \"  (11): Linear(in_features=128, out_features=10, bias=True)\\n\",\n      \")\\n\",\n      \"\\u001b[35mEpoch: \\u001b[36m1/1 \\u001b[35mTrain steps: \\u001b[36m1500 \\u001b[35mVal steps: \\u001b[36m375 \\u001b[32m19.20s \\u001b[35mloss:\\u001b[94m 0.396201\\u001b[35m acc:\\u001b[94m 87.187500\\u001b[35m val_loss:\\u001b[94m 0.087136\\u001b[35m val_acc:\\u001b[94m 97.375000\\u001b[0m\\n\",\n      \"\\u001b[35mTest steps: \\u001b[36m313 \\u001b[32m2.45s \\u001b[35mtest_loss:\\u001b[94m 0.074455\\u001b[35m test_acc:\\u001b[94m 97.590000\\u001b[0m                                                   \\n\",\n      \"Test:\\n\",\n      \"\\tLoss: 0.07445458313710987\\n\",\n      \"\\tAccuracy: 97.59\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"conv_net = create_convolutional_network()\\n\",\n    \"poutyne_train(conv_net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter15/pytorch_lightning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mRequirement already satisfied: torchvision==0.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (1.26.4)\\n\",\n      \"Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (2.31.0)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torchvision==0.17) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.3)\\n\",\n      \"Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2.0->torchvision==0.17) (2024.2.0)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->torchvision==0.17) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mRequirement already satisfied: matplotlib==3.5.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (4.49.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (1.26.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (23.2)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (3.1.1)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mRequirement already satisfied: scikit-image==0.19.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (1.26.4)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (1.12.0)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (9.3.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (2.34.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (2024.2.12)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-image==0.19.3) (23.2)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0mRequirement already satisfied: pytorch-lightning==1.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (1.7.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (1.26.4)\\n\",\n      \"Requirement already satisfied: torch>=1.9.* in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: tqdm>=4.57.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (4.66.2)\\n\",\n      \"Requirement already satisfied: PyYAML>=5.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (6.0.1)\\n\",\n      \"Requirement already satisfied: fsspec!=2021.06.0,>=2021.05.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (2024.2.0)\\n\",\n      \"Requirement already satisfied: tensorboard>=2.9.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (2.15.2)\\n\",\n      \"Requirement already satisfied: torchmetrics>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (0.11.4)\\n\",\n      \"Requirement already satisfied: pyDeprecate>=0.3.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (0.3.2)\\n\",\n      \"Requirement already satisfied: packaging>=17.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (23.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pytorch-lightning==1.7.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (3.9.3)\\n\",\n      \"Requirement already satisfied: absl-py>=0.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: grpcio>=1.48.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (1.60.1)\\n\",\n      \"Requirement already satisfied: google-auth<3,>=1.6.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2.28.0)\\n\",\n      \"Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (1.2.0)\\n\",\n      \"Requirement already satisfied: markdown>=2.6.8 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.5.2)\\n\",\n      \"Requirement already satisfied: protobuf!=4.24.0,>=3.19.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (4.25.3)\\n\",\n      \"Requirement already satisfied: requests<3,>=2.21.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2.31.0)\\n\",\n      \"Requirement already satisfied: setuptools>=41.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (68.2.2)\\n\",\n      \"Requirement already satisfied: six>1.9 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (1.16.0)\\n\",\n      \"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (0.7.2)\\n\",\n      \"Requirement already satisfied: werkzeug>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.0.1)\\n\",\n      \"Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch>=1.9.*->pytorch-lightning==1.7.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch>=1.9.*->pytorch-lightning==1.7.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch>=1.9.*->pytorch-lightning==1.7.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch>=1.9.*->pytorch-lightning==1.7.0) (3.1.3)\\n\",\n      \"Requirement already satisfied: aiosignal>=1.1.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (1.3.1)\\n\",\n      \"Requirement already satisfied: attrs>=17.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (23.2.0)\\n\",\n      \"Requirement already satisfied: frozenlist>=1.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (1.4.1)\\n\",\n      \"Requirement already satisfied: multidict<7.0,>=4.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (6.0.5)\\n\",\n      \"Requirement already satisfied: yarl<2.0,>=1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (1.9.4)\\n\",\n      \"Requirement already satisfied: async-timeout<5.0,>=4.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning==1.7.0) (4.0.3)\\n\",\n      \"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (5.3.2)\\n\",\n      \"Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (0.3.0)\\n\",\n      \"Requirement already satisfied: rsa<5,>=3.1.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (4.9)\\n\",\n      \"Requirement already satisfied: requests-oauthlib>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from google-auth-oauthlib<2,>=0.5->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (1.3.1)\\n\",\n      \"Requirement already satisfied: importlib-metadata>=4.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (7.0.1)\\n\",\n      \"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.3.2)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.6)\\n\",\n      \"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2.2.1)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2024.2.2)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from werkzeug>=1.0.1->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (2.1.5)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch>=1.9.*->pytorch-lightning==1.7.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: zipp>=0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.17.0)\\n\",\n      \"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (0.5.1)\\n\",\n      \"Requirement already satisfied: oauthlib>=3.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard>=2.9.1->pytorch-lightning==1.7.0) (3.2.2)\\n\",\n      \"\\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\\u001b[0m\\u001b[33m\\n\",\n      \"\\u001b[0m\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\\n\",\n    \"!pip install pytorch-lightning==1.7.0\\n\",\n    \"# !pip install torchmetrics==0.11.4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.nn import functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision.datasets import MNIST\\n\",\n    \"from torchvision import transforms\\n\",\n    \"import pytorch_lightning as pl\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(pl.LightningModule):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_num):\\n\",\n    \"        train_x, train_y = batch\\n\",\n    \"        y_pred = self(train_x)\\n\",\n    \"        training_loss = F.cross_entropy(y_pred, train_y)\\n\",\n    \"        # optional\\n\",\n    \"        self.log('train_loss', training_loss, on_epoch=True, prog_bar=True)\\n\",\n    \"        return training_loss\\n\",\n    \"\\n\",\n    \"    def validation_epoch_end(self, outputs):\\n\",\n    \"        # optional\\n\",\n    \"        avg_loss = torch.stack(outputs).mean()\\n\",\n    \"        return avg_loss\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_num):\\n\",\n    \"        # optional\\n\",\n    \"        test_x, test_y = batch\\n\",\n    \"        y_pred = self(test_x)\\n\",\n    \"        test_loss = F.cross_entropy(y_pred, test_y)\\n\",\n    \"        # optional\\n\",\n    \"        self.log('test_loss', test_loss, on_step=True, on_epoch=True, prog_bar=True)\\n\",\n    \"        return test_loss\\n\",\n    \"\\n\",\n    \"    def test_epoch_end(self, outputs):\\n\",\n    \"        # optional\\n\",\n    \"        avg_loss = torch.stack(outputs).mean()\\n\",\n    \"        return avg_loss\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        return torch.optim.Adadelta(self.parameters(), lr=0.5)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(MNIST(os.getcwd(), train=True, download=True, \\n\",\n    \"                                transform=transforms.Compose([transforms.ToTensor(),\\n\",\n    \"                                                              transforms.Normalize((0.1302,), (0.3069,))])), \\n\",\n    \"                                batch_size=32, num_workers=4)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        # optional\\n\",\n    \"        return DataLoader(MNIST(os.getcwd(), train=True, download=True, \\n\",\n    \"                                transform=transforms.Compose([transforms.ToTensor(),\\n\",\n    \"                                                              transforms.Normalize((0.1302,), (0.3069,))])), \\n\",\n    \"                                batch_size=32, num_workers=4)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        # optional\\n\",\n    \"        return DataLoader(MNIST(os.getcwd(), train=False, download=True, \\n\",\n    \"                                transform=transforms.Compose([transforms.ToTensor(),\\n\",\n    \"                                                              transforms.Normalize((0.1302,), (0.3069,))])), \\n\",\n    \"                                batch_size=32, num_workers=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (mps), used: False\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1788: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/pytorch_lightning/trainer/configuration_validator.py:115: UserWarning: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\\n\",\n      \"  rank_zero_warn(\\\"You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\\\")\\n\",\n      \"\\n\",\n      \"  | Name | Type      | Params\\n\",\n      \"-----------------------------------\\n\",\n      \"0 | cn1  | Conv2d    | 160   \\n\",\n      \"1 | cn2  | Conv2d    | 4.6 K \\n\",\n      \"2 | dp1  | Dropout2d | 0     \\n\",\n      \"3 | dp2  | Dropout2d | 0     \\n\",\n      \"4 | fc1  | Linear    | 294 K \\n\",\n      \"5 | fc2  | Linear    | 650   \\n\",\n      \"-----------------------------------\\n\",\n      \"300 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"300 K     Total params\\n\",\n      \"1.202     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b1eeee117e424c98b7c44541c9fe52c0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\",\n      \"`Trainer.fit` stopped: `max_epochs=10` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = ConvNet()\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(max_epochs=10)    \\n\",\n    \"trainer.fit(model)   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1385: UserWarning: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.test(ckpt_path='best')` to use the best model or `.test(ckpt_path='last')` to use the last model. If you pass a value, this warning will be silenced.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"Restoring states from the checkpoint path at /Users/Ashish.Jha/code/personal/MasteringPyTorchV2/Chapter15/lightning_logs/version_14/checkpoints/epoch=9-step=18750.ckpt\\n\",\n      \"Loaded model weights from checkpoint at /Users/Ashish.Jha/code/personal/MasteringPyTorchV2/Chapter15/lightning_logs/version_14/checkpoints/epoch=9-step=18750.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a0b7cfe45d614ce3926f0c20f338ffaa\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"       Test metric             DataLoader 0\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n      \"     test_loss_epoch        0.03324906900525093\\n\",\n      \"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_loss_epoch': 0.03324906900525093}]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"      <iframe id=\\\"tensorboard-frame-d41c6264dc1a7fcc\\\" width=\\\"100%\\\" height=\\\"800\\\" frameborder=\\\"0\\\">\\n\",\n       \"      </iframe>\\n\",\n       \"      <script>\\n\",\n       \"        (function() {\\n\",\n       \"          const frame = document.getElementById(\\\"tensorboard-frame-d41c6264dc1a7fcc\\\");\\n\",\n       \"          const url = new URL(\\\"/\\\", window.location);\\n\",\n       \"          const port = 6006;\\n\",\n       \"          if (port) {\\n\",\n       \"            url.port = port;\\n\",\n       \"          }\\n\",\n       \"          frame.src = url;\\n\",\n       \"        })();\\n\",\n       \"      </script>\\n\",\n       \"    \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Start tensorboard.\\n\",\n    \"%reload_ext tensorboard\\n\",\n    \"%tensorboard --logdir lightning_logs/\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.18\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter15/pytorch_profiler.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.23.4)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 10 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset),\\n\",\n    \"        100. * success / len(test_dataloader.dataset)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\\n\",\n    \"    batch_size=32, shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,)) \\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=500, shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(0)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = optim.Adadelta(model.parameters(), lr=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.310609\\n\",\n      \"epoch: 1 [320/60000 (1%)]\\t training loss: 1.924133\\n\",\n      \"epoch: 1 [640/60000 (1%)]\\t training loss: 1.313337\\n\",\n      \"epoch: 1 [960/60000 (2%)]\\t training loss: 0.796470\\n\",\n      \"epoch: 1 [1280/60000 (2%)]\\t training loss: 0.819801\\n\",\n      \"epoch: 1 [1600/60000 (3%)]\\t training loss: 0.678459\\n\",\n      \"epoch: 1 [1920/60000 (3%)]\\t training loss: 0.477066\\n\",\n      \"epoch: 1 [2240/60000 (4%)]\\t training loss: 0.527125\\n\",\n      \"epoch: 1 [2560/60000 (4%)]\\t training loss: 0.469366\\n\",\n      \"epoch: 1 [2880/60000 (5%)]\\t training loss: 0.239070\\n\",\n      \"epoch: 1 [3200/60000 (5%)]\\t training loss: 0.523908\\n\",\n      \"epoch: 1 [3520/60000 (6%)]\\t training loss: 0.267831\\n\",\n      \"epoch: 1 [3840/60000 (6%)]\\t training loss: 0.464627\\n\",\n      \"epoch: 1 [4160/60000 (7%)]\\t training loss: 0.413977\\n\",\n      \"epoch: 1 [4480/60000 (7%)]\\t training loss: 0.324858\\n\",\n      \"epoch: 1 [4800/60000 (8%)]\\t training loss: 0.495965\\n\",\n      \"epoch: 1 [5120/60000 (9%)]\\t training loss: 0.153463\\n\",\n      \"epoch: 1 [5440/60000 (9%)]\\t training loss: 0.377620\\n\",\n      \"epoch: 1 [5760/60000 (10%)]\\t training loss: 0.097269\\n\",\n      \"epoch: 1 [6080/60000 (10%)]\\t training loss: 0.181864\\n\",\n      \"epoch: 1 [6400/60000 (11%)]\\t training loss: 0.295800\\n\",\n      \"epoch: 1 [6720/60000 (11%)]\\t training loss: 0.082916\\n\",\n      \"epoch: 1 [7040/60000 (12%)]\\t training loss: 0.353389\\n\",\n      \"epoch: 1 [7360/60000 (12%)]\\t training loss: 0.040657\\n\",\n      \"epoch: 1 [7680/60000 (13%)]\\t training loss: 0.365705\\n\",\n      \"epoch: 1 [8000/60000 (13%)]\\t training loss: 0.231380\\n\",\n      \"epoch: 1 [8320/60000 (14%)]\\t training loss: 0.321043\\n\",\n      \"epoch: 1 [8640/60000 (14%)]\\t training loss: 0.211378\\n\",\n      \"epoch: 1 [8960/60000 (15%)]\\t training loss: 0.054053\\n\",\n      \"epoch: 1 [9280/60000 (15%)]\\t training loss: 0.337620\\n\",\n      \"epoch: 1 [9600/60000 (16%)]\\t training loss: 0.216428\\n\",\n      \"epoch: 1 [9920/60000 (17%)]\\t training loss: 0.174468\\n\",\n      \"epoch: 1 [10240/60000 (17%)]\\t training loss: 0.213010\\n\",\n      \"epoch: 1 [10560/60000 (18%)]\\t training loss: 0.357055\\n\",\n      \"epoch: 1 [10880/60000 (18%)]\\t training loss: 0.070653\\n\",\n      \"epoch: 1 [11200/60000 (19%)]\\t training loss: 0.055425\\n\",\n      \"epoch: 1 [11520/60000 (19%)]\\t training loss: 0.230391\\n\",\n      \"epoch: 1 [11840/60000 (20%)]\\t training loss: 0.048202\\n\",\n      \"epoch: 1 [12160/60000 (20%)]\\t training loss: 0.403469\\n\",\n      \"epoch: 1 [12480/60000 (21%)]\\t training loss: 0.025848\\n\",\n      \"epoch: 1 [12800/60000 (21%)]\\t training loss: 0.104852\\n\",\n      \"epoch: 1 [13120/60000 (22%)]\\t training loss: 0.101376\\n\",\n      \"epoch: 1 [13440/60000 (22%)]\\t training loss: 0.178021\\n\",\n      \"epoch: 1 [13760/60000 (23%)]\\t training loss: 0.070927\\n\",\n      \"epoch: 1 [14080/60000 (23%)]\\t training loss: 0.048668\\n\",\n      \"epoch: 1 [14400/60000 (24%)]\\t training loss: 0.090581\\n\",\n      \"epoch: 1 [14720/60000 (25%)]\\t training loss: 0.165846\\n\",\n      \"epoch: 1 [15040/60000 (25%)]\\t training loss: 0.324705\\n\",\n      \"epoch: 1 [15360/60000 (26%)]\\t training loss: 0.077008\\n\",\n      \"epoch: 1 [15680/60000 (26%)]\\t training loss: 0.128724\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 0.084200\\n\",\n      \"epoch: 1 [16320/60000 (27%)]\\t training loss: 0.158558\\n\",\n      \"epoch: 1 [16640/60000 (28%)]\\t training loss: 0.040566\\n\",\n      \"epoch: 1 [16960/60000 (28%)]\\t training loss: 0.287925\\n\",\n      \"epoch: 1 [17280/60000 (29%)]\\t training loss: 0.057604\\n\",\n      \"epoch: 1 [17600/60000 (29%)]\\t training loss: 0.031712\\n\",\n      \"epoch: 1 [17920/60000 (30%)]\\t training loss: 0.046777\\n\",\n      \"epoch: 1 [18240/60000 (30%)]\\t training loss: 0.025740\\n\",\n      \"epoch: 1 [18560/60000 (31%)]\\t training loss: 0.065141\\n\",\n      \"epoch: 1 [18880/60000 (31%)]\\t training loss: 0.220227\\n\",\n      \"epoch: 1 [19200/60000 (32%)]\\t training loss: 0.102033\\n\",\n      \"epoch: 1 [19520/60000 (33%)]\\t training loss: 0.068922\\n\",\n      \"epoch: 1 [19840/60000 (33%)]\\t training loss: 0.075009\\n\",\n      \"epoch: 1 [20160/60000 (34%)]\\t training loss: 0.111961\\n\",\n      \"epoch: 1 [20480/60000 (34%)]\\t training loss: 0.056665\\n\",\n      \"epoch: 1 [20800/60000 (35%)]\\t training loss: 0.234118\\n\",\n      \"epoch: 1 [21120/60000 (35%)]\\t training loss: 0.303591\\n\",\n      \"epoch: 1 [21440/60000 (36%)]\\t training loss: 0.217894\\n\",\n      \"epoch: 1 [21760/60000 (36%)]\\t training loss: 0.091958\\n\",\n      \"epoch: 1 [22080/60000 (37%)]\\t training loss: 0.079766\\n\",\n      \"epoch: 1 [22400/60000 (37%)]\\t training loss: 0.151057\\n\",\n      \"epoch: 1 [22720/60000 (38%)]\\t training loss: 0.262819\\n\",\n      \"epoch: 1 [23040/60000 (38%)]\\t training loss: 0.061579\\n\",\n      \"epoch: 1 [23360/60000 (39%)]\\t training loss: 0.135419\\n\",\n      \"epoch: 1 [23680/60000 (39%)]\\t training loss: 0.285402\\n\",\n      \"epoch: 1 [24000/60000 (40%)]\\t training loss: 0.148586\\n\",\n      \"epoch: 1 [24320/60000 (41%)]\\t training loss: 0.066807\\n\",\n      \"epoch: 1 [24640/60000 (41%)]\\t training loss: 0.126747\\n\",\n      \"epoch: 1 [24960/60000 (42%)]\\t training loss: 0.026213\\n\",\n      \"epoch: 1 [25280/60000 (42%)]\\t training loss: 0.024073\\n\",\n      \"epoch: 1 [25600/60000 (43%)]\\t training loss: 0.600073\\n\",\n      \"epoch: 1 [25920/60000 (43%)]\\t training loss: 0.497171\\n\",\n      \"epoch: 1 [26240/60000 (44%)]\\t training loss: 0.118145\\n\",\n      \"epoch: 1 [26560/60000 (44%)]\\t training loss: 0.040906\\n\",\n      \"epoch: 1 [26880/60000 (45%)]\\t training loss: 0.154461\\n\",\n      \"epoch: 1 [27200/60000 (45%)]\\t training loss: 0.013285\\n\",\n      \"epoch: 1 [27520/60000 (46%)]\\t training loss: 0.027993\\n\",\n      \"epoch: 1 [27840/60000 (46%)]\\t training loss: 0.127872\\n\",\n      \"epoch: 1 [28160/60000 (47%)]\\t training loss: 0.040515\\n\",\n      \"epoch: 1 [28480/60000 (47%)]\\t training loss: 0.402136\\n\",\n      \"epoch: 1 [28800/60000 (48%)]\\t training loss: 0.138493\\n\",\n      \"epoch: 1 [29120/60000 (49%)]\\t training loss: 0.207102\\n\",\n      \"epoch: 1 [29440/60000 (49%)]\\t training loss: 0.012119\\n\",\n      \"epoch: 1 [29760/60000 (50%)]\\t training loss: 0.054411\\n\",\n      \"epoch: 1 [30080/60000 (50%)]\\t training loss: 0.546068\\n\",\n      \"epoch: 1 [30400/60000 (51%)]\\t training loss: 0.144434\\n\",\n      \"epoch: 1 [30720/60000 (51%)]\\t training loss: 0.225871\\n\",\n      \"epoch: 1 [31040/60000 (52%)]\\t training loss: 0.069757\\n\",\n      \"epoch: 1 [31360/60000 (52%)]\\t training loss: 0.068556\\n\",\n      \"epoch: 1 [31680/60000 (53%)]\\t training loss: 0.034509\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 0.015917\\n\",\n      \"epoch: 1 [32320/60000 (54%)]\\t training loss: 0.107069\\n\",\n      \"epoch: 1 [32640/60000 (54%)]\\t training loss: 0.110502\\n\",\n      \"epoch: 1 [32960/60000 (55%)]\\t training loss: 0.077687\\n\",\n      \"epoch: 1 [33280/60000 (55%)]\\t training loss: 0.276489\\n\",\n      \"epoch: 1 [33600/60000 (56%)]\\t training loss: 0.186835\\n\",\n      \"epoch: 1 [33920/60000 (57%)]\\t training loss: 0.017603\\n\",\n      \"epoch: 1 [34240/60000 (57%)]\\t training loss: 0.025609\\n\",\n      \"epoch: 1 [34560/60000 (58%)]\\t training loss: 0.003528\\n\",\n      \"epoch: 1 [34880/60000 (58%)]\\t training loss: 0.123900\\n\",\n      \"epoch: 1 [35200/60000 (59%)]\\t training loss: 0.229518\\n\",\n      \"epoch: 1 [35520/60000 (59%)]\\t training loss: 0.131734\\n\",\n      \"epoch: 1 [35840/60000 (60%)]\\t training loss: 0.015819\\n\",\n      \"epoch: 1 [36160/60000 (60%)]\\t training loss: 0.165748\\n\",\n      \"epoch: 1 [36480/60000 (61%)]\\t training loss: 0.117925\\n\",\n      \"epoch: 1 [36800/60000 (61%)]\\t training loss: 0.140046\\n\",\n      \"epoch: 1 [37120/60000 (62%)]\\t training loss: 0.043650\\n\",\n      \"epoch: 1 [37440/60000 (62%)]\\t training loss: 0.216800\\n\",\n      \"epoch: 1 [37760/60000 (63%)]\\t training loss: 0.032769\\n\",\n      \"epoch: 1 [38080/60000 (63%)]\\t training loss: 0.113341\\n\",\n      \"epoch: 1 [38400/60000 (64%)]\\t training loss: 0.074898\\n\",\n      \"epoch: 1 [38720/60000 (65%)]\\t training loss: 0.488738\\n\",\n      \"epoch: 1 [39040/60000 (65%)]\\t training loss: 0.048389\\n\",\n      \"epoch: 1 [39360/60000 (66%)]\\t training loss: 0.176396\\n\",\n      \"epoch: 1 [39680/60000 (66%)]\\t training loss: 0.021329\\n\",\n      \"epoch: 1 [40000/60000 (67%)]\\t training loss: 0.106253\\n\",\n      \"epoch: 1 [40320/60000 (67%)]\\t training loss: 0.065340\\n\",\n      \"epoch: 1 [40640/60000 (68%)]\\t training loss: 0.202225\\n\",\n      \"epoch: 1 [40960/60000 (68%)]\\t training loss: 0.194045\\n\",\n      \"epoch: 1 [41280/60000 (69%)]\\t training loss: 0.213245\\n\",\n      \"epoch: 1 [41600/60000 (69%)]\\t training loss: 0.155805\\n\",\n      \"epoch: 1 [41920/60000 (70%)]\\t training loss: 0.040492\\n\",\n      \"epoch: 1 [42240/60000 (70%)]\\t training loss: 0.011083\\n\",\n      \"epoch: 1 [42560/60000 (71%)]\\t training loss: 0.037179\\n\",\n      \"epoch: 1 [42880/60000 (71%)]\\t training loss: 0.100338\\n\",\n      \"epoch: 1 [43200/60000 (72%)]\\t training loss: 0.045283\\n\",\n      \"epoch: 1 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[48000/60000 (80%)]\\t training loss: 0.219784\\n\",\n      \"epoch: 1 [48320/60000 (81%)]\\t training loss: 0.173513\\n\",\n      \"epoch: 1 [48640/60000 (81%)]\\t training loss: 0.020131\\n\",\n      \"epoch: 1 [48960/60000 (82%)]\\t training loss: 0.092622\\n\",\n      \"epoch: 1 [49280/60000 (82%)]\\t training loss: 0.143768\\n\",\n      \"epoch: 1 [49600/60000 (83%)]\\t training loss: 0.227249\\n\",\n      \"epoch: 1 [49920/60000 (83%)]\\t training loss: 0.129910\\n\",\n      \"epoch: 1 [50240/60000 (84%)]\\t training loss: 0.071571\\n\",\n      \"epoch: 1 [50560/60000 (84%)]\\t training loss: 0.003748\\n\",\n      \"epoch: 1 [50880/60000 (85%)]\\t training loss: 0.004240\\n\",\n      \"epoch: 1 [51200/60000 (85%)]\\t training loss: 0.232011\\n\",\n      \"epoch: 1 [51520/60000 (86%)]\\t training loss: 0.028602\\n\",\n      \"epoch: 1 [51840/60000 (86%)]\\t training loss: 0.013641\\n\",\n      \"epoch: 1 [52160/60000 (87%)]\\t training loss: 0.010407\\n\",\n      \"epoch: 1 [52480/60000 (87%)]\\t training loss: 0.014092\\n\",\n      \"epoch: 1 [52800/60000 (88%)]\\t training loss: 0.041886\\n\",\n      \"epoch: 1 [53120/60000 (89%)]\\t training loss: 0.029681\\n\",\n      \"epoch: 1 [53440/60000 (89%)]\\t training loss: 0.032927\\n\",\n      \"epoch: 1 [53760/60000 (90%)]\\t training loss: 0.044958\\n\",\n      \"epoch: 1 [54080/60000 (90%)]\\t training loss: 0.014901\\n\",\n      \"epoch: 1 [54400/60000 (91%)]\\t training loss: 0.136652\\n\",\n      \"epoch: 1 [54720/60000 (91%)]\\t training loss: 0.011173\\n\",\n      \"epoch: 1 [55040/60000 (92%)]\\t training loss: 0.002030\\n\",\n      \"epoch: 1 [55360/60000 (92%)]\\t training loss: 0.066842\\n\",\n      \"epoch: 1 [55680/60000 (93%)]\\t training loss: 0.048196\\n\",\n      \"epoch: 1 [56000/60000 (93%)]\\t training loss: 0.052259\\n\",\n      \"epoch: 1 [56320/60000 (94%)]\\t training loss: 0.161982\\n\",\n      \"epoch: 1 [56640/60000 (94%)]\\t training loss: 0.054209\\n\",\n      \"epoch: 1 [56960/60000 (95%)]\\t training loss: 0.028114\\n\",\n      \"epoch: 1 [57280/60000 (95%)]\\t training loss: 0.107114\\n\",\n      \"epoch: 1 [57600/60000 (96%)]\\t training loss: 0.003919\\n\",\n      \"epoch: 1 [57920/60000 (97%)]\\t training loss: 0.093449\\n\",\n      \"epoch: 1 [58240/60000 (97%)]\\t training loss: 0.043330\\n\",\n      \"epoch: 1 [58560/60000 (98%)]\\t training loss: 0.005689\\n\",\n      \"epoch: 1 [58880/60000 (98%)]\\t training loss: 0.087195\\n\",\n      \"epoch: 1 [59200/60000 (99%)]\\t training loss: 0.009148\\n\",\n      \"epoch: 1 [59520/60000 (99%)]\\t training loss: 0.035150\\n\",\n      \"epoch: 1 [59840/60000 (100%)]\\t training loss: 0.024602\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0478, Overall Accuracy: 9845/10000 (98%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 0.075755\\n\",\n      \"epoch: 2 [320/60000 (1%)]\\t training loss: 0.052196\\n\",\n      \"epoch: 2 [640/60000 (1%)]\\t training loss: 0.176723\\n\",\n      \"epoch: 2 [960/60000 (2%)]\\t training loss: 0.084691\\n\",\n      \"epoch: 2 [1280/60000 (2%)]\\t training loss: 0.037865\\n\",\n      \"epoch: 2 [1600/60000 (3%)]\\t training loss: 0.002995\\n\",\n      \"epoch: 2 [1920/60000 (3%)]\\t training loss: 0.085839\\n\",\n      \"epoch: 2 [2240/60000 (4%)]\\t training loss: 0.028786\\n\",\n      \"epoch: 2 [2560/60000 (4%)]\\t training loss: 0.134300\\n\",\n      \"epoch: 2 [2880/60000 (5%)]\\t training loss: 0.009706\\n\",\n      \"epoch: 2 [3200/60000 (5%)]\\t training loss: 0.072933\\n\",\n      \"epoch: 2 [3520/60000 (6%)]\\t training loss: 0.010232\\n\",\n      \"epoch: 2 [3840/60000 (6%)]\\t training loss: 0.069974\\n\",\n      \"epoch: 2 [4160/60000 (7%)]\\t training loss: 0.010989\\n\",\n      \"epoch: 2 [4480/60000 (7%)]\\t training loss: 0.037965\\n\",\n      \"epoch: 2 [4800/60000 (8%)]\\t training loss: 0.048734\\n\",\n      \"epoch: 2 [5120/60000 (9%)]\\t training loss: 0.174570\\n\",\n      \"epoch: 2 [5440/60000 (9%)]\\t training loss: 0.169110\\n\",\n      \"epoch: 2 [5760/60000 (10%)]\\t training loss: 0.078296\\n\",\n      \"epoch: 2 [6080/60000 (10%)]\\t training loss: 0.047879\\n\",\n      \"epoch: 2 [6400/60000 (11%)]\\t training loss: 0.039148\\n\",\n      \"epoch: 2 [6720/60000 (11%)]\\t training loss: 0.041429\\n\",\n      \"epoch: 2 [7040/60000 (12%)]\\t training loss: 0.012437\\n\",\n      \"epoch: 2 [7360/60000 (12%)]\\t training loss: 0.093218\\n\",\n      \"epoch: 2 [7680/60000 (13%)]\\t training loss: 0.025234\\n\",\n      \"epoch: 2 [8000/60000 (13%)]\\t training loss: 0.192158\\n\",\n      \"epoch: 2 [8320/60000 (14%)]\\t training loss: 0.038945\\n\",\n      \"epoch: 2 [8640/60000 (14%)]\\t training loss: 0.018283\\n\",\n      \"epoch: 2 [8960/60000 (15%)]\\t training loss: 0.026005\\n\",\n      \"epoch: 2 [9280/60000 (15%)]\\t training loss: 0.002971\\n\",\n      \"epoch: 2 [9600/60000 (16%)]\\t training loss: 0.055324\\n\",\n      \"epoch: 2 [9920/60000 (17%)]\\t training loss: 0.003880\\n\",\n      \"epoch: 2 [10240/60000 (17%)]\\t training loss: 0.005629\\n\",\n      \"epoch: 2 [10560/60000 (18%)]\\t training loss: 0.001743\\n\",\n      \"epoch: 2 [10880/60000 (18%)]\\t training loss: 0.063067\\n\",\n      \"epoch: 2 [11200/60000 (19%)]\\t training loss: 0.034985\\n\",\n      \"epoch: 2 [11520/60000 (19%)]\\t training loss: 0.007477\\n\",\n      \"epoch: 2 [11840/60000 (20%)]\\t training loss: 0.091652\\n\",\n      \"epoch: 2 [12160/60000 (20%)]\\t training loss: 0.057981\\n\",\n      \"epoch: 2 [12480/60000 (21%)]\\t training loss: 0.045031\\n\",\n      \"epoch: 2 [12800/60000 (21%)]\\t training loss: 0.006746\\n\",\n      \"epoch: 2 [13120/60000 (22%)]\\t training loss: 0.151338\\n\",\n      \"epoch: 2 [13440/60000 (22%)]\\t training loss: 0.051468\\n\",\n      \"epoch: 2 [13760/60000 (23%)]\\t training loss: 0.118806\\n\",\n      \"epoch: 2 [14080/60000 (23%)]\\t training loss: 0.059753\\n\",\n      \"epoch: 2 [14400/60000 (24%)]\\t training loss: 0.023917\\n\",\n      \"epoch: 2 [14720/60000 (25%)]\\t training loss: 0.010549\\n\",\n      \"epoch: 2 [15040/60000 (25%)]\\t training loss: 0.001766\\n\",\n      \"epoch: 2 [15360/60000 (26%)]\\t training loss: 0.083428\\n\",\n      \"epoch: 2 [15680/60000 (26%)]\\t training loss: 0.060596\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 0.054066\\n\",\n      \"epoch: 2 [16320/60000 (27%)]\\t training loss: 0.155204\\n\",\n      \"epoch: 2 [16640/60000 (28%)]\\t training loss: 0.007297\\n\",\n      \"epoch: 2 [16960/60000 (28%)]\\t training loss: 0.391733\\n\",\n      \"epoch: 2 [17280/60000 (29%)]\\t training loss: 0.041596\\n\",\n      \"epoch: 2 [17600/60000 (29%)]\\t training loss: 0.058965\\n\",\n      \"epoch: 2 [17920/60000 (30%)]\\t training loss: 0.140119\\n\",\n      \"epoch: 2 [18240/60000 (30%)]\\t training loss: 0.083102\\n\",\n      \"epoch: 2 [18560/60000 (31%)]\\t training loss: 0.001707\\n\",\n      \"epoch: 2 [18880/60000 (31%)]\\t training loss: 0.050675\\n\",\n      \"epoch: 2 [19200/60000 (32%)]\\t training loss: 0.163005\\n\",\n      \"epoch: 2 [19520/60000 (33%)]\\t training loss: 0.006558\\n\",\n      \"epoch: 2 [19840/60000 (33%)]\\t training loss: 0.071730\\n\",\n      \"epoch: 2 [20160/60000 (34%)]\\t training loss: 0.021590\\n\",\n      \"epoch: 2 [20480/60000 (34%)]\\t training loss: 0.116760\\n\",\n      \"epoch: 2 [20800/60000 (35%)]\\t training loss: 0.007973\\n\",\n      \"epoch: 2 [21120/60000 (35%)]\\t training loss: 0.081753\\n\",\n      \"epoch: 2 [21440/60000 (36%)]\\t training loss: 0.031363\\n\",\n      \"epoch: 2 [21760/60000 (36%)]\\t training loss: 0.012533\\n\",\n      \"epoch: 2 [22080/60000 (37%)]\\t training loss: 0.007146\\n\",\n      \"epoch: 2 [22400/60000 (37%)]\\t training loss: 0.002010\\n\",\n      \"epoch: 2 [22720/60000 (38%)]\\t training loss: 0.007648\\n\",\n      \"epoch: 2 [23040/60000 (38%)]\\t training loss: 0.105152\\n\",\n      \"epoch: 2 [23360/60000 (39%)]\\t training loss: 0.101917\\n\",\n      \"epoch: 2 [23680/60000 (39%)]\\t training loss: 0.163842\\n\",\n      \"epoch: 2 [24000/60000 (40%)]\\t training loss: 0.034108\\n\",\n      \"epoch: 2 [24320/60000 (41%)]\\t training loss: 0.024196\\n\",\n      \"epoch: 2 [24640/60000 (41%)]\\t training loss: 0.089241\\n\",\n      \"epoch: 2 [24960/60000 (42%)]\\t training loss: 0.051603\\n\",\n      \"epoch: 2 [25280/60000 (42%)]\\t training loss: 0.054226\\n\",\n      \"epoch: 2 [25600/60000 (43%)]\\t training loss: 0.058934\\n\",\n      \"epoch: 2 [25920/60000 (43%)]\\t training loss: 0.028085\\n\",\n      \"epoch: 2 [26240/60000 (44%)]\\t training loss: 0.003685\\n\",\n      \"epoch: 2 [26560/60000 (44%)]\\t training loss: 0.005134\\n\",\n      \"epoch: 2 [26880/60000 (45%)]\\t training loss: 0.069611\\n\",\n      \"epoch: 2 [27200/60000 (45%)]\\t training loss: 0.453698\\n\",\n      \"epoch: 2 [27520/60000 (46%)]\\t training loss: 0.077049\\n\",\n      \"epoch: 2 [27840/60000 (46%)]\\t training loss: 0.003867\\n\",\n      \"epoch: 2 [28160/60000 (47%)]\\t training loss: 0.049487\\n\",\n      \"epoch: 2 [28480/60000 (47%)]\\t training loss: 0.097699\\n\",\n      \"epoch: 2 [28800/60000 (48%)]\\t training loss: 0.013503\\n\",\n      \"epoch: 2 [29120/60000 (49%)]\\t training loss: 0.003477\\n\",\n      \"epoch: 2 [29440/60000 (49%)]\\t training loss: 0.046258\\n\",\n      \"epoch: 2 [29760/60000 (50%)]\\t training loss: 0.057281\\n\",\n      \"epoch: 2 [30080/60000 (50%)]\\t training loss: 0.017527\\n\",\n      \"epoch: 2 [30400/60000 (51%)]\\t training loss: 0.025550\\n\",\n      \"epoch: 2 [30720/60000 (51%)]\\t training loss: 0.027727\\n\",\n      \"epoch: 2 [31040/60000 (52%)]\\t training loss: 0.170225\\n\",\n      \"epoch: 2 [31360/60000 (52%)]\\t training loss: 0.108750\\n\",\n      \"epoch: 2 [31680/60000 (53%)]\\t training loss: 0.011146\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 0.003069\\n\",\n      \"epoch: 2 [32320/60000 (54%)]\\t training loss: 0.006169\\n\",\n      \"epoch: 2 [32640/60000 (54%)]\\t training loss: 0.024151\\n\",\n      \"epoch: 2 [32960/60000 (55%)]\\t training loss: 0.001360\\n\",\n      \"epoch: 2 [33280/60000 (55%)]\\t training loss: 0.147938\\n\",\n      \"epoch: 2 [33600/60000 (56%)]\\t training loss: 0.015906\\n\",\n      \"epoch: 2 [33920/60000 (57%)]\\t training loss: 0.013825\\n\",\n      \"epoch: 2 [34240/60000 (57%)]\\t training loss: 0.330136\\n\",\n      \"epoch: 2 [34560/60000 (58%)]\\t training loss: 0.005314\\n\",\n      \"epoch: 2 [34880/60000 (58%)]\\t training loss: 0.150999\\n\",\n      \"epoch: 2 [35200/60000 (59%)]\\t training loss: 0.025108\\n\",\n      \"epoch: 2 [35520/60000 (59%)]\\t training loss: 0.020267\\n\",\n      \"epoch: 2 [35840/60000 (60%)]\\t training loss: 0.003014\\n\",\n      \"epoch: 2 [36160/60000 (60%)]\\t training loss: 0.040846\\n\",\n      \"epoch: 2 [36480/60000 (61%)]\\t training loss: 0.092544\\n\",\n      \"epoch: 2 [36800/60000 (61%)]\\t training loss: 0.002039\\n\",\n      \"epoch: 2 [37120/60000 (62%)]\\t training loss: 0.433007\\n\",\n      \"epoch: 2 [37440/60000 (62%)]\\t training loss: 0.005830\\n\",\n      \"epoch: 2 [37760/60000 (63%)]\\t training loss: 0.054115\\n\",\n      \"epoch: 2 [38080/60000 (63%)]\\t training loss: 0.018357\\n\",\n      \"epoch: 2 [38400/60000 (64%)]\\t training loss: 0.050539\\n\",\n      \"epoch: 2 [38720/60000 (65%)]\\t training loss: 0.087123\\n\",\n      \"epoch: 2 [39040/60000 (65%)]\\t training loss: 0.014614\\n\",\n      \"epoch: 2 [39360/60000 (66%)]\\t training loss: 0.005193\\n\",\n      \"epoch: 2 [39680/60000 (66%)]\\t training loss: 0.177055\\n\",\n      \"epoch: 2 [40000/60000 (67%)]\\t training loss: 0.007170\\n\",\n      \"epoch: 2 [40320/60000 (67%)]\\t training loss: 0.240168\\n\",\n      \"epoch: 2 [40640/60000 (68%)]\\t training loss: 0.587453\\n\",\n      \"epoch: 2 [40960/60000 (68%)]\\t training loss: 0.033368\\n\",\n      \"epoch: 2 [41280/60000 (69%)]\\t training loss: 0.106131\\n\",\n      \"epoch: 2 [41600/60000 (69%)]\\t training loss: 0.142523\\n\",\n      \"epoch: 2 [41920/60000 (70%)]\\t training loss: 0.005451\\n\",\n      \"epoch: 2 [42240/60000 (70%)]\\t training loss: 0.006553\\n\",\n      \"epoch: 2 [42560/60000 (71%)]\\t training loss: 0.057431\\n\",\n      \"epoch: 2 [42880/60000 (71%)]\\t training loss: 0.008507\\n\",\n      \"epoch: 2 [43200/60000 (72%)]\\t training loss: 0.002467\\n\",\n      \"epoch: 2 [43520/60000 (73%)]\\t training loss: 0.038520\\n\",\n      \"epoch: 2 [43840/60000 (73%)]\\t training loss: 0.240774\\n\",\n      \"epoch: 2 [44160/60000 (74%)]\\t training loss: 0.177386\\n\",\n      \"epoch: 2 [44480/60000 (74%)]\\t training loss: 0.010960\\n\",\n      \"epoch: 2 [44800/60000 (75%)]\\t training loss: 0.004259\\n\",\n      \"epoch: 2 [45120/60000 (75%)]\\t training loss: 0.069959\\n\",\n      \"epoch: 2 [45440/60000 (76%)]\\t training loss: 0.044531\\n\",\n      \"epoch: 2 [45760/60000 (76%)]\\t training loss: 0.046592\\n\",\n      \"epoch: 2 [46080/60000 (77%)]\\t training loss: 0.084542\\n\",\n      \"epoch: 2 [46400/60000 (77%)]\\t training loss: 0.021363\\n\",\n      \"epoch: 2 [46720/60000 (78%)]\\t training loss: 0.160489\\n\",\n      \"epoch: 2 [47040/60000 (78%)]\\t training loss: 0.038613\\n\",\n      \"epoch: 2 [47360/60000 (79%)]\\t training loss: 0.050239\\n\",\n      \"epoch: 2 [47680/60000 (79%)]\\t training loss: 0.027921\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 0.073250\\n\",\n      \"epoch: 2 [48320/60000 (81%)]\\t training loss: 0.183588\\n\",\n      \"epoch: 2 [48640/60000 (81%)]\\t training loss: 0.045946\\n\",\n      \"epoch: 2 [48960/60000 (82%)]\\t training loss: 0.006745\\n\",\n      \"epoch: 2 [49280/60000 (82%)]\\t training loss: 0.003105\\n\",\n      \"epoch: 2 [49600/60000 (83%)]\\t training loss: 0.010110\\n\",\n      \"epoch: 2 [49920/60000 (83%)]\\t training loss: 0.116635\\n\",\n      \"epoch: 2 [50240/60000 (84%)]\\t training loss: 0.039653\\n\",\n      \"epoch: 2 [50560/60000 (84%)]\\t training loss: 0.005039\\n\",\n      \"epoch: 2 [50880/60000 (85%)]\\t training loss: 0.006200\\n\",\n      \"epoch: 2 [51200/60000 (85%)]\\t training loss: 0.013030\\n\",\n      \"epoch: 2 [51520/60000 (86%)]\\t training loss: 0.213073\\n\",\n      \"epoch: 2 [51840/60000 (86%)]\\t training loss: 0.005721\\n\",\n      \"epoch: 2 [52160/60000 (87%)]\\t training loss: 0.003327\\n\",\n      \"epoch: 2 [52480/60000 (87%)]\\t training loss: 0.117841\\n\",\n      \"epoch: 2 [52800/60000 (88%)]\\t training loss: 0.050646\\n\",\n      \"epoch: 2 [53120/60000 (89%)]\\t training loss: 0.005108\\n\",\n      \"epoch: 2 [53440/60000 (89%)]\\t training loss: 0.083819\\n\",\n      \"epoch: 2 [53760/60000 (90%)]\\t training loss: 0.174583\\n\",\n      \"epoch: 2 [54080/60000 (90%)]\\t training loss: 0.003345\\n\",\n      \"epoch: 2 [54400/60000 (91%)]\\t training loss: 0.393704\\n\",\n      \"epoch: 2 [54720/60000 (91%)]\\t training loss: 0.115101\\n\",\n      \"epoch: 2 [55040/60000 (92%)]\\t training loss: 0.018684\\n\",\n      \"epoch: 2 [55360/60000 (92%)]\\t training loss: 0.072076\\n\",\n      \"epoch: 2 [55680/60000 (93%)]\\t training loss: 0.012095\\n\",\n      \"epoch: 2 [56000/60000 (93%)]\\t training loss: 0.027326\\n\",\n      \"epoch: 2 [56320/60000 (94%)]\\t training loss: 0.001306\\n\",\n      \"epoch: 2 [56640/60000 (94%)]\\t training loss: 0.058052\\n\",\n      \"epoch: 2 [56960/60000 (95%)]\\t training loss: 0.012479\\n\",\n      \"epoch: 2 [57280/60000 (95%)]\\t training loss: 0.077006\\n\",\n      \"epoch: 2 [57600/60000 (96%)]\\t training loss: 0.074425\\n\",\n      \"epoch: 2 [57920/60000 (97%)]\\t training loss: 0.150789\\n\",\n      \"epoch: 2 [58240/60000 (97%)]\\t training loss: 0.051848\\n\",\n      \"epoch: 2 [58560/60000 (98%)]\\t training loss: 0.003488\\n\",\n      \"epoch: 2 [58880/60000 (98%)]\\t training loss: 0.003011\\n\",\n      \"epoch: 2 [59200/60000 (99%)]\\t training loss: 0.025069\\n\",\n      \"epoch: 2 [59520/60000 (99%)]\\t training loss: 0.021845\\n\",\n      \"epoch: 2 [59840/60000 (100%)]\\t training loss: 0.008774\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0421, Overall Accuracy: 9857/10000 (99%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(1, 3):\\n\",\n    \"    train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"    test(model, device, test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"\\n\",\n    \"plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 7\\n\",\n      \"Ground truth is : 7\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# PROFILING STARTS HERE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ConvNet(\\n\",\n      \"  (cn1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1))\\n\",\n      \"  (cn2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\\n\",\n      \"  (dp1): Dropout2d(p=0.1, inplace=False)\\n\",\n      \"  (dp2): Dropout2d(p=0.25, inplace=False)\\n\",\n      \"  (fc1): Linear(in_features=4608, out_features=64, bias=True)\\n\",\n      \"  (fc2): Linear(in_features=64, out_features=10, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([500, 1, 28, 28])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(sample_data.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## profile model inference on cpu\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### cpu time\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### expand notebook to visualise traces properly\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>.container { width:100% !important; }</style>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from IPython.display import display, HTML\\n\",\n    \"display(HTML(\\\"<style>.container { width:100% !important; }</style>\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.profiler import profile, record_function, ProfilerActivity\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:314] Completed Stage: Warm Up\\n\",\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:320] Completed Stage: Collection\\n\",\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:324] Completed Stage: Post Processing\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:\\n\",\n    \"    with record_function(\\\"model_inference\\\"):\\n\",\n    \"        model(sample_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                             Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                  model_inference        10.54%       4.908ms       100.00%      46.563ms      46.563ms             1  \\n\",\n      \"                     aten::conv2d         1.98%     920.000us        51.67%      24.057ms      12.028ms             2  \\n\",\n      \"                aten::convolution         0.19%      90.000us        49.69%      23.137ms      11.569ms             2  \\n\",\n      \"               aten::_convolution         0.11%      52.000us        49.50%      23.047ms      11.524ms             2  \\n\",\n      \"         aten::mkldnn_convolution        49.11%      22.865ms        49.38%      22.995ms      11.498ms             2  \\n\",\n      \"                 aten::max_pool2d         0.04%      19.000us        20.34%       9.473ms       9.473ms             1  \\n\",\n      \"    aten::max_pool2d_with_indices        20.30%       9.454ms        20.30%       9.454ms       9.454ms             1  \\n\",\n      \"                       aten::relu         0.28%     132.000us         9.35%       4.355ms       1.452ms             3  \\n\",\n      \"                  aten::clamp_min         9.07%       4.223ms         9.07%       4.223ms       1.408ms             3  \\n\",\n      \"                     aten::linear         0.05%      24.000us         5.79%       2.696ms       1.348ms             2  \\n\",\n      \"                      aten::addmm         5.44%       2.533ms         5.62%       2.617ms       1.308ms             2  \\n\",\n      \"                    aten::flatten         2.13%     992.000us         2.18%       1.016ms       1.016ms             1  \\n\",\n      \"                      aten::empty         0.20%      93.000us         0.20%      93.000us      23.250us             4  \\n\",\n      \"                      aten::copy_         0.15%      69.000us         0.15%      69.000us      34.500us             2  \\n\",\n      \"                aten::log_softmax         0.02%       7.000us         0.12%      56.000us      56.000us             1  \\n\",\n      \"                          aten::t         0.06%      29.000us         0.12%      55.000us      27.500us             2  \\n\",\n      \"               aten::_log_softmax         0.11%      49.000us         0.11%      49.000us      49.000us             1  \\n\",\n      \"                aten::as_strided_         0.07%      31.000us         0.07%      31.000us      15.500us             2  \\n\",\n      \"                  aten::transpose         0.04%      18.000us         0.06%      26.000us      13.000us             2  \\n\",\n      \"                       aten::view         0.05%      24.000us         0.05%      24.000us      24.000us             1  \\n\",\n      \"                     aten::expand         0.03%      12.000us         0.03%      15.000us       7.500us             2  \\n\",\n      \"                 aten::as_strided         0.02%      11.000us         0.02%      11.000us       2.750us             4  \\n\",\n      \"                    aten::resize_         0.01%       6.000us         0.01%       6.000us       3.000us             2  \\n\",\n      \"            aten::feature_dropout         0.00%       2.000us         0.00%       2.000us       1.000us             2  \\n\",\n      \"               aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             4  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"Self CPU time total: 46.563ms\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(prof.key_averages().table(sort_by=\\\"cpu_time_total\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  --------------------------------------------------------------------------------  \\n\",\n      \"                             Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg    # of Calls                                                                      Input Shapes  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  --------------------------------------------------------------------------------  \\n\",\n      \"                  model_inference        10.54%       4.908ms       100.00%      46.563ms      46.563ms             1                                                                                []  \\n\",\n      \"                     aten::conv2d         0.03%      13.000us        36.59%      17.038ms      17.038ms             1                         [[500, 16, 26, 26], [32, 16, 3, 3], [32], [], [], [], []]  \\n\",\n      \"                aten::convolution         0.09%      42.000us        36.56%      17.025ms      17.025ms             1                 [[500, 16, 26, 26], [32, 16, 3, 3], [32], [], [], [], [], [], []]  \\n\",\n      \"               aten::_convolution         0.06%      30.000us        36.47%      16.983ms      16.983ms             1  [[500, 16, 26, 26], [32, 16, 3, 3], [32], [], [], [], [], [], [], [], [], [], []  \\n\",\n      \"         aten::mkldnn_convolution        36.26%      16.883ms        36.41%      16.953ms      16.953ms             1                         [[500, 16, 26, 26], [32, 16, 3, 3], [32], [], [], [], []]  \\n\",\n      \"                 aten::max_pool2d         0.04%      19.000us        20.34%       9.473ms       9.473ms             1                                           [[500, 32, 24, 24], [], [], [], [], []]  \\n\",\n      \"    aten::max_pool2d_with_indices        20.30%       9.454ms        20.30%       9.454ms       9.454ms             1                                           [[500, 32, 24, 24], [], [], [], [], []]  \\n\",\n      \"                     aten::conv2d         1.95%     907.000us        15.07%       7.019ms       7.019ms             1                           [[500, 1, 28, 28], [16, 1, 3, 3], [16], [], [], [], []]  \\n\",\n      \"                aten::convolution         0.10%      48.000us        13.13%       6.112ms       6.112ms             1                   [[500, 1, 28, 28], [16, 1, 3, 3], [16], [], [], [], [], [], []]  \\n\",\n      \"               aten::_convolution         0.05%      22.000us        13.02%       6.064ms       6.064ms             1   [[500, 1, 28, 28], [16, 1, 3, 3], [16], [], [], [], [], [], [], [], [], [], []]  \\n\",\n      \"         aten::mkldnn_convolution        12.85%       5.982ms        12.98%       6.042ms       6.042ms             1                           [[500, 1, 28, 28], [16, 1, 3, 3], [16], [], [], [], []]  \\n\",\n      \"                       aten::relu         0.14%      64.000us         7.48%       3.483ms       3.483ms             1                                                               [[500, 32, 24, 24]]  \\n\",\n      \"                  aten::clamp_min         7.34%       3.419ms         7.34%       3.419ms       3.419ms             1                                                           [[500, 32, 24, 24], []]  \\n\",\n      \"                     aten::linear         0.03%      15.000us         5.54%       2.579ms       2.579ms             1                                                   [[500, 4608], [64, 4608], [64]]  \\n\",\n      \"                      aten::addmm         5.32%       2.475ms         5.45%       2.539ms       2.539ms             1                                           [[64], [500, 4608], [4608, 64], [], []]  \\n\",\n      \"                    aten::flatten         2.13%     992.000us         2.18%       1.016ms       1.016ms             1                                                       [[500, 32, 12, 12], [], []]  \\n\",\n      \"                       aten::relu         0.10%      45.000us         1.74%     812.000us     812.000us             1                                                               [[500, 16, 26, 26]]  \\n\",\n      \"                  aten::clamp_min         1.65%     767.000us         1.65%     767.000us     767.000us             1                                                           [[500, 16, 26, 26], []]  \\n\",\n      \"                     aten::linear         0.02%       9.000us         0.25%     117.000us     117.000us             1                                                       [[500, 64], [10, 64], [10]]  \\n\",\n      \"                      aten::empty         0.20%      93.000us         0.20%      93.000us      23.250us             4                                                          [[], [], [], [], [], []]  \\n\",\n      \"                      aten::addmm         0.12%      58.000us         0.17%      78.000us      78.000us             1                                               [[10], [500, 64], [64, 10], [], []]  \\n\",\n      \"                       aten::relu         0.05%      23.000us         0.13%      60.000us      60.000us             1                                                                       [[500, 64]]  \\n\",\n      \"                aten::log_softmax         0.02%       7.000us         0.12%      56.000us      56.000us             1                                                               [[500, 10], [], []]  \\n\",\n      \"                      aten::copy_         0.12%      55.000us         0.12%      55.000us      55.000us             1                                                        [[500, 64], [500, 64], []]  \\n\",\n      \"               aten::_log_softmax         0.11%      49.000us         0.11%      49.000us      49.000us             1                                                               [[500, 10], [], []]  \\n\",\n      \"                  aten::clamp_min         0.08%      37.000us         0.08%      37.000us      37.000us             1                                                                   [[500, 64], []]  \\n\",\n      \"                          aten::t         0.03%      16.000us         0.06%      30.000us      30.000us             1                                                                        [[10, 64]]  \\n\",\n      \"                          aten::t         0.03%      13.000us         0.05%      25.000us      25.000us             1                                                                      [[64, 4608]]  \\n\",\n      \"                       aten::view         0.05%      24.000us         0.05%      24.000us      24.000us             1                                                           [[500, 32, 12, 12], []]  \\n\",\n      \"                aten::as_strided_         0.04%      17.000us         0.04%      17.000us      17.000us             1                                                   [[500, 16, 26, 26], [], [], []]  \\n\",\n      \"                aten::as_strided_         0.03%      14.000us         0.03%      14.000us      14.000us             1                                                   [[500, 32, 24, 24], [], [], []]  \\n\",\n      \"                  aten::transpose         0.02%      11.000us         0.03%      14.000us      14.000us             1                                                                [[10, 64], [], []]  \\n\",\n      \"                      aten::copy_         0.03%      14.000us         0.03%      14.000us      14.000us             1                                                        [[500, 10], [500, 10], []]  \\n\",\n      \"                  aten::transpose         0.02%       7.000us         0.03%      12.000us      12.000us             1                                                              [[64, 4608], [], []]  \\n\",\n      \"                     aten::expand         0.02%       7.000us         0.02%       9.000us       9.000us             1                                                                    [[64], [], []]  \\n\",\n      \"                     aten::expand         0.01%       5.000us         0.01%       6.000us       6.000us             1                                                                    [[10], [], []]  \\n\",\n      \"                 aten::as_strided         0.01%       5.000us         0.01%       5.000us       5.000us             1                                                          [[64, 4608], [], [], []]  \\n\",\n      \"                    aten::resize_         0.01%       3.000us         0.01%       3.000us       3.000us             1                                                       [[500, 16, 26, 26], [], []]  \\n\",\n      \"                    aten::resize_         0.01%       3.000us         0.01%       3.000us       3.000us             1                                                       [[500, 32, 24, 24], [], []]  \\n\",\n      \"                 aten::as_strided         0.01%       3.000us         0.01%       3.000us       3.000us             1                                                            [[10, 64], [], [], []]  \\n\",\n      \"            aten::feature_dropout         0.00%       2.000us         0.00%       2.000us       2.000us             1                                                       [[500, 32, 12, 12], [], []]  \\n\",\n      \"                 aten::as_strided         0.00%       2.000us         0.00%       2.000us       2.000us             1                                                                [[64], [], [], []]  \\n\",\n      \"                 aten::as_strided         0.00%       1.000us         0.00%       1.000us       1.000us             1                                                                [[10], [], [], []]  \\n\",\n      \"               aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             2                                                                       [[500, 64]]  \\n\",\n      \"               aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                                                                     [[500, 4608]]  \\n\",\n      \"            aten::feature_dropout         0.00%       0.000us         0.00%       0.000us       0.000us             1                                                               [[500, 64], [], []]  \\n\",\n      \"               aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us             1                                                                       [[500, 10]]  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  --------------------------------------------------------------------------------  \\n\",\n      \"Self CPU time total: 46.563ms\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(prof.key_averages(group_by_input_shape=True).table(sort_by=\\\"cpu_time_total\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### cpu memory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                             Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem    # of Calls  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                       aten::relu         0.44%     172.000us        14.70%       5.740ms       1.913ms      55.91 Mb           0 b             3  \\n\",\n      \"                  aten::clamp_min        14.26%       5.568ms        14.26%       5.568ms       1.856ms      55.91 Mb      55.91 Mb             3  \\n\",\n      \"                     aten::conv2d         0.06%      22.000us        59.51%      23.232ms      11.616ms      55.79 Mb           0 b             2  \\n\",\n      \"                aten::convolution         0.20%      79.000us        59.45%      23.210ms      11.605ms      55.79 Mb           0 b             2  \\n\",\n      \"               aten::_convolution         0.12%      48.000us        59.25%      23.131ms      11.566ms      55.79 Mb           0 b             2  \\n\",\n      \"         aten::mkldnn_convolution        58.83%      22.966ms        59.13%      23.083ms      11.541ms      55.79 Mb           0 b             2  \\n\",\n      \"                      aten::empty         0.22%      85.000us         0.22%      85.000us      21.250us      55.79 Mb      55.79 Mb             4  \\n\",\n      \"                 aten::max_pool2d         0.05%      19.000us        22.08%       8.622ms       8.622ms      26.37 Mb           0 b             1  \\n\",\n      \"    aten::max_pool2d_with_indices        22.04%       8.603ms        22.04%       8.603ms       8.603ms      26.37 Mb      26.37 Mb             1  \\n\",\n      \"                     aten::linear         0.05%      18.000us         3.49%       1.364ms     682.000us     144.53 Kb           0 b             2  \\n\",\n      \"                      aten::addmm         3.17%       1.239ms         3.33%       1.300ms     650.000us     144.53 Kb     144.53 Kb             2  \\n\",\n      \"                aten::log_softmax         0.01%       5.000us         0.12%      45.000us      45.000us      19.53 Kb           0 b             1  \\n\",\n      \"               aten::_log_softmax         0.10%      40.000us         0.10%      40.000us      40.000us      19.53 Kb      19.53 Kb             1  \\n\",\n      \"                aten::as_strided_         0.07%      26.000us         0.07%      26.000us      13.000us           0 b           0 b             2  \\n\",\n      \"                    aten::resize_         0.02%       6.000us         0.02%       6.000us       3.000us           0 b           0 b             2  \\n\",\n      \"            aten::feature_dropout         0.01%       3.000us         0.01%       3.000us       1.500us           0 b           0 b             2  \\n\",\n      \"                    aten::flatten         0.04%      14.000us         0.09%      35.000us      35.000us           0 b           0 b             1  \\n\",\n      \"                       aten::view         0.05%      21.000us         0.05%      21.000us      21.000us           0 b           0 b             1  \\n\",\n      \"                          aten::t         0.06%      23.000us         0.12%      46.000us      23.000us           0 b           0 b             2  \\n\",\n      \"                  aten::transpose         0.04%      15.000us         0.06%      23.000us      11.500us           0 b           0 b             2  \\n\",\n      \"                 aten::as_strided         0.02%       8.000us         0.02%       8.000us       2.000us           0 b           0 b             4  \\n\",\n      \"                     aten::expand         0.04%      16.000us         0.04%      16.000us       8.000us           0 b           0 b             2  \\n\",\n      \"                      aten::copy_         0.12%      45.000us         0.12%      45.000us      22.500us           0 b           0 b             2  \\n\",\n      \"               aten::resolve_conj         0.00%       0.000us         0.00%       0.000us       0.000us           0 b           0 b             4  \\n\",\n      \"                         [memory]         0.00%       0.000us         0.00%       0.000us       0.000us    -138.22 Mb    -138.22 Mb            10  \\n\",\n      \"---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"Self CPU time total: 39.041ms\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:314] Completed Stage: Warm Up\\n\",\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:320] Completed Stage: Collection\\n\",\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:324] Completed Stage: Post Processing\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with profile(activities=[ProfilerActivity.CPU],\\n\",\n    \"        profile_memory=True, record_shapes=True) as prof:\\n\",\n    \"    model(sample_data)\\n\",\n    \"\\n\",\n    \"print(prof.key_averages().table(sort_by=\\\"cpu_memory_usage\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### gpu time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"STAGE:2024-02-16 16:03:39 3073744:3073744 ActivityProfilerController.cpp:314] Completed Stage: Warm Up\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  \\n\",\n      \"-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"                                        model_inference         0.04%       1.189ms        99.94%        3.252s        3.252s       0.000us         0.00%       3.935ms       3.935ms             1  \\n\",\n      \"                                           aten::conv2d         0.00%      27.000us        95.25%        3.099s        1.550s       0.000us         0.00%       3.024ms       1.512ms             2  \\n\",\n      \"                                      aten::convolution         0.00%      94.000us        95.25%        3.099s        1.550s       0.000us         0.00%       3.024ms       1.512ms             2  \\n\",\n      \"                                     aten::_convolution         0.03%       1.124ms        95.24%        3.099s        1.550s       0.000us         0.00%       3.024ms       1.512ms             2  \\n\",\n      \"                                aten::cudnn_convolution        17.15%     557.947ms        94.37%        3.071s        1.535s     745.000us        34.75%       2.536ms       1.268ms             2  \\n\",\n      \"                                 cudaDeviceGetAttribute         0.00%       3.000us         0.00%       3.000us       0.064us       1.791ms        83.54%       1.791ms      38.106us            47  \\n\",\n      \"             cudnn_volta_scudnn_128x32_relu_small_nn_v1         0.00%       0.000us         0.00%       0.000us       0.000us     538.000us        25.09%     538.000us     538.000us             1  \\n\",\n      \"                                             aten::relu         0.00%     100.000us         0.96%      31.293ms      10.431ms       0.000us         0.00%     493.000us     164.333us             3  \\n\",\n      \"                                        aten::clamp_min         0.08%       2.590ms         0.96%      31.193ms      10.398ms     493.000us        22.99%     493.000us     164.333us             3  \\n\",\n      \"void at::native::vectorized_elementwise_kernel<4, at...         0.00%       0.000us         0.00%       0.000us       0.000us     493.000us        22.99%     493.000us     164.333us             3  \\n\",\n      \"                                             aten::add_         0.00%     107.000us         0.84%      27.318ms      13.659ms     488.000us        22.76%     488.000us     244.000us             2  \\n\",\n      \"void at::native::elementwise_kernel<128, 2, at::nati...         0.00%       0.000us         0.00%       0.000us       0.000us     488.000us        22.76%     488.000us     244.000us             2  \\n\",\n      \"                                       aten::max_pool2d         0.00%       8.000us         0.23%       7.442ms       7.442ms       0.000us         0.00%     272.000us     272.000us             1  \\n\",\n      \"                          aten::max_pool2d_with_indices         0.05%       1.480ms         0.23%       7.434ms       7.434ms     272.000us        12.69%     272.000us     272.000us             1  \\n\",\n      \"void at::native::(anonymous namespace)::max_pool_for...         0.00%       0.000us         0.00%       0.000us       0.000us     272.000us        12.69%     272.000us     272.000us             1  \\n\",\n      \"void implicit_convolve_sgemm<float, float, 1024, 5, ...         0.00%       0.000us         0.00%       0.000us       0.000us     203.000us         9.47%     203.000us     203.000us             1  \\n\",\n      \"                                           aten::linear         0.00%      20.000us         2.25%      73.206ms      36.603ms       0.000us         0.00%     144.000us      72.000us             2  \\n\",\n      \"                                            aten::addmm         0.68%      22.122ms         2.25%      73.134ms      36.567ms     144.000us         6.72%     144.000us      72.000us             2  \\n\",\n      \"                                  volta_sgemm_32x128_tn         0.00%       0.000us         0.00%       0.000us       0.000us     136.000us         6.34%     136.000us     136.000us             1  \\n\",\n      \"                         volta_sgemm_32x32_sliced1x4_tn         0.00%       0.000us         0.00%       0.000us       0.000us       7.000us         0.33%       7.000us       7.000us             1  \\n\",\n      \"                                        Memset (Device)         0.00%       0.000us         0.00%       0.000us       0.000us       3.000us         0.14%       3.000us       1.500us             2  \\n\",\n      \"void cask_cudnn::computeOffsetsKernel<false, false>(...         0.00%       0.000us         0.00%       0.000us       0.000us       2.000us         0.09%       2.000us       2.000us             1  \\n\",\n      \"                                      aten::log_softmax         0.00%      12.000us         1.22%      39.557ms      39.557ms       0.000us         0.00%       2.000us       2.000us             1  \\n\",\n      \"                                     aten::_log_softmax         0.00%      83.000us         1.22%      39.545ms      39.545ms       2.000us         0.09%       2.000us       2.000us             1  \\n\",\n      \"void (anonymous namespace)::softmax_warp_forward<flo...         0.00%       0.000us         0.00%       0.000us       0.000us       2.000us         0.09%       2.000us       2.000us             1  \\n\",\n      \"                                  cudaStreamIsCapturing         0.00%      11.000us         0.00%      11.000us       1.833us       0.000us         0.00%       0.000us       0.000us             6  \\n\",\n      \"                                             cudaMalloc         0.05%       1.568ms         0.05%       1.568ms     142.545us       0.000us         0.00%       0.000us       0.000us            11  \\n\",\n      \"                                     cudaGetDeviceCount         0.00%       0.000us         0.00%       0.000us       0.000us       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                                   cudaDriverGetVersion         0.00%       0.000us         0.00%       0.000us       0.000us       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                                cudaGetDeviceProperties         0.10%       3.184ms         0.10%       3.184ms       3.184ms       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                              cudaStreamCreateWithFlags         7.81%     254.037ms         7.81%     254.037ms      15.877ms       0.000us         0.00%       0.000us       0.000us            16  \\n\",\n      \"                                        cudaMemsetAsync         0.00%      49.000us         0.00%      49.000us      16.333us       0.000us         0.00%       0.000us       0.000us             3  \\n\",\n      \"                                          cudaHostAlloc         0.04%       1.267ms         0.04%       1.267ms       1.267ms       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                               cudaHostGetDevicePointer         0.00%       2.000us         0.00%       2.000us       2.000us       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                                               cudaFree         3.00%      97.473ms         3.00%      97.473ms      19.495ms       0.000us         0.00%       0.000us       0.000us             5  \\n\",\n      \"                                   cudaGetSymbolAddress         0.11%       3.484ms         0.11%       3.484ms       1.742ms       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                        cudaEventRecord         0.00%      14.000us         0.00%      14.000us       7.000us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                  cudaStreamGetPriority         0.00%       2.000us         0.00%       2.000us       1.000us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                       cudaDeviceGetStreamPriorityRange         0.00%       2.000us         0.00%       2.000us       1.000us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                       cudaLaunchKernel        70.49%        2.294s        70.49%        2.294s     191.164ms       0.000us         0.00%       0.000us       0.000us            12  \\n\",\n      \"                                          aten::reshape         0.00%      13.000us         0.00%      23.000us      11.500us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                             aten::view         0.00%      22.000us         0.00%      22.000us       7.333us       0.000us         0.00%       0.000us       0.000us             3  \\n\",\n      \"                                  aten::feature_dropout         0.00%       1.000us         0.00%       1.000us       0.500us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                          aten::flatten         0.00%       7.000us         0.00%      19.000us      19.000us       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"                                                aten::t         0.00%      27.000us         0.00%      52.000us      26.000us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                        aten::transpose         0.00%      15.000us         0.00%      25.000us      12.500us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"                                       aten::as_strided         0.00%      10.000us         0.00%      10.000us       5.000us       0.000us         0.00%       0.000us       0.000us             2  \\n\",\n      \"          cudaOccupancyMaxActiveBlocksPerMultiprocessor         0.31%      10.118ms         0.31%      10.118ms       3.373ms       0.000us         0.00%       0.000us       0.000us             3  \\n\",\n      \"                                  cudaDeviceSynchronize         0.06%       1.967ms         0.06%       1.967ms       1.967ms       0.000us         0.00%       0.000us       0.000us             1  \\n\",\n      \"-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \\n\",\n      \"Self CPU time total: 3.254s\\n\",\n      \"Self CUDA time total: 2.144ms\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"STAGE:2024-02-16 16:03:43 3073744:3073744 ActivityProfilerController.cpp:320] Completed Stage: Collection\\n\",\n      \"STAGE:2024-02-16 16:03:43 3073744:3073744 ActivityProfilerController.cpp:324] Completed Stage: Post Processing\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model=model.cuda()\\n\",\n    \"sample_data=sample_data.cuda()\\n\",\n    \"with profile(activities=[\\n\",\n    \"        ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:\\n\",\n    \"    with record_function(\\\"model_inference\\\"):\\n\",\n    \"        model(sample_data)\\n\",\n    \"\\n\",\n    \"print(prof.key_averages().table(sort_by=\\\"cuda_time_total\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## tracing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"STAGE:2024-02-16 16:03:43 3073744:3073744 ActivityProfilerController.cpp:314] Completed Stage: Warm Up\\n\",\n      \"STAGE:2024-02-16 16:03:43 3073744:3073744 ActivityProfilerController.cpp:320] Completed Stage: Collection\\n\",\n      \"STAGE:2024-02-16 16:03:43 3073744:3073744 ActivityProfilerController.cpp:324] Completed Stage: Post Processing\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:\\n\",\n    \"    model(sample_data)\\n\",\n    \"\\n\",\n    \"prof.export_chrome_trace(\\\"trace.json\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter16/automl-pytorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install autoPyTorch==0.0.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## Need this for some of the submodules to work for autoPyTorch==0.0.2\\n\",\n    \"import sys \\n\",\n    \"sys.modules[\\\"fractions.gcd\\\"] = sys.modules[\\\"math\\\"].gcd\\n\",\n    \"sys.modules[\\\"sklearn.metrics.classification\\\"] = sys.modules[\\\"sklearn.metrics._classification\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torchviz import make_dot\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"from autoPyTorch import AutoNetClassification\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_ds = datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))]))\\n\",\n    \"\\n\",\n    \"test_ds = datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_test, y_train, y_test = train_ds.data.numpy().reshape(-1, 28*28), test_ds.data.numpy().reshape(-1, 28*28) ,train_ds.targets.numpy(), test_ds.targets.numpy()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:30 [AutoNet] Start bohb\\n\",\n      \"13:43:30 WORKER: start listening for jobs\\n\",\n      \"13:43:30 DISPATCHER: started the 'discover_worker' thread\\n\",\n      \"13:43:30 DISPATCHER: started the 'job_runner' thread\\n\",\n      \"13:43:30 DISPATCHER: Pyro daemon running on 192.168.0.4:53569\\n\",\n      \"13:43:30 DISPATCHER: discovered new worker, hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664\\n\",\n      \"13:43:30 HBMASTER: adjusted queue size to (0, 1)\\n\",\n      \"13:43:30 DISPATCHER: A new worker triggered discover_worker\\n\",\n      \"13:43:30 HBMASTER: starting run at 1667223810.3316212\\n\",\n      \"13:43:30 WORKER: start processing job (0, 0, 0)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 64, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010595144218862576, 'OptimizerSelector:sgd:momentum': 0.43522314968819176, 'OptimizerSelector:sgd:weight_decay': 0.022480307117129155, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:30 WORKER: registered result for job (0, 0, 0) with dispatcher\\n\",\n      \"13:43:30 job (0, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.21949100494384766 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:30 WORKER: start processing job (0, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 540, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00276201163707919, 'OptimizerSelector:sgd:momentum': 0.7508712272076359, 'OptimizerSelector:sgd:weight_decay': 0.03034550604284189, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:30 WORKER: registered result for job (0, 0, 1) with dispatcher\\n\",\n      \"13:43:30 job (0, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18489503860473633 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:30 WORKER: start processing job (0, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 61, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05738681059184554, 'OptimizerSelector:sgd:momentum': 0.1930011688749396, 'OptimizerSelector:sgd:weight_decay': 0.08433786886545823, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:30 WORKER: registered result for job (0, 0, 2) with dispatcher\\n\",\n      \"13:43:30 job (0, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1579608917236328 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:30 WORKER: start processing job (0, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 723, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00022402482197861326, 'OptimizerSelector:sgd:momentum': 0.10236862756048729, 'OptimizerSelector:sgd:weight_decay': 0.08424442200432937, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:31 WORKER: registered result for job (0, 0, 3) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15178918838500977 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 602, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009964850222156675, 'OptimizerSelector:sgd:momentum': 0.2487016631083106, 'OptimizerSelector:sgd:weight_decay': 0.09671449463790414, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:31 WORKER: registered result for job (0, 0, 4) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15674710273742676 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 700, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002907543909382822, 'OptimizerSelector:sgd:momentum': 0.8118358757038733, 'OptimizerSelector:sgd:weight_decay': 0.026468253392885076, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:31 WORKER: registered result for job (0, 0, 5) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1623852252960205 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 134, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00027653162316728524, 'OptimizerSelector:sgd:momentum': 0.19694510357349104, 'OptimizerSelector:sgd:weight_decay': 0.07280997550043737, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:31 WORKER: registered result for job (0, 0, 6) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15081191062927246 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 305, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.056682807486965046, 'OptimizerSelector:sgd:momentum': 0.34894347620074306, 'OptimizerSelector:sgd:weight_decay': 0.08558160850731837, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:31 WORKER: registered result for job (0, 0, 7) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15323686599731445 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 138, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0024959713980858118, 'OptimizerSelector:sgd:momentum': 0.6476390779418247, 'OptimizerSelector:sgd:weight_decay': 0.03776926726097081, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:31 WORKER: registered result for job (0, 0, 8) with dispatcher\\n\",\n      \"13:43:31 job (0, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15732812881469727 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:31 WORKER: start processing job (0, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00975424914772041, 'OptimizerSelector:sgd:momentum': 0.13507400883921616, 'OptimizerSelector:sgd:weight_decay': 0.05479390893511718, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:32 WORKER: registered result for job (0, 1, 0) with dispatcher\\n\",\n      \"13:43:32 job (0, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15068793296813965 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (0, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005906849813275808, 'OptimizerSelector:sgd:momentum': 0.7315582950641623, 'OptimizerSelector:sgd:weight_decay': 0.06659161606506454, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:32 WORKER: registered result for job (0, 1, 1) with dispatcher\\n\",\n      \"13:43:32 job (0, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1576089859008789 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (0, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 558, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005882337845250566, 'OptimizerSelector:sgd:momentum': 0.23674292589923868, 'OptimizerSelector:sgd:weight_decay': 0.011423856108354684, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:32 WORKER: registered result for job (0, 1, 2) with dispatcher\\n\",\n      \"13:43:32 job (0, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1514289379119873 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (0, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 109, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.077677498874665, 'OptimizerSelector:sgd:momentum': 0.5221452097160931, 'OptimizerSelector:sgd:weight_decay': 0.09040433567050854, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:32 WORKER: registered result for job (0, 2, 0) with dispatcher\\n\",\n      \"13:43:32 job (0, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14933300018310547 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (1, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01593379651825319, 'OptimizerSelector:sgd:momentum': 0.17007236531779746, 'OptimizerSelector:sgd:weight_decay': 0.06707446360882485, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:32 WORKER: registered result for job (1, 0, 0) with dispatcher\\n\",\n      \"13:43:32 job (1, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1484541893005371 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (1, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00012684568969860088, 'OptimizerSelector:sgd:momentum': 0.16031483751069117, 'OptimizerSelector:sgd:weight_decay': 0.03759988983216864, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:32 WORKER: registered result for job (1, 0, 1) with dispatcher\\n\",\n      \"13:43:32 job (1, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14879703521728516 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:32 WORKER: start processing job (1, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 83, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00028904646631313914, 'OptimizerSelector:sgd:momentum': 0.21683645168092727, 'OptimizerSelector:sgd:weight_decay': 0.07507115838741815, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:33 WORKER: registered result for job (1, 0, 2) with dispatcher\\n\",\n      \"13:43:33 job (1, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1487739086151123 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:33 WORKER: start processing job (1, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 124, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00030093111935990775, 'OptimizerSelector:sgd:momentum': 0.1154923103831836, 'OptimizerSelector:sgd:weight_decay': 0.07692406399151497, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:33 WORKER: registered result for job (1, 1, 0) with dispatcher\\n\",\n      \"13:43:33 job (1, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15006375312805176 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:33 WORKER: start processing job (2, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 583, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008526854922581167, 'OptimizerSelector:sgd:momentum': 0.6038716419825334, 'OptimizerSelector:sgd:weight_decay': 0.056821093732002115, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:33 WORKER: registered result for job (2, 0, 0) with dispatcher\\n\",\n      \"13:43:33 job (2, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15131211280822754 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:33 WORKER: start processing job (2, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 139, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.011552019783943763, 'OptimizerSelector:sgd:momentum': 0.2983122167086651, 'OptimizerSelector:sgd:weight_decay': 0.09285922758532408, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:33 WORKER: registered result for job (2, 0, 1) with dispatcher\\n\",\n      \"13:43:33 job (2, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1488640308380127 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:33 WORKER: start processing job (2, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 221, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.023982431748404347, 'OptimizerSelector:sgd:momentum': 0.5780757045156223, 'OptimizerSelector:sgd:weight_decay': 0.05800578259081473, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:33 WORKER: registered result for job (2, 0, 2) with dispatcher\\n\",\n      \"13:43:33 job (2, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16610479354858398 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:33 WORKER: start processing job (3, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 437, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005314304349046976, 'OptimizerSelector:sgd:momentum': 0.6169659183292192, 'OptimizerSelector:sgd:weight_decay': 0.09574481058172495, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:34 WORKER: registered result for job (3, 0, 0) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17682194709777832 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 115, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006131613483811701, 'OptimizerSelector:sgd:momentum': 0.3824529257100427, 'OptimizerSelector:sgd:weight_decay': 0.0631439596597702, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:34 WORKER: registered result for job (3, 0, 1) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15701794624328613 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 689, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008727492068665412, 'OptimizerSelector:sgd:momentum': 0.10557809614662557, 'OptimizerSelector:sgd:weight_decay': 0.08215753601004686, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:34 WORKER: registered result for job (3, 0, 2) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14830708503723145 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 54, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.049989350740028256, 'OptimizerSelector:sgd:momentum': 0.3011367857206925, 'OptimizerSelector:sgd:weight_decay': 0.09285212667906206, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:34 WORKER: registered result for job (3, 0, 3) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15135478973388672 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 477, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00026478607362395224, 'OptimizerSelector:sgd:momentum': 0.47832709508207977, 'OptimizerSelector:sgd:weight_decay': 0.08883967586850791, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:34 WORKER: registered result for job (3, 0, 4) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14987492561340332 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00039649923510372715, 'OptimizerSelector:sgd:momentum': 0.5935704127445859, 'OptimizerSelector:sgd:weight_decay': 0.09746675117960281, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:34 WORKER: registered result for job (3, 0, 5) with dispatcher\\n\",\n      \"13:43:34 job (3, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14834117889404297 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:34 WORKER: start processing job (3, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 39, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010432172515646846, 'OptimizerSelector:sgd:momentum': 0.49292033911128563, 'OptimizerSelector:sgd:weight_decay': 0.014200168287406396, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:35 WORKER: registered result for job (3, 0, 6) with dispatcher\\n\",\n      \"13:43:35 job (3, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15020203590393066 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 209, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005825551129779436, 'OptimizerSelector:sgd:momentum': 0.14085122712968243, 'OptimizerSelector:sgd:weight_decay': 0.061932061890293016, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:35 WORKER: registered result for job (3, 0, 7) with dispatcher\\n\",\n      \"13:43:35 job (3, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1489260196685791 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 479, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08260774804638367, 'OptimizerSelector:sgd:momentum': 0.4580647579578185, 'OptimizerSelector:sgd:weight_decay': 0.06496001751714107, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:35 WORKER: registered result for job (3, 0, 8) with dispatcher\\n\",\n      \"13:43:35 job (3, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14957213401794434 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 37, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002005980829410402, 'OptimizerSelector:sgd:momentum': 0.1138978642512411, 'OptimizerSelector:sgd:weight_decay': 0.09691740113934026, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:35 WORKER: registered result for job (3, 1, 0) with dispatcher\\n\",\n      \"13:43:35 job (3, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14748311042785645 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 331, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008323997060002418, 'OptimizerSelector:sgd:momentum': 0.4140750224042687, 'OptimizerSelector:sgd:weight_decay': 0.09319808735808577, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:35 WORKER: registered result for job (3, 1, 1) with dispatcher\\n\",\n      \"13:43:35 job (3, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15501070022583008 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 42, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03795036331792613, 'OptimizerSelector:sgd:momentum': 0.6163010100659022, 'OptimizerSelector:sgd:weight_decay': 0.07844315596425958, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:35 WORKER: registered result for job (3, 1, 2) with dispatcher\\n\",\n      \"13:43:35 job (3, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1551048755645752 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:35 WORKER: start processing job (3, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 510, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04436378959995691, 'OptimizerSelector:sgd:momentum': 0.329891900028441, 'OptimizerSelector:sgd:weight_decay': 0.0583926875662652, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:36 WORKER: registered result for job (3, 2, 0) with dispatcher\\n\",\n      \"13:43:36 job (3, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15062785148620605 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (4, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 478, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001911870997303758, 'OptimizerSelector:sgd:momentum': 0.2523033099470685, 'OptimizerSelector:sgd:weight_decay': 0.09147366945544215, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:36 WORKER: registered result for job (4, 0, 0) with dispatcher\\n\",\n      \"13:43:36 job (4, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14214706420898438 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (4, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006170988624739786, 'OptimizerSelector:sgd:momentum': 0.7965307754828058, 'OptimizerSelector:sgd:weight_decay': 0.09343329159197834, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:36 WORKER: registered result for job (4, 0, 1) with dispatcher\\n\",\n      \"13:43:36 job (4, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14132118225097656 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (4, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001427822219603837, 'OptimizerSelector:sgd:momentum': 0.19215397621559915, 'OptimizerSelector:sgd:weight_decay': 0.07294342578221895, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:36 WORKER: registered result for job (4, 0, 2) with dispatcher\\n\",\n      \"13:43:36 job (4, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1472640037536621 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (4, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07238897494680784, 'OptimizerSelector:sgd:momentum': 0.2877621032771319, 'OptimizerSelector:sgd:weight_decay': 0.0922110238569142, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:36 WORKER: registered result for job (4, 1, 0) with dispatcher\\n\",\n      \"13:43:36 job (4, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15346908569335938 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (5, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 213, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00042894294638420026, 'OptimizerSelector:sgd:momentum': 0.22607811488017243, 'OptimizerSelector:sgd:weight_decay': 0.051560867950107385, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:36 WORKER: registered result for job (5, 0, 0) with dispatcher\\n\",\n      \"13:43:36 job (5, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15137290954589844 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:36 WORKER: start processing job (5, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004507708359694497, 'OptimizerSelector:sgd:momentum': 0.19994648180903432, 'OptimizerSelector:sgd:weight_decay': 0.07334288256531414, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:37 WORKER: registered result for job (5, 0, 1) with dispatcher\\n\",\n      \"13:43:37 job (5, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16010022163391113 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (5, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 117, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0015789243740928658, 'OptimizerSelector:sgd:momentum': 0.1861218061931022, 'OptimizerSelector:sgd:weight_decay': 0.02789323651669405, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:37 WORKER: registered result for job (5, 0, 2) with dispatcher\\n\",\n      \"13:43:37 job (5, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15961313247680664 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (6, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 452, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003658890794848185, 'OptimizerSelector:sgd:momentum': 0.12140071240292934, 'OptimizerSelector:sgd:weight_decay': 0.010536291707110877, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:37 WORKER: registered result for job (6, 0, 0) with dispatcher\\n\",\n      \"13:43:37 job (6, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14834094047546387 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (6, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 163, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03924209732420118, 'OptimizerSelector:sgd:momentum': 0.21062972835447158, 'OptimizerSelector:sgd:weight_decay': 0.0690939069924859, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:37 WORKER: registered result for job (6, 0, 1) with dispatcher\\n\",\n      \"13:43:37 job (6, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1507110595703125 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (6, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 711, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02448374442212534, 'OptimizerSelector:sgd:momentum': 0.28955174455916244, 'OptimizerSelector:sgd:weight_decay': 0.002157689652957298, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:37 WORKER: registered result for job (6, 0, 2) with dispatcher\\n\",\n      \"13:43:37 job (6, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14921808242797852 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (6, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 67, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005795215498742253, 'OptimizerSelector:sgd:momentum': 0.41327802137661024, 'OptimizerSelector:sgd:weight_decay': 0.048692787043085, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:37 WORKER: registered result for job (6, 0, 3) with dispatcher\\n\",\n      \"13:43:37 job (6, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1580522060394287 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:37 WORKER: start processing job (6, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 33, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.028717068566369564, 'OptimizerSelector:sgd:momentum': 0.20215713612093378, 'OptimizerSelector:sgd:weight_decay': 0.053650877040952276, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:38 WORKER: registered result for job (6, 0, 4) with dispatcher\\n\",\n      \"13:43:38 job (6, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15336894989013672 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 516, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00014338505997638367, 'OptimizerSelector:sgd:momentum': 0.8679781416584659, 'OptimizerSelector:sgd:weight_decay': 0.04028812605025867, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:38 WORKER: registered result for job (6, 0, 5) with dispatcher\\n\",\n      \"13:43:38 job (6, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15535497665405273 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 179, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.011077643916907229, 'OptimizerSelector:sgd:momentum': 0.9181233461997749, 'OptimizerSelector:sgd:weight_decay': 0.052952377274678254, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:38 WORKER: registered result for job (6, 0, 6) with dispatcher\\n\",\n      \"13:43:38 job (6, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16333723068237305 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0026408043209061017, 'OptimizerSelector:sgd:momentum': 0.14103201147421343, 'OptimizerSelector:sgd:weight_decay': 0.002740926590460932, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:38 WORKER: registered result for job (6, 0, 7) with dispatcher\\n\",\n      \"13:43:38 job (6, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1507110595703125 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 235, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00024456577058764396, 'OptimizerSelector:sgd:momentum': 0.17948065892898957, 'OptimizerSelector:sgd:weight_decay': 0.029582184000494425, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:38 WORKER: registered result for job (6, 0, 8) with dispatcher\\n\",\n      \"13:43:38 job (6, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14594602584838867 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 45, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00025888359498933846, 'OptimizerSelector:sgd:momentum': 0.20591706781833274, 'OptimizerSelector:sgd:weight_decay': 0.06346228130804116, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:38 WORKER: registered result for job (6, 1, 0) with dispatcher\\n\",\n      \"13:43:38 job (6, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14813685417175293 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:38 WORKER: start processing job (6, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 808, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00022109925424379701, 'OptimizerSelector:sgd:momentum': 0.24903410146408117, 'OptimizerSelector:sgd:weight_decay': 0.04667317109426572, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:39 WORKER: registered result for job (6, 1, 1) with dispatcher\\n\",\n      \"13:43:39 job (6, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15268898010253906 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (6, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.027904594283029986, 'OptimizerSelector:sgd:momentum': 0.40360044133988915, 'OptimizerSelector:sgd:weight_decay': 0.011677746893195463, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:39 WORKER: registered result for job (6, 1, 2) with dispatcher\\n\",\n      \"13:43:39 job (6, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15046405792236328 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (6, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010805210566974873, 'OptimizerSelector:sgd:momentum': 0.1494186954564403, 'OptimizerSelector:sgd:weight_decay': 0.002802736386570464, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:39 WORKER: registered result for job (6, 2, 0) with dispatcher\\n\",\n      \"13:43:39 job (6, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14662504196166992 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (7, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 1007, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010745969354773306, 'OptimizerSelector:sgd:momentum': 0.22434592177342214, 'OptimizerSelector:sgd:weight_decay': 0.08105711923552938, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:39 WORKER: registered result for job (7, 0, 0) with dispatcher\\n\",\n      \"13:43:39 job (7, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14794111251831055 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (7, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 422, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04003718592517847, 'OptimizerSelector:sgd:momentum': 0.10990498762932781, 'OptimizerSelector:sgd:weight_decay': 0.004083218706614048, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:39 WORKER: registered result for job (7, 0, 1) with dispatcher\\n\",\n      \"13:43:39 job (7, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14842700958251953 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (7, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 121, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01899870906636, 'OptimizerSelector:sgd:momentum': 0.11156825418742675, 'OptimizerSelector:sgd:weight_decay': 0.04844749712269602, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:39 WORKER: registered result for job (7, 0, 2) with dispatcher\\n\",\n      \"13:43:39 job (7, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15088796615600586 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:39 WORKER: start processing job (7, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 188, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00013342645569281702, 'OptimizerSelector:sgd:momentum': 0.1121721482614833, 'OptimizerSelector:sgd:weight_decay': 0.01477589656977043, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:40 WORKER: registered result for job (7, 1, 0) with dispatcher\\n\",\n      \"13:43:40 job (7, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1460893154144287 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (8, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00011204219209662212, 'OptimizerSelector:sgd:momentum': 0.13719550722302218, 'OptimizerSelector:sgd:weight_decay': 0.06919641046526316, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:40 WORKER: registered result for job (8, 0, 0) with dispatcher\\n\",\n      \"13:43:40 job (8, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14357972145080566 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (8, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09473958977672699, 'OptimizerSelector:sgd:momentum': 0.5299680151269581, 'OptimizerSelector:sgd:weight_decay': 0.0557416813881317, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:40 WORKER: registered result for job (8, 0, 1) with dispatcher\\n\",\n      \"13:43:40 job (8, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15613794326782227 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (8, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 14, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0012471489715909438, 'OptimizerSelector:sgd:momentum': 0.7068773182618407, 'OptimizerSelector:sgd:weight_decay': 0.017843967920006268, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:40 WORKER: registered result for job (8, 0, 2) with dispatcher\\n\",\n      \"13:43:40 job (8, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14671611785888672 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (9, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 35, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000809777033120206, 'OptimizerSelector:sgd:momentum': 0.24084479383527913, 'OptimizerSelector:sgd:weight_decay': 0.04929645734215447, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:40 WORKER: registered result for job (9, 0, 0) with dispatcher\\n\",\n      \"13:43:40 job (9, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1482548713684082 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (9, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 47, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001629967709476201, 'OptimizerSelector:sgd:momentum': 0.2640536699556369, 'OptimizerSelector:sgd:weight_decay': 0.06598928507029395, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:40 WORKER: registered result for job (9, 0, 1) with dispatcher\\n\",\n      \"13:43:40 job (9, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17275714874267578 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:40 WORKER: start processing job (9, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 931, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006648621160095542, 'OptimizerSelector:sgd:momentum': 0.11493670549357195, 'OptimizerSelector:sgd:weight_decay': 0.02921268126737879, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:41 WORKER: registered result for job (9, 0, 2) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15425682067871094 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 263, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.007398415820470201, 'OptimizerSelector:sgd:momentum': 0.28187203094675906, 'OptimizerSelector:sgd:weight_decay': 0.09246265469284386, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:41 WORKER: registered result for job (9, 0, 3) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14739394187927246 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 194, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.022942605359548406, 'OptimizerSelector:sgd:momentum': 0.12616296917557868, 'OptimizerSelector:sgd:weight_decay': 0.042652576999307135, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:41 WORKER: registered result for job (9, 0, 4) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1473841667175293 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02434192573999672, 'OptimizerSelector:sgd:momentum': 0.35307104075114537, 'OptimizerSelector:sgd:weight_decay': 0.02826716978551855, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:41 WORKER: registered result for job (9, 0, 5) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1504828929901123 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 125, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09605857118529917, 'OptimizerSelector:sgd:momentum': 0.17616894360226465, 'OptimizerSelector:sgd:weight_decay': 0.09685483825983784, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:41 WORKER: registered result for job (9, 0, 6) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15209507942199707 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 385, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.013956024034951, 'OptimizerSelector:sgd:momentum': 0.3210357990045702, 'OptimizerSelector:sgd:weight_decay': 0.028068129677741988, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:41 WORKER: registered result for job (9, 0, 7) with dispatcher\\n\",\n      \"13:43:41 job (9, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14703607559204102 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:41 WORKER: start processing job (9, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 57, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00022580790628014743, 'OptimizerSelector:sgd:momentum': 0.1566569131722892, 'OptimizerSelector:sgd:weight_decay': 0.07402060383438167, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:42 WORKER: registered result for job (9, 0, 8) with dispatcher\\n\",\n      \"13:43:42 job (9, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14838004112243652 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (9, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004357221496901233, 'OptimizerSelector:sgd:momentum': 0.14006598801490305, 'OptimizerSelector:sgd:weight_decay': 0.05171086586663404, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:42 WORKER: registered result for job (9, 1, 0) with dispatcher\\n\",\n      \"13:43:42 job (9, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15646100044250488 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (9, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 30, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005568472079763694, 'OptimizerSelector:sgd:momentum': 0.17276520586382843, 'OptimizerSelector:sgd:weight_decay': 0.02287725919983385, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:42 WORKER: registered result for job (9, 1, 1) with dispatcher\\n\",\n      \"13:43:42 job (9, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1525402069091797 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (9, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01054181746120768, 'OptimizerSelector:sgd:momentum': 0.39965777497509347, 'OptimizerSelector:sgd:weight_decay': 0.017393170730496035, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:42 WORKER: registered result for job (9, 1, 2) with dispatcher\\n\",\n      \"13:43:42 job (9, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15362095832824707 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (9, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 41, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0002762339636633511, 'OptimizerSelector:sgd:momentum': 0.6049391840119076, 'OptimizerSelector:sgd:weight_decay': 0.04858807896935882, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:42 WORKER: registered result for job (9, 2, 0) with dispatcher\\n\",\n      \"13:43:42 job (9, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15291690826416016 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (10, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00031099113796501256, 'OptimizerSelector:sgd:momentum': 0.10480726190991699, 'OptimizerSelector:sgd:weight_decay': 0.07050579367172918, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:42 WORKER: registered result for job (10, 0, 0) with dispatcher\\n\",\n      \"13:43:42 job (10, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16576600074768066 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:42 WORKER: start processing job (10, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 44, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05678131753873751, 'OptimizerSelector:sgd:momentum': 0.18662601006787252, 'OptimizerSelector:sgd:weight_decay': 0.0025844703069716445, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:43 WORKER: registered result for job (10, 0, 1) with dispatcher\\n\",\n      \"13:43:43 job (10, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1533660888671875 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (10, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010171867928760193, 'OptimizerSelector:sgd:momentum': 0.6121491757307843, 'OptimizerSelector:sgd:weight_decay': 0.09794344458764084, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:43 WORKER: registered result for job (10, 0, 2) with dispatcher\\n\",\n      \"13:43:43 job (10, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1493370532989502 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (10, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 185, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.012376343750419462, 'OptimizerSelector:sgd:momentum': 0.13470331912682337, 'OptimizerSelector:sgd:weight_decay': 0.02724122651917625, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:43 WORKER: registered result for job (10, 1, 0) with dispatcher\\n\",\n      \"13:43:43 job (10, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15011191368103027 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (11, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 139, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0013411727693087184, 'OptimizerSelector:sgd:momentum': 0.669997255437621, 'OptimizerSelector:sgd:weight_decay': 0.07464120805464816, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:43 WORKER: registered result for job (11, 0, 0) with dispatcher\\n\",\n      \"13:43:43 job (11, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15167903900146484 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (11, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 63, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003597598362957659, 'OptimizerSelector:sgd:momentum': 0.10502679565502859, 'OptimizerSelector:sgd:weight_decay': 0.0838708072203505, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:43 WORKER: registered result for job (11, 0, 1) with dispatcher\\n\",\n      \"13:43:43 job (11, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15988707542419434 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (11, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 432, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06884429362702756, 'OptimizerSelector:sgd:momentum': 0.19345442875829122, 'OptimizerSelector:sgd:weight_decay': 0.03291821360324577, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:43 WORKER: registered result for job (11, 0, 2) with dispatcher\\n\",\n      \"13:43:43 job (11, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15634989738464355 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:43 WORKER: start processing job (12, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 23, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002140142646336111, 'OptimizerSelector:sgd:momentum': 0.32506132042051195, 'OptimizerSelector:sgd:weight_decay': 0.07047073571720446, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:44 WORKER: registered result for job (12, 0, 0) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1503429412841797 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 208, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001850850915532567, 'OptimizerSelector:sgd:momentum': 0.5081819267396694, 'OptimizerSelector:sgd:weight_decay': 0.09378881185257587, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:44 WORKER: registered result for job (12, 0, 1) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14768195152282715 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 281, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.038145860019060986, 'OptimizerSelector:sgd:momentum': 0.6297850838750487, 'OptimizerSelector:sgd:weight_decay': 0.07941642656664019, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:44 WORKER: registered result for job (12, 0, 2) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15104293823242188 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 487, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.012182291367097054, 'OptimizerSelector:sgd:momentum': 0.9088719738713791, 'OptimizerSelector:sgd:weight_decay': 0.06297728622319558, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:44 WORKER: registered result for job (12, 0, 3) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1483309268951416 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08219108277901402, 'OptimizerSelector:sgd:momentum': 0.2042790753713178, 'OptimizerSelector:sgd:weight_decay': 0.08067739261683944, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:44 WORKER: registered result for job (12, 0, 4) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14905905723571777 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006084349034563009, 'OptimizerSelector:sgd:momentum': 0.12478539404317002, 'OptimizerSelector:sgd:weight_decay': 0.08271527552680641, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:44 WORKER: registered result for job (12, 0, 5) with dispatcher\\n\",\n      \"13:43:44 job (12, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1486659049987793 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:44 WORKER: start processing job (12, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 247, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00022478806770752052, 'OptimizerSelector:sgd:momentum': 0.24046160759068924, 'OptimizerSelector:sgd:weight_decay': 0.05814135621651369, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:45 WORKER: registered result for job (12, 0, 6) with dispatcher\\n\",\n      \"13:43:45 job (12, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14901399612426758 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.016330662446548502, 'OptimizerSelector:sgd:momentum': 0.5876677924011967, 'OptimizerSelector:sgd:weight_decay': 0.09910587195609141, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:45 WORKER: registered result for job (12, 0, 7) with dispatcher\\n\",\n      \"13:43:45 job (12, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15272116661071777 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 617, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01284085441771106, 'OptimizerSelector:sgd:momentum': 0.12437735483421902, 'OptimizerSelector:sgd:weight_decay': 0.02655247726828647, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:45 WORKER: registered result for job (12, 0, 8) with dispatcher\\n\",\n      \"13:43:45 job (12, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15527606010437012 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0012908203398059668, 'OptimizerSelector:sgd:momentum': 0.11735262530682322, 'OptimizerSelector:sgd:weight_decay': 0.042606187973297086, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:45 WORKER: registered result for job (12, 1, 0) with dispatcher\\n\",\n      \"13:43:45 job (12, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14994502067565918 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 512, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02354593518473633, 'OptimizerSelector:sgd:momentum': 0.14706514679962196, 'OptimizerSelector:sgd:weight_decay': 0.06787941870084536, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:45 WORKER: registered result for job (12, 1, 1) with dispatcher\\n\",\n      \"13:43:45 job (12, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15166807174682617 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 613, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004986604524051357, 'OptimizerSelector:sgd:momentum': 0.12886049992307833, 'OptimizerSelector:sgd:weight_decay': 0.050806763281765505, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:45 WORKER: registered result for job (12, 1, 2) with dispatcher\\n\",\n      \"13:43:45 job (12, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.156203031539917 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:45 WORKER: start processing job (12, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 279, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00015429932879008738, 'OptimizerSelector:sgd:momentum': 0.11822546537322928, 'OptimizerSelector:sgd:weight_decay': 0.031862213622070784, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:46 WORKER: registered result for job (12, 2, 0) with dispatcher\\n\",\n      \"13:43:46 job (12, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15291190147399902 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (13, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 360, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0011029275784806335, 'OptimizerSelector:sgd:momentum': 0.2702437144412358, 'OptimizerSelector:sgd:weight_decay': 0.06158340530929514, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:46 WORKER: registered result for job (13, 0, 0) with dispatcher\\n\",\n      \"13:43:46 job (13, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15273404121398926 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (13, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 243, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00017363927518089875, 'OptimizerSelector:sgd:momentum': 0.40643411430403636, 'OptimizerSelector:sgd:weight_decay': 0.0537788456845472, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:46 WORKER: registered result for job (13, 0, 1) with dispatcher\\n\",\n      \"13:43:46 job (13, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15139198303222656 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (13, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 304, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008655561151411997, 'OptimizerSelector:sgd:momentum': 0.49334238308258166, 'OptimizerSelector:sgd:weight_decay': 0.08903906827140841, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:46 WORKER: registered result for job (13, 0, 2) with dispatcher\\n\",\n      \"13:43:46 job (13, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15146589279174805 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (13, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 191, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00218270287180964, 'OptimizerSelector:sgd:momentum': 0.10870247781037083, 'OptimizerSelector:sgd:weight_decay': 0.0843281984941576, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:46 WORKER: registered result for job (13, 1, 0) with dispatcher\\n\",\n      \"13:43:46 job (13, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14706730842590332 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (14, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 543, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003528478426239389, 'OptimizerSelector:sgd:momentum': 0.252906261803277, 'OptimizerSelector:sgd:weight_decay': 0.023213445929524092, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:46 WORKER: registered result for job (14, 0, 0) with dispatcher\\n\",\n      \"13:43:46 job (14, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1729729175567627 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:46 WORKER: start processing job (14, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 111, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00035424525610386885, 'OptimizerSelector:sgd:momentum': 0.48015162252405963, 'OptimizerSelector:sgd:weight_decay': 0.052574949516162574, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:47 WORKER: registered result for job (14, 0, 1) with dispatcher\\n\",\n      \"13:43:47 job (14, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16506385803222656 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (14, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 890, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0043036334908961756, 'OptimizerSelector:sgd:momentum': 0.2553155435770928, 'OptimizerSelector:sgd:weight_decay': 0.05217277086503617, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:47 WORKER: registered result for job (14, 0, 2) with dispatcher\\n\",\n      \"13:43:47 job (14, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14811229705810547 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (15, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004676691359174827, 'OptimizerSelector:sgd:momentum': 0.13666204357565412, 'OptimizerSelector:sgd:weight_decay': 0.001967301691917394, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:47 WORKER: registered result for job (15, 0, 0) with dispatcher\\n\",\n      \"13:43:47 job (15, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1489720344543457 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (15, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 37, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005887830740987062, 'OptimizerSelector:sgd:momentum': 0.4147157943708448, 'OptimizerSelector:sgd:weight_decay': 0.02309755446076817, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:47 WORKER: registered result for job (15, 0, 1) with dispatcher\\n\",\n      \"13:43:47 job (15, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14605164527893066 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (15, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 71, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.013971123868734758, 'OptimizerSelector:sgd:momentum': 0.47345708775299417, 'OptimizerSelector:sgd:weight_decay': 0.055037679222979835, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:47 WORKER: registered result for job (15, 0, 2) with dispatcher\\n\",\n      \"13:43:47 job (15, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16234493255615234 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (15, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006748121517862619, 'OptimizerSelector:sgd:momentum': 0.10362375970840766, 'OptimizerSelector:sgd:weight_decay': 0.01584697750049969, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:47 WORKER: registered result for job (15, 0, 3) with dispatcher\\n\",\n      \"13:43:47 job (15, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14837408065795898 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:47 WORKER: start processing job (15, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 43, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0035311725835452026, 'OptimizerSelector:sgd:momentum': 0.48747231690341714, 'OptimizerSelector:sgd:weight_decay': 0.036263095027065644, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:48 WORKER: registered result for job (15, 0, 4) with dispatcher\\n\",\n      \"13:43:48 job (15, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1462860107421875 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 93, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.001283171567684129, 'OptimizerSelector:sgd:momentum': 0.22520541393310586, 'OptimizerSelector:sgd:weight_decay': 0.05986748388761248, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:48 WORKER: registered result for job (15, 0, 5) with dispatcher\\n\",\n      \"13:43:48 job (15, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14760398864746094 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00037519652956588307, 'OptimizerSelector:sgd:momentum': 0.9506994466960823, 'OptimizerSelector:sgd:weight_decay': 0.07024467611753415, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:48 WORKER: registered result for job (15, 0, 6) with dispatcher\\n\",\n      \"13:43:48 job (15, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14831900596618652 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 818, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0002615136231563627, 'OptimizerSelector:sgd:momentum': 0.3953610064522152, 'OptimizerSelector:sgd:weight_decay': 0.024727920593411792, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:48 WORKER: registered result for job (15, 0, 7) with dispatcher\\n\",\n      \"13:43:48 job (15, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14795494079589844 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 561, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0026340671796479913, 'OptimizerSelector:sgd:momentum': 0.6404670760569565, 'OptimizerSelector:sgd:weight_decay': 0.04783113309284806, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:48 WORKER: registered result for job (15, 0, 8) with dispatcher\\n\",\n      \"13:43:48 job (15, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15103483200073242 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 101, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000944535282489476, 'OptimizerSelector:sgd:momentum': 0.3071122966960131, 'OptimizerSelector:sgd:weight_decay': 0.03367956776227898, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:48 WORKER: registered result for job (15, 1, 0) with dispatcher\\n\",\n      \"13:43:48 job (15, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14864802360534668 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:48 WORKER: start processing job (15, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 106, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008700925587913841, 'OptimizerSelector:sgd:momentum': 0.34459571191097216, 'OptimizerSelector:sgd:weight_decay': 0.07647305156734518, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:49 WORKER: registered result for job (15, 1, 1) with dispatcher\\n\",\n      \"13:43:49 job (15, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15100717544555664 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (15, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 903, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04434237377125584, 'OptimizerSelector:sgd:momentum': 0.47307050127553985, 'OptimizerSelector:sgd:weight_decay': 0.020025639933843657, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:49 WORKER: registered result for job (15, 1, 2) with dispatcher\\n\",\n      \"13:43:49 job (15, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14712905883789062 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (15, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 235, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.011539116404045412, 'OptimizerSelector:sgd:momentum': 0.9550863916338242, 'OptimizerSelector:sgd:weight_decay': 0.08539779502552271, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:49 WORKER: registered result for job (15, 2, 0) with dispatcher\\n\",\n      \"13:43:49 job (15, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.149277925491333 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (16, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 232, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009345157495405162, 'OptimizerSelector:sgd:momentum': 0.1978998076076544, 'OptimizerSelector:sgd:weight_decay': 0.027013639642548746, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:49 WORKER: registered result for job (16, 0, 0) with dispatcher\\n\",\n      \"13:43:49 job (16, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14634394645690918 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (16, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 26, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005088386066397251, 'OptimizerSelector:sgd:momentum': 0.31968944715103464, 'OptimizerSelector:sgd:weight_decay': 0.01166486682188598, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:49 WORKER: registered result for job (16, 0, 1) with dispatcher\\n\",\n      \"13:43:49 job (16, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1542971134185791 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (16, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 28, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004645489049636985, 'OptimizerSelector:sgd:momentum': 0.19374003286132024, 'OptimizerSelector:sgd:weight_decay': 0.09608319657439113, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:49 WORKER: registered result for job (16, 0, 2) with dispatcher\\n\",\n      \"13:43:49 job (16, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14621591567993164 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:49 WORKER: start processing job (16, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 33, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.023756950416292666, 'OptimizerSelector:sgd:momentum': 0.3401461971325867, 'OptimizerSelector:sgd:weight_decay': 0.09813128104900658, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:50 WORKER: registered result for job (16, 1, 0) with dispatcher\\n\",\n      \"13:43:50 job (16, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15017271041870117 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (17, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 542, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004562916235508196, 'OptimizerSelector:sgd:momentum': 0.5539213169475979, 'OptimizerSelector:sgd:weight_decay': 0.0370573539707279, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:50 WORKER: registered result for job (17, 0, 0) with dispatcher\\n\",\n      \"13:43:50 job (17, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14593219757080078 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (17, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 84, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.029484190172683392, 'OptimizerSelector:sgd:momentum': 0.1581877276873809, 'OptimizerSelector:sgd:weight_decay': 0.09882787341465421, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:50 WORKER: registered result for job (17, 0, 1) with dispatcher\\n\",\n      \"13:43:50 job (17, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14776182174682617 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (17, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 49, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0029655547633301938, 'OptimizerSelector:sgd:momentum': 0.12737501318423025, 'OptimizerSelector:sgd:weight_decay': 0.029494817796878594, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:50 WORKER: registered result for job (17, 0, 2) with dispatcher\\n\",\n      \"13:43:50 job (17, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14643383026123047 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (18, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 31, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0013397705709860559, 'OptimizerSelector:sgd:momentum': 0.13644185217072743, 'OptimizerSelector:sgd:weight_decay': 0.019005657568358876, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:50 WORKER: registered result for job (18, 0, 0) with dispatcher\\n\",\n      \"13:43:50 job (18, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14979815483093262 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (18, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 243, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006452981497766367, 'OptimizerSelector:sgd:momentum': 0.5655974528969264, 'OptimizerSelector:sgd:weight_decay': 0.0057777780173479745, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:50 WORKER: registered result for job (18, 0, 1) with dispatcher\\n\",\n      \"13:43:50 job (18, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1515958309173584 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:50 WORKER: start processing job (18, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023991701201626124, 'OptimizerSelector:sgd:momentum': 0.34322236020470315, 'OptimizerSelector:sgd:weight_decay': 0.02213684540245658, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:51 WORKER: registered result for job (18, 0, 2) with dispatcher\\n\",\n      \"13:43:51 job (18, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15162014961242676 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:51 WORKER: start processing job (18, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001969287584613355, 'OptimizerSelector:sgd:momentum': 0.8947565293839944, 'OptimizerSelector:sgd:weight_decay': 0.09436540208397137, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:51 WORKER: registered result for job (18, 0, 3) with dispatcher\\n\",\n      \"13:43:51 job (18, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14939498901367188 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"/usr/local/lib/python3.8/site-packages/statsmodels/nonparametric/kernels.py:62: RuntimeWarning: divide by zero encountered in true_divide\\n\",\n      \"  kernel_value = np.ones(Xi.size) * h / (num_levels - 1)\\n\",\n      \"13:43:51 WORKER: start processing job (18, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 33, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08908080084683827, 'OptimizerSelector:sgd:momentum': 0.6398587038758865, 'OptimizerSelector:sgd:weight_decay': 0.017806655330411193, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:51 WORKER: registered result for job (18, 0, 4) with dispatcher\\n\",\n      \"13:43:51 job (18, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1542208194732666 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:51 WORKER: start processing job (18, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:52 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 959, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08842926946836822, 'OptimizerSelector:sgd:momentum': 0.22743896232253924, 'OptimizerSelector:sgd:weight_decay': 0.0079589772609836, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:52 WORKER: registered result for job (18, 0, 5) with dispatcher\\n\",\n      \"13:43:52 job (18, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1468641757965088 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:52 WORKER: start processing job (18, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:52 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05346299355466584, 'OptimizerSelector:sgd:momentum': 0.4024067224453066, 'OptimizerSelector:sgd:weight_decay': 0.07609841795678655, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:52 WORKER: registered result for job (18, 0, 6) with dispatcher\\n\",\n      \"13:43:52 job (18, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14901494979858398 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:52 WORKER: start processing job (18, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:52 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003423964547163991, 'OptimizerSelector:sgd:momentum': 0.9599817012338697, 'OptimizerSelector:sgd:weight_decay': 0.05008807388140505, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:52 WORKER: registered result for job (18, 0, 7) with dispatcher\\n\",\n      \"13:43:52 job (18, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1558210849761963 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:52 WORKER: start processing job (18, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:53 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 65, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0014267051637762485, 'OptimizerSelector:sgd:momentum': 0.5855288712790656, 'OptimizerSelector:sgd:weight_decay': 0.004705514901844385, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:53 WORKER: registered result for job (18, 0, 8) with dispatcher\\n\",\n      \"13:43:53 job (18, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15536093711853027 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:53 WORKER: start processing job (18, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:53 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00012431085333226185, 'OptimizerSelector:sgd:momentum': 0.19162965228250975, 'OptimizerSelector:sgd:weight_decay': 0.038165894846805645, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:53 WORKER: registered result for job (18, 1, 0) with dispatcher\\n\",\n      \"13:43:53 job (18, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15218114852905273 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:53 WORKER: start processing job (18, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:53 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 101, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008305328630531602, 'OptimizerSelector:sgd:momentum': 0.11655985820719925, 'OptimizerSelector:sgd:weight_decay': 0.08770267721110876, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:53 WORKER: registered result for job (18, 1, 1) with dispatcher\\n\",\n      \"13:43:53 job (18, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15085577964782715 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:54 WORKER: start processing job (18, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0027297923410995687, 'OptimizerSelector:sgd:momentum': 0.601186092821379, 'OptimizerSelector:sgd:weight_decay': 0.09823431823287848, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:54 WORKER: registered result for job (18, 1, 2) with dispatcher\\n\",\n      \"13:43:54 job (18, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1613931655883789 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:54 WORKER: start processing job (18, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 181, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09505441672299594, 'OptimizerSelector:sgd:momentum': 0.4521818782179564, 'OptimizerSelector:sgd:weight_decay': 0.0025750748596121884, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:54 WORKER: registered result for job (18, 2, 0) with dispatcher\\n\",\n      \"13:43:54 job (18, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15318894386291504 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:54 WORKER: start processing job (19, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 54, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.048835260579188314, 'OptimizerSelector:sgd:momentum': 0.9076864144759459, 'OptimizerSelector:sgd:weight_decay': 0.0013367365719728543, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:55 WORKER: registered result for job (19, 0, 0) with dispatcher\\n\",\n      \"13:43:55 job (19, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15264201164245605 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:55 WORKER: start processing job (19, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09491087898354969, 'OptimizerSelector:sgd:momentum': 0.9330580571741668, 'OptimizerSelector:sgd:weight_decay': 0.004214515643816406, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:55 WORKER: registered result for job (19, 0, 1) with dispatcher\\n\",\n      \"13:43:55 job (19, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15536189079284668 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:55 WORKER: start processing job (19, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 649, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.044804995836301674, 'OptimizerSelector:sgd:momentum': 0.11425029659775598, 'OptimizerSelector:sgd:weight_decay': 0.02746024444987245, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:55 WORKER: registered result for job (19, 0, 2) with dispatcher\\n\",\n      \"13:43:55 job (19, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1716768741607666 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:55 WORKER: start processing job (19, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 49, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006402418035539099, 'OptimizerSelector:sgd:momentum': 0.49305351553484694, 'OptimizerSelector:sgd:weight_decay': 0.09286071401299663, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:55 WORKER: registered result for job (19, 1, 0) with dispatcher\\n\",\n      \"13:43:55 job (19, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15111827850341797 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (20, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03974700645874211, 'OptimizerSelector:sgd:momentum': 0.1359844634781565, 'OptimizerSelector:sgd:weight_decay': 0.00903081670402624, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:56 WORKER: registered result for job (20, 0, 0) with dispatcher\\n\",\n      \"13:43:56 job (20, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15872907638549805 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (20, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 50, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.046318506631553305, 'OptimizerSelector:sgd:momentum': 0.7123359089964596, 'OptimizerSelector:sgd:weight_decay': 0.050182272665073545, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:56 WORKER: registered result for job (20, 0, 1) with dispatcher\\n\",\n      \"13:43:56 job (20, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15100622177124023 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (20, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 758, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00028002378890950127, 'OptimizerSelector:sgd:momentum': 0.5033657751081735, 'OptimizerSelector:sgd:weight_decay': 0.08489052802418881, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:56 WORKER: registered result for job (20, 0, 2) with dispatcher\\n\",\n      \"13:43:56 job (20, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.161376953125 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (21, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05825471697168684, 'OptimizerSelector:sgd:momentum': 0.3337324521396464, 'OptimizerSelector:sgd:weight_decay': 0.09644466721064439, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:56 WORKER: registered result for job (21, 0, 0) with dispatcher\\n\",\n      \"13:43:56 job (21, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1526319980621338 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (21, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 271, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03693712295074257, 'OptimizerSelector:sgd:momentum': 0.5237442608688025, 'OptimizerSelector:sgd:weight_decay': 0.05119927678073114, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:56 WORKER: registered result for job (21, 0, 1) with dispatcher\\n\",\n      \"13:43:56 job (21, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1559739112854004 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:56 WORKER: start processing job (21, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000187607761017551, 'OptimizerSelector:sgd:momentum': 0.808225368735058, 'OptimizerSelector:sgd:weight_decay': 0.05956929679791681, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:57 WORKER: registered result for job (21, 0, 2) with dispatcher\\n\",\n      \"13:43:57 job (21, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17204904556274414 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:57 WORKER: start processing job (21, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 62, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008351914736872913, 'OptimizerSelector:sgd:momentum': 0.48891503756012367, 'OptimizerSelector:sgd:weight_decay': 0.05164244870264381, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:57 WORKER: registered result for job (21, 0, 3) with dispatcher\\n\",\n      \"13:43:57 job (21, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16438913345336914 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:57 WORKER: start processing job (21, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 358, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003208288802242177, 'OptimizerSelector:sgd:momentum': 0.9183695274815583, 'OptimizerSelector:sgd:weight_decay': 0.051224902019861004, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:57 WORKER: registered result for job (21, 0, 4) with dispatcher\\n\",\n      \"13:43:57 job (21, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16445016860961914 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:57 WORKER: start processing job (21, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03506980655811203, 'OptimizerSelector:sgd:momentum': 0.3379627551778592, 'OptimizerSelector:sgd:weight_decay': 0.007463618893262814, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:58 WORKER: registered result for job (21, 0, 5) with dispatcher\\n\",\n      \"13:43:58 job (21, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.2665398120880127 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:58 WORKER: start processing job (21, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 57, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00718054591102875, 'OptimizerSelector:sgd:momentum': 0.203845545012436, 'OptimizerSelector:sgd:weight_decay': 0.08122953317352141, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:58 WORKER: registered result for job (21, 0, 6) with dispatcher\\n\",\n      \"13:43:58 job (21, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17991876602172852 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:58 WORKER: start processing job (21, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.015824543124236274, 'OptimizerSelector:sgd:momentum': 0.909574437462381, 'OptimizerSelector:sgd:weight_decay': 0.034077144325786644, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:58 WORKER: registered result for job (21, 0, 7) with dispatcher\\n\",\n      \"13:43:58 job (21, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1916670799255371 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:59 WORKER: start processing job (21, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:59 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 14, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005593419039782144, 'OptimizerSelector:sgd:momentum': 0.6467777144853258, 'OptimizerSelector:sgd:weight_decay': 0.01795298482182474, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:59 WORKER: registered result for job (21, 0, 8) with dispatcher\\n\",\n      \"13:43:59 job (21, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18866205215454102 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:43:59 WORKER: start processing job (21, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:59 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 35, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006257435721726726, 'OptimizerSelector:sgd:momentum': 0.5291573840720926, 'OptimizerSelector:sgd:weight_decay': 0.043663167324160874, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:43:59 WORKER: registered result for job (21, 1, 0) with dispatcher\\n\",\n      \"13:43:59 job (21, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1553950309753418 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:59 WORKER: start processing job (21, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:43:59 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 48, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.015583461352137565, 'OptimizerSelector:sgd:momentum': 0.5952863101309174, 'OptimizerSelector:sgd:weight_decay': 0.06846118174981028, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:43:59 WORKER: registered result for job (21, 1, 1) with dispatcher\\n\",\n      \"13:43:59 job (21, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1557939052581787 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:43:59 WORKER: start processing job (21, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0046888150013327376, 'OptimizerSelector:sgd:momentum': 0.590725016753823, 'OptimizerSelector:sgd:weight_decay': 0.029865469068166443, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:00 WORKER: registered result for job (21, 1, 2) with dispatcher\\n\",\n      \"13:44:00 job (21, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15713977813720703 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:00 WORKER: start processing job (21, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003814454124812659, 'OptimizerSelector:sgd:momentum': 0.11421365998360185, 'OptimizerSelector:sgd:weight_decay': 0.07266721417078828, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:00 WORKER: registered result for job (21, 2, 0) with dispatcher\\n\",\n      \"13:44:00 job (21, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15854382514953613 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:00 WORKER: start processing job (22, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.030782910931691658, 'OptimizerSelector:sgd:momentum': 0.808187545814692, 'OptimizerSelector:sgd:weight_decay': 0.08485260412218529, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:00 WORKER: registered result for job (22, 0, 0) with dispatcher\\n\",\n      \"13:44:00 job (22, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15236401557922363 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:01 WORKER: start processing job (22, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0014775370174655673, 'OptimizerSelector:sgd:momentum': 0.9094477894145038, 'OptimizerSelector:sgd:weight_decay': 0.03556167938007811, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:01 WORKER: registered result for job (22, 0, 1) with dispatcher\\n\",\n      \"13:44:01 job (22, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15096211433410645 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:01 WORKER: start processing job (22, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 950, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009343174138284941, 'OptimizerSelector:sgd:momentum': 0.3762415630338462, 'OptimizerSelector:sgd:weight_decay': 0.0011045265285690746, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:01 WORKER: registered result for job (22, 0, 2) with dispatcher\\n\",\n      \"13:44:01 job (22, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16264891624450684 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:01 WORKER: start processing job (22, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 739, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004140173909419955, 'OptimizerSelector:sgd:momentum': 0.15502249311700636, 'OptimizerSelector:sgd:weight_decay': 0.09379440291346024, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:01 WORKER: registered result for job (22, 1, 0) with dispatcher\\n\",\n      \"13:44:01 job (22, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1838059425354004 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:01 WORKER: start processing job (23, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 25, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0013742638601646619, 'OptimizerSelector:sgd:momentum': 0.721113752479556, 'OptimizerSelector:sgd:weight_decay': 0.030940700404411357, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:02 WORKER: registered result for job (23, 0, 0) with dispatcher\\n\",\n      \"13:44:02 job (23, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17298507690429688 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:02 WORKER: start processing job (23, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0017679560661131104, 'OptimizerSelector:sgd:momentum': 0.9873661002841981, 'OptimizerSelector:sgd:weight_decay': 0.07657713188549224, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:02 WORKER: registered result for job (23, 0, 1) with dispatcher\\n\",\n      \"13:44:02 job (23, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17458295822143555 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:02 WORKER: start processing job (23, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00013545104462203426, 'OptimizerSelector:sgd:momentum': 0.19726094618118054, 'OptimizerSelector:sgd:weight_decay': 0.09897622431660877, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:02 WORKER: registered result for job (23, 0, 2) with dispatcher\\n\",\n      \"13:44:02 job (23, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17740273475646973 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:02 WORKER: start processing job (24, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:03 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 65, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0016418817027706108, 'OptimizerSelector:sgd:momentum': 0.9418261520329354, 'OptimizerSelector:sgd:weight_decay': 0.07823610143517977, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:03 WORKER: registered result for job (24, 0, 0) with dispatcher\\n\",\n      \"13:44:03 job (24, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16852712631225586 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:03 WORKER: start processing job (24, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:03 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 288, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00023979077662860635, 'OptimizerSelector:sgd:momentum': 0.8634674637276261, 'OptimizerSelector:sgd:weight_decay': 0.044645604522263654, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:03 WORKER: registered result for job (24, 0, 1) with dispatcher\\n\",\n      \"13:44:03 job (24, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.21063232421875 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:03 WORKER: start processing job (24, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:03 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 71, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0018192675883783244, 'OptimizerSelector:sgd:momentum': 0.519412290245793, 'OptimizerSelector:sgd:weight_decay': 0.006638994470066686, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:03 WORKER: registered result for job (24, 0, 2) with dispatcher\\n\",\n      \"13:44:03 job (24, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.22649598121643066 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:04 WORKER: start processing job (24, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004259239905250138, 'OptimizerSelector:sgd:momentum': 0.13045936872548056, 'OptimizerSelector:sgd:weight_decay': 0.013212525193296073, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:04 WORKER: registered result for job (24, 0, 3) with dispatcher\\n\",\n      \"13:44:04 job (24, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16228508949279785 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:04 WORKER: start processing job (24, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000478578739376374, 'OptimizerSelector:sgd:momentum': 0.7166770784459785, 'OptimizerSelector:sgd:weight_decay': 0.0550026472006108, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:04 WORKER: registered result for job (24, 0, 4) with dispatcher\\n\",\n      \"13:44:04 job (24, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1535019874572754 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:04 WORKER: start processing job (24, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010565297727629071, 'OptimizerSelector:sgd:momentum': 0.5063148044466128, 'OptimizerSelector:sgd:weight_decay': 0.03541102955838973, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:04 WORKER: registered result for job (24, 0, 5) with dispatcher\\n\",\n      \"13:44:04 job (24, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14733314514160156 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:05 WORKER: start processing job (24, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:05 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003305545341172964, 'OptimizerSelector:sgd:momentum': 0.8087323424392047, 'OptimizerSelector:sgd:weight_decay': 0.0006062316477804878, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:05 WORKER: registered result for job (24, 0, 6) with dispatcher\\n\",\n      \"13:44:05 job (24, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15629291534423828 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:05 WORKER: start processing job (24, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:05 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 25, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002451742979084766, 'OptimizerSelector:sgd:momentum': 0.1060264982628568, 'OptimizerSelector:sgd:weight_decay': 0.012134979979932715, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:05 WORKER: registered result for job (24, 0, 7) with dispatcher\\n\",\n      \"13:44:05 job (24, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1482999324798584 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:06 WORKER: start processing job (24, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:06 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.022181073720732795, 'OptimizerSelector:sgd:momentum': 0.11451919072195979, 'OptimizerSelector:sgd:weight_decay': 0.0073703531680762505, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:06 WORKER: registered result for job (24, 0, 8) with dispatcher\\n\",\n      \"13:44:06 job (24, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16839098930358887 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:06 WORKER: start processing job (24, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:06 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 167, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000273659977042633, 'OptimizerSelector:sgd:momentum': 0.5286793852267693, 'OptimizerSelector:sgd:weight_decay': 0.02824651165759117, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:06 WORKER: registered result for job (24, 1, 0) with dispatcher\\n\",\n      \"13:44:06 job (24, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1620030403137207 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:06 WORKER: start processing job (24, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:07 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 41, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.009763227497129844, 'OptimizerSelector:sgd:momentum': 0.7110676047946418, 'OptimizerSelector:sgd:weight_decay': 0.005207942264431833, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:07 WORKER: registered result for job (24, 1, 1) with dispatcher\\n\",\n      \"13:44:07 job (24, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15304112434387207 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:07 WORKER: start processing job (24, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:07 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005829210990203627, 'OptimizerSelector:sgd:momentum': 0.16803717565380616, 'OptimizerSelector:sgd:weight_decay': 0.025920116502353027, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:07 WORKER: registered result for job (24, 1, 2) with dispatcher\\n\",\n      \"13:44:07 job (24, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15009403228759766 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:07 WORKER: start processing job (24, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:07 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 783, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023425449265962062, 'OptimizerSelector:sgd:momentum': 0.19892101086207312, 'OptimizerSelector:sgd:weight_decay': 0.05588174010281082, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:07 WORKER: registered result for job (24, 2, 0) with dispatcher\\n\",\n      \"13:44:07 job (24, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1484220027923584 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:07 WORKER: start processing job (25, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005087256733751786, 'OptimizerSelector:sgd:momentum': 0.14343355045637113, 'OptimizerSelector:sgd:weight_decay': 0.014678933087172554, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:08 WORKER: registered result for job (25, 0, 0) with dispatcher\\n\",\n      \"13:44:08 job (25, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15002679824829102 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:08 WORKER: start processing job (25, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 57, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.007673256476637555, 'OptimizerSelector:sgd:momentum': 0.7664458868114967, 'OptimizerSelector:sgd:weight_decay': 0.06532052196158233, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:08 WORKER: registered result for job (25, 0, 1) with dispatcher\\n\",\n      \"13:44:08 job (25, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14940619468688965 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:08 WORKER: start processing job (25, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.013252435932959862, 'OptimizerSelector:sgd:momentum': 0.10595087254452171, 'OptimizerSelector:sgd:weight_decay': 0.00596748301998612, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:08 WORKER: registered result for job (25, 0, 2) with dispatcher\\n\",\n      \"13:44:08 job (25, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15254497528076172 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:08 WORKER: start processing job (25, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 197, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07940464760792389, 'OptimizerSelector:sgd:momentum': 0.3841328264935759, 'OptimizerSelector:sgd:weight_decay': 0.006003259748762682, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:09 WORKER: registered result for job (25, 1, 0) with dispatcher\\n\",\n      \"13:44:09 job (25, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1509687900543213 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:09 WORKER: start processing job (26, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 80, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0011963572732927477, 'OptimizerSelector:sgd:momentum': 0.9860189382560257, 'OptimizerSelector:sgd:weight_decay': 0.0073867321901024, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:09 WORKER: registered result for job (26, 0, 0) with dispatcher\\n\",\n      \"13:44:09 job (26, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17541718482971191 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:09 WORKER: start processing job (26, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00067384847209607, 'OptimizerSelector:sgd:momentum': 0.25293893873797985, 'OptimizerSelector:sgd:weight_decay': 0.03278511730399739, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:09 WORKER: registered result for job (26, 0, 1) with dispatcher\\n\",\n      \"13:44:09 job (26, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16624093055725098 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:09 WORKER: start processing job (26, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.014646061855654868, 'OptimizerSelector:sgd:momentum': 0.25800782940900185, 'OptimizerSelector:sgd:weight_decay': 0.041238906793138894, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:09 WORKER: registered result for job (26, 0, 2) with dispatcher\\n\",\n      \"13:44:09 job (26, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.164215087890625 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:10 WORKER: start processing job (27, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:10 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 341, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010379055395184221, 'OptimizerSelector:sgd:momentum': 0.5277696436619681, 'OptimizerSelector:sgd:weight_decay': 0.035716928846698096, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:10 WORKER: registered result for job (27, 0, 0) with dispatcher\\n\",\n      \"13:44:10 job (27, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1514418125152588 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:10 WORKER: start processing job (27, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:10 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009073069913683975, 'OptimizerSelector:sgd:momentum': 0.8182282092481012, 'OptimizerSelector:sgd:weight_decay': 0.034326056359394674, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:10 WORKER: registered result for job (27, 0, 1) with dispatcher\\n\",\n      \"13:44:10 job (27, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.27743101119995117 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:11 WORKER: start processing job (27, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:11 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0014943835389273827, 'OptimizerSelector:sgd:momentum': 0.14529708147131742, 'OptimizerSelector:sgd:weight_decay': 0.008287431738144217, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:11 WORKER: registered result for job (27, 0, 2) with dispatcher\\n\",\n      \"13:44:11 job (27, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15482783317565918 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:11 WORKER: start processing job (27, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:11 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 81, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0032091092874762146, 'OptimizerSelector:sgd:momentum': 0.11957446844711446, 'OptimizerSelector:sgd:weight_decay': 0.01010383250479613, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:11 WORKER: registered result for job (27, 0, 3) with dispatcher\\n\",\n      \"13:44:11 job (27, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15303492546081543 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:11 WORKER: start processing job (27, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 69, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06416517809552832, 'OptimizerSelector:sgd:momentum': 0.4156657649254904, 'OptimizerSelector:sgd:weight_decay': 0.023012770924194664, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:12 WORKER: registered result for job (27, 0, 4) with dispatcher\\n\",\n      \"13:44:12 job (27, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1522810459136963 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:12 WORKER: start processing job (27, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09200860227759969, 'OptimizerSelector:sgd:momentum': 0.9379385727987957, 'OptimizerSelector:sgd:weight_decay': 0.05831837055569159, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:12 WORKER: registered result for job (27, 0, 5) with dispatcher\\n\",\n      \"13:44:12 job (27, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1514286994934082 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:12 WORKER: start processing job (27, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008173109102981718, 'OptimizerSelector:sgd:momentum': 0.12358622653213329, 'OptimizerSelector:sgd:weight_decay': 0.006368922553345323, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:12 WORKER: registered result for job (27, 0, 6) with dispatcher\\n\",\n      \"13:44:12 job (27, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1518709659576416 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:12 WORKER: start processing job (27, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 41, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02932828382440975, 'OptimizerSelector:sgd:momentum': 0.404395766079669, 'OptimizerSelector:sgd:weight_decay': 0.0001671413361740985, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:13 WORKER: registered result for job (27, 0, 7) with dispatcher\\n\",\n      \"13:44:13 job (27, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1513371467590332 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:13 WORKER: start processing job (27, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 447, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06906462358399243, 'OptimizerSelector:sgd:momentum': 0.49199165672453343, 'OptimizerSelector:sgd:weight_decay': 0.07491406953413106, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:13 WORKER: registered result for job (27, 0, 8) with dispatcher\\n\",\n      \"13:44:13 job (27, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1444840431213379 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:13 WORKER: start processing job (27, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 1017, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005840326610778783, 'OptimizerSelector:sgd:momentum': 0.2168117589918844, 'OptimizerSelector:sgd:weight_decay': 0.04355017333047543, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:13 WORKER: registered result for job (27, 1, 0) with dispatcher\\n\",\n      \"13:44:13 job (27, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14785194396972656 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:13 WORKER: start processing job (27, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 700, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.019523654852010602, 'OptimizerSelector:sgd:momentum': 0.3090008081207469, 'OptimizerSelector:sgd:weight_decay': 0.005636783288326821, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:13 WORKER: registered result for job (27, 1, 1) with dispatcher\\n\",\n      \"13:44:13 job (27, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15731000900268555 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:13 WORKER: start processing job (27, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.019576043004195305, 'OptimizerSelector:sgd:momentum': 0.4297928018321146, 'OptimizerSelector:sgd:weight_decay': 0.03226851611455622, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:13 WORKER: registered result for job (27, 1, 2) with dispatcher\\n\",\n      \"13:44:13 job (27, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15456891059875488 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:14 WORKER: start processing job (27, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06591858659177753, 'OptimizerSelector:sgd:momentum': 0.6562683206417238, 'OptimizerSelector:sgd:weight_decay': 0.029657410977640016, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:14 WORKER: registered result for job (27, 2, 0) with dispatcher\\n\",\n      \"13:44:14 job (27, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15054798126220703 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:14 WORKER: start processing job (28, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 462, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08728166644657735, 'OptimizerSelector:sgd:momentum': 0.10462671100984106, 'OptimizerSelector:sgd:weight_decay': 0.0556312376382392, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:14 WORKER: registered result for job (28, 0, 0) with dispatcher\\n\",\n      \"13:44:14 job (28, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14732098579406738 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:14 WORKER: start processing job (28, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 278, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005121131240026481, 'OptimizerSelector:sgd:momentum': 0.6594199232782012, 'OptimizerSelector:sgd:weight_decay': 0.044084902301641686, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:14 WORKER: registered result for job (28, 0, 1) with dispatcher\\n\",\n      \"13:44:14 job (28, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14787793159484863 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:14 WORKER: start processing job (28, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:15 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 48, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00406942054496115, 'OptimizerSelector:sgd:momentum': 0.4959299167698369, 'OptimizerSelector:sgd:weight_decay': 0.037749681745406244, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:15 WORKER: registered result for job (28, 0, 2) with dispatcher\\n\",\n      \"13:44:15 job (28, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14592409133911133 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:15 WORKER: start processing job (28, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:15 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005032075246418847, 'OptimizerSelector:sgd:momentum': 0.8763699971273216, 'OptimizerSelector:sgd:weight_decay': 0.04205187002437107, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:15 WORKER: registered result for job (28, 1, 0) with dispatcher\\n\",\n      \"13:44:15 job (28, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15047335624694824 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:15 WORKER: start processing job (29, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:15 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 64, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00032134666469375834, 'OptimizerSelector:sgd:momentum': 0.1634504334190506, 'OptimizerSelector:sgd:weight_decay': 0.04433172177499458, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:15 WORKER: registered result for job (29, 0, 0) with dispatcher\\n\",\n      \"13:44:15 job (29, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1457526683807373 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:15 WORKER: start processing job (29, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 14, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0157900786295888, 'OptimizerSelector:sgd:momentum': 0.823148905832743, 'OptimizerSelector:sgd:weight_decay': 0.0617366198970355, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:16 WORKER: registered result for job (29, 0, 1) with dispatcher\\n\",\n      \"13:44:16 job (29, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1558229923248291 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:16 WORKER: start processing job (29, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 71, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00021586678281786313, 'OptimizerSelector:sgd:momentum': 0.2468177995739147, 'OptimizerSelector:sgd:weight_decay': 0.002612696611657496, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:16 WORKER: registered result for job (29, 0, 2) with dispatcher\\n\",\n      \"13:44:16 job (29, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15163898468017578 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:16 WORKER: start processing job (30, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 376, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009372323253888004, 'OptimizerSelector:sgd:momentum': 0.17848281637365715, 'OptimizerSelector:sgd:weight_decay': 0.006848742665455705, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:16 WORKER: registered result for job (30, 0, 0) with dispatcher\\n\",\n      \"13:44:16 job (30, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15727710723876953 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:16 WORKER: start processing job (30, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 468, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.029351225987317108, 'OptimizerSelector:sgd:momentum': 0.7146348157904504, 'OptimizerSelector:sgd:weight_decay': 0.07560553101350362, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:17 WORKER: registered result for job (30, 0, 1) with dispatcher\\n\",\n      \"13:44:17 job (30, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16565990447998047 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:17 WORKER: start processing job (30, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 57, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001467521852320903, 'OptimizerSelector:sgd:momentum': 0.7060059975885865, 'OptimizerSelector:sgd:weight_decay': 0.0907621916274346, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:17 WORKER: registered result for job (30, 0, 2) with dispatcher\\n\",\n      \"13:44:17 job (30, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14630508422851562 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:17 WORKER: start processing job (30, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005760986793990271, 'OptimizerSelector:sgd:momentum': 0.20143966456319137, 'OptimizerSelector:sgd:weight_decay': 0.022187722662855534, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:17 WORKER: registered result for job (30, 0, 3) with dispatcher\\n\",\n      \"13:44:17 job (30, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15262413024902344 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:17 WORKER: start processing job (30, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010936498567607163, 'OptimizerSelector:sgd:momentum': 0.654007866059726, 'OptimizerSelector:sgd:weight_decay': 0.05279186594030674, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:17 WORKER: registered result for job (30, 0, 4) with dispatcher\\n\",\n      \"13:44:17 job (30, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14734530448913574 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:18 WORKER: start processing job (30, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:18 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 966, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.007608158463182214, 'OptimizerSelector:sgd:momentum': 0.823644705111281, 'OptimizerSelector:sgd:weight_decay': 0.004790909420104664, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:18 WORKER: registered result for job (30, 0, 5) with dispatcher\\n\",\n      \"13:44:18 job (30, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14534592628479004 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:18 WORKER: start processing job (30, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:18 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02741941464938496, 'OptimizerSelector:sgd:momentum': 0.9174833322652775, 'OptimizerSelector:sgd:weight_decay': 0.00996539827739219, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:18 WORKER: registered result for job (30, 0, 6) with dispatcher\\n\",\n      \"13:44:18 job (30, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1468050479888916 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:18 WORKER: start processing job (30, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 77, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07029376319053429, 'OptimizerSelector:sgd:momentum': 0.9833039666478216, 'OptimizerSelector:sgd:weight_decay': 0.08707999400167843, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:19 WORKER: registered result for job (30, 0, 7) with dispatcher\\n\",\n      \"13:44:19 job (30, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1522979736328125 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:19 WORKER: start processing job (30, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.015795703191863488, 'OptimizerSelector:sgd:momentum': 0.9480362460905386, 'OptimizerSelector:sgd:weight_decay': 0.0935667127725677, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:19 WORKER: registered result for job (30, 0, 8) with dispatcher\\n\",\n      \"13:44:19 job (30, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17345094680786133 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:19 WORKER: start processing job (30, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0002434510033339719, 'OptimizerSelector:sgd:momentum': 0.20097703778675846, 'OptimizerSelector:sgd:weight_decay': 0.08703819392074118, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:19 WORKER: registered result for job (30, 1, 0) with dispatcher\\n\",\n      \"13:44:19 job (30, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17433905601501465 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:19 WORKER: start processing job (30, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:20 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 610, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008425165176370958, 'OptimizerSelector:sgd:momentum': 0.5020554582492895, 'OptimizerSelector:sgd:weight_decay': 0.004597389097681334, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:20 WORKER: registered result for job (30, 1, 1) with dispatcher\\n\",\n      \"13:44:20 job (30, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1532599925994873 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:20 WORKER: start processing job (30, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:20 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 52, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0016333296698814949, 'OptimizerSelector:sgd:momentum': 0.7152552789610302, 'OptimizerSelector:sgd:weight_decay': 0.020900184263935518, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:20 WORKER: registered result for job (30, 1, 2) with dispatcher\\n\",\n      \"13:44:20 job (30, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1927506923675537 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:20 WORKER: start processing job (30, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:21 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006283411890546003, 'OptimizerSelector:sgd:momentum': 0.8306597227714551, 'OptimizerSelector:sgd:weight_decay': 0.032882127014463615, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:21 WORKER: registered result for job (30, 2, 0) with dispatcher\\n\",\n      \"13:44:21 job (30, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.20567679405212402 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:21 WORKER: start processing job (31, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:21 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 338, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02527586391340911, 'OptimizerSelector:sgd:momentum': 0.8755198427890122, 'OptimizerSelector:sgd:weight_decay': 0.04652889239353592, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:21 WORKER: registered result for job (31, 0, 0) with dispatcher\\n\",\n      \"13:44:21 job (31, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.21240019798278809 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:21 WORKER: start processing job (31, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 860, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00023208858171608958, 'OptimizerSelector:sgd:momentum': 0.8846125677807638, 'OptimizerSelector:sgd:weight_decay': 0.007216710858093281, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:22 WORKER: registered result for job (31, 0, 1) with dispatcher\\n\",\n      \"13:44:22 job (31, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14884614944458008 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:22 WORKER: start processing job (31, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 161, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.031011130081612895, 'OptimizerSelector:sgd:momentum': 0.3125284983542355, 'OptimizerSelector:sgd:weight_decay': 0.06404595740811495, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:22 WORKER: registered result for job (31, 0, 2) with dispatcher\\n\",\n      \"13:44:22 job (31, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16582727432250977 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:22 WORKER: start processing job (31, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0015112158332342429, 'OptimizerSelector:sgd:momentum': 0.511488445532269, 'OptimizerSelector:sgd:weight_decay': 0.08042560556328787, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:22 WORKER: registered result for job (31, 1, 0) with dispatcher\\n\",\n      \"13:44:22 job (31, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16376590728759766 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:22 WORKER: start processing job (32, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 544, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.041263859334317386, 'OptimizerSelector:sgd:momentum': 0.2548862292290878, 'OptimizerSelector:sgd:weight_decay': 0.02085004338003215, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:22 WORKER: registered result for job (32, 0, 0) with dispatcher\\n\",\n      \"13:44:22 job (32, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16716814041137695 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:22 WORKER: start processing job (32, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09635326406240474, 'OptimizerSelector:sgd:momentum': 0.49691972581407423, 'OptimizerSelector:sgd:weight_decay': 0.06693422151532001, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:23 WORKER: registered result for job (32, 0, 1) with dispatcher\\n\",\n      \"13:44:23 job (32, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17984604835510254 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:23 WORKER: start processing job (32, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 95, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000730033471212262, 'OptimizerSelector:sgd:momentum': 0.23535697093835678, 'OptimizerSelector:sgd:weight_decay': 0.08229780727639877, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:23 WORKER: registered result for job (32, 0, 2) with dispatcher\\n\",\n      \"13:44:23 job (32, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17138385772705078 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:23 WORKER: start processing job (33, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 25, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06521705998860197, 'OptimizerSelector:sgd:momentum': 0.7609206163774076, 'OptimizerSelector:sgd:weight_decay': 0.052706534283421674, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:23 WORKER: registered result for job (33, 0, 0) with dispatcher\\n\",\n      \"13:44:23 job (33, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16330599784851074 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:24 WORKER: start processing job (33, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003410881559206538, 'OptimizerSelector:sgd:momentum': 0.976898401861478, 'OptimizerSelector:sgd:weight_decay': 0.04948903998312342, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:24 WORKER: registered result for job (33, 0, 1) with dispatcher\\n\",\n      \"13:44:24 job (33, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16640686988830566 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:24 WORKER: start processing job (33, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 417, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002443508408005201, 'OptimizerSelector:sgd:momentum': 0.8041393520264506, 'OptimizerSelector:sgd:weight_decay': 0.06722688217393184, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:24 WORKER: registered result for job (33, 0, 2) with dispatcher\\n\",\n      \"13:44:24 job (33, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1803131103515625 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:24 WORKER: start processing job (33, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00013620081148731168, 'OptimizerSelector:sgd:momentum': 0.9122066322737068, 'OptimizerSelector:sgd:weight_decay': 0.021752443651167134, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:24 WORKER: registered result for job (33, 0, 3) with dispatcher\\n\",\n      \"13:44:24 job (33, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16203999519348145 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:25 WORKER: start processing job (33, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:25 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05856099537529629, 'OptimizerSelector:sgd:momentum': 0.5134089226446411, 'OptimizerSelector:sgd:weight_decay': 0.02257330085351209, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:25 WORKER: registered result for job (33, 0, 4) with dispatcher\\n\",\n      \"13:44:25 job (33, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1532299518585205 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:25 WORKER: start processing job (33, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:25 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.016055270471856807, 'OptimizerSelector:sgd:momentum': 0.7154173978664706, 'OptimizerSelector:sgd:weight_decay': 0.039438030676298245, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:25 WORKER: registered result for job (33, 0, 5) with dispatcher\\n\",\n      \"13:44:25 job (33, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1710069179534912 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:26 WORKER: start processing job (33, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 68, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0025532795263211294, 'OptimizerSelector:sgd:momentum': 0.7795790173412898, 'OptimizerSelector:sgd:weight_decay': 0.03259470505414218, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:26 WORKER: registered result for job (33, 0, 6) with dispatcher\\n\",\n      \"13:44:26 job (33, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16104602813720703 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:26 WORKER: start processing job (33, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.017702016314777677, 'OptimizerSelector:sgd:momentum': 0.4609665458791649, 'OptimizerSelector:sgd:weight_decay': 0.03306104031728374, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:26 WORKER: registered result for job (33, 0, 7) with dispatcher\\n\",\n      \"13:44:26 job (33, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17573094367980957 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:26 WORKER: start processing job (33, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 295, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006346407173589697, 'OptimizerSelector:sgd:momentum': 0.10184471865309694, 'OptimizerSelector:sgd:weight_decay': 0.08692349567695665, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:26 WORKER: registered result for job (33, 0, 8) with dispatcher\\n\",\n      \"13:44:26 job (33, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.19363188743591309 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:26 WORKER: start processing job (33, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 159, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.093213987498508, 'OptimizerSelector:sgd:momentum': 0.6534573653944318, 'OptimizerSelector:sgd:weight_decay': 0.00787502585003558, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:27 WORKER: registered result for job (33, 1, 0) with dispatcher\\n\",\n      \"13:44:27 job (33, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.2088301181793213 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:27 WORKER: start processing job (33, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 392, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0028349348119503653, 'OptimizerSelector:sgd:momentum': 0.349269732849897, 'OptimizerSelector:sgd:weight_decay': 0.013484117299844457, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:27 WORKER: registered result for job (33, 1, 1) with dispatcher\\n\",\n      \"13:44:27 job (33, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14821600914001465 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:27 WORKER: start processing job (33, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 41, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004526181036478486, 'OptimizerSelector:sgd:momentum': 0.6321170179754524, 'OptimizerSelector:sgd:weight_decay': 0.06730795181790637, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:27 WORKER: registered result for job (33, 1, 2) with dispatcher\\n\",\n      \"13:44:27 job (33, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1495828628540039 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:27 WORKER: start processing job (33, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 192, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00014438914400745912, 'OptimizerSelector:sgd:momentum': 0.5451891542306367, 'OptimizerSelector:sgd:weight_decay': 0.011139503454249437, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:28 WORKER: registered result for job (33, 2, 0) with dispatcher\\n\",\n      \"13:44:28 job (33, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14879107475280762 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:28 WORKER: start processing job (34, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 251, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05424685760426694, 'OptimizerSelector:sgd:momentum': 0.8233264312975235, 'OptimizerSelector:sgd:weight_decay': 0.0575083242373383, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:28 WORKER: registered result for job (34, 0, 0) with dispatcher\\n\",\n      \"13:44:28 job (34, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15720725059509277 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:28 WORKER: start processing job (34, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 132, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00023864596172687875, 'OptimizerSelector:sgd:momentum': 0.8301745563887655, 'OptimizerSelector:sgd:weight_decay': 0.0258554949539053, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:28 WORKER: registered result for job (34, 0, 1) with dispatcher\\n\",\n      \"13:44:28 job (34, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15380597114562988 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:29 WORKER: start processing job (34, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:29 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 47, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00896014202930808, 'OptimizerSelector:sgd:momentum': 0.9673461956318491, 'OptimizerSelector:sgd:weight_decay': 0.072446872261344, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:29 WORKER: registered result for job (34, 0, 2) with dispatcher\\n\",\n      \"13:44:29 job (34, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14464592933654785 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:29 WORKER: start processing job (34, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:29 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 68, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0046374853723287575, 'OptimizerSelector:sgd:momentum': 0.17369966211574944, 'OptimizerSelector:sgd:weight_decay': 0.09319100795769325, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:29 WORKER: registered result for job (34, 1, 0) with dispatcher\\n\",\n      \"13:44:29 job (34, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1542057991027832 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:29 WORKER: start processing job (35, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:29 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 358, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06851682890270291, 'OptimizerSelector:sgd:momentum': 0.7906713003478543, 'OptimizerSelector:sgd:weight_decay': 0.025407307417784693, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:29 WORKER: registered result for job (35, 0, 0) with dispatcher\\n\",\n      \"13:44:29 job (35, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16950201988220215 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:30 WORKER: start processing job (35, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00015098201320154858, 'OptimizerSelector:sgd:momentum': 0.6632159732810995, 'OptimizerSelector:sgd:weight_decay': 0.039891425959009096, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:30 WORKER: registered result for job (35, 0, 1) with dispatcher\\n\",\n      \"13:44:30 job (35, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15455269813537598 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:30 WORKER: start processing job (35, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07818296578607581, 'OptimizerSelector:sgd:momentum': 0.41032062784297385, 'OptimizerSelector:sgd:weight_decay': 0.030608554510239045, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:30 WORKER: registered result for job (35, 0, 2) with dispatcher\\n\",\n      \"13:44:30 job (35, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1487281322479248 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:31 WORKER: start processing job (36, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 313, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03339743642836555, 'OptimizerSelector:sgd:momentum': 0.7604836239453209, 'OptimizerSelector:sgd:weight_decay': 0.0006163944093833604, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:31 WORKER: registered result for job (36, 0, 0) with dispatcher\\n\",\n      \"13:44:31 job (36, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16636896133422852 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:31 WORKER: start processing job (36, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 29, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02308657564843713, 'OptimizerSelector:sgd:momentum': 0.512225761525992, 'OptimizerSelector:sgd:weight_decay': 0.011271712608634795, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:31 WORKER: registered result for job (36, 0, 1) with dispatcher\\n\",\n      \"13:44:31 job (36, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15899372100830078 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:31 WORKER: start processing job (36, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 46, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003171297894068004, 'OptimizerSelector:sgd:momentum': 0.618516591140205, 'OptimizerSelector:sgd:weight_decay': 0.013643329296522316, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:32 WORKER: registered result for job (36, 0, 2) with dispatcher\\n\",\n      \"13:44:32 job (36, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14995503425598145 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:32 WORKER: start processing job (36, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0037360042205415956, 'OptimizerSelector:sgd:momentum': 0.7566580425773167, 'OptimizerSelector:sgd:weight_decay': 0.013036636483464953, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:32 WORKER: registered result for job (36, 0, 3) with dispatcher\\n\",\n      \"13:44:32 job (36, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15526223182678223 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:32 WORKER: start processing job (36, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 30, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.005237172386154036, 'OptimizerSelector:sgd:momentum': 0.4810114589520514, 'OptimizerSelector:sgd:weight_decay': 0.0773685788832058, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:32 WORKER: registered result for job (36, 0, 4) with dispatcher\\n\",\n      \"13:44:32 job (36, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1479170322418213 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:32 WORKER: start processing job (36, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.013155487201753763, 'OptimizerSelector:sgd:momentum': 0.6083243561220272, 'OptimizerSelector:sgd:weight_decay': 0.02127979399803051, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:32 WORKER: registered result for job (36, 0, 5) with dispatcher\\n\",\n      \"13:44:32 job (36, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17464995384216309 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:32 WORKER: start processing job (36, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 34, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007864524023240691, 'OptimizerSelector:sgd:momentum': 0.3274074631421001, 'OptimizerSelector:sgd:weight_decay': 0.03726042359169067, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:33 WORKER: registered result for job (36, 0, 6) with dispatcher\\n\",\n      \"13:44:33 job (36, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1649007797241211 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:33 WORKER: start processing job (36, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04387459528321816, 'OptimizerSelector:sgd:momentum': 0.16880941371334765, 'OptimizerSelector:sgd:weight_decay': 0.005930128695899953, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:33 WORKER: registered result for job (36, 0, 7) with dispatcher\\n\",\n      \"13:44:33 job (36, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1611640453338623 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:33 WORKER: start processing job (36, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 177, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008748673150828947, 'OptimizerSelector:sgd:momentum': 0.14590452063308051, 'OptimizerSelector:sgd:weight_decay': 0.06903064994594503, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:33 WORKER: registered result for job (36, 0, 8) with dispatcher\\n\",\n      \"13:44:33 job (36, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15463805198669434 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:33 WORKER: start processing job (36, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 379, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03476597250578095, 'OptimizerSelector:sgd:momentum': 0.84346791387331, 'OptimizerSelector:sgd:weight_decay': 0.01664742079358106, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:34 WORKER: registered result for job (36, 1, 0) with dispatcher\\n\",\n      \"13:44:34 job (36, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16206908226013184 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:34 WORKER: start processing job (36, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04270593342815385, 'OptimizerSelector:sgd:momentum': 0.5099458968140685, 'OptimizerSelector:sgd:weight_decay': 0.005612207456371772, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:34 WORKER: registered result for job (36, 1, 1) with dispatcher\\n\",\n      \"13:44:34 job (36, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17627382278442383 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:34 WORKER: start processing job (36, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 75, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00015126317412437386, 'OptimizerSelector:sgd:momentum': 0.9464102715543601, 'OptimizerSelector:sgd:weight_decay': 0.036332192988634424, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:34 WORKER: registered result for job (36, 1, 2) with dispatcher\\n\",\n      \"13:44:34 job (36, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18195199966430664 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:34 WORKER: start processing job (36, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 177, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0030771809274381086, 'OptimizerSelector:sgd:momentum': 0.5651339794153021, 'OptimizerSelector:sgd:weight_decay': 0.0832342507035673, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:34 WORKER: registered result for job (36, 2, 0) with dispatcher\\n\",\n      \"13:44:34 job (36, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1454617977142334 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:35 WORKER: start processing job (37, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003901963746029044, 'OptimizerSelector:sgd:momentum': 0.4156159142791032, 'OptimizerSelector:sgd:weight_decay': 0.0944200604638254, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:35 WORKER: registered result for job (37, 0, 0) with dispatcher\\n\",\n      \"13:44:35 job (37, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1477959156036377 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:35 WORKER: start processing job (37, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0002471847808223088, 'OptimizerSelector:sgd:momentum': 0.654940539681448, 'OptimizerSelector:sgd:weight_decay': 0.08790386269860594, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:35 WORKER: registered result for job (37, 0, 1) with dispatcher\\n\",\n      \"13:44:35 job (37, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17438197135925293 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:35 WORKER: start processing job (37, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0035011477730242818, 'OptimizerSelector:sgd:momentum': 0.6923573787476984, 'OptimizerSelector:sgd:weight_decay': 0.09497648447705889, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:36 WORKER: registered result for job (37, 0, 2) with dispatcher\\n\",\n      \"13:44:36 job (37, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17759013175964355 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:36 WORKER: start processing job (37, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 464, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003057830285417903, 'OptimizerSelector:sgd:momentum': 0.7668688536142807, 'OptimizerSelector:sgd:weight_decay': 0.005582573973276135, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:36 WORKER: registered result for job (37, 1, 0) with dispatcher\\n\",\n      \"13:44:36 job (37, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16081976890563965 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:36 WORKER: start processing job (38, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 180, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004882221356191376, 'OptimizerSelector:sgd:momentum': 0.2492471670582401, 'OptimizerSelector:sgd:weight_decay': 0.04489641432052752, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:36 WORKER: registered result for job (38, 0, 0) with dispatcher\\n\",\n      \"13:44:36 job (38, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14957284927368164 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:36 WORKER: start processing job (38, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 63, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05160528064002106, 'OptimizerSelector:sgd:momentum': 0.8389773755038868, 'OptimizerSelector:sgd:weight_decay': 0.011395807660233855, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:37 WORKER: registered result for job (38, 0, 1) with dispatcher\\n\",\n      \"13:44:37 job (38, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15105676651000977 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:37 WORKER: start processing job (38, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.015389873299265498, 'OptimizerSelector:sgd:momentum': 0.7571075140485474, 'OptimizerSelector:sgd:weight_decay': 0.005757491303395358, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:37 WORKER: registered result for job (38, 0, 2) with dispatcher\\n\",\n      \"13:44:37 job (38, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1615302562713623 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:37 WORKER: start processing job (39, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 40, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.036750539998663, 'OptimizerSelector:sgd:momentum': 0.39553936048457516, 'OptimizerSelector:sgd:weight_decay': 0.009525796637752786, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:37 WORKER: registered result for job (39, 0, 0) with dispatcher\\n\",\n      \"13:44:37 job (39, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16763615608215332 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:37 WORKER: start processing job (39, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 58, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001302126581532445, 'OptimizerSelector:sgd:momentum': 0.7399738059833303, 'OptimizerSelector:sgd:weight_decay': 0.017824668933824284, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:38 WORKER: registered result for job (39, 0, 1) with dispatcher\\n\",\n      \"13:44:38 job (39, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.19164204597473145 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:38 WORKER: start processing job (39, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 357, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000216515870369791, 'OptimizerSelector:sgd:momentum': 0.45703553296230726, 'OptimizerSelector:sgd:weight_decay': 0.08162885696456156, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:38 WORKER: registered result for job (39, 0, 2) with dispatcher\\n\",\n      \"13:44:38 job (39, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16574406623840332 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:38 WORKER: start processing job (39, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0022584776640877284, 'OptimizerSelector:sgd:momentum': 0.9010111767274721, 'OptimizerSelector:sgd:weight_decay': 0.03131616726645616, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:38 WORKER: registered result for job (39, 0, 3) with dispatcher\\n\",\n      \"13:44:38 job (39, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15797710418701172 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:38 WORKER: start processing job (39, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 995, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09495070521122664, 'OptimizerSelector:sgd:momentum': 0.43154686522914504, 'OptimizerSelector:sgd:weight_decay': 0.02094951565852309, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:38 WORKER: registered result for job (39, 0, 4) with dispatcher\\n\",\n      \"13:44:38 job (39, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1521761417388916 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:39 WORKER: start processing job (39, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 63, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0031188156865754046, 'OptimizerSelector:sgd:momentum': 0.9895485456986692, 'OptimizerSelector:sgd:weight_decay': 0.025223842448928124, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:39 WORKER: registered result for job (39, 0, 5) with dispatcher\\n\",\n      \"13:44:39 job (39, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17234301567077637 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:39 WORKER: start processing job (39, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 756, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.043708415577895676, 'OptimizerSelector:sgd:momentum': 0.8800471059401456, 'OptimizerSelector:sgd:weight_decay': 0.003091551352863063, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:39 WORKER: registered result for job (39, 0, 6) with dispatcher\\n\",\n      \"13:44:39 job (39, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16529083251953125 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:40 WORKER: start processing job (39, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00014655636678192913, 'OptimizerSelector:sgd:momentum': 0.6302558429466595, 'OptimizerSelector:sgd:weight_decay': 0.02361508882469328, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:40 WORKER: registered result for job (39, 0, 7) with dispatcher\\n\",\n      \"13:44:40 job (39, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14925193786621094 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:40 WORKER: start processing job (39, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 933, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006881692601269958, 'OptimizerSelector:sgd:momentum': 0.1346030411900748, 'OptimizerSelector:sgd:weight_decay': 0.06684847769852695, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:40 WORKER: registered result for job (39, 0, 8) with dispatcher\\n\",\n      \"13:44:40 job (39, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.170698881149292 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:40 WORKER: start processing job (39, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.037245886731146954, 'OptimizerSelector:sgd:momentum': 0.9037571395230235, 'OptimizerSelector:sgd:weight_decay': 0.055337135333311235, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:40 WORKER: registered result for job (39, 1, 0) with dispatcher\\n\",\n      \"13:44:40 job (39, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15915918350219727 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:41 WORKER: start processing job (39, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 26, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.033510054503900356, 'OptimizerSelector:sgd:momentum': 0.30215340450452755, 'OptimizerSelector:sgd:weight_decay': 0.012741411735752063, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:41 WORKER: registered result for job (39, 1, 1) with dispatcher\\n\",\n      \"13:44:41 job (39, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1508181095123291 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:41 WORKER: start processing job (39, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 120, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09531502677475694, 'OptimizerSelector:sgd:momentum': 0.19810652028323827, 'OptimizerSelector:sgd:weight_decay': 0.07079362882991452, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:41 WORKER: registered result for job (39, 1, 2) with dispatcher\\n\",\n      \"13:44:41 job (39, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14986610412597656 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:41 WORKER: start processing job (39, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 977, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00013017230848854643, 'OptimizerSelector:sgd:momentum': 0.14970535720289024, 'OptimizerSelector:sgd:weight_decay': 0.09765013591724689, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:41 WORKER: registered result for job (39, 2, 0) with dispatcher\\n\",\n      \"13:44:41 job (39, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16463470458984375 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:41 WORKER: start processing job (40, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 30, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04522051406605074, 'OptimizerSelector:sgd:momentum': 0.41612084879321976, 'OptimizerSelector:sgd:weight_decay': 0.046613130477286435, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:41 WORKER: registered result for job (40, 0, 0) with dispatcher\\n\",\n      \"13:44:41 job (40, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15757536888122559 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:42 WORKER: start processing job (40, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00046234764880577115, 'OptimizerSelector:sgd:momentum': 0.47700849343308094, 'OptimizerSelector:sgd:weight_decay': 0.04930274503683957, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:42 WORKER: registered result for job (40, 0, 1) with dispatcher\\n\",\n      \"13:44:42 job (40, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14567971229553223 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:42 WORKER: start processing job (40, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 537, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007130055259609262, 'OptimizerSelector:sgd:momentum': 0.6862986406641638, 'OptimizerSelector:sgd:weight_decay': 0.0010089310335866495, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:42 WORKER: registered result for job (40, 0, 2) with dispatcher\\n\",\n      \"13:44:42 job (40, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17891812324523926 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:43 WORKER: start processing job (40, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 33, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02170106833151227, 'OptimizerSelector:sgd:momentum': 0.7123536996748437, 'OptimizerSelector:sgd:weight_decay': 0.0111054718067987, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:43 WORKER: registered result for job (40, 1, 0) with dispatcher\\n\",\n      \"13:44:43 job (40, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14562201499938965 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:43 WORKER: start processing job (41, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007421164422740701, 'OptimizerSelector:sgd:momentum': 0.12195348367110886, 'OptimizerSelector:sgd:weight_decay': 0.07869356556238112, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:43 WORKER: registered result for job (41, 0, 0) with dispatcher\\n\",\n      \"13:44:43 job (41, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17354488372802734 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:43 WORKER: start processing job (41, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 120, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0012148754311657806, 'OptimizerSelector:sgd:momentum': 0.7684564293951064, 'OptimizerSelector:sgd:weight_decay': 0.02167650328518399, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:43 WORKER: registered result for job (41, 0, 1) with dispatcher\\n\",\n      \"13:44:43 job (41, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15624594688415527 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:44 WORKER: start processing job (41, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023875168299616304, 'OptimizerSelector:sgd:momentum': 0.9355822236495411, 'OptimizerSelector:sgd:weight_decay': 0.002798322830651637, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:44 WORKER: registered result for job (41, 0, 2) with dispatcher\\n\",\n      \"13:44:44 job (41, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14943718910217285 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:44 WORKER: start processing job (42, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004968986562281295, 'OptimizerSelector:sgd:momentum': 0.6073998867924886, 'OptimizerSelector:sgd:weight_decay': 0.05676919967872943, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:44 WORKER: registered result for job (42, 0, 0) with dispatcher\\n\",\n      \"13:44:44 job (42, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15295696258544922 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:44 WORKER: start processing job (42, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00017706701267323363, 'OptimizerSelector:sgd:momentum': 0.5684958048578, 'OptimizerSelector:sgd:weight_decay': 0.04962606894399637, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:45 WORKER: registered result for job (42, 0, 1) with dispatcher\\n\",\n      \"13:44:45 job (42, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15559601783752441 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:45 WORKER: start processing job (42, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004638015761590877, 'OptimizerSelector:sgd:momentum': 0.10385376774506905, 'OptimizerSelector:sgd:weight_decay': 0.035767550035311305, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:45 WORKER: registered result for job (42, 0, 2) with dispatcher\\n\",\n      \"13:44:45 job (42, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15333080291748047 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:45 WORKER: start processing job (42, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 76, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08519907614626465, 'OptimizerSelector:sgd:momentum': 0.8317273689235529, 'OptimizerSelector:sgd:weight_decay': 0.01826666957542978, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:45 WORKER: registered result for job (42, 0, 3) with dispatcher\\n\",\n      \"13:44:45 job (42, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14595794677734375 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:45 WORKER: start processing job (42, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 80, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.019074286707410022, 'OptimizerSelector:sgd:momentum': 0.8361821274706811, 'OptimizerSelector:sgd:weight_decay': 0.0016219693973929426, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:45 WORKER: registered result for job (42, 0, 4) with dispatcher\\n\",\n      \"13:44:45 job (42, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1469433307647705 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:46 WORKER: start processing job (42, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008256292407149384, 'OptimizerSelector:sgd:momentum': 0.5380113804885, 'OptimizerSelector:sgd:weight_decay': 0.07330444223351991, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:46 WORKER: registered result for job (42, 0, 5) with dispatcher\\n\",\n      \"13:44:46 job (42, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15027093887329102 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:46 WORKER: start processing job (42, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 123, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010786325031773617, 'OptimizerSelector:sgd:momentum': 0.1831286992963517, 'OptimizerSelector:sgd:weight_decay': 0.06838915093661742, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:46 WORKER: registered result for job (42, 0, 6) with dispatcher\\n\",\n      \"13:44:46 job (42, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14697003364562988 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:46 WORKER: start processing job (42, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 183, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07904464592508464, 'OptimizerSelector:sgd:momentum': 0.9570392816088958, 'OptimizerSelector:sgd:weight_decay': 0.029450116776042454, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:46 WORKER: registered result for job (42, 0, 7) with dispatcher\\n\",\n      \"13:44:46 job (42, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1481771469116211 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:46 WORKER: start processing job (42, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001162061413719027, 'OptimizerSelector:sgd:momentum': 0.21506753722728114, 'OptimizerSelector:sgd:weight_decay': 0.037615986923003816, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:46 WORKER: registered result for job (42, 0, 8) with dispatcher\\n\",\n      \"13:44:46 job (42, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14423704147338867 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:47 WORKER: start processing job (42, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04352413215912304, 'OptimizerSelector:sgd:momentum': 0.24837922539925736, 'OptimizerSelector:sgd:weight_decay': 0.00759965713058315, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:47 WORKER: registered result for job (42, 1, 0) with dispatcher\\n\",\n      \"13:44:47 job (42, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1497941017150879 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:47 WORKER: start processing job (42, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 14, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00404238399224627, 'OptimizerSelector:sgd:momentum': 0.8498359203013465, 'OptimizerSelector:sgd:weight_decay': 0.05106774108071201, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:47 WORKER: registered result for job (42, 1, 1) with dispatcher\\n\",\n      \"13:44:47 job (42, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14625263214111328 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:47 WORKER: start processing job (42, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:47 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001158210909856447, 'OptimizerSelector:sgd:momentum': 0.7509128443880643, 'OptimizerSelector:sgd:weight_decay': 0.0775529338504411, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:47 WORKER: registered result for job (42, 1, 2) with dispatcher\\n\",\n      \"13:44:47 job (42, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15137696266174316 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:47 WORKER: start processing job (42, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 69, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00040823042900217416, 'OptimizerSelector:sgd:momentum': 0.3573497634925523, 'OptimizerSelector:sgd:weight_decay': 0.038928490352097854, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:48 WORKER: registered result for job (42, 2, 0) with dispatcher\\n\",\n      \"13:44:48 job (42, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14673709869384766 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:48 WORKER: start processing job (43, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09327250627547859, 'OptimizerSelector:sgd:momentum': 0.7108587239014256, 'OptimizerSelector:sgd:weight_decay': 0.0013510809564218984, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:48 WORKER: registered result for job (43, 0, 0) with dispatcher\\n\",\n      \"13:44:48 job (43, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15626907348632812 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:48 WORKER: start processing job (43, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:48 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01208644158100793, 'OptimizerSelector:sgd:momentum': 0.37821081960150643, 'OptimizerSelector:sgd:weight_decay': 0.0392093146176296, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:49 WORKER: registered result for job (43, 0, 1) with dispatcher\\n\",\n      \"13:44:49 job (43, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16619300842285156 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:49 WORKER: start processing job (43, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 566, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010601534320604097, 'OptimizerSelector:sgd:momentum': 0.8102422194686312, 'OptimizerSelector:sgd:weight_decay': 0.00030507353047149084, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:49 WORKER: registered result for job (43, 0, 2) with dispatcher\\n\",\n      \"13:44:49 job (43, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16235613822937012 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:49 WORKER: start processing job (43, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:49 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09551217320923254, 'OptimizerSelector:sgd:momentum': 0.6043110390336631, 'OptimizerSelector:sgd:weight_decay': 0.06969898350106345, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:49 WORKER: registered result for job (43, 1, 0) with dispatcher\\n\",\n      \"13:44:49 job (43, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1595749855041504 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:50 WORKER: start processing job (44, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 198, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003252792960218837, 'OptimizerSelector:sgd:momentum': 0.8299139215737638, 'OptimizerSelector:sgd:weight_decay': 0.004142492844943685, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:50 WORKER: registered result for job (44, 0, 0) with dispatcher\\n\",\n      \"13:44:50 job (44, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15597081184387207 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:50 WORKER: start processing job (44, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 362, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003255039904410724, 'OptimizerSelector:sgd:momentum': 0.2369998487997464, 'OptimizerSelector:sgd:weight_decay': 0.021378166325212163, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:50 WORKER: registered result for job (44, 0, 1) with dispatcher\\n\",\n      \"13:44:50 job (44, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15792584419250488 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:50 WORKER: start processing job (44, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:50 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 90, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.022946916891316848, 'OptimizerSelector:sgd:momentum': 0.5819339441473997, 'OptimizerSelector:sgd:weight_decay': 0.0006260162011584754, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:50 WORKER: registered result for job (44, 0, 2) with dispatcher\\n\",\n      \"13:44:50 job (44, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15176796913146973 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:51 WORKER: start processing job (45, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.020884585033733794, 'OptimizerSelector:sgd:momentum': 0.470527506580459, 'OptimizerSelector:sgd:weight_decay': 0.06694732389298776, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:51 WORKER: registered result for job (45, 0, 0) with dispatcher\\n\",\n      \"13:44:51 job (45, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1513509750366211 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:51 WORKER: start processing job (45, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 47, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01585724129716255, 'OptimizerSelector:sgd:momentum': 0.10188021236135128, 'OptimizerSelector:sgd:weight_decay': 0.06468770311480752, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:51 WORKER: registered result for job (45, 0, 1) with dispatcher\\n\",\n      \"13:44:51 job (45, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14658093452453613 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:51 WORKER: start processing job (45, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:51 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006311576605358061, 'OptimizerSelector:sgd:momentum': 0.780783686314492, 'OptimizerSelector:sgd:weight_decay': 0.06802911103634099, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:51 WORKER: registered result for job (45, 0, 2) with dispatcher\\n\",\n      \"13:44:51 job (45, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16540813446044922 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:52 WORKER: start processing job (45, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:52 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 612, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007902077807840276, 'OptimizerSelector:sgd:momentum': 0.8866558093829741, 'OptimizerSelector:sgd:weight_decay': 0.0041474813316528195, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:52 WORKER: registered result for job (45, 0, 3) with dispatcher\\n\",\n      \"13:44:52 job (45, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1645798683166504 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:52 WORKER: start processing job (45, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:52 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 117, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010572736958423064, 'OptimizerSelector:sgd:momentum': 0.9792366666301211, 'OptimizerSelector:sgd:weight_decay': 0.0020865931488736836, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:52 WORKER: registered result for job (45, 0, 4) with dispatcher\\n\",\n      \"13:44:52 job (45, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16662216186523438 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:53 WORKER: start processing job (45, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:53 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 98, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07998084079081567, 'OptimizerSelector:sgd:momentum': 0.9813554483892775, 'OptimizerSelector:sgd:weight_decay': 0.020547356541717725, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:53 WORKER: registered result for job (45, 0, 5) with dispatcher\\n\",\n      \"13:44:53 job (45, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1740128993988037 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:53 WORKER: start processing job (45, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:53 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004296301730071758, 'OptimizerSelector:sgd:momentum': 0.1415245970301625, 'OptimizerSelector:sgd:weight_decay': 0.0196877779388147, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:53 WORKER: registered result for job (45, 0, 6) with dispatcher\\n\",\n      \"13:44:53 job (45, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15417098999023438 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:53 WORKER: start processing job (45, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.047742562756823835, 'OptimizerSelector:sgd:momentum': 0.5624554216525351, 'OptimizerSelector:sgd:weight_decay': 0.04402203800205821, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:54 WORKER: registered result for job (45, 0, 7) with dispatcher\\n\",\n      \"13:44:54 job (45, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17376065254211426 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:54 WORKER: start processing job (45, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 242, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00025898494802368253, 'OptimizerSelector:sgd:momentum': 0.5905364365937189, 'OptimizerSelector:sgd:weight_decay': 0.08581789700340134, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:54 WORKER: registered result for job (45, 0, 8) with dispatcher\\n\",\n      \"13:44:54 job (45, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17415308952331543 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:54 WORKER: start processing job (45, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.001483921058432098, 'OptimizerSelector:sgd:momentum': 0.13784477585008958, 'OptimizerSelector:sgd:weight_decay': 0.04215045295491232, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:54 WORKER: registered result for job (45, 1, 0) with dispatcher\\n\",\n      \"13:44:54 job (45, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1942610740661621 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:54 WORKER: start processing job (45, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:54 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 89, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0036107205163975046, 'OptimizerSelector:sgd:momentum': 0.6776391470795787, 'OptimizerSelector:sgd:weight_decay': 0.002604533398184409, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:54 WORKER: registered result for job (45, 1, 1) with dispatcher\\n\",\n      \"13:44:54 job (45, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1528911590576172 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:55 WORKER: start processing job (45, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05312550323141495, 'OptimizerSelector:sgd:momentum': 0.7404970323910114, 'OptimizerSelector:sgd:weight_decay': 0.04879209807073244, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:55 WORKER: registered result for job (45, 1, 2) with dispatcher\\n\",\n      \"13:44:55 job (45, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15823888778686523 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:55 WORKER: start processing job (45, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.008619624607190051, 'OptimizerSelector:sgd:momentum': 0.9419026537793834, 'OptimizerSelector:sgd:weight_decay': 0.07019182429953992, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:55 WORKER: registered result for job (45, 2, 0) with dispatcher\\n\",\n      \"13:44:55 job (45, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14880681037902832 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:55 WORKER: start processing job (46, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:55 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 34, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0008762313130761031, 'OptimizerSelector:sgd:momentum': 0.2159008469156782, 'OptimizerSelector:sgd:weight_decay': 0.08523084015991393, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:55 WORKER: registered result for job (46, 0, 0) with dispatcher\\n\",\n      \"13:44:55 job (46, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14844489097595215 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:55 WORKER: start processing job (46, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006012285803000254, 'OptimizerSelector:sgd:momentum': 0.15063783660196844, 'OptimizerSelector:sgd:weight_decay': 0.029906888052872487, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:56 WORKER: registered result for job (46, 0, 1) with dispatcher\\n\",\n      \"13:44:56 job (46, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15241789817810059 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:56 WORKER: start processing job (46, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010545333312454773, 'OptimizerSelector:sgd:momentum': 0.8069801783069847, 'OptimizerSelector:sgd:weight_decay': 0.06768960210288028, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:56 WORKER: registered result for job (46, 0, 2) with dispatcher\\n\",\n      \"13:44:56 job (46, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1508960723876953 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:44:56 WORKER: start processing job (46, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:56 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04500385829223225, 'OptimizerSelector:sgd:momentum': 0.9834817298763385, 'OptimizerSelector:sgd:weight_decay': 0.07935901451038996, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:56 WORKER: registered result for job (46, 1, 0) with dispatcher\\n\",\n      \"13:44:56 job (46, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16823077201843262 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:56 WORKER: start processing job (47, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 237, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004325345999990301, 'OptimizerSelector:sgd:momentum': 0.8492970545689517, 'OptimizerSelector:sgd:weight_decay': 0.00471095982705904, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:57 WORKER: registered result for job (47, 0, 0) with dispatcher\\n\",\n      \"13:44:57 job (47, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15480613708496094 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:57 WORKER: start processing job (47, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00011945823765160973, 'OptimizerSelector:sgd:momentum': 0.9470699478043972, 'OptimizerSelector:sgd:weight_decay': 0.02811980965705507, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:57 WORKER: registered result for job (47, 0, 1) with dispatcher\\n\",\n      \"13:44:57 job (47, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14271998405456543 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:57 WORKER: start processing job (47, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:57 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 175, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08702450281127942, 'OptimizerSelector:sgd:momentum': 0.13413676852551126, 'OptimizerSelector:sgd:weight_decay': 0.06250859125005893, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:57 WORKER: registered result for job (47, 0, 2) with dispatcher\\n\",\n      \"13:44:57 job (47, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1520843505859375 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:44:57 WORKER: start processing job (48, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 337, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02669284737359366, 'OptimizerSelector:sgd:momentum': 0.7904492395761453, 'OptimizerSelector:sgd:weight_decay': 0.002839685074904017, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:58 WORKER: registered result for job (48, 0, 0) with dispatcher\\n\",\n      \"13:44:58 job (48, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1486201286315918 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:58 WORKER: start processing job (48, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 28, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07388304439574875, 'OptimizerSelector:sgd:momentum': 0.5602117771141252, 'OptimizerSelector:sgd:weight_decay': 0.06692130516465254, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:58 WORKER: registered result for job (48, 0, 1) with dispatcher\\n\",\n      \"13:44:58 job (48, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14519500732421875 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:58 WORKER: start processing job (48, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 126, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000211726948812016, 'OptimizerSelector:sgd:momentum': 0.38090311701465523, 'OptimizerSelector:sgd:weight_decay': 0.03104925293726401, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:58 WORKER: registered result for job (48, 0, 2) with dispatcher\\n\",\n      \"13:44:58 job (48, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18708372116088867 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:58 WORKER: start processing job (48, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:58 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004828842428247261, 'OptimizerSelector:sgd:momentum': 0.22498025369586097, 'OptimizerSelector:sgd:weight_decay': 0.05434585780045417, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:44:58 WORKER: registered result for job (48, 0, 3) with dispatcher\\n\",\n      \"13:44:58 job (48, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18302416801452637 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:59 WORKER: start processing job (48, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:59 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 605, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0635263991904113, 'OptimizerSelector:sgd:momentum': 0.5774035972946371, 'OptimizerSelector:sgd:weight_decay': 0.027539013611240466, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:59 WORKER: registered result for job (48, 0, 4) with dispatcher\\n\",\n      \"13:44:59 job (48, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14893674850463867 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:59 WORKER: start processing job (48, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:44:59 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09036768338914714, 'OptimizerSelector:sgd:momentum': 0.5301932178737155, 'OptimizerSelector:sgd:weight_decay': 0.09043639275202911, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:44:59 WORKER: registered result for job (48, 0, 5) with dispatcher\\n\",\n      \"13:44:59 job (48, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14672613143920898 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:44:59 WORKER: start processing job (48, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.029631236889522145, 'OptimizerSelector:sgd:momentum': 0.4819442856578188, 'OptimizerSelector:sgd:weight_decay': 0.0460802024547066, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:00 WORKER: registered result for job (48, 0, 6) with dispatcher\\n\",\n      \"13:45:00 job (48, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1537609100341797 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:00 WORKER: start processing job (48, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0913559749408387, 'OptimizerSelector:sgd:momentum': 0.6735253432503276, 'OptimizerSelector:sgd:weight_decay': 0.08865702619589644, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:00 WORKER: registered result for job (48, 0, 7) with dispatcher\\n\",\n      \"13:45:00 job (48, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15274405479431152 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:00 WORKER: start processing job (48, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:00 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 169, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007749852782154997, 'OptimizerSelector:sgd:momentum': 0.22753478661401694, 'OptimizerSelector:sgd:weight_decay': 0.06101639400300935, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:00 WORKER: registered result for job (48, 0, 8) with dispatcher\\n\",\n      \"13:45:00 job (48, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16197919845581055 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:00 WORKER: start processing job (48, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02210500836464233, 'OptimizerSelector:sgd:momentum': 0.5562664046107797, 'OptimizerSelector:sgd:weight_decay': 0.0725632557640986, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:01 WORKER: registered result for job (48, 1, 0) with dispatcher\\n\",\n      \"13:45:01 job (48, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17492103576660156 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:01 WORKER: start processing job (48, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 870, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.023283344510486522, 'OptimizerSelector:sgd:momentum': 0.3751631163117405, 'OptimizerSelector:sgd:weight_decay': 0.049800345182844014, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:01 WORKER: registered result for job (48, 1, 1) with dispatcher\\n\",\n      \"13:45:01 job (48, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15708589553833008 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:01 WORKER: start processing job (48, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:01 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.047760966686328524, 'OptimizerSelector:sgd:momentum': 0.9229264614665433, 'OptimizerSelector:sgd:weight_decay': 0.08690382927577257, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:01 WORKER: registered result for job (48, 1, 2) with dispatcher\\n\",\n      \"13:45:01 job (48, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.165604829788208 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:02 WORKER: start processing job (48, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.016859067371559098, 'OptimizerSelector:sgd:momentum': 0.6490113143483486, 'OptimizerSelector:sgd:weight_decay': 0.09498532826638674, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:02 WORKER: registered result for job (48, 2, 0) with dispatcher\\n\",\n      \"13:45:02 job (48, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15093302726745605 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:02 WORKER: start processing job (49, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 97, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010891075408064854, 'OptimizerSelector:sgd:momentum': 0.4195205339427786, 'OptimizerSelector:sgd:weight_decay': 0.0755172079222306, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:02 WORKER: registered result for job (49, 0, 0) with dispatcher\\n\",\n      \"13:45:02 job (49, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15100407600402832 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:02 WORKER: start processing job (49, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:02 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.060017959855565385, 'OptimizerSelector:sgd:momentum': 0.3782074111278878, 'OptimizerSelector:sgd:weight_decay': 0.048698972819838295, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:02 WORKER: registered result for job (49, 0, 1) with dispatcher\\n\",\n      \"13:45:02 job (49, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15962910652160645 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:03 WORKER: start processing job (49, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:03 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005588534554112431, 'OptimizerSelector:sgd:momentum': 0.8256306916155564, 'OptimizerSelector:sgd:weight_decay': 0.02925387463306684, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:03 WORKER: registered result for job (49, 0, 2) with dispatcher\\n\",\n      \"13:45:03 job (49, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16669797897338867 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:03 WORKER: start processing job (49, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:03 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 670, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07274172053743616, 'OptimizerSelector:sgd:momentum': 0.771173597433389, 'OptimizerSelector:sgd:weight_decay': 0.0037249022284981956, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:03 WORKER: registered result for job (49, 1, 0) with dispatcher\\n\",\n      \"13:45:03 job (49, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16348481178283691 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:04 WORKER: start processing job (50, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00023793645300250772, 'OptimizerSelector:sgd:momentum': 0.8910689742309733, 'OptimizerSelector:sgd:weight_decay': 0.05832125265666006, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:04 WORKER: registered result for job (50, 0, 0) with dispatcher\\n\",\n      \"13:45:04 job (50, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1471707820892334 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:04 WORKER: start processing job (50, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 476, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01973412237707802, 'OptimizerSelector:sgd:momentum': 0.3163206486066361, 'OptimizerSelector:sgd:weight_decay': 0.0025026968765295005, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:04 WORKER: registered result for job (50, 0, 1) with dispatcher\\n\",\n      \"13:45:04 job (50, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17467880249023438 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:04 WORKER: start processing job (50, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:04 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04889735524030607, 'OptimizerSelector:sgd:momentum': 0.4891342513058652, 'OptimizerSelector:sgd:weight_decay': 0.05720844844190532, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:04 WORKER: registered result for job (50, 0, 2) with dispatcher\\n\",\n      \"13:45:04 job (50, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1521139144897461 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:05 WORKER: start processing job (51, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:05 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 62, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00011347814913591919, 'OptimizerSelector:sgd:momentum': 0.8335863384159622, 'OptimizerSelector:sgd:weight_decay': 0.06628240131938654, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:05 WORKER: registered result for job (51, 0, 0) with dispatcher\\n\",\n      \"13:45:05 job (51, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14608502388000488 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:05 WORKER: start processing job (51, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:05 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 43, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07162553563917574, 'OptimizerSelector:sgd:momentum': 0.2824191582115744, 'OptimizerSelector:sgd:weight_decay': 0.025626668719727342, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:05 WORKER: registered result for job (51, 0, 1) with dispatcher\\n\",\n      \"13:45:05 job (51, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16616487503051758 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:05 WORKER: start processing job (51, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:05 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03808199877285428, 'OptimizerSelector:sgd:momentum': 0.9678614325490671, 'OptimizerSelector:sgd:weight_decay': 0.09049362891040073, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:05 WORKER: registered result for job (51, 0, 2) with dispatcher\\n\",\n      \"13:45:05 job (51, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14982891082763672 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:06 WORKER: start processing job (51, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:06 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 20, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003152392874874194, 'OptimizerSelector:sgd:momentum': 0.7213659004212893, 'OptimizerSelector:sgd:weight_decay': 0.05422403966745101, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:06 WORKER: registered result for job (51, 0, 3) with dispatcher\\n\",\n      \"13:45:06 job (51, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14981913566589355 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:06 WORKER: start processing job (51, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:06 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 203, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00019153855690628456, 'OptimizerSelector:sgd:momentum': 0.5684562962838147, 'OptimizerSelector:sgd:weight_decay': 0.014898398364809164, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:06 WORKER: registered result for job (51, 0, 4) with dispatcher\\n\",\n      \"13:45:06 job (51, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14873600006103516 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:06 WORKER: start processing job (51, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:06 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 269, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.004056245084260981, 'OptimizerSelector:sgd:momentum': 0.1858878434028393, 'OptimizerSelector:sgd:weight_decay': 0.008016482499336102, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:06 WORKER: registered result for job (51, 0, 5) with dispatcher\\n\",\n      \"13:45:06 job (51, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14655375480651855 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:07 WORKER: start processing job (51, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:07 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 58, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08545282039777805, 'OptimizerSelector:sgd:momentum': 0.62893282708365, 'OptimizerSelector:sgd:weight_decay': 0.08371126453101622, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:07 WORKER: registered result for job (51, 0, 6) with dispatcher\\n\",\n      \"13:45:07 job (51, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1500852108001709 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:07 WORKER: start processing job (51, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:07 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00040159397444680987, 'OptimizerSelector:sgd:momentum': 0.4241794566281806, 'OptimizerSelector:sgd:weight_decay': 0.016843130450257458, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:07 WORKER: registered result for job (51, 0, 7) with dispatcher\\n\",\n      \"13:45:07 job (51, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15714192390441895 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:07 WORKER: start processing job (51, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0010753722275500574, 'OptimizerSelector:sgd:momentum': 0.740779476220299, 'OptimizerSelector:sgd:weight_decay': 0.03369775233011721, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:08 WORKER: registered result for job (51, 0, 8) with dispatcher\\n\",\n      \"13:45:08 job (51, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16724205017089844 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:08 WORKER: start processing job (51, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004743407832634717, 'OptimizerSelector:sgd:momentum': 0.6215562644854797, 'OptimizerSelector:sgd:weight_decay': 0.09343047637369331, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:08 WORKER: registered result for job (51, 1, 0) with dispatcher\\n\",\n      \"13:45:08 job (51, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15964388847351074 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:08 WORKER: start processing job (51, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:08 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05407956493174519, 'OptimizerSelector:sgd:momentum': 0.8448440366245455, 'OptimizerSelector:sgd:weight_decay': 0.02106695425359345, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:08 WORKER: registered result for job (51, 1, 1) with dispatcher\\n\",\n      \"13:45:08 job (51, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1540071964263916 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:09 WORKER: start processing job (51, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00024642477825645816, 'OptimizerSelector:sgd:momentum': 0.866122198330071, 'OptimizerSelector:sgd:weight_decay': 0.07454141120696157, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:09 WORKER: registered result for job (51, 1, 2) with dispatcher\\n\",\n      \"13:45:09 job (51, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15678787231445312 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:09 WORKER: start processing job (51, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:09 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 95, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023281963179656736, 'OptimizerSelector:sgd:momentum': 0.26424954644643234, 'OptimizerSelector:sgd:weight_decay': 0.08596923081327953, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:09 WORKER: registered result for job (51, 2, 0) with dispatcher\\n\",\n      \"13:45:09 job (51, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15311193466186523 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:10 WORKER: start processing job (52, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:10 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 18, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0019450611501415894, 'OptimizerSelector:sgd:momentum': 0.70770135231667, 'OptimizerSelector:sgd:weight_decay': 0.05232155483118559, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:10 WORKER: registered result for job (52, 0, 0) with dispatcher\\n\",\n      \"13:45:10 job (52, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15437793731689453 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:10 WORKER: start processing job (52, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:10 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 457, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06734766161136342, 'OptimizerSelector:sgd:momentum': 0.27406813991585893, 'OptimizerSelector:sgd:weight_decay': 0.029826148688358846, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:10 WORKER: registered result for job (52, 0, 1) with dispatcher\\n\",\n      \"13:45:10 job (52, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14706802368164062 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:10 WORKER: start processing job (52, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:10 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 49, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.062336249343647315, 'OptimizerSelector:sgd:momentum': 0.6749177763876845, 'OptimizerSelector:sgd:weight_decay': 0.022683055571173515, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:10 WORKER: registered result for job (52, 0, 2) with dispatcher\\n\",\n      \"13:45:10 job (52, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.17323589324951172 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:11 WORKER: start processing job (52, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:11 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0015317132430885635, 'OptimizerSelector:sgd:momentum': 0.8245178064279921, 'OptimizerSelector:sgd:weight_decay': 0.050915431957982774, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:11 WORKER: registered result for job (52, 1, 0) with dispatcher\\n\",\n      \"13:45:11 job (52, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.147658109664917 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:11 WORKER: start processing job (53, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:11 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 784, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.020584143751243556, 'OptimizerSelector:sgd:momentum': 0.34186543824199755, 'OptimizerSelector:sgd:weight_decay': 0.020029372244665587, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:11 WORKER: registered result for job (53, 0, 0) with dispatcher\\n\",\n      \"13:45:11 job (53, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14854121208190918 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:11 WORKER: start processing job (53, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 526, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.024802385302333706, 'OptimizerSelector:sgd:momentum': 0.4617526507148638, 'OptimizerSelector:sgd:weight_decay': 0.00038843162352162074, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:12 WORKER: registered result for job (53, 0, 1) with dispatcher\\n\",\n      \"13:45:12 job (53, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15505099296569824 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (53, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 274, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.021050979813764005, 'OptimizerSelector:sgd:momentum': 0.17036090734951181, 'OptimizerSelector:sgd:weight_decay': 0.018053618800791767, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:12 WORKER: registered result for job (53, 0, 2) with dispatcher\\n\",\n      \"13:45:12 job (53, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1496269702911377 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (54, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 93, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000397502075355197, 'OptimizerSelector:sgd:momentum': 0.12676269880137225, 'OptimizerSelector:sgd:weight_decay': 0.007701679309729416, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:12 WORKER: registered result for job (54, 0, 0) with dispatcher\\n\",\n      \"13:45:12 job (54, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.23970413208007812 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (54, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.001687972763095927, 'OptimizerSelector:sgd:momentum': 0.4443794876997799, 'OptimizerSelector:sgd:weight_decay': 0.012725561406401619, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:12 WORKER: registered result for job (54, 0, 1) with dispatcher\\n\",\n      \"13:45:12 job (54, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14823698997497559 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (54, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09481941419573661, 'OptimizerSelector:sgd:momentum': 0.13599921033370144, 'OptimizerSelector:sgd:weight_decay': 0.006275258063706044, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:12 WORKER: registered result for job (54, 0, 2) with dispatcher\\n\",\n      \"13:45:12 job (54, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14609408378601074 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (54, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:12 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006764828086105053, 'OptimizerSelector:sgd:momentum': 0.6717546450736629, 'OptimizerSelector:sgd:weight_decay': 0.01920747068035668, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:12 WORKER: registered result for job (54, 0, 3) with dispatcher\\n\",\n      \"13:45:12 job (54, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14830684661865234 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:12 WORKER: start processing job (54, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 87, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002342506858767198, 'OptimizerSelector:sgd:momentum': 0.5891740356931578, 'OptimizerSelector:sgd:weight_decay': 0.0046639514120047, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:13 WORKER: registered result for job (54, 0, 4) with dispatcher\\n\",\n      \"13:45:13 job (54, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1604001522064209 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:13 WORKER: start processing job (54, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 37, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0015753914904145195, 'OptimizerSelector:sgd:momentum': 0.22848207523941266, 'OptimizerSelector:sgd:weight_decay': 0.09616082131957081, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:13 WORKER: registered result for job (54, 0, 5) with dispatcher\\n\",\n      \"13:45:13 job (54, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15332818031311035 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:13 WORKER: start processing job (54, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:13 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06972118849399582, 'OptimizerSelector:sgd:momentum': 0.9052170106471471, 'OptimizerSelector:sgd:weight_decay': 0.012479415679989125, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:13 WORKER: registered result for job (54, 0, 6) with dispatcher\\n\",\n      \"13:45:13 job (54, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1493520736694336 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:14 WORKER: start processing job (54, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005808763729298765, 'OptimizerSelector:sgd:momentum': 0.49073354940495617, 'OptimizerSelector:sgd:weight_decay': 0.06384000540930364, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:14 WORKER: registered result for job (54, 0, 7) with dispatcher\\n\",\n      \"13:45:14 job (54, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15213584899902344 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:14 WORKER: start processing job (54, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 97, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003385189826726627, 'OptimizerSelector:sgd:momentum': 0.10267655861307902, 'OptimizerSelector:sgd:weight_decay': 0.03746883278815019, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:14 WORKER: registered result for job (54, 0, 8) with dispatcher\\n\",\n      \"13:45:14 job (54, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1479048728942871 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:14 WORKER: start processing job (54, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:14 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 16, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0007592138598699704, 'OptimizerSelector:sgd:momentum': 0.5763028841984443, 'OptimizerSelector:sgd:weight_decay': 0.06111566517443004, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:14 WORKER: registered result for job (54, 1, 0) with dispatcher\\n\",\n      \"13:45:14 job (54, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1471550464630127 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:15 WORKER: start processing job (54, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:15 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 14, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001321655515254498, 'OptimizerSelector:sgd:momentum': 0.7508920090240667, 'OptimizerSelector:sgd:weight_decay': 0.06685403787653249, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:15 WORKER: registered result for job (54, 1, 1) with dispatcher\\n\",\n      \"13:45:15 job (54, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15027523040771484 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:15 WORKER: start processing job (54, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:15 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 22, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.039371930961363856, 'OptimizerSelector:sgd:momentum': 0.7867932479568289, 'OptimizerSelector:sgd:weight_decay': 0.0032060080241738177, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:15 WORKER: registered result for job (54, 1, 2) with dispatcher\\n\",\n      \"13:45:15 job (54, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15106892585754395 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:15 WORKER: start processing job (54, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00026733513162154407, 'OptimizerSelector:sgd:momentum': 0.470374414435964, 'OptimizerSelector:sgd:weight_decay': 0.09389086969746098, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:16 WORKER: registered result for job (54, 2, 0) with dispatcher\\n\",\n      \"13:45:16 job (54, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1477491855621338 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:16 WORKER: start processing job (55, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 105, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0037711562927940645, 'OptimizerSelector:sgd:momentum': 0.9690452307345321, 'OptimizerSelector:sgd:weight_decay': 0.09067442055306157, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:16 WORKER: registered result for job (55, 0, 0) with dispatcher\\n\",\n      \"13:45:16 job (55, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14839673042297363 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:16 WORKER: start processing job (55, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:16 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 26, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06011618923491637, 'OptimizerSelector:sgd:momentum': 0.5365429674969985, 'OptimizerSelector:sgd:weight_decay': 0.007713066428189559, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:16 WORKER: registered result for job (55, 0, 1) with dispatcher\\n\",\n      \"13:45:16 job (55, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14864206314086914 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:16 WORKER: start processing job (55, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010370214549342846, 'OptimizerSelector:sgd:momentum': 0.18285809475021036, 'OptimizerSelector:sgd:weight_decay': 0.08604712744891743, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:17 WORKER: registered result for job (55, 0, 2) with dispatcher\\n\",\n      \"13:45:17 job (55, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15598726272583008 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:17 WORKER: start processing job (55, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 315, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00011831613721755772, 'OptimizerSelector:sgd:momentum': 0.33414496395451604, 'OptimizerSelector:sgd:weight_decay': 0.014206404723751809, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:17 WORKER: registered result for job (55, 1, 0) with dispatcher\\n\",\n      \"13:45:17 job (55, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14656996726989746 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:17 WORKER: start processing job (56, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:17 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 61, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07865571563987088, 'OptimizerSelector:sgd:momentum': 0.5065851745273924, 'OptimizerSelector:sgd:weight_decay': 0.00709738550786799, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:17 WORKER: registered result for job (56, 0, 0) with dispatcher\\n\",\n      \"13:45:17 job (56, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15368318557739258 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:18 WORKER: start processing job (56, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:18 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 17, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0004444297504550857, 'OptimizerSelector:sgd:momentum': 0.41584520535215574, 'OptimizerSelector:sgd:weight_decay': 0.09750498630103087, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:18 WORKER: registered result for job (56, 0, 1) with dispatcher\\n\",\n      \"13:45:18 job (56, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14870381355285645 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:18 WORKER: start processing job (56, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:18 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 774, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0028610373833113244, 'OptimizerSelector:sgd:momentum': 0.9240843360355491, 'OptimizerSelector:sgd:weight_decay': 0.05130135761183467, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:18 WORKER: registered result for job (56, 0, 2) with dispatcher\\n\",\n      \"13:45:18 job (56, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14980387687683105 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:18 WORKER: start processing job (57, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 54, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.019173067893260117, 'OptimizerSelector:sgd:momentum': 0.761772828470698, 'OptimizerSelector:sgd:weight_decay': 0.01976233717312623, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:19 WORKER: registered result for job (57, 0, 0) with dispatcher\\n\",\n      \"13:45:19 job (57, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14876604080200195 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:19 WORKER: start processing job (57, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 227, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.02580141622420981, 'OptimizerSelector:sgd:momentum': 0.6026540566771487, 'OptimizerSelector:sgd:weight_decay': 0.04608262915545493, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:19 WORKER: registered result for job (57, 0, 1) with dispatcher\\n\",\n      \"13:45:19 job (57, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14661312103271484 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:19 WORKER: start processing job (57, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.07011469924401126, 'OptimizerSelector:sgd:momentum': 0.3074320560513151, 'OptimizerSelector:sgd:weight_decay': 0.002156251347213576, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:19 WORKER: registered result for job (57, 0, 2) with dispatcher\\n\",\n      \"13:45:19 job (57, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14764189720153809 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:19 WORKER: start processing job (57, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:19 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 45, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0033406602852337217, 'OptimizerSelector:sgd:momentum': 0.5443734420117993, 'OptimizerSelector:sgd:weight_decay': 0.01805309922146261, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:19 WORKER: registered result for job (57, 0, 3) with dispatcher\\n\",\n      \"13:45:19 job (57, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14935088157653809 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:20 WORKER: start processing job (57, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:20 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 26, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08645114253150542, 'OptimizerSelector:sgd:momentum': 0.49079199322576533, 'OptimizerSelector:sgd:weight_decay': 0.004908840489843834, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:20 WORKER: registered result for job (57, 0, 4) with dispatcher\\n\",\n      \"13:45:20 job (57, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15025782585144043 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:20 WORKER: start processing job (57, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:20 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 32, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08257427694303564, 'OptimizerSelector:sgd:momentum': 0.3971500310361957, 'OptimizerSelector:sgd:weight_decay': 0.010166080097931551, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:20 WORKER: registered result for job (57, 0, 5) with dispatcher\\n\",\n      \"13:45:20 job (57, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15133404731750488 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:20 WORKER: start processing job (57, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:20 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 215, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.009432614278505638, 'OptimizerSelector:sgd:momentum': 0.2854835283531922, 'OptimizerSelector:sgd:weight_decay': 0.04486520644617708, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:20 WORKER: registered result for job (57, 0, 6) with dispatcher\\n\",\n      \"13:45:20 job (57, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14803719520568848 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:20 WORKER: start processing job (57, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:21 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 285, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01008291461867419, 'OptimizerSelector:sgd:momentum': 0.5560024498867614, 'OptimizerSelector:sgd:weight_decay': 0.05764949136179947, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:21 WORKER: registered result for job (57, 0, 7) with dispatcher\\n\",\n      \"13:45:21 job (57, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15355229377746582 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:21 WORKER: start processing job (57, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:21 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0014626360945422183, 'OptimizerSelector:sgd:momentum': 0.5503991946624526, 'OptimizerSelector:sgd:weight_decay': 0.09110385550228794, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:21 WORKER: registered result for job (57, 0, 8) with dispatcher\\n\",\n      \"13:45:21 job (57, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14650607109069824 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:21 WORKER: start processing job (57, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:21 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 249, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00020051229132427739, 'OptimizerSelector:sgd:momentum': 0.6037995811327311, 'OptimizerSelector:sgd:weight_decay': 0.016035135986088054, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:21 WORKER: registered result for job (57, 1, 0) with dispatcher\\n\",\n      \"13:45:21 job (57, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1499919891357422 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:21 WORKER: start processing job (57, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 195, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0001085849498539702, 'OptimizerSelector:sgd:momentum': 0.4416074355202097, 'OptimizerSelector:sgd:weight_decay': 0.013301999727363307, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:22 WORKER: registered result for job (57, 1, 1) with dispatcher\\n\",\n      \"13:45:22 job (57, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1484520435333252 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:22 WORKER: start processing job (57, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 23, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0481224632142732, 'OptimizerSelector:sgd:momentum': 0.8681486742359567, 'OptimizerSelector:sgd:weight_decay': 0.030053801324912383, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:22 WORKER: registered result for job (57, 1, 2) with dispatcher\\n\",\n      \"13:45:22 job (57, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15132498741149902 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:22 WORKER: start processing job (57, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:22 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 44, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0020083787825797266, 'OptimizerSelector:sgd:momentum': 0.44857970324236823, 'OptimizerSelector:sgd:weight_decay': 0.011678388966722828, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:22 WORKER: registered result for job (57, 2, 0) with dispatcher\\n\",\n      \"13:45:22 job (57, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14812803268432617 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:23 WORKER: start processing job (58, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 21, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.013639729046569212, 'OptimizerSelector:sgd:momentum': 0.6425967807796439, 'OptimizerSelector:sgd:weight_decay': 0.014698543884678741, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:23 WORKER: registered result for job (58, 0, 0) with dispatcher\\n\",\n      \"13:45:23 job (58, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15369606018066406 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:23 WORKER: start processing job (58, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05518391391099835, 'OptimizerSelector:sgd:momentum': 0.4027600016352872, 'OptimizerSelector:sgd:weight_decay': 0.018822659217809536, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:23 WORKER: registered result for job (58, 0, 1) with dispatcher\\n\",\n      \"13:45:23 job (58, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1478109359741211 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:23 WORKER: start processing job (58, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:23 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 67, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00030840244019458065, 'OptimizerSelector:sgd:momentum': 0.12163766900100689, 'OptimizerSelector:sgd:weight_decay': 0.0029249556078892232, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:23 WORKER: registered result for job (58, 0, 2) with dispatcher\\n\",\n      \"13:45:23 job (58, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1492938995361328 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:24 WORKER: start processing job (58, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.006245002657582654, 'OptimizerSelector:sgd:momentum': 0.987176340495631, 'OptimizerSelector:sgd:weight_decay': 0.007047651143967553, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:24 WORKER: registered result for job (58, 1, 0) with dispatcher\\n\",\n      \"13:45:24 job (58, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15448784828186035 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:24 WORKER: start processing job (59, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 55, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005792132569533022, 'OptimizerSelector:sgd:momentum': 0.7556773597273698, 'OptimizerSelector:sgd:weight_decay': 0.006122877100533009, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:24 WORKER: registered result for job (59, 0, 0) with dispatcher\\n\",\n      \"13:45:24 job (59, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.147813081741333 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:24 WORKER: start processing job (59, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.046375665433728286, 'OptimizerSelector:sgd:momentum': 0.8053382108750624, 'OptimizerSelector:sgd:weight_decay': 0.02386462467017826, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:24 WORKER: registered result for job (59, 0, 1) with dispatcher\\n\",\n      \"13:45:24 job (59, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1528167724609375 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:24 WORKER: start processing job (59, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:24 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 71, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0022337700177432907, 'OptimizerSelector:sgd:momentum': 0.4014312259403996, 'OptimizerSelector:sgd:weight_decay': 0.014564340067483117, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:24 WORKER: registered result for job (59, 0, 2) with dispatcher\\n\",\n      \"13:45:24 job (59, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1457819938659668 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:24 WORKER: start processing job (60, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:25 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 28, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00205631596707456, 'OptimizerSelector:sgd:momentum': 0.7524655822410076, 'OptimizerSelector:sgd:weight_decay': 0.044693605645399405, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:25 WORKER: registered result for job (60, 0, 0) with dispatcher\\n\",\n      \"13:45:25 job (60, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15522408485412598 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:25 WORKER: start processing job (60, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:25 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 934, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010168470461571786, 'OptimizerSelector:sgd:momentum': 0.7774287641308014, 'OptimizerSelector:sgd:weight_decay': 0.03329928396964196, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:25 WORKER: registered result for job (60, 0, 1) with dispatcher\\n\",\n      \"13:45:25 job (60, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.16222810745239258 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:25 WORKER: start processing job (60, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:25 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.007763946606241191, 'OptimizerSelector:sgd:momentum': 0.5107793255928044, 'OptimizerSelector:sgd:weight_decay': 0.00026932024231741047, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:25 WORKER: registered result for job (60, 0, 2) with dispatcher\\n\",\n      \"13:45:25 job (60, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14858484268188477 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:26 WORKER: start processing job (60, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 30, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08132450245956352, 'OptimizerSelector:sgd:momentum': 0.6825848523477823, 'OptimizerSelector:sgd:weight_decay': 0.024599459214959245, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:26 WORKER: registered result for job (60, 0, 3) with dispatcher\\n\",\n      \"13:45:26 job (60, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1480240821838379 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:26 WORKER: start processing job (60, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 10, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.010321228819345595, 'OptimizerSelector:sgd:momentum': 0.13158792897476188, 'OptimizerSelector:sgd:weight_decay': 0.0028279181528957096, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:26 WORKER: registered result for job (60, 0, 4) with dispatcher\\n\",\n      \"13:45:26 job (60, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14921021461486816 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:26 WORKER: start processing job (60, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:26 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 266, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010517650941145307, 'OptimizerSelector:sgd:momentum': 0.23542890320698368, 'OptimizerSelector:sgd:weight_decay': 0.020964890201975447, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:26 WORKER: registered result for job (60, 0, 5) with dispatcher\\n\",\n      \"13:45:26 job (60, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1459028720855713 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:26 WORKER: start processing job (60, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 334, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0005109638306689436, 'OptimizerSelector:sgd:momentum': 0.17635656736209693, 'OptimizerSelector:sgd:weight_decay': 0.029845915216065763, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:27 WORKER: registered result for job (60, 0, 6) with dispatcher\\n\",\n      \"13:45:27 job (60, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15071582794189453 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:27 WORKER: start processing job (60, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 98, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003046251921766663, 'OptimizerSelector:sgd:momentum': 0.5397136389518834, 'OptimizerSelector:sgd:weight_decay': 0.013814295401595537, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:27 WORKER: registered result for job (60, 0, 7) with dispatcher\\n\",\n      \"13:45:27 job (60, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15461397171020508 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:27 WORKER: start processing job (60, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 39, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000358727773704108, 'OptimizerSelector:sgd:momentum': 0.11218621102520926, 'OptimizerSelector:sgd:weight_decay': 0.09111739311288385, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:27 WORKER: registered result for job (60, 0, 8) with dispatcher\\n\",\n      \"13:45:27 job (60, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1469101905822754 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:27 WORKER: start processing job (60, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:27 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.011685346513513476, 'OptimizerSelector:sgd:momentum': 0.710019851092806, 'OptimizerSelector:sgd:weight_decay': 0.0021471338099802845, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:27 WORKER: registered result for job (60, 1, 0) with dispatcher\\n\",\n      \"13:45:27 job (60, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14823007583618164 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:28 WORKER: start processing job (60, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 151, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023276630376518476, 'OptimizerSelector:sgd:momentum': 0.7942610404576841, 'OptimizerSelector:sgd:weight_decay': 0.005849645795072486, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:28 WORKER: registered result for job (60, 1, 1) with dispatcher\\n\",\n      \"13:45:28 job (60, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1507251262664795 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:28 WORKER: start processing job (60, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002432371200188984, 'OptimizerSelector:sgd:momentum': 0.7805991397324816, 'OptimizerSelector:sgd:weight_decay': 0.07484309022712851, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:28 WORKER: registered result for job (60, 1, 2) with dispatcher\\n\",\n      \"13:45:28 job (60, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1462090015411377 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:28 WORKER: start processing job (60, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 33, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.01730456004148795, 'OptimizerSelector:sgd:momentum': 0.5613868304559153, 'OptimizerSelector:sgd:weight_decay': 0.0792828099804624, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:28 WORKER: registered result for job (60, 2, 0) with dispatcher\\n\",\n      \"13:45:28 job (60, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15346717834472656 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:28 WORKER: start processing job (61, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:28 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 23, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08790486327131303, 'OptimizerSelector:sgd:momentum': 0.9281332530956105, 'OptimizerSelector:sgd:weight_decay': 0.01668700053900909, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:28 WORKER: registered result for job (61, 0, 0) with dispatcher\\n\",\n      \"13:45:28 job (61, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15285301208496094 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:29 WORKER: start processing job (61, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:29 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 200, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009850697756623237, 'OptimizerSelector:sgd:momentum': 0.6681287806206425, 'OptimizerSelector:sgd:weight_decay': 0.019653679196243504, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:29 WORKER: registered result for job (61, 0, 1) with dispatcher\\n\",\n      \"13:45:29 job (61, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14650416374206543 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:29 WORKER: start processing job (61, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:29 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 39, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.059038103636660434, 'OptimizerSelector:sgd:momentum': 0.5020801330903518, 'OptimizerSelector:sgd:weight_decay': 0.03937602247176257, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:29 WORKER: registered result for job (61, 0, 2) with dispatcher\\n\",\n      \"13:45:29 job (61, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1497211456298828 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:29 WORKER: start processing job (61, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 12, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.027670765955946336, 'OptimizerSelector:sgd:momentum': 0.6815996965015, 'OptimizerSelector:sgd:weight_decay': 0.029324069645432323, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:30 WORKER: registered result for job (61, 1, 0) with dispatcher\\n\",\n      \"13:45:30 job (61, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14877891540527344 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:30 WORKER: start processing job (62, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 190, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000679087842963771, 'OptimizerSelector:sgd:momentum': 0.9364236532027054, 'OptimizerSelector:sgd:weight_decay': 0.013230626006902729, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:30 WORKER: registered result for job (62, 0, 0) with dispatcher\\n\",\n      \"13:45:30 job (62, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15144991874694824 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:30 WORKER: start processing job (62, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:30 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 655, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0012104468406144264, 'OptimizerSelector:sgd:momentum': 0.8400964686769105, 'OptimizerSelector:sgd:weight_decay': 0.01933032872564818, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:30 WORKER: registered result for job (62, 0, 1) with dispatcher\\n\",\n      \"13:45:30 job (62, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15026092529296875 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:30 WORKER: start processing job (62, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.04489863218517538, 'OptimizerSelector:sgd:momentum': 0.9578988945577426, 'OptimizerSelector:sgd:weight_decay': 0.09006710457569377, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:31 WORKER: registered result for job (62, 0, 2) with dispatcher\\n\",\n      \"13:45:31 job (62, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15002965927124023 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:31 WORKER: start processing job (63, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 613, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0915949160070171, 'OptimizerSelector:sgd:momentum': 0.18837224570361571, 'OptimizerSelector:sgd:weight_decay': 0.057570524355496956, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:31 WORKER: registered result for job (63, 0, 0) with dispatcher\\n\",\n      \"13:45:31 job (63, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1486659049987793 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:31 WORKER: start processing job (63, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 202, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.009990623005689744, 'OptimizerSelector:sgd:momentum': 0.9845580648847984, 'OptimizerSelector:sgd:weight_decay': 0.0023410612671271483, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:31 WORKER: registered result for job (63, 0, 1) with dispatcher\\n\",\n      \"13:45:31 job (63, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14621806144714355 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:31 WORKER: start processing job (63, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:31 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 146, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.011975863582918028, 'OptimizerSelector:sgd:momentum': 0.8013480420237722, 'OptimizerSelector:sgd:weight_decay': 0.006403380078550109, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:31 WORKER: registered result for job (63, 0, 2) with dispatcher\\n\",\n      \"13:45:32 job (63, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14863801002502441 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:32 WORKER: start processing job (63, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 38, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03774201974373043, 'OptimizerSelector:sgd:momentum': 0.5337484249527495, 'OptimizerSelector:sgd:weight_decay': 0.010765129576115447, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:32 WORKER: registered result for job (63, 0, 3) with dispatcher\\n\",\n      \"13:45:32 job (63, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14786934852600098 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:32 WORKER: start processing job (63, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 469, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.003164991978182436, 'OptimizerSelector:sgd:momentum': 0.3353604740869923, 'OptimizerSelector:sgd:weight_decay': 0.059248701814393304, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:32 WORKER: registered result for job (63, 0, 4) with dispatcher\\n\",\n      \"13:45:32 job (63, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14606595039367676 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:32 WORKER: start processing job (63, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:32 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03417040681666091, 'OptimizerSelector:sgd:momentum': 0.7817536650894962, 'OptimizerSelector:sgd:weight_decay': 0.009004914659024341, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:32 WORKER: registered result for job (63, 0, 5) with dispatcher\\n\",\n      \"13:45:32 job (63, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14642095565795898 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:32 WORKER: start processing job (63, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 251, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000245560005177107, 'OptimizerSelector:sgd:momentum': 0.40595681900395564, 'OptimizerSelector:sgd:weight_decay': 0.006345066350284644, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:33 WORKER: registered result for job (63, 0, 6) with dispatcher\\n\",\n      \"13:45:33 job (63, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14484477043151855 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:33 WORKER: start processing job (63, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 119, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0013364374946924194, 'OptimizerSelector:sgd:momentum': 0.4552940262970644, 'OptimizerSelector:sgd:weight_decay': 0.0070567228407571354, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:33 WORKER: registered result for job (63, 0, 7) with dispatcher\\n\",\n      \"13:45:33 job (63, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14892983436584473 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:33 WORKER: start processing job (63, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:33 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 171, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010833605451199027, 'OptimizerSelector:sgd:momentum': 0.6078717871168612, 'OptimizerSelector:sgd:weight_decay': 0.0375104119114585, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:33 WORKER: registered result for job (63, 0, 8) with dispatcher\\n\",\n      \"13:45:33 job (63, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1529839038848877 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:33 WORKER: start processing job (63, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 589, 'NetworkSelector:shapedresnet:num_groups': 4, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0017062388921261491, 'OptimizerSelector:sgd:momentum': 0.33787543390912195, 'OptimizerSelector:sgd:weight_decay': 0.09901160978447283, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:34 WORKER: registered result for job (63, 1, 0) with dispatcher\\n\",\n      \"13:45:34 job (63, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14528322219848633 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:34 WORKER: start processing job (63, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 601, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06463012207858046, 'OptimizerSelector:sgd:momentum': 0.7753417283887233, 'OptimizerSelector:sgd:weight_decay': 0.03882257005593974, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:34 WORKER: registered result for job (63, 1, 1) with dispatcher\\n\",\n      \"13:45:34 job (63, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.149061918258667 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:34 WORKER: start processing job (63, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:34 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 51, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00020528036054833404, 'OptimizerSelector:sgd:momentum': 0.8570854566298052, 'OptimizerSelector:sgd:weight_decay': 0.019635021717583405, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:34 WORKER: registered result for job (63, 1, 2) with dispatcher\\n\",\n      \"13:45:34 job (63, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14538288116455078 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:34 WORKER: start processing job (63, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 39, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.057072308532509435, 'OptimizerSelector:sgd:momentum': 0.7489778249647838, 'OptimizerSelector:sgd:weight_decay': 0.013644418283954532, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:35 WORKER: registered result for job (63, 2, 0) with dispatcher\\n\",\n      \"13:45:35 job (63, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14896893501281738 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:35 WORKER: start processing job (64, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 19, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00012771562544208913, 'OptimizerSelector:sgd:momentum': 0.8532867902426015, 'OptimizerSelector:sgd:weight_decay': 0.01718923149045792, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:35 WORKER: registered result for job (64, 0, 0) with dispatcher\\n\",\n      \"13:45:35 job (64, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15145421028137207 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:35 WORKER: start processing job (64, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:35 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 660, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006549355052824622, 'OptimizerSelector:sgd:momentum': 0.8277019554607578, 'OptimizerSelector:sgd:weight_decay': 0.01513728288095352, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:35 WORKER: registered result for job (64, 0, 1) with dispatcher\\n\",\n      \"13:45:35 job (64, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1478283405303955 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:36 WORKER: start processing job (64, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 241, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006493545447792618, 'OptimizerSelector:sgd:momentum': 0.855257413384546, 'OptimizerSelector:sgd:weight_decay': 0.0027478148901605944, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:36 WORKER: registered result for job (64, 0, 2) with dispatcher\\n\",\n      \"13:45:36 job (64, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14893198013305664 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:36 WORKER: start processing job (64, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 265, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0022202755999824572, 'OptimizerSelector:sgd:momentum': 0.6077274754789564, 'OptimizerSelector:sgd:weight_decay': 0.0688225784966698, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:36 WORKER: registered result for job (64, 1, 0) with dispatcher\\n\",\n      \"13:45:36 job (64, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14925813674926758 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:36 WORKER: start processing job (65, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:36 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 83, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0006661669052095278, 'OptimizerSelector:sgd:momentum': 0.5396862665293816, 'OptimizerSelector:sgd:weight_decay': 0.013267465365864413, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:36 WORKER: registered result for job (65, 0, 0) with dispatcher\\n\",\n      \"13:45:36 job (65, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15161395072937012 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:36 WORKER: start processing job (65, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00044873577936490716, 'OptimizerSelector:sgd:momentum': 0.7278063939425645, 'OptimizerSelector:sgd:weight_decay': 0.03808021392295846, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:37 WORKER: registered result for job (65, 0, 1) with dispatcher\\n\",\n      \"13:45:37 job (65, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14775967597961426 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:37 WORKER: start processing job (65, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 24, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.007846026581776027, 'OptimizerSelector:sgd:momentum': 0.2910845005752566, 'OptimizerSelector:sgd:weight_decay': 0.050555009010230016, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:37 WORKER: registered result for job (65, 0, 2) with dispatcher\\n\",\n      \"13:45:37 job (65, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1482100486755371 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:37 WORKER: start processing job (66, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 36, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000849211838312109, 'OptimizerSelector:sgd:momentum': 0.5206547517829038, 'OptimizerSelector:sgd:weight_decay': 0.004203710647348418, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:37 WORKER: registered result for job (66, 0, 0) with dispatcher\\n\",\n      \"13:45:37 job (66, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14746522903442383 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:37 WORKER: start processing job (66, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:37 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 15, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.001446364398256255, 'OptimizerSelector:sgd:momentum': 0.32585519960702836, 'OptimizerSelector:sgd:weight_decay': 0.09291023175901986, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:37 WORKER: registered result for job (66, 0, 1) with dispatcher\\n\",\n      \"13:45:37 job (66, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14728999137878418 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:38 WORKER: start processing job (66, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 179, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.06581856273438604, 'OptimizerSelector:sgd:momentum': 0.8841480741223932, 'OptimizerSelector:sgd:weight_decay': 0.012879898716242212, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:38 WORKER: registered result for job (66, 0, 2) with dispatcher\\n\",\n      \"13:45:38 job (66, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14698100090026855 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:38 WORKER: start processing job (66, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 224, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.018266107746719464, 'OptimizerSelector:sgd:momentum': 0.8528134744832158, 'OptimizerSelector:sgd:weight_decay': 0.012132787638585283, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:38 WORKER: registered result for job (66, 0, 3) with dispatcher\\n\",\n      \"13:45:38 job (66, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14687395095825195 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:38 WORKER: start processing job (66, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:38 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 88, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.08032354660385631, 'OptimizerSelector:sgd:momentum': 0.800296124318796, 'OptimizerSelector:sgd:weight_decay': 0.007331067614691491, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:38 WORKER: registered result for job (66, 0, 4) with dispatcher\\n\",\n      \"13:45:38 job (66, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15514183044433594 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:39 WORKER: start processing job (66, 0, 5)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 372, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.030395954830874525, 'OptimizerSelector:sgd:momentum': 0.7190540915800705, 'OptimizerSelector:sgd:weight_decay': 0.0022697102032809683, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:39 WORKER: registered result for job (66, 0, 5) with dispatcher\\n\",\n      \"13:45:39 job (66, 0, 5) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15282988548278809 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:39 WORKER: start processing job (66, 0, 6)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 325, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.012860970441650224, 'OptimizerSelector:sgd:momentum': 0.9511555696549857, 'OptimizerSelector:sgd:weight_decay': 0.0020047986025929104, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:39 WORKER: registered result for job (66, 0, 6) with dispatcher\\n\",\n      \"13:45:39 job (66, 0, 6) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14893007278442383 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:39 WORKER: start processing job (66, 0, 7)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:39 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 151, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0016608230570769006, 'OptimizerSelector:sgd:momentum': 0.4738906567865262, 'OptimizerSelector:sgd:weight_decay': 0.011164294724113429, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:39 WORKER: registered result for job (66, 0, 7) with dispatcher\\n\",\n      \"13:45:39 job (66, 0, 7) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14771509170532227 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:40 WORKER: start processing job (66, 0, 8)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 27, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00010482466984694966, 'OptimizerSelector:sgd:momentum': 0.5875744947838859, 'OptimizerSelector:sgd:weight_decay': 0.025942350731086056, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:40 WORKER: registered result for job (66, 0, 8) with dispatcher\\n\",\n      \"13:45:40 job (66, 0, 8) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15318679809570312 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:40 WORKER: start processing job (66, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:40 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 225, 'NetworkSelector:shapedresnet:num_groups': 7, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0030048425312844064, 'OptimizerSelector:sgd:momentum': 0.7277984250161132, 'OptimizerSelector:sgd:weight_decay': 0.017279185285910593, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:40 WORKER: registered result for job (66, 1, 0) with dispatcher\\n\",\n      \"13:45:40 job (66, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15801215171813965 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:41 WORKER: start processing job (66, 1, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 221, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00029020787497147636, 'OptimizerSelector:sgd:momentum': 0.763712467907779, 'OptimizerSelector:sgd:weight_decay': 0.02302170867626374, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:41 WORKER: registered result for job (66, 1, 1) with dispatcher\\n\",\n      \"13:45:41 job (66, 1, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.18522405624389648 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:41 WORKER: start processing job (66, 1, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 97, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0009833010436948778, 'OptimizerSelector:sgd:momentum': 0.1401045781155772, 'OptimizerSelector:sgd:weight_decay': 0.08313210876055267, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:41 WORKER: registered result for job (66, 1, 2) with dispatcher\\n\",\n      \"13:45:41 job (66, 1, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15146899223327637 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:41 WORKER: start processing job (66, 2, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:41 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 911, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00030977182626952933, 'OptimizerSelector:sgd:momentum': 0.1759585853420628, 'OptimizerSelector:sgd:weight_decay': 0.0943292712181745, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:41 WORKER: registered result for job (66, 2, 0) with dispatcher\\n\",\n      \"13:45:41 job (66, 2, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14956998825073242 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:42 WORKER: start processing job (67, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 184, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.00021648932481991437, 'OptimizerSelector:sgd:momentum': 0.6246501774918298, 'OptimizerSelector:sgd:weight_decay': 0.07839782790464038, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:42 WORKER: registered result for job (67, 0, 0) with dispatcher\\n\",\n      \"13:45:42 job (67, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14699196815490723 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:42 WORKER: start processing job (67, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 13, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.009752262831709319, 'OptimizerSelector:sgd:momentum': 0.712142125111583, 'OptimizerSelector:sgd:weight_decay': 0.0020071937104753686, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:42 WORKER: registered result for job (67, 0, 1) with dispatcher\\n\",\n      \"13:45:42 job (67, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14928102493286133 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:42 WORKER: start processing job (67, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:42 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 2, 'NetworkSelector:shapedresnet:max_units': 597, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.002174475509536659, 'OptimizerSelector:sgd:momentum': 0.9878309372674807, 'OptimizerSelector:sgd:weight_decay': 0.008949987517697867, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:42 WORKER: registered result for job (67, 0, 2) with dispatcher\\n\",\n      \"13:45:42 job (67, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15144991874694824 seconds with budget 500.0\\n\",\n      \"\\n\",\n      \"13:45:43 WORKER: start processing job (67, 1, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 287, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000264260021952688, 'OptimizerSelector:sgd:momentum': 0.5865608082375962, 'OptimizerSelector:sgd:weight_decay': 0.06536506962941625, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:43 WORKER: registered result for job (67, 1, 0) with dispatcher\\n\",\n      \"13:45:43 job (67, 1, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.15112709999084473 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:43 WORKER: start processing job (68, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:43 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 195, 'NetworkSelector:shapedresnet:num_groups': 5, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.09406617368817947, 'OptimizerSelector:sgd:momentum': 0.8027266608384972, 'OptimizerSelector:sgd:weight_decay': 0.039955842709229285, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:43 WORKER: registered result for job (68, 0, 0) with dispatcher\\n\",\n      \"13:45:43 job (68, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14628815650939941 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:44 WORKER: start processing job (68, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 885, 'NetworkSelector:shapedresnet:num_groups': 8, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0003918894397603429, 'OptimizerSelector:sgd:momentum': 0.32639374933916493, 'OptimizerSelector:sgd:weight_decay': 0.0055971970438837195, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:44 WORKER: registered result for job (68, 0, 1) with dispatcher\\n\",\n      \"13:45:44 job (68, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1553211212158203 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:44 WORKER: start processing job (68, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 468, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0012625721802493437, 'OptimizerSelector:sgd:momentum': 0.9262544631813933, 'OptimizerSelector:sgd:weight_decay': 0.0033991561313786623, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:44 WORKER: registered result for job (68, 0, 2) with dispatcher\\n\",\n      \"13:45:44 job (68, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1524639129638672 seconds with budget 1500.0\\n\",\n      \"\\n\",\n      \"13:45:44 WORKER: start processing job (69, 0, 0)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:44 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 4, 'NetworkSelector:shapedresnet:max_units': 11, 'NetworkSelector:shapedresnet:num_groups': 1, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.03073355641201278, 'OptimizerSelector:sgd:momentum': 0.7996234835861319, 'OptimizerSelector:sgd:weight_decay': 0.08283576677791246, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:44 WORKER: registered result for job (69, 0, 0) with dispatcher\\n\",\n      \"13:45:44 job (69, 0, 0) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14864587783813477 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:45 WORKER: start processing job (69, 0, 1)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 322, 'NetworkSelector:shapedresnet:num_groups': 2, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.0023517285152466964, 'OptimizerSelector:sgd:momentum': 0.902339979028184, 'OptimizerSelector:sgd:weight_decay': 0.011267247118262552, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:45 WORKER: registered result for job (69, 0, 1) with dispatcher\\n\",\n      \"13:45:45 job (69, 0, 1) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14938068389892578 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:45 WORKER: start processing job (69, 0, 2)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 29, 'NetworkSelector:shapedresnet:num_groups': 3, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.000387388195973696, 'OptimizerSelector:sgd:momentum': 0.5368731679518436, 'OptimizerSelector:sgd:weight_decay': 0.006787775197418459, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:45 WORKER: registered result for job (69, 0, 2) with dispatcher\\n\",\n      \"13:45:45 job (69, 0, 2) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14665508270263672 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:45 WORKER: start processing job (69, 0, 3)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:45 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 3, 'NetworkSelector:shapedresnet:max_units': 245, 'NetworkSelector:shapedresnet:num_groups': 9, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.041136025617806764, 'OptimizerSelector:sgd:momentum': 0.3438980817428258, 'OptimizerSelector:sgd:weight_decay': 0.0916478484231702, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\",\n      \"13:45:45 WORKER: registered result for job (69, 0, 3) with dispatcher\\n\",\n      \"13:45:45 job (69, 0, 3) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.14789724349975586 seconds with budget 166.66666666666666\\n\",\n      \"\\n\",\n      \"13:45:46 WORKER: start processing job (69, 0, 4)\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"<string>\\\", line 1, in <module>\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 116, in spawn_main\\n\",\n      \"    exitcode = _main(fd, parent_sentinel)\\n\",\n      \"  File \\\"/usr/local/Cellar/python@3.8/3.8.13_1/Frameworks/Python.framework/Versions/3.8/lib/python3.8/multiprocessing/spawn.py\\\", line 126, in _main\\n\",\n      \"    self = reduction.pickle.load(from_parent)\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/Pyro4/core.py\\\", line 280, in __getattr__\\n\",\n      \"    raise AttributeError(\\\"remote object '%s' has no exposed attribute or method '%s'\\\" % (self._pyroUri, name))\\n\",\n      \"AttributeError: remote object 'PYRO:hpbandster.run_0.worker.LDN-C02DN1EEMD6T.72049.-14490417664@192.168.0.4:53567' has no exposed attribute or method 'optimize_pipeline'\\n\",\n      \"13:45:46 Exception occurred using config:\\n\",\n      \"{'CreateDataLoader:batch_size': 125, 'Imputation:strategy': 'median', 'InitializationSelector:initialization_method': 'default', 'InitializationSelector:initializer:initialize_bias': 'No', 'LearningrateSchedulerSelector:lr_scheduler': 'cosine_annealing', 'LossModuleSelector:loss_module': 'cross_entropy_weighted', 'NetworkSelector:network': 'shapedresnet', 'NormalizationStrategySelector:normalization_strategy': 'standardize', 'OptimizerSelector:optimizer': 'sgd', 'PreprocessorSelector:preprocessor': 'truncated_svd', 'ResamplingStrategySelector:over_sampling_method': 'none', 'ResamplingStrategySelector:target_size_strategy': 'none', 'ResamplingStrategySelector:under_sampling_method': 'none', 'TrainNode:batch_loss_computation_technique': 'standard', 'LearningrateSchedulerSelector:cosine_annealing:T_max': 10, 'LearningrateSchedulerSelector:cosine_annealing:T_mult': 2, 'NetworkSelector:shapedresnet:activation': 'relu', 'NetworkSelector:shapedresnet:blocks_per_group': 1, 'NetworkSelector:shapedresnet:max_units': 90, 'NetworkSelector:shapedresnet:num_groups': 6, 'NetworkSelector:shapedresnet:resnet_shape': 'brick', 'NetworkSelector:shapedresnet:use_dropout': 0, 'NetworkSelector:shapedresnet:use_shake_drop': 0, 'NetworkSelector:shapedresnet:use_shake_shake': 0, 'OptimizerSelector:sgd:learning_rate': 0.05462920625091088, 'OptimizerSelector:sgd:momentum': 0.8625870876472601, 'OptimizerSelector:sgd:weight_decay': 0.004019526621887905, 'PreprocessorSelector:truncated_svd:target_dim': 100}\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13:45:46 WORKER: registered result for job (69, 0, 4) with dispatcher\\n\",\n      \"13:45:46 job (69, 0, 4) failed with exception\\n\",\n      \"Traceback (most recent call last):\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/hpbandster/core/worker.py\\\", line 206, in start_computation\\n\",\n      \"    result = {'result': self.compute(*args, config_id=id, **kwargs),\\n\",\n      \"  File \\\"/usr/local/lib/python3.8/site-packages/autoPyTorch/core/worker.py\\\", line 86, in compute\\n\",\n      \"    raise Exception(\\\"Exception in train pipeline. Took \\\" + str((time.time()-start_time)) + \\\" seconds with budget \\\" + str(budget))\\n\",\n      \"Exception: Exception in train pipeline. Took 0.1515662670135498 seconds with budget 166.66666666666666\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# running Auto-PyTorch\\n\",\n    \"autoPyTorch = AutoNetClassification(\\\"tiny_cs\\\",  # config preset\\n\",\n    \"                                    log_level='info',\\n\",\n    \"                                    max_runtime=2000,\\n\",\n    \"                                    min_budget=100,\\n\",\n    \"                                    max_budget=1500)\\n\",\n    \"\\n\",\n    \"autoPyTorch.fit(X_train, y_train, validation_split=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_pred = autoPyTorch.predict(X_test)\\n\",\n    \"\\n\",\n    \"print(\\\"Accuracy score\\\", np.mean(y_pred.reshape(-1) == y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model = autoPyTorch.get_pytorch_model()\\n\",\n    \"print(pytorch_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.randn(1, pytorch_model[0].in_features)\\n\",\n    \"y = pytorch_model(x)\\n\",\n    \"arch = make_dot(y.mean(), params=dict(pytorch_model.named_parameters()))\\n\",\n    \"arch.format=\\\"pdf\\\"\\n\",\n    \"arch.filename = \\\"convnet_arch\\\"\\n\",\n    \"arch.render(view=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"autoPyTorch.get_hyperparameter_search_space()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter16/optuna_pytorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Collecting optuna==2.10.1\\n\",\n      \"  Downloading optuna-2.10.1-py3-none-any.whl (308 kB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m308.2/308.2 kB\\u001b[0m \\u001b[31m12.9 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n      \"\\u001b[?25hRequirement already satisfied: alembic in /opt/conda/lib/python3.10/site-packages (from optuna==2.10.1) (1.13.1)\\n\",\n      \"Collecting cliff (from optuna==2.10.1)\\n\",\n      \"  Downloading cliff-4.5.0-py3-none-any.whl.metadata (2.0 kB)\\n\",\n      \"Collecting cmaes>=0.8.2 (from optuna==2.10.1)\\n\",\n      \"  Downloading cmaes-0.10.0-py3-none-any.whl.metadata (19 kB)\\n\",\n      \"Requirement already satisfied: colorlog in /opt/conda/lib/python3.10/site-packages (from optuna==2.10.1) (6.8.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from optuna==2.10.1) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from optuna==2.10.1) (21.3)\\n\",\n      \"Requirement already satisfied: scipy!=1.4.0 in /opt/conda/lib/python3.10/site-packages (from optuna==2.10.1) (1.11.4)\\n\",\n      \"Requirement already satisfied: sqlalchemy>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from 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cliff->optuna==2.10.1) (3.9.0)\\n\",\n      \"Collecting autopage>=0.4.0 (from cliff->optuna==2.10.1)\\n\",\n      \"  Downloading autopage-0.5.2-py3-none-any.whl.metadata (7.9 kB)\\n\",\n      \"Collecting cmd2>=1.0.0 (from cliff->optuna==2.10.1)\\n\",\n      \"  Downloading cmd2-2.4.3-py3-none-any.whl (147 kB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m147.2/147.2 kB\\u001b[0m \\u001b[31m21.9 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n      \"\\u001b[?25hCollecting stevedore>=2.0.1 (from cliff->optuna==2.10.1)\\n\",\n      \"  Downloading stevedore-5.1.0-py3-none-any.whl.metadata (2.2 kB)\\n\",\n      \"Requirement already satisfied: attrs>=16.3.0 in /opt/conda/lib/python3.10/site-packages (from cmd2>=1.0.0->cliff->optuna==2.10.1) (23.2.0)\\n\",\n      \"Collecting pyperclip>=1.6 (from cmd2>=1.0.0->cliff->optuna==2.10.1)\\n\",\n      \"  Downloading pyperclip-1.8.2.tar.gz (20 kB)\\n\",\n      \"  Preparing metadata (setup.py) ... \\u001b[?25ldone\\n\",\n      \"\\u001b[?25hRequirement already satisfied: wcwidth>=0.1.7 in /opt/conda/lib/python3.10/site-packages (from cmd2>=1.0.0->cliff->optuna==2.10.1) (0.2.12)\\n\",\n      \"Collecting pbr!=2.1.0,>=2.0.0 (from stevedore>=2.0.1->cliff->optuna==2.10.1)\\n\",\n      \"  Downloading pbr-6.0.0-py2.py3-none-any.whl.metadata (1.3 kB)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=0.9.2 in /opt/conda/lib/python3.10/site-packages (from Mako->alembic->optuna==2.10.1) (2.1.3)\\n\",\n      \"Downloading cmaes-0.10.0-py3-none-any.whl (29 kB)\\n\",\n      \"Downloading cliff-4.5.0-py3-none-any.whl (81 kB)\\n\",\n      \"\\u001b[2K   \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m81.1/81.1 kB\\u001b[0m \\u001b[31m14.5 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n      \"\\u001b[?25hDownloading autopage-0.5.2-py3-none-any.whl (30 kB)\\n\",\n      \"Downloading stevedore-5.1.0-py3-none-any.whl (49 kB)\\n\",\n      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packages: pyperclip, pbr, cmd2, cmaes, autopage, stevedore, cliff, optuna\\n\",\n      \"  Attempting uninstall: optuna\\n\",\n      \"    Found existing installation: optuna 3.5.0\\n\",\n      \"    Uninstalling optuna-3.5.0:\\n\",\n      \"      Successfully uninstalled optuna-3.5.0\\n\",\n      \"Successfully installed autopage-0.5.2 cliff-4.5.0 cmaes-0.10.0 cmd2-2.4.3 optuna-2.10.1 pbr-6.0.0 pyperclip-1.8.2 stevedore-5.1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install optuna==2.10.1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import optuna\\n\",\n    \"\\n\",\n    \"torch.use_deterministic_algorithms(True)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self, trial):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        num_conv_layers = trial.suggest_int(\\\"num_conv_layers\\\", 1, 4)\\n\",\n    \"        num_fc_layers = trial.suggest_int(\\\"num_fc_layers\\\", 1, 2)\\n\",\n    \"\\n\",\n    \"        self.layers = []\\n\",\n    \"        input_depth = 1 # grayscale image\\n\",\n    \"        for i in range(num_conv_layers):\\n\",\n    \"            output_depth = trial.suggest_int(f\\\"conv_depth_{i}\\\", 16, 64)\\n\",\n    \"            self.layers.append(nn.Conv2d(input_depth, output_depth, 3, 1))\\n\",\n    \"            self.layers.append(nn.ReLU())\\n\",\n    \"            input_depth = output_depth\\n\",\n    \"        self.layers.append(nn.MaxPool2d(2))\\n\",\n    \"        p = trial.suggest_float(f\\\"conv_dropout_{i}\\\", 0.1, 0.4)\\n\",\n    \"        self.layers.append(nn.Dropout(p))\\n\",\n    \"        self.layers.append(nn.Flatten())\\n\",\n    \"\\n\",\n    \"        input_feat = self._get_flatten_shape()\\n\",\n    \"        for i in range(num_fc_layers):\\n\",\n    \"            output_feat = trial.suggest_int(f\\\"fc_output_feat_{i}\\\", 16, 64)\\n\",\n    \"            self.layers.append(nn.Linear(input_feat, output_feat))\\n\",\n    \"            self.layers.append(nn.ReLU())\\n\",\n    \"            p = trial.suggest_float(f\\\"fc_dropout_{i}\\\", 0.1, 0.4)\\n\",\n    \"            self.layers.append(nn.Dropout(p))\\n\",\n    \"            input_feat = output_feat\\n\",\n    \"        self.layers.append(nn.Linear(input_feat, 10))\\n\",\n    \"        self.layers.append(nn.LogSoftmax(dim=1))\\n\",\n    \"        \\n\",\n    \"        self.model = nn.Sequential(*self.layers)\\n\",\n    \"    \\n\",\n    \"    def _get_flatten_shape(self):\\n\",\n    \"        conv_model = nn.Sequential(*self.layers)\\n\",\n    \"        op_feat = conv_model(torch.rand(1, 1, 28, 28))\\n\",\n    \"        n_size = op_feat.data.view(1, -1).size(1)\\n\",\n    \"        return n_size\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_ds = datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))]))\\n\",\n    \"test_ds = datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))]))\\n\",\n    \"\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(train_ds, batch_size=32, shuffle=True)\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(test_ds, batch_size=500, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 500 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # use argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"    \\n\",\n    \"    accuracy = 100. * success / len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset), accuracy))\\n\",\n    \"    \\n\",\n    \"    return accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and model training routine\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def objective(trial):\\n\",\n    \"    \\n\",\n    \"    model = ConvNet(trial)\\n\",\n    \"    opt_name = trial.suggest_categorical(\\\"optimizer\\\", [\\\"Adam\\\", \\\"Adadelta\\\", \\\"RMSprop\\\", \\\"SGD\\\"])\\n\",\n    \"    lr = trial.suggest_float(\\\"lr\\\", 1e-1, 5e-1, log=True)\\n\",\n    \"    optimizer = getattr(optim, opt_name)(model.parameters(), lr=lr)\\n\",\n    \"    \\n\",\n    \"    for epoch in range(1, 3):\\n\",\n    \"        train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"        accuracy = test(model, device, test_dataloader)\\n\",\n    \"        trial.report(accuracy, epoch)\\n\",\n    \"        \\n\",\n    \"        if trial.should_prune():\\n\",\n    \"            raise optuna.exceptions.TrialPruned()\\n\",\n    \"\\n\",\n    \"    return accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run the hyperparameter search\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:15:30,029]\\u001b[0m A new study created in memory with name: mastering_pytorch\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.279864\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 2.347058\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 2.261235\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 2.291524\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3178, Overall Accuracy: 1135/10000 (11%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 2.304916\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 2.303748\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 2.324766\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 2.347335\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:16:58,997]\\u001b[0m Trial 0 finished with value: 9.58 and parameters: {'num_conv_layers': 2, 'num_fc_layers': 1, 'conv_depth_0': 34, 'conv_depth_1': 41, 'conv_dropout_1': 0.15383558720767948, 'fc_output_feat_0': 62, 'fc_dropout_0': 0.15506861406996786, 'optimizer': 'Adam', 'lr': 0.20023572685997063}. Best is trial 0 with value: 9.58.\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3296, Overall Accuracy: 958/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.297234\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 2.321342\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 2.248134\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 2.282013\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.4947, Overall Accuracy: 1009/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 2.338793\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 2.524946\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 2.318933\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 2.332036\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:19:10,390]\\u001b[0m Trial 1 finished with value: 9.8 and parameters: {'num_conv_layers': 3, 'num_fc_layers': 2, 'conv_depth_0': 29, 'conv_depth_1': 49, 'conv_depth_2': 47, 'conv_dropout_2': 0.278335463941041, 'fc_output_feat_0': 51, 'fc_dropout_0': 0.31004103413830064, 'fc_output_feat_1': 58, 'fc_dropout_1': 0.15485667529511574, 'optimizer': 'RMSprop', 'lr': 0.4780748567930119}. Best is trial 1 with value: 9.8.\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3665, Overall Accuracy: 980/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.297451\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 2.322544\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 2.298779\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 2.334569\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3327, Overall Accuracy: 958/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 2.291020\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 2.341492\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 2.317598\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 2.369541\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:41:10,990]\\u001b[0m Trial 2 finished with value: 9.82 and parameters: {'num_conv_layers': 4, 'num_fc_layers': 2, 'conv_depth_0': 34, 'conv_depth_1': 51, 'conv_depth_2': 28, 'conv_depth_3': 55, 'conv_dropout_3': 0.14208996846752817, 'fc_output_feat_0': 35, 'fc_dropout_0': 0.2660624056161214, 'fc_output_feat_1': 36, 'fc_dropout_1': 0.15326719427526703, 'optimizer': 'Adam', 'lr': 0.14119025099148208}. Best is trial 2 with value: 9.82.\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3158, Overall Accuracy: 982/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.295434\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 2.309730\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 2.298417\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 2.383884\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3727, Overall Accuracy: 1010/10000 (10%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 2.310924\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 2.367006\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 2.380785\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 2.387081\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:45:38,374]\\u001b[0m Trial 3 finished with value: 8.92 and parameters: {'num_conv_layers': 3, 'num_fc_layers': 2, 'conv_depth_0': 17, 'conv_depth_1': 29, 'conv_depth_2': 58, 'conv_dropout_2': 0.33864705908649817, 'fc_output_feat_0': 39, 'fc_dropout_0': 0.23750831217409174, 'fc_output_feat_1': 40, 'fc_dropout_1': 0.14041408480339376, 'optimizer': 'Adam', 'lr': 0.310858350845543}. Best is trial 2 with value: 9.82.\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3431, Overall Accuracy: 892/10000 (9%)\\n\",\n      \"\\n\",\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.330726\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 2.365713\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 2.304307\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 2.256113\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3197, Overall Accuracy: 1135/10000 (11%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 2.273054\\n\",\n      \"epoch: 2 [16000/60000 (27%)]\\t training loss: 2.342382\\n\",\n      \"epoch: 2 [32000/60000 (53%)]\\t training loss: 2.342956\\n\",\n      \"epoch: 2 [48000/60000 (80%)]\\t training loss: 2.277427\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[32m[I 2024-02-16 02:49:20,214]\\u001b[0m Trial 4 finished with value: 10.32 and parameters: {'num_conv_layers': 2, 'num_fc_layers': 1, 'conv_depth_0': 26, 'conv_depth_1': 35, 'conv_dropout_1': 0.19941309294192455, 'fc_output_feat_0': 59, 'fc_dropout_0': 0.2574163114292745, 'optimizer': 'RMSprop', 'lr': 0.14980606145925923}. Best is trial 4 with value: 10.32.\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test dataset: Overall Loss: 2.3201, Overall Accuracy: 1032/10000 (10%)\\n\",\n      \"\\n\",\n      \"results: \\n\",\n      \"num_trials_conducted:  5\\n\",\n      \"num_trials_pruned:  0\\n\",\n      \"num_trials_completed:  5\\n\",\n      \"results from best trial:\\n\",\n      \"accuracy:  10.32\\n\",\n      \"hyperparameters: \\n\",\n      \"num_conv_layers: 2\\n\",\n      \"num_fc_layers: 1\\n\",\n      \"conv_depth_0: 26\\n\",\n      \"conv_depth_1: 35\\n\",\n      \"conv_dropout_1: 0.19941309294192455\\n\",\n      \"fc_output_feat_0: 59\\n\",\n      \"fc_dropout_0: 0.2574163114292745\\n\",\n      \"optimizer: RMSprop\\n\",\n      \"lr: 0.14980606145925923\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"study = optuna.create_study(study_name=\\\"mastering_pytorch\\\", direction=\\\"maximize\\\")\\n\",\n    \"study.optimize(objective, n_trials=100, timeout=2000)\\n\",\n    \"\\n\",\n    \"pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED]\\n\",\n    \"complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]\\n\",\n    \"\\n\",\n    \"print(\\\"results: \\\")\\n\",\n    \"print(\\\"num_trials_conducted: \\\", len(study.trials))\\n\",\n    \"print(\\\"num_trials_pruned: \\\", len(pruned_trials))\\n\",\n    \"print(\\\"num_trials_completed: \\\", len(complete_trials))\\n\",\n    \"\\n\",\n    \"print(\\\"results from best trial:\\\")\\n\",\n    \"trial = study.best_trial\\n\",\n    \"\\n\",\n    \"print(\\\"accuracy: \\\", trial.value)\\n\",\n    \"print(\\\"hyperparameters: \\\")\\n\",\n    \"for key, value in trial.params.items():\\n\",\n    \"    print(\\\"{}: {}\\\".format(key, value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter17/captum_interpretability.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17.0 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17.0) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (9.3.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\",\n      \"Collecting captum==0.5.0\\n\",\n      \"  Downloading captum-0.5.0-py3-none-any.whl (1.4 MB)\\n\",\n      \"\\u001b[2K     \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m1.4/1.4 MB\\u001b[0m \\u001b[31m35.9 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m00:01\\u001b[0m\\n\",\n      \"\\u001b[?25hRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from captum==0.5.0) (3.5.2)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from captum==0.5.0) (1.26.2)\\n\",\n      \"Requirement already satisfied: torch>=1.6 in /opt/conda/lib/python3.10/site-packages (from captum==0.5.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.6->captum==0.5.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.6->captum==0.5.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (1.4.5)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->captum==0.5.0) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->captum==0.5.0) (1.16.0)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.6->captum==0.5.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.6->captum==0.5.0) (1.3.0)\\n\",\n      \"Installing collected packages: captum\\n\",\n      \"Successfully installed captum-0.5.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17.0\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\\n\",\n    \"!pip install captum==0.5.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"from captum.attr import IntegratedGradients\\n\",\n    \"from captum.attr import Saliency\\n\",\n    \"from captum.attr import DeepLift\\n\",\n    \"from captum.attr import visualization as viz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(123)\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 10 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset),\\n\",\n    \"        100. * success / len(test_dataloader.dataset)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\\n\",\n    \"    batch_size=32, shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,)) \\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=500, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(0)\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = optim.Adadelta(model.parameters(), lr=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.310609\\n\",\n      \"epoch: 1 [320/60000 (1%)]\\t training loss: 1.924133\\n\",\n      \"epoch: 1 [640/60000 (1%)]\\t training loss: 1.313337\\n\",\n      \"epoch: 1 [960/60000 (2%)]\\t training loss: 0.796470\\n\",\n      \"epoch: 1 [1280/60000 (2%)]\\t training loss: 0.819801\\n\",\n      \"epoch: 1 [1600/60000 (3%)]\\t training loss: 0.678459\\n\",\n      \"epoch: 1 [1920/60000 (3%)]\\t training loss: 0.477066\\n\",\n      \"epoch: 1 [2240/60000 (4%)]\\t training loss: 0.527125\\n\",\n      \"epoch: 1 [2560/60000 (4%)]\\t training loss: 0.469366\\n\",\n      \"epoch: 1 [2880/60000 (5%)]\\t training loss: 0.239070\\n\",\n      \"epoch: 1 [3200/60000 (5%)]\\t training loss: 0.523908\\n\",\n      \"epoch: 1 [3520/60000 (6%)]\\t training loss: 0.267831\\n\",\n      \"epoch: 1 [3840/60000 (6%)]\\t training loss: 0.464627\\n\",\n      \"epoch: 1 [4160/60000 (7%)]\\t training loss: 0.413977\\n\",\n      \"epoch: 1 [4480/60000 (7%)]\\t training loss: 0.324858\\n\",\n      \"epoch: 1 [4800/60000 (8%)]\\t training loss: 0.495965\\n\",\n      \"epoch: 1 [5120/60000 (9%)]\\t training loss: 0.153463\\n\",\n      \"epoch: 1 [5440/60000 (9%)]\\t training loss: 0.377620\\n\",\n      \"epoch: 1 [5760/60000 (10%)]\\t training loss: 0.097269\\n\",\n      \"epoch: 1 [6080/60000 (10%)]\\t training loss: 0.181864\\n\",\n      \"epoch: 1 [6400/60000 (11%)]\\t training loss: 0.295800\\n\",\n      \"epoch: 1 [6720/60000 (11%)]\\t training loss: 0.082916\\n\",\n      \"epoch: 1 [7040/60000 (12%)]\\t training loss: 0.353389\\n\",\n      \"epoch: 1 [7360/60000 (12%)]\\t training loss: 0.040657\\n\",\n      \"epoch: 1 [7680/60000 (13%)]\\t training loss: 0.365705\\n\",\n      \"epoch: 1 [8000/60000 (13%)]\\t training loss: 0.231380\\n\",\n      \"epoch: 1 [8320/60000 (14%)]\\t training loss: 0.321043\\n\",\n      \"epoch: 1 [8640/60000 (14%)]\\t training loss: 0.211378\\n\",\n      \"epoch: 1 [8960/60000 (15%)]\\t training loss: 0.054053\\n\",\n      \"epoch: 1 [9280/60000 (15%)]\\t training loss: 0.337620\\n\",\n      \"epoch: 1 [9600/60000 (16%)]\\t training loss: 0.216428\\n\",\n      \"epoch: 1 [9920/60000 (17%)]\\t training loss: 0.174468\\n\",\n      \"epoch: 1 [10240/60000 (17%)]\\t training loss: 0.213010\\n\",\n      \"epoch: 1 [10560/60000 (18%)]\\t training loss: 0.357055\\n\",\n      \"epoch: 1 [10880/60000 (18%)]\\t training loss: 0.070653\\n\",\n      \"epoch: 1 [11200/60000 (19%)]\\t training loss: 0.055425\\n\",\n      \"epoch: 1 [11520/60000 (19%)]\\t training loss: 0.230391\\n\",\n      \"epoch: 1 [11840/60000 (20%)]\\t training loss: 0.048202\\n\",\n      \"epoch: 1 [12160/60000 (20%)]\\t training loss: 0.403469\\n\",\n      \"epoch: 1 [12480/60000 (21%)]\\t training loss: 0.025848\\n\",\n      \"epoch: 1 [12800/60000 (21%)]\\t training loss: 0.104852\\n\",\n      \"epoch: 1 [13120/60000 (22%)]\\t training loss: 0.101376\\n\",\n      \"epoch: 1 [13440/60000 (22%)]\\t training loss: 0.178021\\n\",\n      \"epoch: 1 [13760/60000 (23%)]\\t training loss: 0.070927\\n\",\n      \"epoch: 1 [14080/60000 (23%)]\\t training loss: 0.048668\\n\",\n      \"epoch: 1 [14400/60000 (24%)]\\t training loss: 0.090581\\n\",\n      \"epoch: 1 [14720/60000 (25%)]\\t training loss: 0.165846\\n\",\n      \"epoch: 1 [15040/60000 (25%)]\\t training loss: 0.324705\\n\",\n      \"epoch: 1 [15360/60000 (26%)]\\t training loss: 0.077008\\n\",\n      \"epoch: 1 [15680/60000 (26%)]\\t training loss: 0.128724\\n\",\n      \"epoch: 1 [16000/60000 (27%)]\\t training loss: 0.084200\\n\",\n      \"epoch: 1 [16320/60000 (27%)]\\t training loss: 0.158558\\n\",\n      \"epoch: 1 [16640/60000 (28%)]\\t training loss: 0.040566\\n\",\n      \"epoch: 1 [16960/60000 (28%)]\\t training loss: 0.287925\\n\",\n      \"epoch: 1 [17280/60000 (29%)]\\t training loss: 0.057604\\n\",\n      \"epoch: 1 [17600/60000 (29%)]\\t training loss: 0.031712\\n\",\n      \"epoch: 1 [17920/60000 (30%)]\\t training loss: 0.046777\\n\",\n      \"epoch: 1 [18240/60000 (30%)]\\t training loss: 0.025740\\n\",\n      \"epoch: 1 [18560/60000 (31%)]\\t training loss: 0.065141\\n\",\n      \"epoch: 1 [18880/60000 (31%)]\\t training loss: 0.220227\\n\",\n      \"epoch: 1 [19200/60000 (32%)]\\t training loss: 0.102033\\n\",\n      \"epoch: 1 [19520/60000 (33%)]\\t training loss: 0.068922\\n\",\n      \"epoch: 1 [19840/60000 (33%)]\\t training loss: 0.075009\\n\",\n      \"epoch: 1 [20160/60000 (34%)]\\t training loss: 0.111961\\n\",\n      \"epoch: 1 [20480/60000 (34%)]\\t training loss: 0.056665\\n\",\n      \"epoch: 1 [20800/60000 (35%)]\\t training loss: 0.234118\\n\",\n      \"epoch: 1 [21120/60000 (35%)]\\t training loss: 0.303591\\n\",\n      \"epoch: 1 [21440/60000 (36%)]\\t training loss: 0.217894\\n\",\n      \"epoch: 1 [21760/60000 (36%)]\\t training loss: 0.091958\\n\",\n      \"epoch: 1 [22080/60000 (37%)]\\t training loss: 0.079766\\n\",\n      \"epoch: 1 [22400/60000 (37%)]\\t training loss: 0.151057\\n\",\n      \"epoch: 1 [22720/60000 (38%)]\\t training loss: 0.262819\\n\",\n      \"epoch: 1 [23040/60000 (38%)]\\t training loss: 0.061579\\n\",\n      \"epoch: 1 [23360/60000 (39%)]\\t training loss: 0.135419\\n\",\n      \"epoch: 1 [23680/60000 (39%)]\\t training loss: 0.285402\\n\",\n      \"epoch: 1 [24000/60000 (40%)]\\t training loss: 0.148586\\n\",\n      \"epoch: 1 [24320/60000 (41%)]\\t training loss: 0.066807\\n\",\n      \"epoch: 1 [24640/60000 (41%)]\\t training loss: 0.126747\\n\",\n      \"epoch: 1 [24960/60000 (42%)]\\t training loss: 0.026213\\n\",\n      \"epoch: 1 [25280/60000 (42%)]\\t training loss: 0.024073\\n\",\n      \"epoch: 1 [25600/60000 (43%)]\\t training loss: 0.600073\\n\",\n      \"epoch: 1 [25920/60000 (43%)]\\t training loss: 0.497171\\n\",\n      \"epoch: 1 [26240/60000 (44%)]\\t training loss: 0.118145\\n\",\n      \"epoch: 1 [26560/60000 (44%)]\\t training loss: 0.040906\\n\",\n      \"epoch: 1 [26880/60000 (45%)]\\t training loss: 0.154461\\n\",\n      \"epoch: 1 [27200/60000 (45%)]\\t training loss: 0.013285\\n\",\n      \"epoch: 1 [27520/60000 (46%)]\\t training loss: 0.027993\\n\",\n      \"epoch: 1 [27840/60000 (46%)]\\t training loss: 0.127872\\n\",\n      \"epoch: 1 [28160/60000 (47%)]\\t training loss: 0.040515\\n\",\n      \"epoch: 1 [28480/60000 (47%)]\\t training loss: 0.402136\\n\",\n      \"epoch: 1 [28800/60000 (48%)]\\t training loss: 0.138493\\n\",\n      \"epoch: 1 [29120/60000 (49%)]\\t training loss: 0.207102\\n\",\n      \"epoch: 1 [29440/60000 (49%)]\\t training loss: 0.012119\\n\",\n      \"epoch: 1 [29760/60000 (50%)]\\t training loss: 0.054411\\n\",\n      \"epoch: 1 [30080/60000 (50%)]\\t training loss: 0.546068\\n\",\n      \"epoch: 1 [30400/60000 (51%)]\\t training loss: 0.144434\\n\",\n      \"epoch: 1 [30720/60000 (51%)]\\t training loss: 0.225871\\n\",\n      \"epoch: 1 [31040/60000 (52%)]\\t training loss: 0.069757\\n\",\n      \"epoch: 1 [31360/60000 (52%)]\\t training loss: 0.068556\\n\",\n      \"epoch: 1 [31680/60000 (53%)]\\t training loss: 0.034509\\n\",\n      \"epoch: 1 [32000/60000 (53%)]\\t training loss: 0.015917\\n\",\n      \"epoch: 1 [32320/60000 (54%)]\\t training loss: 0.107069\\n\",\n      \"epoch: 1 [32640/60000 (54%)]\\t training loss: 0.110502\\n\",\n      \"epoch: 1 [32960/60000 (55%)]\\t training loss: 0.077687\\n\",\n      \"epoch: 1 [33280/60000 (55%)]\\t training loss: 0.276489\\n\",\n      \"epoch: 1 [33600/60000 (56%)]\\t training loss: 0.186835\\n\",\n      \"epoch: 1 [33920/60000 (57%)]\\t training loss: 0.017603\\n\",\n      \"epoch: 1 [34240/60000 (57%)]\\t training loss: 0.025609\\n\",\n      \"epoch: 1 [34560/60000 (58%)]\\t training loss: 0.003528\\n\",\n      \"epoch: 1 [34880/60000 (58%)]\\t training loss: 0.123900\\n\",\n      \"epoch: 1 [35200/60000 (59%)]\\t training loss: 0.229518\\n\",\n      \"epoch: 1 [35520/60000 (59%)]\\t training loss: 0.131734\\n\",\n      \"epoch: 1 [35840/60000 (60%)]\\t training loss: 0.015819\\n\",\n      \"epoch: 1 [36160/60000 (60%)]\\t training loss: 0.165748\\n\",\n      \"epoch: 1 [36480/60000 (61%)]\\t training loss: 0.117925\\n\",\n      \"epoch: 1 [36800/60000 (61%)]\\t training loss: 0.140046\\n\",\n      \"epoch: 1 [37120/60000 (62%)]\\t training loss: 0.043650\\n\",\n      \"epoch: 1 [37440/60000 (62%)]\\t training loss: 0.216800\\n\",\n      \"epoch: 1 [37760/60000 (63%)]\\t training loss: 0.032769\\n\",\n      \"epoch: 1 [38080/60000 (63%)]\\t training loss: 0.113341\\n\",\n      \"epoch: 1 [38400/60000 (64%)]\\t training loss: 0.074898\\n\",\n      \"epoch: 1 [38720/60000 (65%)]\\t training loss: 0.488738\\n\",\n      \"epoch: 1 [39040/60000 (65%)]\\t training loss: 0.048389\\n\",\n      \"epoch: 1 [39360/60000 (66%)]\\t training loss: 0.176396\\n\",\n      \"epoch: 1 [39680/60000 (66%)]\\t training loss: 0.021329\\n\",\n      \"epoch: 1 [40000/60000 (67%)]\\t training loss: 0.106253\\n\",\n      \"epoch: 1 [40320/60000 (67%)]\\t training loss: 0.065340\\n\",\n      \"epoch: 1 [40640/60000 (68%)]\\t training loss: 0.202225\\n\",\n      \"epoch: 1 [40960/60000 (68%)]\\t training loss: 0.194045\\n\",\n      \"epoch: 1 [41280/60000 (69%)]\\t training loss: 0.213245\\n\",\n      \"epoch: 1 [41600/60000 (69%)]\\t training loss: 0.155805\\n\",\n      \"epoch: 1 [41920/60000 (70%)]\\t training loss: 0.040492\\n\",\n      \"epoch: 1 [42240/60000 (70%)]\\t training loss: 0.011083\\n\",\n      \"epoch: 1 [42560/60000 (71%)]\\t training loss: 0.037179\\n\",\n      \"epoch: 1 [42880/60000 (71%)]\\t training loss: 0.100338\\n\",\n      \"epoch: 1 [43200/60000 (72%)]\\t training loss: 0.045283\\n\",\n      \"epoch: 1 [43520/60000 (73%)]\\t training loss: 0.226100\\n\",\n      \"epoch: 1 [43840/60000 (73%)]\\t training loss: 0.040024\\n\",\n      \"epoch: 1 [44160/60000 (74%)]\\t training loss: 0.116498\\n\",\n      \"epoch: 1 [44480/60000 (74%)]\\t training loss: 0.061347\\n\",\n      \"epoch: 1 [44800/60000 (75%)]\\t training loss: 0.250636\\n\",\n      \"epoch: 1 [45120/60000 (75%)]\\t training loss: 0.015089\\n\",\n      \"epoch: 1 [45440/60000 (76%)]\\t training loss: 0.179538\\n\",\n      \"epoch: 1 [45760/60000 (76%)]\\t training loss: 0.005873\\n\",\n      \"epoch: 1 [46080/60000 (77%)]\\t training loss: 0.246703\\n\",\n      \"epoch: 1 [46400/60000 (77%)]\\t training loss: 0.059748\\n\",\n      \"epoch: 1 [46720/60000 (78%)]\\t training loss: 0.086515\\n\",\n      \"epoch: 1 [47040/60000 (78%)]\\t training loss: 0.056783\\n\",\n      \"epoch: 1 [47360/60000 (79%)]\\t training loss: 0.026905\\n\",\n      \"epoch: 1 [47680/60000 (79%)]\\t training loss: 0.229662\\n\",\n      \"epoch: 1 [48000/60000 (80%)]\\t training loss: 0.219784\\n\",\n      \"epoch: 1 [48320/60000 (81%)]\\t training loss: 0.173513\\n\",\n      \"epoch: 1 [48640/60000 (81%)]\\t training loss: 0.020131\\n\",\n      \"epoch: 1 [48960/60000 (82%)]\\t training loss: 0.092622\\n\",\n      \"epoch: 1 [49280/60000 (82%)]\\t training loss: 0.143768\\n\",\n      \"epoch: 1 [49600/60000 (83%)]\\t training loss: 0.227249\\n\",\n      \"epoch: 1 [49920/60000 (83%)]\\t training loss: 0.129910\\n\",\n      \"epoch: 1 [50240/60000 (84%)]\\t training loss: 0.071571\\n\",\n      \"epoch: 1 [50560/60000 (84%)]\\t training loss: 0.003748\\n\",\n      \"epoch: 1 [50880/60000 (85%)]\\t training loss: 0.004240\\n\",\n      \"epoch: 1 [51200/60000 (85%)]\\t training loss: 0.232011\\n\",\n      \"epoch: 1 [51520/60000 (86%)]\\t training loss: 0.028602\\n\",\n      \"epoch: 1 [51840/60000 (86%)]\\t training loss: 0.013641\\n\",\n      \"epoch: 1 [52160/60000 (87%)]\\t training loss: 0.010407\\n\",\n      \"epoch: 1 [52480/60000 (87%)]\\t training loss: 0.014092\\n\",\n      \"epoch: 1 [52800/60000 (88%)]\\t training loss: 0.041886\\n\",\n      \"epoch: 1 [53120/60000 (89%)]\\t training loss: 0.029681\\n\",\n      \"epoch: 1 [53440/60000 (89%)]\\t training loss: 0.032927\\n\",\n      \"epoch: 1 [53760/60000 (90%)]\\t training loss: 0.044958\\n\",\n      \"epoch: 1 [54080/60000 (90%)]\\t training loss: 0.014901\\n\",\n      \"epoch: 1 [54400/60000 (91%)]\\t training loss: 0.136652\\n\",\n      \"epoch: 1 [54720/60000 (91%)]\\t training loss: 0.011173\\n\",\n      \"epoch: 1 [55040/60000 (92%)]\\t training loss: 0.002030\\n\",\n      \"epoch: 1 [55360/60000 (92%)]\\t training loss: 0.066842\\n\",\n      \"epoch: 1 [55680/60000 (93%)]\\t training loss: 0.048196\\n\",\n      \"epoch: 1 [56000/60000 (93%)]\\t training loss: 0.052259\\n\",\n      \"epoch: 1 [56320/60000 (94%)]\\t training loss: 0.161982\\n\",\n      \"epoch: 1 [56640/60000 (94%)]\\t training loss: 0.054209\\n\",\n      \"epoch: 1 [56960/60000 (95%)]\\t training loss: 0.028114\\n\",\n      \"epoch: 1 [57280/60000 (95%)]\\t training loss: 0.107114\\n\",\n      \"epoch: 1 [57600/60000 (96%)]\\t training loss: 0.003919\\n\",\n      \"epoch: 1 [57920/60000 (97%)]\\t training loss: 0.093449\\n\",\n      \"epoch: 1 [58240/60000 (97%)]\\t training loss: 0.043330\\n\",\n      \"epoch: 1 [58560/60000 (98%)]\\t training loss: 0.005689\\n\",\n      \"epoch: 1 [58880/60000 (98%)]\\t training loss: 0.087195\\n\",\n      \"epoch: 1 [59200/60000 (99%)]\\t training loss: 0.009148\\n\",\n      \"epoch: 1 [59520/60000 (99%)]\\t training loss: 0.035150\\n\",\n      \"epoch: 1 [59840/60000 (100%)]\\t training loss: 0.024602\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0478, Overall Accuracy: 9845/10000 (98%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(1, 2):\\n\",\n    \"    train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"    test(model, device, test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"\\n\",\n    \"plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 0\\n\",\n      \"Ground truth is : 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## captum tools\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"captum_input = sample_data[0].unsqueeze(0)\\n\",\n    \"captum_input.requires_grad = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 600x600 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"orig_image = np.tile(np.transpose((sample_data[0].cpu().detach().numpy() / 2) + 0.5, (1, 2, 0)), (1,1,3))\\n\",\n    \"# tmp = np.transpose((sample_data[0].cpu().detach().numpy() / 2) + 0.5, (1, 2, 0))\\n\",\n    \"# orig_image = np.concatenate([tmp, np.zeros(tmp.shape), np.zeros(tmp.shape)], axis=2)\\n\",\n    \"_ = viz.visualize_image_attr(None, orig_image, cmap='gray', method=\\\"original_image\\\", title=\\\"Original Image\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 600x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"saliency = Saliency(model)\\n\",\n    \"gradients = saliency.attribute(captum_input, target=sample_targets[0].item())\\n\",\n    \"gradients = np.reshape(gradients.squeeze().cpu().detach().numpy(), (28, 28, 1))\\n\",\n    \"_ = viz.visualize_image_attr(gradients, orig_image, method=\\\"blended_heat_map\\\", sign=\\\"absolute_value\\\",\\n\",\n    \"                          show_colorbar=True, title=\\\"Overlayed Gradients\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(np.tile(gradients/(np.max(gradients)), (1,1,3)));\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EdicPt7zp16ig+Pt7z/m7atKmaNGmi+fPn69FHH/V9Wd5LL72kwsJCvfjii2Fnq6UfG5Q6+HFy8Bno999/f8R3Qiq6byrrcH9NrV69erruuut03XXX6bvvvtMZZ5yhBx54gHA+DvGZ8wnm9ttvV3x8vK655hrl5OSE3Zabm6trr71W1atXD13KU6pHjx6qVauWnnvuOT333HNq37592NuBaWlp6tq1q6ZMmaJvv/22TL/lXeJUEaVnCgefGezfv1+TJ08ut/15552n1NRUjRs3Tm+99VbYWbMk9ezZU0lJSXrwwQd14MCBQ46zXr16atOmjZ5++umwt85fe+01rV271tNcKqM0XH7+R0j69u2r6OhojRw5sszZkpmFjmm7du1Uu3ZtTZ06VUVFRaE2s2bNqtDb56Wio6PL9PP888+X+f7Aocbbtm1bZWVlacKECWXecpZ+2t/R0dHq2bOn5s+fr6+++ip0+7p167Rw4cIKjXXEiBHasWOHhg4dWu6xrcjZZanyHnd5eXmaPn16WLsePXooJiZGkyZNCmv76KOPHrGPiu6byqpRo0aZ41BcXBz2OJZ+fM6mp6f7ftcCkcGZ8wmmSZMmevrpp3XZZZepZcuWuuqqq5SRkaHNmzdr2rRp2rFjh5599tmwLyxJP54R9+3bV7Nnz1Z+fr4mTJhQ5r4ff/xxnXXWWWrZsqWGDh2qzMxMbd++XUuXLtXWrVvDro2tqE6dOqlmzZoaPHiwbrrpJgUCAc2cOfOQL7QxMTEaMGCAHnvsMUVHR2vgwIFhtyclJemJJ57QoEGDdMYZZ2jAgAGqU6eOvvrqK/373//Wb37zGz322GOSpDFjxqh3794666yzdOWVVyo3N1eTJk1S8+bNy30xPZratm0rSfrTn/6kAQMGKCYmRhdccIGysrJ0//336+6779bmzZt10UUXKTExUZs2bdK8efN09dVX67bbblNsbKxGjBihG2+8Ud26dVO/fv20efNmzZgxQ1lZWRX+W9V9+vTRqFGjdMUVV6hTp05atWqVZs2aVeZzyqysLKWkpOjJJ59UYmKiatSooQ4dOigjI0N/+9vfdN5556l58+a64oorVL9+fX399ddatGiRkpKS9NJLL0mSRo4cqVdeeUWdO3fWddddp6KiotD+PtJHMZJ06aWXavXq1RozZow++OADDRgwQBkZGcrPz9fq1av17LPPKjEx8YifBUvSueeeq9jYWF1wwQW65pprtGfPHk2dOlVpaWlhv3zWqVNHt912m8aMGaM+ffro/PPP1/Lly/Xyyy+HvbNQnqioqArvm8po27atXn/9dT3yyCNKT09XRkaGmjZtqgYNGujiiy9W69atlZCQoNdff10ffvihHn744Ur3AQdE4iviqHorV660gQMHWr169SwmJsbq1q1rAwcOtFWrVh2y5rXXXjNJFggEbMuWLeW22bhxo11++eVWt25di4mJsfr161ufPn1szpw5oTall0t9+OGHZerLu5RqyZIl9utf/9ri4+MtPT3d7rjjDlu4cGGZa0ZLffDBBybJzj333EPOZdGiRdazZ09LTk62uLg4y8rKsiFDhthHH30U1u6f//ynnXbaaRYMBq1Zs2Y2d+5cGzx4sK9Lqcq7nKVRo0ZlLn8ZPXq01a9f36Kiosrsk3/+85921llnWY0aNaxGjRp26qmn2vXXX2+ff/552H385S9/sUaNGlkwGLT27dvbkiVLrG3bttarV6+wfSHJnn/++TLjKigosFtvvdXq1atn8fHx9pvf/MaWLl1q2dnZYZdjmf14OVOzZs2sWrVqZS6rWr58ufXt29dq165twWDQGjVqZP369bM33ngj7D7eeusta9u2rcXGxlpmZqY9+eSTocuIKmrx4sV28cUXhx7bSUlJ1q5dOxs+fLh9++23YW0bNWpkvXv3Lvd+XnzxRWvVqpXFxcVZ48aNbdy4cfbUU0+VORbFxcU2cuTI0D7q2rWrrV69uswxLe8654rum9J98P3334fVlvd8+eyzz6xLly4WHx8fupyrsLDQbr/9dmvdurUlJiZajRo1rHXr1jZ58uQK71e4JWBWifeCAAesWLFCbdq00TPPPKNBgwZFejhOKSkpUZ06ddS3b19NnTo10sMB4BGfOeO4M3XqVCUkJKhv376RHkpEFRQUlHn7/5lnnlFubm7Yn+8EcPzhM2ccN1566SWtXbtWf/3rX3XDDTf84hfSeP/993XzzTfrkksuUe3atfXJJ59o2rRpatGihS655JJIDw+AD7ytjeNG48aNtX37dvXs2VMzZ8484l9CO9Ft3rxZN910kz744APl5uaqVq1aOv/88zV27FilpaVFengAfCCcAQBwDJ85AwDgGMIZAADHROwLYSUlJfrmm2+UmJhY4T+YAABAJJmZdu/erfT09NCKblUhYuH8zTffqGHDhpHqHgAAz7Zs2aIGDRpU2f1HLJxLv2k7bNgwBYPBSA0DAIAKKyws1KOPPlrlV4tELJxL38oOBoOEMwDguFLVH8fyhTAAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4plqkBwDgEMy81wYCR28cxxHzs898CvxC9zmqBmfOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHMOSkUBVitQShn779bH8YfPmzX113e+3v/Vc+8T06Z5rt23b5rk20iK1VCbLZFYdzpwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxrOcMVKUIrbMbHx/vq75Xz56ea09OTvbV9+dr13quzc3N9VxbYiWeayUpKhC5c52Iravs8/Htp/pEX0uaM2cAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOYclIHBfMx9J0EV1azk/fPmq7duvmvV9JrerV81xrPpeM/ODbDZ5rY2NiPNcWFBZ4rpX8LTnpe7lJP0s3RugxKiliS6oeDzhzBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BiWjMQJL6JL+flYUi8zM9Nz7YHduz3XStIP+/Z5rv3nv/7lq++CWO9LNyaemeS944/8LX+Ym5frubZalL+X4qhi749xRUd7LvW74GNEl3N1HGfOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGNZzxnHBfK8cGxlm3sd9TrdunmtTknysayxp6owZnmtzc72vayxJ8rGW9K8atvZc26tfL8+1kvTnJ/7suTYYHfTVdzAqxnuxj8eon/XKcXicOQMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAMS0biuBBQZJamC/hcEq/vJX0918YkxXmufXbOHM+1kpSTk+O51veRio72XFq8o9hzbf5p+Z5rJSkjPcNzrZ/9LUkq9j5vP0tGBqoRIVWFM2cAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAMi3GiwszHuq8m77WSv/Wc69Wr57m2bce2nmslqX5Gfc+1Kz9Y6bn2yy+/9Fwr+V/H2o+SGB8vSwe8l7699W3vxZKiavo41/nOx3rMklRU5L3Wx/rZqDqcOQMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAMS0bimCixEl/1fpaMbNqiqefauLpxnmslqTC/0HPtmlWrPddGcolOlfg81j6Wq9z4nw2ea7OaneK5VpIK070fa/3H5xKdfpb49HO8fCwjK8nfuCPZ9zHAmTMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHsGQkjgvdunXzXLsrZpfn2sR9iZ5rJWnuS//0XLv9++881/pdotMC3pfjiwr4+53fz3KXRSXFnmvbpbfzXCtJK/as8FxbJH/HKyrG+0t5VImPpRd9Lg+q6Gh/9ScwzpwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxrOccCeZj/dRAIDL9Sgr46LtWci1ffdc5pY7n2q9zvvZcm1YtzXOtJG3d7r3vgLzvb79rKvtdDzpS/DxGT0462VffWxK2eK6ND8b76vvA/v3ei4uLvNdGcX5XVdizAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHMOSkZHgZ9nHCPZrPpacbJCe7qvvbfu3ea6tEVPDc+2y197wXCv5W3oxJirGc210VLTnWkkqLin2Xmvea/2qFuX9Jc3PcpOStGnfJs+18cGgr74PFBZ6L67mIwZKfC4t6qc+Uq+jxwhnzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABzDkpGR4GPpRT/LpPlZ8lHytxRgZkaGr77zowo815b4WJbu442fe66VpIAis6yd3379LJ/o93Hmp75+en3PtTkFOZ5rJenkaid7rl2W+46vvhXjfXlRFftY4tPvso0n+LKPfnDmDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjmE950jws4apj7Vu/azR+2Pf3kvXbfyPr67jE+M91zbTqZ5rl5Ys9VwrSTFRPtbZ9aGopMhXfYl5XwPbr+ioaM+1SbWTPNfuLNzpuVaSmtRu4rl2WewyX31Hao14RXs/VpK/tbt9v545jjNnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjmHJyEjws7xbBEUFvP8ut+uHXb76Dhzwvjzc2++947nWz5wlf8sf+un7QPEBz7WSvyUjI7mUX2pqqufaPfv3+Oo7f0e+51o/Syf6FuX9cRYo8be0aMBH3yc69gwAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiG9ZxxTPhd43frrq2ea9MS0zzXxlWL81wrSVE+lukt8rGm8v7i/d479snvPkuvm+65tlWrVp5r9wX2ea6VpHf/433dcD/rZ0v+1g2PKD/rWEdw3fBjgTNnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjmHJyF8SP8uzSYoKRO53uZYntfRcm78n33Nt1Aafc95f6Lm0WkyM51q/SwjWTq7tubZhPe9LPkpSVqdfea6Nq+59ucqSPf6WbfzPhvW+6o9LJ/iyjZHEmTMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHsGRkJERqmbUSf0viRVJmzUzPtUuil3jvuKjIe60ki4313nWJ975TU1I910rSoDPbe65N6NjWV99f5n3puTapWg3PtW++/YbnWsnnkqp+XxJ8LAfrZyHZAEtGVhnOnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGs5/xLEsG1V7dt3+arPu/rPM+1B1IOeK7N6tjUc60knVKU5Lk2+ttvPde2u+QSz7WS9H3Q+1rSftZjlqT0Gumea997803PtctXfOq51q+AzwWdfa2r7GMtaFQdzpwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hiUjUXE+lpaLivL3e+C7r7/rubZVj1aea5fWWOq5VpLi05p7rm18ZjvPtYvyPvVcK0mtTvK+z+rsivPV97x5z3uu3fz1Vl99+2Hy/vzwvWSkj/qoAOdoLuKoAADgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAY1jP+Zck4G/NWF9KSnyV796923Ptilc/9Vyb3jLdc60k5RXkea7dWbDTc218TrznWkl69l/Peq7dk7/HV9979nqvrxbl/SXNfKxXLvlbUzkQyecmnMSZMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMewZCQqzs+ydn6X4/PRd/7evd5rl3mvlaQS875U5obiDb769iM6KtpzbVTA3+/8MVExvuq9Op6XbTyex47yceYMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMSwZiWMjKnK/B/pZttFPrSSZj6Uy/Sy96HfZRj9LRgLwjzNnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOs544QXyXWR5WM9Z18Cgcj0C+Co4MwZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAY1gyEqhKLN0IwAPOnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMYQzAACOIZwBAHAM4QwAgGMIZwAAHEM4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMdUi1THZiZJKiwsjNQQAAColNLMKs2wqhKwqu7hELZu3aqGDRtGomsAAHzZsmWLGjRoUGX3H7FwLikp0TfffKPExEQFAoFIDAEAgEoxM+3evVvp6emKiqq6T4YjFs4AAKB8fCEMAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjIvYXwiSpoKBA+/fvj+QQAAColNjYWMXFxVVpHxEL54KCAmXUqaNte/ZEaggAAFRa3bp1tWnTpioN6IiF8/79+7Vtzx5tuflmJQWDP24MBH78V/r/Vb2t1LHo6+f/9VMbyf79jqkq5lORMR3c9y/gMWb//7+lf2PIzGSy8G06/G1e28nk6/68jqmUn7F7GdNP066a/Xm4Mf28XVX1VV67UP8VaHfUxqTy+/Uydi9jkqTCvYWa2G+i9u/ff2KGc6mkYFBJpRM81i+ckerrRBtnpPtnTGW2RTKcqzIIGdMxDELGdMh2xwJfCAMAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA4hnAGAMAxhDMAAI4hnAEAcAzhDACAYwhnAAAcQzgDAOAYwhkAAMcQzgAAOIZwBgDAMdUiPYBdhYU//RAI/Piv9P+retvB/R7L/v30G+n+/Y6pKuZTkTEd3Pcv4DFm//+/Zhb6r8nCt+nwt3ltJ5Ov+/M6plJ+xu5lTD9Nu2r25+HG9PN2VdVXee1C/Veg3VEbk8rv18vYvYxJkgr3HpRZVShi4WxmSkhIUMOJEyM1BAAAKi0hISEssKtCxMI5EAhoz5492rJli5KSkiI1jKNu165datiwIfM6TpyI8zoR5yQxr+PNiT6vwM/fHTvKIv62dlJS0gl14Eoxr+PLiTivE3FOEvM63pyo86pqfCEMAADHEM4AADgmYuEcDAY1fPhwBYPBSA2hSjCv48uJOK8TcU4S8zreMC9/AlbVXzkDAACVwtvaAAA4hnAGAMAxhDMAAI4hnAEAcEyVhfMDDzygTp06qXr16kpJSalQjZnpvvvuU7169RQfH68ePXpo/fr1YW1yc3N12WWXKSkpSSkpKbrqqqu0Z8+eKphB+Srb/+bNmxUIBMr99/zzz4falXf77Nmzj8WUJHnbr127di0z5muvvTaszVdffaXevXurevXqSktL0+23366ioqKqnEqYys4rNzdXN954o5o2bar4+HidfPLJuummm5SXlxfW7lgfr8cff1yNGzdWXFycOnTooA8++OCw7Z9//nmdeuqpiouLU8uWLbVgwYKw2yvyXDsWKjOvqVOnqnPnzqpZs6Zq1qypHj16lGk/ZMiQMselV69eVT2NMiozrxkzZpQZc1xcXFgbF45XZeZU3mtDIBBQ7969Q21cOFZvv/22LrjgAqWnpysQCGj+/PlHrFm8eLHOOOMMBYNBnXLKKZoxY0aZNpV9vpbLqsh9991njzzyiN1yyy2WnJxcoZqxY8dacnKyzZ8/31asWGG//e1vLSMjw/bt2xdq06tXL2vdurW9//779s4779gpp5xiAwcOrKJZlFXZ/ouKiuzbb78N+zdy5EhLSEiw3bt3h9pJsunTp4e1O3jeVc3Lfs3OzrahQ4eGjTkvLy90e1FRkbVo0cJ69Ohhy5cvtwULFlhqaqrdfffdVT2dkMrOa9WqVda3b1978cUXbcOGDfbGG29YkyZN7L//+7/D2h3L4zV79myLjY21p556ytasWWNDhw61lJQU2759e7ntlyxZYtHR0fbQQw/Z2rVr7Z577rGYmBhbtWpVqE1FnmtVrbLzuvTSS+3xxx+35cuX27p162zIkCGWnJxsW7duDbUZPHiw9erVK+y45ObmHqspmVnl5zV9+nRLSkoKG/O2bdvC2kT6eFV2Tjk5OWHzWb16tUVHR9v06dNDbVw4VgsWLLA//elPNnfuXJNk8+bNO2z7L774wqpXr2633HKLrV271iZNmmTR0dH2yiuvhNpUdl8dSpWFc6np06dXKJxLSkqsbt26Nn78+NC2nTt3WjAYtGeffdbMzNauXWuS7MMPPwy1efnlly0QCNjXX3991Mf+c0er/zZt2tiVV14Ztq0iD4yq4nVe2dnZ9oc//OGQty9YsMCioqLCXmieeOIJS0pKssLCwqMy9sM5WsfrH//4h8XGxtqBAwdC247l8Wrfvr1df/31oZ+Li4stPT3dxowZU277fv36We/evcO2dejQwa655hozq9hz7Vio7Lx+rqioyBITE+3pp58ObRs8eLBdeOGFR3uolVLZeR3pNdKF4+X3WE2cONESExNtz549oW0uHKuDVeQ5fccdd1jz5s3DtvXv39969uwZ+tnvvirlzGfOmzZt0rZt29SjR4/QtuTkZHXo0EFLly6VJC1dulQpKSlq165dqE2PHj0UFRWlZcuWVfkYj0b/H3/8sT799FNdddVVZW67/vrrlZqaqvbt2+upp56q8lVPSvmZ16xZs5SamqoWLVro7rvv1t69e8Put2XLljrppJNC23r27Kldu3ZpzZo1R38iP3O0Hi95eXlKSkpStWrhf4r+WByv/fv36+OPPw57XkRFRalHjx6h58XPLV26NKy99ON+L21fkedaVfMyr5/bu3evDhw4oFq1aoVtX7x4sdLS0tS0aVP9z//8j3Jyco7q2A/H67z27NmjRo0aqWHDhrrwwgvDnh+RPl5H41hNmzZNAwYMUI0aNcK2R/JYeXGk59bR2FelIr7wRalt27ZJUtgLeenPpbdt27ZNaWlpYbdXq1ZNtWrVCrWp6jH67X/atGk67bTT1KlTp7Dto0aNUrdu3VS9enW9+uqruu6667Rnzx7ddNNNR238h+J1XpdeeqkaNWqk9PR0rVy5Unfeeac+//xzzZ07N3S/5R3P0tuq2tE4Xjt27NDo0aN19dVXh20/Vsdrx44dKi4uLnc/fvbZZ+XWHGq/H/w8Kt12qDZVzcu8fu7OO+9Uenp62Athr1691LdvX2VkZGjjxo364x//qPPOO09Lly5VdHT0UZ1DebzMq2nTpnrqqafUqlUr5eXlacKECerUqZPWrFmjBg0aRPx4+T1WH3zwgVavXq1p06aFbY/0sfLiUM+tXbt2ad++ffrhhx98P65LVSqc77rrLo0bN+6wbdatW6dTTz21UoOItIrOy699+/bpf//3f3XvvfeWue3gbaeffrry8/M1fvx4Xy/2VT2vgwOrZcuWqlevnrp3766NGzcqKyvL8/0eybE6Xrt27VLv3r3VrFkzjRgxIuy2qjheqLixY8dq9uzZWrx4cdiXpwYMGBD6/5YtW6pVq1bKysrS4sWL1b1790gM9Yg6duyojh07hn7u1KmTTjvtNE2ZMkWjR4+O4MiOjmnTpqlly5Zq37592Pbj8VgdS5UK51tvvVVDhgw5bJvMzExPA6lbt64kafv27apXr15o+/bt29WmTZtQm++++y6srqioSLm5uaF6Lyo6L7/9z5kzR3v37tXll19+xLYdOnTQ6NGjVVhY6PlvuB6reZXq0KGDJGnDhg3KyspS3bp1y3xLcfv27ZLk/PHavXu3evXqpcTERM2bN08xMTGHbX80jld5UlNTFR0dHdpvpbZv337IOdStW/ew7SvyXKtqXuZVasKECRo7dqxef/11tWrV6rBtMzMzlZqaqg0bNhyTF3w/8yoVExOj008/XRs2bJAU+ePlZ075+fmaPXu2Ro0adcR+jvWx8uJQz62kpCTFx8crOjra9/EPqdQn1B5U9gthEyZMCG3Ly8sr9wthH330UajNwoULj/kXwrz2n52dXeZbv4dy//33W82aNT2PtTKO1n599913TZKtWLHCzH76QtjB31KcMmWKJSUlWUFBwdGbwCF4nVdeXp79+te/tuzsbMvPz69QX1V5vNq3b2833HBD6Ofi4mKrX7/+Yb8Q1qdPn7BtHTt2LPOFsMM9146Fys7LzGzcuHGWlJRkS5curVAfW7ZssUAgYC+88ILv8VaUl3kdrKioyJo2bWo333yzmblxvLzOafr06RYMBm3Hjh1H7CMSx+pgquAXwlq0aBG2beDAgWW+EObn+IfGU6nWlfDll1/a8uXLQ5cNLV++3JYvXx52+VDTpk1t7ty5oZ/Hjh1rKSkp9sILL9jKlSvtwgsvLPdSqtNPP92WLVtm7777rjVp0uSYX0p1uP63bt1qTZs2tWXLloXVrV+/3gKBgL388stl7vPFF1+0qVOn2qpVq2z9+vU2efJkq169ut13331VPp9SlZ3Xhg0bbNSoUfbRRx/Zpk2b7IUXXrDMzEzr0qVLqKb0Uqpzzz3XPv30U3vllVesTp06x/xSqsrMKy8vzzp06GAtW7a0DRs2hF3mUVRUZGbH/njNnj3bgsGgzZgxw9auXWtXX321paSkhL4FP2jQILvrrrtC7ZcsWWLVqlWzCRMm2Lp162z48OHlXkp1pOdaVavsvMaOHWuxsbE2Z86csONS+pqye/duu+2222zp0qW2adMme/311+2MM86wJk2aHJNfBr3Oa+TIkbZw4ULbuHGjffzxxzZgwACLi4uzNWvWhM09ksersnMqddZZZ1n//v3LbHflWO3evTuUTZLskUceseXLl9uXX35pZmZ33XWXDRo0KNS+9FKq22+/3datW2ePP/54uZdSHW5fVVSVhfPgwYNNUpl/ixYt+qnz/79WtFRJSYnde++9dtJJJ1kwGLTu3bvb559/Hna/OTk5NnDgQEtISLCkpCS74oorwgK/qh2p/02bNpWZp5nZ3XffbQ0bNrTi4uIy9/nyyy9bmzZtLCEhwWrUqGGtW7e2J598sty2VaWy8/rqq6+sS5cuVqtWLQsGg3bKKafY7bffHnads5nZ5s2b7bzzzrP4+HhLTU21W2+9NeySJNfmtWjRonIft5Js06ZNZhaZ4zVp0iQ7+eSTLTY21tq3b2/vv/9+6Lbs7GwbPHhwWPt//OMf9qtf/cpiY2OtefPm9u9//zvs9oo8146FysyrUaNG5R6X4cOHm5nZ3r177dxzz7U6depYTEyMNWrUyIYOHVrpF8WjoTLzGjZsWKjtSSedZOeff7598sknYffnwvGq7GPws88+M0n26quvlrkvV47VoZ7vpXMZPHiwZWdnl6lp06aNxcbGWmZmZliGlTrcvqoolowEAMAxzlznDAAAfkQ4AwDgGMIZAADHEM4AADiGcAYAwDGEMwAAjiGcAQBwDOEMAIBjCGcAABxDOAMA4BjCGQAAxxDOAAA45v8AizX3TQVpF7IAAAAASUVORK5CYII=\",\n      \"text/plain\": [\n       \"<Figure size 600x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"integ_grads = IntegratedGradients(model)\\n\",\n    \"attributed_ig, delta = integ_grads.attribute(captum_input, target=sample_targets[0].item(), baselines=captum_input * 0, \\n\",\n    \"                              return_convergence_delta=True)\\n\",\n    \"attributed_ig = np.reshape(attributed_ig.squeeze().cpu().detach().numpy(), (28, 28, 1))\\n\",\n    \"_ = viz.visualize_image_attr(attributed_ig, orig_image, method=\\\"blended_heat_map\\\",sign=\\\"all\\\", show_colorbar=True, \\n\",\n    \"                             title=\\\"Overlayed Integrated Gradients\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/captum/attr/_core/deep_lift.py:336: UserWarning: Setting forward, backward hooks and attributes on non-linear\\n\",\n      \"               activations. The hooks and attributes will be removed\\n\",\n      \"            after the attribution is finished\\n\",\n      \"  warnings.warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 600x600 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"deep_lift = DeepLift(model)\\n\",\n    \"attributed_dl = deep_lift.attribute(captum_input, target=sample_targets[0].item(), baselines=captum_input * 0, \\n\",\n    \"                              return_convergence_delta=False)\\n\",\n    \"attributed_dl = np.reshape(attributed_dl.squeeze(0).cpu().detach().numpy(), (28, 28, 1))\\n\",\n    \"_ = viz.visualize_image_attr(attributed_dl, orig_image, method=\\\"blended_heat_map\\\",sign=\\\"all\\\",show_colorbar=True, \\n\",\n    \"                          title=\\\"Overlayed DeepLift\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter17/pytorch_interpretability.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\\n\",\n      \"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\\n\",\n      \"Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (9.3.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\\n\",\n      \"Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\\n\",\n      \"Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\\n\",\n      \"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (9.3.0)\\n\",\n      \"Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\\n\",\n      \"Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\\n\",\n      \"Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\\n\",\n      \"Requirement already satisfied: opencv-python==4.6.0.66 in /opt/conda/lib/python3.10/site-packages (4.6.0.66)\\n\",\n      \"Requirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python==4.6.0.66) (1.26.2)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install torchvision==0.17\\n\",\n    \"!pip install matplotlib==3.5.2\\n\",\n    \"!pip install scikit-image==0.19.3\\n\",\n    \"!pip install opencv-python==4.6.0.66\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## import modules\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.optim as optim\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets, transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(123)\\n\",\n    \"torch.use_deterministic_algorithms(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define model architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ConvNet(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        self.cn1 = nn.Conv2d(1, 16, 3, 1)\\n\",\n    \"        self.cn2 = nn.Conv2d(16, 32, 3, 1)\\n\",\n    \"        self.dp1 = nn.Dropout2d(0.10)\\n\",\n    \"        self.dp2 = nn.Dropout2d(0.25)\\n\",\n    \"        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\\n\",\n    \"        self.fc2 = nn.Linear(64, 10)\\n\",\n    \" \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.cn1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.cn2(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = F.max_pool2d(x, 2)\\n\",\n    \"        x = self.dp1(x)\\n\",\n    \"        x = torch.flatten(x, 1)\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        x = self.dp2(x)\\n\",\n    \"        x = self.fc2(x)\\n\",\n    \"        op = F.log_softmax(x, dim=1)\\n\",\n    \"        return op\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define training and inference routines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(model, device, train_dataloader, optim, epoch):\\n\",\n    \"    model.train()\\n\",\n    \"    for b_i, (X, y) in enumerate(train_dataloader):\\n\",\n    \"        X, y = X.to(device), y.to(device)\\n\",\n    \"        optim.zero_grad()\\n\",\n    \"        pred_prob = model(X)\\n\",\n    \"        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optim.step()\\n\",\n    \"        if b_i % 10 == 0:\\n\",\n    \"            print('epoch: {} [{}/{} ({:.0f}%)]\\\\t training loss: {:.6f}'.format(\\n\",\n    \"                epoch, b_i * len(X), len(train_dataloader.dataset),\\n\",\n    \"                100. * b_i / len(train_dataloader), loss.item()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def test(model, device, test_dataloader):\\n\",\n    \"    model.eval()\\n\",\n    \"    loss = 0\\n\",\n    \"    success = 0\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        for X, y in test_dataloader:\\n\",\n    \"            X, y = X.to(device), y.to(device)\\n\",\n    \"            pred_prob = model(X)\\n\",\n    \"            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\\n\",\n    \"            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\\n\",\n    \"            success += pred.eq(y.view_as(pred)).sum().item()\\n\",\n    \"\\n\",\n    \"    loss /= len(test_dataloader.dataset)\\n\",\n    \"\\n\",\n    \"    print('\\\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\\\n'.format(\\n\",\n    \"        loss, success, len(test_dataloader.dataset),\\n\",\n    \"        100. * success / len(test_dataloader.dataset)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## create data loaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=True, download=True,\\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\\n\",\n    \"    batch_size=32, shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataloader = torch.utils.data.DataLoader(\\n\",\n    \"    datasets.MNIST('../data', train=False, \\n\",\n    \"                   transform=transforms.Compose([\\n\",\n    \"                       transforms.ToTensor(),\\n\",\n    \"                       transforms.Normalize((0.1302,), (0.3069,)) \\n\",\n    \"                   ])),\\n\",\n    \"    batch_size=500, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## define optimizer and run training epochs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"model = ConvNet()\\n\",\n    \"optimizer = optim.Adadelta(model.parameters(), lr=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\\n\",\n      \"  warnings.warn(warn_msg)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch: 1 [0/60000 (0%)]\\t training loss: 2.294348\\n\",\n      \"epoch: 1 [320/60000 (1%)]\\t training loss: 1.743703\\n\",\n      \"epoch: 1 [640/60000 (1%)]\\t training loss: 1.272358\\n\",\n      \"epoch: 1 [960/60000 (2%)]\\t training loss: 1.964490\\n\",\n      \"epoch: 1 [1280/60000 (2%)]\\t training loss: 0.411745\\n\",\n      \"epoch: 1 [1600/60000 (3%)]\\t training loss: 0.666613\\n\",\n      \"epoch: 1 [1920/60000 (3%)]\\t training loss: 0.852746\\n\",\n      \"epoch: 1 [2240/60000 (4%)]\\t training loss: 0.280735\\n\",\n      \"epoch: 1 [2560/60000 (4%)]\\t training loss: 0.608666\\n\",\n      \"epoch: 1 [2880/60000 (5%)]\\t training loss: 0.459875\\n\",\n      \"epoch: 1 [3200/60000 (5%)]\\t training loss: 0.200465\\n\",\n      \"epoch: 1 [3520/60000 (6%)]\\t training loss: 0.381452\\n\",\n      \"epoch: 1 [3840/60000 (6%)]\\t training loss: 0.143269\\n\",\n      \"epoch: 1 [4160/60000 (7%)]\\t training loss: 0.695926\\n\",\n      \"epoch: 1 [4480/60000 (7%)]\\t training loss: 0.592977\\n\",\n      \"epoch: 1 [4800/60000 (8%)]\\t training loss: 0.347031\\n\",\n      \"epoch: 1 [5120/60000 (9%)]\\t training loss: 0.402775\\n\",\n      \"epoch: 1 [5440/60000 (9%)]\\t training loss: 0.525813\\n\",\n      \"epoch: 1 [5760/60000 (10%)]\\t training loss: 0.373715\\n\",\n      \"epoch: 1 [6080/60000 (10%)]\\t training loss: 0.345820\\n\",\n      \"epoch: 1 [6400/60000 (11%)]\\t training loss: 0.166799\\n\",\n      \"epoch: 1 [6720/60000 (11%)]\\t training loss: 0.345126\\n\",\n      \"epoch: 1 [7040/60000 (12%)]\\t training loss: 0.064074\\n\",\n      \"epoch: 1 [7360/60000 (12%)]\\t training loss: 0.288761\\n\",\n      \"epoch: 1 [7680/60000 (13%)]\\t training loss: 0.035006\\n\",\n      \"epoch: 1 [8000/60000 (13%)]\\t training loss: 0.131319\\n\",\n      \"epoch: 1 [8320/60000 (14%)]\\t training loss: 0.114256\\n\",\n      \"epoch: 1 [8640/60000 (14%)]\\t training loss: 0.177033\\n\",\n      \"epoch: 1 [8960/60000 (15%)]\\t training loss: 0.037013\\n\",\n      \"epoch: 1 [9280/60000 (15%)]\\t training loss: 0.055339\\n\",\n      \"epoch: 1 [9600/60000 (16%)]\\t training loss: 0.079561\\n\",\n      \"epoch: 1 [9920/60000 (17%)]\\t training loss: 0.163464\\n\",\n      \"epoch: 1 [10240/60000 (17%)]\\t training loss: 0.076177\\n\",\n      \"epoch: 1 [10560/60000 (18%)]\\t training loss: 0.183408\\n\",\n      \"epoch: 1 [10880/60000 (18%)]\\t training loss: 0.090588\\n\",\n      \"epoch: 1 [11200/60000 (19%)]\\t training loss: 0.260131\\n\",\n      \"epoch: 1 [11520/60000 (19%)]\\t training loss: 0.043986\\n\",\n      \"epoch: 1 [11840/60000 (20%)]\\t training loss: 0.146020\\n\",\n      \"epoch: 1 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[52480/60000 (87%)]\\t training loss: 0.268109\\n\",\n      \"epoch: 1 [52800/60000 (88%)]\\t training loss: 0.088751\\n\",\n      \"epoch: 1 [53120/60000 (89%)]\\t training loss: 0.105866\\n\",\n      \"epoch: 1 [53440/60000 (89%)]\\t training loss: 0.090061\\n\",\n      \"epoch: 1 [53760/60000 (90%)]\\t training loss: 0.138882\\n\",\n      \"epoch: 1 [54080/60000 (90%)]\\t training loss: 0.107938\\n\",\n      \"epoch: 1 [54400/60000 (91%)]\\t training loss: 0.023027\\n\",\n      \"epoch: 1 [54720/60000 (91%)]\\t training loss: 0.033679\\n\",\n      \"epoch: 1 [55040/60000 (92%)]\\t training loss: 0.153292\\n\",\n      \"epoch: 1 [55360/60000 (92%)]\\t training loss: 0.126298\\n\",\n      \"epoch: 1 [55680/60000 (93%)]\\t training loss: 0.054509\\n\",\n      \"epoch: 1 [56000/60000 (93%)]\\t training loss: 0.021476\\n\",\n      \"epoch: 1 [56320/60000 (94%)]\\t training loss: 0.006900\\n\",\n      \"epoch: 1 [56640/60000 (94%)]\\t training loss: 0.006288\\n\",\n      \"epoch: 1 [56960/60000 (95%)]\\t training loss: 0.018695\\n\",\n      \"epoch: 1 [57280/60000 (95%)]\\t training loss: 0.148758\\n\",\n      \"epoch: 1 [57600/60000 (96%)]\\t training loss: 0.015779\\n\",\n      \"epoch: 1 [57920/60000 (97%)]\\t training loss: 0.088187\\n\",\n      \"epoch: 1 [58240/60000 (97%)]\\t training loss: 0.135229\\n\",\n      \"epoch: 1 [58560/60000 (98%)]\\t training loss: 0.148267\\n\",\n      \"epoch: 1 [58880/60000 (98%)]\\t training loss: 0.040180\\n\",\n      \"epoch: 1 [59200/60000 (99%)]\\t training loss: 0.034478\\n\",\n      \"epoch: 1 [59520/60000 (99%)]\\t training loss: 0.036374\\n\",\n      \"epoch: 1 [59840/60000 (100%)]\\t training loss: 0.014311\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0475, Overall Accuracy: 9844/10000 (98%)\\n\",\n      \"\\n\",\n      \"epoch: 2 [0/60000 (0%)]\\t training loss: 0.119430\\n\",\n      \"epoch: 2 [320/60000 (1%)]\\t training loss: 0.008598\\n\",\n      \"epoch: 2 [640/60000 (1%)]\\t training loss: 0.265510\\n\",\n      \"epoch: 2 [960/60000 (2%)]\\t training loss: 0.067296\\n\",\n      \"epoch: 2 [1280/60000 (2%)]\\t training loss: 0.066289\\n\",\n      \"epoch: 2 [1600/60000 (3%)]\\t training loss: 0.031245\\n\",\n      \"epoch: 2 [1920/60000 (3%)]\\t training loss: 0.009062\\n\",\n      \"epoch: 2 [2240/60000 (4%)]\\t training loss: 0.017542\\n\",\n      \"epoch: 2 [2560/60000 (4%)]\\t training loss: 0.118867\\n\",\n      \"epoch: 2 [2880/60000 (5%)]\\t training loss: 0.068008\\n\",\n      \"epoch: 2 [3200/60000 (5%)]\\t training loss: 0.025926\\n\",\n      \"epoch: 2 [3520/60000 (6%)]\\t training loss: 0.065706\\n\",\n      \"epoch: 2 [3840/60000 (6%)]\\t training loss: 0.021485\\n\",\n      \"epoch: 2 [4160/60000 (7%)]\\t training loss: 0.409869\\n\",\n      \"epoch: 2 [4480/60000 (7%)]\\t training loss: 0.070850\\n\",\n      \"epoch: 2 [4800/60000 (8%)]\\t training loss: 0.012097\\n\",\n      \"epoch: 2 [5120/60000 (9%)]\\t training loss: 0.015393\\n\",\n      \"epoch: 2 [5440/60000 (9%)]\\t training loss: 0.235101\\n\",\n      \"epoch: 2 [5760/60000 (10%)]\\t training loss: 0.060175\\n\",\n      \"epoch: 2 [6080/60000 (10%)]\\t training loss: 0.040447\\n\",\n      \"epoch: 2 [6400/60000 (11%)]\\t training loss: 0.076637\\n\",\n      \"epoch: 2 [6720/60000 (11%)]\\t training loss: 0.016884\\n\",\n      \"epoch: 2 [7040/60000 (12%)]\\t training loss: 0.016561\\n\",\n      \"epoch: 2 [7360/60000 (12%)]\\t training loss: 0.028731\\n\",\n      \"epoch: 2 [7680/60000 (13%)]\\t training loss: 0.101185\\n\",\n      \"epoch: 2 [8000/60000 (13%)]\\t training loss: 0.165041\\n\",\n      \"epoch: 2 [8320/60000 (14%)]\\t training loss: 0.007331\\n\",\n      \"epoch: 2 [8640/60000 (14%)]\\t training loss: 0.278004\\n\",\n      \"epoch: 2 [8960/60000 (15%)]\\t training loss: 0.025074\\n\",\n      \"epoch: 2 [9280/60000 (15%)]\\t training loss: 0.022760\\n\",\n      \"epoch: 2 [9600/60000 (16%)]\\t training loss: 0.007004\\n\",\n      \"epoch: 2 [9920/60000 (17%)]\\t training loss: 0.046825\\n\",\n      \"epoch: 2 [10240/60000 (17%)]\\t training loss: 0.034772\\n\",\n      \"epoch: 2 [10560/60000 (18%)]\\t training loss: 0.004999\\n\",\n      \"epoch: 2 [10880/60000 (18%)]\\t training loss: 0.019550\\n\",\n      \"epoch: 2 [11200/60000 (19%)]\\t training loss: 0.382418\\n\",\n      \"epoch: 2 [11520/60000 (19%)]\\t training loss: 0.084466\\n\",\n      \"epoch: 2 [11840/60000 (20%)]\\t training loss: 0.006346\\n\",\n      \"epoch: 2 [12160/60000 (20%)]\\t training loss: 0.008292\\n\",\n      \"epoch: 2 [12480/60000 (21%)]\\t training loss: 0.116132\\n\",\n      \"epoch: 2 [12800/60000 (21%)]\\t training loss: 0.062028\\n\",\n      \"epoch: 2 [13120/60000 (22%)]\\t training loss: 0.007986\\n\",\n      \"epoch: 2 [13440/60000 (22%)]\\t training loss: 0.020640\\n\",\n      \"epoch: 2 [13760/60000 (23%)]\\t training loss: 0.032395\\n\",\n      \"epoch: 2 [14080/60000 (23%)]\\t training loss: 0.033272\\n\",\n      \"epoch: 2 [14400/60000 (24%)]\\t 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training loss: 0.004861\\n\",\n      \"epoch: 2 [55040/60000 (92%)]\\t training loss: 0.011730\\n\",\n      \"epoch: 2 [55360/60000 (92%)]\\t training loss: 0.050950\\n\",\n      \"epoch: 2 [55680/60000 (93%)]\\t training loss: 0.053503\\n\",\n      \"epoch: 2 [56000/60000 (93%)]\\t training loss: 0.003406\\n\",\n      \"epoch: 2 [56320/60000 (94%)]\\t training loss: 0.006183\\n\",\n      \"epoch: 2 [56640/60000 (94%)]\\t training loss: 0.054964\\n\",\n      \"epoch: 2 [56960/60000 (95%)]\\t training loss: 0.008591\\n\",\n      \"epoch: 2 [57280/60000 (95%)]\\t training loss: 0.006075\\n\",\n      \"epoch: 2 [57600/60000 (96%)]\\t training loss: 0.063095\\n\",\n      \"epoch: 2 [57920/60000 (97%)]\\t training loss: 0.000654\\n\",\n      \"epoch: 2 [58240/60000 (97%)]\\t training loss: 0.016976\\n\",\n      \"epoch: 2 [58560/60000 (98%)]\\t training loss: 0.014165\\n\",\n      \"epoch: 2 [58880/60000 (98%)]\\t training loss: 0.009361\\n\",\n      \"epoch: 2 [59200/60000 (99%)]\\t 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0.118316\\n\",\n      \"epoch: 3 [52800/60000 (88%)]\\t training loss: 0.008898\\n\",\n      \"epoch: 3 [53120/60000 (89%)]\\t training loss: 0.002832\\n\",\n      \"epoch: 3 [53440/60000 (89%)]\\t training loss: 0.001943\\n\",\n      \"epoch: 3 [53760/60000 (90%)]\\t training loss: 0.031312\\n\",\n      \"epoch: 3 [54080/60000 (90%)]\\t training loss: 0.062200\\n\",\n      \"epoch: 3 [54400/60000 (91%)]\\t training loss: 0.035154\\n\",\n      \"epoch: 3 [54720/60000 (91%)]\\t training loss: 0.016859\\n\",\n      \"epoch: 3 [55040/60000 (92%)]\\t training loss: 0.007813\\n\",\n      \"epoch: 3 [55360/60000 (92%)]\\t training loss: 0.008188\\n\",\n      \"epoch: 3 [55680/60000 (93%)]\\t training loss: 0.018315\\n\",\n      \"epoch: 3 [56000/60000 (93%)]\\t training loss: 0.041843\\n\",\n      \"epoch: 3 [56320/60000 (94%)]\\t training loss: 0.040984\\n\",\n      \"epoch: 3 [56640/60000 (94%)]\\t training loss: 0.000689\\n\",\n      \"epoch: 3 [56960/60000 (95%)]\\t training loss: 0.007462\\n\",\n      \"epoch: 3 [57280/60000 (95%)]\\t training loss: 0.011974\\n\",\n      \"epoch: 3 [57600/60000 (96%)]\\t training loss: 0.160275\\n\",\n      \"epoch: 3 [57920/60000 (97%)]\\t training loss: 0.061971\\n\",\n      \"epoch: 3 [58240/60000 (97%)]\\t training loss: 0.002083\\n\",\n      \"epoch: 3 [58560/60000 (98%)]\\t training loss: 0.003416\\n\",\n      \"epoch: 3 [58880/60000 (98%)]\\t training loss: 0.075416\\n\",\n      \"epoch: 3 [59200/60000 (99%)]\\t training loss: 0.205819\\n\",\n      \"epoch: 3 [59520/60000 (99%)]\\t training loss: 0.015730\\n\",\n      \"epoch: 3 [59840/60000 (100%)]\\t training loss: 0.040238\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0344, Overall Accuracy: 9886/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 4 [0/60000 (0%)]\\t training loss: 0.110118\\n\",\n      \"epoch: 4 [320/60000 (1%)]\\t training loss: 0.041794\\n\",\n      \"epoch: 4 [640/60000 (1%)]\\t training loss: 0.016891\\n\",\n      \"epoch: 4 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\"epoch: 4 [55040/60000 (92%)]\\t training loss: 0.057995\\n\",\n      \"epoch: 4 [55360/60000 (92%)]\\t training loss: 0.001988\\n\",\n      \"epoch: 4 [55680/60000 (93%)]\\t training loss: 0.009346\\n\",\n      \"epoch: 4 [56000/60000 (93%)]\\t training loss: 0.001878\\n\",\n      \"epoch: 4 [56320/60000 (94%)]\\t training loss: 0.135942\\n\",\n      \"epoch: 4 [56640/60000 (94%)]\\t training loss: 0.054410\\n\",\n      \"epoch: 4 [56960/60000 (95%)]\\t training loss: 0.087394\\n\",\n      \"epoch: 4 [57280/60000 (95%)]\\t training loss: 0.054197\\n\",\n      \"epoch: 4 [57600/60000 (96%)]\\t training loss: 0.000243\\n\",\n      \"epoch: 4 [57920/60000 (97%)]\\t training loss: 0.185310\\n\",\n      \"epoch: 4 [58240/60000 (97%)]\\t training loss: 0.007459\\n\",\n      \"epoch: 4 [58560/60000 (98%)]\\t training loss: 0.216312\\n\",\n      \"epoch: 4 [58880/60000 (98%)]\\t training loss: 0.010306\\n\",\n      \"epoch: 4 [59200/60000 (99%)]\\t training loss: 0.117581\\n\",\n      \"epoch: 4 [59520/60000 (99%)]\\t training loss: 0.013694\\n\",\n      \"epoch: 4 [59840/60000 (100%)]\\t training loss: 0.017924\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0356, Overall Accuracy: 9885/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 5 [0/60000 (0%)]\\t training loss: 0.035247\\n\",\n      \"epoch: 5 [320/60000 (1%)]\\t training loss: 0.051318\\n\",\n      \"epoch: 5 [640/60000 (1%)]\\t training loss: 0.005964\\n\",\n      \"epoch: 5 [960/60000 (2%)]\\t training loss: 0.002361\\n\",\n      \"epoch: 5 [1280/60000 (2%)]\\t training loss: 0.017584\\n\",\n      \"epoch: 5 [1600/60000 (3%)]\\t training loss: 0.018771\\n\",\n      \"epoch: 5 [1920/60000 (3%)]\\t training loss: 0.003743\\n\",\n      \"epoch: 5 [2240/60000 (4%)]\\t training loss: 0.069095\\n\",\n      \"epoch: 5 [2560/60000 (4%)]\\t training loss: 0.012049\\n\",\n      \"epoch: 5 [2880/60000 (5%)]\\t training loss: 0.004435\\n\",\n      \"epoch: 5 [3200/60000 (5%)]\\t training loss: 0.005285\\n\",\n      \"epoch: 5 [3520/60000 (6%)]\\t training loss: 0.002239\\n\",\n      \"epoch: 5 [3840/60000 (6%)]\\t training loss: 0.001584\\n\",\n      \"epoch: 5 [4160/60000 (7%)]\\t training loss: 0.010120\\n\",\n      \"epoch: 5 [4480/60000 (7%)]\\t training loss: 0.047948\\n\",\n      \"epoch: 5 [4800/60000 (8%)]\\t training loss: 0.009176\\n\",\n      \"epoch: 5 [5120/60000 (9%)]\\t training loss: 0.011103\\n\",\n      \"epoch: 5 [5440/60000 (9%)]\\t training loss: 0.088778\\n\",\n      \"epoch: 5 [5760/60000 (10%)]\\t training loss: 0.001731\\n\",\n      \"epoch: 5 [6080/60000 (10%)]\\t training loss: 0.400679\\n\",\n      \"epoch: 5 [6400/60000 (11%)]\\t training loss: 0.007210\\n\",\n      \"epoch: 5 [6720/60000 (11%)]\\t training loss: 0.004337\\n\",\n      \"epoch: 5 [7040/60000 (12%)]\\t training loss: 0.034019\\n\",\n      \"epoch: 5 [7360/60000 (12%)]\\t training loss: 0.002023\\n\",\n      \"epoch: 5 [7680/60000 (13%)]\\t training loss: 0.034322\\n\",\n      \"epoch: 5 [8000/60000 (13%)]\\t training loss: 0.006112\\n\",\n      \"epoch: 5 [8320/60000 (14%)]\\t training loss: 0.020031\\n\",\n      \"epoch: 5 [8640/60000 (14%)]\\t training loss: 0.081617\\n\",\n      \"epoch: 5 [8960/60000 (15%)]\\t training loss: 0.002687\\n\",\n      \"epoch: 5 [9280/60000 (15%)]\\t training loss: 0.002668\\n\",\n      \"epoch: 5 [9600/60000 (16%)]\\t training loss: 0.016180\\n\",\n      \"epoch: 5 [9920/60000 (17%)]\\t training loss: 0.008328\\n\",\n      \"epoch: 5 [10240/60000 (17%)]\\t training loss: 0.024805\\n\",\n      \"epoch: 5 [10560/60000 (18%)]\\t training loss: 0.031896\\n\",\n      \"epoch: 5 [10880/60000 (18%)]\\t training loss: 0.001545\\n\",\n      \"epoch: 5 [11200/60000 (19%)]\\t training loss: 0.011549\\n\",\n      \"epoch: 5 [11520/60000 (19%)]\\t training loss: 0.014523\\n\",\n      \"epoch: 5 [11840/60000 (20%)]\\t training loss: 0.007609\\n\",\n      \"epoch: 5 [12160/60000 (20%)]\\t training loss: 0.108892\\n\",\n      \"epoch: 5 [12480/60000 (21%)]\\t training loss: 0.001220\\n\",\n      \"epoch: 5 [12800/60000 (21%)]\\t training loss: 0.137178\\n\",\n      \"epoch: 5 [13120/60000 (22%)]\\t training loss: 0.000932\\n\",\n      \"epoch: 5 [13440/60000 (22%)]\\t training loss: 0.005293\\n\",\n      \"epoch: 5 [13760/60000 (23%)]\\t training loss: 0.034460\\n\",\n      \"epoch: 5 [14080/60000 (23%)]\\t training loss: 0.000685\\n\",\n      \"epoch: 5 [14400/60000 (24%)]\\t training loss: 0.002494\\n\",\n      \"epoch: 5 [14720/60000 (25%)]\\t training loss: 0.017084\\n\",\n      \"epoch: 5 [15040/60000 (25%)]\\t training loss: 0.000597\\n\",\n      \"epoch: 5 [15360/60000 (26%)]\\t training loss: 0.000431\\n\",\n      \"epoch: 5 [15680/60000 (26%)]\\t training loss: 0.007383\\n\",\n      \"epoch: 5 [16000/60000 (27%)]\\t training loss: 0.009324\\n\",\n      \"epoch: 5 [16320/60000 (27%)]\\t training loss: 0.001683\\n\",\n      \"epoch: 5 [16640/60000 (28%)]\\t training loss: 0.005019\\n\",\n      \"epoch: 5 [16960/60000 (28%)]\\t training loss: 0.023847\\n\",\n      \"epoch: 5 [17280/60000 (29%)]\\t training loss: 0.021999\\n\",\n      \"epoch: 5 [17600/60000 (29%)]\\t training loss: 0.007341\\n\",\n      \"epoch: 5 [17920/60000 (30%)]\\t training loss: 0.021978\\n\",\n      \"epoch: 5 [18240/60000 (30%)]\\t training loss: 0.036329\\n\",\n      \"epoch: 5 [18560/60000 (31%)]\\t training loss: 0.021755\\n\",\n      \"epoch: 5 [18880/60000 (31%)]\\t training loss: 0.114978\\n\",\n      \"epoch: 5 [19200/60000 (32%)]\\t training loss: 0.149085\\n\",\n      \"epoch: 5 [19520/60000 (33%)]\\t training loss: 0.434260\\n\",\n      \"epoch: 5 [19840/60000 (33%)]\\t training loss: 0.105602\\n\",\n      \"epoch: 5 [20160/60000 (34%)]\\t training loss: 0.016082\\n\",\n      \"epoch: 5 [20480/60000 (34%)]\\t training loss: 0.033174\\n\",\n      \"epoch: 5 [20800/60000 (35%)]\\t training loss: 0.034030\\n\",\n      \"epoch: 5 [21120/60000 (35%)]\\t training loss: 0.113617\\n\",\n      \"epoch: 5 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[30400/60000 (51%)]\\t training loss: 0.015232\\n\",\n      \"epoch: 5 [30720/60000 (51%)]\\t training loss: 0.005438\\n\",\n      \"epoch: 5 [31040/60000 (52%)]\\t training loss: 0.001816\\n\",\n      \"epoch: 5 [31360/60000 (52%)]\\t training loss: 0.001376\\n\",\n      \"epoch: 5 [31680/60000 (53%)]\\t training loss: 0.005161\\n\",\n      \"epoch: 5 [32000/60000 (53%)]\\t training loss: 0.012575\\n\",\n      \"epoch: 5 [32320/60000 (54%)]\\t training loss: 0.032663\\n\",\n      \"epoch: 5 [32640/60000 (54%)]\\t training loss: 0.009155\\n\",\n      \"epoch: 5 [32960/60000 (55%)]\\t training loss: 0.011069\\n\",\n      \"epoch: 5 [33280/60000 (55%)]\\t training loss: 0.001667\\n\",\n      \"epoch: 5 [33600/60000 (56%)]\\t training loss: 0.001417\\n\",\n      \"epoch: 5 [33920/60000 (57%)]\\t training loss: 0.000282\\n\",\n      \"epoch: 5 [34240/60000 (57%)]\\t training loss: 0.029594\\n\",\n      \"epoch: 5 [34560/60000 (58%)]\\t training loss: 0.007990\\n\",\n      \"epoch: 5 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[39360/60000 (66%)]\\t training loss: 0.053617\\n\",\n      \"epoch: 5 [39680/60000 (66%)]\\t training loss: 0.015570\\n\",\n      \"epoch: 5 [40000/60000 (67%)]\\t training loss: 0.003030\\n\",\n      \"epoch: 5 [40320/60000 (67%)]\\t training loss: 0.015480\\n\",\n      \"epoch: 5 [40640/60000 (68%)]\\t training loss: 0.003029\\n\",\n      \"epoch: 5 [40960/60000 (68%)]\\t training loss: 0.000205\\n\",\n      \"epoch: 5 [41280/60000 (69%)]\\t training loss: 0.001174\\n\",\n      \"epoch: 5 [41600/60000 (69%)]\\t training loss: 0.037600\\n\",\n      \"epoch: 5 [41920/60000 (70%)]\\t training loss: 0.000518\\n\",\n      \"epoch: 5 [42240/60000 (70%)]\\t training loss: 0.021980\\n\",\n      \"epoch: 5 [42560/60000 (71%)]\\t training loss: 0.000365\\n\",\n      \"epoch: 5 [42880/60000 (71%)]\\t training loss: 0.280558\\n\",\n      \"epoch: 5 [43200/60000 (72%)]\\t training loss: 0.085476\\n\",\n      \"epoch: 5 [43520/60000 (73%)]\\t training loss: 0.055305\\n\",\n      \"epoch: 5 [43840/60000 (73%)]\\t training loss: 0.010299\\n\",\n      \"epoch: 5 [44160/60000 (74%)]\\t training loss: 0.002546\\n\",\n      \"epoch: 5 [44480/60000 (74%)]\\t training loss: 0.023556\\n\",\n      \"epoch: 5 [44800/60000 (75%)]\\t training loss: 0.169104\\n\",\n      \"epoch: 5 [45120/60000 (75%)]\\t training loss: 0.000253\\n\",\n      \"epoch: 5 [45440/60000 (76%)]\\t training loss: 0.009811\\n\",\n      \"epoch: 5 [45760/60000 (76%)]\\t training loss: 0.004018\\n\",\n      \"epoch: 5 [46080/60000 (77%)]\\t training loss: 0.004791\\n\",\n      \"epoch: 5 [46400/60000 (77%)]\\t training loss: 0.002488\\n\",\n      \"epoch: 5 [46720/60000 (78%)]\\t training loss: 0.023894\\n\",\n      \"epoch: 5 [47040/60000 (78%)]\\t training loss: 0.481144\\n\",\n      \"epoch: 5 [47360/60000 (79%)]\\t training loss: 0.006230\\n\",\n      \"epoch: 5 [47680/60000 (79%)]\\t training loss: 0.000553\\n\",\n      \"epoch: 5 [48000/60000 (80%)]\\t training loss: 0.002279\\n\",\n      \"epoch: 5 [48320/60000 (81%)]\\t training loss: 0.021728\\n\",\n      \"epoch: 5 [48640/60000 (81%)]\\t training loss: 0.011831\\n\",\n      \"epoch: 5 [48960/60000 (82%)]\\t training loss: 0.001271\\n\",\n      \"epoch: 5 [49280/60000 (82%)]\\t training loss: 0.089985\\n\",\n      \"epoch: 5 [49600/60000 (83%)]\\t training loss: 0.016375\\n\",\n      \"epoch: 5 [49920/60000 (83%)]\\t training loss: 0.002314\\n\",\n      \"epoch: 5 [50240/60000 (84%)]\\t training loss: 0.132437\\n\",\n      \"epoch: 5 [50560/60000 (84%)]\\t training loss: 0.062590\\n\",\n      \"epoch: 5 [50880/60000 (85%)]\\t training loss: 0.023316\\n\",\n      \"epoch: 5 [51200/60000 (85%)]\\t training loss: 0.009912\\n\",\n      \"epoch: 5 [51520/60000 (86%)]\\t training loss: 0.021840\\n\",\n      \"epoch: 5 [51840/60000 (86%)]\\t training loss: 0.032781\\n\",\n      \"epoch: 5 [52160/60000 (87%)]\\t training loss: 0.016682\\n\",\n      \"epoch: 5 [52480/60000 (87%)]\\t training loss: 0.180458\\n\",\n      \"epoch: 5 [52800/60000 (88%)]\\t training loss: 0.263259\\n\",\n      \"epoch: 5 [53120/60000 (89%)]\\t training loss: 0.010235\\n\",\n      \"epoch: 5 [53440/60000 (89%)]\\t training loss: 0.002158\\n\",\n      \"epoch: 5 [53760/60000 (90%)]\\t training loss: 0.002053\\n\",\n      \"epoch: 5 [54080/60000 (90%)]\\t training loss: 0.009737\\n\",\n      \"epoch: 5 [54400/60000 (91%)]\\t training loss: 0.000276\\n\",\n      \"epoch: 5 [54720/60000 (91%)]\\t training loss: 0.121469\\n\",\n      \"epoch: 5 [55040/60000 (92%)]\\t training loss: 0.005134\\n\",\n      \"epoch: 5 [55360/60000 (92%)]\\t training loss: 0.005075\\n\",\n      \"epoch: 5 [55680/60000 (93%)]\\t training loss: 0.006333\\n\",\n      \"epoch: 5 [56000/60000 (93%)]\\t training loss: 0.027743\\n\",\n      \"epoch: 5 [56320/60000 (94%)]\\t training loss: 0.004136\\n\",\n      \"epoch: 5 [56640/60000 (94%)]\\t training loss: 0.005325\\n\",\n      \"epoch: 5 [56960/60000 (95%)]\\t training loss: 0.149156\\n\",\n      \"epoch: 5 [57280/60000 (95%)]\\t training loss: 0.001821\\n\",\n      \"epoch: 5 [57600/60000 (96%)]\\t training loss: 0.000234\\n\",\n      \"epoch: 5 [57920/60000 (97%)]\\t training loss: 0.111853\\n\",\n      \"epoch: 5 [58240/60000 (97%)]\\t training loss: 0.000984\\n\",\n      \"epoch: 5 [58560/60000 (98%)]\\t training loss: 0.030338\\n\",\n      \"epoch: 5 [58880/60000 (98%)]\\t training loss: 0.044758\\n\",\n      \"epoch: 5 [59200/60000 (99%)]\\t training loss: 0.002045\\n\",\n      \"epoch: 5 [59520/60000 (99%)]\\t training loss: 0.005412\\n\",\n      \"epoch: 5 [59840/60000 (100%)]\\t training loss: 0.002643\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0318, Overall Accuracy: 9900/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 6 [0/60000 (0%)]\\t training loss: 0.005471\\n\",\n      \"epoch: 6 [320/60000 (1%)]\\t training loss: 0.005731\\n\",\n      \"epoch: 6 [640/60000 (1%)]\\t training loss: 0.195548\\n\",\n      \"epoch: 6 [960/60000 (2%)]\\t training loss: 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0.069707\\n\",\n      \"epoch: 8 [55360/60000 (92%)]\\t training loss: 0.000417\\n\",\n      \"epoch: 8 [55680/60000 (93%)]\\t training loss: 0.129443\\n\",\n      \"epoch: 8 [56000/60000 (93%)]\\t training loss: 0.026754\\n\",\n      \"epoch: 8 [56320/60000 (94%)]\\t training loss: 0.088746\\n\",\n      \"epoch: 8 [56640/60000 (94%)]\\t training loss: 0.001993\\n\",\n      \"epoch: 8 [56960/60000 (95%)]\\t training loss: 0.006843\\n\",\n      \"epoch: 8 [57280/60000 (95%)]\\t training loss: 0.003045\\n\",\n      \"epoch: 8 [57600/60000 (96%)]\\t training loss: 0.024736\\n\",\n      \"epoch: 8 [57920/60000 (97%)]\\t training loss: 0.011806\\n\",\n      \"epoch: 8 [58240/60000 (97%)]\\t training loss: 0.042968\\n\",\n      \"epoch: 8 [58560/60000 (98%)]\\t training loss: 0.005785\\n\",\n      \"epoch: 8 [58880/60000 (98%)]\\t training loss: 0.002075\\n\",\n      \"epoch: 8 [59200/60000 (99%)]\\t training loss: 0.016357\\n\",\n      \"epoch: 8 [59520/60000 (99%)]\\t training loss: 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0.001410\\n\",\n      \"epoch: 9 [57600/60000 (96%)]\\t training loss: 0.171638\\n\",\n      \"epoch: 9 [57920/60000 (97%)]\\t training loss: 0.001689\\n\",\n      \"epoch: 9 [58240/60000 (97%)]\\t training loss: 0.006387\\n\",\n      \"epoch: 9 [58560/60000 (98%)]\\t training loss: 0.001477\\n\",\n      \"epoch: 9 [58880/60000 (98%)]\\t training loss: 0.001063\\n\",\n      \"epoch: 9 [59200/60000 (99%)]\\t training loss: 0.014601\\n\",\n      \"epoch: 9 [59520/60000 (99%)]\\t training loss: 0.000106\\n\",\n      \"epoch: 9 [59840/60000 (100%)]\\t training loss: 0.001231\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0366, Overall Accuracy: 9907/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 10 [0/60000 (0%)]\\t training loss: 0.026886\\n\",\n      \"epoch: 10 [320/60000 (1%)]\\t training loss: 0.069728\\n\",\n      \"epoch: 10 [640/60000 (1%)]\\t training loss: 0.028899\\n\",\n      \"epoch: 10 [960/60000 (2%)]\\t training loss: 0.091336\\n\",\n      \"epoch: 10 [1280/60000 (2%)]\\t training loss: 0.000552\\n\",\n      \"epoch: 10 [1600/60000 (3%)]\\t training loss: 0.007296\\n\",\n      \"epoch: 10 [1920/60000 (3%)]\\t training loss: 0.000568\\n\",\n      \"epoch: 10 [2240/60000 (4%)]\\t training loss: 0.000394\\n\",\n      \"epoch: 10 [2560/60000 (4%)]\\t training loss: 0.027242\\n\",\n      \"epoch: 10 [2880/60000 (5%)]\\t training loss: 0.002901\\n\",\n      \"epoch: 10 [3200/60000 (5%)]\\t training loss: 0.052801\\n\",\n      \"epoch: 10 [3520/60000 (6%)]\\t training loss: 0.001438\\n\",\n      \"epoch: 10 [3840/60000 (6%)]\\t training loss: 0.000124\\n\",\n      \"epoch: 10 [4160/60000 (7%)]\\t training loss: 0.061892\\n\",\n      \"epoch: 10 [4480/60000 (7%)]\\t training loss: 0.002005\\n\",\n      \"epoch: 10 [4800/60000 (8%)]\\t training loss: 0.001308\\n\",\n      \"epoch: 10 [5120/60000 (9%)]\\t training loss: 0.001165\\n\",\n      \"epoch: 10 [5440/60000 (9%)]\\t training loss: 0.022172\\n\",\n      \"epoch: 10 [5760/60000 (10%)]\\t training loss: 0.000326\\n\",\n      \"epoch: 10 [6080/60000 (10%)]\\t training loss: 0.071593\\n\",\n      \"epoch: 10 [6400/60000 (11%)]\\t training loss: 0.041825\\n\",\n      \"epoch: 10 [6720/60000 (11%)]\\t training loss: 0.005946\\n\",\n      \"epoch: 10 [7040/60000 (12%)]\\t training loss: 0.002486\\n\",\n      \"epoch: 10 [7360/60000 (12%)]\\t training loss: 0.004494\\n\",\n      \"epoch: 10 [7680/60000 (13%)]\\t training loss: 0.003781\\n\",\n      \"epoch: 10 [8000/60000 (13%)]\\t training loss: 0.020201\\n\",\n      \"epoch: 10 [8320/60000 (14%)]\\t training loss: 0.340536\\n\",\n      \"epoch: 10 [8640/60000 (14%)]\\t training loss: 0.006305\\n\",\n      \"epoch: 10 [8960/60000 (15%)]\\t training loss: 0.071060\\n\",\n      \"epoch: 10 [9280/60000 (15%)]\\t training loss: 0.027659\\n\",\n      \"epoch: 10 [9600/60000 (16%)]\\t training loss: 0.007930\\n\",\n      \"epoch: 10 [9920/60000 (17%)]\\t training loss: 0.268625\\n\",\n      \"epoch: 10 [10240/60000 (17%)]\\t training loss: 0.137700\\n\",\n      \"epoch: 10 [10560/60000 (18%)]\\t training loss: 0.030525\\n\",\n      \"epoch: 10 [10880/60000 (18%)]\\t training loss: 0.008362\\n\",\n      \"epoch: 10 [11200/60000 (19%)]\\t training loss: 0.173587\\n\",\n      \"epoch: 10 [11520/60000 (19%)]\\t training loss: 0.044864\\n\",\n      \"epoch: 10 [11840/60000 (20%)]\\t training loss: 0.000766\\n\",\n      \"epoch: 10 [12160/60000 (20%)]\\t training loss: 0.000307\\n\",\n      \"epoch: 10 [12480/60000 (21%)]\\t training loss: 0.004981\\n\",\n      \"epoch: 10 [12800/60000 (21%)]\\t training loss: 0.000052\\n\",\n      \"epoch: 10 [13120/60000 (22%)]\\t training loss: 0.003447\\n\",\n      \"epoch: 10 [13440/60000 (22%)]\\t training loss: 0.008660\\n\",\n      \"epoch: 10 [13760/60000 (23%)]\\t training loss: 0.007985\\n\",\n      \"epoch: 10 [14080/60000 (23%)]\\t training loss: 0.000275\\n\",\n      \"epoch: 10 [14400/60000 (24%)]\\t training loss: 0.035583\\n\",\n      \"epoch: 10 [14720/60000 (25%)]\\t training loss: 0.126648\\n\",\n      \"epoch: 10 [15040/60000 (25%)]\\t training loss: 0.005843\\n\",\n      \"epoch: 10 [15360/60000 (26%)]\\t training loss: 0.015040\\n\",\n      \"epoch: 10 [15680/60000 (26%)]\\t training loss: 0.001358\\n\",\n      \"epoch: 10 [16000/60000 (27%)]\\t training loss: 0.000733\\n\",\n      \"epoch: 10 [16320/60000 (27%)]\\t training loss: 0.291124\\n\",\n      \"epoch: 10 [16640/60000 (28%)]\\t training loss: 0.002084\\n\",\n      \"epoch: 10 [16960/60000 (28%)]\\t training loss: 0.012148\\n\",\n      \"epoch: 10 [17280/60000 (29%)]\\t training loss: 0.000373\\n\",\n      \"epoch: 10 [17600/60000 (29%)]\\t training loss: 0.002139\\n\",\n      \"epoch: 10 [17920/60000 (30%)]\\t training loss: 0.012413\\n\",\n      \"epoch: 10 [18240/60000 (30%)]\\t training loss: 0.000695\\n\",\n      \"epoch: 10 [18560/60000 (31%)]\\t training loss: 0.000131\\n\",\n      \"epoch: 10 [18880/60000 (31%)]\\t training loss: 0.014357\\n\",\n      \"epoch: 10 [19200/60000 (32%)]\\t training loss: 0.007379\\n\",\n      \"epoch: 10 [19520/60000 (33%)]\\t training loss: 0.009447\\n\",\n      \"epoch: 10 [19840/60000 (33%)]\\t training loss: 0.063613\\n\",\n      \"epoch: 10 [20160/60000 (34%)]\\t training loss: 0.034716\\n\",\n      \"epoch: 10 [20480/60000 (34%)]\\t training loss: 0.025156\\n\",\n      \"epoch: 10 [20800/60000 (35%)]\\t training loss: 0.000518\\n\",\n      \"epoch: 10 [21120/60000 (35%)]\\t training loss: 0.038810\\n\",\n      \"epoch: 10 [21440/60000 (36%)]\\t training loss: 0.004080\\n\",\n      \"epoch: 10 [21760/60000 (36%)]\\t training loss: 0.112013\\n\",\n      \"epoch: 10 [22080/60000 (37%)]\\t training loss: 0.148753\\n\",\n      \"epoch: 10 [22400/60000 (37%)]\\t training loss: 0.033678\\n\",\n      \"epoch: 10 [22720/60000 (38%)]\\t training loss: 0.120388\\n\",\n      \"epoch: 10 [23040/60000 (38%)]\\t training loss: 0.086190\\n\",\n      \"epoch: 10 [23360/60000 (39%)]\\t training loss: 0.001148\\n\",\n      \"epoch: 10 [23680/60000 (39%)]\\t training loss: 0.000359\\n\",\n      \"epoch: 10 [24000/60000 (40%)]\\t training loss: 0.067003\\n\",\n      \"epoch: 10 [24320/60000 (41%)]\\t training loss: 0.088647\\n\",\n      \"epoch: 10 [24640/60000 (41%)]\\t training loss: 0.000004\\n\",\n      \"epoch: 10 [24960/60000 (42%)]\\t training loss: 0.000339\\n\",\n      \"epoch: 10 [25280/60000 (42%)]\\t training loss: 0.001204\\n\",\n      \"epoch: 10 [25600/60000 (43%)]\\t training loss: 0.245152\\n\",\n      \"epoch: 10 [25920/60000 (43%)]\\t training loss: 0.019873\\n\",\n      \"epoch: 10 [26240/60000 (44%)]\\t training loss: 0.005331\\n\",\n      \"epoch: 10 [26560/60000 (44%)]\\t training loss: 0.086527\\n\",\n      \"epoch: 10 [26880/60000 (45%)]\\t training loss: 0.001321\\n\",\n      \"epoch: 10 [27200/60000 (45%)]\\t training loss: 0.126186\\n\",\n      \"epoch: 10 [27520/60000 (46%)]\\t training loss: 0.117476\\n\",\n      \"epoch: 10 [27840/60000 (46%)]\\t training loss: 0.170149\\n\",\n      \"epoch: 10 [28160/60000 (47%)]\\t training loss: 0.000043\\n\",\n      \"epoch: 10 [28480/60000 (47%)]\\t training loss: 0.001371\\n\",\n      \"epoch: 10 [28800/60000 (48%)]\\t training loss: 0.000092\\n\",\n      \"epoch: 10 [29120/60000 (49%)]\\t training loss: 0.014875\\n\",\n      \"epoch: 10 [29440/60000 (49%)]\\t training loss: 0.005060\\n\",\n      \"epoch: 10 [29760/60000 (50%)]\\t training loss: 0.003418\\n\",\n      \"epoch: 10 [30080/60000 (50%)]\\t training loss: 0.000345\\n\",\n      \"epoch: 10 [30400/60000 (51%)]\\t training loss: 0.000931\\n\",\n      \"epoch: 10 [30720/60000 (51%)]\\t training loss: 0.009843\\n\",\n      \"epoch: 10 [31040/60000 (52%)]\\t training loss: 0.006899\\n\",\n      \"epoch: 10 [31360/60000 (52%)]\\t training loss: 0.001677\\n\",\n      \"epoch: 10 [31680/60000 (53%)]\\t training loss: 0.000378\\n\",\n      \"epoch: 10 [32000/60000 (53%)]\\t training loss: 0.000206\\n\",\n      \"epoch: 10 [32320/60000 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[36800/60000 (61%)]\\t training loss: 0.006895\\n\",\n      \"epoch: 10 [37120/60000 (62%)]\\t training loss: 0.037081\\n\",\n      \"epoch: 10 [37440/60000 (62%)]\\t training loss: 0.000852\\n\",\n      \"epoch: 10 [37760/60000 (63%)]\\t training loss: 0.000867\\n\",\n      \"epoch: 10 [38080/60000 (63%)]\\t training loss: 0.014843\\n\",\n      \"epoch: 10 [38400/60000 (64%)]\\t training loss: 0.001252\\n\",\n      \"epoch: 10 [38720/60000 (65%)]\\t training loss: 0.155282\\n\",\n      \"epoch: 10 [39040/60000 (65%)]\\t training loss: 0.002621\\n\",\n      \"epoch: 10 [39360/60000 (66%)]\\t training loss: 0.010802\\n\",\n      \"epoch: 10 [39680/60000 (66%)]\\t training loss: 0.007977\\n\",\n      \"epoch: 10 [40000/60000 (67%)]\\t training loss: 0.004440\\n\",\n      \"epoch: 10 [40320/60000 (67%)]\\t training loss: 0.090428\\n\",\n      \"epoch: 10 [40640/60000 (68%)]\\t training loss: 0.191506\\n\",\n      \"epoch: 10 [40960/60000 (68%)]\\t training loss: 0.000664\\n\",\n      \"epoch: 10 [41280/60000 (69%)]\\t training loss: 0.001254\\n\",\n      \"epoch: 10 [41600/60000 (69%)]\\t training loss: 0.186390\\n\",\n      \"epoch: 10 [41920/60000 (70%)]\\t training loss: 0.010371\\n\",\n      \"epoch: 10 [42240/60000 (70%)]\\t training loss: 0.235318\\n\",\n      \"epoch: 10 [42560/60000 (71%)]\\t training loss: 0.001861\\n\",\n      \"epoch: 10 [42880/60000 (71%)]\\t training loss: 0.000469\\n\",\n      \"epoch: 10 [43200/60000 (72%)]\\t training loss: 0.014503\\n\",\n      \"epoch: 10 [43520/60000 (73%)]\\t training loss: 0.184187\\n\",\n      \"epoch: 10 [43840/60000 (73%)]\\t training loss: 0.262158\\n\",\n      \"epoch: 10 [44160/60000 (74%)]\\t training loss: 0.201205\\n\",\n      \"epoch: 10 [44480/60000 (74%)]\\t training loss: 0.041799\\n\",\n      \"epoch: 10 [44800/60000 (75%)]\\t training loss: 0.005672\\n\",\n      \"epoch: 10 [45120/60000 (75%)]\\t training loss: 0.064888\\n\",\n      \"epoch: 10 [45440/60000 (76%)]\\t training loss: 0.000393\\n\",\n      \"epoch: 10 [45760/60000 (76%)]\\t training loss: 0.151691\\n\",\n      \"epoch: 10 [46080/60000 (77%)]\\t training loss: 0.001828\\n\",\n      \"epoch: 10 [46400/60000 (77%)]\\t training loss: 0.025387\\n\",\n      \"epoch: 10 [46720/60000 (78%)]\\t training loss: 0.001020\\n\",\n      \"epoch: 10 [47040/60000 (78%)]\\t training loss: 0.000153\\n\",\n      \"epoch: 10 [47360/60000 (79%)]\\t training loss: 0.003716\\n\",\n      \"epoch: 10 [47680/60000 (79%)]\\t training loss: 0.000262\\n\",\n      \"epoch: 10 [48000/60000 (80%)]\\t training loss: 0.005042\\n\",\n      \"epoch: 10 [48320/60000 (81%)]\\t training loss: 0.032340\\n\",\n      \"epoch: 10 [48640/60000 (81%)]\\t training loss: 0.002494\\n\",\n      \"epoch: 10 [48960/60000 (82%)]\\t training loss: 0.001177\\n\",\n      \"epoch: 10 [49280/60000 (82%)]\\t training loss: 0.050250\\n\",\n      \"epoch: 10 [49600/60000 (83%)]\\t training loss: 0.003485\\n\",\n      \"epoch: 10 [49920/60000 (83%)]\\t training loss: 0.001999\\n\",\n      \"epoch: 10 [50240/60000 (84%)]\\t training loss: 0.001679\\n\",\n      \"epoch: 10 [50560/60000 (84%)]\\t training loss: 0.000056\\n\",\n      \"epoch: 10 [50880/60000 (85%)]\\t training loss: 0.295970\\n\",\n      \"epoch: 10 [51200/60000 (85%)]\\t training loss: 0.000410\\n\",\n      \"epoch: 10 [51520/60000 (86%)]\\t training loss: 0.006645\\n\",\n      \"epoch: 10 [51840/60000 (86%)]\\t training loss: 0.001761\\n\",\n      \"epoch: 10 [52160/60000 (87%)]\\t training loss: 0.004712\\n\",\n      \"epoch: 10 [52480/60000 (87%)]\\t training loss: 0.000217\\n\",\n      \"epoch: 10 [52800/60000 (88%)]\\t training loss: 0.037142\\n\",\n      \"epoch: 10 [53120/60000 (89%)]\\t training loss: 0.000138\\n\",\n      \"epoch: 10 [53440/60000 (89%)]\\t training loss: 0.033546\\n\",\n      \"epoch: 10 [53760/60000 (90%)]\\t training loss: 0.000911\\n\",\n      \"epoch: 10 [54080/60000 (90%)]\\t training loss: 0.002457\\n\",\n      \"epoch: 10 [54400/60000 (91%)]\\t training loss: 0.008432\\n\",\n      \"epoch: 10 [54720/60000 (91%)]\\t training loss: 0.001413\\n\",\n      \"epoch: 10 [55040/60000 (92%)]\\t training loss: 0.006463\\n\",\n      \"epoch: 10 [55360/60000 (92%)]\\t training loss: 0.018699\\n\",\n      \"epoch: 10 [55680/60000 (93%)]\\t training loss: 0.029339\\n\",\n      \"epoch: 10 [56000/60000 (93%)]\\t training loss: 0.001272\\n\",\n      \"epoch: 10 [56320/60000 (94%)]\\t training loss: 0.021055\\n\",\n      \"epoch: 10 [56640/60000 (94%)]\\t training loss: 0.010920\\n\",\n      \"epoch: 10 [56960/60000 (95%)]\\t training loss: 0.000721\\n\",\n      \"epoch: 10 [57280/60000 (95%)]\\t training loss: 0.113609\\n\",\n      \"epoch: 10 [57600/60000 (96%)]\\t training loss: 0.140323\\n\",\n      \"epoch: 10 [57920/60000 (97%)]\\t training loss: 0.005767\\n\",\n      \"epoch: 10 [58240/60000 (97%)]\\t training loss: 0.010778\\n\",\n      \"epoch: 10 [58560/60000 (98%)]\\t training loss: 0.029620\\n\",\n      \"epoch: 10 [58880/60000 (98%)]\\t training loss: 0.025632\\n\",\n      \"epoch: 10 [59200/60000 (99%)]\\t training loss: 0.001784\\n\",\n      \"epoch: 10 [59520/60000 (99%)]\\t training loss: 0.102152\\n\",\n      \"epoch: 10 [59840/60000 (100%)]\\t training loss: 0.093881\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0304, Overall Accuracy: 9916/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 11 [0/60000 (0%)]\\t training loss: 0.000799\\n\",\n      \"epoch: 11 [320/60000 (1%)]\\t training loss: 0.008168\\n\",\n      \"epoch: 11 [640/60000 (1%)]\\t training loss: 0.000136\\n\",\n      \"epoch: 11 [960/60000 (2%)]\\t training loss: 0.107621\\n\",\n      \"epoch: 11 [1280/60000 (2%)]\\t training loss: 0.142768\\n\",\n      \"epoch: 11 [1600/60000 (3%)]\\t training loss: 0.027138\\n\",\n      \"epoch: 11 [1920/60000 (3%)]\\t training loss: 0.043573\\n\",\n      \"epoch: 11 [2240/60000 (4%)]\\t training loss: 0.002555\\n\",\n      \"epoch: 11 [2560/60000 (4%)]\\t training loss: 0.000064\\n\",\n      \"epoch: 11 [2880/60000 (5%)]\\t training loss: 0.000294\\n\",\n      \"epoch: 11 [3200/60000 (5%)]\\t training loss: 0.002086\\n\",\n      \"epoch: 11 [3520/60000 (6%)]\\t training loss: 0.014940\\n\",\n      \"epoch: 11 [3840/60000 (6%)]\\t training loss: 0.005636\\n\",\n      \"epoch: 11 [4160/60000 (7%)]\\t training loss: 0.000169\\n\",\n      \"epoch: 11 [4480/60000 (7%)]\\t training loss: 0.000661\\n\",\n      \"epoch: 11 [4800/60000 (8%)]\\t training loss: 0.007436\\n\",\n      \"epoch: 11 [5120/60000 (9%)]\\t training loss: 0.000174\\n\",\n      \"epoch: 11 [5440/60000 (9%)]\\t training loss: 0.003232\\n\",\n      \"epoch: 11 [5760/60000 (10%)]\\t training loss: 0.007645\\n\",\n      \"epoch: 11 [6080/60000 (10%)]\\t training loss: 0.004961\\n\",\n      \"epoch: 11 [6400/60000 (11%)]\\t training loss: 0.003562\\n\",\n      \"epoch: 11 [6720/60000 (11%)]\\t training loss: 0.059615\\n\",\n      \"epoch: 11 [7040/60000 (12%)]\\t training loss: 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\"epoch: 11 [51520/60000 (86%)]\\t training loss: 0.001400\\n\",\n      \"epoch: 11 [51840/60000 (86%)]\\t training loss: 0.008984\\n\",\n      \"epoch: 11 [52160/60000 (87%)]\\t training loss: 0.210046\\n\",\n      \"epoch: 11 [52480/60000 (87%)]\\t training loss: 0.000465\\n\",\n      \"epoch: 11 [52800/60000 (88%)]\\t training loss: 0.001659\\n\",\n      \"epoch: 11 [53120/60000 (89%)]\\t training loss: 0.131058\\n\",\n      \"epoch: 11 [53440/60000 (89%)]\\t training loss: 0.014746\\n\",\n      \"epoch: 11 [53760/60000 (90%)]\\t training loss: 0.005614\\n\",\n      \"epoch: 11 [54080/60000 (90%)]\\t training loss: 0.093949\\n\",\n      \"epoch: 11 [54400/60000 (91%)]\\t training loss: 0.000297\\n\",\n      \"epoch: 11 [54720/60000 (91%)]\\t training loss: 0.008699\\n\",\n      \"epoch: 11 [55040/60000 (92%)]\\t training loss: 0.000619\\n\",\n      \"epoch: 11 [55360/60000 (92%)]\\t training loss: 0.008892\\n\",\n      \"epoch: 11 [55680/60000 (93%)]\\t training loss: 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Loss: 0.0316, Overall Accuracy: 9906/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 12 [0/60000 (0%)]\\t training loss: 0.028874\\n\",\n      \"epoch: 12 [320/60000 (1%)]\\t training loss: 0.000814\\n\",\n      \"epoch: 12 [640/60000 (1%)]\\t training loss: 0.005865\\n\",\n      \"epoch: 12 [960/60000 (2%)]\\t training loss: 0.028923\\n\",\n      \"epoch: 12 [1280/60000 (2%)]\\t training loss: 0.005768\\n\",\n      \"epoch: 12 [1600/60000 (3%)]\\t training loss: 0.001855\\n\",\n      \"epoch: 12 [1920/60000 (3%)]\\t training loss: 0.003594\\n\",\n      \"epoch: 12 [2240/60000 (4%)]\\t training loss: 0.007726\\n\",\n      \"epoch: 12 [2560/60000 (4%)]\\t training loss: 0.001870\\n\",\n      \"epoch: 12 [2880/60000 (5%)]\\t training loss: 0.042527\\n\",\n      \"epoch: 12 [3200/60000 (5%)]\\t training loss: 0.036527\\n\",\n      \"epoch: 12 [3520/60000 (6%)]\\t training loss: 0.018291\\n\",\n      \"epoch: 12 [3840/60000 (6%)]\\t training loss: 0.001410\\n\",\n      \"epoch: 12 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\"epoch: 12 [17600/60000 (29%)]\\t training loss: 0.018087\\n\",\n      \"epoch: 12 [17920/60000 (30%)]\\t training loss: 0.062428\\n\",\n      \"epoch: 12 [18240/60000 (30%)]\\t training loss: 0.002804\\n\",\n      \"epoch: 12 [18560/60000 (31%)]\\t training loss: 0.035469\\n\",\n      \"epoch: 12 [18880/60000 (31%)]\\t training loss: 0.009550\\n\",\n      \"epoch: 12 [19200/60000 (32%)]\\t training loss: 0.005122\\n\",\n      \"epoch: 12 [19520/60000 (33%)]\\t training loss: 0.048845\\n\",\n      \"epoch: 12 [19840/60000 (33%)]\\t training loss: 0.014230\\n\",\n      \"epoch: 12 [20160/60000 (34%)]\\t training loss: 0.000130\\n\",\n      \"epoch: 12 [20480/60000 (34%)]\\t training loss: 0.000131\\n\",\n      \"epoch: 12 [20800/60000 (35%)]\\t training loss: 0.201279\\n\",\n      \"epoch: 12 [21120/60000 (35%)]\\t training loss: 0.102546\\n\",\n      \"epoch: 12 [21440/60000 (36%)]\\t training loss: 0.002923\\n\",\n      \"epoch: 12 [21760/60000 (36%)]\\t training loss: 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0.086216\\n\",\n      \"epoch: 13 [54400/60000 (91%)]\\t training loss: 0.012492\\n\",\n      \"epoch: 13 [54720/60000 (91%)]\\t training loss: 0.033067\\n\",\n      \"epoch: 13 [55040/60000 (92%)]\\t training loss: 0.004850\\n\",\n      \"epoch: 13 [55360/60000 (92%)]\\t training loss: 0.000713\\n\",\n      \"epoch: 13 [55680/60000 (93%)]\\t training loss: 0.050624\\n\",\n      \"epoch: 13 [56000/60000 (93%)]\\t training loss: 0.000266\\n\",\n      \"epoch: 13 [56320/60000 (94%)]\\t training loss: 0.002616\\n\",\n      \"epoch: 13 [56640/60000 (94%)]\\t training loss: 0.000684\\n\",\n      \"epoch: 13 [56960/60000 (95%)]\\t training loss: 0.000011\\n\",\n      \"epoch: 13 [57280/60000 (95%)]\\t training loss: 0.003951\\n\",\n      \"epoch: 13 [57600/60000 (96%)]\\t training loss: 0.002384\\n\",\n      \"epoch: 13 [57920/60000 (97%)]\\t training loss: 0.001241\\n\",\n      \"epoch: 13 [58240/60000 (97%)]\\t training loss: 0.022752\\n\",\n      \"epoch: 13 [58560/60000 (98%)]\\t training loss: 0.011842\\n\",\n      \"epoch: 13 [58880/60000 (98%)]\\t training loss: 0.021472\\n\",\n      \"epoch: 13 [59200/60000 (99%)]\\t training loss: 0.050412\\n\",\n      \"epoch: 13 [59520/60000 (99%)]\\t training loss: 0.002907\\n\",\n      \"epoch: 13 [59840/60000 (100%)]\\t training loss: 0.001168\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0415, Overall Accuracy: 9906/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 14 [0/60000 (0%)]\\t training loss: 0.001558\\n\",\n      \"epoch: 14 [320/60000 (1%)]\\t training loss: 0.007151\\n\",\n      \"epoch: 14 [640/60000 (1%)]\\t training loss: 0.000024\\n\",\n      \"epoch: 14 [960/60000 (2%)]\\t training loss: 0.049642\\n\",\n      \"epoch: 14 [1280/60000 (2%)]\\t training loss: 0.057306\\n\",\n      \"epoch: 14 [1600/60000 (3%)]\\t training loss: 0.046696\\n\",\n      \"epoch: 14 [1920/60000 (3%)]\\t training loss: 0.003430\\n\",\n      \"epoch: 14 [2240/60000 (4%)]\\t training loss: 0.029474\\n\",\n      \"epoch: 14 [2560/60000 (4%)]\\t training loss: 0.000168\\n\",\n      \"epoch: 14 [2880/60000 (5%)]\\t training loss: 0.014985\\n\",\n      \"epoch: 14 [3200/60000 (5%)]\\t training loss: 0.006570\\n\",\n      \"epoch: 14 [3520/60000 (6%)]\\t training loss: 0.011318\\n\",\n      \"epoch: 14 [3840/60000 (6%)]\\t training loss: 0.000126\\n\",\n      \"epoch: 14 [4160/60000 (7%)]\\t training loss: 0.000303\\n\",\n      \"epoch: 14 [4480/60000 (7%)]\\t training loss: 0.004139\\n\",\n      \"epoch: 14 [4800/60000 (8%)]\\t training loss: 0.011673\\n\",\n      \"epoch: 14 [5120/60000 (9%)]\\t training loss: 0.000075\\n\",\n      \"epoch: 14 [5440/60000 (9%)]\\t training loss: 0.000034\\n\",\n      \"epoch: 14 [5760/60000 (10%)]\\t training loss: 0.001164\\n\",\n      \"epoch: 14 [6080/60000 (10%)]\\t training loss: 0.000885\\n\",\n      \"epoch: 14 [6400/60000 (11%)]\\t training loss: 0.003106\\n\",\n      \"epoch: 14 [6720/60000 (11%)]\\t training loss: 0.000054\\n\",\n      \"epoch: 14 [7040/60000 (12%)]\\t training loss: 0.007080\\n\",\n      \"epoch: 14 [7360/60000 (12%)]\\t training loss: 0.106420\\n\",\n      \"epoch: 14 [7680/60000 (13%)]\\t training loss: 0.002377\\n\",\n      \"epoch: 14 [8000/60000 (13%)]\\t training loss: 0.089681\\n\",\n      \"epoch: 14 [8320/60000 (14%)]\\t training loss: 0.000018\\n\",\n      \"epoch: 14 [8640/60000 (14%)]\\t training loss: 0.000303\\n\",\n      \"epoch: 14 [8960/60000 (15%)]\\t training loss: 0.009728\\n\",\n      \"epoch: 14 [9280/60000 (15%)]\\t training loss: 0.004568\\n\",\n      \"epoch: 14 [9600/60000 (16%)]\\t training loss: 0.000016\\n\",\n      \"epoch: 14 [9920/60000 (17%)]\\t training loss: 0.101696\\n\",\n      \"epoch: 14 [10240/60000 (17%)]\\t training loss: 0.000769\\n\",\n      \"epoch: 14 [10560/60000 (18%)]\\t training loss: 0.007521\\n\",\n      \"epoch: 14 [10880/60000 (18%)]\\t training loss: 0.000334\\n\",\n      \"epoch: 14 [11200/60000 (19%)]\\t training loss: 0.008116\\n\",\n      \"epoch: 14 [11520/60000 (19%)]\\t training loss: 0.001287\\n\",\n      \"epoch: 14 [11840/60000 (20%)]\\t training loss: 0.000617\\n\",\n      \"epoch: 14 [12160/60000 (20%)]\\t training loss: 0.001819\\n\",\n      \"epoch: 14 [12480/60000 (21%)]\\t training loss: 0.000017\\n\",\n      \"epoch: 14 [12800/60000 (21%)]\\t training loss: 0.000341\\n\",\n      \"epoch: 14 [13120/60000 (22%)]\\t training loss: 0.000122\\n\",\n      \"epoch: 14 [13440/60000 (22%)]\\t training loss: 0.002477\\n\",\n      \"epoch: 14 [13760/60000 (23%)]\\t training loss: 0.000958\\n\",\n      \"epoch: 14 [14080/60000 (23%)]\\t training loss: 0.164437\\n\",\n      \"epoch: 14 [14400/60000 (24%)]\\t training loss: 0.000509\\n\",\n      \"epoch: 14 [14720/60000 (25%)]\\t training loss: 0.002482\\n\",\n      \"epoch: 14 [15040/60000 (25%)]\\t training loss: 0.007283\\n\",\n      \"epoch: 14 [15360/60000 (26%)]\\t training loss: 0.000670\\n\",\n      \"epoch: 14 [15680/60000 (26%)]\\t training loss: 0.002238\\n\",\n      \"epoch: 14 [16000/60000 (27%)]\\t training loss: 0.016933\\n\",\n      \"epoch: 14 [16320/60000 (27%)]\\t training loss: 0.082954\\n\",\n      \"epoch: 14 [16640/60000 (28%)]\\t training loss: 0.000014\\n\",\n      \"epoch: 14 [16960/60000 (28%)]\\t training loss: 0.014289\\n\",\n      \"epoch: 14 [17280/60000 (29%)]\\t training loss: 0.000156\\n\",\n      \"epoch: 14 [17600/60000 (29%)]\\t training loss: 0.005534\\n\",\n      \"epoch: 14 [17920/60000 (30%)]\\t training loss: 0.003221\\n\",\n      \"epoch: 14 [18240/60000 (30%)]\\t training loss: 0.000052\\n\",\n      \"epoch: 14 [18560/60000 (31%)]\\t training loss: 0.050009\\n\",\n      \"epoch: 14 [18880/60000 (31%)]\\t training loss: 0.000080\\n\",\n      \"epoch: 14 [19200/60000 (32%)]\\t training loss: 0.210345\\n\",\n      \"epoch: 14 [19520/60000 (33%)]\\t training loss: 0.009988\\n\",\n      \"epoch: 14 [19840/60000 (33%)]\\t training loss: 0.041414\\n\",\n      \"epoch: 14 [20160/60000 (34%)]\\t training loss: 0.000053\\n\",\n      \"epoch: 14 [20480/60000 (34%)]\\t training loss: 0.002320\\n\",\n      \"epoch: 14 [20800/60000 (35%)]\\t training loss: 0.000456\\n\",\n      \"epoch: 14 [21120/60000 (35%)]\\t training loss: 0.090086\\n\",\n      \"epoch: 14 [21440/60000 (36%)]\\t training loss: 0.015256\\n\",\n      \"epoch: 14 [21760/60000 (36%)]\\t training loss: 0.029422\\n\",\n      \"epoch: 14 [22080/60000 (37%)]\\t training loss: 0.009844\\n\",\n      \"epoch: 14 [22400/60000 (37%)]\\t training loss: 0.000325\\n\",\n      \"epoch: 14 [22720/60000 (38%)]\\t training loss: 0.001334\\n\",\n      \"epoch: 14 [23040/60000 (38%)]\\t training loss: 0.004360\\n\",\n      \"epoch: 14 [23360/60000 (39%)]\\t training loss: 0.000470\\n\",\n      \"epoch: 14 [23680/60000 (39%)]\\t training loss: 0.000182\\n\",\n      \"epoch: 14 [24000/60000 (40%)]\\t training loss: 0.000247\\n\",\n      \"epoch: 14 [24320/60000 (41%)]\\t training loss: 0.001892\\n\",\n      \"epoch: 14 [24640/60000 (41%)]\\t training loss: 0.001310\\n\",\n      \"epoch: 14 [24960/60000 (42%)]\\t training loss: 0.005494\\n\",\n      \"epoch: 14 [25280/60000 (42%)]\\t training loss: 0.007486\\n\",\n      \"epoch: 14 [25600/60000 (43%)]\\t training loss: 0.153906\\n\",\n      \"epoch: 14 [25920/60000 (43%)]\\t training loss: 0.015749\\n\",\n      \"epoch: 14 [26240/60000 (44%)]\\t training loss: 0.072087\\n\",\n      \"epoch: 14 [26560/60000 (44%)]\\t training loss: 0.002107\\n\",\n      \"epoch: 14 [26880/60000 (45%)]\\t training loss: 0.000902\\n\",\n      \"epoch: 14 [27200/60000 (45%)]\\t training loss: 0.120916\\n\",\n      \"epoch: 14 [27520/60000 (46%)]\\t training loss: 0.000362\\n\",\n      \"epoch: 14 [27840/60000 (46%)]\\t training loss: 0.067336\\n\",\n      \"epoch: 14 [28160/60000 (47%)]\\t training loss: 0.025504\\n\",\n      \"epoch: 14 [28480/60000 (47%)]\\t training loss: 0.000334\\n\",\n      \"epoch: 14 [28800/60000 (48%)]\\t training loss: 0.078901\\n\",\n      \"epoch: 14 [29120/60000 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[33600/60000 (56%)]\\t training loss: 0.000714\\n\",\n      \"epoch: 14 [33920/60000 (57%)]\\t training loss: 0.232115\\n\",\n      \"epoch: 14 [34240/60000 (57%)]\\t training loss: 0.000057\\n\",\n      \"epoch: 14 [34560/60000 (58%)]\\t training loss: 0.033145\\n\",\n      \"epoch: 14 [34880/60000 (58%)]\\t training loss: 0.001415\\n\",\n      \"epoch: 14 [35200/60000 (59%)]\\t training loss: 0.130691\\n\",\n      \"epoch: 14 [35520/60000 (59%)]\\t training loss: 0.033689\\n\",\n      \"epoch: 14 [35840/60000 (60%)]\\t training loss: 0.008842\\n\",\n      \"epoch: 14 [36160/60000 (60%)]\\t training loss: 0.000365\\n\",\n      \"epoch: 14 [36480/60000 (61%)]\\t training loss: 0.015887\\n\",\n      \"epoch: 14 [36800/60000 (61%)]\\t training loss: 0.000146\\n\",\n      \"epoch: 14 [37120/60000 (62%)]\\t training loss: 0.003899\\n\",\n      \"epoch: 14 [37440/60000 (62%)]\\t training loss: 0.023849\\n\",\n      \"epoch: 14 [37760/60000 (63%)]\\t training loss: 0.023794\\n\",\n      \"epoch: 14 [38080/60000 (63%)]\\t training loss: 0.000101\\n\",\n      \"epoch: 14 [38400/60000 (64%)]\\t training loss: 0.007203\\n\",\n      \"epoch: 14 [38720/60000 (65%)]\\t training loss: 0.000342\\n\",\n      \"epoch: 14 [39040/60000 (65%)]\\t training loss: 0.000028\\n\",\n      \"epoch: 14 [39360/60000 (66%)]\\t training loss: 0.009635\\n\",\n      \"epoch: 14 [39680/60000 (66%)]\\t training loss: 0.001624\\n\",\n      \"epoch: 14 [40000/60000 (67%)]\\t training loss: 0.013588\\n\",\n      \"epoch: 14 [40320/60000 (67%)]\\t training loss: 0.005020\\n\",\n      \"epoch: 14 [40640/60000 (68%)]\\t training loss: 0.034141\\n\",\n      \"epoch: 14 [40960/60000 (68%)]\\t training loss: 0.000006\\n\",\n      \"epoch: 14 [41280/60000 (69%)]\\t training loss: 0.017172\\n\",\n      \"epoch: 14 [41600/60000 (69%)]\\t training loss: 0.041846\\n\",\n      \"epoch: 14 [41920/60000 (70%)]\\t training loss: 0.000348\\n\",\n      \"epoch: 14 [42240/60000 (70%)]\\t training loss: 0.002081\\n\",\n      \"epoch: 14 [42560/60000 (71%)]\\t training loss: 0.003732\\n\",\n      \"epoch: 14 [42880/60000 (71%)]\\t training loss: 0.007379\\n\",\n      \"epoch: 14 [43200/60000 (72%)]\\t training loss: 0.000096\\n\",\n      \"epoch: 14 [43520/60000 (73%)]\\t training loss: 0.111127\\n\",\n      \"epoch: 14 [43840/60000 (73%)]\\t training loss: 0.001436\\n\",\n      \"epoch: 14 [44160/60000 (74%)]\\t training loss: 0.022469\\n\",\n      \"epoch: 14 [44480/60000 (74%)]\\t training loss: 0.003625\\n\",\n      \"epoch: 14 [44800/60000 (75%)]\\t training loss: 0.000368\\n\",\n      \"epoch: 14 [45120/60000 (75%)]\\t training loss: 0.000078\\n\",\n      \"epoch: 14 [45440/60000 (76%)]\\t training loss: 0.001520\\n\",\n      \"epoch: 14 [45760/60000 (76%)]\\t training loss: 0.000398\\n\",\n      \"epoch: 14 [46080/60000 (77%)]\\t training loss: 0.025541\\n\",\n      \"epoch: 14 [46400/60000 (77%)]\\t training loss: 0.000386\\n\",\n      \"epoch: 14 [46720/60000 (78%)]\\t training loss: 0.001054\\n\",\n      \"epoch: 14 [47040/60000 (78%)]\\t training loss: 0.009700\\n\",\n      \"epoch: 14 [47360/60000 (79%)]\\t training loss: 0.000046\\n\",\n      \"epoch: 14 [47680/60000 (79%)]\\t training loss: 0.017605\\n\",\n      \"epoch: 14 [48000/60000 (80%)]\\t training loss: 0.163102\\n\",\n      \"epoch: 14 [48320/60000 (81%)]\\t training loss: 0.000148\\n\",\n      \"epoch: 14 [48640/60000 (81%)]\\t training loss: 0.019201\\n\",\n      \"epoch: 14 [48960/60000 (82%)]\\t training loss: 0.000082\\n\",\n      \"epoch: 14 [49280/60000 (82%)]\\t training loss: 0.002347\\n\",\n      \"epoch: 14 [49600/60000 (83%)]\\t training loss: 0.023805\\n\",\n      \"epoch: 14 [49920/60000 (83%)]\\t training loss: 0.233533\\n\",\n      \"epoch: 14 [50240/60000 (84%)]\\t training loss: 0.532788\\n\",\n      \"epoch: 14 [50560/60000 (84%)]\\t training loss: 0.001958\\n\",\n      \"epoch: 14 [50880/60000 (85%)]\\t training loss: 0.006325\\n\",\n      \"epoch: 14 [51200/60000 (85%)]\\t training loss: 0.090712\\n\",\n      \"epoch: 14 [51520/60000 (86%)]\\t training loss: 0.004629\\n\",\n      \"epoch: 14 [51840/60000 (86%)]\\t training loss: 0.015457\\n\",\n      \"epoch: 14 [52160/60000 (87%)]\\t training loss: 0.000955\\n\",\n      \"epoch: 14 [52480/60000 (87%)]\\t training loss: 0.003658\\n\",\n      \"epoch: 14 [52800/60000 (88%)]\\t training loss: 0.029288\\n\",\n      \"epoch: 14 [53120/60000 (89%)]\\t training loss: 0.000768\\n\",\n      \"epoch: 14 [53440/60000 (89%)]\\t training loss: 0.000158\\n\",\n      \"epoch: 14 [53760/60000 (90%)]\\t training loss: 0.008976\\n\",\n      \"epoch: 14 [54080/60000 (90%)]\\t training loss: 0.196080\\n\",\n      \"epoch: 14 [54400/60000 (91%)]\\t training loss: 0.080486\\n\",\n      \"epoch: 14 [54720/60000 (91%)]\\t training loss: 0.014813\\n\",\n      \"epoch: 14 [55040/60000 (92%)]\\t training loss: 0.008611\\n\",\n      \"epoch: 14 [55360/60000 (92%)]\\t training loss: 0.001738\\n\",\n      \"epoch: 14 [55680/60000 (93%)]\\t training loss: 0.000842\\n\",\n      \"epoch: 14 [56000/60000 (93%)]\\t training loss: 0.099776\\n\",\n      \"epoch: 14 [56320/60000 (94%)]\\t training loss: 0.002691\\n\",\n      \"epoch: 14 [56640/60000 (94%)]\\t training loss: 0.002090\\n\",\n      \"epoch: 14 [56960/60000 (95%)]\\t training loss: 0.000279\\n\",\n      \"epoch: 14 [57280/60000 (95%)]\\t training loss: 0.000909\\n\",\n      \"epoch: 14 [57600/60000 (96%)]\\t training loss: 0.004168\\n\",\n      \"epoch: 14 [57920/60000 (97%)]\\t training loss: 0.049469\\n\",\n      \"epoch: 14 [58240/60000 (97%)]\\t training loss: 0.128800\\n\",\n      \"epoch: 14 [58560/60000 (98%)]\\t training loss: 0.005255\\n\",\n      \"epoch: 14 [58880/60000 (98%)]\\t training loss: 0.000143\\n\",\n      \"epoch: 14 [59200/60000 (99%)]\\t training loss: 0.000097\\n\",\n      \"epoch: 14 [59520/60000 (99%)]\\t training loss: 0.002996\\n\",\n      \"epoch: 14 [59840/60000 (100%)]\\t training loss: 0.041795\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0320, Overall Accuracy: 9914/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 15 [0/60000 (0%)]\\t training loss: 0.004177\\n\",\n      \"epoch: 15 [320/60000 (1%)]\\t training loss: 0.018569\\n\",\n      \"epoch: 15 [640/60000 (1%)]\\t training loss: 0.001134\\n\",\n      \"epoch: 15 [960/60000 (2%)]\\t training loss: 0.010670\\n\",\n      \"epoch: 15 [1280/60000 (2%)]\\t training loss: 0.000337\\n\",\n      \"epoch: 15 [1600/60000 (3%)]\\t training loss: 0.000104\\n\",\n      \"epoch: 15 [1920/60000 (3%)]\\t training loss: 0.000030\\n\",\n      \"epoch: 15 [2240/60000 (4%)]\\t training loss: 0.000320\\n\",\n      \"epoch: 15 [2560/60000 (4%)]\\t training loss: 0.000769\\n\",\n      \"epoch: 15 [2880/60000 (5%)]\\t training loss: 0.000095\\n\",\n      \"epoch: 15 [3200/60000 (5%)]\\t training loss: 0.001750\\n\",\n      \"epoch: 15 [3520/60000 (6%)]\\t training loss: 0.000170\\n\",\n      \"epoch: 15 [3840/60000 (6%)]\\t training loss: 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\"epoch: 15 [52800/60000 (88%)]\\t training loss: 0.000571\\n\",\n      \"epoch: 15 [53120/60000 (89%)]\\t training loss: 0.000576\\n\",\n      \"epoch: 15 [53440/60000 (89%)]\\t training loss: 0.039617\\n\",\n      \"epoch: 15 [53760/60000 (90%)]\\t training loss: 0.000371\\n\",\n      \"epoch: 15 [54080/60000 (90%)]\\t training loss: 0.001736\\n\",\n      \"epoch: 15 [54400/60000 (91%)]\\t training loss: 0.045639\\n\",\n      \"epoch: 15 [54720/60000 (91%)]\\t training loss: 0.036406\\n\",\n      \"epoch: 15 [55040/60000 (92%)]\\t training loss: 0.013646\\n\",\n      \"epoch: 15 [55360/60000 (92%)]\\t training loss: 0.013259\\n\",\n      \"epoch: 15 [55680/60000 (93%)]\\t training loss: 0.002978\\n\",\n      \"epoch: 15 [56000/60000 (93%)]\\t training loss: 0.000328\\n\",\n      \"epoch: 15 [56320/60000 (94%)]\\t training loss: 0.000650\\n\",\n      \"epoch: 15 [56640/60000 (94%)]\\t training loss: 0.000154\\n\",\n      \"epoch: 15 [56960/60000 (95%)]\\t training loss: 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\"epoch: 16 [14400/60000 (24%)]\\t training loss: 0.000401\\n\",\n      \"epoch: 16 [14720/60000 (25%)]\\t training loss: 0.007081\\n\",\n      \"epoch: 16 [15040/60000 (25%)]\\t training loss: 0.000845\\n\",\n      \"epoch: 16 [15360/60000 (26%)]\\t training loss: 0.009066\\n\",\n      \"epoch: 16 [15680/60000 (26%)]\\t training loss: 0.007430\\n\",\n      \"epoch: 16 [16000/60000 (27%)]\\t training loss: 0.005587\\n\",\n      \"epoch: 16 [16320/60000 (27%)]\\t training loss: 0.000334\\n\",\n      \"epoch: 16 [16640/60000 (28%)]\\t training loss: 0.000004\\n\",\n      \"epoch: 16 [16960/60000 (28%)]\\t training loss: 0.000008\\n\",\n      \"epoch: 16 [17280/60000 (29%)]\\t training loss: 0.001653\\n\",\n      \"epoch: 16 [17600/60000 (29%)]\\t training loss: 0.019171\\n\",\n      \"epoch: 16 [17920/60000 (30%)]\\t training loss: 0.001156\\n\",\n      \"epoch: 16 [18240/60000 (30%)]\\t training loss: 0.001269\\n\",\n      \"epoch: 16 [18560/60000 (31%)]\\t training loss: 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\"epoch: 16 [58560/60000 (98%)]\\t training loss: 0.024583\\n\",\n      \"epoch: 16 [58880/60000 (98%)]\\t training loss: 0.001876\\n\",\n      \"epoch: 16 [59200/60000 (99%)]\\t training loss: 0.022112\\n\",\n      \"epoch: 16 [59520/60000 (99%)]\\t training loss: 0.012206\\n\",\n      \"epoch: 16 [59840/60000 (100%)]\\t training loss: 0.029706\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0346, Overall Accuracy: 9901/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 17 [0/60000 (0%)]\\t training loss: 0.018773\\n\",\n      \"epoch: 17 [320/60000 (1%)]\\t training loss: 0.007805\\n\",\n      \"epoch: 17 [640/60000 (1%)]\\t training loss: 0.000295\\n\",\n      \"epoch: 17 [960/60000 (2%)]\\t training loss: 0.001735\\n\",\n      \"epoch: 17 [1280/60000 (2%)]\\t training loss: 0.000161\\n\",\n      \"epoch: 17 [1600/60000 (3%)]\\t training loss: 0.000077\\n\",\n      \"epoch: 17 [1920/60000 (3%)]\\t training loss: 0.004241\\n\",\n      \"epoch: 17 [2240/60000 (4%)]\\t 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\"epoch: 17 [51200/60000 (85%)]\\t training loss: 0.018350\\n\",\n      \"epoch: 17 [51520/60000 (86%)]\\t training loss: 0.000008\\n\",\n      \"epoch: 17 [51840/60000 (86%)]\\t training loss: 0.324238\\n\",\n      \"epoch: 17 [52160/60000 (87%)]\\t training loss: 0.076134\\n\",\n      \"epoch: 17 [52480/60000 (87%)]\\t training loss: 0.000070\\n\",\n      \"epoch: 17 [52800/60000 (88%)]\\t training loss: 0.005548\\n\",\n      \"epoch: 17 [53120/60000 (89%)]\\t training loss: 0.004437\\n\",\n      \"epoch: 17 [53440/60000 (89%)]\\t training loss: 0.083054\\n\",\n      \"epoch: 17 [53760/60000 (90%)]\\t training loss: 0.000020\\n\",\n      \"epoch: 17 [54080/60000 (90%)]\\t training loss: 0.000554\\n\",\n      \"epoch: 17 [54400/60000 (91%)]\\t training loss: 0.015819\\n\",\n      \"epoch: 17 [54720/60000 (91%)]\\t training loss: 0.008664\\n\",\n      \"epoch: 17 [55040/60000 (92%)]\\t training loss: 0.108616\\n\",\n      \"epoch: 17 [55360/60000 (92%)]\\t training loss: 0.000956\\n\",\n      \"epoch: 17 [55680/60000 (93%)]\\t training loss: 0.043985\\n\",\n      \"epoch: 17 [56000/60000 (93%)]\\t training loss: 0.021443\\n\",\n      \"epoch: 17 [56320/60000 (94%)]\\t training loss: 0.042536\\n\",\n      \"epoch: 17 [56640/60000 (94%)]\\t training loss: 0.008758\\n\",\n      \"epoch: 17 [56960/60000 (95%)]\\t training loss: 0.000694\\n\",\n      \"epoch: 17 [57280/60000 (95%)]\\t training loss: 0.000232\\n\",\n      \"epoch: 17 [57600/60000 (96%)]\\t training loss: 0.014555\\n\",\n      \"epoch: 17 [57920/60000 (97%)]\\t training loss: 0.039423\\n\",\n      \"epoch: 17 [58240/60000 (97%)]\\t training loss: 0.061491\\n\",\n      \"epoch: 17 [58560/60000 (98%)]\\t training loss: 0.000111\\n\",\n      \"epoch: 17 [58880/60000 (98%)]\\t training loss: 0.064006\\n\",\n      \"epoch: 17 [59200/60000 (99%)]\\t training loss: 0.049887\\n\",\n      \"epoch: 17 [59520/60000 (99%)]\\t training loss: 0.207859\\n\",\n      \"epoch: 17 [59840/60000 (100%)]\\t training loss: 0.002149\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0362, Overall Accuracy: 9908/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 18 [0/60000 (0%)]\\t training loss: 0.001782\\n\",\n      \"epoch: 18 [320/60000 (1%)]\\t training loss: 0.037850\\n\",\n      \"epoch: 18 [640/60000 (1%)]\\t training loss: 0.000087\\n\",\n      \"epoch: 18 [960/60000 (2%)]\\t training loss: 0.000005\\n\",\n      \"epoch: 18 [1280/60000 (2%)]\\t training loss: 0.001660\\n\",\n      \"epoch: 18 [1600/60000 (3%)]\\t training loss: 0.000203\\n\",\n      \"epoch: 18 [1920/60000 (3%)]\\t training loss: 0.083166\\n\",\n      \"epoch: 18 [2240/60000 (4%)]\\t training loss: 0.030593\\n\",\n      \"epoch: 18 [2560/60000 (4%)]\\t training loss: 0.000030\\n\",\n      \"epoch: 18 [2880/60000 (5%)]\\t training loss: 0.001896\\n\",\n      \"epoch: 18 [3200/60000 (5%)]\\t training loss: 0.000047\\n\",\n      \"epoch: 18 [3520/60000 (6%)]\\t training loss: 0.000038\\n\",\n      \"epoch: 18 [3840/60000 (6%)]\\t training loss: 0.000843\\n\",\n      \"epoch: 18 [4160/60000 (7%)]\\t training loss: 0.040955\\n\",\n      \"epoch: 18 [4480/60000 (7%)]\\t training loss: 0.037475\\n\",\n      \"epoch: 18 [4800/60000 (8%)]\\t training loss: 0.000109\\n\",\n      \"epoch: 18 [5120/60000 (9%)]\\t training loss: 0.000057\\n\",\n      \"epoch: 18 [5440/60000 (9%)]\\t training loss: 0.007211\\n\",\n      \"epoch: 18 [5760/60000 (10%)]\\t training loss: 0.006779\\n\",\n      \"epoch: 18 [6080/60000 (10%)]\\t training loss: 0.131247\\n\",\n      \"epoch: 18 [6400/60000 (11%)]\\t training loss: 0.000053\\n\",\n      \"epoch: 18 [6720/60000 (11%)]\\t training loss: 0.000113\\n\",\n      \"epoch: 18 [7040/60000 (12%)]\\t training loss: 0.000648\\n\",\n      \"epoch: 18 [7360/60000 (12%)]\\t training loss: 0.000225\\n\",\n      \"epoch: 18 [7680/60000 (13%)]\\t training loss: 0.000366\\n\",\n      \"epoch: 18 [8000/60000 (13%)]\\t training loss: 0.002339\\n\",\n      \"epoch: 18 [8320/60000 (14%)]\\t training loss: 0.013973\\n\",\n      \"epoch: 18 [8640/60000 (14%)]\\t training loss: 0.024421\\n\",\n      \"epoch: 18 [8960/60000 (15%)]\\t training loss: 0.043944\\n\",\n      \"epoch: 18 [9280/60000 (15%)]\\t training loss: 0.000417\\n\",\n      \"epoch: 18 [9600/60000 (16%)]\\t training loss: 0.002785\\n\",\n      \"epoch: 18 [9920/60000 (17%)]\\t training loss: 0.000320\\n\",\n      \"epoch: 18 [10240/60000 (17%)]\\t training loss: 0.000307\\n\",\n      \"epoch: 18 [10560/60000 (18%)]\\t training loss: 0.002826\\n\",\n      \"epoch: 18 [10880/60000 (18%)]\\t training loss: 0.000158\\n\",\n      \"epoch: 18 [11200/60000 (19%)]\\t training loss: 0.002918\\n\",\n      \"epoch: 18 [11520/60000 (19%)]\\t training loss: 0.000120\\n\",\n      \"epoch: 18 [11840/60000 (20%)]\\t training loss: 0.004766\\n\",\n      \"epoch: 18 [12160/60000 (20%)]\\t training loss: 0.074373\\n\",\n      \"epoch: 18 [12480/60000 (21%)]\\t training loss: 0.055565\\n\",\n      \"epoch: 18 [12800/60000 (21%)]\\t training loss: 0.000055\\n\",\n      \"epoch: 18 [13120/60000 (22%)]\\t training loss: 0.000614\\n\",\n      \"epoch: 18 [13440/60000 (22%)]\\t training loss: 0.115949\\n\",\n      \"epoch: 18 [13760/60000 (23%)]\\t training loss: 0.002902\\n\",\n      \"epoch: 18 [14080/60000 (23%)]\\t training loss: 0.000493\\n\",\n      \"epoch: 18 [14400/60000 (24%)]\\t training loss: 0.001047\\n\",\n      \"epoch: 18 [14720/60000 (25%)]\\t training loss: 0.048845\\n\",\n      \"epoch: 18 [15040/60000 (25%)]\\t training loss: 0.098284\\n\",\n      \"epoch: 18 [15360/60000 (26%)]\\t training loss: 0.010860\\n\",\n      \"epoch: 18 [15680/60000 (26%)]\\t training loss: 0.171926\\n\",\n      \"epoch: 18 [16000/60000 (27%)]\\t training loss: 0.000057\\n\",\n      \"epoch: 18 [16320/60000 (27%)]\\t training loss: 0.000022\\n\",\n      \"epoch: 18 [16640/60000 (28%)]\\t training loss: 0.000127\\n\",\n      \"epoch: 18 [16960/60000 (28%)]\\t training loss: 0.000243\\n\",\n      \"epoch: 18 [17280/60000 (29%)]\\t training loss: 0.226918\\n\",\n      \"epoch: 18 [17600/60000 (29%)]\\t training loss: 0.000492\\n\",\n      \"epoch: 18 [17920/60000 (30%)]\\t training loss: 0.000030\\n\",\n      \"epoch: 18 [18240/60000 (30%)]\\t training loss: 0.000372\\n\",\n      \"epoch: 18 [18560/60000 (31%)]\\t training loss: 0.000027\\n\",\n      \"epoch: 18 [18880/60000 (31%)]\\t training loss: 0.000293\\n\",\n      \"epoch: 18 [19200/60000 (32%)]\\t training loss: 0.017667\\n\",\n      \"epoch: 18 [19520/60000 (33%)]\\t training loss: 0.000010\\n\",\n      \"epoch: 18 [19840/60000 (33%)]\\t training loss: 0.000791\\n\",\n      \"epoch: 18 [20160/60000 (34%)]\\t training loss: 0.005372\\n\",\n      \"epoch: 18 [20480/60000 (34%)]\\t training loss: 0.000340\\n\",\n      \"epoch: 18 [20800/60000 (35%)]\\t training loss: 0.000036\\n\",\n      \"epoch: 18 [21120/60000 (35%)]\\t training loss: 0.000120\\n\",\n      \"epoch: 18 [21440/60000 (36%)]\\t training loss: 0.000019\\n\",\n      \"epoch: 18 [21760/60000 (36%)]\\t training loss: 0.007180\\n\",\n      \"epoch: 18 [22080/60000 (37%)]\\t training loss: 0.012126\\n\",\n      \"epoch: 18 [22400/60000 (37%)]\\t training loss: 0.004780\\n\",\n      \"epoch: 18 [22720/60000 (38%)]\\t training loss: 0.003618\\n\",\n      \"epoch: 18 [23040/60000 (38%)]\\t training loss: 0.005717\\n\",\n      \"epoch: 18 [23360/60000 (39%)]\\t training loss: 0.021251\\n\",\n      \"epoch: 18 [23680/60000 (39%)]\\t training loss: 0.000768\\n\",\n      \"epoch: 18 [24000/60000 (40%)]\\t training loss: 0.006055\\n\",\n      \"epoch: 18 [24320/60000 (41%)]\\t training loss: 0.000475\\n\",\n      \"epoch: 18 [24640/60000 (41%)]\\t training loss: 0.016224\\n\",\n      \"epoch: 18 [24960/60000 (42%)]\\t training loss: 0.000105\\n\",\n      \"epoch: 18 [25280/60000 (42%)]\\t training loss: 0.016141\\n\",\n      \"epoch: 18 [25600/60000 (43%)]\\t training loss: 0.017644\\n\",\n      \"epoch: 18 [25920/60000 (43%)]\\t training loss: 0.001630\\n\",\n      \"epoch: 18 [26240/60000 (44%)]\\t training loss: 0.017068\\n\",\n      \"epoch: 18 [26560/60000 (44%)]\\t training loss: 0.000275\\n\",\n      \"epoch: 18 [26880/60000 (45%)]\\t training loss: 0.010132\\n\",\n      \"epoch: 18 [27200/60000 (45%)]\\t training loss: 0.038492\\n\",\n      \"epoch: 18 [27520/60000 (46%)]\\t training loss: 0.006769\\n\",\n      \"epoch: 18 [27840/60000 (46%)]\\t training loss: 0.000187\\n\",\n      \"epoch: 18 [28160/60000 (47%)]\\t training loss: 0.000037\\n\",\n      \"epoch: 18 [28480/60000 (47%)]\\t training loss: 0.000182\\n\",\n      \"epoch: 18 [28800/60000 (48%)]\\t training loss: 0.014609\\n\",\n      \"epoch: 18 [29120/60000 (49%)]\\t training loss: 0.031931\\n\",\n      \"epoch: 18 [29440/60000 (49%)]\\t training loss: 0.108074\\n\",\n      \"epoch: 18 [29760/60000 (50%)]\\t training loss: 0.000146\\n\",\n      \"epoch: 18 [30080/60000 (50%)]\\t training loss: 0.000938\\n\",\n      \"epoch: 18 [30400/60000 (51%)]\\t training loss: 0.001997\\n\",\n      \"epoch: 18 [30720/60000 (51%)]\\t training loss: 0.001387\\n\",\n      \"epoch: 18 [31040/60000 (52%)]\\t training loss: 0.232617\\n\",\n      \"epoch: 18 [31360/60000 (52%)]\\t training loss: 0.003842\\n\",\n      \"epoch: 18 [31680/60000 (53%)]\\t training loss: 0.000039\\n\",\n      \"epoch: 18 [32000/60000 (53%)]\\t training loss: 0.211244\\n\",\n      \"epoch: 18 [32320/60000 (54%)]\\t training loss: 0.000201\\n\",\n      \"epoch: 18 [32640/60000 (54%)]\\t training loss: 0.000182\\n\",\n      \"epoch: 18 [32960/60000 (55%)]\\t training loss: 0.000054\\n\",\n      \"epoch: 18 [33280/60000 (55%)]\\t training loss: 0.000345\\n\",\n      \"epoch: 18 [33600/60000 (56%)]\\t training loss: 0.000101\\n\",\n      \"epoch: 18 [33920/60000 (57%)]\\t training loss: 0.000270\\n\",\n      \"epoch: 18 [34240/60000 (57%)]\\t training loss: 0.000896\\n\",\n      \"epoch: 18 [34560/60000 (58%)]\\t training loss: 0.000061\\n\",\n      \"epoch: 18 [34880/60000 (58%)]\\t training loss: 0.000036\\n\",\n      \"epoch: 18 [35200/60000 (59%)]\\t training loss: 0.042912\\n\",\n      \"epoch: 18 [35520/60000 (59%)]\\t training loss: 0.001002\\n\",\n      \"epoch: 18 [35840/60000 (60%)]\\t training loss: 0.000098\\n\",\n      \"epoch: 18 [36160/60000 (60%)]\\t training loss: 0.001133\\n\",\n      \"epoch: 18 [36480/60000 (61%)]\\t training loss: 0.005107\\n\",\n      \"epoch: 18 [36800/60000 (61%)]\\t training loss: 0.005131\\n\",\n      \"epoch: 18 [37120/60000 (62%)]\\t training loss: 0.000068\\n\",\n      \"epoch: 18 [37440/60000 (62%)]\\t training loss: 0.000007\\n\",\n      \"epoch: 18 [37760/60000 (63%)]\\t training loss: 0.002049\\n\",\n      \"epoch: 18 [38080/60000 (63%)]\\t training loss: 0.000002\\n\",\n      \"epoch: 18 [38400/60000 (64%)]\\t training loss: 0.008027\\n\",\n      \"epoch: 18 [38720/60000 (65%)]\\t training loss: 0.088446\\n\",\n      \"epoch: 18 [39040/60000 (65%)]\\t training loss: 0.000879\\n\",\n      \"epoch: 18 [39360/60000 (66%)]\\t training loss: 0.000023\\n\",\n      \"epoch: 18 [39680/60000 (66%)]\\t training loss: 0.026291\\n\",\n      \"epoch: 18 [40000/60000 (67%)]\\t training loss: 0.000169\\n\",\n      \"epoch: 18 [40320/60000 (67%)]\\t training loss: 0.012581\\n\",\n      \"epoch: 18 [40640/60000 (68%)]\\t training loss: 0.001175\\n\",\n      \"epoch: 18 [40960/60000 (68%)]\\t training loss: 0.000015\\n\",\n      \"epoch: 18 [41280/60000 (69%)]\\t training loss: 0.000303\\n\",\n      \"epoch: 18 [41600/60000 (69%)]\\t training loss: 0.020271\\n\",\n      \"epoch: 18 [41920/60000 (70%)]\\t training loss: 0.003547\\n\",\n      \"epoch: 18 [42240/60000 (70%)]\\t training loss: 0.487801\\n\",\n      \"epoch: 18 [42560/60000 (71%)]\\t training loss: 0.000543\\n\",\n      \"epoch: 18 [42880/60000 (71%)]\\t training loss: 0.092255\\n\",\n      \"epoch: 18 [43200/60000 (72%)]\\t training loss: 0.000481\\n\",\n      \"epoch: 18 [43520/60000 (73%)]\\t training loss: 0.024013\\n\",\n      \"epoch: 18 [43840/60000 (73%)]\\t training loss: 0.000231\\n\",\n      \"epoch: 18 [44160/60000 (74%)]\\t training loss: 0.000925\\n\",\n      \"epoch: 18 [44480/60000 (74%)]\\t training loss: 0.003930\\n\",\n      \"epoch: 18 [44800/60000 (75%)]\\t training loss: 0.000017\\n\",\n      \"epoch: 18 [45120/60000 (75%)]\\t training loss: 0.004521\\n\",\n      \"epoch: 18 [45440/60000 (76%)]\\t training loss: 0.000017\\n\",\n      \"epoch: 18 [45760/60000 (76%)]\\t training loss: 0.000487\\n\",\n      \"epoch: 18 [46080/60000 (77%)]\\t training loss: 0.045538\\n\",\n      \"epoch: 18 [46400/60000 (77%)]\\t training loss: 0.014161\\n\",\n      \"epoch: 18 [46720/60000 (78%)]\\t training loss: 0.130969\\n\",\n      \"epoch: 18 [47040/60000 (78%)]\\t training loss: 0.000621\\n\",\n      \"epoch: 18 [47360/60000 (79%)]\\t training loss: 0.000042\\n\",\n      \"epoch: 18 [47680/60000 (79%)]\\t training loss: 0.081315\\n\",\n      \"epoch: 18 [48000/60000 (80%)]\\t training loss: 0.002181\\n\",\n      \"epoch: 18 [48320/60000 (81%)]\\t training loss: 0.000064\\n\",\n      \"epoch: 18 [48640/60000 (81%)]\\t training loss: 0.039701\\n\",\n      \"epoch: 18 [48960/60000 (82%)]\\t training loss: 0.000005\\n\",\n      \"epoch: 18 [49280/60000 (82%)]\\t training loss: 0.000423\\n\",\n      \"epoch: 18 [49600/60000 (83%)]\\t training loss: 0.000108\\n\",\n      \"epoch: 18 [49920/60000 (83%)]\\t training loss: 0.025033\\n\",\n      \"epoch: 18 [50240/60000 (84%)]\\t training loss: 0.000560\\n\",\n      \"epoch: 18 [50560/60000 (84%)]\\t training loss: 0.048346\\n\",\n      \"epoch: 18 [50880/60000 (85%)]\\t training loss: 0.021224\\n\",\n      \"epoch: 18 [51200/60000 (85%)]\\t training loss: 0.000248\\n\",\n      \"epoch: 18 [51520/60000 (86%)]\\t training loss: 0.000689\\n\",\n      \"epoch: 18 [51840/60000 (86%)]\\t training loss: 0.000000\\n\",\n      \"epoch: 18 [52160/60000 (87%)]\\t training loss: 0.000245\\n\",\n      \"epoch: 18 [52480/60000 (87%)]\\t training loss: 0.002403\\n\",\n      \"epoch: 18 [52800/60000 (88%)]\\t training loss: 0.000945\\n\",\n      \"epoch: 18 [53120/60000 (89%)]\\t training loss: 0.002774\\n\",\n      \"epoch: 18 [53440/60000 (89%)]\\t training loss: 0.160516\\n\",\n      \"epoch: 18 [53760/60000 (90%)]\\t training loss: 0.021920\\n\",\n      \"epoch: 18 [54080/60000 (90%)]\\t training loss: 0.001036\\n\",\n      \"epoch: 18 [54400/60000 (91%)]\\t training loss: 0.239800\\n\",\n      \"epoch: 18 [54720/60000 (91%)]\\t training loss: 0.000424\\n\",\n      \"epoch: 18 [55040/60000 (92%)]\\t training loss: 0.009514\\n\",\n      \"epoch: 18 [55360/60000 (92%)]\\t training loss: 0.016584\\n\",\n      \"epoch: 18 [55680/60000 (93%)]\\t training loss: 0.000663\\n\",\n      \"epoch: 18 [56000/60000 (93%)]\\t training loss: 0.000081\\n\",\n      \"epoch: 18 [56320/60000 (94%)]\\t training loss: 0.000378\\n\",\n      \"epoch: 18 [56640/60000 (94%)]\\t training loss: 0.000050\\n\",\n      \"epoch: 18 [56960/60000 (95%)]\\t training loss: 0.000158\\n\",\n      \"epoch: 18 [57280/60000 (95%)]\\t training loss: 0.002465\\n\",\n      \"epoch: 18 [57600/60000 (96%)]\\t training loss: 0.004555\\n\",\n      \"epoch: 18 [57920/60000 (97%)]\\t training loss: 0.001574\\n\",\n      \"epoch: 18 [58240/60000 (97%)]\\t training loss: 0.001144\\n\",\n      \"epoch: 18 [58560/60000 (98%)]\\t training loss: 0.005433\\n\",\n      \"epoch: 18 [58880/60000 (98%)]\\t training loss: 0.000281\\n\",\n      \"epoch: 18 [59200/60000 (99%)]\\t training loss: 0.008778\\n\",\n      \"epoch: 18 [59520/60000 (99%)]\\t training loss: 0.054680\\n\",\n      \"epoch: 18 [59840/60000 (100%)]\\t training loss: 0.000720\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0348, Overall Accuracy: 9914/10000 (99%)\\n\",\n      \"\\n\",\n      \"epoch: 19 [0/60000 (0%)]\\t training loss: 0.000451\\n\",\n      \"epoch: 19 [320/60000 (1%)]\\t training loss: 0.107797\\n\",\n      \"epoch: 19 [640/60000 (1%)]\\t training loss: 0.000284\\n\",\n      \"epoch: 19 [960/60000 (2%)]\\t training loss: 0.000057\\n\",\n      \"epoch: 19 [1280/60000 (2%)]\\t training loss: 0.000310\\n\",\n      \"epoch: 19 [1600/60000 (3%)]\\t training loss: 0.001452\\n\",\n      \"epoch: 19 [1920/60000 (3%)]\\t training loss: 0.003497\\n\",\n      \"epoch: 19 [2240/60000 (4%)]\\t training loss: 0.000264\\n\",\n      \"epoch: 19 [2560/60000 (4%)]\\t training loss: 0.000091\\n\",\n      \"epoch: 19 [2880/60000 (5%)]\\t training loss: 0.006180\\n\",\n      \"epoch: 19 [3200/60000 (5%)]\\t training loss: 0.002533\\n\",\n      \"epoch: 19 [3520/60000 (6%)]\\t training loss: 0.000538\\n\",\n      \"epoch: 19 [3840/60000 (6%)]\\t training loss: 0.005232\\n\",\n      \"epoch: 19 [4160/60000 (7%)]\\t training loss: 0.017949\\n\",\n      \"epoch: 19 [4480/60000 (7%)]\\t training loss: 0.013728\\n\",\n      \"epoch: 19 [4800/60000 (8%)]\\t training loss: 0.002291\\n\",\n      \"epoch: 19 [5120/60000 (9%)]\\t training loss: 0.000709\\n\",\n      \"epoch: 19 [5440/60000 (9%)]\\t training loss: 0.000042\\n\",\n      \"epoch: 19 [5760/60000 (10%)]\\t training loss: 0.111945\\n\",\n      \"epoch: 19 [6080/60000 (10%)]\\t training loss: 0.003184\\n\",\n      \"epoch: 19 [6400/60000 (11%)]\\t training loss: 0.000190\\n\",\n      \"epoch: 19 [6720/60000 (11%)]\\t training loss: 0.001799\\n\",\n      \"epoch: 19 [7040/60000 (12%)]\\t training loss: 0.021109\\n\",\n      \"epoch: 19 [7360/60000 (12%)]\\t training loss: 0.002380\\n\",\n      \"epoch: 19 [7680/60000 (13%)]\\t training loss: 0.007744\\n\",\n      \"epoch: 19 [8000/60000 (13%)]\\t training loss: 0.024902\\n\",\n      \"epoch: 19 [8320/60000 (14%)]\\t training loss: 0.001961\\n\",\n      \"epoch: 19 [8640/60000 (14%)]\\t training loss: 0.183349\\n\",\n      \"epoch: 19 [8960/60000 (15%)]\\t training loss: 0.026591\\n\",\n      \"epoch: 19 [9280/60000 (15%)]\\t training loss: 0.000513\\n\",\n      \"epoch: 19 [9600/60000 (16%)]\\t training loss: 0.015319\\n\",\n      \"epoch: 19 [9920/60000 (17%)]\\t training loss: 0.000475\\n\",\n      \"epoch: 19 [10240/60000 (17%)]\\t training loss: 0.066298\\n\",\n      \"epoch: 19 [10560/60000 (18%)]\\t training loss: 0.000500\\n\",\n      \"epoch: 19 [10880/60000 (18%)]\\t training loss: 0.002707\\n\",\n      \"epoch: 19 [11200/60000 (19%)]\\t training loss: 0.010367\\n\",\n      \"epoch: 19 [11520/60000 (19%)]\\t training loss: 0.010082\\n\",\n      \"epoch: 19 [11840/60000 (20%)]\\t training loss: 0.000781\\n\",\n      \"epoch: 19 [12160/60000 (20%)]\\t training loss: 0.001693\\n\",\n      \"epoch: 19 [12480/60000 (21%)]\\t training loss: 0.000593\\n\",\n      \"epoch: 19 [12800/60000 (21%)]\\t training loss: 0.003331\\n\",\n      \"epoch: 19 [13120/60000 (22%)]\\t training loss: 0.001309\\n\",\n      \"epoch: 19 [13440/60000 (22%)]\\t training loss: 0.003293\\n\",\n      \"epoch: 19 [13760/60000 (23%)]\\t training loss: 0.048383\\n\",\n      \"epoch: 19 [14080/60000 (23%)]\\t training loss: 0.005746\\n\",\n      \"epoch: 19 [14400/60000 (24%)]\\t training loss: 0.014414\\n\",\n      \"epoch: 19 [14720/60000 (25%)]\\t training loss: 0.002526\\n\",\n      \"epoch: 19 [15040/60000 (25%)]\\t training loss: 0.011976\\n\",\n      \"epoch: 19 [15360/60000 (26%)]\\t training loss: 0.000049\\n\",\n      \"epoch: 19 [15680/60000 (26%)]\\t training loss: 0.005341\\n\",\n      \"epoch: 19 [16000/60000 (27%)]\\t training loss: 0.155143\\n\",\n      \"epoch: 19 [16320/60000 (27%)]\\t training loss: 0.061360\\n\",\n      \"epoch: 19 [16640/60000 (28%)]\\t training loss: 0.046722\\n\",\n      \"epoch: 19 [16960/60000 (28%)]\\t training loss: 0.000206\\n\",\n      \"epoch: 19 [17280/60000 (29%)]\\t training loss: 0.000140\\n\",\n      \"epoch: 19 [17600/60000 (29%)]\\t training loss: 0.000231\\n\",\n      \"epoch: 19 [17920/60000 (30%)]\\t training loss: 0.019372\\n\",\n      \"epoch: 19 [18240/60000 (30%)]\\t training loss: 0.003675\\n\",\n      \"epoch: 19 [18560/60000 (31%)]\\t training loss: 0.060302\\n\",\n      \"epoch: 19 [18880/60000 (31%)]\\t training loss: 0.029847\\n\",\n      \"epoch: 19 [19200/60000 (32%)]\\t training loss: 0.000241\\n\",\n      \"epoch: 19 [19520/60000 (33%)]\\t training loss: 0.000141\\n\",\n      \"epoch: 19 [19840/60000 (33%)]\\t training loss: 0.096143\\n\",\n      \"epoch: 19 [20160/60000 (34%)]\\t training loss: 0.000013\\n\",\n      \"epoch: 19 [20480/60000 (34%)]\\t training loss: 0.000235\\n\",\n      \"epoch: 19 [20800/60000 (35%)]\\t training loss: 0.007867\\n\",\n      \"epoch: 19 [21120/60000 (35%)]\\t training loss: 0.045290\\n\",\n      \"epoch: 19 [21440/60000 (36%)]\\t training loss: 0.001468\\n\",\n      \"epoch: 19 [21760/60000 (36%)]\\t training loss: 0.000158\\n\",\n      \"epoch: 19 [22080/60000 (37%)]\\t training loss: 0.000010\\n\",\n      \"epoch: 19 [22400/60000 (37%)]\\t training loss: 0.000232\\n\",\n      \"epoch: 19 [22720/60000 (38%)]\\t training loss: 0.000167\\n\",\n      \"epoch: 19 [23040/60000 (38%)]\\t training loss: 0.076485\\n\",\n      \"epoch: 19 [23360/60000 (39%)]\\t training loss: 0.007932\\n\",\n      \"epoch: 19 [23680/60000 (39%)]\\t training loss: 0.000173\\n\",\n      \"epoch: 19 [24000/60000 (40%)]\\t training loss: 0.000557\\n\",\n      \"epoch: 19 [24320/60000 (41%)]\\t training loss: 0.010726\\n\",\n      \"epoch: 19 [24640/60000 (41%)]\\t training loss: 0.006129\\n\",\n      \"epoch: 19 [24960/60000 (42%)]\\t training loss: 0.000057\\n\",\n      \"epoch: 19 [25280/60000 (42%)]\\t training loss: 0.003554\\n\",\n      \"epoch: 19 [25600/60000 (43%)]\\t training loss: 0.023561\\n\",\n      \"epoch: 19 [25920/60000 (43%)]\\t training loss: 0.754961\\n\",\n      \"epoch: 19 [26240/60000 (44%)]\\t training loss: 0.014062\\n\",\n      \"epoch: 19 [26560/60000 (44%)]\\t training loss: 0.068545\\n\",\n      \"epoch: 19 [26880/60000 (45%)]\\t training loss: 0.000116\\n\",\n      \"epoch: 19 [27200/60000 (45%)]\\t training loss: 0.003202\\n\",\n      \"epoch: 19 [27520/60000 (46%)]\\t training loss: 0.006872\\n\",\n      \"epoch: 19 [27840/60000 (46%)]\\t training loss: 0.061262\\n\",\n      \"epoch: 19 [28160/60000 (47%)]\\t training loss: 0.000844\\n\",\n      \"epoch: 19 [28480/60000 (47%)]\\t training loss: 0.001430\\n\",\n      \"epoch: 19 [28800/60000 (48%)]\\t training loss: 0.000008\\n\",\n      \"epoch: 19 [29120/60000 (49%)]\\t training loss: 0.001131\\n\",\n      \"epoch: 19 [29440/60000 (49%)]\\t training loss: 0.000854\\n\",\n      \"epoch: 19 [29760/60000 (50%)]\\t training loss: 0.049546\\n\",\n      \"epoch: 19 [30080/60000 (50%)]\\t training loss: 0.000377\\n\",\n      \"epoch: 19 [30400/60000 (51%)]\\t training loss: 0.000031\\n\",\n      \"epoch: 19 [30720/60000 (51%)]\\t training loss: 0.000064\\n\",\n      \"epoch: 19 [31040/60000 (52%)]\\t training loss: 0.001064\\n\",\n      \"epoch: 19 [31360/60000 (52%)]\\t training loss: 0.001101\\n\",\n      \"epoch: 19 [31680/60000 (53%)]\\t training loss: 0.000536\\n\",\n      \"epoch: 19 [32000/60000 (53%)]\\t training loss: 0.086440\\n\",\n      \"epoch: 19 [32320/60000 (54%)]\\t training loss: 0.000027\\n\",\n      \"epoch: 19 [32640/60000 (54%)]\\t training loss: 0.079773\\n\",\n      \"epoch: 19 [32960/60000 (55%)]\\t training loss: 0.001765\\n\",\n      \"epoch: 19 [33280/60000 (55%)]\\t training loss: 0.000047\\n\",\n      \"epoch: 19 [33600/60000 (56%)]\\t training loss: 0.000006\\n\",\n      \"epoch: 19 [33920/60000 (57%)]\\t training loss: 0.000021\\n\",\n      \"epoch: 19 [34240/60000 (57%)]\\t training loss: 0.018981\\n\",\n      \"epoch: 19 [34560/60000 (58%)]\\t training loss: 0.153440\\n\",\n      \"epoch: 19 [34880/60000 (58%)]\\t training loss: 0.071882\\n\",\n      \"epoch: 19 [35200/60000 (59%)]\\t training loss: 0.009374\\n\",\n      \"epoch: 19 [35520/60000 (59%)]\\t training loss: 0.002273\\n\",\n      \"epoch: 19 [35840/60000 (60%)]\\t training loss: 0.014939\\n\",\n      \"epoch: 19 [36160/60000 (60%)]\\t training loss: 0.001729\\n\",\n      \"epoch: 19 [36480/60000 (61%)]\\t training loss: 0.001771\\n\",\n      \"epoch: 19 [36800/60000 (61%)]\\t training loss: 0.035329\\n\",\n      \"epoch: 19 [37120/60000 (62%)]\\t training loss: 0.129930\\n\",\n      \"epoch: 19 [37440/60000 (62%)]\\t training loss: 0.211464\\n\",\n      \"epoch: 19 [37760/60000 (63%)]\\t training loss: 0.015301\\n\",\n      \"epoch: 19 [38080/60000 (63%)]\\t training loss: 0.016863\\n\",\n      \"epoch: 19 [38400/60000 (64%)]\\t training loss: 0.000139\\n\",\n      \"epoch: 19 [38720/60000 (65%)]\\t training loss: 0.000012\\n\",\n      \"epoch: 19 [39040/60000 (65%)]\\t training loss: 0.031108\\n\",\n      \"epoch: 19 [39360/60000 (66%)]\\t training loss: 0.000149\\n\",\n      \"epoch: 19 [39680/60000 (66%)]\\t training loss: 0.022301\\n\",\n      \"epoch: 19 [40000/60000 (67%)]\\t training loss: 0.125032\\n\",\n      \"epoch: 19 [40320/60000 (67%)]\\t training loss: 0.001330\\n\",\n      \"epoch: 19 [40640/60000 (68%)]\\t training loss: 0.000286\\n\",\n      \"epoch: 19 [40960/60000 (68%)]\\t training loss: 0.014802\\n\",\n      \"epoch: 19 [41280/60000 (69%)]\\t training loss: 0.000014\\n\",\n      \"epoch: 19 [41600/60000 (69%)]\\t training loss: 0.000813\\n\",\n      \"epoch: 19 [41920/60000 (70%)]\\t training loss: 0.000009\\n\",\n      \"epoch: 19 [42240/60000 (70%)]\\t training loss: 0.000076\\n\",\n      \"epoch: 19 [42560/60000 (71%)]\\t training loss: 0.000015\\n\",\n      \"epoch: 19 [42880/60000 (71%)]\\t training loss: 0.001414\\n\",\n      \"epoch: 19 [43200/60000 (72%)]\\t training loss: 0.056795\\n\",\n      \"epoch: 19 [43520/60000 (73%)]\\t training loss: 0.046024\\n\",\n      \"epoch: 19 [43840/60000 (73%)]\\t training loss: 0.000365\\n\",\n      \"epoch: 19 [44160/60000 (74%)]\\t training loss: 0.000008\\n\",\n      \"epoch: 19 [44480/60000 (74%)]\\t training loss: 0.000701\\n\",\n      \"epoch: 19 [44800/60000 (75%)]\\t training loss: 0.009753\\n\",\n      \"epoch: 19 [45120/60000 (75%)]\\t training loss: 0.000146\\n\",\n      \"epoch: 19 [45440/60000 (76%)]\\t training loss: 0.010681\\n\",\n      \"epoch: 19 [45760/60000 (76%)]\\t training loss: 0.025899\\n\",\n      \"epoch: 19 [46080/60000 (77%)]\\t training loss: 0.015466\\n\",\n      \"epoch: 19 [46400/60000 (77%)]\\t training loss: 0.001381\\n\",\n      \"epoch: 19 [46720/60000 (78%)]\\t training loss: 0.000312\\n\",\n      \"epoch: 19 [47040/60000 (78%)]\\t training loss: 0.024625\\n\",\n      \"epoch: 19 [47360/60000 (79%)]\\t training loss: 0.009816\\n\",\n      \"epoch: 19 [47680/60000 (79%)]\\t training loss: 0.017592\\n\",\n      \"epoch: 19 [48000/60000 (80%)]\\t training loss: 0.006236\\n\",\n      \"epoch: 19 [48320/60000 (81%)]\\t training loss: 0.003046\\n\",\n      \"epoch: 19 [48640/60000 (81%)]\\t training loss: 0.001661\\n\",\n      \"epoch: 19 [48960/60000 (82%)]\\t training loss: 0.014432\\n\",\n      \"epoch: 19 [49280/60000 (82%)]\\t training loss: 0.027202\\n\",\n      \"epoch: 19 [49600/60000 (83%)]\\t training loss: 0.000097\\n\",\n      \"epoch: 19 [49920/60000 (83%)]\\t training loss: 0.000089\\n\",\n      \"epoch: 19 [50240/60000 (84%)]\\t training loss: 0.001701\\n\",\n      \"epoch: 19 [50560/60000 (84%)]\\t training loss: 0.001683\\n\",\n      \"epoch: 19 [50880/60000 (85%)]\\t training loss: 0.041290\\n\",\n      \"epoch: 19 [51200/60000 (85%)]\\t training loss: 0.074044\\n\",\n      \"epoch: 19 [51520/60000 (86%)]\\t training loss: 0.000428\\n\",\n      \"epoch: 19 [51840/60000 (86%)]\\t training loss: 0.020164\\n\",\n      \"epoch: 19 [52160/60000 (87%)]\\t training loss: 0.000038\\n\",\n      \"epoch: 19 [52480/60000 (87%)]\\t training loss: 0.078580\\n\",\n      \"epoch: 19 [52800/60000 (88%)]\\t training loss: 0.005430\\n\",\n      \"epoch: 19 [53120/60000 (89%)]\\t training loss: 0.001048\\n\",\n      \"epoch: 19 [53440/60000 (89%)]\\t training loss: 0.034718\\n\",\n      \"epoch: 19 [53760/60000 (90%)]\\t training loss: 0.001970\\n\",\n      \"epoch: 19 [54080/60000 (90%)]\\t training loss: 0.000135\\n\",\n      \"epoch: 19 [54400/60000 (91%)]\\t training loss: 0.002385\\n\",\n      \"epoch: 19 [54720/60000 (91%)]\\t training loss: 0.111812\\n\",\n      \"epoch: 19 [55040/60000 (92%)]\\t training loss: 0.000911\\n\",\n      \"epoch: 19 [55360/60000 (92%)]\\t training loss: 0.001384\\n\",\n      \"epoch: 19 [55680/60000 (93%)]\\t training loss: 0.000391\\n\",\n      \"epoch: 19 [56000/60000 (93%)]\\t training loss: 0.000602\\n\",\n      \"epoch: 19 [56320/60000 (94%)]\\t training loss: 0.000179\\n\",\n      \"epoch: 19 [56640/60000 (94%)]\\t training loss: 0.000022\\n\",\n      \"epoch: 19 [56960/60000 (95%)]\\t training loss: 0.002838\\n\",\n      \"epoch: 19 [57280/60000 (95%)]\\t training loss: 0.001844\\n\",\n      \"epoch: 19 [57600/60000 (96%)]\\t training loss: 0.147678\\n\",\n      \"epoch: 19 [57920/60000 (97%)]\\t training loss: 0.000999\\n\",\n      \"epoch: 19 [58240/60000 (97%)]\\t training loss: 0.037533\\n\",\n      \"epoch: 19 [58560/60000 (98%)]\\t training loss: 0.001186\\n\",\n      \"epoch: 19 [58880/60000 (98%)]\\t training loss: 0.012587\\n\",\n      \"epoch: 19 [59200/60000 (99%)]\\t training loss: 0.000077\\n\",\n      \"epoch: 19 [59520/60000 (99%)]\\t training loss: 0.000058\\n\",\n      \"epoch: 19 [59840/60000 (100%)]\\t training loss: 0.002629\\n\",\n      \"\\n\",\n      \"Test dataset: Overall Loss: 0.0418, Overall Accuracy: 9918/10000 (99%)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(1, 20):\\n\",\n    \"    train(model, device, train_dataloader, optimizer, epoch)\\n\",\n    \"    test(model, device, test_dataloader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## run inference on trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_samples = enumerate(test_dataloader)\\n\",\n    \"b_i, (sample_data, sample_targets) = next(test_samples)\\n\",\n    \"\\n\",\n    \"plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model prediction is : 4\\n\",\n      \"Ground truth is : 4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\\\")\\n\",\n    \"print(f\\\"Ground truth is : {sample_targets[0]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### visualize filters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1)),\\n\",\n       \" Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1)),\\n\",\n       \" Dropout2d(p=0.1, inplace=False),\\n\",\n       \" Dropout2d(p=0.25, inplace=False),\\n\",\n       \" Linear(in_features=4608, out_features=64, bias=True),\\n\",\n       \" Linear(in_features=64, out_features=10, bias=True)]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_children_list = list(model.children())\\n\",\n    \"convolutional_layers = []\\n\",\n    \"model_parameters = []\\n\",\n    \"model_children_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for i in range(len(model_children_list)):\\n\",\n    \"    if type(model_children_list[i]) == nn.Conv2d:\\n\",\n    \"        model_parameters.append(model_children_list[i].weight)\\n\",\n    \"        convolutional_layers.append(model_children_list[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYYAAAFICAYAAABUYlkhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAI+klEQVR4nO3cP6jV9R/H8fepS2qBDYFDkC4WCGlwEcJAsCGvgkPdkMAlJY1uCNFQg5R4kYYQRBC81z+DEAiV6KDLDcKhOzQXDq0NgZM4aKjQt+W3vLZzfvDmHuLxmD+8+HDvufd5P8sdDcMwFAD8zzNrfQEAposwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAMLMuAd//PHHlgt8/PHHLbtVVT///HPL7uzsbMvutBiNRm3bv/32W8vu9u3bW3Yntbi42LL72muvtexWVX300Uctu48ePWrZndS7777bsvv666+37FZVffPNNy274/6jCy8GAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgzIx78ODBgy0X2LVrV8tuVdWtW7dadmdnZ1t2JzU/P9+y+91337XsVlXdv3+/bXsabNq0qWX30KFDLbtVVUtLS23b0+D48eMtu9evX2/Zrap666232rbH4cUAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgjIZhGMY5ODc313KB/fv3t+xWVX3++ectu2N+ydqdOHGiZffJkyctu1VVKysrLbu///57y+6ktmzZ0rK7fv36lt2qqj/++KNtexq8/fbbLbt37txp2a2q+uuvv1p2X3755bHOeTEAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBhNAzDsNaXAGB6eDEAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACDMjHtwNBq1XGBhYaFlt6rq6dOnLbuXL19u2Z3UTz/91LJ7+vTplt2qqtXV1Zbd//r/guz6LFf1fZ4//fTTlt1J3b59u2X3wIEDLbtVVV988UXL7pkzZ8Y658UAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDACEmXEPnj59uuUCr776astuVdVnn33Wtj0NlpeXW3Y3bNjQsltV9eTJk7btaXDp0qWW3V9//bVlt6pqaWmpbXsarKystOzev3+/Zbeq6o033mjbHocXAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgDAahmEY5+Du3btbLvDVV1+17FZVzc3NtW1Pg9Fo1LI75kfi//Lnn3+27G7evLlld1Jd35N33nmnZbeqateuXS27i4uLLbuTWl1dbdnt+p1YVbVx48aW3QcPHox1zosBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIo2EYhrW+BADTw4sBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIM2t9ge3bt7dtLy4utuzOz8+37E7qxo0bLbsvvPBCy25V1Z49e1p2161b17I7qX/++adl99lnn23ZrapaWFho2b1w4ULL7qTOnDnTsnvv3r2W3aqqpaWllt2HDx+Odc6LAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACKNhGIZxDl65cqXlAs8991zLblXVJ5980rL76NGjlt1JrVu3rmX3yJEjLbtVVcvLy23b02Dbtm0tux9++GHLblXVl19+2bL7zDP/7b875+fn27Zv3rzZsjvmr3svBgCSMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAGFm3IPHjh1rucDJkydbdquqVldX27anwUsvvdSye/HixZbdqqrnn3++Zffs2bMtu5O6evVqy+6bb77ZsltV9cEHH7Tsfv/99y27k/r6669bdjdt2tSyW1U1Ozvbtj0OLwYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAARhACCMhmEY1voSAEwPLwYAgjAAEIQBgCAMAARhACAIAwBBGAAIwgBAEAYAgjAAEIQBgCAMAISZcQ9u27at5QLvv/9+y25V1datW1t2Dx8+3LI7LRYWFtq233vvvZbdvXv3tuxOajQatezu27evZbeqaseOHS273377bcvupLp+Xk+cONGyW1V16tSplt1r166Ndc6LAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACMIAQBAGAIIwABCEAYAgDAAEYQAgCAMAQRgACDPjHnzxxRdbLrB169aW3aqqlZWVlt3Dhw+37E7q1KlTLbuPHz9u2a2qmpuba9kdhqFld1KXL19u2f37779bdquqXnnllbbtaXDu3LmW3fPnz7fsVlVdu3atbXscXgwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgDCzFpf4MiRI23bwzC0bU+Do0ePtuzevXu3Zbeqav369W3b06DrM/fDDz+07FZV/fLLL23b02B5ebll9+TJky27VVU7d+5s2d2/f/9Y57wYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAjCAEAQBgCCMAAQhAGAMBqGYVjrSwAwPbwYAAjCAEAQBgCCMAAQhAGAIAwABGEAIAgDAEEYAAj/AhqlEh49LF9fAAAAAElFTkSuQmCC\",\n      \"text/plain\": [\n       \"<Figure size 500x400 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(5, 4))\\n\",\n    \"for i, flt in enumerate(model_parameters[0]):\\n\",\n    \"    plt.subplot(4, 4, i+1)\\n\",\n    \"    plt.imshow(flt[0, :, :].detach(), cmap='gray')\\n\",\n    \"    plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<Figure size 500x800 with 32 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(5, 8))\\n\",\n    \"for i, flt in enumerate(model_parameters[1]):\\n\",\n    \"    plt.subplot(8, 4, i+1)\\n\",\n    \"    plt.imshow(flt[0, :, :].detach(), cmap='gray')\\n\",\n    \"    plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### visualize feature maps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"per_layer_results = [convolutional_layers[0](sample_data)]\\n\",\n    \"for i in range(1, len(convolutional_layers)):\\n\",\n    \"    per_layer_results.append(convolutional_layers[i](per_layer_results[-1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([16, 26, 26])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 500x400 with 16 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(5, 4))\\n\",\n    \"layer_visualisation = per_layer_results[0][0, :, :, :]\\n\",\n    \"layer_visualisation = layer_visualisation.data\\n\",\n    \"print(layer_visualisation.size())\\n\",\n    \"for i, flt in enumerate(layer_visualisation):\\n\",\n    \"    plt.subplot(4, 4, i + 1)\\n\",\n    \"    plt.imshow(flt, cmap='gray')\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([32, 24, 24])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 500x800 with 32 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(5, 8))\\n\",\n    \"layer_visualisation = per_layer_results[1][0, :, :, :]\\n\",\n    \"layer_visualisation = layer_visualisation.data\\n\",\n    \"print(layer_visualisation.size())\\n\",\n    \"for i, flt in enumerate(layer_visualisation):\\n\",\n    \"    plt.subplot(8, 4, i + 1)\\n\",\n    \"    plt.imshow(flt, cmap='gray')\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "Chapter18/torch-recsys.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ec547158\",\n   \"metadata\": {},\n   \"source\": [\n    \"### imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"a6ea781e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: scikit-learn==1.3.2 in /opt/conda/lib/python3.10/site-packages (1.3.2)\\n\",\n      \"Requirement already satisfied: numpy<2.0,>=1.17.3 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.26.2)\\n\",\n      \"Requirement already satisfied: scipy>=1.5.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.11.4)\\n\",\n      \"Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (1.3.2)\\n\",\n      \"Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.3.2) (3.2.0)\\n\",\n      \"Requirement already satisfied: numpy==1.26.2 in /opt/conda/lib/python3.10/site-packages (1.26.2)\\n\",\n      \"Requirement already satisfied: pandas==1.4.3 in /opt/conda/lib/python3.10/site-packages (1.4.3)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (2022.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.21.0 in /opt/conda/lib/python3.10/site-packages (from pandas==1.4.3) (1.26.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas==1.4.3) (1.16.0)\\n\",\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install scikit-learn==1.3.2\\n\",\n    \"!pip install numpy==1.26.2\\n\",\n    \"!pip install pandas==1.4.3\\n\",\n    \"!pip install torch==2.2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"39f8a04a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import math\\n\",\n    \"import copy\\n\",\n    \"from itertools import zip_longest\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"from torch import nn\\n\",\n    \"from torch import optim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c256430\",\n   \"metadata\": {},\n   \"source\": [\n    \"### set random seed for reproducibility\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"cbbce5ef\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_random_seed(state=1):\\n\",\n    \"    gens = (np.random.seed, torch.manual_seed, torch.cuda.manual_seed)\\n\",\n    \"    for set_state in gens:\\n\",\n    \"        set_state(state)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"08676a14\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"RANDOM_STATE = 42\\n\",\n    \"set_random_seed(RANDOM_STATE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b895a888\",\n   \"metadata\": {},\n   \"source\": [\n    \"### download dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"af21cb08\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"DATASET_LINK=\\\"https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"27dc39b2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"File ‘ml-latest-small.zip’ already there; not retrieving.\\n\",\n      \"\\n\",\n      \"Archive:  ml-latest-small.zip\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!wget -nc $DATASET_LINK\\n\",\n    \"!unzip -n ml-latest-small.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"efebdf4f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### load dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"bb874566\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def read_data(path):\\n\",\n    \"    files = {}\\n\",\n    \"    for filename in os.listdir(path):\\n\",\n    \"        stem, suffix =  os.path.splitext(filename)\\n\",\n    \"        file_path = os.path.join(path,filename)\\n\",\n    \"        print(filename)\\n\",\n    \"        if suffix == '.csv':\\n\",\n    \"            files[stem] = pd.read_csv(file_path)\\n\",\n    \"        elif suffix == '.dat':\\n\",\n    \"            if stem == 'ratings':\\n\",\n    \"                columns = ['userId', 'movieId', 'rating', 'timestamp']\\n\",\n    \"            else:\\n\",\n    \"                columns = ['movieId', 'title', 'genres']\\n\",\n    \"            data = pd.read_csv(file_path, sep='::', names=columns, engine='python')\\n\",\n    \"            files[stem] = data\\n\",\n    \"    return files['ratings'], files['movies']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"830f6f1b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"movies.csv\\n\",\n      \"ratings.csv\\n\",\n      \"README.txt\\n\",\n      \"links.csv\\n\",\n      \"tags.csv\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ratings, movies = read_data('./ml-latest-small/')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"80483ccf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>userId</th>\\n\",\n       \"      <th>movieId</th>\\n\",\n       \"      <th>rating</th>\\n\",\n       \"      <th>timestamp</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>964982703</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>964981247</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>964982224</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>964983815</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>964982931</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   userId  movieId  rating  timestamp\\n\",\n       \"0       1        1     4.0  964982703\\n\",\n       \"1       1        3     4.0  964981247\\n\",\n       \"2       1        6     4.0  964982224\\n\",\n       \"3       1       47     5.0  964983815\\n\",\n       \"4       1       50     5.0  964982931\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ratings.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"56b4918a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.5, 5.0)\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"minmax = ratings.rating.min(), ratings.rating.max()\\n\",\n    \"minmax\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"d7bee386\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>movieId</th>\\n\",\n       \"      <th>title</th>\\n\",\n       \"      <th>genres</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>Jumanji (1995)</td>\\n\",\n       \"      <td>Adventure|Children|Fantasy</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>Grumpier Old Men (1995)</td>\\n\",\n       \"      <td>Comedy|Romance</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>Waiting to Exhale (1995)</td>\\n\",\n       \"      <td>Comedy|Drama|Romance</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>Father of the Bride Part II (1995)</td>\\n\",\n       \"      <td>Comedy</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   movieId                               title  \\\\\\n\",\n       \"0        1                    Toy Story (1995)   \\n\",\n       \"1        2                      Jumanji (1995)   \\n\",\n       \"2        3             Grumpier Old Men (1995)   \\n\",\n       \"3        4            Waiting to Exhale (1995)   \\n\",\n       \"4        5  Father of the Bride Part II (1995)   \\n\",\n       \"\\n\",\n       \"                                        genres  \\n\",\n       \"0  Adventure|Animation|Children|Comedy|Fantasy  \\n\",\n       \"1                   Adventure|Children|Fantasy  \\n\",\n       \"2                               Comedy|Romance  \\n\",\n       \"3                         Comedy|Drama|Romance  \\n\",\n       \"4                                       Comedy  \"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"movies.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"ab05e616\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(9742, 3)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"movies.drop_duplicates().shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"cc03525f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ratings = ratings.merge(movies[[\\\"movieId\\\", \\\"title\\\"]], on=\\\"movieId\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"0b7d02a5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>userId</th>\\n\",\n       \"      <th>movieId</th>\\n\",\n       \"      <th>rating</th>\\n\",\n       \"      <th>timestamp</th>\\n\",\n       \"      <th>title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>964982703</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>847434962</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>1106635946</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1510577970</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>1305696483</td>\\n\",\n       \"      <td>Toy Story (1995)</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   userId  movieId  rating   timestamp             title\\n\",\n       \"0       1        1     4.0   964982703  Toy Story (1995)\\n\",\n       \"1       5        1     4.0   847434962  Toy Story (1995)\\n\",\n       \"2       7        1     4.5  1106635946  Toy Story (1995)\\n\",\n       \"3      15        1     2.5  1510577970  Toy Story (1995)\\n\",\n       \"4      17        1     4.5  1305696483  Toy Story (1995)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ratings.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"2ecbd7b9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tabular_preview(ratings, n=15):\\n\",\n    \"    \\\"\\\"\\\"Creates a cross-tabular view of users vs movies.\\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    user_groups = ratings.groupby('userId')['rating'].count()\\n\",\n    \"    top_users = user_groups.sort_values(ascending=False)[:n]\\n\",\n    \"\\n\",\n    \"    movie_groups = ratings.groupby('movieId')['rating'].count()\\n\",\n    \"    top_movies = movie_groups.sort_values(ascending=False)[:n]\\n\",\n    \"\\n\",\n    \"    top = (\\n\",\n    \"        ratings.\\n\",\n    \"        join(top_users, rsuffix='_r', how='inner', on='userId').\\n\",\n    \"        join(top_movies, rsuffix='_r', how='inner', on='movieId'))\\n\",\n    \"\\n\",\n    \"    return pd.crosstab(top.userId, top.movieId, top.rating, aggfunc=np.sum)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"2ac12fea\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th>movieId</th>\\n\",\n       \"      <th>110</th>\\n\",\n       \"      <th>260</th>\\n\",\n       \"      <th>296</th>\\n\",\n       \"      <th>318</th>\\n\",\n       \"      <th>356</th>\\n\",\n       \"      <th>480</th>\\n\",\n       \"      <th>527</th>\\n\",\n       \"      <th>589</th>\\n\",\n       \"      <th>593</th>\\n\",\n       \"      <th>2571</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>userId</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>68</th>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>274</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>288</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>380</th>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>414</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>448</th>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>2.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>474</th>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>606</th>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"movieId  110   260   296   318   356   480   527   589   593   2571\\n\",\n       \"userId                                                             \\n\",\n       \"68        2.5   5.0   2.0   3.0   3.5   3.5   4.0   3.5   3.5   4.5\\n\",\n       \"274       4.5   3.0   5.0   4.5   4.5   3.5   4.0   4.5   4.0   4.0\\n\",\n       \"288       5.0   5.0   5.0   5.0   5.0   2.0   5.0   4.0   5.0   3.0\\n\",\n       \"380       4.0   5.0   5.0   3.0   5.0   5.0   NaN   5.0   5.0   4.5\\n\",\n       \"414       5.0   5.0   5.0   5.0   5.0   4.0   4.0   5.0   4.0   5.0\\n\",\n       \"448       NaN   5.0   5.0   NaN   3.0   3.0   NaN   3.0   5.0   2.0\\n\",\n       \"474       3.0   4.0   4.0   5.0   3.0   4.5   5.0   4.0   4.5   4.5\\n\",\n       \"599       3.5   5.0   5.0   4.0   3.5   4.0   NaN   4.5   3.0   5.0\\n\",\n       \"606       3.5   4.5   5.0   3.5   4.0   2.5   5.0   3.5   4.5   5.0\\n\",\n       \"610       4.5   5.0   5.0   3.0   3.0   5.0   3.5   5.0   4.5   5.0\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tabular_preview(ratings, 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"23c1f9f9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_dataset(ratings, top=None):\\n\",\n    \"    if top is not None:\\n\",\n    \"        ratings.groupby('userId')['rating'].count()\\n\",\n    \"    \\n\",\n    \"    unique_users = ratings.userId.unique()\\n\",\n    \"    user_to_index = {old: new for new, old in enumerate(unique_users)}\\n\",\n    \"    new_users = ratings.userId.map(user_to_index)\\n\",\n    \"    \\n\",\n    \"    unique_movies = ratings.movieId.unique()\\n\",\n    \"    movie_to_index = {old: new for new, old in enumerate(unique_movies)}\\n\",\n    \"    new_movies = ratings.movieId.map(movie_to_index)\\n\",\n    \"        \\n\",\n    \"    n_users = unique_users.shape[0]\\n\",\n    \"    n_movies = unique_movies.shape[0]\\n\",\n    \"    \\n\",\n    \"    X = pd.DataFrame({'user_id': new_users, 'movie_id': new_movies})\\n\",\n    \"    y = ratings['rating'].astype(np.float32)\\n\",\n    \"    return (n_users, n_movies), (X, y), (user_to_index, movie_to_index)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"562dfed4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Embeddings: 610 users, 9724 movies\\n\",\n      \"Dataset shape: (100836, 2)\\n\",\n      \"Target shape: (100836,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"(n, m), (X, y), (user_to_index, movie_to_index) = create_dataset(ratings)\\n\",\n    \"print(f'Embeddings: {n} users, {m} movies')\\n\",\n    \"print(f'Dataset shape: {X.shape}')\\n\",\n    \"print(f'Target shape: {y.shape}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a9b01ad1\",\n   \"metadata\": {},\n   \"source\": [\n    \"### create dataloader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"461b564e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ReviewsIterator:\\n\",\n    \"    \\n\",\n    \"    def __init__(self, X, y, batch_size=32, shuffle=True):\\n\",\n    \"        X, y = np.asarray(X), np.asarray(y)\\n\",\n    \"        \\n\",\n    \"        if shuffle:\\n\",\n    \"            index = np.random.permutation(X.shape[0])\\n\",\n    \"            X, y = X[index], y[index]\\n\",\n    \"            \\n\",\n    \"        self.X = X\\n\",\n    \"        self.y = y\\n\",\n    \"        self.batch_size = batch_size\\n\",\n    \"        self.shuffle = shuffle\\n\",\n    \"        self.n_batches = int(math.ceil(X.shape[0] // batch_size))\\n\",\n    \"        self._current = 0\\n\",\n    \"        \\n\",\n    \"    def __iter__(self):\\n\",\n    \"        return self\\n\",\n    \"    \\n\",\n    \"    def __next__(self):\\n\",\n    \"        return self.next()\\n\",\n    \"    \\n\",\n    \"    def next(self):\\n\",\n    \"        if self._current >= self.n_batches:\\n\",\n    \"            raise StopIteration()\\n\",\n    \"        k = self._current\\n\",\n    \"        self._current += 1\\n\",\n    \"        bs = self.batch_size\\n\",\n    \"        return self.X[k*bs:(k + 1)*bs], self.y[k*bs:(k + 1)*bs]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"fc07fdf7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def batches(X, y, bs=32, shuffle=True):\\n\",\n    \"    for xb, yb in ReviewsIterator(X, y, bs, shuffle):\\n\",\n    \"        xb = torch.LongTensor(xb)\\n\",\n    \"        yb = torch.FloatTensor(yb)\\n\",\n    \"        yield xb, yb.view(-1, 1) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"63861618\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([[ 341, 1891],\\n\",\n      \"        [  83,  907],\\n\",\n      \"        [ 106, 5749],\\n\",\n      \"        [ 146,   61]])\\n\",\n      \"tensor([[4.],\\n\",\n      \"        [2.],\\n\",\n      \"        [4.],\\n\",\n      \"        [5.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for x_batch, y_batch in batches(X, y, bs=4):\\n\",\n    \"    print(x_batch)\\n\",\n    \"    print(y_batch)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"361dee73\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=RANDOM_STATE)\\n\",\n    \"datasets = {'train': (X_train, y_train), 'val': (X_valid, y_valid)}\\n\",\n    \"dataset_sizes = {'train': len(X_train), 'val': len(X_valid)}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"316f7f94\",\n   \"metadata\": {},\n   \"source\": [\n    \"### define recsys model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"106018f1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class EmbeddingNet(nn.Module):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Creates a dense network with embedding layers.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"    \\n\",\n    \"        n_users:            \\n\",\n    \"            Number of unique users in the dataset.\\n\",\n    \"\\n\",\n    \"        n_movies: \\n\",\n    \"            Number of unique movies in the dataset.\\n\",\n    \"\\n\",\n    \"        n_factors: \\n\",\n    \"            Number of columns in the embeddings matrix.\\n\",\n    \"\\n\",\n    \"        embedding_dropout: \\n\",\n    \"            Dropout rate to apply right after embeddings layer.\\n\",\n    \"\\n\",\n    \"        hidden:\\n\",\n    \"            A single integer or a list of integers defining the number of \\n\",\n    \"            units in hidden layer(s).\\n\",\n    \"\\n\",\n    \"        dropouts: \\n\",\n    \"            A single integer or a list of integers defining the dropout \\n\",\n    \"            layers rates applyied right after each of hidden layers.\\n\",\n    \"            \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    def __init__(self, n_users, n_movies,\\n\",\n    \"                 n_factors=50, embedding_dropout=0.02, \\n\",\n    \"                 hidden=10, dropouts=0.2):\\n\",\n    \"        super().__init__()\\n\",\n    \"        hidden = get_list(hidden)\\n\",\n    \"        dropouts = get_list(dropouts)\\n\",\n    \"        n_last = hidden[-1]\\n\",\n    \"        \\n\",\n    \"        def gen_layers(n_in):\\n\",\n    \"            \\\"\\\"\\\"\\n\",\n    \"            A generator that yields a sequence of hidden layers and \\n\",\n    \"            their activations/dropouts.\\n\",\n    \"            \\n\",\n    \"            Note that the function captures `hidden` and `dropouts` \\n\",\n    \"            values from the outer scope.\\n\",\n    \"            \\\"\\\"\\\"\\n\",\n    \"            nonlocal hidden, dropouts\\n\",\n    \"            assert len(dropouts) <= len(hidden)\\n\",\n    \"            \\n\",\n    \"            for n_out, rate in zip_longest(hidden, dropouts):\\n\",\n    \"                yield nn.Linear(n_in, n_out)\\n\",\n    \"                yield nn.ReLU()\\n\",\n    \"                if rate is not None and rate > 0.:\\n\",\n    \"                    yield nn.Dropout(rate)\\n\",\n    \"                n_in = n_out\\n\",\n    \"            \\n\",\n    \"        self.u = nn.Embedding(n_users, n_factors)\\n\",\n    \"        self.m = nn.Embedding(n_movies, n_factors)\\n\",\n    \"        self.drop = nn.Dropout(embedding_dropout)\\n\",\n    \"        self.hidden = nn.Sequential(*list(gen_layers(n_factors * 2)))\\n\",\n    \"        self.fc = nn.Linear(n_last, 1)\\n\",\n    \"        self._init()\\n\",\n    \"        \\n\",\n    \"    def forward(self, users, movies, minmax=None):\\n\",\n    \"        features = torch.cat([self.u(users), self.m(movies)], dim=1)\\n\",\n    \"        x = self.drop(features)\\n\",\n    \"        x = self.hidden(x)\\n\",\n    \"        out = torch.sigmoid(self.fc(x))\\n\",\n    \"        if minmax is not None:\\n\",\n    \"            min_rating, max_rating = minmax\\n\",\n    \"            out = out*(max_rating - min_rating + 1) + min_rating - 0.5\\n\",\n    \"        return out\\n\",\n    \"    \\n\",\n    \"    def _init(self):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        Setup embeddings and hidden layers with reasonable initial values.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        def init(m):\\n\",\n    \"            if type(m) == nn.Linear:\\n\",\n    \"                torch.nn.init.xavier_uniform_(m.weight)\\n\",\n    \"                m.bias.data.fill_(0.01)\\n\",\n    \"                \\n\",\n    \"        self.u.weight.data.uniform_(-0.05, 0.05)\\n\",\n    \"        self.m.weight.data.uniform_(-0.05, 0.05)\\n\",\n    \"        self.hidden.apply(init)\\n\",\n    \"        init(self.fc)\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"def get_list(n):\\n\",\n    \"    if isinstance(n, (int, float)):\\n\",\n    \"        return [n]\\n\",\n    \"    elif hasattr(n, '__iter__'):\\n\",\n    \"        return list(n)\\n\",\n    \"    raise TypeError('layers configuraiton should be a single number or a list of numbers')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"1a002fbd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"net = EmbeddingNet(\\n\",\n    \"    n_users=n, n_movies=m, \\n\",\n    \"    n_factors=150, hidden=[500, 500, 500], \\n\",\n    \"    embedding_dropout=0.05, dropouts=[0.5, 0.5, 0.25])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"b6eed23a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"EmbeddingNet(\\n\",\n       \"  (u): Embedding(610, 150)\\n\",\n       \"  (m): Embedding(9724, 150)\\n\",\n       \"  (drop): Dropout(p=0.05, inplace=False)\\n\",\n       \"  (hidden): Sequential(\\n\",\n       \"    (0): Linear(in_features=300, out_features=500, bias=True)\\n\",\n       \"    (1): ReLU()\\n\",\n       \"    (2): Dropout(p=0.5, inplace=False)\\n\",\n       \"    (3): Linear(in_features=500, out_features=500, bias=True)\\n\",\n       \"    (4): ReLU()\\n\",\n       \"    (5): Dropout(p=0.5, inplace=False)\\n\",\n       \"    (6): Linear(in_features=500, out_features=500, bias=True)\\n\",\n       \"    (7): ReLU()\\n\",\n       \"    (8): Dropout(p=0.25, inplace=False)\\n\",\n       \"  )\\n\",\n       \"  (fc): Linear(in_features=500, out_features=1, bias=True)\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"net\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c986d497\",\n   \"metadata\": {},\n   \"source\": [\n    \"### model training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"ef43de4c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss improvement on epoch: 1\\n\",\n      \"[001/200] train: 1.1996 - val: 1.0651\\n\",\n      \"loss improvement on epoch: 2\\n\",\n      \"[002/200] train: 1.0806 - val: 1.0494\\n\",\n      \"loss improvement on epoch: 3\\n\",\n      \"[003/200] train: 1.0632 - val: 1.0300\\n\",\n      \"loss improvement on epoch: 4\\n\",\n      \"[004/200] train: 1.0376 - val: 0.9992\\n\",\n      \"loss improvement on epoch: 5\\n\",\n      \"[005/200] train: 0.9992 - val: 0.9553\\n\",\n      \"loss improvement on epoch: 6\\n\",\n      \"[006/200] train: 0.9511 - val: 0.9160\\n\",\n      \"loss improvement on epoch: 7\\n\",\n      \"[007/200] train: 0.9073 - val: 0.8842\\n\",\n      \"loss improvement on epoch: 8\\n\",\n      \"[008/200] train: 0.8785 - val: 0.8654\\n\",\n      \"loss improvement on epoch: 9\\n\",\n      \"[009/200] train: 0.8545 - val: 0.8508\\n\",\n      \"loss improvement on epoch: 10\\n\",\n      \"[010/200] train: 0.8364 - val: 0.8395\\n\",\n      \"loss improvement on epoch: 11\\n\",\n      \"[011/200] train: 0.8192 - val: 0.8360\\n\",\n      \"loss improvement on epoch: 12\\n\",\n      \"[012/200] train: 0.8053 - val: 0.8224\\n\",\n      \"loss improvement on epoch: 13\\n\",\n      \"[013/200] train: 0.7895 - val: 0.8203\\n\",\n      \"loss improvement on epoch: 14\\n\",\n      \"[014/200] train: 0.7779 - val: 0.8162\\n\",\n      \"loss improvement on epoch: 15\\n\",\n      \"[015/200] train: 0.7675 - val: 0.8094\\n\",\n      \"loss improvement on epoch: 16\\n\",\n      \"[016/200] train: 0.7576 - val: 0.8031\\n\",\n      \"loss improvement on epoch: 17\\n\",\n      \"[017/200] train: 0.7487 - val: 0.8008\\n\",\n      \"loss improvement on epoch: 18\\n\",\n      \"[018/200] train: 0.7407 - val: 0.7978\\n\",\n      \"loss improvement on epoch: 19\\n\",\n      \"[019/200] train: 0.7294 - val: 0.7919\\n\",\n      \"loss improvement on epoch: 20\\n\",\n      \"[020/200] train: 0.7241 - val: 0.7912\\n\",\n      \"[021/200] train: 0.7160 - val: 0.7924\\n\",\n      \"loss improvement on epoch: 22\\n\",\n      \"[022/200] train: 0.7119 - val: 0.7877\\n\",\n      \"loss improvement on epoch: 23\\n\",\n      \"[023/200] train: 0.7058 - val: 0.7843\\n\",\n      \"[024/200] train: 0.7033 - val: 0.7878\\n\",\n      \"loss improvement on epoch: 25\\n\",\n      \"[025/200] train: 0.6957 - val: 0.7810\\n\",\n      \"[026/200] train: 0.6898 - val: 0.7813\\n\",\n      \"[027/200] train: 0.6836 - val: 0.7823\\n\",\n      \"[028/200] train: 0.6811 - val: 0.7815\\n\",\n      \"loss improvement on epoch: 29\\n\",\n      \"[029/200] train: 0.6783 - val: 0.7792\\n\",\n      \"loss improvement on epoch: 30\\n\",\n      \"[030/200] train: 0.6755 - val: 0.7769\\n\",\n      \"[031/200] train: 0.6715 - val: 0.7811\\n\",\n      \"[032/200] train: 0.6690 - val: 0.7794\\n\",\n      \"[033/200] train: 0.6631 - val: 0.7799\\n\",\n      \"loss improvement on epoch: 34\\n\",\n      \"[034/200] train: 0.6615 - val: 0.7727\\n\",\n      \"[035/200] train: 0.6599 - val: 0.7747\\n\",\n      \"[036/200] train: 0.6566 - val: 0.7736\\n\",\n      \"[037/200] train: 0.6546 - val: 0.7737\\n\",\n      \"[038/200] train: 0.6540 - val: 0.7777\\n\",\n      \"loss improvement on epoch: 39\\n\",\n      \"[039/200] train: 0.6497 - val: 0.7697\\n\",\n      \"[040/200] train: 0.6483 - val: 0.7790\\n\",\n      \"[041/200] train: 0.6478 - val: 0.7701\\n\",\n      \"[042/200] train: 0.6445 - val: 0.7740\\n\",\n      \"[043/200] train: 0.6428 - val: 0.7775\\n\",\n      \"[044/200] train: 0.6413 - val: 0.7772\\n\",\n      \"[045/200] train: 0.6398 - val: 0.7759\\n\",\n      \"[046/200] train: 0.6369 - val: 0.7781\\n\",\n      \"[047/200] train: 0.6349 - val: 0.7768\\n\",\n      \"[048/200] train: 0.6331 - val: 0.7778\\n\",\n      \"[049/200] train: 0.6326 - val: 0.7714\\n\",\n      \"early stopping after epoch 049\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"lr = 1e-5\\n\",\n    \"wd = 1e-5\\n\",\n    \"bs = 200 \\n\",\n    \"n_epochs = 200\\n\",\n    \"patience = 10\\n\",\n    \"no_improvements = 0\\n\",\n    \"best_loss = np.inf\\n\",\n    \"best_weights = None\\n\",\n    \"history = []\\n\",\n    \"\\n\",\n    \"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\\n\",\n    \"\\n\",\n    \"net.to(device)\\n\",\n    \"criterion = nn.MSELoss(reduction='sum')\\n\",\n    \"optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=wd)\\n\",\n    \"iterations_per_epoch = int(math.ceil(dataset_sizes['train'] // bs))\\n\",\n    \"\\n\",\n    \"for epoch in range(n_epochs):\\n\",\n    \"    stats = {'epoch': epoch + 1, 'total': n_epochs}\\n\",\n    \"    \\n\",\n    \"    for phase in ('train', 'val'):\\n\",\n    \"        training = phase == 'train'\\n\",\n    \"        running_loss = 0.0\\n\",\n    \"        n_batches = 0\\n\",\n    \"        batch_num = 0\\n\",\n    \"        for batch in batches(*datasets[phase], shuffle=training, bs=bs):\\n\",\n    \"            x_batch, y_batch = [b.to(device) for b in batch]\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            # compute gradients only during 'train' phase\\n\",\n    \"            with torch.set_grad_enabled(training):\\n\",\n    \"                outputs = net(x_batch[:, 0], x_batch[:, 1], minmax)\\n\",\n    \"                loss = criterion(outputs, y_batch)\\n\",\n    \"                \\n\",\n    \"                # don't update weights and rates when in 'val' phase\\n\",\n    \"                if training:\\n\",\n    \"                    loss.backward()\\n\",\n    \"                    optimizer.step()\\n\",\n    \"                    \\n\",\n    \"            running_loss += loss.item()\\n\",\n    \"            \\n\",\n    \"        epoch_loss = running_loss / dataset_sizes[phase]\\n\",\n    \"        stats[phase] = epoch_loss\\n\",\n    \"        \\n\",\n    \"        # early stopping: save weights of the best model so far\\n\",\n    \"        if phase == 'val':\\n\",\n    \"            if epoch_loss < best_loss:\\n\",\n    \"                print('loss improvement on epoch: %d' % (epoch + 1))\\n\",\n    \"                best_loss = epoch_loss\\n\",\n    \"                best_weights = copy.deepcopy(net.state_dict())\\n\",\n    \"                no_improvements = 0\\n\",\n    \"            else:\\n\",\n    \"                no_improvements += 1\\n\",\n    \"                \\n\",\n    \"    history.append(stats)\\n\",\n    \"    print('[{epoch:03d}/{total:03d}] train: {train:.4f} - val: {val:.4f}'.format(**stats))\\n\",\n    \"    if no_improvements >= patience:\\n\",\n    \"        print('early stopping after epoch {epoch:03d}'.format(**stats))\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"6d557cc2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"ax = pd.DataFrame(history).drop(columns='total').plot(x='epoch')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d6f55ed\",\n   \"metadata\": {},\n   \"source\": [\n    \"### evaluate model on validation set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"d2eb35c8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"groud_truth, predictions = [], []\\n\",\n    \"\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    for batch in batches(*datasets['val'], shuffle=False, bs=bs):\\n\",\n    \"        x_batch, y_batch = [b.to(device) for b in batch]\\n\",\n    \"        outputs = net(x_batch[:, 0], x_batch[:, 1], minmax)\\n\",\n    \"        groud_truth.extend(y_batch.tolist())\\n\",\n    \"        predictions.extend(outputs.tolist())\\n\",\n    \"\\n\",\n    \"groud_truth = np.asarray(groud_truth).ravel()\\n\",\n    \"predictions = np.asarray(predictions).ravel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"7b1c4a6b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Final RMSE: 0.8816\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"final_loss = np.sqrt(np.mean((np.array(predictions) - np.array(groud_truth))**2))\\n\",\n    \"print(f'Final RMSE: {final_loss:.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"ee6e04ed\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([3.22362065, 2.88916826, 3.34224343, ..., 3.88819361, 3.34277844,\\n\",\n       \"       3.67311811])\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.array(predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"5103f29a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>user_id</th>\\n\",\n       \"      <th>movie_id</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>67037</th>\\n\",\n       \"      <td>341</td>\\n\",\n       \"      <td>1891</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42175</th>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>907</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>93850</th>\\n\",\n       \"      <td>106</td>\\n\",\n       \"      <td>5749</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6187</th>\\n\",\n       \"      <td>146</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12229</th>\\n\",\n       \"      <td>267</td>\\n\",\n       \"      <td>154</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>57416</th>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>1378</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>67290</th>\\n\",\n       \"      <td>83</td>\\n\",\n       \"      <td>1898</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33423</th>\\n\",\n       \"      <td>157</td>\\n\",\n       \"      <td>696</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>98552</th>\\n\",\n       \"      <td>154</td>\\n\",\n       \"      <td>7775</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>87803</th>\\n\",\n       \"      <td>267</td>\\n\",\n       \"      <td>4174</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>20168 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       user_id  movie_id\\n\",\n       \"67037      341      1891\\n\",\n       \"42175       83       907\\n\",\n       \"93850      106      5749\\n\",\n       \"6187       146        61\\n\",\n       \"12229      267       154\\n\",\n       \"...        ...       ...\\n\",\n       \"57416      102      1378\\n\",\n       \"67290       83      1898\\n\",\n       \"33423      157       696\\n\",\n       \"98552      154      7775\\n\",\n       \"87803      267      4174\\n\",\n       \"\\n\",\n       \"[20168 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"datasets[\\\"val\\\"][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"ebc0e488\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"67037    4.0\\n\",\n       \"42175    2.0\\n\",\n       \"93850    4.0\\n\",\n       \"6187     5.0\\n\",\n       \"12229    4.0\\n\",\n       \"        ... \\n\",\n       \"73415    5.0\\n\",\n       \"59374    4.0\\n\",\n       \"89587    3.5\\n\",\n       \"30918    4.5\\n\",\n       \"46482    4.5\\n\",\n       \"Name: rating, Length: 20000, dtype: float32\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"datasets[\\\"val\\\"][1][:20000]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"82d352bd\",\n   \"metadata\": {},\n   \"source\": [\n    \"### build a recommendation system using trained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"14e7b21c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"device=\\\"cpu\\\"\\n\",\n    \"def recommender_system(user_id, model, n_movies):\\n\",\n    \"    model = model.to(device)\\n\",\n    \"    seen_movies = set(X[X['user_id'] == user_id]['movie_id'])\\n\",\n    \"    print(f\\\"Total movies seen by the user: {len(seen_movies)}\\\")\\n\",\n    \"    user_ratings = y[X['user_id'] == user_id]\\n\",\n    \"    print(\\\"=====================================================================\\\")\\n\",\n    \"    print(f\\\"Some top rated movies (rating = {user_ratings.max()}) seen by the user:\\\")\\n\",\n    \"    print(\\\"=====================================================================\\\\n\\\")\\n\",\n    \"    top_rated_movie_ids = X.loc[(X['user_id'] == user_id) & (y == user_ratings.max()), \\\"movie_id\\\"]\\n\",\n    \"    print(\\\"\\\\n\\\".join(movies[movies.movieId.isin(top_rated_movie_ids)].title.iloc[:10].tolist()))\\n\",\n    \"    print(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    unseen_movies = list(set(ratings.movieId) - set(seen_movies))\\n\",\n    \"    unseen_movies_index = [movie_to_index[i] for i in unseen_movies]\\n\",\n    \"    \\n\",\n    \"    model_input = (torch.tensor([user_id]*len(unseen_movies_index), device=device), \\n\",\n    \"                   torch.tensor(unseen_movies_index, device=device))\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        predicted_ratings = model(*model_input, minmax).detach().numpy()\\n\",\n    \"    \\n\",\n    \"    zipped_pred = zip(unseen_movies, predicted_ratings)\\n\",\n    \"    sorted_movie_index = list(zip(*sorted(zipped_pred, key=lambda c: c[1], reverse=True)))[0]\\n\",\n    \"    recommended_movies = movies[movies.movieId.isin(sorted_movie_index)].title.tolist()\\n\",\n    \"    \\n\",\n    \"    print(\\\"=====================================================================\\\")\\n\",\n    \"    print(\\\"Top \\\"+str(n_movies)+\\\" Movie recommendations for the user \\\"+str(user_id)+ \\\" are:\\\")\\n\",\n    \"    print(\\\"=====================================================================\\\\n\\\")\\n\",\n    \"    print(\\\"\\\\n\\\".join(recommended_movies[:n_movies]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"fcd48302\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total movies seen by the user: 575\\n\",\n      \"=====================================================================\\n\",\n      \"Some top rated movies (rating = 5.0) seen by the user:\\n\",\n      \"=====================================================================\\n\",\n      \"\\n\",\n      \"Jumanji (1995)\\n\",\n      \"GoldenEye (1995)\\n\",\n      \"Friday (1995)\\n\",\n      \"From Dusk Till Dawn (1996)\\n\",\n      \"Fair Game (1995)\\n\",\n      \"Big Bully (1996)\\n\",\n      \"Screamers (1995)\\n\",\n      \"Nico Icon (1995)\\n\",\n      \"Doom Generation, The (1995)\\n\",\n      \"Mighty Morphin Power Rangers: The Movie (1995)\\n\",\n      \"\\n\",\n      \"=====================================================================\\n\",\n      \"Top 20 Movie recommendations for the user 32 are:\\n\",\n      \"=====================================================================\\n\",\n      \"\\n\",\n      \"Father of the Bride Part II (1995)\\n\",\n      \"Heat (1995)\\n\",\n      \"Sudden Death (1995)\\n\",\n      \"American President, The (1995)\\n\",\n      \"Four Rooms (1995)\\n\",\n      \"Get Shorty (1995)\\n\",\n      \"Assassins (1995)\\n\",\n      \"Powder (1995)\\n\",\n      \"Persuasion (1995)\\n\",\n      \"It Takes Two (1995)\\n\",\n      \"Clueless (1995)\\n\",\n      \"Restoration (1995)\\n\",\n      \"How to Make an American Quilt (1995)\\n\",\n      \"Seven (a.k.a. Se7en) (1995)\\n\",\n      \"Pocahontas (1995)\\n\",\n      \"When Night Is Falling (1995)\\n\",\n      \"Usual Suspects, The (1995)\\n\",\n      \"Mighty Aphrodite (1995)\\n\",\n      \"Lamerica (1994)\\n\",\n      \"Big Green, The (1995)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"recommender_system(32, net, 20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"890d1c51\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"5b6d580e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter19/HuggingFaceAccelerate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"fe2fbcf0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: accelerate==0.25.0 in /opt/conda/lib/python3.10/site-packages (0.25.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (21.3)\\n\",\n      \"Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (5.9.3)\\n\",\n      \"Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (6.0)\\n\",\n      \"Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.25.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->accelerate==0.25.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.25.0) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate==0.25.0) (12.3.101)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate==0.25.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate==0.25.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.10.0->accelerate==0.25.0) (2.1.3)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate==0.25.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.10.0->accelerate==0.25.0) (1.3.0)\\n\",\n      \"Requirement already satisfied: transformers==4.36.0 in /opt/conda/lib/python3.10/site-packages (4.36.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2022.6.15)\\n\",\n      \"Requirement already satisfied: datasets==2.3.2 in /opt/conda/lib/python3.10/site-packages (2.3.2)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (1.23.4)\\n\",\n      \"Requirement already satisfied: pyarrow>=6.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (8.0.0)\\n\",\n      \"Requirement already satisfied: dill<0.3.6 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.3.5.1)\\n\",\n      \"Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (1.4.3)\\n\",\n      \"Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (2.28.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (4.66.1)\\n\",\n      \"Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (3.0.0)\\n\",\n      \"Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets==2.3.2) (0.70.13)\\n\",\n      \"Requirement already satisfied: 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already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (6.0.2)\\n\",\n      \"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (4.0.2)\\n\",\n      \"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.7.2)\\n\",\n      \"Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.3.2) (1.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets==2.3.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->datasets==2.3.2) (3.0.9)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.3.2) (2022.6.15)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.3.2) (2.8.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.3.2) (2022.1)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas->datasets==2.3.2) (1.16.0)\\n\",\n      \"Requirement already satisfied: numpy==1.23.4 in /opt/conda/lib/python3.10/site-packages (1.23.4)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install accelerate==0.25.0\\n\",\n    \"!pip install transformers==4.36.0\\n\",\n    \"!pip install datasets==2.3.2\\n\",\n    \"!pip install numpy==1.23.4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"62d977e0-8760-46bb-b08f-b335fbeb5a79\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"Using custom data configuration default\\n\",\n      \"Reusing dataset rotten_tomatoes (/home/ashish.jha/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3358322d2b0c41ca9058fba51b9b9fd3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Parameter 'function'=<function tokenize_function at 0x7fbc826b0040> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"75b23b72a9854a4fb99fe68d79e66cb1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/9 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3b4f985d0eac4fada1bfeeb0a90a16fe\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/2 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5a41bbf4d64d4c9cb81463096ca6d3a1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/2 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from datasets import load_dataset\\n\",\n    \"from transformers import BertTokenizer\\n\",\n    \"from accelerate import Accelerator\\n\",\n    \"\\n\",\n    \"# Initialize the accelerator\\n\",\n    \"accelerator = Accelerator(cpu=False, mixed_precision=\\\"fp16\\\")\\n\",\n    \"\\n\",\n    \"# Loading a dataset from HuggingFace Datasets library\\n\",\n    \"dataset = load_dataset(\\\"rotten_tomatoes\\\")\\n\",\n    \"\\n\",\n    \"# Initializing a tokenizer\\n\",\n    \"tokenizer = BertTokenizer.from_pretrained(\\\"bert-base-uncased\\\")\\n\",\n    \"\\n\",\n    \"# Tokenizing and preparing the dataset for PyTorch\\n\",\n    \"def tokenize_function(example):\\n\",\n    \"    return tokenizer(example[\\\"text\\\"], padding=\\\"max_length\\\", truncation=True)\\n\",\n    \"\\n\",\n    \"tokenized_dataset = dataset.map(tokenize_function, batched=True)\\n\",\n    \"tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])\\n\",\n    \"\\n\",\n    \"# Creating PyTorch DataLoader\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(tokenized_dataset['train'], batch_size=8, shuffle=True)\\n\",\n    \"eval_dataloader = torch.utils.data.DataLoader(tokenized_dataset['test'], batch_size=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"7de76a32-8fe4-43e0-9c17-f9b4ca259af1\",\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\\n\",\n      \"  warnings.warn(\\n\",\n      \"  0%|                                                                                                                                                                                                                                                                      | 0/1067 [00:00<?, ?it/s]2024-02-16 16:00:57.026641: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\",\n      \"2024-02-16 16:00:57.026707: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\",\n      \"2024-02-16 16:00:57.028280: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\",\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [05:14<00:00,  3.39it/s]\\n\",\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134/134 [00:11<00:00, 11.24it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1 - Evaluation Accuracy: 0.5\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [05:07<00:00,  3.47it/s]\\n\",\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134/134 [00:12<00:00, 11.16it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 2 - Evaluation Accuracy: 0.5\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [05:07<00:00,  3.47it/s]\\n\",\n      \"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134/134 [00:11<00:00, 11.28it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 3 - Evaluation Accuracy: 0.5\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from tqdm import tqdm\\n\",\n    \"from transformers import BertForSequenceClassification, AdamW\\n\",\n    \"\\n\",\n    \"# Load pre-trained BERT model for sequence classification\\n\",\n    \"model = BertForSequenceClassification.from_pretrained(\\\"bert-base-uncased\\\")\\n\",\n    \"\\n\",\n    \"# Move model to the device managed by Accelerator\\n\",\n    \"model, train_dataloader, eval_dataloader = accelerator.prepare(model, train_dataloader, eval_dataloader)\\n\",\n    \"\\n\",\n    \"# Optimizer and learning rate scheduler setup\\n\",\n    \"optimizer = AdamW(model.parameters(), lr=5e-5)\\n\",\n    \"\\n\",\n    \"# Training loop using PyTorch\\n\",\n    \"for epoch in range(3):  # Train for 3 epochs as an example\\n\",\n    \"    model.train()\\n\",\n    \"    for batch in tqdm(train_dataloader):\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        input_ids = batch['input_ids']\\n\",\n    \"        attention_mask = batch['attention_mask']\\n\",\n    \"        labels = batch['label']\\n\",\n    \"\\n\",\n    \"        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        loss = outputs.loss\\n\",\n    \"        accelerator.backward(loss)\\n\",\n    \"        optimizer.step()\\n\",\n    \"\\n\",\n    \"    # Evaluation loop\\n\",\n    \"    model.eval()\\n\",\n    \"    total_correct = 0\\n\",\n    \"    total_samples = 0\\n\",\n    \"    for batch in tqdm(eval_dataloader):\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            input_ids = batch['input_ids']\\n\",\n    \"            attention_mask = batch['attention_mask']\\n\",\n    \"            labels = batch['label']\\n\",\n    \"\\n\",\n    \"            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\\n\",\n    \"            predictions = torch.argmax(outputs.logits, dim=1)\\n\",\n    \"\\n\",\n    \"            total_correct += accelerator.gather(predictions.eq(labels)).sum().item()\\n\",\n    \"            total_samples += len(labels)\\n\",\n    \"\\n\",\n    \"    accuracy = total_correct / total_samples\\n\",\n    \"    print(f\\\"Epoch {epoch + 1} - Evaluation Accuracy: {accuracy}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"2c7ad201-d253-4c52-81b8-18885ea8c782\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"4bf97faf-eb1a-4c12-a97b-f107f53f05cf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter19/HuggingFaceDatasets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"fd1a39e2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install huggingface_hub==0.20.3\\n\",\n    \"!pip install transformers==4.37.2\\n\",\n    \"!pip install datasets==2.17.0\\n\",\n    \"!pip install numpy==1.26.4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8f8eb2a9-d2d8-44df-b98c-bc1f401ac372\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from huggingface_hub import hf_api\\n\",\n    \"datasets = hf_api.list_datasets()\\n\",\n    \"len([d for d in datasets])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"b662538f-c500-4711-a346-4aa084ad7244\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default\\n\",\n      \"Reusing dataset rotten_tomatoes (/Users/ashish.jha/.cache/huggingface/datasets/rotten_tomatoes/default/1.0.0/40d411e45a6ce3484deed7cc15b82a53dad9a72aafd9f86f8f227134bec5ca46)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1c49a19ccd6f48329ddf591fd5bd91b6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c5893ab9a1e34d469f0744b02c10f2d8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/9 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6e498af0ba34485a95d12ea4b2376411\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/2 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b557eb2a4bd840d59e75f9b89605f6ba\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/2 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from datasets import load_dataset\\n\",\n    \"from transformers import BertTokenizer\\n\",\n    \"\\n\",\n    \"# Loading a dataset from HuggingFace Datasets library\\n\",\n    \"dataset = load_dataset(\\\"rotten_tomatoes\\\")\\n\",\n    \"\\n\",\n    \"# Initializing a tokenizer\\n\",\n    \"tokenizer = BertTokenizer.from_pretrained(\\\"bert-base-uncased\\\")\\n\",\n    \"\\n\",\n    \"# Tokenizing and preparing the dataset for PyTorch\\n\",\n    \"def tokenize_function(example):\\n\",\n    \"    return tokenizer(example[\\\"text\\\"], padding=\\\"max_length\\\", truncation=True)\\n\",\n    \"\\n\",\n    \"tokenized_dataset = dataset.map(tokenize_function, batched=True)\\n\",\n    \"tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])\\n\",\n    \"\\n\",\n    \"# Creating PyTorch DataLoader\\n\",\n    \"train_dataloader = torch.utils.data.DataLoader(tokenized_dataset['train'], batch_size=8, shuffle=True)\\n\",\n    \"eval_dataloader = torch.utils.data.DataLoader(tokenized_dataset['test'], batch_size=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"29b5af5e-7fc3-48e0-9c63-134fb550d778\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\",\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [4:03:15<00:00, 13.68s/it]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1 - Evaluation Accuracy: 0.800187617260788\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [4:10:06<00:00, 14.06s/it]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 2 - Evaluation Accuracy: 0.8292682926829268\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1067/1067 [4:15:38<00:00, 14.38s/it]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 3 - Evaluation Accuracy: 0.8461538461538461\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from tqdm import tqdm\\n\",\n    \"from transformers import BertForSequenceClassification, AdamW\\n\",\n    \"\\n\",\n    \"# Load pre-trained BERT model for sequence classification\\n\",\n    \"model = BertForSequenceClassification.from_pretrained(\\\"bert-base-uncased\\\")\\n\",\n    \"\\n\",\n    \"# Optimizer and learning rate scheduler setup\\n\",\n    \"optimizer = AdamW(model.parameters(), lr=5e-5)\\n\",\n    \"\\n\",\n    \"# Training loop using PyTorch\\n\",\n    \"for epoch in range(3):  # Train for 3 epochs as an example\\n\",\n    \"    model.train()\\n\",\n    \"    for batch in tqdm(train_dataloader):\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        input_ids = batch['input_ids']\\n\",\n    \"        attention_mask = batch['attention_mask']\\n\",\n    \"        labels = batch['label']\\n\",\n    \"\\n\",\n    \"        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        loss = outputs.loss\\n\",\n    \"        loss.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"\\n\",\n    \"    # Evaluation loop\\n\",\n    \"    model.eval()\\n\",\n    \"    total_correct = 0\\n\",\n    \"    total_samples = 0\\n\",\n    \"    for batch in tqdm(eval_dataloader):\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            input_ids = batch['input_ids']\\n\",\n    \"            attention_mask = batch['attention_mask']\\n\",\n    \"            labels = batch['label']\\n\",\n    \"\\n\",\n    \"            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\\n\",\n    \"            predictions = torch.argmax(outputs.logits, dim=1)\\n\",\n    \"\\n\",\n    \"            total_correct += (predictions == labels).sum().item()\\n\",\n    \"            total_samples += len(labels)\\n\",\n    \"\\n\",\n    \"    accuracy = total_correct / total_samples\\n\",\n    \"    print(f\\\"Epoch {epoch + 1} - Evaluation Accuracy: {accuracy}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f15d030f-dae8-4403-a505-658af0cec891\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"93962529-a401-4611-aa1f-8b5a63cba99f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter19/HuggingFaceHub.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"3a69c9a1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: huggingface_hub==0.20.1 in /opt/conda/lib/python3.10/site-packages (0.20.1)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (3.13.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (2023.12.2)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (2.28.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (4.66.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (4.9.0)\\n\",\n      \"Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub==0.20.1) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface_hub==0.20.1) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub==0.20.1) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub==0.20.1) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub==0.20.1) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface_hub==0.20.1) (2022.6.15)\\n\",\n      \"Requirement already satisfied: transformers==4.36.0 in /opt/conda/lib/python3.10/site-packages (4.36.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2022.6.15)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install huggingface_hub==0.20.1\\n\",\n    \"!pip install transformers==4.36.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"c3406aa6-8626-4062-a4c6-688975f22f82\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from huggingface_hub import hf_api\\n\",\n    \"models = hf_api.list_models()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"24139f7b-90c7-46a7-a410-1b1d1092a76a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"508842\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len([t for t in models])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"798dd2b8-c71e-4082-82bf-f80a964c7015\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"text_gen_models = [model.id for model in models \\n\",\n    \"                   if \\\"text-generation-inference\\\" in model.tags\\n\",\n    \"                   and model.downloads>1000000]\\n\",\n    \"print(text_gen_models)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"426003df-7daf-4e33-a5b6-1e5e4d79759d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3820fded1ab3432fa5d19ae63475e46f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"35d6fce281594965981ad0c9e280722b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"35c09509b4c14ec98851145f9d7d88a3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\\n\",\n      \"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"75c845730b2e43b499ea2665b0af3e81\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"0f8b6b0adadd4152bdd933b88be70609\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1377130de5334e2d895f3317aaa2e49c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from transformers import T5Tokenizer, T5ForConditionalGeneration\\n\",\n    \"tokenizer = T5Tokenizer.from_pretrained(\\\"t5-small\\\")\\n\",\n    \"model = T5ForConditionalGeneration.from_pretrained(\\\"t5-small\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"bace0450-1a86-49c1-a217-f91487f97a0a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\\n\",\n      \"  warnings.warn(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Ich liebe PyTorch.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"input_ids = tokenizer(\\\"translate English to German: I love PyTorch.\\\", return_tensors=\\\"pt\\\").input_ids\\n\",\n    \"outputs = model.generate(input_ids)\\n\",\n    \"print(tokenizer.decode(outputs[0], skip_special_tokens=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"dbda3f93-30bf-4f7e-942a-1e28e2d1cfbf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"5819ac6e-baf3-4022-893e-2f3a341dd72d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter19/HuggingFaceOptimum.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"a1199016\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: optimum==1.16.1 in /opt/conda/lib/python3.10/site-packages (1.16.1)\\n\",\n      \"Requirement already satisfied: coloredlogs in /opt/conda/lib/python3.10/site-packages (from optimum==1.16.1) (15.0.1)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from optimum==1.16.1) (1.12)\\n\",\n      \"Requirement already satisfied: transformers>=4.26.0 in /opt/conda/lib/python3.10/site-packages (from transformers[sentencepiece]>=4.26.0->optimum==1.16.1) (4.36.0)\\n\",\n      \"Requirement already satisfied: torch>=1.9 in /opt/conda/lib/python3.10/site-packages (from optimum==1.16.1) (2.2.0)\\n\",\n      \"Requirement already satisfied: packaging in 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tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.8.0->optimum==1.16.1) (4.66.1)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.8.0->optimum==1.16.1) (6.0)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.8.0->optimum==1.16.1) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->optimum==1.16.1) (3.0.9)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->optimum==1.16.1) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->optimum==1.16.1) (3.1.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from 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satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2022.6.15)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install optimum==1.16.1\\n\",\n    \"!pip install transformers==4.36.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"b0471b34-bdaa-42f1-af0f-1468c560b0fa\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"2024-02-16 16:01:44.185209: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\",\n      \"2024-02-16 16:01:44.185256: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\",\n      \"2024-02-16 16:01:44.186646: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification, AutoTokenizer\\n\",\n    \"from optimum.onnxruntime import ORTModelForSequenceClassification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"9c22987b-f4a6-415e-9a40-327d9b69a2fe\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# a pre-trained model from HuggingFace\\n\",\n    \"model_name = \\\"bert-base-uncased\\\"\\n\",\n    \"onnx_directory = \\\"bert-base-uncased_onnx\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"6428193d-f0e4-4ad8-b140-6f5fe797dd65\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load the pre-trained model and tokenizer\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"bert-base-uncased\\\", export=True)\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(model_name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"fb324d63-3bee-41ed-95bd-40bf49b13d63\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SequenceClassifierOutput(loss=None, logits=tensor([[-0.0306,  0.2759]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"input_ids = tokenizer(\\\"I love PyTorch!\\\", return_tensors=\\\"pt\\\")\\n\",\n    \"model(**input_ids)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"62746dc3-f43c-44eb-8c4c-c17283d26a94\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Framework not specified. Using pt to export to ONNX.\\n\",\n      \"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\",\n      \"Using the export variant default. Available variants are:\\n\",\n      \"    - default: The default ONNX variant.\\n\",\n      \"Using framework PyTorch: 2.2.0+cu121\\n\",\n      \"Overriding 1 configuration item(s)\\n\",\n      \"\\t- use_cache -> False\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1623, -0.0475]]), hidden_states=None, attentions=None)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_onnx = ORTModelForSequenceClassification.from_pretrained(\\\"bert-base-uncased\\\", export=True)\\n\",\n    \"model_onnx(**input_ids)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"1f88c2bc-4bea-4fa0-8487-ad406e94719d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"('bert-base-uncased_onnx/tokenizer_config.json',\\n\",\n       \" 'bert-base-uncased_onnx/special_tokens_map.json',\\n\",\n       \" 'bert-base-uncased_onnx/vocab.txt',\\n\",\n       \" 'bert-base-uncased_onnx/added_tokens.json',\\n\",\n       \" 'bert-base-uncased_onnx/tokenizer.json')\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_onnx.save_pretrained(onnx_directory)\\n\",\n    \"tokenizer.save_pretrained(onnx_directory)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"4df44901-4dd0-4670-a348-94a027e63419\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from optimum.onnxruntime.configuration import AutoQuantizationConfig\\n\",\n    \"from optimum.onnxruntime import ORTQuantizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"668a7a50-daf8-4648-bc54-720643f0411a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Creating dynamic quantizer: QOperator (mode: IntegerOps, schema: u8/s8, channel-wise: False)\\n\",\n      \"Quantizing model...\\n\",\n      \"Saving quantized model at: bert-base-uncased_onnx (external data format: False)\\n\",\n      \"Configuration saved in bert-base-uncased_onnx/ort_config.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"PosixPath('bert-base-uncased_onnx')\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"qconfig = AutoQuantizationConfig.arm64(is_static=False, per_channel=False)\\n\",\n    \"quantizer = ORTQuantizer.from_pretrained(model_onnx)\\n\",\n    \"quantizer.quantize(save_dir=onnx_directory, quantization_config=qconfig)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"92b1f6a7-bf31-48d3-aabf-bdea6aa617d0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SequenceClassifierOutput(loss=None, logits=tensor([[ 0.1623, -0.0475]]), hidden_states=None, attentions=None)\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model_quantized = ORTModelForSequenceClassification.from_pretrained(onnx_directory, file_name=\\\"model.onnx\\\")\\n\",\n    \"model_quantized(**input_ids)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Chapter19/HuggingFacePyTorch.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"c087c5fe\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.13.1)\\n\",\n      \"Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (4.9.0)\\n\",\n      \"Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\\n\",\n      \"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.8.5)\\n\",\n      \"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (3.1.2)\\n\",\n      \"Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\\n\",\n      \"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\\n\",\n      \"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\\n\",\n      \"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\\n\",\n      \"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\\n\",\n      \"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\\n\",\n      \"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\\n\",\n      \"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\\n\",\n      \"Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\\n\",\n      \"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\\n\",\n      \"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\\n\",\n      \"Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\\n\",\n      \"Requirement already satisfied: transformers==4.36.0 in /opt/conda/lib/python3.10/site-packages (4.36.0)\\n\",\n      \"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (3.13.1)\\n\",\n      \"Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.20.1)\\n\",\n      \"Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (1.23.4)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (21.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (6.0)\\n\",\n      \"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2022.7.9)\\n\",\n      \"Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (2.28.1)\\n\",\n      \"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.15.2)\\n\",\n      \"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (0.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.0) (4.66.1)\\n\",\n      \"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (2023.12.2)\\n\",\n      \"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0) (4.9.0)\\n\",\n      \"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.0) (3.0.9)\\n\",\n      \"Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2.1.0)\\n\",\n      \"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (3.3)\\n\",\n      \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (1.26.10)\\n\",\n      \"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.0) (2022.6.15)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install torch==2.2\\n\",\n    \"!pip install transformers==4.36.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"96f49e9f-e7b1-4c9d-9b78-b9992fe1edf7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\\n\",\n      \"  _torch_pytree._register_pytree_node(\\n\",\n      \"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from transformers import AutoModelForSequenceClassification, AutoTokenizer\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# a pre-trained model from HuggingFace\\n\",\n    \"model_name = \\\"bert-base-uncased\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Load the pre-trained model and tokenizer\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(model_name)\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(model_name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"d774cc11-d384-4959-bfd7-789127d91ddd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"input_text = \\\"I love PyTorch!\\\"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Tokenize the input text using the tokenizer\\n\",\n    \"inputs = tokenizer(input_text, return_tensors=\\\"pt\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Perform inference using the pre-trained model\\n\",\n    \"with torch.no_grad():\\n\",\n    \"    outputs = model(**inputs)\\n\",\n    \"\\n\",\n    \"# Access the model predictions or outputs\\n\",\n    \"predicted_class = torch.argmax(outputs.logits, dim=1).item()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"cb986349-3b40-43e1-bb40-386d62910617\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"036ddd1e-6d82-45aa-96c7-9223adb90db0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Example: Using GPU for model inference\\n\",\n    \"device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"model.to(device)\\n\",\n    \"inputs.to(device)\\n\",\n    \"outputs = model(**inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3cc47824-0687-464c-9452-114696ef19db\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (Local)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "README.md",
    "content": "# Mastering PyTorch, Second Edition\n\nThis is the code repository for the book [Mastering PyTorch, Second Edition](https://www.amazon.com/Mastering-PyTorch-powerful-learning-architectures-ebook/dp/1801074305), published by Packt.\n\n<img src=\"https://github.com/arj7192/MasteringPyTorchV2/blob/main/MasteringPyTorch2E.jpg?raw=tru\" alt=\"drawing\" width=\"400\"/>\n\n# What is this book about?\n\nPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.\n\n# What you will learn\n* Implement text, vision, and music generation models using PyTorch  \n* Build a deep Q-network (DQN) model in PyTorch  \n* Deploy PyTorch models on mobile devices (Android and iOS)  \n* Become well versed in rapid prototyping using PyTorch with fastai  \n* Perform neural architecture search effectively using AutoML  \n* Easily interpret machine learning models using Captum  \n* Design ResNets, LSTMs, and graph neural networks (GNNs)  \n* Create language and vision transformer models using Hugging Face\n\n# Who This Book Is for?\nThis deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.\n\n# Notebooks in each chapter\n\n\nYou can run the notebooks directly from the table below: \n\n| Chapter no. |Chapter title | Notebook/Utility Script (GitHub) | Open in Kaggle | Open in Colab\n|:-- |:-- | :-------- | :-------- |  :-------- |\n| 1 | Overview of Deep Learning Using PyTorch | [mnist_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) |\n| | | [mnist_tensorflow.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) |\n| 2 |  Deep CNN Architectures | [DenseNetBlock.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) |\n|  |   | [GoogLeNet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) |\n|  |   | [ResNetBlock.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) |\n|  |   | [lenet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) |\n|  |   | [transfer_learning_alexnet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) |\n|  |   | [vgg13_pretrained_run_inference.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) |\n| 3 |  Combining CNNs and LSTMs | [image_captioning_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) |\n| 4 |  Deep Recurrent Model Architectures | [lstm.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) |\n|  |   | [rnn.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) |\n| 5 |   Advanced Hybrid Models | [out_of_the_box_transformers.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) |\n|  |   | [rand_wire_nn.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) |\n|  |   | [transformer.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) |\n| 6 |   Graph Neural Networks | [GNN.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) |\n| 7 |    Music and Text Generation with PyTorch | [music_generation.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) |\n|  |   | [text_generation.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) |\n|  |   | [text_generation_out_of_the_box.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) |\n|  |   | [text_generation_out_of_the_box_gpt3.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) |\n| 8 |    Neural Style Transfer  | [neural_style_transfer.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) |\n| 9 |    Deep Convolutional GANs  | [dcgan.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) |\n|  |   | [pix2pix_architecture.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) |\n| 10 |    Image Generation Using Diffusion  | [image_generation_using_diffusion.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) |\n|  |   | [taj_mahal_image.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) |\n|  |   | [text_to_image_generation_using_stable_diffusion_v1_5.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) |\n| 11 |    Deep Reinforcement Learning  | [pong.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) |\n| 13 |    Operationalizing PyTorch Models into Production  | [mnist_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) |\n|  |   | [model_scripting.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) |\n|  |   | [model_tracing.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) |\n|  |   | [onnx.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) |\n|  |   | [run_inference.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) |\n| 15 |    Rapid Prototyping with PyTorch  | [fastai.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) |\n|  |   | [poutyne.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) |\n|  |   | [pytorch_lightning.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) |\n|  |   | [pytorch_profiler.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) |\n| 16 |    PyTorch and AutoML  | [automl-pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) |\n|  |   | [optuna_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) |\n| 17 |    PyTorch and Explainable AI  | [captum_interpretability.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) |\n|  |   | [pytorch_interpretability.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) |\n| 18 |    Recommendation Systems with PyTorch  | [torch-recsys.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) |\n| 19 |    PyTorch and Hugging Face  | [HuggingFaceAccelerate.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) |\n|  |   | [HuggingFaceDatasets.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) |\n|  |   | [HuggingFaceHub.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) |\n|  |   | [HuggingFaceOptimum.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) |\n|  |   | [HuggingFacePyTorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) | [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) |\n\n\n## Know more on the Discord server <img alt=\"Coding\" height=\"25\" width=\"32\"  src=\"https://cliply.co/wp-content/uploads/2021/08/372108630_DISCORD_LOGO_400.gif\">\nYou can get more engaged on the discord server for more latest updates and discussions in the community at [Discord](https://packt.link/mastorch)\n\n## Download a free PDF <img alt=\"Coding\" height=\"25\" width=\"40\" src=\"https://emergency.com.au/wp-content/uploads/2021/03/free.gif\">\n\n_If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF._\n[Free-Ebook](https://download.packt.com/free-ebook/9781801074308) <img alt=\"Coding\" height=\"15\" width=\"35\"  src=\"https://media.tenor.com/ex_HDD_k5P8AAAAi/habbo-habbohotel.gif\">\n\nWe also provide a PDF file that has color images of the screenshots/diagrams used in this book at [GraphicBundle](https://packt.link/gbp/9781801074308) <img alt=\"Coding\" height=\"15\" width=\"35\"  src=\"https://media.tenor.com/ex_HDD_k5P8AAAAi/habbo-habbohotel.gif\">\n\n\n## Get to know the Author\n_Ashish Ranjan Jha_ studied electrical engineering at IIT Roorkee, computer science at École Polytechnique\nFédérale de Lausanne (EPFL), and he also completed his MBA at Quantic School of Business,\nwith a distinction in all three degrees. He has worked for bigger tech companies like Oracle and Sony,\nand recent tech unicorns – Revolut and Tractable, in the fields of data science, machine learning and\nartificial intelligence. He currently works as head of ML and AI at XYZ Reality, based in London (a\nconstruction tech start-up where construction meets AR/VR meets ML/AI to enable real-time data\ndriven construction intelligence). He is also an advisor to SUIND, an agritech startup that uses drones\nfor intelligence. Along with that, he has also authored a book, _Fight Fraud with Machine Learning_.\n\n## Other Related Books\n- [Machine Learning with PyTorch and Scikit-Learn](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312)\n- [Python Data Cleaning Cookbook – Second Edition](https://www.packtpub.com/product/python-data-cleaning-cookbook-second-edition/9781803239873)\n\n"
  }
]